Practical aspects of imputation-driven meta-analysis of genome-wide association studies

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

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

Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls

The Wellcome Trust Case Control Consortium, Management Committee, Burton, P. R., Clayton, D. G., Cardon, L. R., Craddock, N., Deloukas, P., Duncanson, A., Kwiatkowski, D. P., McCarthy, M. I., et al. (2007) · Nature · doi:10.1038/nature05911 · Cited by 9,442

Genetics of Food Intake Self-Regulation in Childhood: Literature Review and Research Opportunities

Faith, M. S., Carnell, S., & Kral, T. V. E. (2013) · Human Heredity · doi:10.1159/000353879 · Cited by 7,633

Impact of Informing Overweight Individuals about the Role of Genetics in Obesity: An Online Experimental Study

Lippa, N. C., & Sanderson, S. C. (2013) · Human Heredity · doi:10.1159/000353712 · Cited by 6,095

Population Structure and Eigenanalysis

Patterson, N., Price, A. L., & Reich, D. (2006) · PLoS Genetics · doi:10.1371/journal.pgen.0020190 · Cited by 5,280

A Common Variant in the FTO Gene Is Associated with Body Mass Index and Predisposes to Childhood and Adult Obesity

Frayling, T. M., Timpson, N. J., Weedon, M. N., Zeggini, E., Freathy, R. M., Lindgren, C. M., Perry, J. R. B., Elliott, K. S., Lango, H., Rayner, N. W., et al. (2007) · Science · doi:10.1126/science.1141634 · Cited by 4,531

A second generation human haplotype map of over 3.1 million SNPs

The International HapMap Consortium (2007) · Nature · doi:10.1038/nature06258 · Cited by 4,438

Genomic Control for Association Studies

Devlin, B., & Roeder, K. (1999) · Biometrics · doi:10.1111/j.0006-341x.1999.00997.x · Cited by 3,162 · cited 1×

Genome-wide association analysis identifies loci for type 2 diabetes and triglyceride levels

Saxena, R., Voight, B. F., Lyssenko, V., Burtt, N. P., de Bakker, P. I. W., Chen, H., Roix, J. J., Kathiresan, S., Hirschhorn, J. N., Daly, M. J., et al. (2007) · Science · doi:10.1126/science.1142358 · Cited by 2,843

Genomic analysis of 11,555 probands identifies 60 dominant congenital heart disease genes

Sierant, M. C., Jin, S. C., Bilgüvar, K., Morton, S. U., Dong, W., Jiang, W., Lu, Z., Li, B., López‐Giráldez, F., Tikhonova, I., et al. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2420343122 · Cited by 31

A Genome-Wide Screen for Interactions Reveals a New Locus on 4p15 Modifying the Effect of Waist-to-Hip Ratio on Total Cholesterol

Surakka, I. L., Isaacs, A. J., Karssen, L. C., Laurila, P., Middelberg, R. P. S., Tikkanen, E., Ried, J. S., Lamina, C., Mangino, M., Igl, W., et al. (2011) · PLoS Genetics · doi:10.1371/journal.pgen.1002333 · Cited by 31

FGF21 signalling pathway and metabolic traits – genetic association analysis

Kaess, B. M., Barnes, T. A., Stark, K. J., Charchar, F. J., Waterworth, D. M., Song, K. S., Wang, W. Y. S., Vollenweider, P., Waeber, G., Mooser, V., et al. (2010) · European Journal of Human Genetics · doi:10.1038/ejhg.2010.130 · Cited by 30

Population genomics of divergence within an obligate pollination mutualism: Selection maintains differences between Joshua tree species

Royer, A. M., Streisfeld, M. A., & Smith, C. I. (2016) · American Journal of Botany · doi:10.3732/ajb.1600069 · Cited by 30

Genome-Wide Association Study of Prostate Cancer–Specific Survival

Szulkin, R., Karlsson, R., Whitington, T., Aly, M., Grönberg, H., Eeles, R. A., Easton, D. F., Kote‐Jarai, Z., Olama, A. A. A., Benlloch, S., et al. (2015) · Cancer Epidemiology Biomarkers & Prevention · doi:10.1158/1055-9965.epi-15-0543 · Cited by 29

Accounting for Linkage Disequilibrium in Association Analysis of Diverse Populations

Charles, B. A., Shriner, D., & Rotimi, C. N. (2014) · Genetic Epidemiology · doi:10.1002/gepi.21788 · Cited by 31

Replication of a Genome-Wide Association Study of Birth Weight in Preterm Neonates

Ryckman, K. K., Feenstra, B., Shaffer, J. R., Bream, E. N. A., Geller, F., Feingold, E., Weeks, D. E., Gadow, E. C., Cosentino, V., Saleme, C., et al. (2011) · The Journal of Pediatrics · doi:10.1016/j.jpeds.2011.07.038 · Cited by 29

