Statistical power and significance testing in large-scale genetic studies

Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482

PLINK: a tool set for whole-genome association and population-based linkage analyses.

Purcell, S., Neale, B., Todd-Brown, K., Thomas, L., Ferreira, M. A. R., Bender, D., Maller, J., Sklar, P., de Bakker, P. I. W., Daly, M. J., et al. (2007) · The American Journal of Human Genetics · doi:10.1086/519795 · Cited by 34,089

A method and server for predicting damaging missense mutations.

Adzhubei, I. A., Schmidt, S., Peshkin, L., Ramensky, V. E., Gerasimova, A., Bork, P., Kondrashov, A. S., & Sunyaev, S. R. (2010) · Nature Methods · doi:10.1038/nmeth0410-248 · Cited by 13,194

Statistical Methods for Research Workers

Fisher, R. A. (1945) · American Journal of Clinical Pathology · doi:10.1093/ajcp/15.11.552b · Cited by 10,883

Principal components analysis corrects for stratification in genome-wide association studies

Price, A. L., Patterson, N. J., Plenge, R. M., Weinblatt, M. E., Shadick, N. A., & Reich, D. (2006) · Nature Genetics · doi:10.1038/ng1847 · Cited by 10,248

Bootstrap Methods and their Application

Davison, A. C., & Hinkley, D. V. (1997) · doi:10.1017/cbo9780511802843 · Cited by 7,350

A haplotype map of the human genome

The International HapMap Consortium (2005) · Nature · doi:10.1038/nature04226 · Cited by 5,858

Bootstrap Methods and Their Application

Loughin, T. M., Davison, A. C., & HINKLEY, D. V. (1999) · Journal of the American Statistical Association · doi:10.2307/2669707 · Cited by 5,478

Genome-wide association studies for common diseases and complex traits

Hirschhorn, J. N., & Daly, M. J. (2005) · Nature Reviews Genetics · doi:10.1038/nrg1521 · Cited by 2,923

Rectangular Confidence Regions for the Means of Multivariate Normal Distributions

Šidák, Z. (1967) · Journal of the American Statistical Association · doi:10.1080/01621459.1967.10482935 · Cited by 2,904

A global reference for human genetic variation

The 1000 Genomes Project Consortium, authors, C., Auton, A., Abecasis, G. R., Steering committee, Altshuler, D. M., Durbin, R. M., Abecasis, G. R., Bentley, D. R., Chakravarti, A., et al. (2015) · Nature · doi:10.1038/nature15393 · Cited by 18,247

Common schizophrenia alleles are enriched in mutation-intolerant genes and in regions under strong background selection

GERAD1 Consortium, Pardiñas, A. F., CRESTAR Consortium, Holmans, P., Pocklington, A. J., Escott-Price, V., Ripke, S., Carrera, N., Legge, S. E., Bishop, S., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0059-2 · Cited by 1,552

All for One and One for All: Mental Disorders in One Dimension

Caspi, A., & Moffitt, T. E. (2018) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2018.17121383 · Cited by 918

Finding the genomic basis of local adaptation: pitfalls, practical solutions, and future directions

Hoban, S., Kelley, J. L., Lotterhos, K. E., Antolin, M. F., Bradburd, G., Lowry, D. B., Poss, M. L., Reed, L. K., Storfer, A., & Whitlock, M. C. (2016) · The American Naturalist · doi:10.1086/688018 · Cited by 876

Advancing the understanding of autism disease mechanisms through genetics

de la Torre-Ubieta, L., Won, H., Stein, J. L., & Geschwind, D. H. (2016) · Nature Medicine · doi:10.1038/nm.4071 · Cited by 827

Evaluating the Clinical Validity of Gene-Disease Associations: An Evidence-Based Framework Developed by the Clinical Genome Resource

Strande, N. T., Riggs, E. R., Buchanan, A. H., Ceyhan‐Birsoy, O., DiStefano, M. T., Dwight, S. S., Goldstein, J., Ghosh, R., Seifert, B. A., Sneddon, T. P., et al. (2017) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2017.04.015 · Cited by 602

Large-scale plasma proteomics comparisons through genetics and disease associations

Eldjarn, G. H., Ferkingstad, E., Lund, S. H., Helgason, H., Magnusson, O. T., Gunnarsdottir, K., Olafsdottir, T. A., Halldorsson, B. V., Olason, P. I., Zink, F., et al. (2023) · Nature · doi:10.1038/s41586-023-06563-x · Cited by 583

