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Your search keyword '"Memari, Y."' showing total 7 results
Start Over Author "Memari, Y." Topic genetic variation
7 results on '"Memari, Y."'

1. Common genetic variation drives molecular heterogeneity in human iPSCs.

Author

Kilpinen H, Goncalves A, Leha A, Afzal V, Alasoo K, Ashford S, Bala S, Bensaddek D, Casale FP, Culley OJ, Danecek P, Faulconbridge A, Harrison PW, Kathuria A, McCarthy D, McCarthy SA, Meleckyte R, Memari Y, Moens N, Soares F, Mann A, Streeter I, Agu CA, Alderton A, Nelson R, Harper S, Patel M, White A, Patel SR, Clarke L, Halai R, Kirton CM, Kolb-Kokocinski A, Beales P, Birney E, Danovi D, Lamond AI, Ouwehand WH, Vallier L, Watt FM, Durbin R, Stegle O, and Gaffney DJ

Subjects
Cells, Cultured, Cellular Reprogramming genetics, DNA Copy Number Variations genetics, Gene Expression Regulation genetics, Genotype, Humans, Organ Specificity, Phenotype, Quality Control, Quantitative Trait Loci genetics, Transcriptome genetics, Genetic Variation genetics, Induced Pluripotent Stem Cells metabolism
Abstract

Technology utilizing human induced pluripotent stem cells (iPS cells) has enormous potential to provide improved cellular models of human disease. However, variable genetic and phenotypic characterization of many existing iPS cell lines limits their potential use for research and therapy. Here we describe the systematic generation, genotyping and phenotyping of 711 iPS cell lines derived from 301 healthy individuals by the Human Induced Pluripotent Stem Cells Initiative. Our study outlines the major sources of genetic and phenotypic variation in iPS cells and establishes their suitability as models of complex human traits and cancer. Through genome-wide profiling we find that 5-46% of the variation in different iPS cell phenotypes, including differentiation capacity and cellular morphology, arises from differences between individuals. Additionally, we assess the phenotypic consequences of genomic copy-number alterations that are repeatedly observed in iPS cells. In addition, we present a comprehensive map of common regulatory variants affecting the transcriptome of human pluripotent cells.

Published
2017
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2. An interactive genome browser of association results from the UK10K cohorts project.

Author

Geihs M, Yan Y, Walter K, Huang J, Memari Y, Min JL, Mead D, Hubbard TJ, Timpson NJ, Down TA, and Soranzo N

Subjects
High-Throughput Nucleotide Sequencing, Humans, Linkage Disequilibrium, Genetic Association Studies, Genetic Variation, Genome, Human, Software
Abstract

Unlabelled: High-throughput sequencing technologies survey genetic variation at genome scale and are increasingly used to study the contribution of rare and low-frequency genetic variants to human traits. As part of the Cohorts arm of the UK10K project, genetic variants called from low-read depth (average 7×) whole genome sequencing of 3621 cohort individuals were analysed for statistical associations with 64 different phenotypic traits of biomedical importance. Here, we describe a novel genome browser based on the Biodalliance platform developed to provide interactive access to the association results of the project., Availability and Implementation: The browser is available at http://www.uk10k.org/dalliance.html. Source code for the Biodalliance platform is available under a BSD license from http://github.com/dasmoth/dalliance, and for the LD-display plugin and backend from http://github.com/dasmoth/ldserv., (© The Author 2015. Published by Oxford University Press.)

Published
2015
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3. The UK10K project identifies rare variants in health and disease.