Molecular Epidemiology of Heart Failure

Smith, J. G. (2017) · JACC Basic to Translational Science · doi:10.1016/j.jacbts.2017.07.010 · Cited by 30

Genome-Wide Meta-Analysis of Longitudinal Alcohol Consumption Across Youth and Early Adulthood

Adkins, D. E., Clark, S. L., Copeland, W., Kennedy, M. A., Conway, K. P., Angold, A. C., MAES, H. H. M., Liu, Y., Kumar, G., Erkanli, A., et al. (2015) · Twin Research and Human Genetics · doi:10.1017/thg.2015.36 · Cited by 28

DNAH5 is associated with total lung capacity in chronic obstructive pulmonary disease

Lee, J. H., McDonald, M., Cho, M. H., Wan, E. S., Castaldi, P. J., Hunninghake, G. M., Marchetti, N. T., Lynch, D. A., Crapo, J. D., Lomas, D. A., et al. (2014) · Respiratory Research · doi:10.1186/s12931-014-0097-y · Cited by 28

Analysis of Genes Associated With Monogenic Primary Immunodeficiency Identifies Rare Variants in XIAP in Patients With Crohn’s Disease

Amininejad, L., Charloteaux, B., Théâtre, E., Liefferinckx, C., Dmitrieva, J., Hayard, P., Muls, V., Maisin, J.-M., Schapira, M., Ghislain, J.-M., et al. (2018) · Gastroenterology · doi:10.1053/j.gastro.2018.02.028 · Cited by 27

Imputation aware meta‐analysis of genome‐wide association studies

Zaitlen, N., & Eskin, E. (2010) · Genetic Epidemiology · doi:10.1002/gepi.20507 · Cited by 27

Systematic assessment of imputation performance using the 1000 Genomes reference panels

Liu, Q., Cirulli, E. T., Han, Y., Yao, S., Liu, S., & Zhu, Q. (2014) · Briefings in Bioinformatics · doi:10.1093/bib/bbu035 · Cited by 27

Lack of replication in polymorphisms reported to be associated with atrial fibrillation

Sinner, M. F., Lubitz, S. A., Pfeufer, A., Makino, S., Beckmann, B.-M., Lunetta, K. L., Steinbeck, G., Perz, S., Rahman, R. M. A., Sonni, A., et al. (2010) · Heart Rhythm · doi:10.1016/j.hrthm.2010.11.003 · Cited by 27

Gene- or region-based association study via kernel principal component analysis

Gao, Q., He, Y., Yuan, Z., Zhao, J., Zhang, B., & Xue, F. (2011) · BMC Genetics · doi:10.1186/1471-2156-12-75 · Cited by 26

QCGWAS: A flexible R package for automated quality control of genome-wide association results

van der Most, P. J., Vaez, A., Prins, B. P., Muñoz, M. L., Snieder, H., Alizadeh, B. Z., & Nolte, I. M. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btt745 · Cited by 26

A Genome-Wide Association Study of Renal Cell Carcinoma among African Americans

Purdue, M. P., Ye, Y., Wang, Z., Colt, J. S., Schwartz, K., Davis, F. G., Rothman, N., Chow, W., Wu, X., & Chanock, S. J. (2013) · Cancer Epidemiology Biomarkers & Prevention · doi:10.1158/1055-9965.epi-13-0818 · Cited by 26

Mendelian randomisation study of the relationship between vitamin D and risk of glioma

Takahashi, H., Cornish, A. J., Sud, A., Law, P., Kinnersley, B., Ostrom, Q. T., Labrèche, K., Eckel‐Passow, J. E., Armstrong, G., Claus, E. B., et al. (2018) · Scientific Reports · doi:10.1038/s41598-018-20844-w · Cited by 26

A Coalescent Model for Genotype Imputation

Jewett, E. M., Zawistowski, M., Rosenberg, N. A., & Zöllner, S. K. (2012) · Genetics · doi:10.1534/genetics.111.137984 · Cited by 25

Gene Level Meta-Analysis of Quantitative Traits by Functional Linear Models

Fan, R., Wang, Y., Boehnke, M., Chen, W., Li, Y., Ren, H., Lobach, I. V., & Xiong, M. (2015) · Genetics · doi:10.1534/genetics.115.178343 · Cited by 24

GWAR: robust analysis and meta-analysis of genome-wide association studies

Dimou, N. L., Tsirigos, K. D., Elofsson, A., & Bagos, P. G. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx008 · Cited by 26

