Hints of hidden heritability in GWAS

Finding the missing heritability of complex diseases

Manolio, T. A., Collins, F. S., Cox, N. J., Goldstein, D. B., Hindorff, L. A., Hunter, D. J., McCarthy, M. I., Ramos, E. M., Cardon, L. R., Chakravarti, A., et al. (2009) · Nature · doi:10.1038/nature08494 · Cited by 8,336

Common polygenic variation contributes to risk of schizophrenia and bipolar disorder

The International Schizophrenia Consortium (2009) · Nature · doi:10.1038/nature08185 · Cited by 4,821

Common SNPs explain a large proportion of the heritability for human height

Yang, J., Benyamin, B., McEvoy, B. P., Gordon, S., Henders, A. K., Nyholt, D. R., Madden, P. A., Heath, A. C., Martin, N. G., Montgomery, G. W., et al. (2010) · Nature Genetics · doi:10.1038/ng.608 · Cited by 4,446

XV.—The Correlation between Relatives on the Supposition of Mendelian Inheritance.

Fisher, R. A. (1919) · Transactions of the Royal Society of Edinburgh · doi:10.1017/s0080456800012163 · Cited by 4,387

Missing heritability and strategies for finding the underlying causes of complex disease

Eichler, E. E., Flint, J., Gibson, G. C., Kong, A., Leal, S. M., Moore, J. H., & Nadeau, J. H. (2010) · Nature Reviews Genetics · doi:10.1038/nrg2809 · Cited by 1,728

The investigation of linkage between a quantitative trait and a marker locus

Haseman, J. K., & Elston, R. C. (1972) · Behavior Genetics · doi:10.1007/bf01066731 · Cited by 1,367

Estimation of effect size distribution from genome-wide association studies and implications for future discoveries

Park, J.-H., Wacholder, S., Gail, M. H., Peters, U., Jacobs, K. B., Chanock, S. J., & Chatterjee, N. (2010) · Nature Genetics · doi:10.1038/ng.610 · Cited by 678

The genetic interpretation of area under the ROC curve in genomic profiling

Wray, N. R., Yang, J., Goddard, M. E., & Visscher, P. M. (2010) · PLoS Genetics · doi:10.1371/journal.pgen.1000864 · Cited by 334

Harnessing the information contained within genome-wide association studies to improve individual prediction of complex disease risk

Evans, D. M., Visscher, P. M., & Wray, N. R. (2009) · Human Molecular Genetics · doi:10.1093/hmg/ddp295 · Cited by 297

Reaching new heights: insights into the genetics of human stature

Weedon, M. N., & Frayling, T. M. (2008) · Trends in Genetics · doi:10.1016/j.tig.2008.09.006 · Cited by 147

Statistical power and heritability in whole-genome association studies for quantitative traits

Khanzadeh, H., Hossein‐Zadeh, N. G., & Ghovvati, S. (2021) · Meta Gene · doi:10.1016/j.mgene.2021.100869 · Cited by 6

Exploring the hidden heritability of inflammatory bowel disease

Satsangi, J., Kennedy, N. A., Henderson, P., Wilson, D. C., & Nimmo, E. R. (2011) · Gut · doi:10.1136/gutjnl-2011-300804 · Cited by 8

A forest-based feature screening approach for large-scale genome data with complex structures

Wang, G., Fu, G., & Corcoran, C. D. (2015) · BMC Genetics · doi:10.1186/s12863-015-0294-9 · Cited by 7

EXOME SEQUENCING AND THE DIAGNOSTICS OF COMPLEX DISEASE PREDISPOSITION IN PREVENTIVE MEDICINE

Щербакова, Н. В., Meshkov, A. N., & Boytsov, S. A. (2013) · CARDIOVASCULAR THERAPY AND PREVENTION · doi:10.15829/1728-8800-2013-6-24-28 · Cited by 6

Finding the Missing Heritability of Genome-wide Association Study Using Genotype Imputation

