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81 results on '"Genomes Project"'

1. Reconstructing Native American migrations from whole-genome and whole-exome data.

Author: Simon Gravel, Fouad Zakharia, Andres Moreno-Estrada, Jake K Byrnes, Marina Muzzio, Juan L Rodriguez-Flores, Eimear E Kenny, Christopher R Gignoux, Brian K Maples, Wilfried Guiblet, Julie Dutil, Marc Via, Karla Sandoval, Gabriel Bedoya, Genomes Project, Taras K Oleksyk, Andres Ruiz-Linares, Esteban G Burchard, Juan Carlos Martinez-Cruzado, and Carlos D Bustamante
Subjects: Genetics, QH426-470
Abstract: There is great scientific and popular interest in understanding the genetic history of populations in the Americas. We wish to understand when different regions of the continent were inhabited, where settlers came from, and how current inhabitants relate genetically to earlier populations. Recent studies unraveled parts of the genetic history of the continent using genotyping arrays and uniparental markers. The 1000 Genomes Project provides a unique opportunity for improving our understanding of population genetic history by providing over a hundred sequenced low coverage genomes and exomes from Colombian (CLM), Mexican-American (MXL), and Puerto Rican (PUR) populations. Here, we explore the genomic contributions of African, European, and especially Native American ancestry to these populations. Estimated Native American ancestry is 48% in MXL, 25% in CLM, and 13% in PUR. Native American ancestry in PUR is most closely related to populations surrounding the Orinoco River basin, confirming the Southern American ancestry of the Taíno people of the Caribbean. We present new methods to estimate the allele frequencies in the Native American fraction of the populations, and model their distribution using a demographic model for three ancestral Native American populations. These ancestral populations likely split in close succession: the most likely scenario, based on a peopling of the Americas 16 thousand years ago (kya), supports that the MXL Ancestors split 12.2kya, with a subsequent split of the ancestors to CLM and PUR 11.7kya. The model also features effective populations of 62,000 in Mexico, 8,700 in Colombia, and 1,900 in Puerto Rico. Modeling Identity-by-descent (IBD) and ancestry tract length, we show that post-contact populations also differ markedly in their effective sizes and migration patterns, with Puerto Rico showing the smallest effective size and the earlier migration from Europe. Finally, we compare IBD and ancestry assignments to find evidence for relatedness among European founders to the three populations.
Published: 2013
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2. A comprehensive map of mobile element insertion polymorphisms in humans.

Author: Chip Stewart, Deniz Kural, Michael P Strömberg, Jerilyn A Walker, Miriam K Konkel, Adrian M Stütz, Alexander E Urban, Fabian Grubert, Hugo Y K Lam, Wan-Ping Lee, Michele Busby, Amit R Indap, Erik Garrison, Chad Huff, Jinchuan Xing, Michael P Snyder, Lynn B Jorde, Mark A Batzer, Jan O Korbel, Gabor T Marth, and Genomes Project
Subjects: Genetics, QH426-470
Abstract: As a consequence of the accumulation of insertion events over evolutionary time, mobile elements now comprise nearly half of the human genome. The Alu, L1, and SVA mobile element families are still duplicating, generating variation between individual genomes. Mobile element insertions (MEI) have been identified as causes for genetic diseases, including hemophilia, neurofibromatosis, and various cancers. Here we present a comprehensive map of 7,380 MEI polymorphisms from the 1000 Genomes Project whole-genome sequencing data of 185 samples in three major populations detected with two detection methods. This catalog enables us to systematically study mutation rates, population segregation, genomic distribution, and functional properties of MEI polymorphisms and to compare MEI to SNP variation from the same individuals. Population allele frequencies of MEI and SNPs are described, broadly, by the same neutral ancestral processes despite vastly different mutation mechanisms and rates, except in coding regions where MEI are virtually absent, presumably due to strong negative selection. A direct comparison of MEI and SNP diversity levels suggests a differential mobile element insertion rate among populations.
Published: 2011
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3. Germline selection shapes human mitochondrial DNA diversity

Author: NIHR BioResource-Rare Diseases, 1001000 Genomes Project-Rare Diseases Pilot, Wei, Wei, Tuna, Salih, Keogh, Michael J., Smith, Katherine R., Aitman, Timothy J., Beales, Phil L., Bennett, David L., Gale, Daniel P., Bitner-Glindzicz, Maria A. K., Black, Graeme C., Brennan, Paul, Elliott, Perry, Flinter, Frances A., Floto, R. Andres, Houlden, Henry, Irving, Melita, Koziell, Ania, Maher, Eamonn R., Markus, Hugh S., Morrell, Nicholas W., Newman, William G., Roberts, Irene, Sayer, John A., Smith, Kenneth G. C., Taylor, Jenny C., Watkins, Hugh, Webster, Andrew R., Wilkie, Andrew O. M., Williamson, Catherine, Ashford, Sofie, Penkett, Christopher J., Stirrups, Kathleen E., Rendon, Augusto, Ouwehand, Willem H., Bradley, John R., Raymond, F. Lucy, Caulfield, Mark, Turro, Ernest, and Chinnery, Patrick F.
Published: 2019

4. An integrated map of structural variation in 2,504 human genomes.

Author: Sudmant, Peter H, Rausch, Tobias, Gardner, Eugene J, Handsaker, Robert E, Abyzov, Alexej, Huddleston, John, Zhang, Yan, Ye, Kai, Jun, Goo, Fritz, Markus Hsi-Yang, Konkel, Miriam K, Malhotra, Ankit, Stütz, Adrian M, Shi, Xinghua, Casale, Francesco Paolo, Chen, Jieming, Hormozdiari, Fereydoun, Dayama, Gargi, Chen, Ken, Malig, Maika, Chaisson, Mark JP, Walter, Klaudia, Meiers, Sascha, Kashin, Seva, Garrison, Erik, Auton, Adam, Lam, Hugo YK, Mu, Xinmeng Jasmine, Alkan, Can, Antaki, Danny, Bae, Taejeong, Cerveira, Eliza, Chines, Peter, Chong, Zechen, Clarke, Laura, Dal, Elif, Ding, Li, Emery, Sarah, Fan, Xian, Gujral, Madhusudan, Kahveci, Fatma, Kidd, Jeffrey M, Kong, Yu, Lameijer, Eric-Wubbo, McCarthy, Shane, Flicek, Paul, Gibbs, Richard A, Marth, Gabor, Mason, Christopher E, Menelaou, Androniki, Muzny, Donna M, Nelson, Bradley J, Noor, Amina, Parrish, Nicholas F, Pendleton, Matthew, Quitadamo, Andrew, Raeder, Benjamin, Schadt, Eric E, Romanovitch, Mallory, Schlattl, Andreas, Sebra, Robert, Shabalin, Andrey A, Untergasser, Andreas, Walker, Jerilyn A, Wang, Min, Yu, Fuli, Zhang, Chengsheng, Zhang, Jing, Zheng-Bradley, Xiangqun, Zhou, Wanding, Zichner, Thomas, Sebat, Jonathan, Batzer, Mark A, McCarroll, Steven A, 1000 Genomes Project Consortium, Mills, Ryan E, Gerstein, Mark B, Bashir, Ali, Stegle, Oliver, Devine, Scott E, Lee, Charles, Eichler, Evan E, and Korbel, Jan O
Subjects: Genomes Project Consortium, Humans, Genetic Predisposition to Disease, Physical Chromosome Mapping, Sequence Analysis, DNA, Genetics, Medical, Genetics, Population, Genomics, Sequence Deletion, Amino Acid Sequence, Genotype, Haplotypes, Homozygote, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Genome, Human, Molecular Sequence Data, Genetic Variation, Genome-Wide Association Study, Mutation Rate, Sequence Analysis, DNA, Genetics, Medical, Population, Polymorphism, Single Nucleotide, Genome, Human, General Science & Technology
Abstract: Structural variants are implicated in numerous diseases and make up the majority of varying nucleotides among human genomes. Here we describe an integrated set of eight structural variant classes comprising both balanced and unbalanced variants, which we constructed using short-read DNA sequencing data and statistically phased onto haplotype blocks in 26 human populations. Analysing this set, we identify numerous gene-intersecting structural variants exhibiting population stratification and describe naturally occurring homozygous gene knockouts that suggest the dispensability of a variety of human genes. We demonstrate that structural variants are enriched on haplotypes identified by genome-wide association studies and exhibit enrichment for expression quantitative trait loci. Additionally, we uncover appreciable levels of structural variant complexity at different scales, including genic loci subject to clusters of repeated rearrangement and complex structural variants with multiple breakpoints likely to have formed through individual mutational events. Our catalogue will enhance future studies into structural variant demography, functional impact and disease association.
Published: 2015

5. A global reference for human genetic variation.

Author: 1000 Genomes Project Consortium, Auton, Adam, Brooks, Lisa D, Durbin, Richard M, Garrison, Erik P, Kang, Hyun Min, Korbel, Jan O, Marchini, Jonathan L, McCarthy, Shane, McVean, Gil A, and Abecasis, Gonçalo R
Subjects: Genomes Project Consortium, Humans, Disease Susceptibility, Rare Diseases, Physical Chromosome Mapping, Sequence Analysis, DNA, Genetics, Medical, Genetics, Population, Genomics, Demography, Genotype, Haplotypes, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Genome, Human, Reference Standards, Internationality, INDEL Mutation, Genetic Variation, Genome-Wide Association Study, High-Throughput Nucleotide Sequencing, Exome, Datasets as Topic, Biotechnology, Genetics, Human Genome, 2.1 Biological and endogenous factors, General Science & Technology
Abstract: The 1000 Genomes Project set out to provide a comprehensive description of common human genetic variation by applying whole-genome sequencing to a diverse set of individuals from multiple populations. Here we report completion of the project, having reconstructed the genomes of 2,504 individuals from 26 populations using a combination of low-coverage whole-genome sequencing, deep exome sequencing, and dense microarray genotyping. We characterized a broad spectrum of genetic variation, in total over 88 million variants (84.7 million single nucleotide polymorphisms (SNPs), 3.6 million short insertions/deletions (indels), and 60,000 structural variants), all phased onto high-quality haplotypes. This resource includes >99% of SNP variants with a frequency of >1% for a variety of ancestries. We describe the distribution of genetic variation across the global sample, and discuss the implications for common disease studies.
Published: 2015

6. Integrating sequence and array data to create an improved 1000 Genomes Project haplotype reference panel.

Author: Delaneau, Olivier, Marchini, Jonathan, and 1000 Genomes Project Consortium
Subjects: Genomes Project Consortium, Humans, Microarray Analysis, Gene Frequency, Haplotypes, Polymorphism, Single Nucleotide, Alleles, Genome, Human, Algorithms, Genome-Wide Association Study, Genome, Human, Polymorphism, Single Nucleotide
Abstract: A major use of the 1000 Genomes Project (1000 GP) data is genotype imputation in genome-wide association studies (GWAS). Here we develop a method to estimate haplotypes from low-coverage sequencing data that can take advantage of single-nucleotide polymorphism (SNP) microarray genotypes on the same samples. First the SNP array data are phased to build a backbone (or 'scaffold') of haplotypes across each chromosome. We then phase the sequence data 'onto' this haplotype scaffold. This approach can take advantage of relatedness between sequenced and non-sequenced samples to improve accuracy. We use this method to create a new 1000 GP haplotype reference set for use by the human genetic community. Using a set of validation genotypes at SNP and bi-allelic indels we show that these haplotypes have lower genotype discordance and improved imputation performance into downstream GWAS samples, especially at low-frequency variants.
Published: 2014

7. An integrated map of genetic variation from 1,092 human genomes.

Author: 1000 Genomes Project Consortium, Abecasis, Goncalo R, Auton, Adam, Brooks, Lisa D, DePristo, Mark A, Durbin, Richard M, Handsaker, Robert E, Kang, Hyun Min, Marth, Gabor T, and McVean, Gil A
Subjects: Genomes Project Consortium, Humans, Transcription Factors, Genetics, Medical, Genetics, Population, Genomics, Evolution, Molecular, Sequence Deletion, Binding Sites, Conserved Sequence, Haplotypes, Polymorphism, Single Nucleotide, Alleles, Genome, Human, Continental Population Groups, Genetic Variation, Genome-Wide Association Study, Nucleotide Motifs, General Science & Technology
Abstract: By characterizing the geographic and functional spectrum of human genetic variation, the 1000 Genomes Project aims to build a resource to help to understand the genetic contribution to disease. Here we describe the genomes of 1,092 individuals from 14 populations, constructed using a combination of low-coverage whole-genome and exome sequencing. By developing methods to integrate information across several algorithms and diverse data sources, we provide a validated haplotype map of 38 million single nucleotide polymorphisms, 1.4 million short insertions and deletions, and more than 14,000 larger deletions. We show that individuals from different populations carry different profiles of rare and common variants, and that low-frequency variants show substantial geographic differentiation, which is further increased by the action of purifying selection. We show that evolutionary conservation and coding consequence are key determinants of the strength of purifying selection, that rare-variant load varies substantially across biological pathways, and that each individual contains hundreds of rare non-coding variants at conserved sites, such as motif-disrupting changes in transcription-factor-binding sites. This resource, which captures up to 98% of accessible single nucleotide polymorphisms at a frequency of 1% in related populations, enables analysis of common and low-frequency variants in individuals from diverse, including admixed, populations.
Published: 2012

8. 100,000 genomes pilot on rare-disease diagnosis in health care preliminary report

Author: The Genomes-Project-Pilot-Investigators
Subjects: General Economics, Econometrics and Finance
Published: 2022
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9. 100,000 genomes pilot on rare-disease diagnosis in health care preliminary report

Author: Genomes-Project-Pilot-Investigators, The, primary
Published: 2022
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10. A Systematic Survey of Loss-of-Function Variants in Human Protein-Coding Genes

Author: 1000 Genomes Project Consortium, MacArthur, Daniel G., Balasubramanian, Suganthi, Frankish, Adam, Huang, Ni, Morris, James, Walter, Klaudia, Jostins, Luke, Habegger, Lukas, Pickrell, Joseph K., Montgomery, Stephen B., Albers, Cornelis A., Zhang, Zhengdong D., Conrad, Donald F., Lunter, Gerton, Zheng, Hancheng, Ayub, Qasim, DePristo, Mark A., Banks, Eric, Hu, Min, Handsaker, Robert E., Rosenfeld, Jeffrey A., Fromer, Menachem, Jin, Mike, Mu, Xinmeng Jasmine, Khurana, Ekta, Ye, Kai, Kay, Mike, Saunders, Gary Ian, Suner, Marie-Marthe, Hunt, Toby, Barnes, H. A., Amid, Clara, Carvalho-Silva, Denise R., Bignell, Alexandra H., Snow, Catherine, Yngvadottir, Bryndis, Bumpstead, Suzannah, Cooper, David N., Xue, Yali, Romero, Irene Gallego, Wang, Jun, Li, Yingrui, Gibbs, Richard A., McCarroll, Steven A., Dermitzakis, Emmanouil T., Pritchard, Jonathan K., Barrett, Jeffrey C., Harrow, Jennifer, Hurles, Matthew E., Gerstein, Mark B., and Tyler-Smith, Chris
Published: 2012
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11. Demographic history and rare allele sharing among human populations

Author: The 1000 Genomes Project, Gravel, Simon, Henn, Brenna M., Gutenkunst, Ryan N., Indap, Amit R., Marth, Gabor T., Clark, Andrew G., Yu, Fuli, Gibbs, Richard A., and Bustamante, Carlos D.
Published: 2011

12. Diversity of Human Copy Number Variation and Multicopy Genes

Author: 1000 Genomes Project, Sudmant, Peter H., Kitzman, Jacob O., Antonacci, Francesca, Alkan, Can, Malig, Maika, Tsalenko, Anya, Sampas, Nick, Bruhn, Laurakay, Shendure, Jay, and Eichler, Evan E.
Published: 2010

13. The genome sequence of the channel bull blenny, Cottoperca gobio (Günther, 1861)

Author: Bista, Iliana, McCarthy, Shane A, Wood, Jonathan, Ning, Zemin, Detrich Iii, H William, Desvignes, Thomas, Postlethwait, John, Chow, William, Howe, Kerstin, Torrance, James, Smith, Michelle, Oliver, Karen, Vertebrate Genomes Project Consortium, Miska, Eric A, Durbin, Richard, Bista, Iliana [0000-0002-6155-3093], McCarthy, Shane A [0000-0002-2715-4187], Wood, Jonathan [0000-0002-7545-2162], Detrich Iii, H William [0000-0002-0783-4505], Desvignes, Thomas [0000-0001-5126-8785], Howe, Kerstin [0000-0003-2237-513X], Torrance, James [0000-0002-6117-8190], and Apollo - University of Cambridge Repository
Subjects: channel bull blenny, genome assembly chromosomal, Cottoperca gobio, Notothenioidei
Abstract: We present a genome assembly for Cottoperca gobio (channel bull blenny, (Günther, 1861)); Chordata; Actinopterygii (ray-finned fishes), a temperate water outgroup for Antarctic Notothenioids. The size of the genome assembly is 609 megabases, with the majority of the assembly scaffolded into 24 chromosomal pseudomolecules. Gene annotation on Ensembl of this assembly has identified 21,662 coding genes.
Published: 2020
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14. Whole-genome sequencing of patients with rare diseases in a national health system

Author: Turro, Ernest, Astle, William J, Megy, Karyn, Gräf, Stefan, Greene, Daniel, Shamardina, Olga, Allen, Hana Lango, Sanchis-Juan, Alba, Frontini, Mattia, Thys, Chantal, Stephens, Jonathan, Mapeta, Rutendo, Burren, Oliver S, Downes, Kate, Haimel, Matthias, Tuna, Salih, Deevi, Sri, Aitman, Timothy J, Bennett, David L, Calleja, Paul, Carss, Keren, Caulfield, Mark J, Chinnery, Patrick F, Dixon, Peter H, Gale, Daniel P, James, Roger, Koziell, Ania, Laffan, Michael A, Levine, Adam P, Maher, Eamonn R, Markus, Hugh S, Morales, Joannella, Morrell, Nicholas W, Mumford, Andrew D, Ormondroyd, Elizabeth, Rankin, Stuart, Rendon, Augusto, Richardson, Sylvia, Roberts, Irene, Roy, Noemi BA, Saleem, Moin A, Smith, Kenneth GC, Stark, Hannah, Tan, Rhea YY, Themistocleous, Andreas C, Thrasher, Adrian J, Watkins, Hugh, Webster, Andrew R, Wilkins, Martin R, Williamson, Catherine, Whitworth, James, Humphray, Sean, Bentley, David R, NIHR BioResource For The 100,000 Genomes Project, Kingston, Nathalie, Walker, Neil, Bradley, John R, Ashford, Sofie, Penkett, Christopher J, Freson, Kathleen, Stirrups, Kathleen E, Raymond, F Lucy, Ouwehand, Willem H, Ouwehand, Willem H [0000-0002-7744-1790], and Apollo - University of Cambridge Repository
Subjects: Erythrocytes, Internationality, Databases, Factual, National Health Programs, Whole Genome Sequencing, Quantitative Trait Loci, Actin-Related Protein 2-3 Complex, State Medicine, United Kingdom, Phenotype, Rare Diseases, Humans, GATA1 Transcription Factor, Receptors, Thrombopoietin, Alleles, Adaptor Proteins, Signal Transducing
Abstract: Most patients with rare diseases do not receive a molecular diagnosis and the aetiological variants and causative genes for more than half such disorders remain to be discovered1. Here we used whole-genome sequencing (WGS) in a national health system to streamline diagnosis and to discover unknown aetiological variants in the coding and non-coding regions of the genome. We generated WGS data for 13,037 participants, of whom 9,802 had a rare disease, and provided a genetic diagnosis to 1,138 of the 7,065 extensively phenotyped participants. We identified 95 Mendelian associations between genes and rare diseases, of which 11 have been discovered since 2015 and at least 79 are confirmed to be aetiological. By generating WGS data of UK Biobank participants2, we found that rare alleles can explain the presence of some individuals in the tails of a quantitative trait for red blood cells. Finally, we identified four novel non-coding variants that cause disease through the disruption of transcription of ARPC1B, GATA1, LRBA and MPL. Our study demonstrates a synergy by using WGS for diagnosis and aetiological discovery in routine healthcare.
Published: 2020
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15. The genome sequence of the channel bull blenny, Cottoperca gobio (Günther, 1861) [version 1; peer review: 2 approved]