Author

Walter K, Min JL, Huang J, Crooks L, Memari Y, McCarthy S, Perry JR, Xu C, Futema M, Lawson D, Iotchkova V, Schiffels S, Hendricks AE, Danecek P, Li R, Floyd J, Wain LV, Barroso I, Humphries SE, Hurles ME, Zeggini E, Barrett JC, Plagnol V, Richards JB, Greenwood CM, Timpson NJ, Durbin R, and Soranzo N

Subjects
Adiponectin blood, Alleles, Cohort Studies, Exome genetics, Female, Genetic Predisposition to Disease genetics, Genetics, Medical, Genetics, Population, Genome-Wide Association Study, Genomics, Humans, Lipid Metabolism genetics, Male, Molecular Sequence Annotation, Receptors, LDL genetics, Reference Standards, Sequence Analysis, DNA, Triglycerides blood, United Kingdom, Disease genetics, Genetic Variation genetics, Genome, Human genetics, Health
Abstract

The contribution of rare and low-frequency variants to human traits is largely unexplored. Here we describe insights from sequencing whole genomes (low read depth, 7×) or exomes (high read depth, 80×) of nearly 10,000 individuals from population-based and disease collections. In extensively phenotyped cohorts we characterize over 24 million novel sequence variants, generate a highly accurate imputation reference panel and identify novel alleles associated with levels of triglycerides (APOB), adiponectin (ADIPOQ) and low-density lipoprotein cholesterol (LDLR and RGAG1) from single-marker and rare variant aggregation tests. We describe population structure and functional annotation of rare and low-frequency variants, use the data to estimate the benefits of sequencing for association studies, and summarize lessons from disease-specific collections. Finally, we make available an extensive resource, including individual-level genetic and phenotypic data and web-based tools to facilitate the exploration of association results.

Published
2015
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4. Improved imputation of low-frequency and rare variants using the UK10K haplotype reference panel.

Author

Huang J, Howie B, McCarthy S, Memari Y, Walter K, Min JL, Danecek P, Malerba G, Trabetti E, Zheng HF, Gambaro G, Richards JB, Durbin R, Timpson NJ, Marchini J, and Soranzo N

Subjects
Adolescent, Adult, Aged, Aged, 80 and over, Alleles, Genome, Human, Humans, Italy, Middle Aged, Models, Genetic, Polymorphism, Single Nucleotide, United Kingdom, Young Adult, Gene Frequency, Genetic Variation, Genotype, Haplotypes, Models, Statistical, White People genetics
Abstract

Imputing genotypes from reference panels created by whole-genome sequencing (WGS) provides a cost-effective strategy for augmenting the single-nucleotide polymorphism (SNP) content of genome-wide arrays. The UK10K Cohorts project has generated a data set of 3,781 whole genomes sequenced at low depth (average 7x), aiming to exhaustively characterize genetic variation down to 0.1% minor allele frequency in the British population. Here we demonstrate the value of this resource for improving imputation accuracy at rare and low-frequency variants in both a UK and an Italian population. We show that large increases in imputation accuracy can be achieved by re-phasing WGS reference panels after initial genotype calling. We also present a method for combining WGS panels to improve variant coverage and downstream imputation accuracy, which we illustrate by integrating 7,562 WGS haplotypes from the UK10K project with 2,184 haplotypes from the 1000 Genomes Project. Finally, we introduce a novel approximation that maintains speed without sacrificing imputation accuracy for rare variants.

Published
2015
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5. A GWAS sequence variant for platelet volume marks an alternative DNM3 promoter in megakaryocytes near a MEIS1 binding site

