Imputing Phenotypes for Genome-wide Association Studies

Support-vector networks

Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421 · see citing works

Regression shrinkage and selection via the lasso

Tibshirani, R. (1996) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1996.tb02080.x · Cited by 51,482 · see citing works

Support-Vector Networks

Cortes, C., & Vapnik, V. N. (1995) · Machine Learning · doi:10.1023/a:1022627411411 · Cited by 34,565 · see citing works

Regularization and Variable Selection Via the Elastic Net

Zou, H., & Hastie, T. (2005) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.1467-9868.2005.00503.x · Cited by 20,957 · see citing works

Multiple Imputation for Nonresponse in Surveys

Rubin, D. B. (1987) · Wiley Series in Probability and Statistics · doi:10.1002/9780470316696 · Cited by 20,701 · see citing works

Statistical Analysis With Missing Data

Lahiff, M., Little, R. J. A., & Rubin, D. B. (1989) · Journal of the American Statistical Association · doi:10.2307/2289883 · Cited by 17,384 · see citing works

Inference and missing data

RUBIN, D. B. (1976) · Biometrika · doi:10.1093/biomet/63.3.581 · Cited by 9,925 · see citing works

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 7,628 · see citing works

Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls

Sterne, J. A. C., White, I. R., Carlin, J. B., Spratt, M., Royston, P., Kenward, M. G., Wood, A. M., & Carpenter, J. R. (2009) · BMJ · doi:10.1136/bmj.b2393 · Cited by 7,299 · see citing works

The Genotype-Tissue Expression (GTEx) pilot analysis: Multitissue gene regulation in humans

The GTEx Consortium, Ardlie, K. G., Deluca, D. S., Segrè, A. V., Sullivan, T. J., Young, T. R., Gelfand, E. T., Trowbridge, C. A., Maller, J. B., Tukiainen, T., et al. (2015) · Science · doi:10.1126/science.1262110 · Cited by 5,062 · see citing works

High density lipoprotein as a protective factor against coronary heart disease

Gordon, T., Castelli, W. P., Hjortland, M. C., Kannel, W. B., & Dawber, T. R. (1977) · The American Journal of Medicine · doi:10.1016/0002-9343(77)90874-9 · Cited by 5,059 · see citing works

Statistical Analysis With Missing Data

Ghosh, S., Little, R. J. A., & Rubin, D. B. (1988) · Technometrics · doi:10.2307/1269814 · Cited by 4,730 · see citing works

A Flexible and Accurate Genotype Imputation Method for the Next Generation of Genome-Wide Association Studies

Howie, B. N., Donnelly, P., & Marchini, J. (2009) · PLoS Genetics · doi:10.1371/journal.pgen.1000529 · Cited by 3,928 · see citing works

Generalized, Linear, and Mixed Models

McCulloch, C. E., & Searle, S. R. (2000) · Wiley series in probability and statistics · doi:10.1002/0471722073 · Cited by 3,019 · see citing works

The Bayesian Lasso

Park, T., & Casella, G. (2008) · Journal of the American Statistical Association · doi:10.1198/016214508000000337 · Cited by 2,941 · see citing works

Variance component model to account for sample structure in genome-wide association studies

Kang, H. M., Sul, J. H., Service, S. K., Zaitlen, N. A., Kong, S.-Y., Freimer, N. B., Sabatti, C., & Eskin, E. (2010) · Nature Genetics · doi:10.1038/ng.548 · Cited by 2,817 · see citing works

Statistical Analysis With Missing Data

Lazar, N. A. (2003) · Technometrics · doi:10.1198/tech.2003.s167 · Cited by 2,479 · see citing works

Large-scale association analysis identifies 13 new susceptibility loci for coronary artery disease

Cardiogenics, Schunkert, H., the CARDIoGRAM Consortium, König, I. R., Kathiresan, S., Reilly, M. P., Assimes, T. L., Holm, H., Preuss, M., Stewart, A. F. R., et al. (2011) · Nature Genetics · doi:10.1038/ng.784 · Cited by 1,866 · see citing works

MaCH: using sequence and genotype data to estimate haplotypes and unobserved genotypes

