Genome-wide association studies

[1]  D. Posthuma,et al.  An integrated framework for local genetic correlation analysis , 2022, Nature Genetics.

[2]  Hongyu Zhao,et al.  SUPERGNOVA: local genetic correlation analysis reveals heterogeneous etiologic sharing of complex traits , 2021, Genome biology.

[3]  Zachary F. Gerring,et al.  Symptom-level modelling unravels the shared genetic architecture of anxiety and depression , 2021, Nature Human Behaviour.

[4]  Neville E. Sanjana,et al.  Discovery of target genes and pathways of blood trait loci using pooled CRISPR screens and single cell RNA sequencing , 2021, bioRxiv.

[5]  Ryan L. Collins,et al.  Genome-wide enhancer maps link risk variants to disease genes , 2021, Nature.

[6]  P. Donnelly,et al.  Validation of an Integrated Risk Tool, Including Polygenic Risk Score, for Atherosclerotic Cardiovascular Disease in Multiple Ethnicities and Ancestries. , 2021, The American journal of cardiology.

[7]  P. Donnelly,et al.  Integrated Polygenic Tool Substantially Enhances Coronary Artery Disease Prediction , 2021, Circulation. Genomic and precision medicine.

[8]  S. Fullerton,et al.  Toward better governance of human genomic data , 2021, Nature Genetics.

[9]  J. Danesh,et al.  Polygenic risk scores in cardiovascular risk prediction: A cohort study and modelling analyses , 2021, PLoS medicine.

[10]  Trevor Hastie,et al.  Genetics of 35 blood and urine biomarkers in the UK Biobank , 2020, Nature Genetics.

[11]  J. Witte,et al.  Inclusion of variants discovered from diverse populations improves polygenic risk score transferability. , 2020, HGG advances.

[12]  Arslan A. Zaidi,et al.  Demographic history mediates the effect of stratification on polygenic scores , 2020, eLife.

[13]  Alexander E. Lopez,et al.  Exome sequencing and characterization of 49,960 individuals in the UK Biobank , 2020, Nature.

[14]  Howard Y. Chang,et al.  The road ahead in genetics and genomics , 2020, Nature Reviews Genetics.

[15]  K. Rawlik,et al.  Sexual differences in genetic architecture in UK Biobank , 2020, bioRxiv.

[16]  A. Auton,et al.  Avoiding dynastic, assortative mating, and population stratification biases in Mendelian randomization through within-family analyses , 2020, Nature Communications.

[17]  Alicia R. Martin,et al.  How robust are cross-population signatures of polygenic adaptation in humans? , 2020, bioRxiv.

[18]  A. Kong,et al.  Mendelian imputation of parental genotypes for genome-wide estimation of direct and indirect genetic effects , 2020, bioRxiv.

[19]  M. Donnellan,et al.  Registered Reports With Developmental and Secondary Data: Some Brief Observations and Introduction to the Special Issue , 2020, Emerging Adulthood.

[20]  Gonçalo Abecasis,et al.  Computationally efficient whole-genome regression for quantitative and binary traits , 2020, Nature Genetics.

[21]  Manuel A. R. Ferreira,et al.  Age-of-onset information helps identify 76 genetic variants associated with allergic disease , 2020, PLoS genetics.

[22]  Tom R. Gaunt,et al.  The variant call format provides efficient and robust storage of GWAS summary statistics , 2020, Genome Biology.

[23]  J. de Vries,et al.  Stigma in African genomics research: Gendered blame, polygamy, ancestry and disease causal beliefs impact on the risk of harm , 2020, Social science & medicine.

[24]  D. Posthuma,et al.  Emerging Methods and Resources for Biological Interrogation of Neuropsychiatric Polygenic Signal , 2020, Biological Psychiatry.

[25]  J. Witte,et al.  Inclusion of variants discovered from diverse populations improves polygenic risk score transferability , 2020, bioRxiv.

[26]  S. A. Lambert,et al.  The Polygenic Score Catalog: an open database for reproducibility and systematic evaluation , 2020, medRxiv.

[27]  The COVID-19 Host Genetics Initiative, a global initiative to elucidate the role of host genetic factors in susceptibility and severity of the SARS-CoV-2 virus pandemic , 2020, European Journal of Human Genetics.

[28]  C. Kooperberg,et al.  Improving reporting standards for polygenic scores in risk prediction studies , 2020, Nature.

[29]  Talishiea Croxton,et al.  Protection of Personal Information Act 2013 and data protection for health research in South Africa , 2020 .

[30]  Ryan L. Collins,et al.  The mutational constraint spectrum quantified from variation in 141,456 humans , 2020, Nature.

[31]  Bjarni J. Vilhjálmsson,et al.  LDpred2: better, faster, stronger , 2020, bioRxiv.

[32]  M. Rivas,et al.  GWAS of three molecular traits highlights core genes and pathways alongside a highly polygenic background , 2020, bioRxiv.

[33]  Deborah A. Bolnick,et al.  Rights, interests and expectations: Indigenous perspectives on unrestricted access to genomic data , 2020, Nature Reviews Genetics.

[34]  Audrey Y. Chu,et al.  Polygenic and clinical risk scores and their impact on age at onset and prediction of cardiometabolic diseases and common cancers , 2020, Nature Medicine.