Fan, Y., & Song, Y. (2016) · Matters · doi:10.19185/matters.201604000013 · Cited by 5

2D association and integrative omics analysis in rice provides systems biology view in trait analysis

Zhang, W., Dai, X., Xu, S., & Zhao, P. X. (2018) · Communications Biology · doi:10.1038/s42003-018-0159-7 · Cited by 5

de CODE and Iceland: A Critique

Árnason, E., & D, B. A. M. (2013) · Wiley eBooks · doi:10.1002/9780470015902.a0005180.pub2 · Cited by 6

A parallelized strategy for epistasis analysis based on Empirical Bayesian Elastic Net models

Wen, J., Ford, C. T., Janies, D. A., & Shi, X. M. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa216 · Cited by 4

Concept of Genome-Wide Association Studies

Lee, C., Kim, T., Lee, S., & Park, Y. (2015) · Springer eBooks · doi:10.1007/978-94-017-9996-6_6 · Cited by 4

Unveiling the Genomic Architecture of Phenotypic Plasticity Using Multiple GWAS Approaches Under Contrasting Conditions of Water Availability: A Model for Barley

Arenas, S., & Cortés, A. J. (2026) · International Journal of Molecular Sciences · doi:10.3390/ijms27020652 · Cited by 3

Integrative bioinformatics frameworks for abdominal aortic aneurysm using GWAS meta-analysis, biological network construction, and structural modeling

Sagulkoo, P., Chuntakaruk, H., Suratanee, A., Plaimas, K., & Teerakulkittipong, N. (2025) · Scientific Reports · doi:10.1038/s41598-025-07989-1 · Cited by 4

Candidate genes revisited in the genetics of hypertension and blood pressure

Kato, N. (2013) · Hypertension Research · doi:10.1038/hr.2013.89 · Cited by 3

Structural and Functional Characterization of a Caenorhabditis elegans Genetic Interaction Network within Pathways

Boucher, B., Lee, A. Y., Hallett, M. T., & Jenna, S. (2016) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004738 · Cited by 3

Modified bagging of maximal information coefficient for genome-wide identification

Liu, H. M., Rao, N., Yang, D., Yang, L. X., Li, Y., & Guo, F. B. (2016) · International Journal of Data Mining and Bioinformatics · doi:10.1504/ijdmb.2016.074875 · Cited by 3

Promises and challenges of translational research in neuropsychiatry

BRAFF, D. L. (2012) · Cambridge University Press eBooks · doi:10.1017/cbo9780511980053.021 · Cited by 3

First-degree relatives with similar phenotypic characterisation of acute myocardial infarction: a case report and review of the literature

Hao, Y.-D., Ohene, B. E., Yang, S.-W., & Zhou, Y. (2019) · BMC Cardiovascular Disorders · doi:10.1186/s12872-019-01303-4 · Cited by 3

Immunogenetics and the mapping of immunological functions

Lamont, S. J., Dekkers, J. C. M., Wolc, A., & Zhou, H. (2022) · Elsevier eBooks · doi:10.1016/b978-0-12-818708-1.00011-7 · Cited by 2

EpiVisR: exploratory data analysis and visualization in epigenome-wide association analyses

Röder, S. W., Herberth, G., Zenclussen, A. C., & Bauer, M. (2022) · BMC Bioinformatics · doi:10.1186/s12859-022-04836-2 · Cited by 2

Trans-Acting Genotypes Associated with mRNA Expression Affect Metabolic and Thermal Tolerance Traits

Drown, M. K., Oleksiak, M. F., & Crawford, D. L. (2023) · Genome Biology and Evolution · doi:10.1093/gbe/evad123 · Cited by 3

A powerful score-based test statistic for detecting gene-gene co-association

Xu, J., Yuan, Z., Ji, J., Zhang, X., Li, H., Wu, X., Xue, F., & Liu, Y. (2016) · BMC Genetics · doi:10.1186/s12863-016-0331-3 · Cited by 3