Statistical significance: p value, 0.05 threshold, and applications to radiomics—reasons for a conservative approach

Leo, G. D., & Sardanelli, F. (2020) · European Radiology Experimental · doi:10.1186/s41747-020-0145-y · Cited by 555

Characterizing sleep spindles in 11,630 individuals from the National Sleep Research Resource

Purcell, S. M., Manoach, D. S., Demanuele, C., Cade, B. E., Mariani, S., Cox, R., Panagiotaropoulou, G., Saxena, R., Pan, J. Q., Smoller, J. W., et al. (2017) · Nature Communications · doi:10.1038/ncomms15930 · Cited by 439

Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders

Parikshak, N. N., Gandal, M. J., & Geschwind, D. H. (2015) · Nature Reviews Genetics · doi:10.1038/nrg3934 · Cited by 430

Genome-wide association studies of cancer: current insights and future perspectives

Sud, A., Kinnersley, B., & Houlston, R. S. (2017) · Nature reviews. Cancer · doi:10.1038/nrc.2017.82 · Cited by 391

A population genetic interpretation of GWAS findings for human quantitative traits

Simons, Y. B., Bullaughey, K., Hudson, R. R., & Sella, G. (2018) · PLoS Biology · doi:10.1371/journal.pbio.2002985 · Cited by 367

The (in)famous GWAS P-value threshold revisited and updated for low-frequency variants

Fadista, J., Manning, A. K., Florez, J. C., & Groop, L. (2016) · European Journal of Human Genetics · doi:10.1038/ejhg.2015.269 · Cited by 376

CHCHD2 mutations in autosomal dominant late-onset Parkinson's disease: a genome-wide linkage and sequencing study

Funayama, M., Ohe, K., Amo, T., Furuya, N., Yamaguchi, J., Saiki, S., Li, Y., Ogaki, K., Ando, M., Yoshino, H., et al. (2015) · The Lancet Neurology · doi:10.1016/s1474-4422(14)70266-2 · Cited by 360

Large-scale analyses of common and rare variants identify 12 new loci associated with atrial fibrillation

Christophersen, I. E., Rienstra, M., Roselli, C., Yin, X., Geelhoed, B., Barnard, J., Lin, H., Arking, D. E., Smith, A. V., Albert, C. M., et al. (2017) · Nature Genetics · doi:10.1038/ng.3843 · Cited by 342

Parkinson's disease age at onset genome‐wide association study: Defining heritability, genetic loci, and α‐synuclein mechanisms

Blauwendraat, C., Heilbron, K., Vallerga, C. L., Bandrés‐Ciga, S., von Coelln, R., Pihlstrøm, L., Simón‐Sánchez, J., Schulte, C., Sharma, M., Krohn, L., et al. (2019) · Movement Disorders · doi:10.1002/mds.27659 · Cited by 338

Identification of genetic variants associated with Huntington's disease progression: a genome-wide association study

Moss, D. J. H., Pardiñas, A. F., Langbehn, D., Lo, K., Leavitt, B. R., Roos, R., Durr, A., Mead, S., Coleman, A., Santos, R. D., et al. (2017) · The Lancet Neurology · doi:10.1016/s1474-4422(17)30161-8 · Cited by 312

Thinking About the Evolution of Complex Traits in the Era of Genome-Wide Association Studies

Sella, G., & Barton, N. (2019) · Annual Review of Genomics and Human Genetics · doi:10.1146/annurev-genom-083115-022316 · Cited by 290

Rare variant association studies: considerations, challenges and opportunities

Auer, P. L., & Lettre, G. (2015) · Genome Medicine · doi:10.1186/s13073-015-0138-2 · Cited by 261

On the reproductive success of early‐generation hatchery fish in the wild

Christie, M. R., Ford, M. J., & Blouin, M. S. (2014) · Evolutionary Applications · doi:10.1111/eva.12183 · Cited by 238

The road to precision psychiatry: translating genetics into disease mechanisms

Gandal, M. J., Leppa, V., Won, H., Parikshak, N. N., & Geschwind, D. H. (2016) · Nature Neuroscience · doi:10.1038/nn.4409 · Cited by 223

Rare-variant collapsing analyses for complex traits: guidelines and applications

Povysil, G., Petrovski, S., Hostyk, J. A., Aggarwal, V. S., Allen, A. S., & Goldstein, D. B. (2019) · Nature Reviews Genetics · doi:10.1038/s41576-019-0177-4 · Cited by 211