On individual genome-wide association studies and their meta-analysis

Pei, Y., Zhang, L., Papasian, C. J., Wang, Y., & Deng, H. (2013) · Human Genetics · doi:10.1007/s00439-013-1366-4 · Cited by 24

Optimal methods for meta-analysis of genome-wide association studies

Zhou, B., Shi, J., & Whittemore, A. S. (2011) · Genetic Epidemiology · doi:10.1002/gepi.20603 · Cited by 24

A comparative analysis of cell-type adjustment methods for epigenome-wide association studies based on simulated and real data sets

Brägelmann, J., & Bermejo, J. L. (2018) · Briefings in Bioinformatics · doi:10.1093/bib/bby068 · Cited by 23

Trans‐Ethnic Fine‐Mapping of the Major Histocompatibility Complex Region Linked to Parkinson's Disease

Naito, T., Satake, W., Ogawa, K., Ken, S., Hirata, J., Foo, J. N., Tan, E., Toda, T., & Okada, Y. (2021) · Movement Disorders · doi:10.1002/mds.28583 · Cited by 24

Planning and Executing a Genome Wide Association Study (GWAS)

Sale, M. M., Mychaleckyj, J. C., & Chen, W. (2009) · Methods in molecular biology · doi:10.1007/978-1-60327-378-7_25 · Cited by 23

Identification of Novel Genetic Markers for the Risk of Spinal Pathologies

Bovonratwet, P., Kulm, S., Kolin, D. A., Song, J., Morse, K. W., Cunningham, M. E., Albert, T. J., Sandhu, H. S., Kim, H. J., Iyer, S., et al. (2023) · Journal of Bone and Joint Surgery · doi:10.2106/jbjs.22.00872 · Cited by 22

Heritability and genome-wide analyses of problematic peer relationships during childhood and adolescence

Pourcain, B. S., Haworth, C., Davis, O., Wang, K., Timpson, N., Evans, D. M., Kemp, J., Ronald, A., Price, T., Meaburn, E., et al. (2014) · Human Genetics · doi:10.1007/s00439-014-1514-5 · Cited by 25

Discovery and fine-mapping of loci associated with MUFAs through trans-ethnic meta-analysis in Chinese and European populations

Hu, Y., Tanaka, T., Zhu, J., Guan, W., Wu, J. H. Y., Psaty, B. M., McKnight, B., King, I. B., Sun, Q., Richard, M. A., et al. (2017) · Journal of Lipid Research · doi:10.1194/jlr.p071860 · Cited by 20

Introduction to Linkage Disequilibrium, the HapMap, and ImputationPreprint

Neale, B. M. (2010) · Cold Spring Harbor Protocols · doi:10.1101/pdb.top74 · Cited by 23

False positive findings during genome-wide association studies with imputation: influence of allele frequency and imputation accuracy

Zhang, Z., Xiao, X., Zhou, W., Zhu, D. K., & Amos, C. I. (2021) · Human Molecular Genetics · doi:10.1093/hmg/ddab203 · Cited by 25

Fundamentals of Complex Trait Genetics and Association Studies

Alghamdi, J., & Padmanabhan, S. (2014) · Elsevier eBooks · doi:10.1016/b978-0-12-386882-4.00012-8 · Cited by 24

The Genomics of Heart Failure: Design and Rationale of the HERMES Consortium

Lumbers, R. T., Shah, S. H., Lin, H., Czuba, T., Henry, A., Swerdlow, D. I., Mälarstig, A., Andersson, C., Verweij, N., Holmes, M. V., et al. (2021) · ESC Heart Failure · doi:10.1002/ehf2.13517 · Cited by 21

Development and Application of Genomic Control Methods for Genome-Wide Association Studies Using Non-Additive Models

Tsepilov, Y. A., Ried, J. S., Strauch, K., Grallert, H., van Duijn, C. M., Axenovich, T. I., & Aulchenko, Y. S. (2013) · PLoS ONE · doi:10.1371/journal.pone.0081431 · Cited by 21

Investigating a Potential Causal Relationship Between Maternal Blood Pressure During Pregnancy and Future Offspring Cardiometabolic Health

Wang, G., Bhatta, L., Moen, G.-H., Hwang, L., Kemp, J. P., Bond, T. A., Åsvold, B. O., Brumpton, B. M., Evans, D. M., & Warrington, N. M. (2021) · Hypertension · doi:10.1161/hypertensionaha.121.17701 · Cited by 22

An Empirical Comparison of Joint and Stratified Frameworks for Studying G × E Interactions: Systolic Blood Pressure and Smoking in the CHARGE Gene‐Lifestyle Interactions Working Group