Author: Iliana Bista, Shane A. McCarthy, Jonathan Wood, Zemin Ning, H. William Detrich III, Thomas Desvignes, John Postlethwait, William Chow, Kerstin Howe, James Torrance, Michelle Smith, Karen Oliver, Vertebrate Genomes Project Consortium, Eric A. Miska, and Richard Durbin
Subjects: lcsh:R, lcsh:Medicine, lcsh:Q, lcsh:Science
Abstract: We present a genome assembly for Cottoperca gobio (channel bull blenny, (Günther, 1861)); Chordata; Actinopterygii (ray-finned fishes), a temperate water outgroup for Antarctic Notothenioids. The size of the genome assembly is 609 megabases, with the majority of the assembly scaffolded into 24 chromosomal pseudomolecules. Gene annotation on Ensembl of this assembly has identified 21,662 coding genes.
Published: 2020

16. Prediction of Susceptibility to First-Line Tuberculosis Drugs by DNA Sequencing.

Author: CRyPTIC Consortium and the 100,000 Genomes Project, CRyPTIC Consortium and the 100,000 Genomes Project, Allix-Béguec, Caroline, Arandjelovic, Irena, Bi, Lijun, Beckert, Patrick, Bonnet, Maryline, Bradley, Phelim, Cabibbe, Andrea M, Cancino-Muñoz, Irving, Caulfield, Mark J, Chaiprasert, Angkana, Cirillo, Daniela M, Clifton, David A, Comas, Iñaki, Crook, Derrick W, De Filippo, Maria R, de Neeling, Han, Diel, Roland, Drobniewski, Francis A, Faksri, Kiatichai, Farhat, Maha R, Fleming, Joy, Fowler, Philip, Fowler, Tom A, Gao, Qian, Gardy, Jennifer, Gascoyne-Binzi, Deborah, Gibertoni-Cruz, Ana-Luiza, Gil-Brusola, Ana, Golubchik, Tanya, Gonzalo, Ximena, Grandjean, Louis, He, Guangxue, Guthrie, Jennifer L, Hoosdally, Sarah, Hunt, Martin, Iqbal, Zamin, Ismail, Nazir, Johnston, James, Khanzada, Faisal M, Khor, Chiea C, Kohl, Thomas A, Kong, Clare, Lipworth, Sam, Liu, Qingyun, Maphalala, Gugu, Martinez, Elena, Mathys, Vanessa, Merker, Matthias, Miotto, Paolo, Mistry, Nerges, Moore, David AJ, Murray, Megan, Niemann, Stefan, Omar, Shaheed V, Ong, Rick T-H, Peto, Tim EA, Posey, James E, Prammananan, Therdsak, Pym, Alexander, Rodrigues, Camilla, Rodrigues, Mabel, Rodwell, Timothy, Rossolini, Gian M, Sánchez Padilla, Elisabeth, Schito, Marco, Shen, Xin, Shendure, Jay, Sintchenko, Vitali, Sloutsky, Alex, Smith, E Grace, Snyder, Matthew, Soetaert, Karine, Starks, Angela M, Supply, Philip, Suriyapol, Prapat, Tahseen, Sabira, Tang, Patrick, Teo, Yik-Ying, Thuong, Thuong NT, Thwaites, Guy, Tortoli, Enrico, van Soolingen, Dick, Walker, A Sarah, Walker, Timothy M, Wilcox, Mark, Wilson, Daniel J, Wyllie, David, Yang, Yang, Zhang, Hongtai, Zhao, Yanlin, Zhu, Baoli, CRyPTIC Consortium and the 100,000 Genomes Project, CRyPTIC Consortium and the 100,000 Genomes Project, Allix-Béguec, Caroline, Arandjelovic, Irena, Bi, Lijun, Beckert, Patrick, Bonnet, Maryline, Bradley, Phelim, Cabibbe, Andrea M, Cancino-Muñoz, Irving, Caulfield, Mark J, Chaiprasert, Angkana, Cirillo, Daniela M, Clifton, David A, Comas, Iñaki, Crook, Derrick W, De Filippo, Maria R, de Neeling, Han, Diel, Roland, Drobniewski, Francis A, Faksri, Kiatichai, Farhat, Maha R, Fleming, Joy, Fowler, Philip, Fowler, Tom A, Gao, Qian, Gardy, Jennifer, Gascoyne-Binzi, Deborah, Gibertoni-Cruz, Ana-Luiza, Gil-Brusola, Ana, Golubchik, Tanya, Gonzalo, Ximena, Grandjean, Louis, He, Guangxue, Guthrie, Jennifer L, Hoosdally, Sarah, Hunt, Martin, Iqbal, Zamin, Ismail, Nazir, Johnston, James, Khanzada, Faisal M, Khor, Chiea C, Kohl, Thomas A, Kong, Clare, Lipworth, Sam, Liu, Qingyun, Maphalala, Gugu, Martinez, Elena, Mathys, Vanessa, Merker, Matthias, Miotto, Paolo, Mistry, Nerges, Moore, David AJ, Murray, Megan, Niemann, Stefan, Omar, Shaheed V, Ong, Rick T-H, Peto, Tim EA, Posey, James E, Prammananan, Therdsak, Pym, Alexander, Rodrigues, Camilla, Rodrigues, Mabel, Rodwell, Timothy, Rossolini, Gian M, Sánchez Padilla, Elisabeth, Schito, Marco, Shen, Xin, Shendure, Jay, Sintchenko, Vitali, Sloutsky, Alex, Smith, E Grace, Snyder, Matthew, Soetaert, Karine, Starks, Angela M, Supply, Philip, Suriyapol, Prapat, Tahseen, Sabira, Tang, Patrick, Teo, Yik-Ying, Thuong, Thuong NT, Thwaites, Guy, Tortoli, Enrico, van Soolingen, Dick, Walker, A Sarah, Walker, Timothy M, Wilcox, Mark, Wilson, Daniel J, Wyllie, David, Yang, Yang, Zhang, Hongtai, Zhao, Yanlin, and Zhu, Baoli
Abstract: BackgroundThe World Health Organization recommends drug-susceptibility testing of Mycobacterium tuberculosis complex for all patients with tuberculosis to guide treatment decisions and improve outcomes. Whether DNA sequencing can be used to accurately predict profiles of susceptibility to first-line antituberculosis drugs has not been clear.MethodsWe obtained whole-genome sequences and associated phenotypes of resistance or susceptibility to the first-line antituberculosis drugs isoniazid, rifampin, ethambutol, and pyrazinamide for isolates from 16 countries across six continents. For each isolate, mutations associated with drug resistance and drug susceptibility were identified across nine genes, and individual phenotypes were predicted unless mutations of unknown association were also present. To identify how whole-genome sequencing might direct first-line drug therapy, complete susceptibility profiles were predicted. These profiles were predicted to be susceptible to all four drugs (i.e., pansusceptible) if they were predicted to be susceptible to isoniazid and to the other drugs or if they contained mutations of unknown association in genes that affect susceptibility to the other drugs. We simulated the way in which the negative predictive value changed with the prevalence of drug resistance.ResultsA total of 10,209 isolates were analyzed. The largest proportion of phenotypes was predicted for rifampin (9660 [95.4%] of 10,130) and the smallest was predicted for ethambutol (8794 [89.8%] of 9794). Resistance to isoniazid, rifampin, ethambutol, and pyrazinamide was correctly predicted with 97.1%, 97.5%, 94.6%, and 91.3% sensitivity, respectively, and susceptibility to these drugs was correctly predicted with 99.0%, 98.8%, 93.6%, and 96.8% specificity. Of the 7516 isolates with complete phenotypic drug-susceptibility profiles, 5865 (78.0%) had complete genotypic predictions, among which 5250 profiles (89.5%) were correctly predicted. Among the 4037 phenotypic profil
Published: 2018

17. Variant calling on the GRCh38 assembly with the data from phase three of the 1000 Genomes Project [version 2; peer review: 2 approved]

Author: Ernesto Lowy-Gallego, Susan Fairley, Xiangqun Zheng-Bradley, Magali Ruffier, Laura Clarke, Paul Flicek, and The 1000 Genomes Project Consortium
Subjects: lcsh:R, lcsh:Medicine, lcsh:Q, lcsh:Science
Abstract: We present a set of biallelic SNVs and INDELs, from 2,548 samples spanning 26 populations from the 1000 Genomes Project, called de novo on GRCh38. We believe this will be a useful reference resource for those using GRCh38. It represents an improvement over the “lift-overs” of the 1000 Genomes Project data that have been available to date by encompassing all of the GRCh38 primary assembly autosomes and pseudo-autosomal regions, including novel, medically relevant loci. Here, we describe how the data set was created and benchmark our call set against that produced by the final phase of the 1000 Genomes Project on GRCh37 and the lift-over of that data to GRCh38.
Published: 2019

18. Prediction of Susceptibility to First-Line Tuberculosis Drugs by DNA Sequencing

Author: CRyPTIC Consortium, 100000 Genomes Project, Allix-Beguec, C, Arandjelovic, I, Bi, L, Beckert, P, Bonnet, M, Bradley, P, Cabibbe, AM, Cancino-Munoz, I, Caulfield, MJ, Chaiprasert, A, Cirillo, DM, Clifton, D, Comas, I, Crook, DW, De Filippo, MR, de Neeling, H, Diel, R, Drobniewski, FA, Faksri, K, Farhat, MR, Fleming, J, Fowler, P, Fowler, TA, Gao, Q, Gardy, J, Gascoyne-Binzi, D, Gibertoni-Cruz, A-L, Gil-Brusola, A, Golubchik, T, Gonzalo, X, Grandjean, L, He, G, Guthrie, JL, Hoosdally, S, Hunt, M, Iqbal, Z, Ismail, N, Johnston, J, Khanzada, FM, Khor, CC, Kohl, TA, Kong, C, Lipworth, S, Liu, Q, Maphalala, G, Martinez, E, Mathys, V, Merker, M, Miotto, P, Mistry, N, Moore, DAJ, Murray, M, Niemann, S, Ong, RT-H, Peto, TEA, Posey, JE, Prammananan, T, Pym, A, Rodrigues, C, Rodrigues, M, Rodwell, T, Rossolini, GM, Padilla, ES, Schito, M, Shen, X, Shendure, J, Sintchenko, V, Sloutsky, A, Smith, EG, Snyder, M, Soetaert, K, Starks, AM, Supply, P, Suriyapol, P, Tahseen, S, Tang, P, Teo, Y-Y, Thuong, TNT, Thwaites, G, Tortoli, E, Omar, SV, van Soolingen, D, Walker, AS, Walker, TM, Wilcox, M, Wilson, DJ, Wyllie, D, Yang, Y, Zhang, H, Zhao, Y, and Zhu, B
Abstract: Background: The World Health Organization recommends drug-susceptibility testing of Mycobacterium tuberculosis complex for all patients with tuberculosis to guide treatment decisions and improve outcomes. Whether DNA sequencing can be used to accurately predict profiles of susceptibility to first-line antituberculosis drugs has not been clear.\ud \ud \ud \ud Methods: We obtained whole-genome sequences and associated phenotypes of resistance or susceptibility to the first-line antituberculosis drugs isoniazid, rifampin, ethambutol, and pyrazinamide for isolates from 16 countries across six continents. For each isolate, mutations associated with drug resistance and drug susceptibility were identified across nine genes, and individual phenotypes were predicted unless mutations of unknown association were also present. To identify how whole-genome sequencing might direct first-line drug therapy, complete susceptibility profiles were predicted. These profiles were predicted to be susceptible to all four drugs (i.e., pansusceptible) if they were predicted to be susceptible to isoniazid and to the other drugs or if they contained mutations of unknown association in genes that affect susceptibility to the other drugs. We simulated the way in which the negative predictive value changed with the prevalence of drug resistance.\ud \ud \ud \ud Results: A total of 10,209 isolates were analyzed. The largest proportion of phenotypes was predicted for rifampin (9660 [95.4%] of 10,130) and the smallest was predicted for ethambutol (8794 [89.8%] of 9794). Resistance to isoniazid, rifampin, ethambutol, and pyrazinamide was correctly predicted with 97.1%, 97.5%, 94.6%, and 91.3% sensitivity, respectively, and susceptibility to these drugs was correctly predicted with 99.0%, 98.8%, 93.6%, and 96.8% specificity. Of the 7516 isolates with complete phenotypic drug-susceptibility profiles, 5865 (78.0%) had complete genotypic predictions, among which 5250 profiles (89.5%) were correctly predicted. Among the 4037 phenotypic profiles that were predicted to be pansusceptible, 3952 (97.9%) were correctly predicted.\ud \ud \ud \ud Conclusions: Genotypic predictions of the susceptibility of M. tuberculosis to first-line drugs were found to be correlated with phenotypic susceptibility to these drugs. (Funded by the Bill and Melinda Gates Foundation and others.)
Published: 2018

19. Alignment of 1000 Genomes Project reads to reference assembly GRCh38

Author: Zheng-Bradley, X., Streeter, I., Fairley, S., Richardson, D., Clarke, L., Flicek, P., 1000 Genomes Project , C., and Timmermann, B.
Subjects: 0301 basic medicine, European Nucleotide Archive, Computer science, read mapping, Health Informatics, Computational biology, Data Note, Genome, 03 medical and health sciences, Contig Mapping, 0302 clinical medicine, Human Genome Project, Humans, 1000 Genomes Project, reference genome, Sequence (medicine), Information retrieval, Contig, Whole Genome Sequencing, alignment, Reference Standards, Short read, Computer Science Applications, 030104 developmental biology, 030220 oncology & carcinogenesis, sequence reads, GRCh38, Sequence Alignment, Project analysis, Algorithms, Reference genome
Abstract: The 1000 Genomes Project produced more than 100 trillion basepairs of short read sequence from more than 2600 samples in 26 populations over a period of five years. In its final phase, the project released over 85 million genotyped and phased variants on human reference genome assembly GRCh37. An updated reference assembly, GRCh38, was released in late 2013, but there was insufficient time for the final phase of the project analysis to change to the new assembly. Although it is possible to lift the coordinates of the 1000 Genomes Project variants to the new assembly, this is a potentially error-prone process as coordinate remapping is most appropriate only for non-repetitive regions of the genome and those that did not see significant change between the two assemblies. It will also miss variants in any region that was newly added to GRCh38. Thus, to produce the highest quality variants and genotypes on GRCh38, the best strategy is to realign the reads and recall the variants based on the new alignment. As the first step of variant calling for the 1000 Genomes Project data, we have finished remapping all of the 1000 Genomes sequence reads to GRCh38 with alternative scaffold–aware BWA-MEM. The resulting alignments are available as CRAM, a reference-based sequence compression format. The data have been released on our FTP site and are also available from European Nucleotide Archive to facilitate researchers discovering variants on the primary sequences and alternative contigs of GRCh38.
Published: 2017

20. Variant calling on the GRCh38 assembly with the data from phase three of the 1000 Genomes Project

Author: Lowy-Gallego, E., Fairley, S., Zheng-Bradley, X., Ruffier, M., Clarke, L., Flicek, P., 1000 Genomes Project , C., and Timmermann, B.
Subjects: single nucleotide variation, 0303 health sciences, Computer science, variant calling, population genetics, Medicine (miscellaneous), Genomics, Computational biology, Articles, Data Note, General Biochemistry, Genetics and Molecular Biology, Set (abstract data type), Data set, variant discovery, 03 medical and health sciences, Resource (project management), 0302 clinical medicine, Benchmark (computing), 1000 Genomes Project, Indel, 030217 neurology & neurosurgery, 030304 developmental biology
Abstract: We present a set of biallelic SNVs and INDELs, from 2,548 samples spanning 26 populations from the 1000 Genomes Project, called de novo on GRCh38. We believe this will be a useful reference resource for those using GRCh38. It represents an improvement over the “lift-overs” of the 1000 Genomes Project data that have been available to date by encompassing all of the GRCh38 primary assembly autosomes and pseudo-autosomal regions, including novel, medically relevant loci. Here, we describe how the data set was created and benchmark our call set against that produced by the final phase of the 1000 Genomes Project on GRCh37 and the lift-over of that data to GRCh38.
Published: 2019
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21. Prediction of Susceptibility to First-Line Tuberculosis Drugs by DNA Sequencing

Author: Ministerio de Economía y Competitividad (España), European Research Council, Comas, Iñaki [0000-0001-5504-9408], CRyPTIC Consortium and the 100,000 Genomes Project, Allix-Beguec, Caroline, Arandjelovic, Irena, Bi, Lijun, Beckert, Patrick, Bonnet, Maryline, Bradley, Phelim, Cabibbe, Andrea M., Cancino-Muñoz, Irving, Caulfield, Mark J., Chaiprasert, Angkana, Cirillo, Daniela M., Clifton, David A., Comas, Iñaki, Crook, Derrick W., De Filippo, Maria R., de Neeling, Han, Diel, Roland, Drobniewski, Francis A, Faksri, Kiatichai, Farhat, Maha R., Fleming, Joy, Fowler, Philip, Fowler, Tom A, Gao, Qian, Gardy, Jennifer, Gascoyne-Binzi, Deborah, Gibertoni-Cruz, Ana-Luiza, Gil-Brusola, Ana, Golubchik, Tanya, Gonzalo, Ximena, Grandjean, Louis, He, Guangxue, Guthrie, Jennifer L., Hoosdally, Sarah, Hunt, Martin, Iqbal, Zamin, Ismail, Nazir, Johnston, James, Khanzada, Faisal M., Khor, Chiea C., Kohl, Thomas A, Kong, Clare, Lipworth, Sam, Liu, Qingyun, Maphalala, Gugu, Martínez, Elena, Mathys, Vanessa, Merker, Matthias, Miotto, Paolo, Mistry, Nerges, Moore, David A. J., Murray, Megan, Niemann, Stefan, Omar, Shaheed V., Ong, Rick T-H., Peto, Tim E.A., Posey, James E., Prammananan, Therdsak, Pym, Alexander, Rodrigues, Camilla, Rodrigues, Mabel, Rodwell, Timothy, Rossolini, Gian M., Sanchez Padilla, Elisabeth, Schito, Marco, Shen, Xin, Shendure, Jay, Sintchenko, Vitali, Sloutsky, Alex, Smith, E. Grace, Snyder, Matthew, Soetaert, Karine, Starks, Angela M., Supply, Philip, Suriyapol, Prapat, Tahseen, Sabira, Tang, Patrick, Teo, Yik-Ying, Thuong, Thuong N. T., Thwaites, Guy, Tortoli, Enrico, van Soolingen, Dick, Walker, A. Sarah, Walker, Timothy M., Wilcox, Mark, Wilson, Daniel J., Wyllie, David, Yang, Yang, Zhang, Hongtai, Zhao, Yanlin, Zhu, Baoli, Ministerio de Economía y Competitividad (España), European Research Council, Comas, Iñaki [0000-0001-5504-9408], CRyPTIC Consortium and the 100,000 Genomes Project, Allix-Beguec, Caroline, Arandjelovic, Irena, Bi, Lijun, Beckert, Patrick, Bonnet, Maryline, Bradley, Phelim, Cabibbe, Andrea M., Cancino-Muñoz, Irving, Caulfield, Mark J., Chaiprasert, Angkana, Cirillo, Daniela M., Clifton, David A., Comas, Iñaki, Crook, Derrick W., De Filippo, Maria R., de Neeling, Han, Diel, Roland, Drobniewski, Francis A, Faksri, Kiatichai, Farhat, Maha R., Fleming, Joy, Fowler, Philip, Fowler, Tom A, Gao, Qian, Gardy, Jennifer, Gascoyne-Binzi, Deborah, Gibertoni-Cruz, Ana-Luiza, Gil-Brusola, Ana, Golubchik, Tanya, Gonzalo, Ximena, Grandjean, Louis, He, Guangxue, Guthrie, Jennifer L., Hoosdally, Sarah, Hunt, Martin, Iqbal, Zamin, Ismail, Nazir, Johnston, James, Khanzada, Faisal M., Khor, Chiea C., Kohl, Thomas A, Kong, Clare, Lipworth, Sam, Liu, Qingyun, Maphalala, Gugu, Martínez, Elena, Mathys, Vanessa, Merker, Matthias, Miotto, Paolo, Mistry, Nerges, Moore, David A. J., Murray, Megan, Niemann, Stefan, Omar, Shaheed V., Ong, Rick T-H., Peto, Tim E.A., Posey, James E., Prammananan, Therdsak, Pym, Alexander, Rodrigues, Camilla, Rodrigues, Mabel, Rodwell, Timothy, Rossolini, Gian M., Sanchez Padilla, Elisabeth, Schito, Marco, Shen, Xin, Shendure, Jay, Sintchenko, Vitali, Sloutsky, Alex, Smith, E. Grace, Snyder, Matthew, Soetaert, Karine, Starks, Angela M., Supply, Philip, Suriyapol, Prapat, Tahseen, Sabira, Tang, Patrick, Teo, Yik-Ying, Thuong, Thuong N. T., Thwaites, Guy, Tortoli, Enrico, van Soolingen, Dick, Walker, A. Sarah, Walker, Timothy M., Wilcox, Mark, Wilson, Daniel J., Wyllie, David, Yang, Yang, Zhang, Hongtai, Zhao, Yanlin, and Zhu, Baoli
Abstract: BACKGROUND: The World Health Organization recommends drug-susceptibility testing of Mycobacterium tuberculosis complex for all patients with tuberculosis to guide treatment decisions and improve outcomes. Whether DNA sequencing can be used to accurately predict profiles of susceptibility to first-line antituberculosis drugs has not been clear. METHODS: We obtained whole-genome sequences and associated phenotypes of resistance or susceptibility to the first-line antituberculosis drugs isoniazid, rifampin, ethambutol, and pyrazinamide for isolates from 16 countries across six continents. For each isolate, mutations associated with drug resistance and drug susceptibility were identified across nine genes, and individual phenotypes were predicted unless mutations of unknown association were also present. To identify how whole-genome sequencing might direct first-line drug therapy, complete susceptibility profiles were predicted. These profiles were predicted to be susceptible to all four drugs (i.e., pansusceptible) if they were predicted to be susceptible to isoniazid and to the other drugs or if they contained mutations of unknown association in genes that affect susceptibility to the other drugs. We simulated the way in which the negative predictive value changed with the prevalence of drug resistance. RESULTS: A total of 10,209 isolates were analyzed. The largest proportion of phenotypes was predicted for rifampin (9660 [95.4%] of 10,130) and the smallest was predicted for ethambutol (8794 [89.8%] of 9794). Resistance to isoniazid, rifampin, ethambutol, and pyrazinamide was correctly predicted with 97.1%, 97.5%, 94.6%, and 91.3% sensitivity, respectively, and susceptibility to these drugs was correctly predicted with 99.0%, 98.8%, 93.6%, and 96.8% specificity. Of the 7516 isolates with complete phenotypic drug-susceptibility profiles, 5865 (78.0%) had complete genotypic predictions, among which 5250 profiles (89.5%) were correctly predicted. Among the 4037 phenotypi
Published: 2018