Author

Nürnberg ST, Rendon A, Smethurst PA, Paul DS, Voss K, Thon JN, Lloyd Jones H, Sambrook JG, Tijssen MR, Gieger C, Radhakrishnan A, Cvejic A, Tang W, Porcu E, Pistis G, Serbanovic Canic J, Elling U, Goodall AH, Labrune Y, Lopez LM, Mägi R, Meacham S, Okada Y, Sorice R, Teumer A, Zhang W, Ramirez Solis R, Bis JC, Ellinghaus D, Gögele M, Hottenga JJ, Langenberg C, Kovacs P, F. P, Shin SY, Esko T, Hartiala J, Kanoni S, Murgia F, Parsa A, Stephens J, van der Harst P, van der Schoot C, Allayee H, Attwood A, Balkau B, Bastardot F, Basu S, Baumeister SE, Biino G, Bomba L, Bonnefond A, Cambien F, Chambers JC, Cucca F, Davies G, de Geus EJ, de Boer RA, Döring A, Elliott P, Erdmann J, Feng W, Evans DM, Falchi M, Folsom AR, Frazer IH, Gibson QD, Glazer NL, Hammond C, Hartikainen AL, Heckbert SR, Hengstenberg C, Hersch M, Illig T, Loos RJ, Jolley J, Khaw KT, Kühnel B, Kyrtsonis MC, Lagou V, Lumley T, Mangino M, Maschio A, Mateo Leach I, McKnight B, Memari Y, Mitchell BD, Montgomery GW, Nöthlings U, Nakamura Y, Nauck M, Navis G, Nolte IM, Porteous DJ, Pouta A, Pramstaller PP, Pullat J, Ring SM, Rotter JI, Ruggiero D, Ruokonen A, Sala C, Samani NJ, Sambrook J, Schlessinger D, Schreiber S, Schunkert H, Scott J, Smith NL, Snieder H, Starr JM, Stumvoll M, Takahashi A, Taylor K, Tenesa A, Thein SL, Tönjes A, Uda M, Ulivi S, Wichmann HE, Yang TP, van Veldhuisen DJ, Visscher PM, Völker U, Wiggins KL, Willemsen G, Zhao JH, Zitting P, Bradley JR, Dedoussis GV, Hazen SL, Metspalu A, Pirastu M, Shuldiner AR, van Pelt L, Zwaginga JJ, Boomsma DI, Deary IJ, Franke A, Froguel P, Ganesh SK, Jarvelin MR, Martin NG, Meisinger C, Psaty BM, Spector TD, Wareham NJ, Akkerman JW, Ciullo M, Deloukas P, Greinacher A, Jupe S, Kamatani N, Khadake J, Kooner JS, Penninger J, Prokopenko I, Stemple D, Toniolo D, Wernisch L, Sanna S, Hicks AA, Ferreira MA, Italiano JE Jr, Gottgens B, Soranzo N, Ouwehand WH, PIRASTU, Nicola, D'ADAMO, ADAMO PIO, GASPARINI, PAOLO, Nürnberg, St, Rendon, A, Smethurst, Pa, Paul, D, Voss, K, Thon, Jn, Lloyd Jones, H, Sambrook, Jg, Tijssen, Mr, Gieger, C, Radhakrishnan, A, Cvejic, A, Tang, W, Porcu, E, Pistis, G, Serbanovic Canic, J, Elling, U, Goodall, Ah, Labrune, Y, Lopez, Lm, Mägi, R, Meacham, S, Okada, Y, Pirastu, Nicola, Sorice, R, Teumer, A, Zhang, W, Ramirez Solis, R, Bis, Jc, Ellinghaus, D, Gögele, M, Hottenga, Jj, Langenberg, C, Kovacs, P, F., P, Shin, Sy, Esko, T, Hartiala, J, Kanoni, S, Murgia, F, Parsa, A, Stephens, J, van der Harst, P, van der Schoot, C, Allayee, H, Attwood, A, Balkau, B, Bastardot, F, Basu, S, Baumeister, Se, Biino, G, Bomba, L, Bonnefond, A, Cambien, F, Chambers, Jc, Cucca, F, D'Adamo, ADAMO PIO, Davies, G, de Geus, Ej, de Boer, Ra, Döring, A, Elliott, P, Erdmann, J, Feng, W, Evans, Dm, Falchi, M, Folsom, Ar, Frazer, Ih, Gibson, Qd, Glazer, Nl, Hammond, C, Hartikainen, Al, Heckbert, Sr, Hengstenberg, C, Hersch, M, Illig, T, Loos, Rj, Jolley, J, Khaw, Kt, Kühnel, B, Kyrtsonis, Mc, Lagou, V, Lumley, T, Mangino, M, Maschio, A, Mateo Leach, I, Mcknight, B, Memari, Y, Mitchell, Bd, Montgomery, Gw, Nöthlings, U, Nakamura, Y, Nauck, M, Navis, G, Nolte, Im, Porteous, Dj, Pouta, A, Pramstaller, Pp, Pullat, J, Ring, Sm, Rotter, Ji, Ruggiero, D, Ruokonen, A, Sala, C, Samani, Nj, Sambrook, J, Schlessinger, D, Schreiber, S, Schunkert, H, Scott, J, Smith, Nl, Snieder, H, Starr, Jm, Stumvoll, M, Takahashi, A, Taylor, K, Tenesa, A, Thein, Sl, Tönjes, A, Uda, M, Ulivi, S, Wichmann, He, Yang, Tp, van Veldhuisen, Dj, Visscher, Pm, Völker, U, Wiggins, Kl, Willemsen, G, Zhao, Jh, Zitting, P, Bradley, Jr, Dedoussis, Gv, Gasparini, Paolo, Hazen, Sl, Metspalu, A, Pirastu, M, Shuldiner, Ar, van Pelt, L, Zwaginga, Jj, Boomsma, Di, Deary, Ij, Franke, A, Froguel, P, Ganesh, Sk, Jarvelin, Mr, Martin, Ng, Meisinger, C, Psaty, Bm, Spector, Td, Wareham, Nj, Akkerman, Jw, Ciullo, M, Deloukas, P, Greinacher, A, Jupe, S, Kamatani, N, Khadake, J, Kooner, J, Penninger, J, Prokopenko, I, Stemple, D, Toniolo, D, Wernisch, L, Sanna, S, Hicks, Aa, Ferreira, Ma, Italiano JE, Jr, Gottgens, B, Soranzo, N, Ouwehand, Wh, Biological Psychology, and EMGO+ - Mental Health

