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1. Bayesian neural networks with variable selection for prediction of genotypic values

Author

Giel H. H. van Bergen, Pascal Duenk, Cornelis A. Albers, Piter Bijma, Mario P. L. Calus, Yvonne C. J. Wientjes, and Hilbert J. Kappen

Subjects
Animal culture, SF1-1100, Genetics, QH426-470
Abstract

Abstract Background Estimating the genetic component of a complex phenotype is a complicated problem, mainly because there are many allele effects to estimate from a limited number of phenotypes. In spite of this difficulty, linear methods with variable selection have been able to give good predictions of additive effects of individuals. However, prediction of non-additive genetic effects is challenging with the usual prediction methods. In machine learning, non-additive relations between inputs can be modeled with neural networks. We developed a novel method (NetSparse) that uses Bayesian neural networks with variable selection for the prediction of genotypic values of individuals, including non-additive genetic effects. Results We simulated several populations with different phenotypic models and compared NetSparse to genomic best linear unbiased prediction (GBLUP), BayesB, their dominance variants, and an additive by additive method. We found that when the number of QTL was relatively small (10 or 100), NetSparse had 2 to 28 percentage points higher accuracy than the reference methods. For scenarios that included dominance or epistatic effects, NetSparse had 0.0 to 3.9 percentage points higher accuracy for predicting phenotypes than the reference methods, except in scenarios with extreme overdominance, for which reference methods that explicitly model dominance had 6 percentage points higher accuracy than NetSparse. Conclusions Bayesian neural networks with variable selection are promising for prediction of the genetic component of complex traits in animal breeding, and their performance is robust across different genetic models. However, their large computational costs can hinder their use in practice.

Published
2020
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2. Immuno-detection by sequencing enables large-scale high-dimensional phenotyping in cells

Author

Jessie A. G. van Buggenum, Jan P. Gerlach, Sabine E. J. Tanis, Mark Hogeweg, Pascal W. T. C. Jansen, Jesse Middelwijk, Ruud van der Steen, Michiel Vermeulen, Hendrik G. Stunnenberg, Cornelis A. Albers, and Klaas W. Mulder

Subjects
Science
Abstract

Detecting proteins and post-translational modifications is important for drug screens, but the number of proteins measurable simultaneously is limited. Here the authors use antibodies tagged with DNA barcodes and high-throughput sequencing to detect up to 70 (phospho-)proteins in stem cells.

Published
2018
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3. Quantification of differential gene expression by multiplexed targeted resequencing of cDNA

Author

Peer Arts, Jori van der Raadt, Sebastianus H.C. van Gestel, Marloes Steehouwer, Jay Shendure, Alexander Hoischen, and Cornelis A. Albers

Subjects
Science
Abstract

Transcriptome sequencing is a powerful tool for functional analysis of different types of RNA molecules in a wide range of applications. Here the authors use targeted resequencing of cDNA with single-molecule molecular inversion probes as a cost-effective, high-throughput tool for mRNA quantification.

Published
2017
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4. Cell type-specific changes in transcriptomic profiles of endothelial cells, iPSC-derived neurons and astrocytes cultured on microfluidic chips

Author

Nael Nadif Kasri, T. M. Klein Gunnewiek, A. D. van der Meer, Anouk H.A. Verboven, Heleen H.T. Middelkamp, Peter A C 't Hoen, Robert Passier, Chantal Schoenmaker, Cornelis A. Albers, A G De Sá Vivas, Biomedical and Environmental Sensorsystems, TechMed Centre, MESA+ Institute, and Applied Stem Cell Technologies

Subjects
0301 basic medicine, Cell division, Molecular biology, Endothelial cells, Microfluidics, Stem cells, Transcriptome, 0302 clinical medicine, Induced pluripotent stem cell, Cells, Cultured, Neurons, Multidisciplinary, Stem cell, Chemistry, Biological techniques, Cell Differentiation, RNA sequencing, Cell biology, medicine.anatomical_structure, Blood-Brain Barrier, Medicine, Organ-on-chip, Molecular Developmental Biology, Biotechnology, Cell type, Science, Induced Pluripotent Stem Cells, Blood–brain barrier, Article, 03 medical and health sciences, medicine, Cell Adhesion, Human Umbilical Vein Endothelial Cells, Animals, Humans, Cell adhesion, Transcriptomics, Neurodevelopmental disorders Donders Center for Medical Neuroscience [Radboudumc 7], Gene Expression Profiling, In vitro, Rats, Computational biology and bioinformatics, Microfluidic chip, 030104 developmental biology, Gene Ontology, Gene Expression Regulation, Astrocytes, Nanomedicine Radboud Institute for Molecular Life Sciences [Radboudumc 19], 030217 neurology & neurosurgery
Abstract

Contains fulltext : 231651.pdf (Publisher’s version ) (Open Access) In vitro neuronal models are essential for studying neurological physiology, disease mechanisms and potential treatments. Most in vitro models lack controlled vasculature, despite its necessity in brain physiology and disease. Organ-on-chip models offer microfluidic culture systems with dedicated micro-compartments for neurons and vascular cells. Such multi-cell type organs-on-chips can emulate neurovascular unit (NVU) physiology, however there is a lack of systematic data on how individual cell types are affected by culturing on microfluidic systems versus conventional culture plates. This information can provide perspective on initial findings of studies using organs-on-chip models, and further optimizes these models in terms of cellular maturity and neurovascular physiology. Here, we analysed the transcriptomic profiles of co-cultures of human induced pluripotent stem cell (hiPSC)-derived neurons and rat astrocytes, as well as one-day monocultures of human endothelial cells, cultured on microfluidic chips. For each cell type, large gene expression changes were observed when cultured on microfluidic chips compared to conventional culture plates. Endothelial cells showed decreased cell division, neurons and astrocytes exhibited increased cell adhesion, and neurons showed increased maturity when cultured on a microfluidic chip. Our results demonstrate that culturing NVU cell types on microfluidic chips changes their gene expression profiles, presumably due to distinct surface-to-volume ratios and substrate materials. These findings inform further NVU organ-on-chip model optimization and support their future application in disease studies and drug testing.