Sung, Y. J., Winkler, T. W., Manning, A. K., Aschard, H., Gudnason, V. G., Harris, T. B., Smith, A. V., Boerwinkle, E., Brown, M. R., Morrison, A. C., et al. (2016) · Genetic Epidemiology · doi:10.1002/gepi.21978 · Cited by 19

Polymorphisms of the SAMHD1 Gene Are Not Associated with the Infection and Natural Control of HIV Type 1 in Europeans and African-Americans

Coon, S., Wang, D., & Wu, L. J. (2012) · AIDS Research and Human Retroviruses · doi:10.1089/aid.2012.0039 · Cited by 20

Prenatal and postnatal genetic influence on lung function development

Kreiner‐Møller, E., Bisgaard, H. F., & Bønnelykke, K. (2014) · Journal of Allergy and Clinical Immunology · doi:10.1016/j.jaci.2014.04.003 · Cited by 19

Mendelian Randomization and GWAS Meta Analysis Revealed the Risk-Increasing Effect of Schizophrenia on Cancers

Yuan, K., Song, W., Liu, Z., Lin, G. N., & Yu, S. (2022) · Biology · doi:10.3390/biology11091345 · Cited by 19

Genotype imputation for Han Chinese population using Haplotype Reference Consortium as reference

Lin, Y., Liu, L., Yang, S., Li, Y., Lin, D., Zhang, X., & Yin, X. (2018) · Human Genetics · doi:10.1007/s00439-018-1894-z · Cited by 17

Clinical and biological data integration for biomarker discovery

Sorani, M. D., Ortmann, W. A., Bierwagen, E. P., & Behrens, T. W. (2010) · Drug Discovery Today · doi:10.1016/j.drudis.2010.06.005 · Cited by 19

Strategies for genetic model specification in the screening of genome-wide meta-analysis signals for further replication

Pereira, T., Patsopoulos, N. A., Pereira, A. C., & Krieger, J. E. (2010) · International Journal of Epidemiology · doi:10.1093/ije/dyq203 · Cited by 19

Type 1 diabetes genome-wide association analysis with imputation identifies five new risk regionsPreprint

Cooper, N. J., Wallace, C., Burren, O. S., Cutler, A. J., Walker, N. M., & Todd, J. A. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/120022 · Cited by 20

Considering hormone-sensitive cancers as a single disease in the UK biobank reveals shared aetiology

Ahmed, M. B., Mäkinen, V., Mulugeta, A., Shin, J., Boyle, T., Hyppönen, E., & Lee, S. (2022) · Communications Biology · doi:10.1038/s42003-022-03554-y · Cited by 17

MagicalRsq: Machine-learning-based genotype imputation quality calibration

Sun, Q., Yang, Y., Rosen, J. D., Jiang, M.-Z., Chen, J. C., Liu, W., Wen, J., Raffield, L. M., Pace, R. G., Zhou, Y., et al. (2022) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2022.09.009 · Cited by 18

Exome sequence genotype imputation in globally diverse hexaploid wheat accessions

Shi, F., Tibbits, J. F. G., Pasam, R., Kay, P., Wong, D., Petkowski, J., Forrest, K., Hayes, B. J., Akhunova, A. R., Davies, J. P., et al. (2017) · Theoretical and Applied Genetics · doi:10.1007/s00122-017-2895-3 · Cited by 18

The Value of Statistical or Bioinformatics Annotation for Rare Variant Association With Quantitative Trait

Byrnes, A., Wu, M. C., Wright, F. A., Li, M., & Li, Y. (2013) · Genetic Epidemiology · doi:10.1002/gepi.21747 · Cited by 17

Comprehensive evaluation of imputation performance in African Americans

Chanda, P., Yuhki, N., Li, M., Bader, J. S., Hartz, A., Boerwinkle, E., Kao, W. L., & Arking, D. E. (2012) · Journal of Human Genetics · doi:10.1038/jhg.2012.43 · Cited by 17

Nordic OCD & Related Disorders Consortium: Rationale, design, and methods

Mataix‐Cols, D., Hansen, B. K. A., Mattheisen, M., Karlsson, E. K., Addington, A. M., Boberg, J., Djurfeldt, D. R., Halvorsen, M., Lichtenstein, P., Solem, S., et al. (2019) · American Journal of Medical Genetics Part B Neuropsychiatric Genetics · doi:10.1002/ajmg.b.32756 · Cited by 16

Probing the diabetes and colorectal cancer relationship using gene – environment interaction analyses

Dimou, N. L., Kim, A. E., Flanagan, O., Murphy, N., Díez‐Obrero, V., Shcherbina, A., Aglago, E. K., Bouras, E., Campbell, P. T., Casey, G., et al. (2023) · British Journal of Cancer · doi:10.1038/s41416-023-02312-z · Cited by 16