Subjects
Netherlands Twin Register (NTR), Transcription, Genetic, Gene Expression, Biochemistry, megakaryocyte, Mice, DNM3 promoter, Transcription (biology), GWAS, Platelet, Thrombopoiesis, Myeloid Ecotropic Viral Integration Site 1 Protein, Promoter Regions, Genetic, Cells, Cultured, Reverse Transcriptase Polymerase Chain Reaction, Hematology, Neoplasm Proteins, Haematopoiesis, Transcription Initiation Site, Megakaryocytes, platelet volume, DNM3, megakaryocytes, MEIS1, Blood Platelets, Chromatin Immunoprecipitation, Immunology, Biology, Polymorphism, Single Nucleotide, MEIS1 binding site, SDG 3 - Good Health and Well-being, Cell Line, Tumor, Animals, Humans, Cell Lineage, Binding site, Gene, Transcription factor, Homeodomain Proteins, Binding Sites, Genome, Human, Platelet Count, Hydrazones, Genetic Variation, Cell Biology, Sequence Analysis, DNA, Platelets and Thrombopoiesis, Molecular biology, Dynamin III
Abstract

We recently identified 68 genomic loci where common sequence variants are associated with platelet count and volume. Platelets are formed in the bone marrow by megakaryocytes, which are derived from hematopoietic stem cells by a process mainly controlled by transcription factors. The homeobox transcription factor MEIS1 is uniquely transcribed in megakaryocytes and not in the other lineage-committed blood cells. By ChIP-seq, we show that 5 of the 68 loci pinpoint a MEIS1 binding event within a group of 252 MK-overexpressed genes. In one such locus in DNM3, regulating platelet volume, the MEIS1 binding site falls within a region acting as an alternative promoter that is solely used in megakaryocytes, where allelic variation dictates different levels of a shorter transcript. The importance of dynamin activity to the latter stages of thrombopoiesis was confirmed by the observation that the inhibitor Dynasore reduced murine proplatelet for-mation in vitro.