Li, Y., Willer, C. J., Ding, J., Scheet, P., & Abecasis, G. R. (2010) · Genetic Epidemiology · doi:10.1002/gepi.20533 · Cited by 1,843 · see citing works

Twelve type 2 diabetes susceptibility loci identified through large-scale association analysis

The MAGIC investigators, Voight, B. F., The GIANT Consortium, Scott, L. J., Steinthorsdottir, V., Morris, A. P., Dina, C., Welch, R. P., Zeggini, E., Huth, C., et al. (2010) · Nature Genetics · doi:10.1038/ng.609 · Cited by 1,761 · see citing works

Fast and accurate genotype imputation in genome-wide association studies through pre-phasing

Howie, B., Fuchsberger, C., Stephens, M., Marchini, J., & Abecasis, G. R. (2012) · Nature Genetics · doi:10.1038/ng.2354 · Cited by 1,737 · see citing works

Six new loci associated with body mass index highlight a neuronal influence on body weight regulation

the GIANT Consortium (2009) · Nature Genetics · doi:10.1038/ng.287 · Cited by 1,720 · see citing works

Prospects for whole-genome linkage disequilibrium mapping of common disease genes

Kruglyak, L. (1999) · Nature Genetics · doi:10.1038/9642 · Cited by 1,416 · see citing works

Linkage disequilibrium in humans: models and data.

Pritchard, J., & Przeworski, M. (2001) · American Journal of Human Genetics · doi:10.1086/321275 · Cited by 1,340 · see citing works

Intermediate phenotypes and genetic mechanisms of psychiatric disorders

Meyer-Lindenberg, A., & Weinberger, D. R. (2006) · Nature reviews. Neuroscience · doi:10.1038/nrn1993 · Cited by 1,216 · see citing works

FaST linear mixed models for genome-wide association studies

Lippert, C., Listgarten, J., Liu, Y., Kadie, C. M., Davidson, R. I., & Heckerman, D. (2011) · Nature Methods · doi:10.1038/nmeth.1681 · Cited by 1,209 · see citing works

Genome-wide association analysis identifies variants associated with nonalcoholic fatty liver disease that have distinct effects on metabolic traits

Speliotes, E. K., Yerges-Armstrong, L. M., Wu, J., Hernaez, R., Kim, L. J., Palmer, C. D., Gudnason, V., Eiriksdottir, G., Garcia, M. E., Launer, L. J., et al. (2011) · PLoS Genetics · doi:10.1371/journal.pgen.1001324 · Cited by 984 · see citing works

Efficient multivariate linear mixed model algorithms for genome-wide association studies

Zhou, X., & Stephens, M. (2014) · Nature Methods · doi:10.1038/nmeth.2848 · Cited by 841 · see citing works

Prediction by Supervised Principal Components

Bair, E., Hastie, T. J., Paul, D., & Tibshirani, R. J. (2006) · Journal of the American Statistical Association · doi:10.1198/016214505000000628 · Cited by 822 · see citing works

Polygenic Modeling with Bayesian Sparse Linear Mixed Models

Zhou, X., Carbonetto, P. S., & Stephens, M. (2013) · PLoS Genetics · doi:10.1371/journal.pgen.1003264 · Cited by 801 · see citing works

[Environment favorable to female physicians: introduction].

Kawashiro, N., Iino, Y., Kudo, F., & Suzuka, Y. (2010) · PubMed · doi:10.3950/jibiinkoka.113.719 · Cited by 776 · see citing works

Genome-wide association analysis of metabolic traits in a birth cohort from a founder population

Sabatti, C., Service, S. K., Hartikainen, A., Pouta, A. M., Ripatti, S., Brodsky, J. F., Jones, C. G., Zaitlen, N., Varilo, T., Kaakinen, M., et al. (2008) · Nature Genetics · doi:10.1038/ng.271 · Cited by 721 · see citing works

SIRE EVALUATION AND GENETIC TRENDS

Henderson, C. (1973) · Journal of Animal Science · doi:10.1093/ansci/1973.symposium.10 · Cited by 648 · see citing works

Designing Genome-Wide Association Studies: Sample Size, Power, Imputation, and the Choice of Genotyping Chip