Association study of CRP gene polymorphism and hypertension in Han Chinese population

Zhao, Y., Wang, H., Liu, S., Zhao, X., Yanchun, C., Yang, Y., Wang, W., Yi-ming, W., Chen, A., Tang, J., et al. (2012) · Gene · doi:10.1016/j.gene.2012.09.107 · Cited by 14

Successful Replication of GWAS Hits for Multiple Sclerosis in 10,000 Germans Using the Exome Array

Dankowski, T., Buck, D., Andlauer, T. F. M., Antony, G., Bayas, A., Bechmann, L., Berthele, A., Bettecken, T., Chan, A., Franke, A., et al. (2015) · Genetic Epidemiology · doi:10.1002/gepi.21933 · Cited by 18

Genome-Wide Association and Replication Study of Hepatotoxicity Induced by Antiretrovirals Alone or with Concomitant Anti-Tuberculosis Drugs

Petros, Z., Lee, M. T. M., Takahashi, A., Zhang, Y., Yimer, G., Habtewold, A., Schuppe‐Koistinen, I., Mushiroda, T., Makonnen, E., Kubo, M., et al. (2017) · OMICS A Journal of Integrative Biology · doi:10.1089/omi.2017.0019 · Cited by 16

Identifying markers for resistance to sugarcane orange rust (Puccinia kuehnii) via selective genotyping and capture sequencing

McCord, P. H., Glynn, N. C., & Comstock, J. C. (2019) · Euphytica · doi:10.1007/s10681-019-2340-6 · Cited by 16

Identification of two novel breast cancer loci through large-scale genome-wide association study in the Japanese population

Low, S., Chin, Y. M., Ito, H. M., Matsuo, K., Tanikawa, C., Matsuda, K., Saito, H., Sakurai‐Yageta, M., Nakaya, N., Shimizu, A., et al. (2019) · Scientific Reports · doi:10.1038/s41598-019-53654-9 · Cited by 15

APOE is not Associated with Alzheimer Disease: a Cautionary tale of Genotype Imputation

Beecham, G. W., Martin, E. R., Gilbert, J. R., Haines, J. L., & Pericak‐Vance, M. A. (2010) · Annals of Human Genetics · doi:10.1111/j.1469-1809.2010.00573.x · Cited by 14

Identifying genetic loci associated with antidepressant drug response with drug–gene interaction models in a population-based study

Noordam, R., Direk, N., Sitlani, C. M., Aarts, N., Tiemeier, H. W., Albert Hofman, D., Uitterlinden, A. G., Psaty, B. M., Stricker, B. H. C., & Visser, L. E. (2015) · Journal of Psychiatric Research · doi:10.1016/j.jpsychires.2015.01.005 · Cited by 15

Evaluation of the imputation performance of the program IMPUTE in an admixed sample from Mexico City using several model designs

Krithika, S., Valladares‐Salgado, A., Peralta‐Romero, J., Escobedo, J., Kumate-Rodríguez, J., Cruz, M., & Parra, E. J. (2012) · BMC Medical Genomics · doi:10.1186/1755-8794-5-12 · Cited by 15

Genome-Wide Interaction Study of Omega-3 PUFAs and Other Fatty Acids on Inflammatory Biomarkers of Cardiovascular Health in the Framingham Heart Study

Veenstra, J., Kalsbeek, A., Westra, J., Disselkoen, C., Smith, C. E., & Tintle, N. (2017) · Nutrients · doi:10.3390/nu9080900 · Cited by 15

Identification of a Novel Genetic Marker for Risk of Degenerative Rotator Cuff Disease Surgery in the UK Biobank

Yanik, E. L., Keener, J. D., Lin, S. J., Colditz, G. A., Wright, R. W., Evanoff, B., Jain, N. B., & Saccone, N. L. (2021) · Journal of Bone and Joint Surgery · doi:10.2106/jbjs.20.01474 · Cited by 15

Genome-Wide Association Studies—Data Generation, Storage, Interpretation, and Bioinformatics

Paré, G. (2010) · Journal of Cardiovascular Translational Research · doi:10.1007/s12265-010-9181-y · Cited by 16

Increasing Association Mapping Power and Resolution in Mouse Genetic Studies Through the Use of Meta-Analysis for Structured Populations

Furlotte, N. A., Kang, E. Y., van Nas, A., Farber, C. R., Lusis, A. J., & Eskin, E. (2012) · Genetics · doi:10.1534/genetics.112.140277 · Cited by 15

Genetic variants predicting left ventricular hypertrophy in a diabetic population: a Go-DARTS study including meta-analysis

