The DOI matches its Crossref record; 40 of the 41 references it cites were found in a registry or an index.
Dominance Genetic Variation Contributes Little to the Missing Heritability for Human Complex Traits
Open accessAbstract
For human complex traits, non-additive genetic variation has been invoked to explain "missing heritability," but its discovery is often neglected in genome-wide association studies. Here we propose a method of using SNP data to partition and estimate the proportion of phenotypic variance attributed to additive and dominance genetic variation at all SNPs (hSNP(2) and δSNP(2)) in unrelated individuals based on an orthogonal model where the estimate of hSNP(2) is independent of that of δSNP(2). With this method, we analyzed 79 quantitative traits in 6,715 unrelated European Americans. The estimate of δSNP(2) averaged across all the 79 quantitative traits was 0.03, approximately a fifth of that for additive variation (average hSNP(2) = 0.15). There were a few traits that showed substantial estimates of δSNP(2), none of which were replicated in a larger sample of 11,965 individuals. We further performed genome-wide association analyses of the 79 quantitative traits and detected SNPs with genome-wide significant dominance effects only at the ABO locus for factor VIII and von Willebrand factor. All these results suggest that dominance variation at common SNPs explains only a small fraction of phenotypic variation for human complex traits and contributes little to the missing narrow-sense heritability problem.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“While non-linear genetic factors explain little variation in complex traits beyond a purely additive model51,”
From Clinical use of current polygenic risk scores may exacerbate health disparities (2019) · cites this paper as [51]
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
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
Introduction to Quantitative Genetics.
Edwards, A. W. F., & Falconer, D. S. (1982) · Biometrics · doi:10.2307/2529912 · Cited by 21,447
The genetical theory of natural selection
Fisher, R. A. (1930) · doi:10.5962/bhl.title.27468 · Cited by 17,617
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
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
Self-citationGCTA: a tool for genome-wide complex trait analysis
Yang, J., Lee, S. H., Goddard, M. E., & Visscher, P. M. (2011) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2010.11.011 · Cited by 8,279
Self-citationA map of human genome variation from population-scale sequencing.
The 1000 Genomes Project Consortium (2010) · Nature · doi:10.1038/nature09534 · Cited by 7,877
Genetics and Analysis of Quantitative Traits
(2002) · Journal of Animal Breeding and Genetics · doi:10.1046/j.1439-0388.2002.00356.x · Cited by 6,176
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
Self-citationXV.—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
Cited by
Sorted by citations
Clinical use of current polygenic risk scores may exacerbate health disparities
Martin, A. R., Kanai, M., Kamatani, Y., Okada, Y., Neale, B. M., & Daly, M. J. (2019) · Nature Genetics · doi:10.1038/s41588-019-0379-x · Cited by 2,821
The personal and clinical utility of polygenic risk scores
Torkamani, A., Wineinger, N. E., & Topol, E. J. (2018) · Nature Reviews Genetics · doi:10.1038/s41576-018-0018-x · Cited by 1,655
Polygenic Prediction of Weight and Obesity Trajectories from Birth to Adulthood
Khera, A. V., Chaffin, M., Wade, K. H., Zahid, S., Brancale, J., Xia, R., Distefano, M., Senol-Cosar, O., Haas, M. E., Bick, A., et al. (2019) · Cell · doi:10.1016/j.cell.2019.03.028 · Cited by 789
Concepts, estimation and interpretation of SNP-based heritability
Yang, J., Zeng, J., Goddard, M. E., Wray, N. R., & Visscher, P. M. (2017) · Nature Genetics · doi:10.1038/ng.3941 · Cited by 455
Self-citationThe Hunger Genes: Pathways to Obesity
van der Klaauw, A. A., & Farooqi, I. S. (2015) · Cell · doi:10.1016/j.cell.2015.03.008 · Cited by 414
Genetics of Parkinson's disease: An introspection of its journey towards precision medicine
Bandrés‐Ciga, S., Díez-Fairén, M., Kim, J. J., & Singleton, A. (2020) · Neurobiology of Disease · doi:10.1016/j.nbd.2020.104782 · Cited by 386
The Fourth Law of Behavior Genetics
Chabris, C. F., Lee, J. J., Cesarini, D. A., Benjamin, D. J., & Laibson, D. (2015) · Current Directions in Psychological Science · doi:10.1177/0963721415580430 · Cited by 377
Transethnic Genetic-Correlation Estimates from Summary Statistics
Brown, B. C., Ye, C. J., Price, A. L., & Zaitlen, N. (2016) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2016.05.001 · Cited by 340
Genome-wide analysis identifies 12 loci influencing human reproductive behavior
Barban, N., Study, L. C., Jansen, R., de Vlaming, R., Vaez, A., Mandemakers, J. J., Tropf, F. C., Shen, X., Wilson, J. F., Chasman, D. I., et al. (2016) · Nature Genetics · doi:10.1038/ng.3698 · Cited by 324
Self-citationCommon Disease Is More Complex Than Implied by the Core Gene Omnigenic Model
Wray, N. R., Wijmenga, C., Sullivan, P. F., Yang, J., & Visscher, P. M. (2018) · Cell · doi:10.1016/j.cell.2018.05.051 · Cited by 309
Self-citationThinking 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
Howard, J., Pryce, J. E., Baes, C. F., & Maltecca, C. (2017) · Journal of Dairy Science · doi:10.3168/jds.2017-12787 · Cited by 260
Haplotype Reference Consortium, Evans, L. M., Tahmasbi, R., Vrieze, S. I., Abecasis, G. R., Das, S., Gazal, S., Bjelland, D. W., de Candia, T. R., Goddard, M. E., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0108-x · Cited by 239
Self-citationMissing heritability of complex diseases: case solved?