Short Tandem Repeats as a High-Resolution Marker for Capturing Recent Orangutan Population Evolution

Voicu, A.-A., Krützen, M., & Sonay, T. B. (2021) · Frontiers in Bioinformatics · doi:10.3389/fbinf.2021.695784 · Cited by 2

Effects of Six Functional SNPs on the Urinary 8-Isoprostane Level in a General Japanese Population; Shimane COHRE Study

Suyama, Y., Matsuda, C., Isomura, M., Hamano, T., Karino, K., Yamasaki, M., Yamaguchi, S., Shiwaku, K., Masuda, J., & Nabika, T. (2011) · Disease Markers · doi:10.1155/2011/469854 · Cited by 2

Discussion of “Scaling-up process characterization”

Woodall, W. H., Kodali, L., & Yashchin, E. (2017) · Quality Engineering · doi:10.1080/08982112.2017.1381487 · Cited by 2

Cell fitness is an omniphenotypePreprint

Jacobs, N. C., Park, J., Saccone, N. L., & Peterson, T. R. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/487157 · Cited by 2

Divide and conquer approach for genome-wide association studies

Özkaraca, M. İ., Agung, M., Navarro, P., & Tenesa, A. (2025) · Genetics · doi:10.1093/genetics/iyaf019 · Cited by 1

DisSNPNet: Predicting disease-associated single-nucleotide polymorphisms using linkage disequilibrium, disease similarity, and 1000 Genomes Project datasets with evidence-based validation

Le, D. (2026) · Biology Methods and Protocols · doi:10.1093/biomethods/bpag004 · Cited by 1

Identification of Protein Signatures Reflecting Latent Variation in Aptamer-Based Affinity Proteomics

Stephan, N., Halama, A., Thareja, G., Sarwath, H., Grallert, H., Peters, A., Gieger, C., Schmidt, F., Graumann, J., & Suhre, K. (2026) · Journal of Proteome Research · doi:10.1021/acs.jproteome.5c00887 · Cited by 1

Avances en enfoques multidisciplinarios y en diversas especies para el examen de la neurobiología de los trastornos psiquiátricos

Kas, M. J., Krishnan, V., Gould, T. D., Collier, D. A., Olivier, B., Lesch, K., Domenici, E., Fuchs, E., Gross, C. T., & Ċastrén, E. (2012) · Psiquiatría Biológica · doi:10.1016/j.psiq.2012.03.001 · Cited by 1

Protein interaction networks define the genetic architecture of preterm birth

Uzun, A., Schuster, J. S., Stabila, J. P., Zarate, V., Tollefson, G. A., Agudelo, A., Kothiyal, P., Wong, W. S. W., & Padbury, J. F. (2022) · Scientific Reports · doi:10.1038/s41598-021-03427-0 · Cited by 1

New approaches to the selection of genetic markers associated with multifactorial phenotypic traits

Кутелев, Г. Г., Krivoruchko, A. B., Евгеньевна, Т. А., Ivanov, A. M., Cherkashin, D. V., Марченко, А. А., & Гришаев, С. Л. (2020) · Bulletin of the Russian Military Medical Academy · doi:10.17816/brmma50074 · Cited by 1

Molecular Genetics of Bipolar Disorder

Offord, J. (2013) · Wiley eBooks · doi:10.1002/9780470015902.a0022426 · Cited by 1

Selection and Complex Multigene Traits

Hasson, E., Fanara, J. J., & Frankel, N. (2013) · Wiley eBooks · doi:10.1002/9780470015902.a0002295 · Cited by 1

Discovery of multivariate phenotypes using association rule mining and their application to genome-wide association studies

Park, S. H., & Kim, S. (2010) · IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW) · doi:10.1109/bibmw.2010.5703822 · Cited by 1

Unraveling genetic factors involved in intelligence, educational attainment and socioeconomic standing: what are the implications for childhood mental health care professionals?