[35]  Jason H. Moore,et al.  Electronic health records and polygenic risk scores for predicting disease risk , 2020, Nature Reviews Genetics.

[36]  Till F. M. Andlauer,et al.  Minimal phenotyping yields genome-wide association signals of low specificity for major depression , 2020, Nature Genetics.

[37]  Pietro Della Briotta Parolo,et al.  Genetic analyses identify widespread sex-differential participation bias , 2020, Nature Genetics.

[38]  N. Patterson,et al.  Liability threshold modeling of case-control status and family history of disease increases association power , 2020, Nature Genetics.

[39]  Matthias Heinig,et al.  The single-cell eQTLGen consortium , 2020, eLife.

[40]  Y. Kamatani,et al.  Genetic and phenotypic landscape of the mitochondrial genome in the Japanese population , 2020, Communications Biology.

[41]  B. Bierer,et al.  Disruptive and avoidable: GDPR challenges to secondary research uses of data , 2020, European Journal of Human Genetics.

[42]  A. Gronowski,et al.  The Ethics of Direct-to-Consumer Testing. , 2020, Clinics in laboratory medicine.

[43]  M. Mills,et al.  The GWAS Diversity Monitor tracks diversity by disease in real time , 2020, Nature Genetics.

[44]  J. Korbel,et al.  Genomic data sharing in Europe is stumbling—Could a code of conduct prevent its fall? , 2020, EMBO molecular medicine.

[45]  J. Dougherty,et al.  Massively Parallel Reporter Assays: Defining Functional Psychiatric Genetic Variants Across Biological Contexts , 2020, Biological Psychiatry.

[46]  Andrew D. Yates,et al.  eQTL Catalogue: a compendium of uniformly processed human gene expression and splicing QTLs , 2020, bioRxiv.

[47]  J. Haines,et al.  Multitrait analysis of glaucoma identifies new risk loci and enables polygenic prediction of disease susceptibility and progression , 2020, Nature Genetics.

[48]  William Jones,et al.  The open targets post-GWAS analysis pipeline , 2020, Bioinform..

[49]  Jirimutu,et al.  Whole-genome sequencing of 128 camels across Asia reveals origin and migration of domestic Bactrian camels , 2020, Communications Biology.

[50]  Faisal M. Fadlelmola,et al.  Enabling Genomic Revolution in Africa , 2019, The Genetics of African Populations in Health and Disease.

[51]  J. de Vries,et al.  Returning incidental findings in African genomics research , 2019, Nature Genetics.

[52]  Stuart J. Ritchie,et al.  Preregistration of secondary data analysis: A template and tutorial , 2019, Meta-Psychology.

[53]  T. Werge,et al.  A major role for common genetic variation in anxiety disorders , 2019, Molecular Psychiatry.

[54]  Kathleen M. Brelsford,et al.  Legal and Ethical Challenges of International Direct-to-Participant Genomic Research: Conclusions and Recommendations , 2019, Journal of Law, Medicine & Ethics.

[55]  J. Denny,et al.  The "All of Us" Research Program. , 2019, The New England journal of medicine.

[56]  N. Tiffin,et al.  A framework for tiered informed consent for health genomic research in Africa , 2019, Nature Genetics.

[57]  Jane E. Carpenter,et al.  Fine-mapping of 150 breast cancer risk regions identifies 191 likely target genes , 2019, Nature Genetics.

[58]  Christopher D. Brown,et al.  Widespread dose-dependent effects of RNA expression and splicing on complex diseases and traits , 2019, bioRxiv.

[59]  Brendan P. Zietsch,et al.  Genetic correlates of social stratification in Great Britain , 2019, Nature Human Behaviour.

[60]  Stephane E. Castel,et al.  Cell type–specific genetic regulation of gene expression across human tissues , 2019, Science.

[61]  Christopher D. Brown,et al.  The GTEx Consortium atlas of genetic regulatory effects across human tissues , 2019, Science.

[62]  Max W. Y. Lam,et al.  Genome-wide Association Studies in Ancestrally Diverse Populations: Opportunities, Methods, Pitfalls, and Recommendations , 2019, Cell.

[63]  Y. Kamatani,et al.  Functional variants in ADH1B and ALDH2 are non-additively associated with all-cause mortality in Japanese population , 2019, European Journal of Human Genetics.

[64]  N. Patterson,et al.  Extreme Polygenicity of Complex Traits Is Explained by Negative Selection. , 2019, American journal of human genetics.

[65]  M. McCarthy,et al.  Cohort Profile: East London Genes & Health (ELGH), a community-based population genomics and health study in British Bangladeshi and British Pakistani people , 2019, International journal of epidemiology.

[66]  O. V. D. Akker.  Preregistration of Secondary Data Analysis - A template and tutorial , 2019 .

[67]  O. Andreassen,et al.  A global overview of pleiotropy and genetic architecture in complex traits , 2019, Nature Genetics.

[68]  A. Philippakis,et al.  The "All of Us" Research Program. , 2019, The New England journal of medicine.

[69]  Marie Verbanck,et al.  HOPS: a quantitative score reveals pervasive horizontal pleiotropy in human genetic variation is driven by extreme polygenicity of human traits and diseases , 2019, Genome Biology.