Génin, E. (2019) · Human Genetics · doi:10.1007/s00439-019-02034-4 · Cited by 219
The Genetic Architecture of Quantitative Traits Cannot Be Inferred from Variance Component Analysis
Huang, W., & Mackay, T. F. C. (2016) · PLoS Genetics · doi:10.1371/journal.pgen.1006421 · Cited by 191
Role of Personalized Nutrition in Chronic-Degenerative Diseases
Renzo, L. D., Gualtieri, P., Romano, L., Marrone, G., Noce, A., Pujia, A. M., Perrone, M. A., Aiello, V., Colica, C., & LORENZO, A. D. (2019) · Nutrients · doi:10.3390/nu11081707 · Cited by 194
Hidden heritability due to heterogeneity across seven populations
Tropf, F. C., Lee, S. H., Verweij, R. M., Stulp, G., van der Most, P. J., de Vlaming, R., Bakshi, A., Briley, D. A., Rahal, C., Hellpap, R., et al. (2017) · Nature Human Behaviour · doi:10.1038/s41562-017-0195-1 · Cited by 180
Self-citationGenetic interactions contribute less than additive effects to quantitative trait variation in yeast
Bloom, J. S., Kotenko, I., Sadhu, M. J., Treusch, S., Albert, F. W., & Kruglyak, L. (2015) · Nature Communications · doi:10.1038/ncomms9712 · Cited by 167
Genomic analysis of family data reveals additional genetic effects on intelligence and personality
Hill, W. D., Arslan, R. C., Xia, C., Luciano, M., Amador, C., Navarro, P., Hayward, C., Nagy, R., Porteous, D. J., McIntosh, A. M., et al. (2018) · Molecular Psychiatry · doi:10.1038/s41380-017-0005-1 · Cited by 158
Self-citationGenotype–covariate interaction effects and the heritability of adult body mass index
Robinson, M. R., English, G., Moser, G., Lloyd‐Jones, L. R., Triplett, M. A., Zhu, Z., Nolte, I. M., van Vliet‐Ostaptchouk, J. V., Snieder, H., Study, T. L. C., et al. (2017) · Nature Genetics · doi:10.1038/ng.3912 · Cited by 150
Self-citationGenotypic Context and Epistasis in Individuals and Populations
Sackton, T. B., & Hartl, D. L. (2016) · Cell · doi:10.1016/j.cell.2016.06.047 · Cited by 160
Genetic variation, brain, and intelligence differences
Deary, I. J., Cox, S. R., & Hill, W. D. (2022) · Molecular Psychiatry · doi:10.1038/s41380-021-01027-y · Cited by 137
Self-citationHivert, V., Sidorenko, J., Rohart, F., Goddard, M. E., Yang, J., Wray, N. R., Yengo, L., & Visscher, P. M. (2021) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2021.02.014 · Cited by 136
Self-citationThe Intergenerational Transmission of Cognitive and Noncognitive Abilities
Grönqvist, E., Öckert, B., & Vlachos, J. (2016) · The Journal of Human Resources · doi:10.3368/jhr.52.4.0115-6882r1 · Cited by 144
The impact of non-additive genetic associations on age-related complex diseases
Guindo-Martínez, M., Amela, R., Bonàs‐Guarch, S., Puiggròs, M., Salvoro, C., Miguel-Escalada, I., Carey, C. E., Cole, J. B., Rüeger, S., Atkinson, E. G., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-21952-4 · Cited by 124
Shi, H., Gazal, S., Kanai, M., Koch, E. M., Schoech, A. P., Siewert, K. M., Kim, S. S., Luo, Y., Amariuta, T., Huang, H., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-21286-1 · Cited by 119
Dias, K. O. D. G., Gezan, S. A., Guimarães, C. T., Nazarian, A., da Costa e Silva, L., Parentoni, S. N., de Oliveira Guimarães, P. E., de Oliveira Anoni, C., Pádua, J. M. V., de Oliveira Pinto, M., et al. (2018) · Heredity · doi:10.1038/s41437-018-0053-6 · Cited by 115
Utility of whole-genome sequence data for across-breed genomic prediction
Raymond, B., Bouwman, A. C., Schrooten, C., Houwing‐Duistermaat, J. J., & Veerkamp, R. F. (2018) · Genetics Selection Evolution · doi:10.1186/s12711-018-0396-8 · Cited by 99
PPARG (Pro12Ala) genetic variant and risk of T2DM: a systematic review and meta-analysis