Melchior, M., & Hebebrand, J. (2018) · European Child & Adolescent Psychiatry · doi:10.1007/s00787-018-1142-z · Cited by 1

The fog of genetics: Known unknowns and unknown unknowns in the genetics of complex traits and diseasesPreprint

de Magalhães, J. P., & Wang, J. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/553685 · Cited by 1

Proteinarium: Multi-Sample Protein-Protein Interaction Analysis and Visualization ToolPreprint

Armanious, D., Schuster, J. S., Tollefson, G. A., Agudelo, A., DeWan, A. T., Istrail, S., Padbury, J. F., & Uzun, A. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/589085 · Cited by 1

Rare Variants Analysis in Unrelated Individuals

Feng, T., & Zhu, X. (2012) · Translational bioinformatics · doi:10.1007/978-94-007-5558-1_8

Scale effects on genomic modelling and prediction

Berger, S. (2015) · eDiss (Georg-August-Universität Göttingen) · doi:10.53846/goediss-5230

Common mtDNA Polymorphisms and Neurodegenerative Disorders

Elson, J. L., & Samuels, D. C. (2011) · Springer eBooks · doi:10.1007/978-0-85729-701-3_4

Some Clarifications about the Use of the Relation between Height and Education as a Proxy for Social Inequalities: a Response to the Comments

Sarti, S. (2013) · Sociologia · doi:10.2383/74860

Machine Learning-Based Imputation of Missing SNP Genotypes in SNP Genotype Arrays

Mihajlović, A. (2013) · Springer eBooks · doi:10.1007/978-1-4614-8785-2_6

Kernel Methods for Genes and Networks to Study Genome-Wide Associations of Lung Cancer and Rheumatoid Arthritis

Freytag, S. (2014) · doi:10.53846/goediss-4373

Design and Implementation of a Computational Platform and a Parallelized Interaction Analysis for Large Scale Genomics Data in Multiple Sclerosis

Uvehag, D. (2013) · KTH Publication Database DiVA (KTH Royal Institute of Technology)

Visualization of biological data: Infrastructure, design and application

Yin, T. (2013) · doi:10.31274/etd-180810-3615

Original article dbPTB: a database for preterm birth

Uzun, A., Laliberte, A., Parker, J., Andrew, C., Winterrowd, E., Sharma, S., Istrail, S., & Padbury, J. F. (2013)

On Identifying Rare Variants for Complex Human Traits

Fan, R. (2015) · doi:10.7916/d8n29vt4

Increasing the Discovery Power and Condence Levels of Disease Association Studies: A Survey

Mousavi, P., & Shatkay, H. (2014)

Novel insights into the genetic basis of chronic obstructive pulmonary disease

Radder, J. E. (2016) · D-Scholarship@Pitt (University of Pittsburgh)

Statistical analysis for aggregated count data in genetic association studies

Choi, H., Jung, H. Y., & Park, T. (2016) · International Journal of Data Mining and Bioinformatics · doi:10.1504/ijdmb.2016.079802

How Genes Modulate Patterns of Aging-Related Changes on the Way to 100: Lessons from Biodemographic Analyses of Longitudinal Data

Yashin, A. I., Arbeev, K. G., Wu, D., Kulminski, A. M., Arbeeva, L. S., Kulminskaya, I., Akushevich, I. V., & Ukraintseva, S. V. (2014)

Natural genetic variation for regulation of photosynthesis response to light in Arabidopsis thaliana

van Rooijen, R. (2016) · doi:10.18174/382144

Algorithms towards haplotype-sharing based association studies of case-control traits on pedigree data

Sabaa, H. (2011) · ERA: Education and Research Archive (University of Alberta) · doi:10.7939/r3gs6d