[70]  Alicia R. Martin,et al.  Predicting Polygenic Risk of Psychiatric Disorders , 2019, Biological Psychiatry.

[71]  Alexander I. Young.  Solving the missing heritability problem , 2019, PLoS genetics.

[72]  Alicia R. Martin,et al.  Geographic Variation and Bias in the Polygenic Scores of Complex Diseases and Traits in Finland. , 2019, American journal of human genetics.

[73]  Tanya M. Teslovich,et al.  Exome sequencing of 20,791 cases of type 2 diabetes and 24,440 controls , 2019, Nature.

[74]  Neil M Davies,et al.  Population phenomena inflate genetic associations of complex social traits , 2019, Science Advances.

[75]  J. Pritchard,et al.  Variable prediction accuracy of polygenic scores within an ancestry group , 2019, bioRxiv.

[76]  N. Timpson,et al.  Is population structure in the genetic biobank era irrelevant, a challenge, or an opportunity? , 2019, Human Genetics.

[77]  P. Visscher,et al.  A resource-efficient tool for mixed model association analysis of large-scale data , 2019, Nature Genetics.

[78]  Robert Karlsson,et al.  RICOPILI: Rapid Imputation for COnsortias PIpeLIne , 2019, bioRxiv.

[79]  Stuart J. Ritchie,et al.  Comparing Within- and Between-Family Polygenic Score Prediction , 2019, bioRxiv.

[80]  Alicia R. Martin,et al.  Clinical use of current polygenic risk scores may exacerbate health disparities , 2019, Nature Genetics.

[81]  M. Blell,et al.  Direct-to-Consumer Genetic Testing's Red Herring: “Genetic Ancestry” and Personalized Medicine , 2019, Front. Med..

[82]  Ryan D. Hernandez,et al.  Recovery of trait heritability from whole genome sequence data , 2019, bioRxiv.

[83]  Robert M. Maier,et al.  Polygenic adaptation on height is overestimated due to uncorrected stratification in genome-wide association studies , 2019, eLife.

[84]  Stuart J. Ritchie,et al.  Genomic SEM Provides Insights into the Multivariate Genetic Architecture of Complex Traits , 2019, Nature Human Behaviour.

[85]  Yukinori Okada,et al.  GREP: genome for REPositioning drugs , 2019, Bioinform..

[86]  Brian E. Cade,et al.  Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program , 2019, Nature.

[87]  A. Auton,et al.  Genome-wide analysis of insomnia in 1,331,010 individuals identifies new risk loci and functional pathways , 2019, Nature Genetics.

[88]  Alkes L. Price,et al.  Quantification of frequency-dependent genetic architectures in 25 UK Biobank traits reveals action of negative selection , 2019, Nature Communications.

[89]  M. Kanai,et al.  Genetic and phenotypic landscape of the major histocompatibilty complex region in the Japanese population , 2019, Nature Genetics.

[90]  D. Lawson,et al.  Apparent latent structure within the UK Biobank sample has implications for epidemiological analysis , 2019, Nature Communications.

[91]  Naomi R. Wray,et al.  Improved polygenic prediction by Bayesian multiple regression on summary statistics , 2019, Nature Communications.

[92]  Michel G. Nivard,et al.  Multivariate genome-wide analyses of the well-being spectrum , 2019, Nature Genetics.

[93]  N. Wald,et al.  The illusion of polygenic disease risk prediction , 2019, Genetics in Medicine.

[94]  Jacob M. Schreiber,et al.  A Genome-wide Framework for Mapping Gene Regulation via Cellular Genetic Screens , 2019, Cell.

[95]  Matthew Stephens,et al.  A simple new approach to variable selection in regression, with application to genetic fine-mapping , 2018, bioRxiv.

[96]  Alicia R. Martin,et al.  Geographic variation and bias in polygenic scores of complex diseases and traits in Finland , 2018, bioRxiv.

[97]  Vijaya Raghavan Rangamaran,et al.  EasyQC: Tool with Interactive User Interface for Efficient Next-Generation Sequencing Data Quality Control , 2018, J. Comput. Biol..

[98]  Dan J Stein,et al.  Potential use of clinical polygenic risk scores in psychiatry – ethical implications and communicating high polygenic risk , 2018, Philosophy, Ethics, and Humanities in Medicine.

[99]  Helen E. Parkinson,et al.  The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019 , 2018, Nucleic Acids Res..

[100]  Sara M. Willems,et al.  Trans-ethnic association study of blood pressure determinants in over 750,000 individuals , 2018, Nature Genetics.

[101]  H. Tilg,et al.  IL-12, IL-23 and IL-17 in IBD: immunobiology and therapeutic targeting , 2018, Nature Reviews Gastroenterology & Hepatology.

[102]  Sina A. Gharib,et al.  Unraveling the polygenic architecture of complex traits using blood eQTL metaanalysis , 2018, bioRxiv.

[103]  Doug Speed,et al.  SumHer better estimates the SNP heritability of complex traits from summary statistics , 2018, Nature Genetics.

[104]  Brielin C. Brown,et al.  Comparative genetic architectures of schizophrenia in East Asian and European populations , 2018, Nature Genetics.

[105]  P. Donnelly,et al.  The UK Biobank resource with deep phenotyping and genomic data , 2018, Nature.