22. A global reference for human genetic variation

Author: Colonna V. (1000 Genomes Project Consortium) Adam Auton, Gonçalo R Abecasis, David M Altshuler, Richard M Durbin, David R Bentley, Aravinda Chakravarti, Andrew G Clark, Peter Donnelly, Evan E Eichler, Paul Flicek, Stacey B Gabriel, Richard A Gibbs, Eric D Green, Matthew E Hurles, Bartha M Knoppers, Jan O Korbel, Eric S Lander, Charles Lee, Hans Lehrach, Elaine R Mardis, Gabor T Marth, Gil A McVean, Deborah A Nickerson, Jeanette P Schmidt, Stephen T Sherry, Jun Wang, Richard K Wilson, Eric Boerwinkle, Harsha Doddapaneni, Yi Han, Viktoriya Korchina, Christie Kovar, Sandra Lee, Donna Muzny, Jeffrey G Reid, Yiming Zhu, Yuqi Chang, Qiang Feng, Xiaodong Fang, Xiaosen Guo, Min Jian, Hui Jiang, Xin Jin, Tianming Lan, Guoqing Li, Jingxiang Li, Yingrui Li, Shengmao Liu, Xiao Liu, Yao Lu, Xuedi Ma, Meifang Tang, Bo Wang, Guangbiao Wang, Honglong Wu, Renhua Wu, Xun Xu, Ye Yin, Dandan Zhang, Wenwei Zhang, Jiao Zhao, Meiru Zhao, Xiaole Zheng, Namrata Gupta, Neda Gharani, Lorraine H Toji, Norman P Gerry, Alissa M Resch, Jonathan Barker, Laura Clarke, Laurent Gil, Sarah E Hunt, Gavin Kelman, Eugene Kulesha, Rasko Leinonen, William M McLaren, Rajesh Radhakrishnan, Asier Roa, Dmitriy Smirnov, Richard E Smith, Ian Streeter, Anja Thormann, Iliana Toneva, Brendan Vaughan, Xiangqun Zheng-Bradley, Russell Grocock, Sean Humphray, Terena James, Zoya Kingsbury, Ralf Sudbrak, Marcus W Albrecht, Vyacheslav S Amstislavskiy, Tatiana A Borodina, Matthias Lienhard, Florian Mertes, Marc Sultan, Bernd Timmermann, Marie-Laure Yaspo, Lucinda Fulton, Robert Fulton, Victor Ananiev, Zinaida Belaia, Dimitriy Beloslyudtsev, Nathan Bouk, Chao Chen, Deanna Church, Robert Cohen, Charles Cook, John Garner, Timothy Hefferon, Mikhail Kimelman, Chunlei Liu, John Lopez, Peter Meric, Chris O'Sullivan, Yuri Ostapchuk, Lon Phan, Sergiy Ponomarov, Valerie Schneider, Eugene Shekhtman, Karl Sirotkin, Douglas Slotta, Hua Zhang, Senduran Balasubramaniam, John Burton, Petr Danecek, Thomas M Keane, Anja Kolb-Kokocinski, Shane McCarthy, James Stalker, Michael Quail, Christopher J Davies, Jeremy Gollub, Teresa Webster, Brant Wong, Yiping Zhan, Adam Auton, Christopher L Campbell, Yu Kong, Anthony Marcketta, Fuli Yu, Lilian Antunes, Matthew Bainbridge, Aniko Sabo, Zhuoyi Huang, Lachlan J M Coin, Lin Fang, Qibin Li, Zhenyu Li, Haoxiang Lin, Binghang Liu, Ruibang Luo, Haojing Shao, Yinlong Xie, Chen Ye, Chang Yu, Fan Zhang, Hancheng Zheng, Hongmei Zhu, Can Alkan, Elif Dal, Fatma Kahveci, Erik P Garrison, Deniz Kural, Wan-Ping Lee, Wen Fung Leong, Michael Stromberg, Alistair N Ward, Jiantao Wu, Mengyao Zhang, Mark J Daly, Mark A DePristo, Robert E Handsaker, Eric Banks, Gaurav Bhatia, Guillermo Del Angel, Giulio Genovese, Heng Li, Seva Kashin, Steven A McCarroll, James C Nemesh, Ryan E Poplin, Seungtai C Yoon, Jayon Lihm, Vladimir Makarov, Srikanth Gottipati, Alon Keinan, Juan L Rodriguez-Flores, Tobias Rausch, Markus H Fritz, Adrian M Stütz, Kathryn Beal, Avik Datta, Javier Herrero, Graham R S Ritchie, Daniel Zerbino, Pardis C Sabeti, Ilya Shlyakhter, Stephen F Schaffner, Joseph Vitti, David N Cooper, Edward V Ball, Peter D Stenson, Bret Barnes, Markus Bauer, R Keira Cheetham, Anthony Cox, Michael Eberle, Scott Kahn, Lisa Murray, John Peden, Richard Shaw, Eimear E Kenny, Mark A Batzer, Miriam K Konkel, Jerilyn A Walker, Daniel G MacArthur, Monkol Lek, Ralf Herwig, Li Ding, Daniel C Koboldt, David Larson, Kai Ye, Simon Gravel, Anand Swaroop, Emily Chew, Tuuli Lappalainen, Yaniv Erlich, Melissa Gymrek, Thomas Frederick Willems, Jared T Simpson, Mark D Shriver, Jeffrey A Rosenfeld, Carlos D Bustamante, Stephen B Montgomery, Francisco M De La Vega, Jake K Byrnes, Andrew W Carroll, Marianne K DeGorter, Phil Lacroute, Brian K Maples, Alicia R Martin, Andres Moreno-Estrada, Suyash S Shringarpure, Fouad Zakharia, Eran Halperin, Yael Baran, Eliza Cerveira, Jaeho Hwang, Ankit Malhotra, Dariusz Plewczynski, Kamen Radew, Mallory Romanovitch, Chengsheng Zhang, Fiona C L Hyland, David W Craig, Alexis Christoforides, Nils Homer, Tyler Izatt, Ahmet A Kurdoglu, Shripad A Sinari, Kevin Squire, Chunlin Xiao, Jonathan Sebat, Danny Antaki, Madhusudan Gujral, Amina Noor, Kenny Ye, Esteban G Burchard, Ryan D Hernandez, Christopher R Gignoux, David Haussler, Sol J Katzman, W James Kent, Bryan Howie, Andres Ruiz-Linares, Emmanouil T Dermitzakis, Scott E Devine, Hyun Min Kang, Jeffrey M Kidd, Tom Blackwell, Sean Caron, Wei Chen, Sarah Emery, Lars Fritsche, Christian Fuchsberger, Goo Jun, Bingshan Li, Robert Lyons, Chris Scheller, Carlo Sidore, Shiya Song, Elzbieta Sliwerska, Daniel Taliun, Adrian Tan, Ryan Welch, Mary Kate Wing, Xiaowei Zhan, Philip Awadalla, Alan Hodgkinson, Yun Li, Xinghua Shi, Andrew Quitadamo, Gerton Lunter, Jonathan L Marchini, Simon Myers, Claire Churchhouse, Olivier Delaneau, Anjali Gupta-Hinch, Warren Kretzschmar, Zamin Iqbal, Iain Mathieson, Androniki Menelaou, Andy Rimmer, Dionysia K Xifara, Taras K Oleksyk, Yunxin Fu, Xiaoming Liu, Momiao Xiong, Lynn Jorde, David Witherspoon, Jinchuan Xing, Brian L Browning, Sharon R Browning, Fereydoun Hormozdiari, Peter H Sudmant, Ekta Khurana, Chris Tyler-Smith, Cornelis A Albers, Qasim Ayub, Yuan Chen, Vincenza Colonna, Luke Jostins, Klaudia Walter, Yali Xue, Mark B Gerstein, Alexej Abyzov, Suganthi Balasubramanian, Jieming Chen, Declan Clarke, Yao Fu, Arif O Harmanci, Mike Jin, Donghoon Lee, Jeremy Liu, Xinmeng Jasmine Mu, Jing Zhang, Yan Zhang, Chris Hartl, Khalid Shakir, Jeremiah Degenhardt, Sascha Meiers, Benjamin Raeder, Francesco Paolo Casale, Oliver Stegle, Eric-Wubbo Lameijer, Ira Hall, Vineet Bafna, Jacob Michaelson, Eugene J Gardner, Ryan E Mills, Gargi Dayama, Ken Chen, Xian Fan, Zechen Chong, Tenghui Chen, Mark J Chaisson, John Huddleston, Maika Malig, Bradley J Nelson, Nicholas F Parrish, Ben Blackburne, Sarah J Lindsay, Zemin Ning, Yujun Zhang, Hugo Lam, Cristina Sisu, Danny Challis, Uday S Evani, James Lu, Uma Nagaswamy, Jin Yu, Wangshen Li, Lukas Habegger, Haiyuan Yu, Fiona Cunningham, Ian Dunham, Kasper Lage, Jakob Berg Jespersen, Heiko Horn, Donghoon Kim, Rob Desalle, Apurva Narechania, Melissa A Wilson Sayres, Fernando L Mendez, G David Poznik, Peter A Underhill, Lachlan Coin, David Mittelman, Ruby Banerjee, Maria Cerezo, Thomas W Fitzgerald, Sandra Louzada, Andrea Massaia, Graham R Ritchie, Fengtang Yang, Divya Kalra, Walker Hale, Xu Dan, Kathleen C Barnes, Christine Beiswanger, Hongyu Cai, Hongzhi Cao, Brenna Henn, Danielle Jones, Jane S Kaye, Alastair Kent, Angeliki Kerasidou, Rasika Mathias, Pilar N Ossorio, Michael Parker, Charles N Rotimi, Charmaine D Royal, Karla Sandoval, Yeyang Su, Zhongming Tian, Sarah Tishkoff, Marc Via, Yuhong Wang, Huanming Yang, Ling Yang, Jiayong Zhu, Walter Bodmer, Gabriel Bedoya, Zhiming Cai, Yang Gao, Jiayou Chu, Leena Peltonen, Andres Garcia-Montero, Alberto Orfao, Julie Dutil, Juan C Martinez-Cruzado, Rasika A Mathias, Anselm Hennis, Harold Watson, Colin McKenzie, Firdausi Qadri, Regina LaRocque, Xiaoyan Deng, Danny Asogun, Onikepe Folarin, Christian Happi, Omonwunmi Omoniwa, Matt Stremlau, Ridhi Tariyal, Muminatou Jallow, Fatoumatta Sisay Joof, Tumani Corrah, Kirk Rockett, Dominic Kwiatkowski, Jaspal Kooner, Trân T?nh Hiên, Sarah J Dunstan, Nguyen Thuy Hang, Richard Fonnie, Robert Garry, Lansana Kanneh, Lina Moses, John Schieffelin, Donald S Grant, Carla Gallo, Giovanni Poletti, Danish Saleheen, Asif Rasheed, Lisa D Brooks, Adam L Felsenfeld, Jean E McEwen, Yekaterina Vaydylevich, Audrey Duncanson, Michael Dunn, Jeffery A Schloss, 1000 Genomes Project Consortium, Institute for Medical Engineering and Science, Broad Institute of MIT and Harvard, Lincoln Laboratory, Massachusetts Institute of Technology. Department of Biology, Gabriel, Stacey, Lander, Eric Steven, Daly, Mark J, Banks, Eric, Bhatia, Gaurav, Kashin, Seva, McCarroll, Steven A, Nemesh, James, Poplin, Ryan E., Sabeti, Pardis, Shlyakhter, Ilya, Schaffner, Stephen F, Vitti, Joseph, Gymrek, Melissa A, Hartler, Christina M., and Tariyal, Ridhi
Subjects: demography, genetic association, genotype, Human genomics, Genome-wide association study, Review, SUSCEPTIBILITY, DISEASE, polymorphism, 0302 clinical medicine, quantitative trait locus, INDEL Mutation, genetics, GeneralLiterature_REFERENCE(e.g.,dictionaries,encyclopedias,glossaries), MUTATION, Exome sequencing, 0303 health sciences, public health, Sequence analysis, High-Throughput Nucleotide Sequencing, standard, Genomics, Reference Standards, Physical Chromosome Mapping, 3. Good health, priority journal, Science & Technology - Other Topics, BAYES FACTORS, Molecular Developmental Biology, Genotype, Genetics, Medical, Quantitative Trait Loci, DNA sequence, rare disease, human genetics, information processing, Article, 03 medical and health sciences, SDG 3 - Good Health and Well-being, POPULATION HISTORY, human genome, Humans, retroposon, Genetic variability, human, GENOME-WIDE ASSOCIATION, 1000 Genomes Project, Demography, Science & Technology, ancestry, disease predisposition, Genetic Variation, MACULAR DEGENERATION, major clinical study, gene linkage disequilibrium, purl.org/pe-repo/ocde/ford#3.01.02 [https], Genetics, Population, 030217 neurology & neurosurgery, haplotype, Internationality, VARIANT, Datasets as Topic, Human genetic variation, COMPLEMENT FACTOR-H, single nucleotide polymorphism, genetic variability, Exome, chromosome map, Genetics, Variant Call Format, Genome, Multidisciplinary, 1000 Genomes Project Consortium, international cooperation, Multidisciplinary Sciences, standards, Disease Susceptibility, medical genetics, General Science & Technology, Population, Computational biology, Biology, gene frequency, Polymorphism, Single Nucleotide, high throughput sequencing, Rare Diseases, promoter region, MD Multidisciplinary, Genetic variation, QH426, 030304 developmental biology, Neurodevelopmental disorders Donders Center for Medical Neuroscience [Radboudumc 7], Genome, Human, population genetics, population structure, Sequence Analysis, DNA, gene structure, INDIVIDUALS, Haplotypes, Genome-Wide Association Study, purl.org/pe-repo/ocde/ford#1.06.07 [https]
Abstract: The 1000 Genomes Project set out to provide a comprehensive description of common human genetic variation by applying whole-genome sequencing to a diverse set of individuals from multiple populations. Here we report completion of the project, having reconstructed the genomes of 2,504 individuals from 26 populations using a combination of low-coverage whole-genome sequencing, deep exome sequencing, and dense microarray genotyping. We characterized a broad spectrum of genetic variation, in total over 88 million variants (84.7 million single nucleotide polymorphisms (SNPs), 3.6 million short insertions/deletions (indels), and 60,000 structural variants), all phased onto high-quality haplotypes. This resource includes >99% of SNP variants with a frequency of >1% for a variety of ancestries. We describe the distribution of genetic variation across the global sample, and discuss the implications for common disease studies., Wellcome Trust (London, England) (Core Award 090532/Z/09/Z), Wellcome Trust (London, England) (Senior Investigator Award 095552/Z/11/Z ), Wellcome Trust (London, England) (WT095908), Wellcome Trust (London, England) (WT109497), Wellcome Trust (London, England) (WT098051), Wellcome Trust (London, England) (WT086084/Z/08/Z), Wellcome Trust (London, England) (WT100956/Z/13/Z ), Wellcome Trust (London, England) (WT097307), Wellcome Trust (London, England) (WT0855322/Z/08/Z ), Wellcome Trust (London, England) (WT090770/Z/09/Z ), Wellcome Trust (London, England) (Major Overseas program in Vietnam grant 089276/Z.09/Z), Medical Research Council (Great Britain) (grant G0801823), Biotechnology and Biological Sciences Research Council (Great Britain) (grant BB/I02593X/1), Biotechnology and Biological Sciences Research Council (Great Britain) (grant BB/I021213/1), Zhongguo ke xue ji shu qing bao yan jiu suo. Office of 863 Programme of China (2012AA02A201), National Basic Research Program of China (2011CB809201), National Basic Research Program of China (2011CB809202), National Basic Research Program of China (2011CB809203), National Natural Science Foundation of China (31161130357), Shenzhen Municipal Government of China (grant ZYC201105170397A), Canadian Institutes of Health Research (grant 136855), Quebec Ministry of Economic Development, Innovation, and Exports (PSR-SIIRI-195), Germany. Bundesministerium für Bildung und Forschung (0315428A), Germany. Bundesministerium für Bildung und Forschung (01GS08201), Germany. Bundesministerium für Bildung und Forschung (BMBF-EPITREAT grant 0316190A), Deutsche Forschungsgemeinschaft (Emmy Noether Grant KO4037/1-1), Beatriu de Pinos Program (2006 BP-A 10144), Beatriu de Pinos Program (2009 BP-B 00274), Spanish National Institute for Health (grant PRB2 IPT13/0001-ISCIII-SGEFI/FEDER), Japan Society for the Promotion of Science (fellowship number PE13075), Marie Curie Actions Career Integration (grant 303772), Fonds National Suisse del la Recherche, SNSF, Scientifique (31003A_130342), National Center for Biotechnology Information (U.S.) (U54HG3067), National Center for Biotechnology Information (U.S.) (U54HG3273), National Center for Biotechnology Information (U.S.) (U01HG5211), National Center for Biotechnology Information (U.S.) (U54HG3079), National Center for Biotechnology Information (U.S.) (R01HG2898), National Center for Biotechnology Information (U.S.) (R01HG2385), National Center for Biotechnology Information (U.S.) (RC2HG5552), National Center for Biotechnology Information (U.S.) (U01HG6513), National Center for Biotechnology Information (U.S.) (U01HG5214), National Center for Biotechnology Information (U.S.) (U01HG5715), National Center for Biotechnology Information (U.S.) (U01HG5718), National Center for Biotechnology Information (U.S.) (U01HG5728), National Center for Biotechnology Information (U.S.) (U41HG7635), National Center for Biotechnology Information (U.S.) (U41HG7497), National Center for Biotechnology Information (U.S.) (R01HG4960), National Center for Biotechnology Information (U.S.) (R01HG5701), National Center for Biotechnology Information (U.S.) (R01HG5214), National Center for Biotechnology Information (U.S.) (R01HG6855), National Center for Biotechnology Information (U.S.) (R01HG7068), National Center for Biotechnology Information (U.S.) (R01HG7644), National Center for Biotechnology Information (U.S.) (DP2OD6514), National Center for Biotechnology Information (U.S.) (DP5OD9154), National Center for Biotechnology Information (U.S.) (R01CA166661), National Center for Biotechnology Information (U.S.) (R01CA172652), National Center for Biotechnology Information (U.S.) (P01GM99568), National Center for Biotechnology Information (U.S.) (R01GM59290), National Center for Biotechnology Information (U.S.) (R01GM104390), National Center for Biotechnology Information (U.S.) (T32GM7790), National Center for Biotechnology Information (U.S.) (R01HL87699), National Center for Biotechnology Information (U.S.) (R01HL104608), National Center for Biotechnology Information (U.S.) (T32HL94284), National Center for Biotechnology Information (U.S.) (HHSN268201100040C), National Center for Biotechnology Information (U.S.) (HHSN272201000025C), Lundbeck Foundation (grant R170-2014-1039, Simons Foundation (SFARI award SF51), National Science Foundation (U.S.) (Research Fellowship DGE-1147470)
Published: 2015
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23. The Mobile Element Locator Tool (MELT): population-scale mobile element discovery and biology