Published
2013

6. Improved imputation of low-frequency and rare variants using the UK10K haplotype reference panel

Author

Huang, J, Howie, B, Mccarthy, S, Memari, Y, Walter, K, Min, Jl, Danecek, P, Malerba, Giovanni, Trabetti, Elisabetta, Zheng, Hf, Gambaro, G, Richards, Jb, Durbin, R, Timpson, Nj, Marchini, J, Soranzo, N, Al Turki, S, Amuzu, A, Anderson, Ca, Anney, R, Antony, D, Artigas, Ms, Ayub, M, Bala, S, Barrett, Jc, Barroso, I, Beales, P, Benn, M, Bentham, J, Bhattacharya, S, Birney, E, Blackwood, D, Bobrow, M, Bochukova, E, Bolton, Pf, Bounds, R, Boustred, C, Breen, G, Calissano, M, Carss, K, Casas, Jp, Chambers, Jc, Charlton, R, Chatterjee, K, Chen, L, Ciampi, A, Cirak, S, Clapham, P, Clement, G, Coates, G, Cocca, M, Collier, Da, Cosgrove, C, Cox, T, Craddock, N, Crooks, L, Curran, S, Curtis, D, Daly, A, Day, In, Day Williams, A, Dedoussis, G, Down, T, Du, Y, van Duijn, Cm, Dunham, I, Edkins, S, Ekong, R, Ellis, P, Evans, Dm, Farooqi, Is, Fitzpatrick, Dr, Flicek, P, Floyd, J, Foley, Ar, Franklin, Cs, Futema, M, Gallagher, L, Gasparini, P, Gaunt, Tr, Geihs, M, Geschwind, D, Greenwood, C, Griffin, H, Grozeva, D, Guo, X, Gurling, H, Hart, D, Hendricks, Ae, Holmans, P, Huang, L, Hubbard, T, Humphries, Se, Hurles, Me, Hysi, P, Iotchkova, V, Isaacs, A, Jackson, Dk, Jamshidi, Y, Johnson, J, Joyce, C, Karczewski, Kj, Kaye, J, Keane, T, Kemp, Jp, Kennedy, K, Kent, A, Keogh, J, Khawaja, F, Kleber, Me, van Kogelenberg, M, Kolb Kokocinski, A, Kooner, Js, Lachance, G, Langenberg, C, Langford, C, Lawson, D, Lee, I, van Leeuwen, Em, Lek, M, Li, R, Li, Y, Liang, J, Lin, H, Liu, R, Lönnqvist, J, Lopes, Lr, Lopes, M, Luan, J, Macarthur, Dg, Mangino, M, Marenne, G, März, W, Maslen, J, Matchan, A, Mathieson, I, Mcguffin, P, Mcintosh, Am, Mckechanie, Ag, Mcquillin, A, Metrustry, S, Migone, N, Mitchison, Hm, Moayyeri, A, Morris, J, Morris, R, Muddyman, D, Muntoni, F, Nordestgaard, Bg, Northstone, K, O'Donovan, Mc, O'Rahilly, S, Onoufriadis, A, Oualkacha, K, Owen, Mj, Palotie, A, Panoutsopoulou, K, Parker, V, Parr, Jr, Paternoster, L, Paunio, T, Payne, F, Payne, Sj, Perry, Jr, Pietilainen, O, Plagnol, V, Pollitt, Rc, Povey, S, Quail, Ma, Quaye, L, Raymond, L, Rehnström, K, Ridout, Ck, Ring, S, Ritchie, Gr, Roberts, N, Robinson, Rl, Savage, Db, Scambler, P, Schiffels, S, Schmidts, M, Schoenmakers, N, Scott, Rh, Scott, Ra, Semple, Rk, Serra, E, Sharp, Si, Shaw, A, Shihab, Ha, Shin, Sy, Skuse, D, Small, Ks, Smee, C, Smith, Gd, Southam, L, Spasic Boskovic, O, Spector, Td, St Clair, D, St Pourcain, B, Stalker, J, Stevens, E, Sun, J, Surdulescu, G, Suvisaari, J, Syrris, P, Tachmazidou, I, Taylor, R, Tian, J, Tobin, Md, Toniolo, D, Traglia, M, Tybjaerg Hansen, A, Valdes, Am, Vandersteen, Am, Varbo, A, Vijayarangakannan, P, Visscher, Pm, Wain, Lv, Walters, Jt, Wang, G, Wang, J, Wang, Y, Ward, K, Wheeler, E, Whincup, P, Whyte, T, Williams, Hj, Williamson, Ka, Wilson, C, Wilson, Sg, Wong, K, Xu, C, Yang, J, Zaza, Gianluigi, Zeggini, E, Zhang, F, Zhang, P, Zhang, W., Clinicum, Department of Psychiatry, Jie, Huang, Bryan, Howie, Shane, Mccarthy, Yasin, Memari, Klaudia, Walter, Josine L., Min, Petr, Danecek, Giovanni, Malerba, Elisabetta, Trabetti, Hou Feng, Zheng, Saeed Al, Turki, Antoinette, Amuzu, Carl A., Anderson, Richard, Anney, Dinu, Antony, María Soler, Artiga, Muhammad, Ayub, Senduran, Bala, Jeffrey C., Barrett, Inês, Barroso, Phil, Beale, Marianne, Benn, Jamie, Bentham, Shoumo, Bhattacharya, Ewan, Birney, Douglas, Blackwood, Martin, Bobrow, Elena, Bochukova, Patrick F., Bolton, Rebecca, Bound, Chris, Boustred, Gerome, Breen, Mattia, Calissano, Keren, Car, Juan