[106]  J. Marchini,et al.  Genome-wide association studies of brain imaging phenotypes in UK Biobank , 2018, Nature.

[107]  Yang I Li,et al.  Trans Effects on Gene Expression Can Drive Omnigenic Inheritance , 2018, Cell.

[108]  Yang Ni,et al.  Polygenic prediction via Bayesian regression and continuous shrinkage priors , 2018, Nature Communications.

[109]  Timothy Shin Heng Mak,et al.  Tutorial: a guide to performing polygenic risk score analyses , 2018, bioRxiv.

[110]  C. Fisher,et al.  Genomics, Big Data, and Broad Consent: a New Ethics Frontier for Prevention Science , 2018, Prevention Science.

[111]  Matthew Z. Anderson,et al.  A framework for enhancing ethical genomic research with Indigenous communities , 2018, Nature Communications.

[112]  Alkes L. Price,et al.  Modeling functional enrichment improves polygenic prediction accuracy in UK Biobank and 23andMe data sets , 2018, bioRxiv.

[113]  Gerardus A. Meddens,et al.  Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals , 2018, Nature Genetics.

[114]  D. Gudbjartsson,et al.  Relatedness disequilibrium regression estimates heritability without environmental bias , 2018, Nature Genetics.

[115]  B. Browning,et al.  A one penny imputed genome from next generation reference panels , 2018, bioRxiv.

[116]  G. Coop,et al.  Reduced signal for polygenic adaptation of height in UK Biobank , 2018, bioRxiv.

[117]  Mary E. Haas,et al.  Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations , 2018, Nature Genetics.

[118]  D. Schaid,et al.  From genome-wide associations to candidate causal variants by statistical fine-mapping , 2018, Nature Reviews Genetics.

[119]  B. Neale,et al.  Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases , 2018, Nature Genetics.

[120]  Nchangwi Syntia Munung,et al.  Model framework for governance of genomic research and biobanking in Africa – a content description , 2018, AAS open research.

[121]  P. Visscher,et al.  Signatures of negative selection in the genetic architecture of human complex traits , 2018, Nature Genetics.

[122]  P. Visscher,et al.  Meta-analysis of genome-wide association studies for height and body mass index in ∼700,000 individuals of European ancestry , 2018, bioRxiv.

[123]  Timothy C. Bates,et al.  The Nature of Nurture: Using a Virtual-Parent Design to Test Parenting Effects on Children's Educational Attainment in Genotyped Families , 2018, Twin Research and Human Genetics.

[124]  Brian A. Nosek,et al.  The preregistration revolution , 2018, Proceedings of the National Academy of Sciences.

[125]  D. Posthuma,et al.  Item-level analyses reveal genetic heterogeneity in neuroticism , 2018, Nature Communications.

[126]  M. Kanai,et al.  Genetic analysis of quantitative traits in the Japanese population links cell types to complex human diseases , 2018, Nature Genetics.

[127]  Stuart J. Ritchie,et al.  A combined analysis of genetically correlated traits identifies 187 loci and a role for neurogenesis and myelination in intelligence , 2018, Molecular Psychiatry.

[128]  Céline Bellenguez,et al.  Strategies for phasing and imputation in a population isolate , 2018, Genetic epidemiology.

[129]  Po-Ru Loh,et al.  Mixed-model association for biobank-scale datasets , 2018, Nature Genetics.

[130]  Calliope A. Dendrou,et al.  HLA variation and disease , 2018, Nature Reviews Immunology.

[131]  Louise Bezuidenhout,et al.  Hidden concerns of sharing research data by low/middle-income country scientists , 2018, Global bioethics = Problemi di bioetica.

[132]  Erdogan Taskesen,et al.  Functional mapping and annotation of genetic associations with FUMA , 2017, Nature Communications.

[133]  Bjarni V. Halldórsson,et al.  The nature of nurture: Effects of parental genotypes , 2017, Science.

[134]  Lars G Fritsche,et al.  Efficiently controlling for case-control imbalance and sample relatedness in large-scale genetic association studies , 2017, Nature Genetics.

[135]  S. Bull,et al.  ‘It is an entrustment’: Broad consent for genomic research and biobanks in sub‐Saharan Africa , 2017, Developing world bioethics.

[136]  K. Rawlik,et al.  An atlas of genetic associations in UK Biobank , 2017, Nature Genetics.

[137]  P. Visscher,et al.  10 Years of GWAS Discovery: Biology, Function, and Translation. , 2017, American journal of human genetics.

[138]  Hailiang Huang,et al.  Fine-mapping inflammatory bowel disease loci to single variant resolution , 2017, Nature.

[139]  Shane A. McCarthy,et al.  Enrichment of low-frequency functional variants revealed by whole-genome sequencing of multiple isolated European populations , 2017, Nature Communications.

[140]  C. Sudlow,et al.  Comparison of Sociodemographic and Health-Related Characteristics of UK Biobank Participants With Those of the General Population , 2017, American journal of epidemiology.

[141]  Yang I Li,et al.  An Expanded View of Complex Traits: From Polygenic to Omnigenic , 2017, Cell.

[142]  D. Geschwind,et al.  The contribution of rare variants to risk of schizophrenia in individuals with and without intellectual disability , 2017, Nature Genetics.