GWAS for systemic sclerosis identifies multiple risk loci and highlights fibrotic and vasculopathy pathways

López‐Isac, E., Acosta‐Herrera, M., Kerick, M., Assassi, S., Satpathy, A. T., Granja, J. M., Mumbach, M. R., Beretta, L., Simeón‐Aznar, C. P., Carreira, P. E., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-12760-y · Cited by 188

A Large-Scale Genetic Analysis Reveals a Strong Contribution of the HLA Class II Region to Giant Cell Arteritis Susceptibility

Carmona, F. D., Mackie, S. L., Martín, J., Taylor, J., Vaglio, A., Eyre, S., Bossini‐Castillo, L., Castañeda, S., Cid, M. C., Hernández‐Rodríguez, J., et al. (2015) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2015.02.009 · Cited by 180

Weighting sequence variants based on their annotation increases power of whole-genome association studies

Sveinbjörnsson, G., Albrechtsen, A., Zink, F., Gudjonsson, S. A., Oddsson, Á., Másson, G., Hólm, H., Kong, A., Þorsteinsdóttir, U., Sulem, P., et al. (2016) · Nature Genetics · doi:10.1038/ng.3507 · Cited by 175

Genome-wide association study of lifetime cannabis use based on a large meta-analytic sample of 32 330 subjects from the International Cannabis Consortium

Stringer, S., Minică, C. C., Verweij, K. J. H., Mbarek, H., Bernard, M., Derringer, J. L., van Eijk, K. R., Isen, J. D., Loukola, A., Maciejewski, D. F., et al. (2016) · Translational Psychiatry · doi:10.1038/tp.2016.36 · Cited by 160

Precision radiology: predicting longevity using feature engineering and deep learning methods in a radiomics framework

Oakden-Rayner, L., Carneiro, G., Bessen, T., Nascimento, J. C., Bradley, A. P., & Palmer, L. J. (2017) · Scientific Reports · doi:10.1038/s41598-017-01931-w · Cited by 154

Inherited genetic variation in childhood acute lymphoblastic leukemia

Moriyama, T., Relling, M. V., & Yang, J. J. J. (2015) · Blood · doi:10.1182/blood-2014-12-580001 · Cited by 149

Facial Genetics: A Brief Overview

Richmond, S., Howe, L. J., Lewis, S. J., Stergiakouli, E., & Zhurov, A. I. (2018) · Frontiers in Genetics · doi:10.3389/fgene.2018.00462 · Cited by 146

Analysis of genome-wide association data highlights candidates for drug repositioning in psychiatry

So, H., Chau, C. K., Chiu, W.-T., Ho, K.-S., Lo, C.-P., Yim, S. H.-Y., & Sham, P. C. (2017) · Nature Neuroscience · doi:10.1038/nn.4618 · Cited by 142

Association between a common immunoglobulin heavy chain allele and rheumatic heart disease risk in Oceania

Parks, T., Mirabel, M., Kado, J. H., Auckland, K., Nowak, J., Rautanen, A., Mentzer, A. J., Marijon, É., Jouven, X., Perman, M. L., et al. (2017) · Nature Communications · doi:10.1038/ncomms14946 · Cited by 142

ELMER v.2: an R/Bioconductor package to reconstruct gene regulatory networks from DNA methylation and transcriptome profiles

Silva, T. C., Coetzee, S. G., Gull, N., Yao, L., Hazelett, D. J., Noushmehr, H., Lin, D., & Berman, B. P. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty902 · Cited by 131

Gene × Environment Determinants of Stress- and Anxiety-Related Disorders

Sharma, S. K., Powers, A. D., Bradley, B., & Ressler, K. J. (2015) · Annual Review of Psychology · doi:10.1146/annurev-psych-122414-033408 · Cited by 134

Characterizing the genetic basis of bacterial phenotypes using genome-wide association studies: a new direction for bacteriology

Read, T. D., & Massey, R. C. (2014) · Genome Medicine · doi:10.1186/s13073-014-0109-z · Cited by 137

Sex differences in disease genetics: evidence, evolution, and detection

Gilks, W. P., Abbott, J. K., & Morrow, E. H. (2014) · Trends in Genetics · doi:10.1016/j.tig.2014.08.006 · Cited by 128

Coselected genes determine adaptive variation in herbivore resistance throughout the native range of Arabidopsis thaliana

Brachi, B., Meyer, C. G., Villoutreix, R., Platt, A., Morton, T. C., Roux, F., & Bergelson, J. M. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1421416112 · Cited by 125