Published
2021

5. Bayesian neural networks with variable selection for prediction of genotypic values

Author

Pascal Duenk, Cornelis A. Albers, Giel H. H. van Bergen, Yvonne C. J. Wientjes, Piter Bijma, Mario P. L. Calus, and Hilbert J. Kappen

Subjects
Multifactorial Inheritance, [SDV]Life Sciences [q-bio], Bayes' theorem, Gene Frequency, Statistics, lcsh:SF1-1100, 0303 health sciences, Artificial neural network, Percentage point, Genomics, 04 agricultural and veterinary sciences, General Medicine, Phenotype, Molecular Developmental Biology, Algorithms, Research Article, Genotype, lcsh:QH426-470, Quantitative Trait Loci, Biophysics, Feature selection, Quantitative trait locus, Biology, Best linear unbiased prediction, Animal Breeding and Genomics, Polymorphism, Single Nucleotide, 03 medical and health sciences, Genetic model, Genetics, Life Science, Animals, Humans, Fokkerij en Genomica, Selection, Genetic, Alleles, Ecology, Evolution, Behavior and Systematics, 030304 developmental biology, Neurodevelopmental disorders Donders Center for Medical Neuroscience [Radboudumc 7], Models, Genetic, 0402 animal and dairy science, Bayes Theorem, 040201 dairy & animal science, lcsh:Genetics, Genetics, Population, WIAS, Epistasis, Animal Science and Zoology, Neural Networks, Computer, lcsh:Animal culture, Forecasting
Abstract

Background Estimating the genetic component of a complex phenotype is a complicated problem, mainly because there are many allele effects to estimate from a limited number of phenotypes. In spite of this difficulty, linear methods with variable selection have been able to give good predictions of additive effects of individuals. However, prediction of non-additive genetic effects is challenging with the usual prediction methods. In machine learning, non-additive relations between inputs can be modeled with neural networks. We developed a novel method (NetSparse) that uses Bayesian neural networks with variable selection for the prediction of genotypic values of individuals, including non-additive genetic effects. Results We simulated several populations with different phenotypic models and compared NetSparse to genomic best linear unbiased prediction (GBLUP), BayesB, their dominance variants, and an additive by additive method. We found that when the number of QTL was relatively small (10 or 100), NetSparse had 2 to 28 percentage points higher accuracy than the reference methods. For scenarios that included dominance or epistatic effects, NetSparse had 0.0 to 3.9 percentage points higher accuracy for predicting phenotypes than the reference methods, except in scenarios with extreme overdominance, for which reference methods that explicitly model dominance had 6 percentage points higher accuracy than NetSparse. Conclusions Bayesian neural networks with variable selection are promising for prediction of the genetic component of complex traits in animal breeding, and their performance is robust across different genetic models. However, their large computational costs can hinder their use in practice.

Published
2020

6. Quantification of differential gene expression by multiplexed targeted resequencing of cDNA

Author

Alexander Hoischen, Jori van der Raadt, Sebastianus H C van Gestel, Jay Shendure, Peer Arts, Cornelis A. Albers, Marloes Steehouwer, Arts, Peer, Van Der Raadt, Jori, Van Gestel, Sebastianus HC, Steehouwer, Marloes, Shendure, Jay, Hoischen, Alexander, and Albers, Cornelis A

Subjects
0301 basic medicine, DNA, Complementary, Sequence analysis, Science, lnfectious Diseases and Global Health Radboud Institute for Molecular Life Sciences [Radboudumc 4], General Physics and Astronomy, Biology, Article, General Biochemistry, Genetics and Molecular Biology, 03 medical and health sciences, chemistry.chemical_compound, Complementary DNA, Exome Sequencing, Gene expression, Humans, RNA, Messenger, Gene, Exome sequencing, Cell Line, Transformed, Genetics, Multidisciplinary, Neurodevelopmental disorders Donders Center for Medical Neuroscience [Radboudumc 7], Sequence Analysis, RNA, Gene Expression Profiling, High-Throughput Nucleotide Sequencing, RNA, General Chemistry, Cell Transformation, Viral, Gene expression profiling, HEK293 Cells, 030104 developmental biology, chemistry, Molecular Developmental Biology, Neurodevelopmental disorders Radboud Institute for Molecular Life Sciences [Radboudumc 7], DNA
Abstract

Whole-transcriptome or RNA sequencing (RNA-Seq) is a powerful and versatile tool for functional analysis of different types of RNA molecules, but sample reagent and sequencing cost can be prohibitive for hypothesis-driven studies where the aim is to quantify differential expression of a limited number of genes. Here we present an approach for quantification of differential mRNA expression by targeted resequencing of complementary DNA using single-molecule molecular inversion probes (cDNA-smMIPs) that enable highly multiplexed resequencing of cDNA target regions of ∼100 nucleotides and counting of individual molecules. We show that accurate estimates of differential expression can be obtained from molecule counts for hundreds of smMIPs per reaction and that smMIPs are also suitable for quantification of relative gene expression and allele-specific expression. Compared with low-coverage RNA-Seq and a hybridization-based targeted RNA-Seq method, cDNA-smMIPs are a cost-effective high-throughput tool for hypothesis-driven expression analysis in large numbers of genes (10 to 500) and samples (hundreds to thousands)., Transcriptome sequencing is a powerful tool for functional analysis of different types of RNA molecules in a wide range of applications. Here the authors use targeted resequencing of cDNA with single-molecule molecular inversion probes as a cost-effective, high-throughput tool for mRNA quantification.

Published
2017

7. Rapid neuronal differentiation of induced pluripotent stem cells for measuring network activity on micro-electrode arrays

Author

Katrin Linda, Cornelis A. Albers, Nael Nadif Kasri, Monica Frega, Jori van der Raadt, Dirk Schubert, Jason M. Keller, Jon-Ruben van Rhijn, and Sebastianus H C van Gestel

Subjects
0301 basic medicine, induced pluripotent stem cells, Cellular differentiation, Neurogenesis, General Chemical Engineering, Population, Genetic Vectors, Neurophysiology, Nerve Tissue Proteins, Biology, General Biochemistry, Genetics and Molecular Biology, Cell Line, 03 medical and health sciences, Biological neural network, Basic Helix-Loop-Helix Transcription Factors, Humans, Issue 119, Induced pluripotent stem cell, education, neuronal differentiation, Neurons, education.field_of_study, Neurodevelopmental disorders Donders Center for Medical Neuroscience [Radboudumc 7], General Immunology and Microbiology, General Neuroscience, Lentivirus, lentiviral transduction, Cell Differentiation, Fibroblasts, Cellular Reprogramming, Microarray Analysis, astrocyte isolation, Electrophysiology, 030104 developmental biology, nervous system, Cell culture, micro-electrode arrays, neuronal network, Molecular Developmental Biology, Neurodevelopmental disorders Radboud Institute for Molecular Life Sciences [Radboudumc 7], Neuroscience, Developmental biology, Microelectrodes, Transcription Factors, Developmental Biology
Abstract

Contains fulltext : 168902.pdf (Publisher’s version ) (Open Access) Neurons derived from human induced Pluripotent Stem Cells (hiPSCs) provide a promising new tool for studying neurological disorders. In the past decade, many protocols for differentiating hiPSCs into neurons have been developed. However, these protocols are often slow with high variability, low reproducibility, and low efficiency. In addition, the neurons obtained with these protocols are often immature and lack adequate functional activity both at the single-cell and network levels unless the neurons are cultured for several months. Partially due to these limitations, the functional properties of hiPSC-derived neuronal networks are still not well characterized. Here, we adapt a recently published protocol that describes production of human neurons from hiPSCs by forced expression of the transcription factor neurogenin-212. This protocol is rapid (yielding mature neurons within 3 weeks) and efficient, with nearly 100% conversion efficiency of transduced cells (>95% of DAPI-positive cells are MAP2 positive). Furthermore, the protocol yields a homogeneous population of excitatory neurons that would allow the investigation of cell-type specific contributions to neurological disorders. We modified the original protocol by generating stably transduced hiPSC cells, giving us explicit control over the total number of neurons. These cells are then used to generate hiPSC-derived neuronal networks on micro-electrode arrays. In this way, the spontaneous electrophysiological activity of hiPSC-derived neuronal networks can be measured and characterized, while retaining interexperimental consistency in terms of cell density. The presented protocol is broadly applicable, especially for mechanistic and pharmacological studies on human neuronal networks. 10 p.