Parry, H., Donnelly, L. A., Zuydam, N. V., Doney, A. S. F., Elder, D. H. J., Morris, A. D., Struthers, A. D., Palmer, C. N., & Lang, C. C. (2013) · Cardiovascular Diabetology · doi:10.1186/1475-2840-12-109 · Cited by 15

Comparative genome-wide association studies of a depressive symptom phenotype in a repeated measures setting by race/ethnicity in the multi-ethnic study of atherosclerosis

Ware, E. B., Mukherjee, B., Sun, Y. V., Roux, A. V. D., Kardia, S. L. R., & Smith, J. A. (2015) · BMC Genetics · doi:10.1186/s12863-015-0274-0 · Cited by 15

Local Exome Sequences Facilitate Imputation of Less Common Variants and Increase Power of Genome Wide Association Studies

Joshi, P. K., Prendergast, J. G. D., Fraser, R. M., Huffman, J. E., Vitart, V., Hayward, C., McQuillan, R., Głodzik, D., Polašek, O., Hastie, N. D., et al. (2013) · PLoS ONE · doi:10.1371/journal.pone.0068604 · Cited by 16

Nuclear genetic control of mtDNA copy number and heteroplasmy in humansPreprint

Gupta, R., Kanai, M., Durham, T., Tsuo, K., McCoy, J. G., Chinnery, P. F., Karczewski, K. J., Calvo, S. E., Neale, B. M., & Mootha, V. K. (2023) · medRxiv · doi:10.1101/2023.01.19.23284696 · Cited by 15

InterPregGen:genetic studies of pre-eclampsia in three continents

Morgan, L., McGinnis, R. E., Steinthórsdóttir, V., Svyatova, G., Zakhidova, N., Lee, W. K., Iversen, A., Magnus, P. M., Walker, J. J., Casas, J. P., et al. (2014) · Norsk Epidemiologi · doi:10.5324/nje.v24i1-2.1815 · Cited by 14

Population‐Based Case‐Control Association Studies

Hancock, D. B., & Scott, W. K. (2012) · Current Protocols in Human Genetics · doi:10.1002/0471142905.hg0117s74 · Cited by 14

Genome-wide association study of plasma triglycerides, phospholipids and relation to cardio-metabolic risk factorsPreprint

Demirkan, A., Pool, R., Deelen, J., Beekman, M., Liu, J., Harms, A. C., Vaarhorst, A. A. M., Hagenbeek, F. A., Willemsen, G. A. H. M., Verhoeven, A., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/621334 · Cited by 15

Genome-wide interaction study of dietary intake of fibre, fruits, and vegetables with risk of colorectal cancer

Papadimitriou, N., Kim, A., Kawaguchi, E. S., Morrison, J. L., Díez‐Obrero, V., Albanes, D., Berndt, S. I., Bezieau, S., Bien, S. A., Bishop, D. T., et al. (2024) · EBioMedicine · doi:10.1016/j.ebiom.2024.105146 · Cited by 14

IGG3: a tool to rapidly integrate large genotype datasets for whole-genome imputation and individual-level meta-analysis

Li, M., Jiang, L., Kao, P. Y. P., Sham, P.-C., & Song, Y. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp183 · Cited by 13

Genetic associations with childhood brain growth, defined in two longitudinal cohorts

Székely, E., Schwantes-An, T.-H. L., Justice, C. M., Sabourin, J., Jansen, P. R., Muetzel, R. L., Sharp, W. S., Tiemeier, H. W., Sung, H., White, T. J. H., et al. (2018) · Genetic Epidemiology · doi:10.1002/gepi.22122 · Cited by 12

The Role of 39 Psoriasis Risk Variants on Age of Psoriasis Onset

Lu, Y., Kane, S., Chen, H., Leon, A., Levin, E., Nguyen, T. V., Debbaneh, M. G., Millsop, J. W., Gupta, R., Huynh, M., et al. (2013) · ISRN Dermatology · doi:10.1155/2013/203941 · Cited by 13

Genotype Imputation for African Americans using data from HapMap Phase II versus 1000 Genomes Projects

Sung, Y. J., Gu, C., Tiwari, H., Arnett, D., Broeckel, U., & Rao, D. C. (2012) · Genetic Epidemiology · doi:10.1002/gepi.21647 · Cited by 14

Genome-wide association studies - A summary for theclinical gastroenterologist

Melum, E., Franke, A., & Karlsen, T. H. (2009) · World Journal of Gastroenterology · doi:10.3748/wjg.15.5377 · Cited by 13

Genome-wide meta-analysis of over 29,000 people with epilepsy reveals 26 loci and subtype-specific genetic architecturePreprint

Berkovic, S. F., Cavalleri, G. L., & Koeleman, B. P. C. (2022) · medRxiv · doi:10.1101/2022.06.08.22276120 · Cited by 13