Multitrait genome‐wide association analysis of Populus trichocarpa identifies key polymorphisms controlling morphological and physiological traits

Chhetri, H. B., Macaya‐Sanz, D., Kainer, D., Biswal, A. K., Evans, L. M., Chen, J., Collins, C., Hunt, K., Mohanty, S. S., Rosenstiel, T. N., et al. (2019) · New Phytologist · doi:10.1111/nph.15777 · Cited by 119

Variants in saposin D domain of prosaposin gene linked to Parkinson’s disease

Oji, Y., Hatano, T., Ueno, S.-I., Funayama, M., Ishikawa, K.-I., Okuzumi, A., Noda, S., Sato, S., Satake, W., Toda, T., et al. (2020) · Brain · doi:10.1093/brain/awaa064 · Cited by 116

Genetics of neurodegenerative diseases: an overview

Pihlstrøm, L., Wiethoff, S., & Houlden, H. (2017) · Handbook of clinical neurology · doi:10.1016/b978-0-12-802395-2.00022-5 · Cited by 120

Genetic diversity, linkage disequilibrium and power of a large grapevine (Vitis vinifera L) diversity panel newly designed for association studies

Nicolas, S., Péros, J., Lacombe, T., Launay, A., Paslier, M. L., Berard, A., Mangin, B., Valière, S., Martins, F., Cunff, L. L., et al. (2016) · BMC Plant Biology · doi:10.1186/s12870-016-0754-z · Cited by 122

Statistical correction of the Winner’s Curse explains replication variability in quantitative trait genome-wide association studies

Palmer, C. D., & Pe’er, I. (2017) · PLoS Genetics · doi:10.1371/journal.pgen.1006916 · Cited by 121

Strategies to design clinical studies to identify predictive biomarkers in cancer research

Perez‐Gracia, J. L., Sanmamed, M. F., Bosch, A., Patiño‐García, A., Schalper, K. A., Segura, V., Joaquim Bellmunt, B., Tabernero, J. M., Sweeney, C. J., Choueiri, T. K., et al. (2016) · Cancer Treatment Reviews · doi:10.1016/j.ctrv.2016.12.005 · Cited by 112

Human candidate gene polymorphisms and risk of severe malaria in children in Kilifi, Kenya: a case-control association study

Ndila, C. M., Uyoga, S., Macharia, A. W., Nyutu, G., Peshu, N., Ojal, J., Shebe, M., Awuondo, K. O., Mturi, N., Tsofa, B., et al. (2018) · The Lancet Haematology · doi:10.1016/s2352-3026(18)30107-8 · Cited by 108

Childhood gene-environment interactions and age-dependent effects of genetic variants associated with refractive error and myopia: The CREAM Consortium

Fan, Q., Guo, X., Tideman, J. W. L., Williams, K. M., Yazar, S., Hosseini, S. M., Howe, L. D., Pourcain, B. S., Evans, D. M., Timpson, N. J., et al. (2016) · Scientific Reports · doi:10.1038/srep25853 · Cited by 113

What can genome‐wide association studies tell us about the evolutionary forces maintaining genetic variation for quantitative traits?

Josephs, E. B., Stinchcombe, J. R., & Wright, S. (2017) · New Phytologist · doi:10.1111/nph.14410 · Cited by 117

Post-GWAS in prostate cancer: from genetic association to biological contribution

Farashi, S., Kryza, T., Clements, J. A., & Batra, J. (2018) · Nature reviews. Cancer · doi:10.1038/s41568-018-0087-3 · Cited by 106

Large-scale genome-wide enrichment analyses identify new trait-associated genes and pathways across 31 human phenotypes

Zhu, X., & Stephens, M. (2018) · Nature Communications · doi:10.1038/s41467-018-06805-x · Cited by 103

Reproducibility and replicability in zebrafish behavioral neuroscience research

Gerlai, R. (2019) · Pharmacology, Biochemistry and Behavior · doi:10.1016/j.pbb.2018.02.005 · Cited by 99

A multi-trait meta-analysis with imputed sequence variants reveals twelve QTL for mammary gland morphology in Fleckvieh cattle

Pausch, H., Emmerling, R., Schwarzenbacher, H., & Fries, R. (2016) · Genetics Selection Evolution · doi:10.1186/s12711-016-0190-4 · Cited by 101

Multimodal Brain Imaging Reveals Structural Differences in Alzheimer’s Disease Polygenic Risk Carriers: A Study in Healthy Young Adults