Spencer, C. C. A., Su, Z., Donnelly, P., & Marchini, J. (2009) · PLoS Genetics · doi:10.1371/journal.pgen.1000477 · Cited by 606 · see citing works

Progress and promise of genome-wide association studies for human complex trait genetics

Stranger, B. E., Stahl, E. A., & Raj, T. (2011) · Genetics · doi:10.1534/genetics.110.120907 · Cited by 567 · see citing works

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

de Bakker, P. I. W., Ferreira, M. A. R., Jia, X., Neale, B. M., Raychaudhuri, S., & Voight, B. F. (2008) · Human Molecular Genetics · doi:10.1093/hmg/ddn288 · Cited by 531 · see citing works

Identifying Causal Variants at Loci with Multiple Signals of Association

Hormozdiari, F., Kostem, E., Kang, E. Y., Pasaniuc, B., & Eskin, E. (2014) · Genetics · doi:10.1534/genetics.114.167908 · Cited by 465 · see citing works

Accurate Prediction of Genetic Values for Complex Traits by Whole-Genome Resequencing

Meuwissen, T. H. E., & Goddard, M. (2010) · Genetics · doi:10.1534/genetics.110.116590 · Cited by 426 · see citing works

Improved linear mixed models for genome-wide association studies

Listgarten, J., Lippert, C., Kadie, C. M., Davidson, R. I., Eskin, E., & Heckerman, D. E. (2012) · Nature Methods · doi:10.1038/nmeth.2037 · Cited by 403 · see citing works

Extent and Distribution of Linkage Disequilibrium in Three Genomic Regions

Abecasis, G. R., Noguchi, E., Heinzmann, A., Traherne, J. A., Bhattacharyya, S., Leaves, N. I., Anderson, G. G., Zhang, Y., Lench, N., Carey, A. H., et al. (2001) · The American Journal of Human Genetics · doi:10.1086/316944 · Cited by 328 · see citing works

A high-resolution association mapping panel for the dissection of complex traits in micePreprint

Bennett, B. J., Farber, C. R., Orozco, L. D., Kang, H. M., Ghazalpour, A., Siemers, N. O., Neubauer, M., Neuhaus, I., Yordanova, R., Guan, B., et al. (2010) · Genome Research · doi:10.1101/gr.099234.109 · Cited by 314 · see citing works

Common variants at CDKAL1 and KLF9 are associated with body mass index in east Asian populations

GIANT consortium, Okada, Y., Kubo, M., Ohmiya, H., Takahashi, A., Kumasaka, N., Hosono, N., Maeda, S., Wen, W., Dorajoo, R., et al. (2012) · Nature Genetics · doi:10.1038/ng.1086 · Cited by 256 · see citing works

Using Whole-Genome Sequence Data to Predict Quantitative Trait Phenotypes in Drosophila melanogaster

Ober, U., Ayroles, J. F., Stone, E. A., Richards, S., Zhu, D., Gibbs, R. A., Stricker, C., Daniel Gianola, B., Schlather, M., Mackay, T. F. C., et al. (2012) · PLoS Genetics · doi:10.1371/journal.pgen.1002685 · Cited by 213 · see citing works

The extent of linkage disequilibrium in four populations with distinct demographic histories.

Dunning, A., Durocher, F., Healey, C., Teare, M., McBride, S., Carlomagno, F., Xu, C.-F., Dawson, E., Rhodes, S., Ueda, S., et al. (2000) · American Journal of Human Genetics · doi:10.1086/316906 · Cited by 190 · see citing works

Missing data imputation and haplotype phase inference for genome-wide association studies

Browning, S. R. (2008) · Human Genetics · doi:10.1007/s00439-008-0568-7 · Cited by 187 · see citing works

Rapid and Accurate Multiple Testing Correction and Power Estimation for Millions of Correlated Markers

Han, B., Kang, H. M., & Eskin, E. (2009) · PLoS Genetics · doi:10.1371/journal.pgen.1000456 · Cited by 175 · see citing works

Multiple Imputation for Nonresponse

Drechsler, J. (2011) · Lecture notes in statistics · doi:10.1007/978-1-4614-0326-5_5 · Cited by 135 · see citing works