Pablo, Casa, John C., Chamber, Ruth, Charlton, Krishna, Chatterjee, Lu, Chen, Antonio, Ciampi, Sebahattin, Cirak, Peter, Clapham, Gail, Clement, Guy, Coate, Cocca, Massimiliano, David A., Collier, Catherine, Cosgrove, Tony, Cox, Nick, Craddock, Lucy, Crook, Sarah, Curran, David, Curti, Allan, Daly, Ian N. M., Day, Aaron Day, William, George, Dedoussi, Thomas, Down, Yuanping, Du, Cornelia M., van Duijn, Ian, Dunham, Sarah, Edkin, Rosemary, Ekong, Peter, Elli, David M., Evan, I., Sadaf Farooqi, David R., Fitzpatrick, Paul, Flicek, James, Floyd, A., Reghan Foley, Christopher S., Franklin, Marta, Futema, Louise, Gallagher, Gasparini, Paolo, Tom R., Gaunt, Matthias, Geih, Daniel, Geschwind, Celia, Greenwood, Heather, Griffin, Detelina, Grozeva, Xiaosen, Guo, Xueqin, Guo, Hugh, Gurling, Deborah, Hart, Audrey E., Hendrick, Peter, Holman, Liren, Huang, Tim, Hubbard, Steve E., Humphrie, Matthew E., Hurle, Pirro, Hysi, Valentina, Iotchkova, Aaron, Isaac, David K., Jackson, Yalda, Jamshidi, Jon, Johnson, Chris, Joyce, Konrad J., Karczewski, Jane, Kaye, Thomas, Keane, John P., Kemp, Karen, Kennedy, Alastair, Kent, Julia, Keogh, Farrah, Khawaja, Marcus E., Kleber, Margriet van, Kogelenberg, Anja Kolb, Kokocinski, Jaspal S., Kooner, Genevieve, Lachance, Claudia, Langenberg, Cordelia, Langford, Daniel, Lawson, Irene, Lee, Elisabeth M., van Leeuwen, Monkol, Lek, Rui, Li, Yingrui, Li, Jieqin, Liang, Hong, Lin, Ryan, Liu, Jouko, Lönnqvist, Luis R., Lope, Margarida, Lope, Jian'An, Luan, Daniel G., Macarthur, Massimo, Mangino, Gaëlle, Marenne, Winfried, März, John, Maslen, Angela, Matchan, Iain, Mathieson, Peter, Mcguffin, Andrew M., Mcintosh, Andrew G., Mckechanie, Andrew, Mcquillin, Sarah, Metrustry, Nicola, Migone, Hannah M., Mitchison, Alireza, Moayyeri, James, Morri, Richard, Morri, Dawn, Muddyman, Francesco, Muntoni, Børge G., Nordestgaard, Kate, Northstone, Michael C., O'Donovan, Stephen, O'Rahilly, Alexandros, Onoufriadi, Karim, Oualkacha, Michael J., Owen, Aarno, Palotie, Kalliope, Panoutsopoulou, Victoria, Parker, Jeremy R., Parr, Lavinia, Paternoster, Tiina, Paunio, Felicity, Payne, Stewart J., Payne, John R. B., Perry, Olli, Pietilainen, Vincent, Plagnol, Rebecca C., Pollitt, Sue, Povey, Michael A., Quail, Lydia, Quaye, Lucy, Raymond, Karola, Rehnström, Cheryl K., Ridout, Susan, Ring, Graham R. S., Ritchie, Nicola, Robert, Rachel L., Robinson, David B., Savage, Peter, Scambler, Stephan, Schiffel, Miriam, Schmidt, Nadia, Schoenmaker, Richard H., Scott, Robert A., Scott, Robert K., Semple, Eva, Serra, Sally I., Sharp, Adam, Shaw, Hashem A., Shihab, So Youn, Shin, David, Skuse, Kerrin S., Small, Carol, Smee, George Davey, Smith, Lorraine, Southam, Olivera Spasic, Boskovic, Timothy D., Spector, David St, Clair, Beate St, Pourcain, Jim, Stalker, Elizabeth, Steven, Jianping, Sun, Gabriela, Surdulescu, Jaana, Suvisaari, Petros, Syrri, Ioanna, Tachmazidou, Rohan, Taylor, Jing, Tian, Martin D., Tobin, Daniela, Toniolo, Michela, Traglia, Anne Tybjaerg, Hansen, Ana M., Valde, Anthony M., Vandersteen, Anette, Varbo, Parthiban, Vijayarangakannan, Peter M., Visscher, Louise V., Wain, James T. R., Walter, Guangbiao, Wang, Jun, Wang, Yu, Wang, Kirsten, Ward, Eleanor, Wheeler, Peter, Whincup, Tamieka, Whyte, Hywel J., William, Kathleen A., Williamson, Crispian, Wilson, Scott G., Wilson, Kim, Wong, Changjiang, Xu, Jian, Yang, Gianluigi, Zaza, Eleftheria, Zeggini, Feng, Zhang, Pingbo, Zhang, Weihua, Zhang, Giovanni, Gambaro, J., Brent Richard, Richard, Durbin, Nicholas J., Timpson, Jonathan, Marchini, and Nicole, Soranzo