Published
2017

8. Immuno-Detection by sequencing (ID-seq) enables large-scale high-dimensional phenotyping in cells

Author

Cornelis A. Albers, Pascal W.T.C. Jansen, Mark Hogeweg, Michiel Vermeulen, Klaas W. Mulder, Sabine E.J. Tanis, Jesse Middelwijk, Jan P. Gerlach, Jessie A.G.L. van Buggenum, and Ruud van der Steen

Subjects
biology, Downregulation and upregulation, Kinase, biology.protein, Genomics, Antibody, Stem cell, Phenotype, Small molecule, PI3K/AKT/mTOR pathway, Cell biology
Abstract

Cell-based small molecule screening is an effective strategy leading to new medicines. Scientists in the pharmaceutical industry as well as in academia have made tremendous progress in developing both large-scale and smaller-scale screening assays. However, an accessible and universal technology for measuring large numbers of molecular and cellular phenotypes in many samples in parallel is not available. Here, we present the Immuno-Detection by sequencing (ID-seq) technology that combines antibody-based protein detection and DNA-sequencing via DNA-tagged antibodies. We used ID-seq to simultaneously measure 84 (phospho-)proteins in hundreds of samples and screen the effects of ~300 kinase inhibitor probes on primary human epidermal stem cells to characterise the role of 225 kinases. Our work highlighted a previously unrecognized downregulation of mTOR signaling during differentiation and uncovered 13 kinases regulating epidermal renewal through distinct mechanisms.

Published
2017
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9. SMIM1 underlies the Vel blood group and influences red blood cell traits

Author

Dorine W. Swinkels, Heather Lloyd-Jones, Hendrik G. Stunnenberg, Graham Kiddle, Hindrik H. D. Kerstens, Gregory E. Jordan, Lonneke Haer-Wigman, Rudolf S N Fehrmann, Juha Karjalainen, Herman H W Silljé, Peter A. Smethurst, Cornelis A. Albers, Harm-Jan Westra, Hein Schepers, Jonathan Stephens, J. Poole, Willem H. Ouwehand, Mattia Frontini, Derek L. Stemple, Nicole Soranzo, Nicholas A. Watkins, Niek Verweij, Samantha Farrow, Augusto Rendon, Emile van den Akker, Vincent G. Haver, Alan Gray, C. Ellen van der Schoot, Ana Cvejic, Jennifer G. Sambrook, Myrto Kostadima, Nick Goldman, Ewa Bielczyk-Maczyńska, Botond Sipos, Malcolm Needs, Asif U. Tamuri, Aicha Ait Soussan, Lude Franke, Klaus Rieneck, Paul Bertone, Pim van der Harst, Masja de Haas, Damage and Repair in Cancer Development and Cancer Treatment (DARE), Guided Treatment in Optimal Selected Cancer Patients (GUTS), Stem Cell Aging Leukemia and Lymphoma (SALL), Cardiovascular Centre (CVC), Groningen Institute for Gastro Intestinal Genetics and Immunology (3GI), Landsteiner Laboratory, and Clinical Haematology

Subjects
Erythrocytes, Molecular Sequence Data, Quantitative Trait Loci, Iron metabolism Pathogenesis and modulation of inflammation [IGMD 7], GENOMES, Electrophoretic Mobility Shift Assay, 030204 cardiovascular system & hematology, Biology, VARIANTS, Article, Frameshift mutation, Genomic disorders and inherited multi-system disorders DCN MP - Plasticity and memory [IGMD 3], 03 medical and health sciences, 0302 clinical medicine, Isoantibodies, Pregnancy, Gene expression, Genetics, medicine, Animals, Humans, Exome, Gene Regulatory Networks, Allele, Gene, Molecular Biology, Alleles, Zebrafish, 030304 developmental biology, Oligonucleotide Array Sequence Analysis, GENE-EXPRESSION, 0303 health sciences, Gene knockdown, Gene Expression Profiling, Erythrocyte Membrane, Homozygote, Membrane Proteins, Molecular biology, 3. Good health, Gene expression profiling, Red blood cell, ALIGNMENT, medicine.anatomical_structure, biology.protein, Blood Group Antigens, Female, Antibody, Biomarkers, Gene Deletion
Abstract

The blood group Vel was discovered 60 years ago(1), but the underlying gene is unknown. Individuals negative for the Vel antigen are rare and are required for the safe transfusion of patients with antibodies to Vel(2). To identify the responsible gene, we sequenced the exomes of five individuals negative for the Vel antigen and found that four were homozygous and one was heterozygous for a low-frequency 17-nucleotide frameshift deletion in the gene encoding the 78-amino-acid transmembrane protein SMIM1. A follow-up study showing that 59 of 64 Vel-negative individuals were homozygous for the same deletion and expression of the Vel antigen on SMIM1-transfected cells confirm SMIM1 as the gene underlying the Vel blood group. An expression quantitative trait locus (eQTL), the common SNP rs1175550 contributes to variable expression of the Vel antigen (P = 0.003) and influences the mean hemoglobin concentration of red blood cells (RBCs; P = 8.6 x 10(-15))(3). In vivo, zebrafish with smim1 knockdown showed a mild reduction in the number of RBCs, identifying SMIM1 as a new regulator of RBC formation. Our findings are of immediate relevance, as the homozygous presence of the deletion allows the unequivocal identification of Vel-negative blood donors.