A multiple-phenotype imputation method for genetic studies

Dahl, A., Iotchkova, V., Baud, A., Johansson, Å., Gyllensten, U. B., Soranzo, N., Mott, R., Kranis, A., & Marchini, J. (2016) · Nature Genetics · doi:10.1038/ng.3513 · Cited by 95 · see citing works

Identification of causal genes for complex traits

Hormozdiari, F., Kichaev, G., Yang, W.-Y., Paşaniuc, B., & Eskin, E. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv240 · Cited by 78 · see citing works

The power of genome-wide association studies of complex disease genes: statistical limitations of indirect approaches using SNP markers

Ohashi, J., & Tokunaga, K. (2001) · Journal of Human Genetics · doi:10.1007/s100380170048 · Cited by 74 · see citing works

Efficient Multiple-Trait Association and Estimation of Genetic Correlation Using the Matrix-Variate Linear Mixed Model

Furlotte, N. A., & Eskin, E. (2015) · Genetics · doi:10.1534/genetics.114.171447 · Cited by 71 · see citing works

Increasing power in association studies by using linkage disequilibrium structure and molecular function as prior informationPreprint

Eskin, E. (2008) · Genome Research · doi:10.1101/gr.072785.107 · Cited by 53 · see citing works

Increasing Power in Association Studies by Using Linkage Disequilibrium Structure and Molecular Function as Prior Information

Eskin, E. (2008) · Springer eBooks · doi:10.1007/978-3-540-78839-3_38 · Cited by 49 · see citing works

Comparing Algorithms for Genotype Imputation

Marchini, J., & Howie, B. (2008) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2008.09.007 · Cited by 39 · see citing works

Imputation aware meta‐analysis of genome‐wide association studies

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

Integrating Multiple Correlated Phenotypes for Genetic Association Analysis by Maximizing Heritability

Zhou, J. J., Cho, M. H., Lange, C., Lutz, S. M., Silverman, E. K., & Laird, N. M. (2015) · Human Heredity · doi:10.1159/000381641 · Cited by 22 · see citing works

Postassociation cleaning using linkage disequilibrium information

Han, B., Hackel, B. M., & Eskin, E. (2010) · Genetic Epidemiology · doi:10.1002/gepi.20544 · Cited by 20 · see citing works

Multiple Imputation of Missing Phenotype Data for QTL Mapping

Bobb, J. F., Scharfstein, D. O., Daniels, M. J., Collins, F. S., & Kelada, S. N. P. (2011) · Statistical Applications in Genetics and Molecular Biology · doi:10.2202/1544-6115.1676 · Cited by 9 · see citing works

Analysis of the progression of systolic blood pressure using imputation of missing phenotype values

Vaitsiakhovich, T., Drichel, D., Angisch, M., Becker, T., Herold, C., & Lacour, A. (2014) · BMC Proceedings · doi:10.1186/1753-6561-8-s1-s83 · Cited by 4 · see citing works

Imputation of Missing Ages in Pedigree Data

Balise, R. R., Chen, Y., Dite, G. S., Felberg, A., Sun, L., Ziogas, A., & Whittemore, A. S. (2007) · Human Heredity · doi:10.1159/000099829 · Cited by 1 · see citing works

Multi-omics approaches to disease

Hasin, Y., Seldin, M., & Lusis, A. (2017) · Genome Biology · doi:10.1186/s13059-017-1215-1 · Cited by 2,953 · see citing works

Colocalization of GWAS and eQTL Signals Detects Target Genes

Hormozdiari, F., van de Bunt, M., Segrè, A. V., Li, X., Joo, J. W. J., Bilow, M., Sul, J. H., Sankararaman, S., Pasaniuc, B., & Eskin, E. (2016) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2016.10.003 · Cited by 697 · see citing works

Dissecting the genetics of complex traits using summary association statistics

Paşaniuc, B., & Price, A. L. (2016) · Nature Reviews Genetics · doi:10.1038/nrg.2016.142 · Cited by 444 · see citing works

The emerging landscape of health research based on biobanks linked to electronic health records: Existing resources, statistical challenges, and potential opportunities

Beesley, L. J., Salvatore, M., Fritsche, L. G., Pandit, A., Rao, A., Brummett, C. M., Willer, C. J., Lisabeth, L. D., & Mukherjee, B. (2019) · Statistics in Medicine · doi:10.1002/sim.8445 · Cited by 106 · see citing works