Subjects
Computer science, General Physics and Astronomy, Genome-wide association study, 0302 clinical medicine, Gene Frequency, Haplotype, Genetics,Biological sciences, Settore MED/14 - NEFROLOGIA, Aged, 80 and over, Genetics, 0303 health sciences, education.field_of_study, Multidisciplinary, TWINSUK, Middle Aged, single-nucleotide polymorphism, Whole-genome sequencing, WGS imputation panel, single-nucleotide polymorphism, Biological sciences, Italy, MAP, Adult, Adolescent, Genotype, WGS imputation panel, Population, Single-nucleotide polymorphism, FORMAT, Computational biology, GENOTYPE IMPUTATION, Polymorphism, Single Nucleotide, Article, White People, General Biochemistry, Genetics and Molecular Biology, Young Adult, 03 medical and health sciences, Humans, GENOME-WIDE ASSOCIATION, 1000 Genomes Project, education, Allele frequency, Alleles, Aged, 030304 developmental biology, Whole-genome sequencing, Models, Statistical, Models, Genetic, Genome, Human, Genetic Variation, General Chemistry, United Kingdom, Minor allele frequency, Renal disorders Radboud Institute for Molecular Life Sciences [Radboudumc 11], Haplotypes, 3111 Biomedicine, 030217 neurology & neurosurgery, Imputation (genetics)
Abstract