Published
2013

10. 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

11. The origin, evolution, and functional impact of short insertion-deletion variants identified in 179 human genomes

Author

Zhengdong D. Zhang, Arend Sidow, Xinmeng Jasmine Mu, Cornelis A. Albers, David L Goode, Kevin S. Smith, Rhea Richardson, Jonathan Marchini, Laurent Duret, Bryan Howie, Daniel G. MacArthur, Kateryna D. Makova, Stephen B. Montgomery, Konrad J. Karczewski, Gerton Lunter, Joseph S. Davis, Guruprasad Ananda, Erika Kvikstad, Vanessa Anaya, Gil McVean, Mark Gerstein, Bioinformatique, phylogénie et génomique évolutive (BPGE), Département PEGASE [LBBE] (PEGASE), Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), and Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Mutation rate, [SDV]Life Sciences [q-bio], Population, Genome-wide association study, Human genetic variation, Biology, Polymorphism, Single Nucleotide, Evolution, Molecular, 03 medical and health sciences, Negative selection, 0302 clinical medicine, INDEL Mutation, Mutation Rate, Genetics, Humans, Indel, education, Genetics (clinical), 030304 developmental biology, 0303 health sciences, education.field_of_study, Genome, Human, Research, High-Throughput Nucleotide Sequencing, food and beverages, Mutagenesis, Insertional, Genetics, Population, Human genome, 030217 neurology & neurosurgery, Genome-Wide Association Study
Abstract

Short insertions and deletions (indels) are the second most abundant form of human genetic variation, but our understanding of their origins and functional effects lags behind that of other types of variants. Using population-scale sequencing, we have identified a high-quality set of 1.6 million indels from 179 individuals representing three diverse human populations. We show that rates of indel mutagenesis are highly heterogeneous, with 43%–48% of indels occurring in 4.03% of the genome, whereas in the remaining 96% their prevalence is 16 times lower than SNPs. Polymerase slippage can explain upwards of three-fourths of all indels, with the remainder being mostly simple deletions in complex sequence. However, insertions do occur and are significantly associated with pseudo-palindromic sequence features compatible with the fork stalling and template switching (FoSTeS) mechanism more commonly associated with large structural variations. We introduce a quantitative model of polymerase slippage, which enables us to identify indel-hypermutagenic protein-coding genes, some of which are associated with recurrent mutations leading to disease. Accounting for mutational rate heterogeneity due to sequence context, we find that indels across functional sequence are generally subject to stronger purifying selection than SNPs. We find that indel length modulates selection strength, and that indels affecting multiple functionally constrained nucleotides undergo stronger purifying selection. We further find that indels are enriched in associations with gene expression and find evidence for a contribution of nonsense-mediated decay. Finally, we show that indels can be integrated in existing genome-wide association studies (GWAS); although we do not find direct evidence that potentially causal protein-coding indels are enriched with associations to known disease-associated SNPs, our findings suggest that the causal variant underlying some of these associations may be indels.

Published
2013

12. Compound inheritance of a low-frequency regulatory SNP and a rare null mutation in exon-junction complex subunit RBM8A causes TAR syndrome

Author

K Smith, Chantal Thys, Najet Debili, Paul Bertone, Paquita Nurden, Gabriele Strauss, Kathleen Freson, Rémi Favier, Jennifer Jolley, Cornelis A. Albers, Jennifer G. Sambrook, Dirk S. Paul, Myrto Kostadima, Ingrid P.C. Krapels, Matthew E. Hurles, Graham Kiddle, Derek L. Stemple, Jonathan Stephens, Catherine M. Hobbs, Ana Cvejic, Janine Fiedler, Willem H. Ouwehand, Christel Van Geet, Cedric Ghevaert, Claudia A. L. Ruivenkamp, Panos Deloukas, Martijn H. Breuning, Ni Huang, Ruth Newbury-Ecob, Peter A. Smethurst, Harald Schulze, MUMC+: DA KG Polikliniek (9), Genetica & Celbiologie, and RS: GROW - School for Oncology and Reproduction

Subjects
Adult, Male, Adolescent, Protein subunit, Single-nucleotide polymorphism, Biology, Polymorphism, Single Nucleotide, Article, Frameshift mutation, Genomic disorders and inherited multi-system disorders DCN MP - Plasticity and memory [IGMD 3], Young Adult, 03 medical and health sciences, 0302 clinical medicine, Transcription (biology), Genetics, medicine, Animals, Congenital Bone Marrow Failure Syndromes, Humans, Genetic Predisposition to Disease, Amino Acid Sequence, Upper Extremity Deformities, Congenital, Child, Zebrafish, 030304 developmental biology, 0303 health sciences, Base Sequence, Platelet Count, TAR syndrome, Infant, Newborn, Genetic Variation, Infant, RNA-Binding Proteins, Sequence Analysis, DNA, medicine.disease, Thrombocytopenia, Molecular biology, Null allele, Radius, Regulatory sequence, Child, Preschool, 030220 oncology & carcinogenesis, Mutation, Exon junction complex, Female, 5' Untranslated Regions, Sequence Alignment
Abstract

Item does not contain fulltext The exon-junction complex (EJC) performs essential RNA processing tasks. Here, we describe the first human disorder, thrombocytopenia with absent radii (TAR), caused by deficiency in one of the four EJC subunits. Compound inheritance of a rare null allele and one of two low-frequency SNPs in the regulatory regions of RBM8A, encoding the Y14 subunit of EJC, causes TAR. We found that this inheritance mechanism explained 53 of 55 cases (P < 5 x 10(-228)) of the rare congenital malformation syndrome. Of the 53 cases with this inheritance pattern, 51 carried a submicroscopic deletion of 1q21.1 that has previously been associated with TAR, and two carried a truncation or frameshift null mutation in RBM8A. We show that the two regulatory SNPs result in diminished RBM8A transcription in vitro and that Y14 expression is reduced in platelets from individuals with TAR. Our data implicate Y14 insufficiency and, presumably, an EJC defect as the cause of TAR syndrome.