Foley, S. F., Tansey, K. E., Caseras, X., Lancaster, T. M., Bracht, T., Parker, G. D., Hall, J., Williams, J. H., & Linden, D. E. J. (2016) · Biological Psychiatry · doi:10.1016/j.biopsych.2016.02.033 · Cited by 98

Genetic variants associated with antithyroid drug-induced agranulocytosis: a genome-wide association study in a European population

Hallberg, P., Eriksson, N., Ibáñez, L., Bondon‐Guitton, E., Kreutz, R. H., Carvajal, A., Lucena, M. I., Ponce, E. S., Molokhia, M., Martin, J. J., et al. (2016) · The Lancet Diabetes & Endocrinology · doi:10.1016/s2213-8587(16)00113-3 · Cited by 94

Discovering Health Benefits of Phytochemicals with Integrated Analysis of the Molecular Network, Chemical Properties and Ethnopharmacological Evidence

Yoo, S., Kim, K., Nam, H., & Lee, D. (2018) · Nutrients · doi:10.3390/nu10081042 · Cited by 96

Meta-analysis of sequence-based association studies across three cattle breeds reveals 25 QTL for fat and protein percentages in milk at nucleotide resolution

Pausch, H., Emmerling, R., Gredler, B., Fries, R., Daetwyler, H. D., & Goddard, M. E. (2017) · BMC Genomics · doi:10.1186/s12864-017-4263-8 · Cited by 93

Common single nucleotide variants underlying drug addiction: more than a decade of research

Bühler, K., Giné, E., Echeverry‐Alzate, V., Calleja‐Conde, J., de Fonseca, F. R., & López‐Moreno, J. A. (2015) · Addiction Biology · doi:10.1111/adb.12204 · Cited by 96

Combined Treatment With Environmental Enrichment and (-)-Epigallocatechin-3-Gallate Ameliorates Learning Deficits and Hippocampal Alterations in a Mouse Model of Down Syndrome

Catuara‐Solarz, S., Espinosa‐Carrasco, J., Erb, I., Langohr, K., González, J. R., Notredame, C., & Dierssen, M. (2016) · eNeuro · doi:10.1523/eneuro.0103-16.2016 · Cited by 90

Combined genetic analysis of juvenile idiopathic arthritis clinical subtypes identifies novel risk loci, target genes and key regulatory mechanisms

López‐Isac, E., Smith, S. L., Marion, M. C., Wood, A., Sudman, M., Yarwood, A., Shi, C., Gaddi, V. P., Martin, P., Prahalad, S., et al. (2020) · Annals of the Rheumatic Diseases · doi:10.1136/annrheumdis-2020-218481 · Cited by 89

Single nucleotide polymorphisms in clinics: Fantasy or reality for cancer?

Srinivasan, S., Clements, J. A., & Batra, J. (2015) · Critical Reviews in Clinical Laboratory Sciences · doi:10.3109/10408363.2015.1075469 · Cited by 87

Power and Sample Size Calculations for Genetic Association Studies in the Presence of Genetic Model Misspecification

Moore, C. M., Jacobson, S. A., & Fingerlin, T. E. (2019) · Human Heredity · doi:10.1159/000508558 · Cited by 84

HLA-DRB1*15:01 and HLA-DRB3*02:02 in PLA2R-Related Membranous Nephropathy

Le, W., Shi, J., Zhang, T., Liu, L., Qin, H.-Z., Liang, S., Zhang, Y., Zheng, C.-X., Jiang, S., Qin, W., et al. (2016) · Journal of the American Society of Nephrology · doi:10.1681/asn.2016060644 · Cited by 80

A power fallacy

Wagenmakers, E., Verhagen, J., Ly, A., Bakker, M., Lee, M. D., Matzke, D., Rouder, J. N., & Morey, R. D. (2014) · Behavior Research Methods · doi:10.3758/s13428-014-0517-4 · Cited by 83

HLA‐associated antiepileptic drug‐induced cutaneous adverse reactions

Mullan, K. A., Anderson, A., Illing, P. T., Kwan, P., Purcell, A. W., & Mifsud, N. A. (2019) · HLA · doi:10.1111/tan.13530 · Cited by 79

Genetic architecture of nonadditive inheritance in Arabidopsis thaliana hybrids

Seymour, D. K., Chae, E., Grimm, D. G., Martín‐Pizarro, C., Habring, A., Vasseur, F., Rakitsch, B., Borgwardt, K. M., Koenig, D., & Weigel, D. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1615268113 · Cited by 78