Inherited Susceptibility to Complex Diseases

Vercelli, D. (2017) · Elsevier eBooks · doi:10.1016/b978-0-12-801238-3.65387-4

Human Being as a “Superorganism”; Genetic Control of Microbiome

Gündoğdu, A. (2016) · Türk Mikrobiyoloji Cemiyeti Dergisi · doi:10.5222/tmcd.2016.147 · Cited by 1

Exploring the resistance against root parasitic plants in Arabidopsis and tomato

Cheng, X. (2017) · doi:10.18174/423176

The Application of Genetic Tests in an Assisted Reproduction Unit: Karyotype

Henkel, R., & Fielding, B. C. (2017) · CRC Press eBooks · doi:10.4324/9781315151632-4

Polygene Methods in Genome-Wide Association Studies (GWAS)

Cooley, P., & Folsom, R. (2016) · doi:10.3768/rtipress.2016.bk.0016.1608.8

A Method for Analyzing Two-locus Epistasis of Complex Diseases based on Decision Tree and Mutual Entropy

Li, X., Yang, H., Wen, K., Zhong, X., Xia, X., Liu, L., & Qin, D. (2019) · Current Proteomics · doi:10.2174/1570164616666190123150236

Genetic Risk Prediction in IBD

Marigorta, U. M. (2019) · Springer eBooks · doi:10.1007/978-3-030-28703-0_7 · Cited by 1

Phenome-wide association study of a comprehensive health check-up database in a Korea population: Clinical application & trans-ethnic comparisonPreprint

Choe, E. K., Shivakumar, M., Verma, A., Verma, S. S., Choi, S. H., Kim, J. S., & Kim, D. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.05.31.126201

Exploiting genetics to understand paediatric eye disorders

Plotnikov, D. (2020) · ORCA Online Research @Cardiff (Cardiff University)

E-index Method for Identifying Dynamic Growth Traits of PoplarPreprint

Wang, L., Wang, J., & Qi, H. (2021) · Research Square · doi:10.21203/rs.3.rs-1034398/v1

Faculty Opinions recommendation of More than the sum of its parts: a complex epistatic network underlies natural variation in thermal preference behavior in Caenorhabditis elegans.

Promislow, D. (2012) · Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature · doi:10.3410/f.717967450.793467100

Peer Review #1 of "A surge of p-values between 0.041 and 0.049 in recent decades (but negative results are increasing rapidly too) (v0.1)"

(2015) · PeerJ · doi:10.7287/peerj.733v0.1/reviews/1

Peer Review #1 of "A surge of p-values between 0.041 and 0.049 in recent decades (but negative results are increasing rapidly too) (v0.2)"

(2015) · PeerJ · doi:10.7287/peerj.733v0.2/reviews/1

Correction: Heritability and Genetic Correlations Explained by Common SNPs for Metabolic Syndrome Traits

Vattikuti, S., Guo, J., & Chow, C. C. (2012) · PLoS Genetics · doi:10.1371/annotation/8f3f1f56-6731-486e-8d55-5fa09b476cad

Peer Review #2 of "A surge of p-values between 0.041 and 0.049 in recent decades (but negative results are increasing rapidly too) (v0.1)"

(2015) · PeerJ · doi:10.7287/peerj.733v0.1/reviews/2

Microarrays

Wilkerson, P. M., Natrajan, R., & Reis-Filho, J. S. (2013) · Elsevier eBooks · doi:10.1016/b978-0-12-374984-0.00940-2

Rare Variant Analysis in Unrelated Individuals

Feng, T., & Zhu, X. (2018) · Translational bioinformatics · doi:10.1007/978-981-13-1071-3_4

The Application of Genetic Tests in an Assisted Reproduction Unit: Karyotype

(2017) · CRC Press eBooks · doi:10.1201/9781315151632-5

Trans-Acting Genotypes Drive mRNA Expression Affecting Metabolic And Thermal Tolerance TraitsPreprint

Drown, M. K., Oleksiak, M. F., & Crawford, D. L. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.01.15.524165