Gene–environment interactions in human health

Luis, E. H., Benke, K. S., Volk, H. E., Ladd‐Acosta, C., & Wojcik, G. L. (2024) · Nature Reviews Genetics · doi:10.1038/s41576-024-00731-z · Cited by 111 · see citing works

Large-scale machine-learning-based phenotyping significantly improves genomic discovery for optic nerve head morphology

Alipanahi, B., Hormozdiari, F., Behsaz, B., Cosentino, J., McCaw, Z. R., Schorsch, E., Sculley, D., Dorfman, E. H., Foster, P. J., Peng, L. H., et al. (2021) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2021.05.004 · Cited by 71 · see citing works

Genome-wide association study of breakfast skipping links clock regulation with food timing

Dashti, H. S., Merino, J., Lane, J. M., Song, Y., Smith, C. E., Tanaka, T., McKeown, N. M., Tucker, C., Sun, D., Bartz, T. M., et al. (2019) · American Journal of Clinical Nutrition · doi:10.1093/ajcn/nqz076 · Cited by 61 · see citing works

Phenotype integration improves power and preserves specificity in biobank-based genetic studies of major depressive disorder

Dahl, A., Thompson, M., An, U., Krebs, M. D., Appadurai, V., Border, R., Bacanu, S., Werge, T. M., Jonathan Flint, C., Schork, A. J., et al. (2023) · Nature Genetics · doi:10.1038/s41588-023-01559-9 · Cited by 59 · see citing works

Deep learning-based phenotype imputation on population-scale biobank data increases genetic discoveries

An, U., Pazokitoroudi, A., Alvarez, M., Huang, L., Bacanu, S., Schork, A. J., Kendler, K. S., Pajukanta, P., Jonathan Flint, C., Zaitlen, N., et al. (2023) · Nature Genetics · doi:10.1038/s41588-023-01558-w · Cited by 55 · see citing works

Informatics and machine learning to define the phenotype

Basile, A. O., & Ritchie, M. D. (2018) · Expert Review of Molecular Diagnostics · doi:10.1080/14737159.2018.1439380 · Cited by 51 · see citing works

Valid inference for machine learning-assisted genome-wide association studies

Miao, J., Wu, Y., Sun, Z., Miao, X., Lu, T., Zhao, J., & Lu, Q. (2024) · Nature Genetics · doi:10.1038/s41588-024-01934-0 · Cited by 28 · see citing works

GxEsum: a novel approach to estimate the phenotypic variance explained by genome-wide GxE interaction based on GWAS summary statistics for biobank-scale data

Shin, J., & Lee, S. (2021) · Genome biology · doi:10.1186/s13059-021-02403-1 · Cited by 31 · see citing works

Leveraging human genetic and adverse outcome pathway (AOP) data to inform susceptibility in human health risk assessment

Mortensen, H. M., Chamberlin, J., Joubert, B. R., Angrish, M. M., Sipes, N. S., Lee, J. S., & Euling, S. Y. (2018) · Mammalian Genome · doi:10.1007/s00335-018-9738-7 · Cited by 27 · see citing works

Reference Trait Analysis Reveals Correlations Between Gene Expression and Quantitative Traits in Disjoint Samples

Skelly, D. A., Raghupathy, N., Robledo, R. F., Graber, J. H., & Chesler, E. J. (2019) · Genetics · doi:10.1534/genetics.118.301865 · Cited by 19 · see citing works

Synthetic surrogates improve power for genome-wide association studies of partially missing phenotypes in population biobanks

McCaw, Z. R., Gao, J., Lin, X., & Gronsbell, J. L. (2024) · Nature Genetics · doi:10.1038/s41588-024-01793-9 · Cited by 19 · see citing works

Endophenotype 2.0: updated definitions and criteria for endophenotypes of psychiatric disorders, incorporating new technologies and findings

Liu, C., & Gershon, E. S. (2024) · Translational Psychiatry · doi:10.1038/s41398-024-03195-1 · Cited by 19 · see citing works