[143]  G. Smith,et al.  Mendelian randomization in cardiometabolic disease: challenges in evaluating causality , 2017, Nature Reviews Cardiology.

[144]  O. Andreassen,et al.  Beyond SNP heritability: Polygenicity and discoverability of phenotypes estimated with a univariate Gaussian mixture model , 2017, bioRxiv.

[145]  Fabian J Theis,et al.  The Human Cell Atlas , 2017, bioRxiv.

[146]  Klaudia Walter,et al.  The impact of rare and low-frequency genetic variants in common disease , 2017, Genome Biology.

[147]  Henry J. Lin,et al.  Large-scale analyses of common and rare variants identify 12 new loci associated with atrial fibrillation , 2017, Nature Genetics.

[148]  M. Parker,et al.  Psychiatric Genomics and Mental Health Treatment: Setting the Ethical Agenda , 2017, The American journal of bioethics : AJOB.

[149]  Y. Kamatani,et al.  Overview of the BioBank Japan Project: Study design and profile , 2017, Journal of epidemiology.

[150]  Mark W. Youngblood,et al.  Integrated genomic analyses of de novo pathways underlying atypical meningiomas , 2017, Nature Communications.

[151]  Yaniv Erlich,et al.  Case–control association mapping by proxy using family history of disease , 2017, Nature Genetics.

[152]  Gad Abraham,et al.  FlashPCA2: principal component analysis of biobank-scale genotype datasets , 2016, bioRxiv.

[153]  Bogdan Pasaniuc,et al.  Local genetic correlation gives insights into the shared genetic architecture of complex traits , 2016, bioRxiv.

[154]  Thomas M. Norman,et al.  A Multiplexed Single-Cell CRISPR Screening Platform Enables Systematic Dissection of the Unfolded Protein Response , 2016, Cell.

[155]  Thomas M. Norman,et al.  Perturb-Seq: Dissecting Molecular Circuits with Scalable Single-Cell RNA Profiling of Pooled Genetic Screens , 2016, Cell.

[156]  Bull Susan.  Review: Ensuring global equity in open research , 2016 .

[157]  S. Fullerton,et al.  Genomics is failing on diversity , 2016, Nature.

[158]  X. Wen,et al.  Integrating molecular QTL data into genome-wide genetic association analysis: Probabilistic assessment of enrichment and colocalization , 2016, bioRxiv.

[159]  Christopher R. Gignoux,et al.  Human demographic history impacts genetic risk prediction across diverse populations , 2016, bioRxiv.

[160]  Olivier Delaneau,et al.  A complete tool set for molecular QTL discovery and analysis , 2016, Nature Communications.

[161]  Ayellet V. Segrè,et al.  Colocalization of GWAS and eQTL Signals Detects Target Genes , 2016, bioRxiv.

[162]  L. Eaves,et al.  Education policy and the heritability of educational attainment , 2016, Nature.

[163]  Shane A. McCarthy,et al.  Whole-genome view of the consequences of a population bottleneck using 2926 genome sequences from Finland and United Kingdom , 2016, European Journal of Human Genetics.

[164]  Herman Aguinis,et al.  HARKing's Threat to Organizational Research: Evidence From Primary and Meta‐Analytic Sources , 2016 .

[165]  Shane A. McCarthy,et al.  Exploring the genetic architecture of inflammatory bowel disease by whole genome sequencing identifies association at ADCY7 , 2016, Nature Genetics.

[166]  Po-Ru Loh,et al.  Multi-ethnic polygenic risk scores improve risk prediction in diverse populations , 2016, bioRxiv.

[167]  David A. Knowles,et al.  RNA splicing is a primary link between genetic variation and disease , 2016, Science.

[168]  E. Zeggini,et al.  Insights into metabolic disease from studying genetics in isolated populations: stories from Greece to Greenland , 2016, Diabetologia.

[169]  Erik Schultes,et al.  The FAIR Guiding Principles for scientific data management and stewardship , 2016, Scientific Data.

[170]  F. Cunningham,et al.  The Ensembl Variant Effect Predictor , 2016, Genome Biology.

[171]  P. Farnham,et al.  Making sense of GWAS: using epigenomics and genome engineering to understand the functional relevance of SNPs in non-coding regions of the human genome , 2015, Epigenetics & Chromatin.

[172]  Gil McVean,et al.  Imputation of KIR Types from SNP Variation Data , 2015, American journal of human genetics.

[173]  Gabor T. Marth,et al.  A global reference for human genetic variation , 2015, Nature.

[174]  Matti Pirinen,et al.  FINEMAP: efficient variable selection using summary data from genome-wide association studies , 2015, bioRxiv.

[175]  P. Visscher,et al.  Population genetic differentiation of height and body mass index across Europe , 2015, Nature Genetics.

[176]  D. Balding,et al.  Using Genetic Distance to Infer the Accuracy of Genomic Prediction , 2015, PLoS genetics.

[177]  Matthew C. Canver,et al.  BCL11A enhancer dissection by Cas9-mediated in situ saturating mutagenesis , 2015, Nature.

[178]  David M. Evans,et al.  Mendelian Randomization: New Applications in the Coming Age of Hypothesis-Free Causality. , 2015, Annual review of genomics and human genetics.

