The phenotype-genotype reference map: Improving biobank data science through replication.

[1]  Jacqueline,et al.  Systematic single-variant and gene-based association testing of thousands of phenotypes in 394,841 UK Biobank exomes , 2022, Cell Genomics.

[2]  N. Chatterjee,et al.  Pathogen exposure misclassification can bias association signals in GWAS of infectious diseases when using population-based common controls , 2022, medRxiv.

[3]  P. Msaouel.  The Big Data Paradox in Clinical Practice , 2022, Cancer investigation.

[4]  V. Escott-Price,et al.  Genome-wide association studies for Alzheimer’s disease: bigger is not always better , 2022, Brain communications.

[5]  Alicia R. Martin,et al.  Diversity in Genomic Studies: A Roadmap to Address the Imbalance , 2022, Nature Medicine.

[6]  D. Roden,et al.  Phenome-Wide Association Studies. , 2022, JAMA.

[7]  L. Winchester,et al.  Validation of UK Biobank data for mental health outcomes: A pilot study using secondary care electronic health records , 2022, Int. J. Medical Informatics.

[8]  G. Abecasis,et al.  The Michigan Genomics Initiative: A biobank linking genotypes and electronic clinical records in Michigan Medicine patients , 2021, medRxiv.

[9]  Wei Zhou,et al.  Global Biobank Meta-analysis Initiative: powering genetic discovery across human diseases , 2021, medRxiv.

[10]  M. Rivas,et al.  A cross-population atlas of genetic associations for 220 human phenotypes , 2021, Nature Genetics.

[11]  Alicia R. Martin,et al.  Genome-wide association studies , 2021, Nature Reviews Methods Primers.

[12]  L. Bastarache.  Using Phecodes for Research with the Electronic Health Record: From PheWAS to PheRS. , 2021, Annual review of biomedical data science.

[13]  M. Munafo,et al.  Has GWAS lost its status as a paragon of open science? , 2021, PLoS biology.

[14]  Kevin B. Johnson,et al.  Phenotyping coronavirus disease 2019 during a global health pandemic: Lessons learned from the characterization of an early cohort , 2021, Journal of Biomedical Informatics.

[15]  D. Curtis.  Analysis of 50,000 exome-sequenced UK Biobank subjects fails to identify genes influencing probability of developing a mood disorder resulting in psychiatric referral. , 2020, Journal of affective disorders.

[16]  J. Ioannidis,et al.  Reproducibility in the UK biobank of genome-wide significant signals discovered in earlier genome-wide association studies , 2020, medRxiv.

[17]  Andrew P. Boughton,et al.  Exploring and visualizing large-scale genetic associations by using PheWeb , 2020, Nature Genetics.

[18]  J. A. Goldstein,et al.  LabWAS: Novel findings and study design recommendations from a meta-analysis of clinical labs in two independent biobanks , 2020, medRxiv.

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

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

[21]  Joshua C. Denny,et al.  WikiMedMap: Expanding the Phenotyping Mapping Toolbox Using Wikipedia , 2019, bioRxiv.

[22]  T. Manolio.  Using the Data We Have: Improving Diversity in Genomic Research. , 2019, American journal of human genetics.

[23]  Arturo Gonzalez-Izquierdo,et al.  UK phenomics platform for developing and validating electronic health record phenotypes: CALIBER , 2019, J. Am. Medical Informatics Assoc..

[24]  P. Gruber.  Genetic association studies: Is non-replication failure or progress? , 2019, The Journal of thoracic and cardiovascular surgery.

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

[26]  J. Denny,et al.  Cox regression increases power to detect genotype-phenotype associations in genomic studies using the electronic health record , 2019, BMC Genomics.

[27]  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..

[28]  J. Huffman.  Examining the current standards for genetic discovery and replication in the era of mega-biobanks , 2018, Nature Communications.

[29]  Arcadi Navarro,et al.  Replicability and Prediction: Lessons and Challenges from GWAS. , 2018, Trends in genetics : TIG.

[30]  Xiao-Li Meng,et al.  Statistical paradises and paradoxes in big data (I): Law of large populations, big data paradox, and the 2016 US presidential election , 2018, The Annals of Applied Statistics.

[31]  David M. Evans,et al.  Collider scope: when selection bias can substantially influence observed associations , 2016, bioRxiv.

[32]  Laura W. Harris,et al.  A standardized framework for representation of ancestry data in genomics studies, with application to the NHGRI-EBI GWAS Catalog , 2018, Genome Biology.

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

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

[35]  Mary Brophy,et al.  Million Veteran Program: A mega-biobank to study genetic influences on health and disease. , 2016, Journal of clinical epidemiology.

[36]  R. Mägi,et al.  Cohort Profile Cohort Profile : Estonian Biobank of the Estonian Genome Center , University of Tartu , 2015 .

[37]  P. Elliott,et al.  UK Biobank: An Open Access Resource for Identifying the Causes of a Wide Range of Complex Diseases of Middle and Old Age , 2015, PLoS medicine.

[38]  Melissa A. Basford,et al.  Systematic comparison of phenome-wide association study of electronic medical record data and genome-wide association study data , 2013, Nature Biotechnology.

[39]  Arcadi Navarro,et al.  High Trans-ethnic Replicability of GWAS Results Implies Common Causal Variants , 2013, PLoS genetics.

[40]  Dana C Crawford,et al.  Pitfalls of merging GWAS data: lessons learned in the eMERGE network and quality control procedures to maintain high data quality , 2011, Genetic epidemiology.

[41]  Michael Boehnke,et al.  Quantifying and correcting for the winner's curse in quantitative‐trait association studies , 2011, Genetic epidemiology.

[42]  B. Henderson,et al.  Generalizability of Associations from Prostate Cancer Genome-Wide Association Studies in Multiple Populations , 2009, Cancer Epidemiology Biomarkers & Prevention.

[43]  D. Roden,et al.  Development of a Large‐Scale De‐Identified DNA Biobank to Enable Personalized Medicine , 2008, Clinical pharmacology and therapeutics.

[44]  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.

[45]  Simon C. Potter,et al.  Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls , 2007, Nature.

[46]  P. Donnelly,et al.  Replicating genotype–phenotype associations , 2007, Nature.

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

[48]  A. Brazma,et al.  Databases and ontologies Advance Access publication March 3, 2010 Modeling sample variables with an Experimental Factor Ontology , 2009 .