Rapid and accurate multi-phenotype imputation for millions of individuals

Gu, L.-L., Wu, H.-S., Liu, T., Zhang, Y., He, J.-C., Liu, X., Wang, Z., Chen, G., Jiang, D. L., & Fang, M. (2025) · Nature Communications · doi:10.1038/s41467-024-55496-0 · Cited by 11 · see citing works

Comparison of two multi-trait association testing methods and sequence-based fine mapping of six additive QTL in Swiss Large White pigs

Nosková, A., Mehrotra, A., Kadri, N. K., Lloret‐Villas, A., Neuenschwander, S., Andreas Hofer, A., & Pausch, H. (2023) · BMC Genomics · doi:10.1186/s12864-023-09295-4 · Cited by 10 · see citing works

Coupled mixed model for joint genetic analysis of complex disorders with two independently collected data sets

Wang, H., Pei, F., Vanyukov, M. M., Bahar, İ., Wu, W., & Xing, E. P. (2021) · BMC Bioinformatics · doi:10.1186/s12859-021-03959-2 · Cited by 10 · see citing works

GxEsum: a novel approach to estimate the phenotypic variance explained by genome-wide GxE interaction based on GWAS summary statistics for biobank-scale dataPreprint

Shin, J., & Lee, S. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.05.31.122549 · Cited by 9 · see citing works

Genetic association studies using disease liabilities from deep neural networks

Yang, L., Sadler, M. C., & Altman, R. B. (2025) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2025.01.019 · Cited by 8 · see citing works

The Emerging Landscape of Epidemiological Research Based on Biobanks Linked to Electronic Health Records: Existing Resources, Analytic Challenges and Potential OpportunitiesPreprint

Beesley, L. J., Salvatore, M., Fritsche, L. G., Pandit, A., Rao, A., Brummett, C. M., Willer, C. J., Lisabeth, L. D., & Mukherjee, B. (2018) · Preprints.org · doi:10.20944/preprints201809.0388.v1 · Cited by 7 · see citing works

Using GWAS summary data to impute traits for genotyped individuals

Ren, J., Lin, Z., He, R., Shen, X., & Pan, W. (2023) · Human Genetics and Genomics Advances · doi:10.1016/j.xhgg.2023.100197 · Cited by 6 · see citing works

Missingness adapted group informed clustered (MAGIC)-LASSO: a novel paradigm for phenotype prediction to improve power for genetic loci discovery

Gentry, A. E., Kirkpatrick, R. M., Peterson, R. E., & Webb, B. T. (2023) · Frontiers in Genetics · doi:10.3389/fgene.2023.1162690 · Cited by 4 · see citing works

Lifestyle Risk Score: handling missingness of individual lifestyle components in meta-analysis of gene-by-lifestyle interactions

Xu, H., Schwander, K., Brown, M. R., Wang, W., Waken, R. J., Boerwinkle, E., Cupples, L. A., Fuentes, L. D. L., van Heemst, D., Osazuwa‐Peters, O. L., et al. (2021) · European Journal of Human Genetics · doi:10.1038/s41431-021-00808-x · Cited by 5 · see citing works

Evaluation of a phenotype imputation approach using GAW20 simulated data

Chen, Y., Peloso, G. M., & Dupuis, J. (2018) · BMC Proceedings · doi:10.1186/s12919-018-0134-9 · Cited by 5 · see citing works

Phenotype integration improves power and preserves specificity in biobank-based genetic studies of MDDPreprint

Dahl, A. W., Thompson, M. J., An, U., Krebs, M., Appadurai, V., Border, R., Bacanu, S., Werge, T., Flint, J., Schork, A., et al. (2023) · bioRxiv · doi:10.1101/2022.08.15.503980 · Cited by 6 · see citing works

When a phenotype is not the genotype: Implications of phenotype misclassification and pedigree errors in genomics-assisted breeding of sweetpotato [ Ipomoea batatas (L.) Lam.]Preprint

Gemenet, D. C., Boeck, B. D., Pereira, G. D. S., Kitavi, M. N., Ssali, R., Utoblo, O. G., Swanckaert, J., Carey, E. E., Grüneberg, W. J., Yada, B., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/747469 · Cited by 4 · see citing works