Additional file 1 of Deep sequencing reveals a DAP1 regulatory haplotype that potentiates autoimmunity in systemic lupus erythematosus

Raj, P., Song, R., Zhu, H., Riediger, L., Jun, D., Liang, C., Arana, C., Zhang, B., Gao, Y., Wakeland, B., et al. (2020) · Figshare · doi:10.6084/m9.figshare.13265907

An extended omnigenic model explains genome-phenome relationships for complex traits in global sorghum diversityPreprint

Hu, Z., Wang, X., Marla, S., Poland, J., & Morris, G. P. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.04.29.591686 · Cited by 1

A high-dimensional omnibus test for set-based association analysis

Yang, H., Wang, X., Zhang, Z., Chen, F., Cao, H., Yan, L., Gao, X., Dong, H., & Cui, Y. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae456

Additional file 2 of Deep sequencing reveals a DAP1 regulatory haplotype that potentiates autoimmunity in systemic lupus erythematosus

Raj, P., SONG, R., Zhu, H., Riediger, L., Jun, D.-J., Liang, C., Arana, C., Zhang, B., Gao, Y., Wakeland, B., et al. (2020) · Figshare · doi:10.6084/m9.figshare.13265910

Additional file 3 of Deep sequencing reveals a DAP1 regulatory haplotype that potentiates autoimmunity in systemic lupus erythematosus

Raj, P., SONG, R., Zhu, H., Riediger, L., Jun, D.-J., Liang, C., Arana, C., Zhang, B., Gao, Y., Wakeland, B., et al. (2020) · Figshare · doi:10.6084/m9.figshare.13265913

Bridging the gap between genome-wide association studies and network medicine with GNExTPreprint

Arend, L., Woller, F., Rehor, B., Emmert, D. B., Frasnelli, J., Fuchsberger, C., Blumenthal, D. B., & List, M. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.01.30.702559

Unravelling the genetic architecture of cardiovascular disease through structural variant detection with whole-genome sequencing

Colombage, D. N. P., Moses, E. K., & Melton, P. E. (2026) · Frontiers in Genetics · doi:10.3389/fgene.2026.1747711

Polygenic modeling of genetic effects on both phenotypic mean and variance: distributional regression for BMI, blood and urine biomarkers in the UK Biobank

Kunwar, K., Wu, Q., Klinkhammer, H., Staerk, C., Mayr, A. J., & Maj, C. (2026) · Frontiers in Bioinformatics · doi:10.3389/fbinf.2026.1800403

Integrating genomics and endocrinology to investigate prolactin dysregulation in female African Savannah elephants housed in north American zoos

Gregory, J. T., Maldonado, J. E., Brown, J. L., McInerney, N. R., Rogers, R., Campana, M. G., & Prado, N. A. (2026) · General and Comparative Endocrinology · doi:10.1016/j.ygcen.2026.115017

Using Genetic Information in Risk Prediction for Alcohol Dependence

Yan, J. (2012) · VCU Scholars Compass (Virginia Commonwealth University) · doi:10.25772/6sk0-8c03

On beyond GWAS

(2010) · Nature Genetics · doi:10.1038/ng0710-551 · Cited by 30

Quantifying Missing Heritability at Known GWAS Loci

Paşaniuc, B., & Zaitlen, N. (2013) · eScholarship (California Digital Library)

Uncovering the total heritability explained by all true susceptibility variants in a genome-wide association study

So, H., Li, M., & Sham, P. C. (2011) · Genetic Epidemiology · doi:10.1002/gepi.20593 · Cited by 70

Missing Heritability of Complex Traits and Diseases

Nolte, I. M., Tropf, F. C., & Snieder, H. (2019) · Wiley eBooks · doi:10.1002/9780470015902.a0028223 · Cited by 13

Heritability, Weak Effects, and Rare Variants in Genomewide Association Studies

Cambien, F. (2011) · Clinical Chemistry · doi:10.1373/clinchem.2010.155655 · Cited by 7