Published
2012

13. Seventy-five genetic loci influencing the human red blood cell

Author

Teresa Nutile, Anna-Liisa Hartikainen, Stavroula Kanoni, Johannes H. Smit, Harm-Jan Westra, Beben Benyamin, Gonneke Willemsen, Clara S. Tang, Maria Dimitriou, Peter Vollenweider, Olle Melander, Inga Prokopenko, W.H. Wilson Tang, John P. Kemp, Tim D. Spector, Evelin Mihailov, Paul F. O'Reilly, Eleonora Porcu, Marcus E. Kleber, Sheila Ulivi, George Dedoussis, Manuela Uda, Matthias Nauck, Brenda W.J.H. Penninx, Daniela Ruggiero, Xinzhong Li, Dirk S. Paul, Niek Verweij, Bernd Genser, Harold Snieder, Willem H. Ouwehand, Ido P. Kema, Miriam F. Moffatt, Carmel Moore, Hilma Holm, Nicola Pirastu, Adamo Pio D'Adamo, Michael Stumvoll, Rossella Sorice, Kay-Tee Khaw, Heather Lloyd-Jones, Federico Murgia, Stephen F. Garner, Jing Hua Zhao, Laura Portas, Abdel Abdellaoui, Ursula Puc, Andres Metspalu, Marleen H. M. de Moor, Isleifur Olafsson, Ruth J. F. Loos, Andres Salumets, François Bastardot, James Scott, Jian Yang, Braxton D. Mitchell, Debora Parracciani, Maria Novatchkova, Panos Deloukas, William O.C.M. Cookson, Lorna M. Lopez, Andrew A. Hicks, Aude Saint-Pierre, Daniel F. Gudbjartsson, Vinicius Tragante, Mario Pirastu, Jacques S. Beckmann, L. Joost van Pelt, Winfried Maerz, Hooman Allayee, Jouke-Jan Hottenga, Kari Stefansson, Debashish Das, Weihua Zhang, Anke Tönjes, Michela Traglia, Stuart Meacham, Antonio Piga, Cornelis A. Albers, Nicholas G. Martin, John C. Chambers, Christa Meisinger, Leo-Pekka Lyytikäinen, Nicole Soranzo, Mika Kähönen, Katrin Voss, Micha Hersch, Claudia Langenberg, Sian Tsung Tan, Sandosh Padmanabhan, Christian X. Weichenberger, J. Gustav Smith, Antony P. Attwood, Claire E. Hastie, Gunnar Engström, Janina S. Ried, Carsten Oliver Schmidt, Pall T. Onundarson, Kathy Miller, Francisco S. Domingues, Stefania Bandinelli, Ulrich Elling, Augusto Rendon, Paul Elliott, Quince Gibson, Tõnu Esko, Robert Sladek, Marina Ciullo, Gerald Wirnsberger, Franco Anni, Antonietta Robino, Serena Sanna, Hein Schepers, Jonathan Stephens, Joban Sehmi, Beverley Balkau, Jennifer G. Sambrook, Lucia Perseu, Ilja M. Nolte, Herman H W Silljé, John M. Starr, Cinzia Sala, Peter P. Pramstaller, David M. Evans, Renzo Galanello, Uwe Völker, Philippe Froguel, Dorret I. Boomsma, Vasiliki Lagou, Gerjan Navis, Christian Gieger, Susan M. Ring, Alexander Teumer, Angela Döring, Ale Algra, Toshiko Tanaka, Bo Hedblad, Anneli Pouta, Unnur Thorsteinsdottir, Bernhard R. Winkelmann, Liming Liang, John Danesh, Paolo Gasparini, Sarah E. Medland, Ian J. Deary, Martin Gögele, Pim van der Harst, Giorgia Girotto, Josef M. Penninger, Jaspal S. Kooner, Patrick Sulem, Thomas Illig, Yasin Memari, Sarah E. Harris, Wiek H. van Gilst, Francesco Cucca, Dirk J. van Veldhuisen, So-Youn Shin, Giorgio Pistis, Olli T. Raitakari, Lude Franke, Folkert W. Asselbergs, Abtehale Al-Hussani, Stanley L. Hazen, Manuel A. R. Ferreira, Aimo Ruokonen, Christian Dina, Aparna Radhakrishnan, Irene Mateo Leach, Nicholas J. Wareham, George Davey Smith, Eric E. Schadt, Alan R. Shuldiner, John Whitfield, Gudmundur I. Eyjolfsson, Eco J. C. de Geus, Grant W. Montgomery, Afshin Parsa, Terho Lehtimäki, Paolo Fortina, Luigi Ferrucci, Andreas Greinacher, Marjo-Riitta Järvelin, Krista Fischer, Fabrice Danjou, Rudolf A. de Boer, Paul I.W. de Bakker, Peter M. Visscher, Anna F. Dominiczak, Ramiro Ramirez-Solis, Jennifer Jolley, Bruce H. R. Wolffenbuttel, Jaana Hartiala, Daniela Toniolo, Bernhard O. Boehm, van der Harst, P, Zhang, W, Mateo Leach, I, Rendon, A, Verweij, N, Sehmi, J, Paul, D, Elling, U, Allayee, H, Li, X, Radhakrishnan, A, Tan, St, Voss, K, Weichenberger, Cx, Albers, Ca, Al Hussani, A, Asselbergs, Fw, Ciullo, M, Danjou, F, Dina, C, Esko, T, Evans, Dm, Franke, L, Gögele, M, Hartiala, J, Hersch, M, Holm, H, Hottenga, Jj, Kanoni, S, Kleber, Me, Lagou, V, Langenberg, C, Lopez, Lm, Lyytikäinen, Lp, Melander, O, Murgia, F, Nolte, Im, O'Reilly, Pf, Padmanabhan, S, Parsa, A, Pirastu, Nicola, Porcu, E, Portas, L, Prokopenko, I, Ried, J, Shin, Sy, Tang, C, Teumer, A, Traglia, Michela, Ulivi, S, Westra, Hj, Yang, J, Zhao, Jh, Anni, F, Abdellaoui, A, Attwood, A, Balkau, B, Bandinelli, S, Bastardot, F, Benyamin, B, Boehm, Bo, Cookson, Wo, Das, D, de Bakker, Pi, de Boer, Ra, de Geus, Ej, de Moor, Mh, Dimitriou, M, Domingues, F, Döring, A, Engström, G, Eyjolfsson, Gi, Ferrucci, L, Fischer, K, Galanello, R, Garner, Sf, Genser, B, Gibson, Qd, Girotto, Giorgia, Gudbjartsson, Df, Harris, Se, Hartikainen, Al, Hastie, Ce, Hedblad, B, Illig, T, Jolley, J, Kähönen, M, Kema, Ip, Kemp, Jp, Liang, L, Lloyd Jones, H, Loos, Rj, Meacham, S, Medland, Se, Meisinger, C, Memari, Y, Mihailov, E, Miller, K, Moffatt, Mf, Nauck, M, Novatchkova, M, Nutile, T, Olafsson, I, Onundarson, Pt, Parracciani, D, Penninx, Bw, Perseu, L, Piga, A, Pistis, G, Pouta, A, Puc, U, Raitakari, O, Ring, Sm, Robino, Antonietta, Ruggiero, D, Ruokonen, A, Saint Pierre, A, Sala, C, Salumets, A, Sambrook, J, Schepers, H, Schmidt, Co, Silljé, Hh, Sladek, R, Smit, Jh, Starr, Jm, Stephens, J, Sulem, P, Tanaka, T, Thorsteinsdottir, U, Tragante, V, van Gilst, Wh, van Pelt, Lj, van Veldhuisen, Dj, Völker, U, Whitfield, Jb, Willemsen, G, Winkelmann, Br, Wirnsberger, G, Algra, A, Cucca, F, D'Adamo, ADAMO PIO, Danesh, J, Deary, Ij, Dominiczak, Af, Elliott, P, Fortina, P, Froguel, P, Gasparini, Paolo, Greinacher, A, Hazen, Sl, Jarvelin, Mr, Khaw, Kt, Lehtimäki, T, Maerz, W, Martin, Ng, Metspalu, A, Mitchell, Bd, Montgomery, Gw, Moore, C, Navis, G, Pirastu, M, Pramstaller, Pp, Ramirez Solis, R, Schadt, E, Scott, J, Shuldiner, Ar, Smith, Gd, Smith, Jg, Snieder, H, Sorice, R, Spector, Td, Stefansson, K, Stumvoll, M, Tang, Wh, Toniolo, D, Tönjes, A, Visscher, Pm, Vollenweider, P, Wareham, Nj, Wolffenbuttel, Bh, Boomsma, Di, Beckmann, J, Dedoussis, Gv, Deloukas, P, Ferreira, Ma, Sanna, S, Uda, M, Hicks, Aa, Penninger, Jm, Gieger, C, Kooner, J, Ouwehand, Wh, Soranzo, N, Chambers, J. C., Psychiatry, EMGO - Mental health, NCA - Anxiety & Depression, Biological Psychology, Neuroscience Campus Amsterdam - Anxiety & Depression, EMGO+ - Mental Health, Van Der Harst, Pim, Zhang, Weihua, Mateo Leach, Irene, Rendon, Augusto, Benyamin, Beben, Chambers, John C, Cardiovascular Centre (CVC), Groningen Institute for Gastro Intestinal Genetics and Immunology (3GI), Life Course Epidemiology (LCE), Guided Treatment in Optimal Selected Cancer Patients (GUTS), Lifestyle Medicine (LM), Stem Cell Aging Leukemia and Lymphoma (SALL), Groningen Kidney Center (GKC), Vascular Ageing Programme (VAP), and Center for Liver, Digestive and Metabolic Diseases (CLDM)