Author: Gardner, E., Lam, V., Harris, D., Chuang, N., Scott, E., Pittard, W., Mills, R., 1000 Genomes Project, C., Timmermann, B., and Devine, S.
Subjects: 0301 basic medicine, Pan troglodytes, Population, Method, Population genetics, Bioinformatics, Polymorphism, Single Nucleotide, Evolution, Molecular, 03 medical and health sciences, Mobile Element Locator Tool (MELT), Databases, Genetic, Genetics, Animals, Humans, 1000 Genomes Project, education, Denisovan, Genetics (clinical), Neanderthals, Whole genome sequencing, Clinical genomics, education.field_of_study, Whole Genome Sequencing, biology, Cloud systems, Computational Biology, High-Throughput Nucleotide Sequencing, biology.organism_classification, 030104 developmental biology, Evolutionary biology, DNA Transposable Elements, Human genome, Software
Abstract: Mobile element insertions (MEIs) represent ∼25% of all structural variants in human genomes. Moreover, when they disrupt genes, MEIs can influence human traits and diseases. Therefore, MEIs should be fully discovered along with other forms of genetic variation in whole genome sequencing (WGS) projects involving population genetics, human diseases, and clinical genomics. Here, we describe the Mobile Element Locator Tool (MELT), which was developed as part of the 1000 Genomes Project to perform MEI discovery on a population scale. Using both Illumina WGS data and simulations, we demonstrate that MELT outperforms existing MEI discovery tools in terms of speed, scalability, specificity, and sensitivity, while also detecting a broader spectrum of MEI-associated features. Several run modes were developed to perform MEI discovery on local and cloud systems. In addition to using MELT to discover MEIs in modern humans as part of the 1000 Genomes Project, we also used it to discover MEIs in chimpanzees and ancient (Neanderthal and Denisovan) hominids. We detected diverse patterns of MEI stratification across these populations that likely were caused by (1) diverse rates of MEI production from source elements, (2) diverse patterns of MEI inheritance, and (3) the introgression of ancient MEIs into modern human genomes. Overall, our study provides the most comprehensive map of MEIs to date spanning chimpanzees, ancient hominids, and modern humans and reveals new aspects of MEI biology in these lineages. We also demonstrate that MELT is a robust platform for MEI discovery and analysis in a variety of experimental settings.
Published: 2017
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24. A global reference for human genetic variation

Author: The 1000 Genomes Project Consortium
Abstract: The 1000 Genomes Project set out to provide a comprehensive description of common human genetic variation by applying whole-genome sequencing to a diverse set of individuals from multiple populations. Here we report completion of the project, having reconstructed the genomes of 2,504 individuals from 26 populations using a combination of low-coverage whole-genome sequencing, deep exome sequencing, and dense microarray genotyping. We characterized a broad spectrum of genetic variation, in total over 88 million variants (84.7 million single nucleotide polymorphisms (SNPs), 3.6 million short insertions/deletions (indels), and 60,000 structural variants), all phased onto high-quality haplotypes. This resource includes >99% of SNP variants with a frequency of >1% for a variety of ancestries. We describe the distribution of genetic variation across the global sample, and discuss the implications for common disease studies.
Published: 2015

25. A global reference for human genetic variation

Author: Institute for Medical Engineering and Science, Broad Institute of MIT and Harvard, Lincoln Laboratory, Massachusetts Institute of Technology. Department of Biology, Gabriel, Stacey, Lander, Eric Steven, Daly, Mark J, Banks, Eric, Bhatia, Gaurav, Kashin, Seva, McCarroll, Steven A, Nemesh, James, Poplin, Ryan E., Sabeti, Pardis, Shlyakhter, Ilya, Schaffner, Stephen F, Vitti, Joseph, Gymrek, Melissa A, Hartler, Christina M., Tariyal, Ridhi, The 1000 Genomes Project Consortium, Institute for Medical Engineering and Science, Broad Institute of MIT and Harvard, Lincoln Laboratory, Massachusetts Institute of Technology. Department of Biology, Gabriel, Stacey, Lander, Eric Steven, Daly, Mark J, Banks, Eric, Bhatia, Gaurav, Kashin, Seva, McCarroll, Steven A, Nemesh, James, Poplin, Ryan E., Sabeti, Pardis, Shlyakhter, Ilya, Schaffner, Stephen F, Vitti, Joseph, Gymrek, Melissa A, Hartler, Christina M., Tariyal, Ridhi, and The 1000 Genomes Project Consortium
Abstract: The 1000 Genomes Project set out to provide a comprehensive description of common human genetic variation by applying whole-genome sequencing to a diverse set of individuals from multiple populations. Here we report completion of the project, having reconstructed the genomes of 2,504 individuals from 26 populations using a combination of low-coverage whole-genome sequencing, deep exome sequencing, and dense microarray genotyping. We characterized a broad spectrum of genetic variation, in total over 88 million variants (84.7 million single nucleotide polymorphisms (SNPs), 3.6 million short insertions/deletions (indels), and 60,000 structural variants), all phased onto high-quality haplotypes. This resource includes >99% of SNP variants with a frequency of >1% for a variety of ancestries. We describe the distribution of genetic variation across the global sample, and discuss the implications for common disease studies., Wellcome Trust (London, England) (Core Award 090532/Z/09/Z), Wellcome Trust (London, England) (Senior Investigator Award 095552/Z/11/Z ), Wellcome Trust (London, England) (WT095908), Wellcome Trust (London, England) (WT109497), Wellcome Trust (London, England) (WT098051), Wellcome Trust (London, England) (WT086084/Z/08/Z), Wellcome Trust (London, England) (WT100956/Z/13/Z ), Wellcome Trust (London, England) (WT097307), Wellcome Trust (London, England) (WT0855322/Z/08/Z ), Wellcome Trust (London, England) (WT090770/Z/09/Z ), Wellcome Trust (London, England) (Major Overseas program in Vietnam grant 089276/Z.09/Z), Medical Research Council (Great Britain) (grant G0801823), Biotechnology and Biological Sciences Research Council (Great Britain) (grant BB/I02593X/1), Biotechnology and Biological Sciences Research Council (Great Britain) (grant BB/I021213/1), Zhongguo ke xue ji shu qing bao yan jiu suo. Office of 863 Programme of China (2012AA02A201), National Basic Research Program of China (2011CB809201), National Basic Research Program of China (2011CB809202), National Basic Research Program of China (2011CB809203), National Natural Science Foundation of China (31161130357), Shenzhen Municipal Government of China (grant ZYC201105170397A), Canadian Institutes of Health Research (grant 136855), Quebec Ministry of Economic Development, Innovation, and Exports (PSR-SIIRI-195), Germany. Bundesministerium für Bildung und Forschung (0315428A), Germany. Bundesministerium für Bildung und Forschung (01GS08201), Germany. Bundesministerium für Bildung und Forschung (BMBF-EPITREAT grant 0316190A), Deutsche Forschungsgemeinschaft (Emmy Noether Grant KO4037/1-1), Beatriu de Pinos Program (2006 BP-A 10144), Beatriu de Pinos Program (2009 BP-B 00274), Spanish National Institute for Health (grant PRB2 IPT13/0001-ISCIII-SGEFI/FEDER), Japan Society for the Promotion of Science (fellowship number PE13075), Marie Curie Actions Career Integration (grant 303772), Fonds National Suisse del la Recherche, SNSF, Scientifique (31003A_130342), National Center for Biotechnology Information (U.S.) (U54HG3067), National Center for Biotechnology Information (U.S.) (U54HG3273), National Center for Biotechnology Information (U.S.) (U01HG5211), National Center for Biotechnology Information (U.S.) (U54HG3079), National Center for Biotechnology Information (U.S.) (R01HG2898), National Center for Biotechnology Information (U.S.) (R01HG2385), National Center for Biotechnology Information (U.S.) (RC2HG5552), National Center for Biotechnology Information (U.S.) (U01HG6513), National Center for Biotechnology Information (U.S.) (U01HG5214), National Center for Biotechnology Information (U.S.) (U01HG5715), National Center for Biotechnology Information (U.S.) (U01HG5718), National Center for Biotechnology Information (U.S.) (U01HG5728), National Center for Biotechnology Information (U.S.) (U41HG7635), National Center for Biotechnology Information (U.S.) (U41HG7497), National Center for Biotechnology Information (U.S.) (R01HG4960), National Center for Biotechnology Information (U.S.) (R01HG5701), National Center for Biotechnology Information (U.S.) (R01HG5214), National Center for Biotechnology Information (U.S.) (R01HG6855), National Center for Biotechnology Information (U.S.) (R01HG7068), National Center for Biotechnology Information (U.S.) (R01HG7644), National Center for Biotechnology Information (U.S.) (DP2OD6514), National Center for Biotechnology Information (U.S.) (DP5OD9154), National Center for Biotechnology Information (U.S.) (R01CA166661), National Center for Biotechnology Information (U.S.) (R01CA172652), National Center for Biotechnology Information (U.S.) (P01GM99568), National Center for Biotechnology Information (U.S.) (R01GM59290), National Center for Biotechnology Information (U.S.) (R01GM104390), National Center for Biotechnology Information (U.S.) (T32GM7790), National Center for Biotechnology Information (U.S.) (R01HL87699), National Center for Biotechnology Information (U.S.) (R01HL104608), National Center for Biotechnology Information (U.S.) (T32HL94284), National Center for Biotechnology Information (U.S.) (HHSN268201100040C), National Center for Biotechnology Information (U.S.) (HHSN272201000025C), Lundbeck Foundation (grant R170-2014-1039, Simons Foundation (SFARI award SF51), National Science Foundation (U.S.) (Research Fellowship DGE-1147470)
Published: 2017

26. Population Structure Analysis of Bull Genomes of European and Western Ancestry.

Author: Chung, NC, Szyda, J, Frąszczak, M, 1000 Bull Genomes Project, Chung, NC, Szyda, J, Frąszczak, M, and 1000 Bull Genomes Project
Abstract: Since domestication, population bottlenecks, breed formation, and selective breeding have radically shaped the genealogy and genetics of Bos taurus. In turn, characterization of population structure among diverse bull (males of Bos taurus) genomes enables detailed assessment of genetic resources and origins. By analyzing 432 unrelated bull genomes from 13 breeds and 16 countries, we demonstrate genetic diversity and structural complexity among the European/Western cattle population. Importantly, we relaxed a strong assumption of discrete or admixed population, by adapting latent variable models for individual-specific allele frequencies that directly capture a wide range of complex structure from genome-wide genotypes. As measured by magnitude of differentiation, selection pressure on SNPs within genes is substantially greater than that on intergenic regions. Additionally, broad regions of chromosome 6 harboring largest genetic differentiation suggest positive selection underlying population structure. We carried out gene set analysis using SNP annotations to identify enriched functional categories such as energy-related processes and multiple development stages. Our population structure analysis of bull genomes can support genetic management strategies that capture structural complexity and promote sustainable genetic breadth.
Published: 2017

27. The Landscape of Human STR Variation

Author: Willems, T., Gymrek, M., Highnam, G., The 1000 Genomes Project, C., Herwig, R., Lehrach, H., Sudbrak, R., Timmermann, B., Mittelman, D., Erlich, Y., Harvard University--MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology. Computational and Systems Biology Program, Willems, Thomas Frederick, and Gymrek, Melissa A.
Subjects: Resource, Linkage disequilibrium, congenital, hereditary, and neonatal diseases and abnormalities, Genotype, Genetic genealogy, STR multiplex system, Population, Population genetics, Genomics, Single-nucleotide polymorphism, Biology, Polymorphism, Single Nucleotide, Linkage Disequilibrium, Gene Frequency, Genetic variation, Genetics, Humans, 1000 Genomes Project, education, Alleles, Genetics (clinical), education.field_of_study, Genome, Human, Genetic Variation, eye diseases, humanities, Genetics, Population, Evolutionary biology, Microsatellite Repeats, Reference genome
Abstract: Short tandem repeats are among the most polymorphic loci in the human genome. These loci play a role in the etiology of a range of genetic diseases and have been frequently utilized in forensics, population genetics, and genetic genealogy. Despite this plethora of applications, little is known about the variation of most STRs in the human population. Here, we report the largest-scale analysis of human STR variation to date. We collected information for nearly 700,000 STR loci across more than 1000 individuals in Phase 1 of the 1000 Genomes Project. Extensive quality controls show that reliable allelic spectra can be obtained for close to 90% of the STR loci in the genome. We utilize this call set to analyze determinants of STR variation, assess the human reference genome’s representation of STR alleles, find STR loci with common loss-of-function alleles, and obtain initial estimates of the linkage disequilibrium between STRs and common SNPs. Overall, these analyses further elucidate the scale of genetic variation beyond classical point mutations., American Society for Engineering Education. National Defense Science and Engineering Graduate Fellowship
Published: 2014
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28. Analysis of variable retroduplications in human populations suggests coupling of retrotransposition to cell division

Author: Abyzov, A., Iskow, R., Gokcumen, O., Radke, D., Balasubramanian, S., Pei, B., Habegger, L., 1000 Genomes Project, C., Timmermann, B., Lee, C., and Gerstein, M.
Abstract: In primates and other animals, reverse transcription of mRNA followed by genomic integration creates retroduplications. Expressed retroduplications are either “retrogenes” coding for functioning proteins, or expressed “processed pseudogenes,” which can function as noncoding RNAs. To date, little is known about the variation in retroduplications in terms of their presence or absence across individuals in the human population. We have developed new methodologies that allow us to identify “novel” retroduplications (i.e., those not present in the reference genome), to find their insertion points, and to genotype them. Using these methods, we catalogued and analyzed 174 retroduplication variants in almost one thousand humans, which were sequenced as part of Phase 1 of The 1000 Genomes Project Consortium. The accuracy of our data set was corroborated by (1) multiple lines of sequencing evidence for retroduplication (e.g., depth of coverage in exons vs. introns), (2) experimental validation, and (3) the fact that we can reconstruct a correct phylogenetic tree of human subpopulations based solely on retroduplications. We also show that parent genes of retroduplication variants tend to be expressed at the M-to-G1 transition in the cell cycle and that M-to-G1 expressed genes have more copies of fixed retroduplications than genes expressed at other times. These findings suggest that cell division is coupled to retrotransposition and, perhaps, is even a requirement for it.
Published: 2013

29. Reconstructing Native American Migrations from Whole-Genome and Whole-Exome Data

Author: Gravel, Simon, Zakharia, Fouad, Moreno Estrada, Andrés, Byrnes, Jake K., Muzzio, Marina, Rodriguez Flores, Juan L., Kenny, Eimear E., Gignoux, Christopher R., Maples, Brian K., Guiblet, Wilfried, Dutil, Julie, Via, Marc, Sandoval, Karla, Bedoya, Gabriel, Oleksyk, Taras K., Ruiz Linares, Andrés, Burchard, Esteban G., Martinez Cruzado, Juan Carlos, Bustamante, Carlos D., The 1000 Genomes Project, and Universitat de Barcelona
Subjects: Cancer Research, Population genetics, Amèrica, Genoma humà, 92D25, purl.org/becyt/ford/1 [https], Effective population size, Gene Frequency, Models, Human Genome Project, Mexican Americans, Sequencing, Exome, Aborígens, Genetics (clinical), Native American Migrations, Genetics, 0303 health sciences, education.field_of_study, Human migration, Population size, 030305 genetics & heredity, Chromosome Mapping, Hispanic or Latino, Bioquímica y Biología Molecular, Full Genomes, CIENCIAS NATURALES Y EXACTAS, Research Article, lcsh:QH426-470, Human Migration, Population, Black People, America, Biology, White People, Ciencias Biológicas, 03 medical and health sciences, Pobles indígenes, Migració de pobles, Humans, Ciencias Naturales, Quantitative Biology - Genomics, 1000 Genomes Project, purl.org/becyt/ford/1.6 [https], education, Quantitative Biology - Populations and Evolution, Molecular Biology, Genotyping, Allele frequency, Mexico, Ecology, Evolution, Behavior and Systematics, 030304 developmental biology, Genomics (q-bio.GN), Human genome, business.industry, Genome, Human, Puerto Rico, Racial Groups, Native American, Populations and Evolution (q-bio.PE), 15. Life on land, Migrations of nations, lcsh:Genetics, Genetics, Population, Evolutionary biology, FOS: Biological sciences, Indians, North American, Indigenous peoples, business
Abstract: There is great scientific and popular interest in understanding the genetic history of populations in the Americas. We wish to understand when different regions of the continent were inhabited, where settlers came from, and how current inhabitants relate genetically to earlier populations. Recent studies unraveled parts of the genetic history of the continent using genotyping arrays and uniparental markers. The 1000 Genomes Project provides a unique opportunity for improving our understanding of population genetic history by providing over a hundred sequenced low coverage genomes and exomes from Colombian (CLM), Mexican-American (MXL), and Puerto Rican (PUR) populations. Here, we explore the genomic contributions of African, European, and especially Native American ancestry to these populations. Estimated Native American ancestry is 48% in MXL, 25% in CLM, and 13% in PUR. Native American ancestry in PUR is most closely related to populations surrounding the Orinoco River basin, confirming the Southern America ancestry of the Taíno people of the Caribbean. We present new methods to estimate the allele frequencies in the Native American fraction of the populations, and model their distribution using a demographic model for three ancestral Native American populations. These ancestral populations likely split in close succession: the most likely scenario, based on a peopling of the Americas 16 thousand years ago (kya), supports that the MXL Ancestors split 12.2kya, with a subsequent split of the ancestors to CLM and PUR 11.7kya. The model also features effective populations of 62,000 in Mexico, 8,700 in Colombia, and 1,900 in Puerto Rico. Modeling Identity-by-descent (IBD) and ancestry tract length, we show that post-contact populations also differ markedly in their effective sizes and migration patterns, with Puerto Rico showing the smallest effective size and the earlier migration from Europe. Finally, we compare IBD and ancestry assignments to find evidence for relatedness among European founders to the three populations., Facultad de Ciencias Naturales y Museo, Instituto Multidisciplinario de Biología Celular
Published: 2013