[179]  T. Lehtimäki,et al.  Integrative approaches for large-scale transcriptome-wide association studies , 2015, Nature Genetics.

[180]  Alan M. Kwong,et al.  Genome sequencing elucidates Sardinian genetic architecture and augments association analyses for lipid and blood inflammatory markers , 2015, Nature Genetics.

[181]  Kaanan P. Shah,et al.  A gene-based association method for mapping traits using reference transcriptome data , 2015, Nature Genetics.

[182]  N. Schork.  Personalized medicine: Time for one-person trials , 2015, Nature.

[183]  Joris M. Mooij,et al.  MAGMA: Generalized Gene-Set Analysis of GWAS Data , 2015, PLoS Comput. Biol..

[184]  N. Timpson,et al.  Using published data in Mendelian randomization: a blueprint for efficient identification of causal risk factors , 2015, European Journal of Epidemiology.

[185]  P. Visscher,et al.  Modeling Linkage Disequilibrium Increases Accuracy of Polygenic Risk Scores , 2015, bioRxiv.

[186]  J. Hirschhorn,et al.  Biological interpretation of genome-wide association studies using predicted gene functions , 2015, Nature Communications.

[187]  C. Rotimi,et al.  The H3Africa policy framework: negotiating fairness in genomics , 2015, Trends in genetics : TIG.

[188]  Jack Euesden,et al.  PRSice: Polygenic Risk Score software , 2014, Bioinform..

[189]  S. Rosset,et al.  Measuring missing heritability: Inferring the contribution of common variants , 2014, Proceedings of the National Academy of Sciences.

[190]  Carmen Cadarso-Suárez,et al.  OptimalCutpoints: An R Package for Selecting Optimal Cutpoints in Diagnostic Tests , 2014 .

[191]  Bartha Maria Knoppers,et al.  Framework for responsible sharing of genomic and health-related data , 2014, The HUGO Journal.

[192]  E. Eskin,et al.  Integrating Functional Data to Prioritize Causal Variants in Statistical Fine-Mapping Studies , 2014, PLoS genetics.

[193]  Christine Weiner.  Anticipate and communicate: Ethical management of incidental and secondary findings in the clinical, research, and direct-to-consumer contexts (December 2013 report of the Presidential Commission for the Study of Bioethical Issues). , 2014, American journal of epidemiology.

[194]  Ian J. Deary,et al.  Common genetic variants associated with cognitive performance identified using the proxy-phenotype method , 2014, Proceedings of the National Academy of Sciences.

[195]  Common genetic variants associated with cognitive performance identified using the proxy-phenotype method , 2014, Proceedings of the National Academy of Sciences.

[196]  B. Berger,et al.  Efficient Bayesian mixed model analysis increases association power in large cohorts , 2014, Nature Genetics.

[197]  Eleazar Eskin,et al.  Identifying Causal Variants at Loci with Multiple Signals of Association , 2014, Genetics.

[198]  E. Zeggini,et al.  Using population isolates in genetic association studies , 2014, Briefings in functional genomics.

[199]  Andres Metspalu,et al.  Distribution and Medical Impact of Loss-of-Function Variants in the Finnish Founder Population , 2014, PLoS genetics.

[200]  B. Berkman,et al.  A Framework for Analyzing the Ethics of Disclosing Genetic Research Findings , 2014, Journal of Law, Medicine & Ethics.

[201]  M. Pangalos,et al.  Lessons learned from the fate of AstraZeneca's drug pipeline: a five-dimensional framework , 2014, Nature Reviews Drug Discovery.

[202]  S. Muthukumaraswamy,et al.  Instead of "playing the game" it is time to change the rules: Registered Reports at AIMS Neuroscience and beyond , 2014 .

[203]  Zoltán Kutalik,et al.  Quality control and conduct of genome-wide association meta-analyses , 2014, Nature Protocols.

[204]  N. Cox,et al.  Obesity-associated variants within FTO form long-range functional connections with IRX3 , 2014, Nature.

[205]  M. Daly,et al.  LD Score regression distinguishes confounding from polygenicity in genome-wide association studies , 2014, Nature Genetics.

[206]  Louis Bodmer.  ACKNOWLEDGEMENTS , 2013, Journal of Biosciences.

[207]  R. Green,et al.  Communicating genetic risk information for common disorders in the era of genomic medicine. , 2013, Annual review of genomics and human genetics.

[208]  D. Altshuler,et al.  Validating therapeutic targets through human genetics , 2013, Nature Reviews Drug Discovery.

[209]  F. Song,et al.  Publication bias: what is it? How do we measure it? How do we avoid it? , 2013 .

[210]  Buhm Han,et al.  Imputing Amino Acid Polymorphisms in Human Leukocyte Antigens , 2013, PloS one.

[211]  B S Weir,et al.  HIBAG—HLA genotype imputation with attribute bagging , 2013, The Pharmacogenomics Journal.

[212]  C. Wallace,et al.  Bayesian Test for Colocalisation between Pairs of Genetic Association Studies Using Summary Statistics , 2013, PLoS genetics.

[213]  Chris Haley,et al.  Inference of identity by descent in population isolates and optimal sequencing studies , 2013, European Journal of Human Genetics.