Quick, “Imputation-free” meta-analysis with proxy-SNPs

Meesters, C., Leber, M., Herold, C., Angisch, M., Mattheisen, M., Drichel, D., Lacour, A., & Becker, T. (2012) · BMC Bioinformatics · doi:10.1186/1471-2105-13-231 · Cited by 12

XPAT: a toolkit to conduct cross-platform association studies with heterogeneous sequencing datasets

Yu, Y., Hu, H., Bohlender, R. J., Hu, F., Chen, J., Holt, C., Fowler, J., Guthery, S. L., Scheet, P. A., Hildebrandt, M. A. T., et al. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkx1280 · Cited by 12

Genome-Wide Association Study Identifies Candidate Loci Associated with Opioid Analgesic Requirements in the Treatment of Cancer Pain

Nishizawa, D., Terui, T., Ishitani, K., Kasai, S., Hasegawa, J., Nakayama, K., Ebata, Y., & Ikeda, K. (2022) · Cancers · doi:10.3390/cancers14194692 · Cited by 12

An empirical evaluation of imputation accuracy for association statistics reveals increased type-I error rates in genome-wide associations

Almeida, M., de Oliveira, P. S. L., Pereira, T., Krieger, J. E., & Pereira, A. C. (2011) · BMC Genetics · doi:10.1186/1471-2156-12-10 · Cited by 12

Genetic meta-analysis of 15,901 African Americans identifies variation in EXOC3L1 is associated with HDL concentration

Lanktree, M. B., Elbers, C. C., Li, Y., Zhang, G., Duan, Q., Karczewski, K. J., Guo, Y., Tragante, V., North, K. E., Cushman, M., et al. (2015) · Journal of Lipid Research · doi:10.1194/jlr.p059477 · Cited by 12

Reviews of Science for Science Librarians: Genome-Wide Association Studies (GWAS)

Jeong, S. H., & Kim, S. (2011) · Science & Technology Libraries · doi:10.1080/0194262x.2011.595315 · Cited by 11

Evaluation of Genetic and Nongenetic Risk Factors for Degenerative Cervical Myelopathy

Shlykov, M. A., Giles, E. M., Kelly, M. P., Lin, S. J., Pham, V., Saccone, N. L., & Yanik, E. L. (2023) · Spine · doi:10.1097/brs.0000000000004735 · Cited by 11

Unveiling the predominance of Saccharum spontaneum alleles for resistance to orange rust in sugarcane using genome-wide association

Dijoux, J., Rio, S., Hervouet, C., Garsmeur, O., Barau, L., Dumont, T., Rott, P., D’Hont, A., & Hoarau, J.-Y. (2024) · Theoretical and Applied Genetics · doi:10.1007/s00122-024-04583-3 · Cited by 11

SNP Genotype Imputation in Forensics—A Performance Study

Tillmar, A. O., & Kling, D. (2024) · Genes · doi:10.3390/genes15111386 · Cited by 11

FCER2 T2206C variant associated with FENO levels in asthmatic children using inhaled corticosteroids: The PACMAN study

Karimi, L., Vijverberg, S. J. H., Farzan, N., Ghanbari, M., Verhamme, K. M. C., & der Zee, A. H. M. (2019) · Clinical & Experimental Allergy · doi:10.1111/cea.13460 · Cited by 11

Genetic susceptibility to autoimmunity—Current status and challenges

Huang, M., & Xu, H. (2022) · Advances in immunology · doi:10.1016/bs.ai.2022.08.004 · Cited by 11

Gene-Based Rare Allele Analysis Identified a Risk Gene of Alzheimer’s Disease

Kim, J. H., Song, P., Lim, H., Lee, J., Lee, J. H., Park, S. A., & Initiative, A. D. N. (2014) · PLoS ONE · doi:10.1371/journal.pone.0107983 · Cited by 11

Genome-Wide Association Meta-Analysis of Single-Nucleotide Polymorphisms and Symptomatic Venous Thromboembolism during Therapy for Acute Lymphoblastic Leukemia and Lymphoma in Caucasian Children

Mateos, M. K., Tulstrup, M., Quinn, M. C. J., Tuckuviene, R., Marshall, G. M., Gupta, R., Mayoh, C., Wolthers, B. O., Barbaro, P., Ruud, E., et al. (2020) · Cancers · doi:10.3390/cancers12051285 · Cited by 11

Rostral middle frontal gyrus thickness mediates the relationship between genetic risk and neuroticism trait

Li, S., Zhou, Z., Meng, J., Zhu, X., Wang, K., Wei, D., & Qiu, J. (2020) · Psychophysiology · doi:10.1111/psyp.13728 · Cited by 12