Analysis of Gene‐Gene Interactions

Cole, B. S., Hall, M. A., Urbanowicz, R. J., Gilbert‐Diamond, D., & Moore, J. H. (2017) · Current Protocols in Human Genetics · doi:10.1002/cphg.45 · Cited by 78

Neurology Individualized Medicine: When to Use Next-Generation Sequencing Panels

Klein, C. J., & Foroud, T. (2017) · Mayo Clinic Proceedings · doi:10.1016/j.mayocp.2016.09.008 · Cited by 80

Pharmacogenomics of acute lymphoblastic leukemia

Kager, L., & Evans, W. E. (2006) · Current Opinion in Hematology · doi:10.1097/01.moh.0000231424.46148.f9 · Cited by 75

Genome-wide Approaches to Investigate Anthelmintic Resistance

Doyle, S. R., & Cotton, J. A. (2019) · Trends in Parasitology · doi:10.1016/j.pt.2019.01.004 · Cited by 74

Exploring human brain lateralization with molecular genetics and genomics

Francks, C. (2015) · Annals of the New York Academy of Sciences · doi:10.1111/nyas.12770 · Cited by 76

Meta-analysis of genome-wide association studies for loin muscle area and loin muscle depth in two Duroc pig populations

Zhuang, Z., Li, S., Ding, R., Yang, M., Zheng, E., Yang, H., Gu, T., Xu, Z., Cai, G., Wu, Z., et al. (2019) · PLoS ONE · doi:10.1371/journal.pone.0218263 · Cited by 71

The virtuous cycle of human genetics and mouse models in drug discovery

Nadeau, J. H., & Auwerx, J. H. (2019) · Nature Reviews Drug Discovery · doi:10.1038/s41573-018-0009-9 · Cited by 78

Complement system biomarkers in first episode psychosis

Kopczynska, M., Zelek, W. M., Touchard, S., Gaughran, F., Forti, M. D., Mondelli, V., Murray, R. M., O’Donovan, M. C., & Morgan, B. P. (2017) · Schizophrenia Research · doi:10.1016/j.schres.2017.12.012 · Cited by 70

Pathway analysis of complex diseases for GWAS, extending to consider rare variants, multi-omics and interactions

Kao, P. Y. P., Leung, K. H., Chan, L., Yip, S. P., & Yap, M. K. H. (2016) · Biochimica et Biophysica Acta (BBA) - General Subjects · doi:10.1016/j.bbagen.2016.11.030 · Cited by 73

Sequencing and imputation in GWAS: Cost‐effective strategies to increase power and genomic coverage across diverse populations

Quick, C., Anugu, P., Musani, S. K., Weiss, S. T., Burchard, E. G., White, M. J., Keys, K. L., Cucca, F., Sidore, C., Boehnke, M., et al. (2020) · Genetic Epidemiology · doi:10.1002/gepi.22326 · Cited by 77

Genome-wide association analysis identifies genetic variations in subjects with myalgic encephalomyelitis/chronic fatigue syndrome

Schlauch, K. A., Khaiboullina, S. F., Meirleir, K. L. D., Rawat, S., Petereit, J., Rizvanov, A. A., Blatt, N. L., Mijatovic, T., Kulick, D., Palotás, A., et al. (2016) · Translational Psychiatry · doi:10.1038/tp.2015.208 · Cited by 71

Formalising recall by genotype as an efficient approach to detailed phenotyping and causal inference

Corbin, L. J., Tan, V. Y., Hughes, D. A., Wade, K. H., Paul, D. S., Tansey, K. E., Butcher, F. C., Dudbridge, F., Howson, J. M. M., Jallow, M. W., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-03109-y · Cited by 72

Association between polygenic risk scores for attention-deficit hyperactivity disorder and educational and cognitive outcomes in the general population

Stergiakouli, E., Martin, J., Hamshere, M. L., Heron, J., Pourcain, B. S., Timpson, N. J., Thapar, A., & Smith, G. D. (2016) · International Journal of Epidemiology · doi:10.1093/ije/dyw216 · Cited by 65

Powerful detection of polygenic selection and evidence of environmental adaptation in US beef cattle

Rowan, T. N., Durbin, H. J., Seabury, C. M., Schnabel, R. D., & Decker, J. E. (2021) · PLoS Genetics · doi:10.1371/journal.pgen.1009652 · Cited by 62