Machine learning: a long way from implementation in cardiovascular disease

Chen, S., & Banerjee, A. (2020) · Heart · doi:10.1136/heartjnl-2019-316030 · Cited by 4 · see citing works

Genome-wide associations spanning 194 in-hospital drug dosage change phenotypes highlight diverse genetic backgrounds in concurrent drug therapy

Henriksen, A. P., Rodríguez, C. L., Currant, H., Louloudis, I., Biel, J. H., Herrero-Zazo, M., Birney, E., Hansen, T. F., Mazzoni, G., Haue, A. D., et al. (2025) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2025.06.042 · Cited by 3 · see citing works

A multiple-phenotype imputation procedure as a method for prediction of cheese-making efficiency in Spanish Assaf sheep

Marina, H., Reverter, A., Gutiérrez‐Gil, B., Alexandre, P. A., Pelayo, R., Suárez‐Vega, A., Esteban‐Blanco, C., & Arranz, J. J. (2020) · Journal of Animal Science · doi:10.1093/jas/skaa370 · Cited by 3 · see citing works

Improving Imputation Accuracy by Inferring Causal Variants in Genetic Studies

Wu, Y., Hormozdiari, F., Joo, J. W. J., & Eskin, E. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-56970-3_19 · Cited by 2 · see citing works

Integrating GWAS summary statistics, individual-level genotypic and omic data to enhance the performance for large-scale trait imputation

Ren, J., Lin, Z., & Pan, W. (2023) · Human Molecular Genetics · doi:10.1093/hmg/ddad097 · Cited by 2 · see citing works

Machine learning-based quantification for disease uncertainty increases the statistical power of genetic association studies

Park, J. Y., Lee, J. J., Lee, Y., Lee, D., Gim, J., Farrer, L. A., Lee, K. H., & Won, S. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad534 · Cited by 2 · see citing works

Statistical inference with large‐scale trait imputation

Ren, J., & Pan, W. (2023) · Statistics in Medicine · doi:10.1002/sim.9975 · Cited by 2 · see citing works

The Multivariate Normal Distribution Framework for Analyzing Association StudiesPreprint

Lozano, J. A., Hormozdiari, F., Joo, J. W. J., Han, B., & Eskin, E. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/208199 · Cited by 2 · see citing works

An Empirical Study of ML-based Phenotyping and Denoising for Improved Genomic DiscoveryPreprint

Yuan, B., Hormozdiari, F., McLean, C. Y., & Cosentino, J. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.11.17.516907 · Cited by 2 · see citing works

Trait imputation enhances nonlinear genetic prediction for some traits

He, R., Fu, J., Ren, J., & Pan, W. (2024) · Genetics · doi:10.1093/genetics/iyae148 · Cited by 1 · see citing works

A novel phenotype imputation method with copula model

Zhang, J., Zhao, J. Z., Gonzales, S., Wang, X., & Sha, Q. (2024) · BMC Bioinformatics · doi:10.1186/s12859-024-05990-5 · Cited by 1 · see citing works

Missingness Adapted Group Informed Clustered (MAGIC)-LASSO: A novel paradigm for prediction in data with widespread non-random missingnessPreprint

Gentry, A. E., Kirkpatrick, R. M., Peterson, R. E., & Webb, B. T. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.04.29.442057 · Cited by 1 · see citing works

Improving Imputation Accuracy by Inferring Causal Variants in Genetic Studies

Wu, Y., Hormozdiari, F., Joo, J. W. J., & Eskin, E. (2018) · Journal of Computational Biology · doi:10.1089/cmb.2018.0139

Methods and results from the genome-wide association group at GAW20

Wang, X., Boekstegers, F., & Brinster, R. (2018) · BMC Genetics · doi:10.1186/s12863-018-0649-0

Comparison of two multi-trait association testing methods and sequence-based fine mapping of six QTL in Swiss Large White pigsPreprint

Nosková, A., Mehrotra, A., Kadri, N. K., Lloret‐Villas, A., Neuenschwander, S., Andreas Hofer, A., & Pausch, H. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.12.13.520268

Linking Phenotypes and Genotypes with Matrix Factorizations

Li, J., Guan, Y., Xu, X., Ma, Z., & Pei, Y. (2023) · Current Pharmaceutical Biotechnology · doi:10.2174/1389201024666230207153738