30. Integrative annotation of variants from 1092 humans: application to cancer genomics

Author: Khurana, E., Fu, Y., Colonna, V., Mu, X., Kang, H., Lappalainen, T., Sboner, A., Lochovsky, L., Chen, J., Harmanci, A., Das, J., Abyzov, A., Balasubramanian, S., Beal, K., Chakravarty, D., Challis, D., Chen, Y., Clarke, D., Clarke, L., Cunningham, F., Evani, U., Flicek, P., Fragoza, R., Garrison, E., Gibbs, R., Gumus, Z., Herrero, J., Kitabayashi, N., Kong, Y., Lage, K., Liluashvili, V., Lipkin, S., MacArthur, D., Marth, G., Muzny, D., Pers, T., Ritchie, G., Rosenfeld, J., Sisu, C., Wei, X., Wilson, M., Xue, Y., Yu, F., 1000 Genomes Project, C., Lehrach, H., Sudbrak, R., Albrecht, M., Amstislavskiy, V., Borodina, T., Lienhard, M., Mertes, F., Sultan, M., Timmermann, B., Yaspo, M., Dermitzakis, E., Yu, H., Rubin, M., Tyler-Smith, C., Gerstein, M., and Broad Institute of MIT and Harvard
Subjects: RNA, Untranslated, Population, Kruppel-Like Transcription Factors, Molecular Sequence Annotation/methods, RNA, Untranslated/genetics, Genomics, Computational biology, Biology, medicine.disease_cause, Genome, Binding Sites/genetics, Polymorphism, Single Nucleotide, Article, Population/genetics, Neoplasms, medicine, Humans, ddc:576.5, Selection, Genetic, education, Enhancer, Genetics, Mutation, education.field_of_study, Binding Sites, Multidisciplinary, Natural selection, Genome, Human, Genetic Variation, Molecular Sequence Annotation, Neoplasms/genetics, Human genome, Kruppel-Like Transcription Factors/metabolism
Abstract: Identifying Important Identifiers Each of us has millions of sequence variations in our genomes. Signatures of purifying or negative selection should help identify which of those variations is functionally important. Khurana et al. ( 1235587 ) used sequence polymorphisms from 1092 humans across 14 populations to identify patterns of selection, especially in noncoding regulatory regions. Noncoding regions under very strong negative selection included binding sites of some chromatin and general transcription factors (TFs) and core motifs of some important TF families. Positive selection in TF binding sites tended to occur in network hub promoters. Many recurrent somatic cancer variants occurred in noncoding regulatory regions and thus might indicate mutations that drive cancer.
Published: 2013
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31. Integrative annotation of variants from 1092 humans: application to cancer genomics

Author: Khurana E, Fu Y, Colonna V, Mu XJ, Kang HM, Lappalainen T, Sboner A, Lochovsky L, Chen J, Harmanci A, Das J, Abyzov A, Balasubramanian S, Beal K, Chakravarty D, Challis D, Chen Y, Clarke D, Clarke L, Cunningham F, Evani US, Flicek P, Fragoza R, Garrison E, Gibbs R, Gümüs ZH, Herrero J, Kitabayashi N, Kong Y, Lage K, Liluashvili V, Lipkin SM, MacArthur DG, Marth G, Muzny D, Pers TH, Ritchie GR, Rosenfeld JA, Sisu C, Wei X, Wilson M, Xue Y, Yu F and 1000 Genomes Project Consortium, Dermitzakis ET, Yu H, Rubin MA, Tyler-Smith C, Gerstein M, Abecasis GR, Auton A, Brooks LD, DePristo MA, Durbin RM, Handsaker RE, McVean GA.
Abstract: Interpreting variants, especially noncoding ones, in the increasing number of personal genomes is challenging. We used patterns of polymorphisms in functionally annotated regions in 1092 humans to identify deleterious variants; then we experimentally validated candidates. We analyzed both coding and noncoding regions, with the former corroborating the latter. We found regions particularly sensitive to mutations ("ultrasensitive") and variants that are disruptive because of mechanistic effects on transcription-factor binding (that is, "motif-breakers"). We also found variants in regions with higher network centrality tend to be deleterious. Insertions and deletions followed a similar pattern to single-nucleotide variants, with some notable exceptions (e.g., certain deletions and enhancers). On the basis of these patterns, we developed a computational tool (FunSeq), whose application to ~90 cancer genomes reveals nearly a hundred candidate noncoding drivers.
Published: 2013
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32. Deleterious- and Disease-Allele Prevalence in Healthy Individuals: Insights from Current Predictions, Mutation Databases, and Population-Scale Resequencing

Author: Xue, Y., Chen, Y., Ayub, Q., Huang, N., Ball, E., Mort, M., Phillips, A., Shaw, K., Stenson, P., Cooper, D., Tyler-Smith, C., The 1000 Genomes Project Consortium, Timmermann, B., Lehrach, H., and Herwig, R.
Abstract: We have assessed the numbers of potentially deleterious variants in the genomes of apparently healthy humans by using (1) low-coverage whole-genome sequence data from 179 individuals in the 1000 Genomes Pilot Project and (2) current predictions and databases of deleterious variants. Each individual carried 281–515 missense substitutions, 40–85 of which were homozygous, predicted to be highly damaging. They also carried 40–110 variants classified by the Human Gene Mutation Database (HGMD) as disease-causing mutations (DMs), 3–24 variants in the homozygous state, and many polymorphisms putatively associated with disease. Whereas many of these DMs are likely to represent disease-allele-annotation errors, between 0 and 8 DMs (0–1 homozygous) per individual are predicted to be highly damaging, and some of them provide information of medical relevance. These analyses emphasize the need for improved annotation of disease alleles both in mutation databases and in the primary literature; some HGMD mutation data have been recategorized on the basis of the present findings, an iterative process that is both necessary and ongoing. Our estimates of deleterious-allele numbers are likely to be subject to both overcounting and undercounting. However, our current best mean estimates of ∼400 damaging variants and ∼2 bona fide disease mutations per individual are likely to increase rather than decrease as sequencing studies ascertain rare variants more effectively and as additional disease alleles are discovered.
Published: 2012

33. Mapping copy number variation by population-scale genome sequencing

Author: Mills, Ryan E, Walter, Klaudia, Stewart, Chip, Handsaker, Robert E, Chen, Ken, Alkan, Can, Abyzov, Alexej, Yoon, Seungtai Chris, Ye, Kai, Cheetham, R Keira, Chinwalla, Asif, Conrad, Donald F, Fu, Yutao, Grubert, Fabian, Hajirasouliha, Iman, Hormozdiari, Fereydoun, Iakoucheva, Lilia M, Iqbal, Zamin, Kang, Shuli, Kidd, Jeffrey M, Konkel, Miriam K, Korn, Joshua, Khurana, Ekta, Kural, Deniz, Lam, Hugo YK, Leng, Jing, Li, Ruiqiang, Li, Yingrui, Lin, Chang-Yun, Luo, Ruibang, Mu, Xinmeng Jasmine, Nemesh, James, Peckham, Heather E, Rausch, Tobias, Scally, Aylwyn, Shi, Xinghua, Stromberg, Michael P, Stütz, Adrian M, Urban, Alexander Eckehart, Walker, Jerilyn A, Wu, Jiantao, Zhang, Yujun, Zhang, Zhengdong D, Batzer, Mark A, Ding, Li, Marth, Gabor T, McVean, Gil, Sebat, Jonathan, Snyder, Michael, Wang, Jun, Ye, Kenny, Eichler, Evan E, Gerstein, Mark B, Hurles, Matthew E, Lee, Charles, McCarroll, Steven A, Korbel, Jan O, and 1000 Genomes Project
Subjects: Genome, Genotype, DNA Copy Number Variations, General Science & Technology, Population, Human Genome, Genomes Project, Reproducibility of Results, DNA, Genomics, Mutagenesis, Insertional, Gene Duplication, Genetics, Humans, Genetic Predisposition to Disease, Generic health relevance, Sequence Analysis, Sequence Deletion, Human, Biotechnology
Abstract: Genomic structural variants (SVs) are abundant in humans, differing from other forms of variation in extent, origin and functional impact. Despite progress in SV characterization, the nucleotide resolution architecture of most SVs remains unknown. We constructed a map of unbalanced SVs (that is, copy number variants) based on whole genome DNA sequencing data from 185 human genomes, integrating evidence from complementary SV discovery approaches with extensive experimental validations. Our map encompassed 22,025 deletions and 6,000 additional SVs, including insertions and tandem duplications. Most SVs (53%) were mapped to nucleotide resolution, which facilitated analysing their origin and functional impact. We examined numerous whole and partial gene deletions with a genotyping approach and observed a depletion of gene disruptions amongst high frequency deletions. Furthermore, we observed differences in the size spectra of SVs originating from distinct formation mechanisms, and constructed a map of SV hotspots formed by common mechanisms. Our analytical framework and SV map serves as a resource for sequencing-based association studies.
Published: 2011

34. A comprehensive map of mobile element insertion polymorphisms in humans

Author: Chip Stewart, Deniz Kural, Michael P Strömberg, Jerilyn A Walker, Miriam K Konkel, Adrian M Stütz, Alexander E Urban, Fabian Grubert, Hugo Y K Lam, Wan-Ping Lee, Michele Busby, Amit R Indap, Erik Garrison, Chad Huff, Jinchuan Xing, Michael P Snyder, Lynn B Jorde, Mark A Batzer, Jan O Korbel, Gabor T Marth, and 1000 Genomes Project
Subjects: Cancer Research, Mutation rate, 0302 clinical medicine, Gene Frequency, Mutation Rate, Genome Databases, Genome Sequencing, Genome Evolution, Genetics (clinical), Genetics, 0303 health sciences, education.field_of_study, Genomics, Genome Scans, Functional Genomics, Research Article, Heterozygote, lcsh:QH426-470, Genotype, Population, Alu element, Single-nucleotide polymorphism, Biology, Polymorphism, Single Nucleotide, 03 medical and health sciences, Genome Analysis Tools, Genetic Mutation, Humans, 1000 Genomes Project, education, Molecular Biology, Allele frequency, Ecology, Evolution, Behavior and Systematics, 030304 developmental biology, Genome, Human, Mutation Types, Computational Biology, Human Genetics, lcsh:Genetics, Mutagenesis, Insertional, Mutation, Neutral Theory, Mutation Databases, DNA Transposable Elements, Genetic Polymorphism, Human genome, Mobile genetic elements, 030217 neurology & neurosurgery, Population Genetics
Abstract: As a consequence of the accumulation of insertion events over evolutionary time, mobile elements now comprise nearly half of the human genome. The Alu, L1, and SVA mobile element families are still duplicating, generating variation between individual genomes. Mobile element insertions (MEI) have been identified as causes for genetic diseases, including hemophilia, neurofibromatosis, and various cancers. Here we present a comprehensive map of 7,380 MEI polymorphisms from the 1000 Genomes Project whole-genome sequencing data of 185 samples in three major populations detected with two detection methods. This catalog enables us to systematically study mutation rates, population segregation, genomic distribution, and functional properties of MEI polymorphisms and to compare MEI to SNP variation from the same individuals. Population allele frequencies of MEI and SNPs are described, broadly, by the same neutral ancestral processes despite vastly different mutation mechanisms and rates, except in coding regions where MEI are virtually absent, presumably due to strong negative selection. A direct comparison of MEI and SNP diversity levels suggests a differential mobile element insertion rate among populations., Author Summary We embarked on this study to explore the 1000 Genomes Project (1000GP) pilot dataset as a substrate for Mobile Element Insertion (MEI) discovery and analysis. MEI is already well known as a significant component of genetic variation in the human population. However the full extent and effects of MEI can only be assessed by accurate detection in large whole-genome sequencing efforts such as the 1000GP. In this study we identified 7,380 distinct genomic locations of variant MEI and carried out rigorous validation experiments that confirmed the high accuracy of the detected events. We were able to measure the frequency of each variant in three continental population groups and found that inherited MEI variants propagate through populations in much the same way as single nucleotide polymorphisms, except that MEI are more strongly suppressed in protein coding parts of the genome. We also found evidence that the MEI mutation rate has not been constant over human population history, rather that different populations appear to have different characteristic MEI mutation rates.
Published: 2010

35. Missense variants in the X-linked gene PRPS1 cause retinal degeneration in females.

Author: Fiorentino, Alessia, Fujinami, Kaoru, Arno, Gavin, Robson, Anthony G., Pontikos, Nikolas, Arasanz Armengol, Monica, Plagnol, Vincent, Hayashi, Takaaki, Iwata, Takeshi, Parker, Matthew, Fowler, Tom, Rendon, Augusto, Gardner, Jessica C., Henderson, Robert H., Cheetham, Michael E., Webster, Andrew R., Michaelides, Michel, Hardcastle, Alison J., and for the 100,000 Genomes Project, the Japan Eye Genetic Consortium and the UK Inherited Retinal Dystrophy Consortium
Abstract: Retinal dystrophies are a heterogeneous group of disorders of visual function leading to partial or complete blindness. We report the genetic basis of an unusual retinal dystrophy in five families with affected females and no affected males. Heterozygous missense variants were identified in the X-linked phosphoribosyl pyrophosphate synthetase 1 ( PRPS1) gene: c.47C > T, p.(Ser16Phe); c.586C > T, p.(Arg196Trp); c.641G > C, p.(Arg214Pro); and c.640C > T, p.(Arg214Trp). Missense variants in PRPS1 are usually associated with disease in male patients, including Arts syndrome, Charcot-Marie-Tooth, and nonsyndromic sensorineural deafness. In our study families, affected females manifested a retinal dystrophy with interocular asymmetry. Three unrelated females from these families had hearing loss leading to a diagnosis of Usher syndrome. Other neurological manifestations were also observed in three individuals. Our data highlight the unexpected X-linked inheritance of retinal degeneration in females caused by variants in PRPS1 and suggest that tissue-specific skewed X-inactivation or variable levels of pyrophosphate synthetase-1 deficiency are the underlying mechanism(s). We speculate that the absence of affected males in the study families suggests that some variants may be male embryonic lethal when inherited in the hemizygous state. The unbiased nature of next-generation sequencing enables all possible modes of inheritance to be considered for association of gene variants with novel phenotypic presentation. [ABSTRACT FROM AUTHOR]
Published: 2018
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36. The landscape of human STR variation

Author: Harvard University--MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology. Computational and Systems Biology Program, Willems, Thomas Frederick, Gymrek, Melissa A., Highnam, Gareth, 1000 Genomes Project Consortium, Mittelman, David, Erlich, Yaniv, Harvard University--MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology. Computational and Systems Biology Program, Willems, Thomas Frederick, Gymrek, Melissa A., Highnam, Gareth, 1000 Genomes Project Consortium, Mittelman, David, and Erlich, Yaniv
Abstract: Short tandem repeats are among the most polymorphic loci in the human genome. These loci play a role in the etiology of a range of genetic diseases and have been frequently utilized in forensics, population genetics, and genetic genealogy. Despite this plethora of applications, little is known about the variation of most STRs in the human population. Here, we report the largest-scale analysis of human STR variation to date. We collected information for nearly 700,000 STR loci across more than 1000 individuals in Phase 1 of the 1000 Genomes Project. Extensive quality controls show that reliable allelic spectra can be obtained for close to 90% of the STR loci in the genome. We utilize this call set to analyze determinants of STR variation, assess the human reference genome’s representation of STR alleles, find STR loci with common loss-of-function alleles, and obtain initial estimates of the linkage disequilibrium between STRs and common SNPs. Overall, these analyses further elucidate the scale of genetic variation beyond classical point mutations., American Society for Engineering Education. National Defense Science and Engineering Graduate Fellowship
Published: 2014

37. The Landscape of Human STR Variation

Author: Willems, Thomas F., primary, Gymrek, Melissa, additional, Highnam, Gareth, additional, The Genomes Project, -, additional, Mittelman, David, additional, and Erlich, Yaniv, additional
Published: 2014
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38. A systematic survey of loss-of-function variants in human protein-coding genes

Author: MacArthur, D.G., Balasubramanian, S., Frankish, A., Huang, N., Morris, J., Walter, K., Jostins, L., Habegger, L., Pickrell, J.K., Montgomery, S.B., Albers, C.A., Zhang, Z.D., Conrad, D.F., Lunter, G., Zheng, H., Ayub, Q., DePristo, M.A., Banks, E., Hu, M., Handsaker, R.E., Rosenfeld, J.A., Fromer, M., Jin, M., Mu, X.J., Khurana, E., Ye, K., Kay, M., Saunders, G.I., Suner, M.M., Hunt, T., Barnes, I.H., Amid, C., Carvalho-Silva, D.R., Bignell, A.H., Snow, C., Yngvadottir, B., Bumpstead, S., Cooper, D.N., Xue, Y., Romero, I.G., Genomes Project, C., Wang, J, Li, Y., Gibbs, R.A., McCarroll, S.A., Dermitzakis, E.T., Pritchard, J.K., Barrett, J.C., Harrow, J., Hurles, M.E., Gerstein, M.B., Tyler-Smith, C., MacArthur, D.G., Balasubramanian, S., Frankish, A., Huang, N., Morris, J., Walter, K., Jostins, L., Habegger, L., Pickrell, J.K., Montgomery, S.B., Albers, C.A., Zhang, Z.D., Conrad, D.F., Lunter, G., Zheng, H., Ayub, Q., DePristo, M.A., Banks, E., Hu, M., Handsaker, R.E., Rosenfeld, J.A., Fromer, M., Jin, M., Mu, X.J., Khurana, E., Ye, K., Kay, M., Saunders, G.I., Suner, M.M., Hunt, T., Barnes, I.H., Amid, C., Carvalho-Silva, D.R., Bignell, A.H., Snow, C., Yngvadottir, B., Bumpstead, S., Cooper, D.N., Xue, Y., Romero, I.G., Genomes Project, C., Wang, J, Li, Y., Gibbs, R.A., McCarroll, S.A., Dermitzakis, E.T., Pritchard, J.K., Barrett, J.C., Harrow, J., Hurles, M.E., Gerstein, M.B., and Tyler-Smith, C.
Abstract: Item does not contain fulltext, Genome-sequencing studies indicate that all humans carry many genetic variants predicted to cause loss of function (LoF) of protein-coding genes, suggesting unexpected redundancy in the human genome. Here we apply stringent filters to 2951 putative LoF variants obtained from 185 human genomes to determine their true prevalence and properties. We estimate that human genomes typically contain ~100 genuine LoF variants with ~20 genes completely inactivated. We identify rare and likely deleterious LoF alleles, including 26 known and 21 predicted severe disease-causing variants, as well as common LoF variants in nonessential genes. We describe functional and evolutionary differences between LoF-tolerant and recessive disease genes and a method for using these differences to prioritize candidate genes found in clinical sequencing studies.
Published: 2012

39. Mining data from 1000 genomes to identify the causal variant in regions under positive selection

Author: Whitaker College of Health Sciences and Technology, Massachusetts Institute of Technology. Department of Biology, Lander, Eric S., Grossman, Sharon Rachel, Shlyakhter, Ilya, Karlsson, Elinor K., Tabrizi, Shervin, Andersen, Kristian, Rinn, John L., Schaffner, Steve, Sabeti, Pardis C., Andersen, Kristian G., 1000 Genomes Project Consortium, Whitaker College of Health Sciences and Technology, Massachusetts Institute of Technology. Department of Biology, Lander, Eric S., Grossman, Sharon Rachel, Shlyakhter, Ilya, Karlsson, Elinor K., Tabrizi, Shervin, Andersen, Kristian, Rinn, John L., Schaffner, Steve, Sabeti, Pardis C., Andersen, Kristian G., and 1000 Genomes Project Consortium
Abstract: The human genome contains hundreds of regions in which the patterns of genetic variation indicate recent positive natural selection, yet for most of these the underlying gene and the advantageous mutation remain unknown. We recently reported the development of a method, Composite of Multiple Signals (CMS), that combines tests for multiple signals of natural selection and increases resolution by up to 100-fold.
Published: 2012

40. Expanding SPTAN1 monoallelic variant associated disorders: From epileptic encephalopathy to pure spastic paraplegia and ataxia