Imputing genotypes from reference panels created by whole-genome sequencing (WGS) provides a cost-effective strategy for augmenting the single-nucleotide polymorphism (SNP) content of genome-wide arrays. The UK10K Cohorts project has generated a data set of 3,781 whole genomes sequenced at low depth (average 7x), aiming to exhaustively characterize genetic variation down to 0.1% minor allele frequency in the British population. Here we demonstrate the value of this resource for improving imputation accuracy at rare and low-frequency variants in both a UK and an Italian population. We show that large increases in imputation accuracy can be achieved by re-phasing WGS reference panels after initial genotype calling. We also present a method for combining WGS panels to improve variant coverage and downstream imputation accuracy, which we illustrate by integrating 7,562 WGS haplotypes from the UK10K project with 2,184 haplotypes from the 1000 Genomes Project. Finally, we introduce a novel approximation that maintains speed without sacrificing imputation accuracy for rare variants., Imputation uses genotype information from SNP arrays to infer the genotypes of missing markers. Here, the authors show that an imputation reference panel derived from whole-genome sequencing of 3,781 samples from the UK10K project improves the imputation accuracy and coverage of low frequency variants compared to existing methods.

Published
2015

7. Discovery and refinement of genetic loci associated with cardiometabolic risk using dense imputation maps

Author

Iotchkova, V, Huang, J, Morris, JA, Jain, D, Barbieri, C, Walter, K, Min, JL, Chen, L, Astle, W, Cocca, M, Deelen, P, Elding, H, Farmaki, A-E, Franklin, CS, Franberg, M, Gaunt, TR, Hofman, A, Jiang, T, Kleber, ME, Lachance, G, Luan, J, Malerba, G, Matchan, A, Mead, D, Memari, Y, Ntalla, I, Panoutsopoulou, K, Pazoki, R, Perry, JRB, Rivadeneira, F, Sabater-Lleal, M, Sennblad, B, Shin, S-Y, Southam, L, Traglia, M, Van Dijk, F, Van Leeuwen, EM, Zaza, G, Zhang, W, UK10K Consortium, Amin, N, Butterworth, A, Chambers, JC, Dedoussis, G, Dehghan, A, Franco, OH, Franke, L, Frontini, M, Gambaro, G, Gasparini, P, Hamsten, A, Issacs, A, Kooner, JS, Kooperberg, C, Langenberg, C, Marz, W, Scott, RA, Swertz, MA, Toniolo, D, Uitterlinden, AG, Van Duijn, CM, Watkins, H, Zeggini, E, Maurano, MT, Timpson, NJ, Reiner, AP, Auer, PL, and Soranzo, N

Subjects
Male, Heart Diseases, Genetic Loci, Genome, Human, Quantitative Trait Loci, Genetic Variation, Humans, Female, Genetic Predisposition to Disease, Sequence Analysis, DNA, Hematologic Diseases, 3. Good health, Genome-Wide Association Study
Abstract

Large-scale whole-genome sequence data sets offer novel opportunities to identify genetic variation underlying human traits. Here we apply genotype imputation based on whole-genome sequence data from the UK10K and 1000 Genomes Project into 35,981 study participants of European ancestry, followed by association analysis with 20 quantitative cardiometabolic and hematological traits. We describe 17 new associations, including 6 rare (minor allele frequency (MAF) < 1%) or low-frequency (1% < MAF < 5%) variants with platelet count (PLT), red blood cell indices (MCH and MCV) and HDL cholesterol. Applying fine-mapping analysis to 233 known and new loci associated with the 20 traits, we resolve the associations of 59 loci to credible sets of 20 or fewer variants and describe trait enrichments within regions of predicted regulatory function. These findings improve understanding of the allelic architecture of risk factors for cardiometabolic and hematological diseases and provide additional functional insights with the identification of potentially novel biological targets.

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