Subjects
Male, Netherlands Twin Register (NTR), Candidate gene, Erythrocytes, PROTEIN, Genome-wide association study, DISEASE, Hemoglobins, Mice, 0302 clinical medicine, Genetics, Genomics, blood, 0303 health sciences, Multidisciplinary, biology, Cell Cycle, COMMON VARIANTS, Genomics, Phenotype, anemia, 3. Good health, Haematopoiesis, DROSOPHILA, Drosophila melanogaster, medicine.anatomical_structure, HEMOGLOBIN LEVELS, Organ Specificity, 030220 oncology & carcinogenesis, Cytokines, Female, RNA Interference, TRAITS, Signal Transduction, EXPRESSION, Polymorphism, Single Nucleotide, Article, Genomic disorders and inherited multi-system disorders DCN MP - Plasticity and memory [IGMD 3], 03 medical and health sciences, SDG 3 - Good Health and Well-being, Genetic, blood, medicine, Animals, Humans, GENOME-WIDE ASSOCIATION, 030304 developmental biology, RECEPTOR, CONSORTIUM, ta3121, hemoglobin, biology.organism_classification, Hematopoiesis, meta-analysis, Red blood cell, Gene Expression Regulation, Genetic Loci, Expression quantitative trait loci, genome-wide association studies, Genome-Wide Association Study
Abstract

Item does not contain fulltext Anaemia is a chief determinant of global ill health, contributing to cognitive impairment, growth retardation and impaired physical capacity. To understand further the genetic factors influencing red blood cells, we carried out a genome-wide association study of haemoglobin concentration and related parameters in up to 135,367 individuals. Here we identify 75 independent genetic loci associated with one or more red blood cell phenotypes at P < 10(-8), which together explain 4-9% of the phenotypic variance per trait. Using expression quantitative trait loci and bioinformatic strategies, we identify 121 candidate genes enriched in functions relevant to red blood cell biology. The candidate genes are expressed preferentially in red blood cell precursors, and 43 have haematopoietic phenotypes in Mus musculus or Drosophila melanogaster. Through open-chromatin and coding-variant analyses we identify potential causal genetic variants at 41 loci. Our findings provide extensive new insights into genetic mechanisms and biological pathways controlling red blood cell formation and function.

Published
2012

14. Dindel: accurate indel calls from short-read data

Author

Gerton Lunter, Richard Durbin, Willem H. Ouwehand, Cornelis A. Albers, Gilean McVean, and Daniel G. MacArthur

Subjects
Resource, Genetics, Likelihood Functions, Models, Genetic, Sequence analysis, Bayesian probability, Inference, Bayes Theorem, Sequence Analysis, DNA, Computational biology, Biology, Identification (information), Bayes' theorem, Haplotypes, INDEL Mutation, 1000 Genomes Project, Indel, Algorithms, Software, Genetics (clinical), Sequence (medicine)
Abstract

Small insertions and deletions (indels) are a common and functionally important type of sequence polymorphism. Most of the focus of studies of sequence variation is on single nucleotide variants (SNVs) and large structural variants. In principle, high-throughput sequencing studies should allow identification of indels just as SNVs. However, inference of indels from next-generation sequence data is challenging, and so far methods for identifying indels lag behind methods for calling SNVs in terms of sensitivity and specificity. We propose a Bayesian method to call indels from short-read sequence data in individuals and populations by realigning reads to candidate haplotypes that represent alternative sequence to the reference. The candidate haplotypes are formed by combining candidate indels and SNVs identified by the read mapper, while allowing for known sequence variants or candidates from other methods to be included. In our probabilistic realignment model we account for base-calling errors, mapping errors, and also, importantly, for increased sequencing error indel rates in long homopolymer runs. We show that our method is sensitive and achieves low false discovery rates on simulated and real data sets, although challenges remain. The algorithm is implemented in the program Dindel, which has been used in the 1000 Genomes Project call sets.