Author: Heba Morsy, Mehdi Benkirane, Elisa Cali, Clarissa Rocca, Kristina Zhelcheska, Valentina Cipriani, Evangelia Galanaki, Reza Maroofian, Stephanie Efthymiou, David Murphy, Mary O’Driscoll, Mohnish Suri, Siddharth Banka, Jill Clayton-Smith, Thomas Wright, Melody Redman, Jennifer A. Bassetti, Mathilde Nizon, Benjamin Cogne, Rami Abu Jamra, Tobias Bartolomaeus, Marion Heruth, Ilona Krey, Janina Gburek-Augustat, Dagmar Wieczorek, Felix Gattermann, Meriel Mcentagart, Alice Goldenberg, Lucie Guyant-Marechal, Hector Garcia-Moreno, Paola Giunti, Brigitte Chabrol, Severine Bacrot, Roger Buissonnière, Virginie Magry, Vykuntaraju K. Gowda, Varunvenkat M. Srinivasan, Béla Melegh, András Szabó, Katalin Sümegi, Mireille Cossée, Monica Ziff, Russell Butterfield, David Hunt, Georgina Bird-Lieberman, Michael Hanna, Michel Koenig, Michael Stankewich, Jana Vandrovcova, Henry Houlden, J.C. Ambrose, P. Arumugam, E.L. Baple, M. Bleda, F. Boardman-Pretty, J.M. Boissiere, C.R. Boustred, H. Brittain, M.J. Caulfield, G.C. Chan, C.E.H. Craig, L.C. Daugherty, A. de Burca, A. Devereau, G. Elgar, R.E. Foulger, T. Fowler, P. Furió-Tarí, J.M. Hackett, D. Halai, A. Hamblin, S. Henderson, J.E. Holman, T.J.P. Hubbard, K. Ibáñez, R. Jackson, L.J. Jones, D. Kasperaviciute, M. Kayikci, L. Lahnstein, K. Lawson, S.E.A. Leigh, I.U.S. Leong, F.J. Lopez, F. Maleady-Crowe, J. Mason, E.M. McDonagh, L. Moutsianas, M. Mueller, N. Murugaesu, A.C. Need, C.A. Odhams, C. Patch, D. Perez-Gil, D. Polychronopoulos, J. Pullinger, T. Rahim, A. Rendon, P. Riesgo-Ferreiro, T. Rogers, M. Ryten, K. Savage, K. Sawant, R.H. Scott, A. Siddiq, A. Sieghart, D. Smedley, K.R. Smith, A. Sosinsky, W. Spooner, H.E. Stevens, A. Stuckey, R. Sultana, E.R.A. Thomas, S.R. Thompson, C. Tregidgo, A. Tucci, E. Walsh, S.A. Watters, M.J. Welland, E. Williams, K. Witkowska, S.M. Wood, M. Zarowiecki, UCL, Institute of Neurology [London], Physiologie & médecine expérimentale du Cœur et des Muscles [U 1046] (PhyMedExp), Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Centre Hospitalier Régional Universitaire [Montpellier] (CHRU Montpellier), William Harvey Research Institute, Barts and the London Medical School, Birmingham Women’s and Children’s Hospitals NHS Foundation Trust, Nottingham Clinical Genetics Service, Nottingham University Hospitals NHS Trust, Manchester Centre for Genomic Medicine [Manchester, UK] (MCGM), St Mary's Hospital Manchester-Manchester Academic Health Science Centre (MAHSC), University of Manchester [Manchester]-University of Manchester [Manchester]-Manchester University NHS Foundation Trust (MFT)-Faculty of Biology, Medicine and Health [Manchester, UK], University of Manchester [Manchester], Manchester University NHS Foundation Trust (MFT), Chapel Allerton Hospital, Leeds Teaching Hospitals NHS Trust, Weill Cornell Medicine [New York], unité de recherche de l'institut du thorax UMR1087 UMR6291 (ITX), Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Nantes Université - UFR de Médecine et des Techniques Médicales (Nantes Univ - UFR MEDECINE), Nantes Université - pôle Santé, Nantes Université (Nantes Univ)-Nantes Université (Nantes Univ)-Nantes Université - pôle Santé, Nantes Université (Nantes Univ)-Nantes Université (Nantes Univ), University Hospital Leipzig, University Hospital Düsseldorf, St George’s University Hospitals, Département de génétique [CHU Rouen] (Centre Normandie de Génomique et de Médecine Personnalisée), CHU Rouen, Normandie Université (NU)-Normandie Université (NU), Service de pédiatrie et neurologie pédiatrique, Université de la Méditerranée - Aix-Marseille 2-Assistance Publique - Hôpitaux de Marseille (APHM)- Hôpital de la Timone [CHU - APHM] (TIMONE), University College London Hospitals NHS Foundation Trust [London, UK] (UCLH), Centre Hospitalier de Versailles André Mignot (CHV), Unité de génétique médicale et oncogénétique [CHU Amiens Picardie], CHU Amiens-Picardie, Auteur indépendant, University of Pécs Medical School (UP MS), University of Pecs, Great Ormond Street Hospital for Children NHS Foundation Trust [London, UK] (GOSHC), University of Utah School of Medicine [Salt Lake City], Princess Anne Hospital [Southampton, UK] (PAH), University of Southampton, University Hospital Southampton NHS Foundation Trust, Department of Pathology [Yale], Yale School of Medicine [New Haven, Connecticut] (YSM), H.M., J.V., and H.H. are supported by an Medical Research Council strategic award, MR/S005021/1, to establish International Centre for Genomic Medicine in Neuromuscular Diseases. H.M. is supported by Wellcome Trust grant 220906/Z/20/Z. H.H. is funded by the Medical Research Council (MR/S01165X/1, MR/S005021/1, G0601943), NIHR University College London Hospitals Biomedical Research Centre, Rosetree Trust UK, Ataxia UK, Multiple System Atrophy Trust, Brain Research UK, Sparks GOSH Charity, Muscular Dystrophy UK, and Multiple System Atrophy Trust. R.But. is supported by the Penelope Rare and Undiagnosed Disease Program at the University of Utah with funding from the Center for Genomic Medicine and with support from Matt Velinder (Department of Human Genetics, University of Utah) and Rong Mao and Pinar Bayrak-Toydemir (ARUP Laboratories). B.M. is supported by NKFIH K138669. This research was made possible through access to the data and findings generated by the 100,000 Genomes Project. The 100,000 Genomes Project is managed by Genomics England Limited (a wholly owned company of the Department of Health and Social Care). The 100,000 Genomes Project is funded by the National Institute for Health and Care Research and NHS England. The Wellcome Trust, Cancer Research UK, and the Medical Research Council have also funded the research infrastructure. The 100,000 Genomes Project uses data provided by patients and collected by the National Health Service as part of their care and support. This study makes use of data generated by the DatabasE of genomiC varIation and Phenotype in Humans using Ensembl Resources (DECIPHER) community. A full list of centers which contributed to the generation of the data is available at https://deciphergenomics.org/about/stats and via email from contact@deciphergenomics.org. Funding for the DECIPHER project was provided by Wellcome. We are thankful to the Deciphering Developmental Disorders Study for the invaluable collaboration. The Deciphering Developmental Disorders Study (Cambridge South Research Ethics Committee approval 10/H0305/83 and the Republic of Ireland Research Ethics Committee GEN/284/12) presents independent research commissioned by the Health Innovation Challenge Fund (grant number HICF-1009-003), a parallel funding partnership between the Wellcome Trust and Department of Health and the Wellcome Trust Sanger Institute (grant number WT098051), MORNET, Dominique, Nottingham University Hospitals NHS Trust (NUH), Weill Cornell Medicine [Cornell University], and Cornell University [New York]
Subjects: [SDV] Life Sciences [q-bio], Developmental delay, [SDV]Life Sciences [q-bio], Hereditary spastic paraplegia, Genetics (clinical), Hereditary ataxia, SPTAN1, Developmental epileptic encephalopathy
Abstract: On behalf of Queen Square Genomics On behalf of Genomics England Research Consortium; International audience; Purpose: Nonerythrocytic αII-spectrin (SPTAN1) variants have been previously associated with intellectual disability and epilepsy. We conducted this study to delineate the phenotypic spectrum of SPTAN1 variants.Methods: We carried out SPTAN1 gene enrichment analysis in the rare disease component of the 100,000 Genomes Project and screened 100,000 Genomes Project, DECIPHER database, and GeneMatcher to identify individuals with SPTAN1 variants. Functional studies were performed on fibroblasts from 2 patients.Results: Statistically significant enrichment of rare (minor allele frequency < 1 × 10-5) probably damaging SPTAN1 variants was identified in families with hereditary ataxia (HA) or hereditary spastic paraplegia (HSP) (12/1142 cases vs 52/23,847 controls, p = 2.8 × 10-5). We identified 31 individuals carrying SPTAN1 heterozygous variants or deletions. A total of 10 patients presented with pure or complex HSP/HA. The remaining 21 patients had developmental delay and seizures. Irregular αII-spectrin aggregation was noted in fibroblasts derived from 2 patients with p.(Arg19Trp) and p.(Glu2207del) variants.Conclusion: We found that SPTAN1 is a genetic cause of neurodevelopmental disorder, which we classified into 3 distinct subgroups. The first comprises developmental epileptic encephalopathy. The second group exhibits milder phenotypes of developmental delay with or without seizures. The final group accounts for patients with pure or complex HSP/HA.
Published: 2022
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41. Complete vertebrate mitogenomes reveal widespread repeats and gene duplications

Author: Giulio Formenti, Arang Rhie, Jennifer Balacco, Bettina Haase, Jacquelyn Mountcastle, Olivier Fedrigo, Samara Brown, Marco Rosario Capodiferro, Farooq O. Al-Ajli, Roberto Ambrosini, Peter Houde, Sergey Koren, Karen Oliver, Michelle Smith, Jason Skelton, Emma Betteridge, Jale Dolucan, Craig Corton, Iliana Bista, James Torrance, Alan Tracey, Jonathan Wood, Marcela Uliano-Silva, Kerstin Howe, Shane McCarthy, Sylke Winkler, Woori Kwak, Jonas Korlach, Arkarachai Fungtammasan, Daniel Fordham, Vania Costa, Simon Mayes, Matteo Chiara, David S. Horner, Eugene Myers, Richard Durbin, Alessandro Achilli, Edward L. Braun, Adam M. Phillippy, Erich D. Jarvis, and The Vertebrate Genomes Project Consortium
Subjects: Mitochondrial DNA, Vertebrate, Assembly, Long reads, Sequencing, Duplications, Biology (General), QH301-705.5, Genetics, QH426-470
Abstract: Abstract Background Modern sequencing technologies should make the assembly of the relatively small mitochondrial genomes an easy undertaking. However, few tools exist that address mitochondrial assembly directly. Results As part of the Vertebrate Genomes Project (VGP) we develop mitoVGP, a fully automated pipeline for similarity-based identification of mitochondrial reads and de novo assembly of mitochondrial genomes that incorporates both long (> 10 kbp, PacBio or Nanopore) and short (100–300 bp, Illumina) reads. Our pipeline leads to successful complete mitogenome assemblies of 100 vertebrate species of the VGP. We observe that tissue type and library size selection have considerable impact on mitogenome sequencing and assembly. Comparing our assemblies to purportedly complete reference mitogenomes based on short-read sequencing, we identify errors, missing sequences, and incomplete genes in those references, particularly in repetitive regions. Our assemblies also identify novel gene region duplications. The presence of repeats and duplications in over half of the species herein assembled indicates that their occurrence is a principle of mitochondrial structure rather than an exception, shedding new light on mitochondrial genome evolution and organization. Conclusions Our results indicate that even in the “simple” case of vertebrate mitogenomes the completeness of many currently available reference sequences can be further improved, and caution should be exercised before claiming the complete assembly of a mitogenome, particularly from short reads alone.
Published: 2021
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42. The 100 000 Genomes Project : bringing whole genome sequencing to the NHS

Author: 100 000 Genomes Project, Turnbull, Clare, Scott, Richard H, Thomas, Ellen, Jones, Louise, Murugaesu, Nirupa, Pretty, Freya Boardman, Halai, Dina, Baple, Emma, Craig, Clare, Hamblin, Angela, Henderson, Shirley, Patch, Christine, O’Neill, Amanda, Devereaux, Andrew, Smith, Katherine, Martin, Antonio Rueda, Sosinsky, Alona, McDonagh, Ellen M, Sultana, Razvan, Mueller, Michael, Smedley, Damian, Toms, Adam, Dinh, Lisa, Fowler, Tom, Bale, Mark, Hubbard, Tim, Rendon, Augusto, Hill, Sue, and Caulfield, Mark J

43. Human genomic regions with exceptionally high levels of population differentiation identified from 911 whole-genome sequences

Author: Colonna, V, Ayub, Q, Chen, Y, Pagani, L, Luisi, P, Pybus, M, Garrison, E, Xue, Y, Tyler-Smith, C, Abecasis, GR, Auton, A, Brooks, LD, Depristo, MA, Durbin, RM, Handsaker, RE, Kang, HM, Marth, GT, McVean, G, Altshuler, DM, Bentley, DR, Chakravarti, A, Clark, AG, Donnelly, P, Eichler, EE, Flicek, P, Gabriel, SB, Gibbs, RA, Green, ED, Hurles, ME, Knoppers, BM, Korbel, JO, Lander, ES, Lee, C, Lehrach, H, Mardis, ER, McVean, GA, Nickerson, DA, Schmidt, JP, Sherry, ST, Wang, J, Wilson, RK, Dinh, H, Kovar, C, Lee, S, Lewis, L, Muzny, D, Reid, J, Wang, M, Fang, X, Guo, X, Jian, M, Jiang, H, Jin, X, Li, G, Li, J, Li, Y, Li, Z, Liu, X, Lu, Y, Ma, X, Su, Z, Tai, S, Tang, M, Wang, B, Wang, G, Wu, H, Wu, R, Yin, Y, Zhang, W, Zhao, J, Zhao, M, Zheng, X, Zhou, Y, Gupta, N, Clarke, L, Leinonen, R, Smith, RE, Zheng-Bradley, X, Grocock, R, Humphray, S, James, T, Kingsbury, Z, Sudbrak, R, Albrecht, MW, Amstislavskiy, VS, Borodina, TA, Lienhard, M, Mertes, F, Sultan, M, Timmermann, B, Yaspo, ML, Fulton, L, Fulton, R, Weinstock, GM, Balasubramaniam, S, Burton, J, Danecek, P, Keane, TM, Kolb-Kokocinski, A, McCarthy, S, Molecular Dynamics, Biomimetics, Urban and Regional Studies Institute, Nanomedicine & Drug Targeting, Artificial Intelligence, Micromechanics, Molecular Cell Biology, Van Swinderen Institute for Particle Physics and G, Archaeology of Northwestern Europe, Polymer Chemistry and Bioengineering, Christianity and the History of Ideas, Scientific Visualization and Computer Graphics, Chemical Technology, Macromolecular Chemistry & New Polymeric Materials, Bernoulli Institute, Surfaces and Thin Films, Hemelrijk group, Groningen Institute for Gastro Intestinal Genetics and Immunology (3GI), Falcao Salles lab, Synthetic Organic Chemistry, Psychometrics and Statistics, Bio-inspired systems and circuits, Advanced Production Engineering, Drug Design, The 1000 Genomes Project Consortium, Faculteit Medische Wetenschappen/UMCG, Wellcome Trust, Consiglio Nazionale delle Ricerche, EMBO, and 1000 Genomes Project Consortium
Subjects: Historia y Arqueología, lactase persistence, POSITIVE SELECTION, BALANCING SELECTION, SOFT SWEEP, Biología, standing variation, Population, Biology, Balancing selection, Genome, Polymorphism, Single Nucleotide, Genètica de poblacions humanes, Ciencias Biológicas, purl.org/becyt/ford/1 [https], selective sweep, functional annotation cluster, Genética y Herencia, HUMANIDADES, Genetic drift, Gene Frequency, INDEL Mutation, Humans, Selection, Genetic, education, purl.org/becyt/ford/1.6 [https], Selection (genetic algorithm), education.field_of_study, purl.org/becyt/ford/6 [https], Genome, Human, Research, Genetic Drift, Levenshtein distance, Selecció natural, Sequence Analysis, DNA, Human genetics, Otras Historia y Arqueología, Evolutionary biology, Human genome, purl.org/becyt/ford/6.1 [https], Selective sweep, Genètica humana -- Variació, CIENCIAS NATURALES Y EXACTAS
Abstract: It contains associated material.-- The 1000 Genomes Project Consortium, [Background] Population differentiation has proved to be effective for identifying loci under geographically localized positive selection, and has the potential to identify loci subject to balancing selection. We have previously investigated the pattern of genetic differentiation among human populations at 36.8 million genomic variants to identify sites in the genome showing high frequency differences. Here, we extend this dataset to include additional variants, survey sites with low levels of differentiation, and evaluate the extent to which highly differentiated sites are likely to result from selective or other processes., [Results] We demonstrate that while sites with low differentiation represent sampling effects rather than balancing selection, sites showing extremely high population differentiation are enriched for positive selection events and that one half may be the result of classic selective sweeps. Among these, we rediscover known examples, where we actually identify the established functional SNP, and discover novel examples including the genes ABCA12, CALD1 and ZNF804, which we speculate may be linked to adaptations in skin, calcium metabolism and defense, respectively., [Conclusions] We identify known and many novel candidate regions for geographically restricted positive selection, and suggest several directions for further research. © 2014 Colonna et al., This work was supported by The Wellcome Trust (098051), an Italian National Research Council (CNR) short-term mobility fellowship from the 2013 program to VC, and an EMBO Short Term Fellowship ASTF 324–2010 to VC.
Published: 2014
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44. 100,000 Genomes Pilot on Rare-Disease Diagnosis in Health Care — Preliminary Report