[214]  D. Altshuler,et al.  Informed Conditioning on Clinical Covariates Increases Power in Case-Control Association Studies , 2012, PLoS genetics.

[215]  Shane J. Neph,et al.  Systematic Localization of Common Disease-Associated Variation in Regulatory DNA , 2012, Science.

[216]  Nathan C. Sheffield,et al.  The accessible chromatin landscape of the human genome , 2012, Nature.

[217]  M. Pirinen,et al.  Including known covariates can reduce power to detect genetic effects in case-control studies , 2012, Nature Genetics.

[218]  Cameron D. Palmer,et al.  Evidence of widespread selection on standing variation in Europe at height-associated SNPs , 2012, Nature Genetics.

[219]  Jonathan P. Beauchamp,et al.  The Promises and Pitfalls of Genoeconomics* , 2012, Annual review of economics.

[220]  M. Stephens,et al.  Genome-wide Efficient Mixed Model Analysis for Association Studies , 2012, Nature Genetics.

[221]  Lawrence H. Uricchio,et al.  Accurate Imputation of Rare and Common Variants in a Founder Population From a Small Number of Sequenced Individuals , 2012, Genetic epidemiology.

[222]  P. Visscher,et al.  Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits , 2012, Nature Genetics.

[223]  G. Loewenstein,et al.  Measuring the Prevalence of Questionable Research Practices With Incentives for Truth Telling , 2012, Psychological science.

[224]  Robert M. Plenge,et al.  Five amino acids in three HLA proteins explain most of the association between MHC and seropositive rheumatoid arthritis , 2011, Nature Genetics.

[225]  R. Collins,et al.  China Kadoorie Biobank of 0.5 million people: survey methods, baseline characteristics and long-term follow-up. , 2011, International journal of epidemiology.

[226]  J. Marchini,et al.  Genotype Imputation with Thousands of Genomes , 2011, G3: Genes | Genomes | Genetics.

[227]  R. Durbin,et al.  Identity-by-Descent-Based Phasing and Imputation in Founder Populations Using Graphical Models , 2011, Genetic epidemiology.

[228]  S. Raychaudhuri.  Mapping Rare and Common Causal Alleles for Complex Human Diseases , 2011, Cell.

[229]  B. Browning,et al.  Population structure can inflate SNP-based heritability estimates. , 2011, American journal of human genetics.

[230]  Xu Shi.  Rare-variant association testing for sequencing data with the sequence kernel association test. , 2011, American journal of human genetics.

[231]  Xihong Lin,et al.  Rare-variant association testing for sequencing data with the sequence kernel association test. , 2011, American journal of human genetics.

[232]  Alexander T. Dilthey,et al.  HLA*IMP - an integrated framework for imputing classical HLA alleles from SNP genotypes , 2011, Bioinform..

[233]  P. Visscher,et al.  Estimating missing heritability for disease from genome-wide association studies. , 2011, American journal of human genetics.

[234]  Kathryn Roeder,et al.  Testing for an Unusual Distribution of Rare Variants , 2011, PLoS genetics.

[235]  P. Visscher,et al.  GCTA: a tool for genome-wide complex trait analysis. , 2011, American journal of human genetics.

[236]  R. C. Rose,et al.  The Major Genetic Determinants of HIV-1 Control Affect HLA Class I Peptide Presentation , 2010, Science.

[237]  D. Altshuler,et al.  A map of human genome variation from population-scale sequencing , 2010, Nature.

[238]  Olle Melander,et al.  From noncoding variant to phenotype via SORT1 at the 1p13 cholesterol locus , 2010, Nature.

[239]  Michael Boehnke,et al.  LocusZoom: regional visualization of genome-wide association scan results , 2010, Bioinform..

[240]  Yun Li,et al.  METAL: fast and efficient meta-analysis of genomewide association scans , 2010, Bioinform..

[241]  H. Hakonarson,et al.  ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data , 2010, Nucleic acids research.

[242]  Reedik Mägi,et al.  GWAMA: software for genome-wide association meta-analysis , 2010, BMC Bioinformatics.

[243]  Judy H. Cho,et al.  Finding the missing heritability of complex diseases , 2009, Nature.

[244]  P. Visscher,et al.  Common polygenic variation contributes to risk of schizophrenia and bipolar disorder , 2009, Nature.

[245]  P. Donnelly,et al.  A Flexible and Accurate Genotype Imputation Method for the Next Generation of Genome-Wide Association Studies , 2009, PLoS genetics.

[246]  David C. Wilson,et al.  Diverse genome-wide association studies associate the IL12/IL23 pathway with Crohn Disease. , 2009, American journal of human genetics.

[247]  P. Visscher,et al.  Family-based genome-wide association studies. , 2009, Pharmacogenomics.

[248]  Amit R. Indap,et al.  Genes mirror geography within Europe , 2008, Nature.

[249]  Pall I. Olason,et al.  Detection of sharing by descent, long-range phasing and haplotype imputation , 2008, Nature Genetics.

[250]  Montgomery Slatkin,et al.  Linkage disequilibrium — understanding the evolutionary past and mapping the medical future , 2008, Nature Reviews Genetics.