Estimating the cis -heritability of gene expression using single cell expression profiles controls false positive rate of eGene detectionPreprint

Xu, Z., Massarat, A. R., Rumker, L., Gymrek, M., Raychaudhuri, S., Zhou, W., & Amariuta, T. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.02.24.639892

Additional file 4 of Comparison of two multi-trait association testing methods and sequence-based fine mapping of six additive QTL in Swiss Large White pigs

Nosková, A., Mehrotra, A., Kadri, N. K., Lloret‐Villas, A., Neuenschwander, S., Andreas Hofer, A., & Pausch, H. (2023) · Figshare · doi:10.6084/m9.figshare.22626415

Additional file 2 of Comparison of two multi-trait association testing methods and sequence-based fine mapping of six additive QTL in Swiss Large White pigs

Nosková, A., Mehrotra, A., Kadri, N. K., Lloret‐Villas, A., Neuenschwander, S., Andreas Hofer, A., & Pausch, H. (2023) · Figshare · doi:10.6084/m9.figshare.22626409

Additional file 1 of Coupled mixed model for joint genetic analysis of complex disorders with two independently collected data sets

Wang, H., Pei, F., Vanyukov, M. M., Bahar, I., Wu, W., & Xing, E. P. (2021) · Figshare · doi:10.6084/m9.figshare.13725332

Additional file 1 of Comparison of two multi-trait association testing methods and sequence-based fine mapping of six additive QTL in Swiss Large White pigs

Nosková, A., Mehrotra, A., Kadri, N. K., Lloret‐Villas, A., Neuenschwander, S., Andreas Hofer, A., & Pausch, H. (2023) · Figshare · doi:10.6084/m9.figshare.22626406

Additional file 9 of Comparison of two multi-trait association testing methods and sequence-based fine mapping of six additive QTL in Swiss Large White pigs

Nosková, A., Mehrotra, A., Kadri, N. K., Lloret‐Villas, A., Neuenschwander, S., Andreas Hofer, A., & Pausch, H. (2023) · Figshare · doi:10.6084/m9.figshare.22626430

Additional file 10 of Comparison of two multi-trait association testing methods and sequence-based fine mapping of six additive QTL in Swiss Large White pigs

Nosková, A., Mehrotra, A., Kadri, N. K., Lloret‐Villas, A., Neuenschwander, S., Andreas Hofer, A., & Pausch, H. (2023) · Figshare · doi:10.6084/m9.figshare.22626433

The association of a common HFE gene variant with stroke disability utilizing predictive machine learning and electronic health records

Markus, H., Helmuth, T. B., Connor, J. R., Liu, D. J., & Simon, S. D. (2026) · Scientific Reports · doi:10.1038/s41598-026-46129-1

Phenotype imputation using high‐throughput phenotyping produces a new secondary trait for further selection modeling

Gevartosky, R., Munaro, L. B., & Rutkoski, J. E. (2026) · The Plant Phenome Journal · doi:10.1002/ppj2.70081

Evaluation of a phenotype imputation approach using GAW20 simulated data

Chen, Y., Peloso, G. M., & Dupuis, J. (2018) · BMC Proceedings · doi:10.1186/s12919-018-0134-9 · Cited by 5 · see citing works

Genotype imputation for genome-wide association studies

Marchini, J., & Howie, B. (2010) · Nature Reviews Genetics · doi:10.1038/nrg2796 · Cited by 1,597 · see citing works

Genome-wide association test of multiple continuous traits using imputed SNPs

Wu, B., & Pankow, J. S. (2017) · Statistics and Its Interface · doi:10.4310/sii.2017.v10.n3.a2 · Cited by 4 · see citing works

A new multipoint method for genome-wide association studies by imputation of genotypes

Marchini, J., Howie, B., Myers, S., McVean, G., & Donnelly, P. (2007) · Nature Genetics · doi:10.1038/ng2088 · Cited by 2,609 · see citing works

REPORT The Relationship between Imputation Error and Statistical Power in Genetic Association Studies in Diverse Populations

Huang, L., Wang, C., & Rosenberg, N. A. (2013)