Published
2011

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

Author

L. McDade, Eric D. Green, Aravinda Chakravarti, Susan Lindsay, Justin Paschall, Aylwyn Scally, Deborah A. Nickerson, Chip Stewart, Stephen T. Sherry, Chunlin Xiao, Alex Reynolds, Carol Scott, H. M. Khouri, Pardis C. Sabeti, Xinmeng Jasmine Mu, Stephen B. Montgomery, Eric Banks, Gabor T. Marth, A. Caprio, Xiaole Zheng, Philip Awadalla, Qunyuan Zhang, Wei Chen, Matthew N. Bainbridge, Donna Muzny, Steven A. McCarroll, Jeffrey M. Kidd, Honglong Wu, Audrey Duncanson, Vladimir Makarov, Lilia M. Iakoucheva, Mark Gerstein, Han-Jun Jin, Can Alkan, Iman Hajirasouliha, T. J. Fennell, C. R. Juenger, J. Kidd, Chris Tyler-Smith, Qasim Ayub, D. Ashworth, Kristian Cibulskis, Yutao Fu, William M. McLaren, Sol Katzman, Yujun Zhang, Rajini R Haraksingh, A. Kebbel, Stuart L. Schreiber, Manual Rivas, Onur Sakarya, Tobias Rausch, Yuan Chen, M. Bachorski, Matthew E. Hurles, N. C. Clemm, Wei Wang, Xiangqun Zheng-Bradley, Adrian M. Sütz, Thomas M. Keane, E. Bank, Stephen F. McLaughlin, Javier Herrero, Jon Keebler, Simon Myers, Aleksandr Morgulis, James Nemesh, Jing Leng, Molly Przeworski, Alon Keinan, Lorraine Toji, Ilya Shlyakhter, Joshua M. Korn, Martine Zilversmit, Luke Jostins, Jun Wang, Jared Maguire, J. M. Korn, Ryan E. Mills, Seungtai Yoon, Bo Wang, F. M. De La Vega, Heng Li, L. Guccione, Laura Clarke, Huisong Zheng, Jeffrey K. Ichikawa, K. Kao, Kirill Rotmistrovsky, L. Gu, David B. Jaffe, David Haussler, Toby Bloom, Tara Skelly, S. Yoon, Gil McVean, Carrie Sougnez, Mark A. Batzer, A. De Witte, Ralf Herwig, Jane Wilkinson, Min Hu, K. Pareja, John V. Pearson, Robert E. Handsaker, Jerilyn A. Walker, Fuli Yu, Anthony A. Philippakis, Aniko Sabo, Jonathan Marchini, Ryan D. Hernandez, Guoqing Li, Peter Donnelly, Eric S. Lander, David J. Dooling, Jun Ding, Lukas Habegger, Pilar N. Ossorio, Andreas Dahl, Wilfried Nietfeld, Miriam F. Moffatt, Alexej Abyzov, Sebastian Zöllner, Ekta Khurana, Jean E. McEwen, Robert S. Fulton, Alexey Soldatov, Fiona Hyland, Philippe Lacroute, Richa Agarwala, Paul Flicek, Weichun Huang, Alison J. Coffey, Tony Cox, John W. Wallis, Robert Sanders, David Neil Cooper, Jason P. Affourtit, Mark A. DePristo, D Wheeler, Christopher Celone, Eugene Kulesha, Craig Elder Mealmaker, B. Desany, Zhengdong D. Zhang, Jonathan M. Manning, Cynthia L. Turcotte, Lisa D Brooks, Xiuqing Zhang, C. Coafra, Rajesh Radhakrishnan, Alan J. Schafer, Jonathan Sebat, Ken Chen, Andrew G. Clark, Alexis Christoforides, Edward V. Ball, Mark S. Guyer, Sharon R. Grossman, Philip Rosenstiel, J. Knowlton, Gonçalo R. Abecasis, Min Jian, James O. Burton, S. Wang, Lucinda Murray, George M. Weinstock, Mark Lathrop, Harold Swerdlow, Michael L. Metzker, Xiaowei Zhan, Yeyang Su, Ruibang Luo, Charles Lee, Huanming Yang, P. Marquardt, Charles N. Rotimi, Lynne V. Nazareth, Michael Snyder, Faheem Niazi, Quan Long, Jane Kaye, Michael Strömberg, Adam Auton, Michael Bauer, Cheng-Sheng Lee, S. Gabriel, Jim Stalker, Heather E. Peckham, D. Conners, Raffaella Smith, Yingrui Li, Niall Anthony Gormley, Megan Hanna, Jinchuan Xing, Hugo Y. K. Lam, S. Giles, Evan E. Eichler, Justin Jee, Loukas Moutsianas, Jiang Du, Hyun Min Kang, Eric F. Tsung, Ni Huang, Kai Ye, Stephen F. Schaffner, Suleyman Cenk Sahinalp, Xinghua Shi, Sean Humphray, Ahmet Kurdoglu, Amy L. McGuire, Sandra J. Lee, Linnea Fulton, Francis S. Collins, Huiqing Liang, S. C. Melton, A. Nawrocki, Aaron R. Quinlan, Tatjana Borodina, Lynn B. Jorde, Leopold Parts, Michael D. McLellan, Adrian M. Stütz, Paul Scheet, Amit Indap, Vyacheslav Amstislavskiy, Waibhav Tembe, S. Attiya, Jin Yu, Dmitri Parkhomchuk, Si Quang Le, Fabian Grubert, E. Buglione, Ruiqiang Li, Yan Zhou, Fiona Cunningham, Gilean McVean, Wan-Ping Lee, W. Song, Richard Durbin, Andrew Kernytsky, Stephen M. Beckstrom-Sternberg, Xin Ma, J. Jeng, Lauren Ambrogio, Carol Churcher, Ryan Poplin, William O.C.M. Cookson, Rasko Leinonen, Alexey N. Davydov, Kenny Ye, Paige Anderson, Alexander E. Urban, Adam Felsenfeld, Jeffrey S. Reid, Cornelis A. Albers, Jan O. Korbel, Senduran Balasubramaniam, Elaine R. Mardis, Gozde Aksay, Peter H. Sudmant, Aaron McKenna, M. Labrecque, Amanda J. Price, Vadim Zalunin, Donald F. Conrad, Florian Mertes, Christie Kovar, Danny Challis, A. D. Ball, Petr Danecek, Kiran V. Garimella, Bryan Howie, Scott Kahn, Shuaishuai Tai, E. P. Garrison, Robert D. Bjornson, Shankar Balasubramanian, Fereydoun Hormozdiari, Geng Tian, S. Clark, Joanna L. Kelley, Asif T. Chinwalla, Ramenani Ravi K, Ralf Sudbrak, Mark Kaganovich, Jeffrey C. Barrett, David Rio Deiros, Jeremiah D. Degenhardt, A. Palotie, Alistair Ward, Gianna Costa, Huyen Dinh, M. Minderman, R. Keira Cheetham, Jingxiang Li, Michael A. Quail, P. Koko-Gonzales, Alastair Kent, Martin Shumway, David R. Bentley, Ferran Casals, Leena Peltonen, Klaudia Walter, Christopher Hartl, Erica Shefler, Zhaolei Zhang, Hans Lehrach, Jessica L. Peterson, Roger Winer, Daniel C. Koboldt, D. Riches, Terena James, Wen Fung Leong, Michael Egholm, Thomas W. Blackwell, Peter D. Stenson, Anthony J. Cox, Andrew D. Kern, David M. Carter, M. Tolzmann, Daniel G. MacArthur, Jiantao Wu, Jennifer Stone, Angie S. Hinrichs, M. Albrecht, Jo Knight, Chang-Yun Lin, Adam R. Boyko, Dan Turner, Xiaodong Fang, Youssef Idaghdour, Liming Liang, Ryan N. Gutenkunst, David Craig, Mark J. Daly, Xiaosen Guo, Neda Gharani, Gerton Lunter, Shuli Kang, A. Burke, Shripad Sinari, Yongming A. Sun, Zoya Kingsbury, Robert M. Kuhn, Miriam K. Konkel, T. Li, Kevin McKernan, Simon Gravel, Brian L. Browning, C Sidore, Zamin Iqbal, Matthew Mort, Afidalina Tumian, Michael C. Wendl, Adam Phillips, Bernd Timmermann, Carlos Bustamante, H. Y. Lam, Deniz Kural, Richard A. Gibbs, Bartha Maria Knoppers, Emmanouil T. Dermitzakis, Lon Phan, Richard K. Wilson, D. L. Altshuler, S. Keenen, Assya Abdallah, Eric A. Stone, Michael A. Eberle, Li Ding, and Broad Institute of MIT and Harvard