[251]  Manuel A. R. Ferreira,et al.  PLINK: a tool set for whole-genome association and population-based linkage analyses. , 2007, American journal of human genetics.

[252]  P. Donnelly,et al.  A new multipoint method for genome-wide association studies by imputation of genotypes , 2007, Nature Genetics.

[253]  Kenneth S Kendler,et al.  Age-Related Changes in Heritability of Behavioral Phenotypes Over Adolescence and Young Adulthood: A Meta-Analysis , 2007, Twin Research and Human Genetics.

[254]  V. Cameron,et al.  Risk-taking: behind the warrior gene story. , 2007, The New Zealand medical journal.

[255]  D. Reich,et al.  Principal components analysis corrects for stratification in genome-wide association studies , 2006, Nature Genetics.

[256]  Chiara Sabatti,et al.  Magnitude and distribution of linkage disequilibrium in population isolates and implications for genome-wide association studies , 2006, Nature Genetics.

[257]  G. Abecasis,et al.  Joint analysis is more efficient than replication-based analysis for two-stage genome-wide association studies , 2006, Nature Genetics.

[258]  N. Craddock,et al.  Design of Case‐controls Studies with Unscreened Controls , 2005, Annals of human genetics.

[259]  Pak Sham,et al.  Parental phenotypes in family-based association analysis. , 2005, American journal of human genetics.

[260]  K. Siminovitch.  PTPN22 and autoimmune disease , 2004, Nature Genetics.

[261]  P. Donnelly,et al.  The effects of human population structure on large genetic association studies , 2004, Nature Genetics.

[262]  M. Olivier.  A haplotype map of the human genome. , 2003, Nature.

[263]  S. Ebrahim,et al.  'Mendelian randomization': can genetic epidemiology contribute to understanding environmental determinants of disease? , 2003, International journal of epidemiology.

[264]  N Risch,et al.  The relative power of family-based and case-control designs for linkage disequilibrium studies of complex human diseases. II. Individual genotyping. , 1999, Genome research.

[265]  N. Kerr.  HARKing: Hypothesizing After the Results are Known , 1998, Personality and social psychology review : an official journal of the Society for Personality and Social Psychology, Inc.

[266]  J. Witte,et al.  Genetic dissection of complex traits , 1996, Nature Genetics.

[267]  Eric S. Lander,et al.  Genetic dissection of complex traits. , 1994, Science.

[268]  William J. Astle,et al.  Allelic Landscape of Human Blood Cell Trait Variation and Links , 2016 .

[269]  Exome sequencing identifies rare LDLR and APOA 5 alleles conferring risk for myocardial infarction , 2016 .

[270]  Andrew D. Johnson,et al.  Multiple rare alleles at LDLR and APOA5 confer risk for early-onset myocardial infarction , 2015 .

[271]  Jun S. Liu,et al.  Genetics of rheumatoid arthritis contributes to biology and drug discovery , 2013 .

[272]  T. Harris.  Anticipate and Communicate: Ethical Management of Incidental and Secondary Findings in the Clinical, Research, and Direct-to-Consumer Contexts , 2013 .

[273]  V. Singal.  The Ethics of Direct-to-consumer Testing , 2012 .

[274]  D. Kleinjan,et al.  Long-range control of gene expression: emerging mechanisms and disruption in disease. , 2005, American journal of human genetics.

[275]  The International HapMap Consortium.  A haplotype map of the human genome , 2005 .

[276]  Pak Chung Sham,et al.  Genetic Power Calculator: design of linkage and association genetic mapping studies of complex traits , 2003, Bioinform..

[277]  L..  HARKing: Hypothesizing After the Results are Known , 2002 .

[278]  G. Abecasis,et al.  A general test of association for quantitative traits in nuclear families. , 2000, American journal of human genetics.

[279]  J K Hewitt,et al.  Combined linkage and association sib-pair analysis for quantitative traits. , 1999, American journal of human genetics.

[280]  R. Fisher.  XV.—The Correlation between Relatives on the Supposition of Mendelian Inheritance. , 1919, Transactions of the Royal Society of Edinburgh.

[281]  A. Hofman,et al.  American Journal of Epidemiology Practice of Epidemiology Improvement of Risk Prediction by Genomic Profiling: Reclassification Measures versus the Area under the Receiver Operating Characteristic Curve , 2022 .

[282]  G. Abecasis,et al.  Supporting Online Material Materials and Methods Figs. S1 to S8 Tables S1 to S10 References a Genome-wide Association Study of Type 2 Diabetes in Finns Detects Multiple Susceptibility Variants , 2022 .

[283]  P. McKeigue,et al.  Problems of reporting genetic associations with complex outcomes , 2003, The Lancet.

[284]  M. Jarvelin,et al.  References and Notes Supporting Online Material Materials and Methods Som Text Figs. S1 to S7 References a Common Variant in the Fto Gene Is Associated with Body Mass Index and Predisposes to Childhood and Adult Obesity , 2022 .

[285]  L. Penrose,et al.  THE CORRELATION BETWEEN RELATIVES ON THE SUPPOSITION OF MENDELIAN INHERITANCE , 2022 .

[286]  M. Jarvelin,et al.  Edinburgh Research Explorer A common variant in the FTO gene is associated with body mass index and predisposes to childhood and adult obesity , 2022 .