Genetic Complexity of Crohn’s Disease in Two Large Ashkenazi Jewish Families

Levine, A. P., Pontikos, N., Schiff, E., Jostins, L., Speed, D., Lovat, L. B., Barrett, J. C., Grasberger, H., Plagnol, V., & Segal, A. W. (2016) · Gastroenterology · doi:10.1053/j.gastro.2016.06.040 · Cited by 61

How Powerful is the Evidence in Criminology? On Whether We Should Fear a Coming Crisis of Confidence

Barnes, J. C., TenEyck, M. F., Pratt, T. C., & Cullen, F. T. (2019) · Justice Quarterly · doi:10.1080/07418825.2018.1495252 · Cited by 62

Barriers in a sea of elasmobranchs: Fromfishingfor populations to testing hypotheses in population genetics

Hirschfeld, M., Dudgeon, C. L., Sheaves, M. J., & Barnett, A. (2021) · Global Ecology and Biogeography · doi:10.1111/geb.13379 · Cited by 58

Root and shoot variation in relation to potential intermittent drought adaptation of Mesoamerican wild common bean (Phaseolus vulgaris L.)

y Terán, J. C. B. M., Konzen, E. R., Medina, V., Palkovic, A., Ariani, A., Tsai, S. M., Gilbert, M. E., & Gepts, P. (2018) · Annals of Botany · doi:10.1093/aob/mcy221 · Cited by 58

Bilingualism Is Associated with a Delayed Onset of Dementia but Not with a Lower Risk of Developing it: a Systematic Review with Meta-Analyses

Brini, S., Sohrabi, H. R., Hébert, J. J., Forrest, M. R. L., Laine, M., Hämäläinen, H., Karrasch, M., Peiffer, J. J., Martins, R. N., & Fairchild, T. J. (2020) · Neuropsychology Review · doi:10.1007/s11065-020-09426-8 · Cited by 68

Pandemrix-induced narcolepsy is associated with genes related to immunity and neuronal survival

Hallberg, P., Smedje, H., Eriksson, N., Kohnke, H., Daniilidou, M., Öhman, I., Yue, Q., Cavalli, M., Wadelius, C., Magnusson, P. K. E., et al. (2019) · EBioMedicine · doi:10.1016/j.ebiom.2019.01.041 · Cited by 58

Invited review: Good practices in genome-wide association studies to identify candidate sequence variants in dairy cattle

Sahana, G., Cai, Z., Sanchez, M.-P., Bouwman, A. C., & Boichard, D. (2023) · Journal of Dairy Science · doi:10.3168/jds.2022-22694 · Cited by 64

Genetics of Complex Traits in Psychiatry

Gelernter, J. E. (2014) · Biological Psychiatry · doi:10.1016/j.biopsych.2014.08.005 · Cited by 59

An Integrated Systems Genetics and Omics Toolkit to Probe Gene Function

Li, H., Wang, X., Rukina, D., Huang, Q. D., Lin, T., Sorrentino, V., Zhang, H., Sleiman, M. S. B., Arends, D., McDaid, A. F., et al. (2017) · Cell Systems · doi:10.1016/j.cels.2017.10.016 · Cited by 57

Functional signaling and gene regulatory networks between the oocyte and the surrounding cumulus cells

Biase, F. H., & Kimble, K. M. (2018) · BMC Genomics · doi:10.1186/s12864-018-4738-2 · Cited by 55

Maternal smoking impacts key biological pathways in newborns through epigenetic modification in Utero

Rotroff, D. M., Joubert, B. R., Marvel, S. W., Håberg, S. E., Wu, M. C., Nilsen, R. M., Ueland, P. M., Nystad, W., London, S. J., & Motsinger‐Reif, A. A. (2016) · BMC Genomics · doi:10.1186/s12864-016-3310-1 · Cited by 54

Genome-Wide Association Study of Meat Quality Traits in Hanwoo Beef Cattle Using Imputed Whole-Genome Sequence Data

Bedhane, M., van der Werf, J. H. J., Gondro, C., Duijvesteijn, N., Lim, D., Park, B., Park, M. N., Hee, R. S., & Clark, S. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.01235 · Cited by 58

Exome sequencing in genetic disease: recent advances and considerations

Ross, J. P., Dion, P. A., & Rouleau, G. A. (2020) · F1000Research · doi:10.12688/f1000research.19444.1 · Cited by 54

Genetics and Brain Morphology

Strike, L. T., Couvy‐Duchesne, B., Hansell, N. K., Cuéllar-Partida, G., Medland, S. E., & Wright, M. J. (2015) · Neuropsychology Review · doi:10.1007/s11065-015-9281-1 · Cited by 53