Subjects
DNA Copy Number Variations, Genotype, Population, Genomic Structural Variation, Genomics, Computational biology, Biology, Genome, Article, DNA sequencing, structural variation segmental duplications short-read rearrangements disorders disease common schizophrenia polymorphism insertions, 03 medical and health sciences, 0302 clinical medicine, Gene Duplication, Insertional, Genetics, Humans, Genetic Predisposition to Disease, Copy-number variation, 1000 Genomes Project, education, Sequence Deletion, 030304 developmental biology, 0303 health sciences, education.field_of_study, Multidisciplinary, Genome, Human, Reproducibility of Results, Sequence Analysis, DNA, DNA, Mutagenesis, Insertional, Genetics, Population, Mutagenesis, Human genome, Sequence Analysis, 030217 neurology & neurosurgery, Human
Abstract

Summary Genomic structural variants (SVs) are abundant in humans, differing from other variation classes 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 (i.e., 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 analyzing 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 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

16. Multipoint Approximations of Identity-by-Descent Probabilities for Accurate Linkage Analysis of Distantly Related Individuals

Author

Hilbert J. Kappen, Cornelis A. Albers, Russell Thomson, Melanie Bahlo, and Jim Stankovich

Subjects
Genetic Linkage, Computer science, Biophysics, Neuroinformatics [DCN 3], Identity by descent, Article, Cognitive neurosciences [UMCN 3.2], Chromosome (genetic algorithm), Genetics, Humans, Computer Simulation, Genetics(clinical), Hidden Markov model, Genetics (clinical), Probability, Parametric statistics, Variable (mathematics), Linkage (software), Markov chain, Nonparametric statistics, Reproducibility of Results, Markov Chains, Pedigree, Algorithm, Software
Abstract

Contains fulltext : 70751.pdf (Publisher’s version ) (Closed access) We propose an analytical approximation method for the estimation of multipoint identity by descent (IBD) probabilities in pedigrees containing a moderate number of distantly related individuals. We show that in large pedigrees where cases are related through untyped ancestors only, it is possible to formulate the hidden Markov model of the Lander-Green algorithm in terms of the IBD configurations of the cases. We use a first-order Markov approximation to model the changes in this IBD-configuration variable along the chromosome. In simulated and real data sets, we demonstrate that estimates of parametric and nonparametric linkage statistics based on the first-order Markov approximation are accurate. The computation time is exponential in the number of cases instead of in the number of meioses separating the cases. We have implemented our approach in the computer program ALADIN (accurate linkage analysis of distantly related individuals). ALADIN can be applied to general pedigrees and marker types and has the ability to model marker-marker linkage disequilibrium with a clustered-markers approach. Using ALADIN is straightforward: It requires no parameters to be specified and accepts standard input files.

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17. Modeling linkage disequilibrium in exact linkage computations: a comparison of first-order Markov approaches and the clustered-markers approach

Author

Hilbert J. Kappen and Cornelis A. Albers

Subjects
Linkage (software), Linkage disequilibrium, Markov chain, Computer science, lcsh:R, Bayesian probability, lcsh:Medicine, General Medicine, computer.software_genre, Markov model, General Biochemistry, Genetics and Molecular Biology, Set (abstract data type), Data set, Proceedings, lcsh:Q, Data mining, lcsh:Science, Hidden Markov model, computer
Abstract

Recent studies have shown that linkage disequilibrium (LD) between single-nucleotide polymorphism (SNP) markers is widespread. Assuming linkage equilibrium has been shown to cause false positives in linkage studies where parental genotypes are not available. Therefore, linkage analysis methods that can deal with LD are required to accurately analyze SNP marker data sets. We compared three approaches to deal with LD between markers: 1) The clustered-markers approach implemented in the computer program MERLIN; 2) The standard hidden Markov model (HMM) multipoint model augmented with a first-order Markov model for the allele frequencies of the founders, in which we considered both a Bayesian and a maximum-likelihood implementation of this approach; 3) The 'independent' SNPs approach, i.e., removing SNPs from the data set until the remaining SNPs have low levels of LD. We evaluated these approaches on the Illumina 6K SNP data set of affected sib-pairs of Problem 2. We found that the first-order Markov model was able to account for most of the strong LD in this data set. The difference between the Bayesian and maximum- likelihood implementation was small. An advantage of the first-order Markov model is that it does not require the user to specify parameters.

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18. Investigating the effect of dependence between conditions with Bayesian Linear Mixed Models for motif activity analysis.

Author

Simone Lederer, Tom Heskes, Simon J van Heeringen, and Cornelis A Albers

Subjects
Medicine, Science
Abstract

MOTIVATION:Cellular identity and behavior is controlled by complex gene regulatory networks. Transcription factors (TFs) bind to specific DNA sequences to regulate the transcription of their target genes. On the basis of these TF motifs in cis-regulatory elements we can model the influence of TFs on gene expression. In such models of TF motif activity the data is usually modeled assuming a linear relationship between the motif activity and the gene expression level. A commonly used method to model motif influence is based on Ridge Regression. One important assumption of linear regression is the independence between samples. However, if samples are generated from the same cell line, tissue, or other biological source, this assumption may be invalid. This same assumption of independence is also applied to different yet similar experimental conditions, which may also be inappropriate. In theory, the independence assumption between samples could lead to loss in signal detection. Here we investigate whether a Bayesian model that allows for correlations results in more accurate inference of motif activities. RESULTS:We extend the Ridge Regression to a Bayesian Linear Mixed Model, which allows us to model dependence between different samples. In a simulation study, we investigate the differences between the two model assumptions. We show that our Bayesian Linear Mixed Model implementation outperforms Ridge Regression in a simulation scenario where the noise, which is the signal that can not be explained by TF motifs, is uncorrelated. However, we demonstrate that there is no such gain in performance if the noise has a similar covariance structure over samples as the signal that can be explained by motifs. We give a mathematical explanation to why this is the case. Using four representative real datasets we show that at most ∼​40% of the signal is explained by motifs using the linear model. With these data there is no advantage to using the Bayesian Linear Mixed Model, due to the similarity of the covariance structure. AVAILABILITY & IMPLEMENTATION:The project implementation is available at https://github.com/Sim19/SimGEXPwMotifs.

Published
2020
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