Author: Gill Wilson, Anna de Burca, Marta Bleda, Lucy R. Wedderburn, Matthew Welland, Kathleen Stirrups, Valentina Cipriani, Kerrie Woods, Vijeya Ganesan, Susan Hill, Rosaline Quinlivan, Georgia Chan, Mehul T. Dattani, Robert McFarland, Graeme C.M. Black, Rutendo Mapeta, Augusto Rendon, Francesco Muntoni, James O.J. Davies, Mina Ryten, Rebecca E. Foulger, Arianna Tucci, Dina Halai, Tom Fowler, Noemi B.A. Roy, Sarah Leigh, Dragana Josifova, Philip Twiss, Ana L.T. Tavares, Zerin Hyder, Detlef Bockenhauer, Patrick Yu-Wai-Man, Lara Abulhoul, Nikolas Pontikos, Anthony T. Moore, Huw R. Morris, Patrick F. Chinnery, Nicholas W. Wood, Ellen A. Thomas, Shehla Mohammed, Sofia Douzgou, Tanya Lam, Kate Gibson, Robert Sarkany, Teofila Bueser, Wei Wei, Siddharth Banka, Alexander Broomfield, Hiva Fassihi, Nils Koelling, Carolyn Campbell, James Buchanan, Melita Irving, Sandrine Compeyrot-Lacassagne, Karola Rehmström, Austen Worth, Nikhil Thapar, Andrew R. Webster, Paul Brennan, Rita Horvath, Gavin Arno, Richard H Scott, Sam Malka, Andrew O.M. Wilkie, Sofie Ashford, Maria Bitner-Glindzicz, Jana Vandrovcova, William G. Newman, Caroline F. Wright, Andrew M. Schaefer, Roger F.L. James, Robert W. Taylor, Melanie Babcock, Arjune Sen, Emma Baple, Ellen M. McDonagh, Stephanie Grunewald, Loukas Moutsianas, Melissa A. Haendel, Olivera Spasic-Boskovic, Eleanor G. Seaby, Anna Need, Clarissa Pilkington, Sarah Wordsworth, Shamima Rahman, Christine Patch, Colin Wallis, Kristina Ibanez, Bishoy Habib, Eik Haraldsdottir, Huw B. Thomas, Razvan Sultana, Andrea H. Németh, Agata Wolejko, Claire Palles, Phil Beales, Adam C. Shaw, Letizia Vestito, Emily Li, Sarah Rose, Sarah Hunter, Angela Matchan, Genevieve Say, Dalia Kasperaviciute, Henry Houlden, Raymond T. O’Keefe, R. Andres Floto, Jill Clayton-Smith, John B. Taylor, Hywel J. Williams, Volker Straub, Val Davison, Helen Savage, John Chisholm, Eleanor Dewhurst, Charles Crichton, Andrea Haworth, Clare Turnbull, Carolyn Tregidgo, Carme Camps, Christopher Penkett, Emer O’Connor, Georgina Hall, Lyn S. Chitty, Sally Halsall, Andrew D. Mumford, Annette G. Wagner, Eleanor Williams, Mark Bale, Julius O. Jacobsen, Willem H. Ouwehand, Charu Deshpande, Gavin Burns, Smita Y. Patel, James Polke, Thiloka Ratnaike, Gavin Fuller, John Burn, Kenneth E. S. Poole, Emma Footitt, John R. Bradley, Suzanne Wood, Russell J. Grocock, Jenny C. Taylor, Louise Izatt, Kikkeri N. Naresh, Katherine R. Smith, Nigel Burrows, Katrina Newland, Peter N. Robinson, Sarju G. Mehta, Michael A. Simpson, Michael R. Barnes, Pilar Cacheiro, Olivia Niblock, Tracy Lester, Dimitris Polychronopoulos, Helen Brittain, John A. Sayer, Antonio Martin, Eshika Haque, Sean Humphray, Douglass M. Turnbull, Damian Smedley, Andrew Devereau, Stefan Gräf, Sian Ellard, Ivone U.S. Leong, Martin G. Reese, Matthias Wielscher, Louise C. Daugherty, Perry M. Elliott, F. Lucy Raymond, Cecilia Compton, David Bentley, Catherine Snow, James Welch, Frances Flinter, Dom McMullan, Mark J. Caulfield, Paul Aurora, Mark Gurnell, Mary Kasanicki, I. Karen Temple, Michel Michaelides, Deborah Ruddy, Leema Robert, Janice Yip, Grainne S. Gorman, Andrew C. Browning, Richard Quinton, Maureen Cleary, Jamie M. Ellingford, Angela Douglas, Christopher Boustred, and Investigators, The 100,000 Genomes Project Pilot
Subjects: Adult, Male, Proband, medicine.medical_specialty, Adolescent, Pilot Projects, Genomics, Polymerase Chain Reaction, Genome, State Medicine, Young Adult, 03 medical and health sciences, 0302 clinical medicine, Health care, Human Phenotype Ontology, Humans, Medicine, Child, Exome sequencing, 030304 developmental biology, Family Characteristics, 0303 health sciences, Whole Genome Sequencing, Genome, Human, business.industry, Genetic Variation, Rare Diseases/diagnosis, General Medicine, Middle Aged, United Kingdom, 3. Good health, Child, Preschool, Family medicine, Medical genetics, Female, business, Bristol, 030217 neurology & neurosurgery, Rare disease
Abstract: BACKGROUND: The U.K. 100,000 Genomes Project is in the process of investigating the role of genome sequencing in patients with undiagnosed rare diseases after usual care and the alignment of this research with health care implementation in the U.K. National Health Service. Other parts of this project focus on patients with cancer and infection.METHODS: We conducted a pilot study involving 4660 participants from 2183 families, among whom 161 disorders covering a broad spectrum of rare diseases were present. We collected data on clinical features with the use of Human Phenotype Ontology terms, undertook genome sequencing, applied automated variant prioritization on the basis of applied virtual gene panels and phenotypes, and identified novel pathogenic variants through research analysis.RESULTS: Diagnostic yields varied among family structures and were highest in family trios (both parents and a proband) and families with larger pedigrees. Diagnostic yields were much higher for disorders likely to have a monogenic cause (35%) than for disorders likely to have a complex cause (11%). Diagnostic yields for intellectual disability, hearing disorders, and vision disorders ranged from 40 to 55%. We made genetic diagnoses in 25% of the probands. A total of 14% of the diagnoses were made by means of the combination of research and automated approaches, which was critical for cases in which we found etiologic noncoding, structural, and mitochondrial genome variants and coding variants poorly covered by exome sequencing. Cohortwide burden testing across 57,000 genomes enabled the discovery of three new disease genes and 19 new associations. Of the genetic diagnoses that we made, 25% had immediate ramifications for clinical decision making for the patients or their relatives.CONCLUSIONS: Our pilot study of genome sequencing in a national health care system showed an increase in diagnostic yield across a range of rare diseases. (Funded by the National Institute for Health Research and others.).
Published: 2021
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45. Integrating sequence and array data to create an improved 1000 Genomes Project haplotype reference panel

Author: Delaneau O., Marchini J., McVeanh G.A., Donnelly P., Lunter G., Marchini J.L., Myers, S., Gupta-Hinch, A., Iqbal, Z., Mathieson I., Rimmer, A., Xifara, D.K., Kerasidou, A., Churchhouse, C., Altshuler, D.M., Gabriel, S.B., Lander, E.S., Gupta, N., Daly, M.J., DePristo, M.A., Banks, E., Bhatia G., Carneiro, M.O., Del Angel G., Genovese G., Handsaker, R.E., Hartl, C., McCarroll, S.A., Nemesh J.C., Poplin, R.E., Schaffner, S.F., Shakir, K., Sabeti P.C., Grossman, S.R., Tabrizi, S., Tariyal, R., Li H., Reich, D., Durbin, R.M., Hurles, M.E., Balasubramaniam, S., Burton J., Danecek P., Keane, T.M., Kolb-Kokocinski, A., McCarthy, S., Stalker J., Quail, M., Ayub Q., Chen, Y., Coffey, A.J., Colonna V., Huang, N., Jostins L., Scally, A., Walter, K., Xue, Y., Zhang, Y., Blackburne, B., Lindsay, S.J., Ning, Z., Frankish, A., Harrow J., Chris, T.-S., Abecasis G.R., Kang H.M., Anderson P., Blackwell, T., Busonero F., Fuchsberger, C., Jun G., Maschio, A., Porcu, E., Sidore, C., Tan, A., Trost, M.K., Bentley, D.R., Grocock, R., Humphray, S., James, T., Kingsbury, Z., Bauer, M., Cheetham, R.K., Cox, T., Eberle, M., Murray L., Shaw, R., Chakravarti, A., Clark, A.G., Keinan, A., Rodriguez-Flores J.L., De LaVega F.M., Degenhardt J., Eichler, E.E., Flicek P., Clarke L., Leinonen, R., Smith, R.E., Zheng-Bradley X., Beal, K., Cunningham F., Herrero J., McLaren W.M., Ritchie G.R.S., Barker J., Kelman G., Kulesha, E., Radhakrishnan, R., Roa, A., Smirnov, D., Streeter I., Toneva I., Gibbs, R.A., Dinh H., Kovar, C., Lee, S., Lewis L., Muzny, D., Reid J., Wang, M., Yu F., Bainbridge, M., Challis, D., Evani, U.S., Lu J., Nagaswamy, U., Sabo, A., Wang, Y., Yu J., Fowler G., Hale W., Kalra, D., Green, E.D., Knoppers, B.M., Korbel J.O., Rausch, T., Sttz, A.M., Lee, C., Griffin L., Hsieh, C.-H., Mills, R.E., Von Grotthuss, M., Zhang, C., Shi X., Lehrach H., Sudbrak, R., Amstislavskiy V.S., Lienhard, M., Mertes F., Sultan, M., Timmermann, B., Yaspo, M.L., Herwig, S.R., Mardis, E.R., Wilson, R.K., Fulton L., Fulton, R., Weinstock G.M., Chinwalla, A., Ding L., Dooling, D., Koboldt, D.C., McLellan, M.D., Wallis J.W., Wendl, M.C., Zhang Q., Marth G.T., Garrison, E.P., Kural, D., Lee W.-P., Leong W.F., Ward, A.N., Wu J., Zhang, M., Nickerson, D.A., Alkan, C., Hormozdiari F., Ko, A., Sudmant P.H., Schmidt J.P., Davies, C.J., Gollub J., Webster, T., Wong, B., Zhan, Y., Sherry, S.T., Xiao, C., Church, D., Ananiev V., Belaia, Z., Beloslyudtsev, D., Bouk, N., Chen, C., Cohen, R., Cook, C., Garner J., Hefferon, T., Kimelman, M., Liu, C., Lopez J., Meric P., Ostapchuk, Y., Phan L., Ponomarov, S., Schneider V., Shekhtman, E., Sirotkin, K., Slotta, D., Zhang H., Wang J., Fang X., Guo X., Jian, M., Jiang H., Jin X., Li G., Li J., Li, Y., Liu X., Lu, Y., Ma X., Tai, S., Tang, M., Wang, B., Wang G., Wu H., Wu, R., Yin, Y., Zhang W., Zhao J., Zhao, M., Zheng X., Lachlan H., Fang L., Li Q., Li, Z., Lin H., Liu, B., Luo, R., Shao H., Xie, Y., Ye, C., Yu, C., Zheng H., Zhu H., Cai H., Cao H., Su, Y., Tian, Z., Yang H., Yang L., Zhu J., Cai, Z., Albrecht, M.W., Borodina, T.A., Auton, A., Yoon, S.C., Lihm J., Makarov V., Jin H., Kim W., Kim, K.C., Gottipati, S., Jones, D., Cooper, D.N., Ball, E.V., Stenson P.D., Barnes, B., Kahn, S., Ye, K., Batzer, M.A., Konkel, M.K., Walker J.A., MacArthur, D.G., Lek, M., Shriver, M.D., Bustamante, C.D., Gravel, S., Kenny, E.E., Kidd J.M., Lacroute P., Maples, B.K., Moreno-Estrada, A., Zakharia F., Henn, B., Sandoval, K., Byrnes J.K., Halperin, E., Baran, Y., Craig, D.W., Christoforides, A., Izatt, T., Kurdoglu, A.A., Sinari, S.A., Homer, N., Squire, K., Sebat J., Bafna V., Burchard, E.G., Hernandez, R.D., Gignoux, C.R., Haussler, D., Katzman, S.J., Kent W.J., Howie, B., Ruiz-Linares, A., Dermitzakis, E.T., Lappalainen, T., Devine, S.E., Maroo, A., Tallon L.J., Rosenfeld J.A., Michelson L.P., Angius, A., Cucca F., Sanna, S., Bigham, A., Jones, C., Reinier F., Lyons, R., Schlessinger, D., Awadalla P., Hodgkinson, A., Oleksyk, T.K., Martinez-Cruzado J.C., Fu, Y., Xiong, M., Jorde L., Witherspoon, D., Xing J., Browning, B.L., Hajirasouliha I., Chen, K., Albers, C.A., Gerstein, M.B., Abyzov, A., Chen J., Habegger L., Harmanci, A.O., Mu X.J., Sisu, C., Balasubramanian, S., Jin, M., Khurana, E., Clarke, D., Michaelson J.J., OSullivan, C., Barnes, K.C., Gharani, N., Toji L.H., Gerry, N., Kaye J.S., Kent, A., Mathias, R., Ossorio P.N., Parker, M., Rotimi, C.N., Royal, C.D., Tishkoff, S., Via, M., Bodmer W., Bedoya G., Yang G., You, C.J., Garcia-Montero, A., Orfao, A., Dutil J., Brooks L.D., Felsenfeld, A.L., McEwen J.E., Clemm, N.C., Guyer, M.S., Peterson J.L., Duncanson, A., Dunn, M., Peltonen L., and 1000 Genomes Project Consortium
Subjects: haplotype, genetic association, genotype, General Physics and Astronomy, Genome-wide association study, genetic analysis, gene sequence, Biology, gene frequency, Polymorphism, Single Nucleotide, General Biochemistry, Genetics and Molecular Biology, Article, polymorphism, Gene Frequency, single nucleotide polymorphism, Humans, chromosome, human, 1000 Genomes Project, indel mutation, genome, Alleles, Genetic association, Genetics, Whole genome sequencing, Multidisciplinary, accuracy, Genome, Human, Haplotype, allele, article, reference database, General Chemistry, Microarray Analysis, chromosome 20, Haplotypes, Human genome, microarray analysis, Imputation (genetics), Algorithms, SNP array, Genome-Wide Association Study
Abstract: A major use of the 1000 Genomes Project (1000GP) data is genotype imputation in genome-wide association studies (GWAS). Here we develop a method to estimate haplotypes from low-coverage sequencing data that can take advantage of single-nucleotide polymorphism (SNP) microarray genotypes on the same samples. First the SNP array data are phased to build a backbone (or 'scaffold') of haplotypes across each chromosome. We then phase the sequence data 'onto' this haplotype scaffold. This approach can take advantage of relatedness between sequenced and non-sequenced samples to improve accuracy. We use this method to create a new 1000GP haplotype reference set for use by the human genetic community. Using a set of validation genotypes at SNP and bi-allelic indels we show that these haplotypes have lower genotype discordance and improved imputation performance into downstream GWAS samples, especially at low-frequency variants. © 2014 Macmillan Publishers Limited. All rights reserved.
Published: 2014
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46. An integrated map of genetic variation from 1,092 human genomes

Author: Altshuler, DM, Durbin, RM, Abecasis, GR, Bentley, DR, Chakravarti, A, Clark, AG, Donnelly, P, Eichler, EE, Flicek, P, Gabriel, SB, Gibbs, RA, Green, ED, Hurles, ME, Knoppers, BM, Korbel, JO, Lander, ES, Lee, C, Lehrach, H, Mardis, ER, Marth, GT, McVean, GA, Nickerson, DA, Schmidt, JP, Sherry, ST, Wang, J, Wilson, RK, Dinh, H, Kovar, C, Lee, S, Lewis, L, Muzny, D, Reid, J, Wang, M, Fang, X, Guo, X, Jian, M, Jiang, H, Jin, X, Li, G, Li, J, Li, Y, Li, Z, Liu, X, Lu, Y, Ma, X, Su, Z, Tai, S, Tang, M, Wang, B, Wang, G, Wu, H, Wu, R, Yin, Y, Zhang, W, Zhao, J, Zhao, M, Zheng, X, Zhou, Y, Gupta, N, Clarke, L, Leinonen, R, Smith, RE, Zheng-Bradley, X, Grocock, R, Humphray, S, James, T, Kingsbury, Z, Sudbrak, R, Albrecht, MW, Amstislavskiy, VS, Borodina, TA, Lienhard, M, Mertes, F, Sultan, M, Timmermann, B, Yaspo, M-L, Fulton, L, Fulton, R, Weinstock, GM, Balasubramaniam, S, Burton, J, Danecek, P, Keane, TM, Kolb-Kokocinski, A, McCarthy, S, Stalker, J, Quail, M, Davies, CJ, Gollub, J, Webster, T, Wong, B, Zhan, Y, Auton, A, Yu, F, Bainbridge, M, Challis, D, Evani, US, Lu, J, Nagaswamy, U, Sabo, A, Wang, Y, Yu, J, Coin, LJM, Fang, L, Li, Q, Lin, H, Liu, B, Luo, R, Qin, N, Shao, H, Xie, Y, Ye, C, Yu, C, Zhang, F, Zheng, H, Zhu, H, Garrison, EP, Kural, D, Lee, W-P, Leong, WF, Ward, AN, Wu, J, Zhang, M, Griffin, L, Hsieh, C-H, Mills, RE, Shi, X, Von Grotthuss, M, Zhang, C, Daly, MJ, DePristo, MA, Banks, E, Bhatia, G, Carneiro, MO, Del Angel, G, Genovese, G, Handsaker, RE, Hartl, C, McCarroll, SA, Nemesh, JC, Poplin, RE, Schaffner, SF, Shakir, K, Yoon, SC, Lihm, J, Makarov, V, Jin, H, Kim, W, Kim, KC, Rausch, T, Beal, K, Cunningham, F, Herrero, J, McLaren, WM, Ritchie, GRS, Gottipati, S, Keinan, A, Rodriguez-Flores, JL, Sabeti, PC, Grossman, SR, Tabrizi, S, Tariyal, R, Cooper, DN, Ball, EV, Stenson, PD, Barnes, B, Bauer, M, Cheetham, RK, Cox, T, Eberle, M, Kahn, S, Murray, L, Peden, J, Shaw, R, Ye, K, Batzer, MA, Konkel, MK, Walker, JA, MacArthur, DG, Lek, M, Herwig, R, Shriver, MD, Bustamante, CD, Byrnes, JK, De la Vega, FM, Gravel, S, Kenny, EE, Kidd, JM, Lacroute, P, Maples, BK, Moreno-Estrada, A, Zakharia, F, Halperin, E, Baran, Y, Craig, DW, Christoforides, A, Homer, N, Izatt, T, Kurdoglu, AA, Sinari, SA, Squire, K, Xiao, C, Sebat, J, Bafna, V, Burchard, EG, Hernandez, RD, Gignoux, CR, Haussler, D, Katzman, SJ, Kent, WJ, Howie, B, Ruiz-Linares, A, Dermitzakis, ET, Lappalainen, T, Devine, SE, Maroo, A, Tallon, LJ, Rosenfeld, JA, Michelson, LP, Kang, HM, Anderson, P, Angius, A, Bigham, A, Blackwell, T, Busonero, F, Cucca, F, Fuchsberger, C, Jones, C, Jun, G, Lyons, R, Maschio, A, Porcu, E, Reinier, F, Sanna, S, Schlessinger, D, Sidore, C, Tan, A, Trost, MK, Awadalla, P, Hodgkinson, A, Lunter, G, Marchini, JL, Myers, S, Churchhouse, C, Delaneau, O, Gupta-Hinch, A, Iqbal, Z, Mathieson, I, Rimmer, A, Xifara, DK, Oleksyk, TK, Fu, Y, Xiong, M, Jorde, L, Witherspoon, D, Xing, J, Browning, BL, Alkan, C, Hajirasouliha, I, Hormozdiari, F, Ko, A, Sudmant, PH, Chen, K, Chinwalla, A, Ding, L, Dooling, D, Koboldt, DC, McLellan, MD, Wallis, JW, Wendl, MC, Zhang, Q, Tyler-Smith, C, Albers, CA, Ayub, Q, Chen, Y, Coffey, AJ, Colonna, V, Huang, N, Jostins, L, Li, H, Scally, A, Walter, K, Xue, Y, Zhang, Y, Gerstein, MB, Abyzov, A, Balasubramanian, S, Chen, J, Clarke, D, Habegger, L, Harmanci, AO, Jin, M, Khurana, E, Mu, XJ, Sisu, C, Degenhardt, J, Stuetz, AM, Church, D, Michaelson, JJ, Ben, B, Lindsay, SJ, Ning, Z, Frankish, A, Harrow, J, Fowler, G, Hale, W, Kalra, D, Barker, J, Kelman, G, Kulesha, E, Radhakrishnan, R, Roa, A, Smirnov, D, Streeter, I, Toneva, I, Vaughan, B, Ananiev, V, Belaia, Z, Beloslyudtsev, D, Bouk, N, Chen, C, Cohen, R, Cook, C, Garner, J, Hefferon, T, Kimelman, M, Liu, C, Lopez, J, Meric, P, O'Sullivan, C, Ostapchuk, Y, Phan, L, Ponomarov, S, Schneider, V, Shekhtman, E, Sirotkin, K, Slotta, D, Zhang, H, Barnes, KC, Beiswanger, C, Cai, H, Cao, H, Gharani, N, Henn, B, Jones, D, Kaye, JS, Kent, A, Kerasidou, A, Mathias, R, Ossorio, PN, Parker, M, Reich, D, Rotimi, CN, Royal, CD, Sandoval, K, Su, Y, Tian, Z, Tishkoff, S, Toji, LH, Via, M, Yang, H, Yang, L, Zhu, J, Bodmer, W, Bedoya, G, Ming, CZ, Yang, G, You, CJ, Peltonen, L, Garcia-Montero, A, Orfao, A, Dutil, J, Martinez-Cruzado, JC, Brooks, LD, Felsenfeld, AL, McEwen, JE, Clemm, NC, Duncanson, A, Dunn, M, Guyer, MS, Peterson, JL, 1000 Genomes Project Consortium, Dermitzakis, Emmanouil, Universitat de Barcelona, Massachusetts Institute of Technology. Department of Biology, Altshuler, David, and Lander, Eric S.
Subjects: Natural selection, LOCI, Genome-wide association study, Evolutionary biology, Continental Population Groups/genetics, Human genetic variation, VARIANTS, Genoma humà, Binding Sites/genetics, 0302 clinical medicine, RARE, Sequence Deletion/genetics, WIDE ASSOCIATION, ddc:576.5, Copy-number variation, MUTATION, Exome sequencing, transcription factor, Conserved Sequence, Human evolution, Sequence Deletion, Genetics, RISK, 0303 health sciences, Multidisciplinary, Continental Population Groups, 1000 Genomes Project Consortium, Genetic analysis, Genomics, Polymorphism, Single Nucleotide/genetics, Research Highlight, 3. Good health, Algorithm, Multidisciplinary Sciences, Genetic Variation/genetics, Map, Science & Technology - Other Topics, Conserved Sequence/genetics, Integrated approach, General Science & Technology, Genetics, Medical, Haplotypes/genetics, Biology, Polymorphism, Single Nucleotide, Evolution, Molecular, 03 medical and health sciences, Genetic variation, Humans, Transcription Factors/metabolism, POPULATION-STRUCTURE, 1000 Genomes Project, Polymorphism, Nucleotide Motifs, Alleles, 030304 developmental biology, COPY NUMBER VARIATION, Science & Technology, Binding Sites, Human genome, Genome, Human, Racial Groups, Genetic Variation, Genetics, Population, Haplotypes, Genome, Human/genetics, untranslated RNA, 030217 neurology & neurosurgery, Transcription Factors, Genome-Wide Association Study
Abstract: By characterizing the geographic and functional spectrum of human genetic variation, the 1000 Genomes Project aims to build a resource to help to understand the genetic contribution to disease. Here we describe the genomes of 1,092 individuals from 14 populations, constructed using a combination of low-coverage whole-genome and exome sequencing. By developing methods to integrate information across several algorithms and diverse data sources, we provide a validated haplotype map of 38 million single nucleotide polymorphisms, 1.4 million short insertions and deletions, and more than 14,000 larger deletions. We show that individuals from different populations carry different profiles of rare and common variants, and that low-frequency variants show substantial geographic differentiation, which is further increased by the action of purifying selection. We show that evolutionary conservation and coding consequence are key determinants of the strength of purifying selection, that rare-variant load varies substantially across biological pathways, and that each individual contains hundreds of rare non-coding variants at conserved sites, such as motif-disrupting changes in transcription-factor-binding sites. This resource, which captures up to 98% of accessible single nucleotide polymorphisms at a frequency of 1% in related populations, enables analysis of common and low-frequency variants in individuals from diverse, including admixed, populations., National Institutes of Health (U.S.) (Grant RC2HL102925), National Institutes of Health (U.S.) (Grant U54HG3067)
Published: 2012