Integrating convolution and self-attention improves language model of human genome for interpreting non-coding regions at base-resolution

Yang, M., Huang, L., Huang, H., Tang, H., Zhang, N., Yang, H., Wu, J., & Mu, F. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac326 · Cited by 56

Association between genetic risk scoring for schizophrenia and bipolar disorder with regional subcortical volumes

Caseras, X., Tansey, K. E., Foley, S. F., & Linden, D. E. J. (2015) · Translational Psychiatry · doi:10.1038/tp.2015.195 · Cited by 51

Identification of Elongated Primary Cilia with Impaired Mechanotransduction in Idiopathic Scoliosis Patients

Oliazadeh, N., Gorman, K. F., Eveleigh, R. J. M., Bourque, G., & Moreau, A. (2017) · Scientific Reports · doi:10.1038/srep44260 · Cited by 51

Comprehensive analysis of the major histocompatibility complex in systemic sclerosis identifies differential HLA associations by clinical and serological subtypes

Acosta‐Herrera, M., Kerick, M., López‐Isac, E., Assassi, S., Beretta, L., Simeón‐Aznar, C. P., Ortego‐Centeno, N., Proudman, S., Hunzelmann, N., Moroncini, G., et al. (2021) · Annals of the Rheumatic Diseases · doi:10.1136/annrheumdis-2021-219884 · Cited by 52

Genome-wide studies to identify risk factors for kidney disease with a focus on patients with diabetes

Regele, F., Jelencsics, K. E., Shiffman, D., Paré, G., McQueen, M., Mann, J. F. E., & Oberbauer, R. W. (2015) · Nephrology Dialysis Transplantation · doi:10.1093/ndt/gfv087 · Cited by 50

Statistical Hypothesis Testing versus Machine Learning Binary Classification: Distinctions and Guidelines

Li, J. J., & Tong, X. (2020) · Patterns · doi:10.1016/j.patter.2020.100115 · Cited by 57

A frameshift mutation in ARMC3 is associated with a tail stump sperm defect in Swedish Red (Bos taurus) cattle

Pausch, H., Venhoranta, H., Wurmser, C., Hakala, K., Iso‐Touru, T., Sironen, A. I., Vingborg, R., Lohi, H., Söderquist, L., Fries, R., et al. (2016) · BMC Genetics · doi:10.1186/s12863-016-0356-7 · Cited by 48

Defining Phenotypes from Clinical Data to Drive Genomic Research

Robinson, J. R., Wei, W., Roden, D. M., & Denny, J. C. (2018) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-080917-013335 · Cited by 48

Genome-wide association studies for fatty acid metabolic traits in five divergent pig populations

Zhang, W., Yang, B., Zhang, J., Cui, L., Ma, J., Chen, C., Ai, H., Xiao, S., Ren, J., & Huang, L. (2016) · Scientific Reports · doi:10.1038/srep24718 · Cited by 48

From Common to Rare Variants: The Genetic Component of Alzheimer Disease

Nicolas, G., Charbonnier, C., & Campion, D. (2016) · Human Heredity · doi:10.1159/000452256 · Cited by 50

Significance Thresholds for Rare Variant Signals

Greenwood, C. M. T., Xu, C., & Ciampi, A. (2015) · Springer eBooks · doi:10.1007/978-1-4939-2824-8_12 · Cited by 1

The Power of Gene-Based Rare Variant Methods to Detect Disease-Associated Variation and Test Hypotheses About Complex Disease

Moutsianas, L., Agarwala, V., Fuchsberger, C., Flannick, J. A., Rivas, M. A., Gaulton, K. J., Albers, P. K., McVean, G., Boehnke, M., Altshuler, D., et al. (2015) · PLoS Genetics · doi:10.1371/journal.pgen.1005165 · Cited by 135

Pooled Association Tests for Rare Genetic Variants: A Review and Some New Results

Derkach, A., Lawless, J. F., & Sun, L. (2014) · Statistical Science · doi:10.1214/13-sts456 · Cited by 78

Assessment of Pooled Association Tests for Rare Genetic Variants within a Unified FrameworkPreprint

Derkach, A., Lawless, J. F., & Sun, L. (2012) · arXiv (Cornell University) · Cited by 2

Power Analysis Provides Bounds for Genetic Architecture and Insights to Challenges for Rare Variant Association StudiesPreprint

Derkach, A., Zhang, H., & Chatterjee, N. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/100891 · Cited by 1