Common Genetic Variant in VIT Is Associated with Human Brain Asymmetry

Tadayon, S. H., Vaziri-Pashkam, M., Kahali, P., Dezfouli, M. A., & Abbassian, A. (2016) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2016.00236 · Cited by 11

Meta-Analysis of Common and Rare Variants

Michailidou, K. (2018) · Methods in molecular biology · doi:10.1007/978-1-4939-7868-7_6 · Cited by 11

Associations of perfluoroalkyl and polyfluoroalkyl substances with markers of glycaemic control, insulin secretion and sensitivity, and diabetes risk: a systematic review and meta-analyses

Aldana, S. I., Yu, X., Yao, M., Cohen, N., Markopoulou, E., Chowdhury, M., Midya, V., Eick, S. M., Trowbridge, J., Starling, A. P., et al. (2026) · EClinicalMedicine · doi:10.1016/j.eclinm.2025.103747 · Cited by 10

Genotype-Based Ancestral Background Consistently Predicts Efficacy and Side Effects across Treatments in CATIE and STAR*D

Adkins, D. E., de Souza, R. P., Åberg, K. A., Clark, S. L., McClay, J. L., Sullivan, P. F., & van den Oord, E. J. C. G. (2013) · PLoS ONE · doi:10.1371/journal.pone.0055239 · Cited by 10

The Candidate Chromosomal Regions Responsible for Milk Yield of Cow: A GWAS Meta-Analysis

Taherkhani, L., Banabazi, M. H., Emamjomeh-Kashan, N., Noshary, A., & Imumorin, I. G. (2022) · Animals · doi:10.3390/ani12050582 · Cited by 10

Colocalization analysis of pancreas eQTLs with risk loci from alcoholic and novel non-alcoholic chronic pancreatitis GWAS suggests potential disease causing mechanisms

Schmidt, A. W., Kühnapfel, A., Kirsten, H., Grallert, H., Hellerbrand, C., Kiefer, F., Mann, K. F., Mueller, S., Nöthen, M. M., Peters, A., et al. (2022) · Pancreatology · doi:10.1016/j.pan.2022.03.007 · Cited by 10

Meta-analysis for genome-wide association studies using case-control design: application and practice

Shim, S. R., Kim, J., Jung, W., Shin, I., & Bae, J. (2016) · Epidemiology and Health · doi:10.4178/epih.e2016058 · Cited by 10

Common and Rare Variants Genetic Association Analysis of Cigarettes per Day Among Ever-Smokers in Chronic Obstructive Pulmonary Disease Cases and Controls

Lutz, S. M., Frederiksen, B. N., Begum, F., McDonald, M., Cho, M. H., Hobbs, B. D., Parker, M. M., DeMeo, D. L., Hersh, C. P., Ehringer, M. A., et al. (2018) · Nicotine & Tobacco Research · doi:10.1093/ntr/nty095 · Cited by 10

Genetically determined intelligence and coronary artery disease risk

Li, L., Pang, S., Zeng, L., Güldener, U., & Schunkert, H. (2020) · Clinical Research in Cardiology · doi:10.1007/s00392-020-01721-x · Cited by 9

Evaluation of a Two‐Stage Approach in Trans‐Ethnic Meta‐Analysis in Genome‐Wide Association Studies

Hong, J., Lunetta, K. L., Cupples, L. A., Dupuis, J., & Liu, C. (2016) · Genetic Epidemiology · doi:10.1002/gepi.21963 · Cited by 10

Data Integration, Imputation, and Meta-analysis for Genome-Wide Association Studies

Joukhadar, R., & Daetwyler, H. D. (2022) · Methods in molecular biology · doi:10.1007/978-1-0716-2237-7_11 · Cited by 5

Imputation aware meta‐analysis of genome‐wide association studies

Zaitlen, N., & Eskin, E. (2010) · Genetic Epidemiology · doi:10.1002/gepi.20507 · Cited by 27

Imputation-free GWAS Meta-Analysis with YAMAS

Becker, T., Drichel, D., Herold, C., Meesters, C., & Leber, M. (2011) · German Medical Science (German Research Foundation) · doi:10.3205/11gmds053

Meta-Analysis of Genome-Wide Association Studies

de Bakker, P. I. W., Neale, B. M., & Daly, M. J. (2010) · Cold Spring Harbor Protocols · doi:10.1101/pdb.top81 · Cited by 48

Meta-imputation: An efficient method to combine genotype data after imputation with multiple reference panels

Yu, K., Das, S., LeFaive, J., Kwong, A. M., Pleiness, J., Forer, L., Schönherr, S., Fuchsberger, C., Smith, A. V., & Abecasis, G. R. (2022) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2022.04.002 · Cited by 39