47. A map of human genome variation from population-scale sequencing

Author: Durbin, Richard M., Altshuler, David L., Abecasis, Gonçalo R., Bentley, David R., Chakravarti, Aravinda, Clark, Andrew G., Collins, Francis S., De La Vega, Francisco M., Donnelly, Peter, Egholm, Michael, Flicek, Paul, Gabriel, Stacey B., Gibbs, Richard A., Knoppers, Bartha M., Lander, Eric S., Lehrach, Hans, Mardis, Elaine R., McVean, Gil A., Nickerson, Debbie A., Peltonen, Leena, Schafer, Alan J., Sherry, Stephen T., Wang, Jun, Wilson, Richard K., Deiros, David, Metzker, Mike, Muzny, Donna, Reid, Jeff, Wheeler, David, Li, Jingxiang, Jian, Min, Li, Guoqing, Li, Ruiqiang, Liang, Huiqing, Tian, Geng, Wang, Bo, Wang, Jian, Wang, Wei, Yang, Huanming, Zhang, Xiuqing, Zheng, Huisong, Ambrogio, Lauren, Bloom, Toby, Cibulskis, Kristian, Fennell, Tim J., Jaffe, David B., Shefler, Erica, Sougnez, Carrie L., Gormley, Niall, Humphray, Sean, Kingsbury, Zoya, Koko-Gonzales, Paula, Stone, Jennifer, McKernan, Kevin J., Costa, Gina L., Ichikawa, Jeffry K., Lee, Clarence C., Sudbrak, Ralf, Borodina, Tatiana A., Dahl, Andreas, Davydov, Alexey N., Marquardt, Peter, Mertes, Florian, Nietfeld, Wilfiried, Rosenstiel, Philip, Schreiber, Stefan, Soldatov, Aleksey V., Timmermann, Bernd, Tolzmann, Marius, Affourtit, Jason, Ashworth, Dana, Attiya, Said, Bachorski, Melissa, Buglione, Eli, Burke, Adam, Caprio, Amanda, Celone, Christopher, Clark, Shauna, Conners, David, Desany, Brian, Gu, Lisa, Guccione, Lorri, Kao, Kalvin, Kebbel, Andrew, Knowlton, Jennifer, Labrecque, Matthew, McDade, Louise, Mealmaker, Craig, Minderman, Melissa, Nawrocki, Anne, Niazi, Faheem, Pareja, Kristen, Ramenani, Ravi, Riches, David, Song, Wanmin, Turcotte, Cynthia, Wang, Shally, Dooling, David, Fulton, Lucinda, Fulton, Robert, Weinstock, George, Burton, John, Carter, David M., Churcher, Carol, Coffey, Alison, Cox, Anthony, Palotie, Aarno, Quail, Michael, Skelly, Tom, Stalker, James, Swerdlow, Harold P., Turner, Daniel, De Witte, Anniek, Giles, Shane, Bainbridge, Matthew, Challis, Danny, Sabo, Aniko, Yu, Fuli, Yu, Jin, Fang, Xiaodong, Guo, Xiaosen, Li, Yingrui, Luo, Ruibang, Tai, Shuaishuai, Wu, Honglong, Zheng, Hancheng, Zheng, Xiaole, Zhou, Yan, Marth, Gabor T., Garrison, Erik P., Huang, Weichun, Indap, Amit, Kural, Deniz, Lee, Wan-Ping, Fung Leong, Wen, Quinlan, Aaron R., Stewart, Chip, Stromberg, Michael P., Ward, Alistair N., Wu, Jiantao, Lee, Charles, Mills, Ryan E., Shi, Xinghua, Daly, Mark J., DePristo, Mark A., Ball, Aaron D., Banks, Eric, Browning, Brian L., Garimella, Kiran V., Grossman, Sharon R., Handsaker, Robert E., Hanna, Matt, Hartl, Chris, Kernytsky, Andrew M., Korn, Joshua M., Li, Heng, Maguire, Jared R., McCarroll, Steven A., McKenna, Aaron, Nemesh, James C., Philippakis, Anthony A., Poplin, Ryan E., Price, Alkes, Rivas, Manuel A., Sabeti, Pardis C., Schaffner, Stephen F., Shlyakhter, Ilya A., Cooper, David N., Ball, Edward V., Mort, Matthew, Phillips, Andrew D., Stenson, Peter D., Sebat, Jonathan, Makarov, Vladimir, Ye, Kenny, Yoon, Seungtai C., Bustamante, Carlos D., Boyko, Adam, Degenhardt, Jeremiah, Gravel, Simon, Gutenkunst, Ryan N., Kaganovich, Mark, Keinan, Alon, Lacroute, Phil, Ma, Xin, Reynolds, Andy, Clarke, Laura, Cunningham, Fiona, Herrero, Javier, Keenen, Stephen, Kulesha, Eugene, Leinonen, Rasko, McLaren, William M., Radhakrishnan, Rajesh, Smith, Richard E., Zalunin, Vadim, Zheng-Bradley, Xiangqun, Korbel, Jan O., Stütz, Adrian M., Bauer, Markus, Keira Cheetham, R., Cox, Tony, Eberle, Michael, James, Terena, Kahn, Scott, Murray, Lisa, Ye, Kai, Fu, Yutao, Hyland, Fiona C. L., Manning, Jonathan M., McLaughlin, Stephen F., Peckham, Heather E., Sakarya, Onur, Sun, Yongming A., Tsung, Eric F., Batzer, Mark A., Konkel, Miriam K., Walker, Jerilyn A., Albrecht, Marcus W., Amstislavskiy, Vyacheslav S., Herwig, Ralf, Parkhomchuk, Dimitri V., Agarwala, Richa, Khouri, Hoda M., Morgulis, Aleksandr O., Paschall, Justin E., Phan, Lon D., Rotmistrovsky, Kirill E., Sanders, Robert D., Shumway, Martin F., Xiao, Chunlin, Auton, Adam, Iqbal, Zamin, Lunter, Gerton, Marchini, Jonathan L., Moutsianas, Loukas, Myers, Simon, Tumian, Afidalina, Knight, James, Winer, Roger, Craig, David W., Beckstrom-Sternberg, Steve M., Christoforides, Alexis, Kurdoglu, Ahmet A., Pearson, John V., Sinari, Shripad A., Tembe, Waibhav D., Haussler, David, Hinrichs, Angie S., Katzman, Sol J., Kern, Andrew, Kuhn, Robert M., Przeworski, Molly, Hernandez, Ryan D., Howie, Bryan, Kelley, Joanna L., Cord Melton, S., Li, Yun, Anderson, Paul, Blackwell, Tom, Chen, Wei, Cookson, William O., Ding, Jun, Min Kang, Hyun, Lathrop, Mark, Liang, Liming, Moffatt, Miriam F., Scheet, Paul, Sidore, Carlo, Snyder, Matthew, Zhan, Xiaowei, Zöllner, Sebastian, Awadalla, Philip, Casals, Ferran, Idaghdour, Youssef, Keebler, John, Stone, Eric A., Zilversmit, Martine, Jorde, Lynn, Xing, Jinchuan, Eichler, Evan E., Aksay, Gozde, Alkan, Can, Hajirasouliha, Iman, Hormozdiari, Fereydoun, Kidd, Jeffrey M., Cenk Sahinalp, S., Sudmant, Peter H., Chen, Ken, Chinwalla, Asif, Ding, Li, Koboldt, Daniel C., McLellan, Mike D., Wallis, John W., Wendl, Michael C., Zhang, Qunyuan, Albers, Cornelis A., Ayub, Qasim, Balasubramaniam, Senduran, Barrett, Jeffrey C., Chen, Yuan, Conrad, Donald F., Danecek, Petr, Dermitzakis, Emmanouil T., Hu, Min, Huang, Ni, Hurles, Matt E., Jin, Hanjun, Jostins, Luke, Keane, Thomas M., Quang Le, Si, Lindsay, Sarah, Long, Quan, MacArthur, Daniel G., Montgomery, Stephen B., Parts, Leopold, Tyler-Smith, Chris, Walter, Klaudia, Zhang, Yujun, Gerstein, Mark B., Snyder, Michael, Abyzov, Alexej, Balasubramanian, Suganthi, Bjornson, Robert, Du, Jiang, Grubert, Fabian, Habegger, Lukas, Haraksingh, Rajini, Jee, Justin, Khurana, Ekta, Lam, Hugo Y. K., Leng, Jing, Jasmine Mu, Xinmeng, Urban, Alexander E., Zhang, Zhengdong, Coafra, Cristian, Dinh, Huyen, Kovar, Christie, Lee, Sandy, Nazareth, Lynne, Wilkinson, Jane, Coffey, Allison, Scott, Carol, Gharani, Neda, Kaye, Jane S., Kent, Alastair, Li, Taosha, McGuire, Amy L., Ossorio, Pilar N., Rotimi, Charles N., Su, Yeyang, Toji, Lorraine H., Brooks, Lisa D., Felsenfeld, Adam L., McEwen, Jean E., Abdallah, Assya, Juenger, Christopher R., Clemm, Nicholas C., Duncanson, Audrey, Green, Eric D., Guyer, Mark S., Peterson, Jane L., The Wellcome Trust Sanger Institute [Cambridge], Centre National de Génotypage (CNG), Commissariat à l'énergie atomique et aux énergies alternatives (CEA), University of Michigan [Ann Arbor], University of Michigan System, The 1000 Genomes Project Consortium, Harvard University--MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology. Department of Biology, Lander, Eric S., Altshuler, David, Daly, Mark J., Grossman, Sharon Rachel, Jaffe, David B., Korn, Joshua M., and Dermitzakis, Emmanouil
Subjects: Male, [SDV]Life Sciences [q-bio], DNA Mutational Analysis, Pilot Projects, Chromosomes, Human, Y/genetics, DNA, Mitochondrial/genetics, 0302 clinical medicine, ddc:576.5, 2. Zero hunger, Genetics, Recombination, Genetic, 0303 health sciences, Multidisciplinary, Genetics, Population/methods, Genomics, Polymorphism, Single Nucleotide/genetics, Recombination, Genetic/genetics, 3. Good health, Genetic Variation/genetics, Sequence Analysis, DNA/methods, Calibration, Female, Mutation/genetics, Genotype, Haplotypes/genetics, Biology, Instability, DNA, Mitochondrial, Polymorphism, Single Nucleotide, Article, Selection, Genetic/genetics, Factor (chord), Evolution, Molecular, 03 medical and health sciences, Table (landform), Humans, Selection, Genetic, Genetic Association Studies, 030304 developmental biology, Genotype imputation, Chromosomes, Human, Y, Genome, Human, Computational Biology, Genetic Variation, Sequence Analysis, DNA, Genomics/methods, Genetics, Population, Haplotypes, Sample Size, wide association gene-expression recombination hotspots meiotic recombination genotype imputation rare variants haplotype map nucleotide diseases common, Mutation, Genome, Human/genetics, Trinucleotide repeat expansion, Sequence Alignment, 030217 neurology & neurosurgery, Genome-Wide Association Study
Abstract: April 1, 2011, The 1000 Genomes Project aims to provide a deep characterization of human genome sequence variation as a foundation for investigating the relationship between genotype and phenotype. Here we present results of the pilot phase of the project, designed to develop and compare different strategies for genome-wide sequencing with high-throughput platforms. We undertook three projects: low-coverage whole-genome sequencing of 179 individuals from four populations; high-coverage sequencing of two mother–father–child trios; and exon-targeted sequencing of 697 individuals from seven populations. We describe the location, allele frequency and local haplotype structure of approximately 15 million single nucleotide polymorphisms, 1 million short insertions and deletions, and 20,000 structural variants, most of which were previously undescribed. We show that, because we have catalogued the vast majority of common variation, over 95% of the currently accessible variants found in any individual are present in this data set. On average, each person is found to carry approximately 250 to 300 loss-of-function variants in annotated genes and 50 to 100 variants previously implicated in inherited disorders. We demonstrate how these results can be used to inform association and functional studies. From the two trios, we directly estimate the rate of de novo germline base substitution mutations to be approximately 10[superscript −8] per base pair per generation. We explore the data with regard to signatures of natural selection, and identify a marked reduction of genetic variation in the neighbourhood of genes, due to selection at linked sites. These methods and public data will support the next phase of human genetic research., Ministry of Science and Technology of the People's Republic of China (973 program no. 2011CB809200), National Natural Science Foundation (China) (30725008), National Natural Science Foundation (China) (30890032), National Natural Science Foundation (China) (30811130531), National Natural Science Foundation (China) (30221004), China (Chinese 863 program 2006AA02Z177), China (Chinese 863 program 2006AA02Z334), China (Chinese 863 program 2006AA02A302), China (Chinese 863 program 2009AA022707), National Institutes of Health (U.S.) (Grant U54HG2750), National Institutes of Health (U.S.) (Grant U01HG5208), National Institutes of Health (U.S.) (Grant U54HG3067)
Published: 2010
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48. Split Hand-Foot Malformations-Unveiling Unique Molecular Diagnosis From a Brazilian Cohort.

Author: Cás ED, Ceroni JRM, Yamamoto GL, Castro MAA, Bisneto EF, Jorge AAL, Filho JBO, Kim CA, Krepischi ACV, and Bertola DR
Abstract: Split hand-foot malformation (SHFM) is a congenital limb malformation affecting primarily the central rays of the hands and/or feet, with variable expressivity, incomplete penetrance and syndromic forms. It is genetically heterogeneous, including point mutations and structural variants in different loci. Five individuals with SHFM were clinically evaluated in a Tertiary Center in Brazil: four of them presented additional, nonskeletal findings, including one individual with split foot, hand syndactyly, and ectodermal findings. Structural variants and point mutations in genes associated with SHFM were identified in all individuals. Our results highlight genetic heterogeneity observed in this group of skeletal disorders, alongside incomplete penetrance, a challenging task imposed on genetic counseling. Of note, an individual harboring a recurrent heterozygous variant in MAP3K20 presented a phenotype reminiscent of TP63-related disorders, contrary to the one recently reported in the literature with prominent facial dysmorphisms, expanding the phenotypic spectrum of this newly recognized syndromic form of SHFM., (© 2024 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.)
Published: 2024
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49. Syndromic Retinitis Pigmentosa: A 15-Patient Study.

Author: Holanda IP, Rim PHH, Rare Genomes Project Consortium, Guaragna MS, Gil-da-Silva-Lopes VL, and Steiner CE
Subjects: Humans, Male, Female, Adult, Adolescent, Child, Young Adult, Usher Syndromes genetics, Usher Syndromes pathology, Usher Syndromes diagnosis, Brazil epidemiology, Middle Aged, High-Throughput Nucleotide Sequencing, Bardet-Biedl Syndrome genetics, Mutation, Retinitis Pigmentosa genetics, Retinitis Pigmentosa pathology, Retinitis Pigmentosa diagnosis
Abstract: Retinitis pigmentosa is a group of genetically determined retinal dystrophies characterized by primary photoreceptor apoptosis and can occur in isolated or syndromic conditions. This study reviewed the clinical data of 15 patients with syndromic retinitis pigmentosa from a Rare Disease Reference Center in Brazil and the results of their next-generation sequencing tests. Five males and ten females participated, with the mean ages for ocular disease onset, fundoscopic diagnosis, and molecular evaluation being 9, 19, and 29 years, respectively. Bardet-Biedl syndrome ( n = 5) and Usher syndrome ( n = 3) were the most frequent diagnoses, followed by other rare conditions. Among the patients, fourteen completed molecular studies, with three negative results and eleven revealing findings in known genes, including novel variants in MKKS (c.432_435del, p.Phe144Leufs*14), USH2A (c.(7301+1_7302-1)_(9369+1_9370-1)del), and CEP250 (c.5383dup, p.Glu1795Glyfs*13, and c.5050del, p.Asp1684Thrfs*9). Except for Kearn-Sayre, all presented an autosomal recessive inheritance pattern with 64% homozygosity results. The long gap between symptom onset and diagnosis highlights the diagnostic challenges faced by the patients. This study reaffirms the clinical heterogeneity of syndromic retinitis pigmentosa and underscores the pivotal role of molecular analysis in advancing our understanding of these diseases.
Published: 2024
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50. Genome Sequencing in an Individual Presenting with 22q11.2 Deletion Syndrome and Juvenile Idiopathic Arthritis.

Author: de Oliveira-Sobrinho RP, Appenzeller S, Holanda IP, Heleno JL, Jorente J, On Behalf Of The Rare Genomes Project Consortium, Vieira TP, and Steiner CE
Subjects: Humans, Male, Intramolecular Oxidoreductases genetics, Child, Preschool, Macrophage Migration-Inhibitory Factors genetics, Child, Arthritis, Juvenile genetics, DiGeorge Syndrome genetics, Whole Genome Sequencing
Abstract: Juvenile idiopathic arthritis is a heterogeneous group of diseases characterized by arthritis with poorly known causes, including monogenic disorders and multifactorial etiology. 22q11.2 proximal deletion syndrome is a multisystemic disease with over 180 manifestations already described. In this report, the authors describe a patient presenting with a short stature, neurodevelopmental delay, and dysmorphisms, who had an episode of polyarticular arthritis at the age of three years and eight months, resulting in severe joint limitations, and was later diagnosed with 22q11.2 deletion syndrome. Investigation through Whole Genome Sequencing revealed that he had no pathogenic or likely-pathogenic variants in both alleles of the MIF gene or in genes associated with monogenic arthritis ( LACC1 , LPIN2 , MAFB , NFIL3 , NOD2 , PRG4 , PRF1 , STX11 , TNFAIP3 , TRHR , UNC13DI ). However, the patient presented 41 risk polymorphisms for juvenile idiopathic arthritis. Thus, in the present case, arthritis seems coincidental to 22q11.2 deletion syndrome, probably caused by a multifactorial etiology. The association of the MIF gene in individuals previously described with juvenile idiopathic arthritis and 22q11.2 deletion seems unlikely since it is located in the distal and less-frequently deleted region of 22q11.2 deletion syndrome.
Published: 2024
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