Sarhangi, N., Sharifi, F., Hashemian, L., Doabsari, M. H., Heshmatzad, K., Rahbaran, M., Jamaldini, S. H., Meybodi, H. R. A., & Hasanzad, M. (2020) · Scientific Reports · doi:10.1038/s41598-020-69363-7 · Cited by 106
Global genetic differentiation of complex traits shaped by natural selection in humans
Guo, J., Wu, Y., Zhu, Z., Zheng, Z., Trzaskowski, M., Zeng, J., Robinson, M. R., Visscher, P. M., & Yang, J. (2018) · Nature Communications · doi:10.1038/s41467-018-04191-y · Cited by 91
Self-citationTwin studies to GWAS: there and back again
Friedman, N. P., Banich, M. T., & Keller, M. C. (2021) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2021.06.007 · Cited by 88
Detection and quantification of inbreeding depression for complex traits from SNP data
Yengo, L., Zhu, Z., Wray, N. R., Weir, B. S., Yang, J., Robinson, M. R., & Visscher, P. M. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1621096114 · Cited by 82
Self-citationHuman Genetics of Obesity and Type 2 Diabetes Mellitus
Ingelsson, E., & McCarthy, M. I. (2018) · Circulation Genomic and Precision Medicine · doi:10.1161/circgen.118.002090 · Cited by 86
Nolte, I. M., van der Most, P. J., Alizadeh, B. Z., de Bakker, P. I. W., Boezen, H. M., Bruinenberg, M., Franke, L., van der Harst, P., Navis, G. J., Postma, D. S., et al. (2017) · European Journal of Human Genetics · doi:10.1038/ejhg.2017.50 · Cited by 83
Self-citationThe GIANT consortium, Wood, A. R., Tyrrell, J., Beaumont, R., Jones, S. E., Tuke, M. A., Ruth, K. S., Yaghootkar, H., Freathy, R. M., Murray, A., et al. (2016) · Diabetologia · doi:10.1007/s00125-016-3908-5 · Cited by 74
Analysis of genetic dominance in the UK Biobank
Palmer, D. S., Zhou, W., Abbott, L. E., Wigdor, E. M., Baya, N. A., Churchhouse, C. L., Seed, C., Poterba, T., King, D. A., Kanai, M., et al. (2023) · Science · doi:10.1126/science.abn8455 · Cited by 73
Maltecca, C., Tiezzi, F., Cole, J. B., & Baes, C. F. (2020) · Journal of Dairy Science · doi:10.3168/jds.2019-17846 · Cited by 70
Jiang, J., Shen, B., O’Connell, J. R., VanRaden, P. M., Cole, J. B., & Ma, L. (2017) · BMC Genomics · doi:10.1186/s12864-017-3821-4 · Cited by 69
Shah, R. L., & Guggenheim, J. A. (2018) · Human Genetics · doi:10.1007/s00439-018-1942-8 · Cited by 68
Genetic architecture and selective sweeps after polygenic adaptation to distant trait optima
Stetter, M. G., Thornton, K., & Ross‐Ibarra, J. (2018) · PLoS Genetics · doi:10.1371/journal.pgen.1007794 · Cited by 63
Inbreeding depression across the genome of Dutch Holstein Friesian dairy cattle
Doekes, H. P., Bijma, P., Veerkamp, R. F., de Jong, G., Wientjes, Y. C. J., & WINDIG, J. J. (2020) · Genetics Selection Evolution · doi:10.1186/s12711-020-00583-1 · Cited by 57
Non-additive association analysis using proxy phenotypes identifies novel cattle syndromes
Reynolds, E., Neeley, C., Lopdell, T., Keehan, M., Dittmer, K. E., Harland, C., Couldrey, C., Johnson, T., Tiplady, K., Worth, G. M., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00872-5 · Cited by 56
Direct and Indirect Effects of Maternal, Paternal, and Offspring Genotypes: Trio-GCTA
Eilertsen, E. M., Jami, E. S., McAdams, T. A., Hannigan, L. J., Havdahl, A., Magnus, P. M., Evans, D. M., & Ystrøm, E. (2021) · Behavior Genetics · doi:10.1007/s10519-020-10036-6 · Cited by 55
Histopathologic changes in laboratory animals resulting from various methods of euthanasia.
Feldman, D. B., & Gupta, B. N. (1976) · PubMed · Cited by 54
Chen, X., Kuja‐Halkola, R., Rahman, I., Arpegård, J., Viktorin, A., Karlsson, R., Hägg, S., Svensson, P., Pedersen, N. L., & Magnusson, P. K. E. (2015) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2015.10.004 · Cited by 57
Pappa, I., Fedko, I. O., Mileva‐Seitz, V. R., Hottenga, J., Bakermans‐Kranenburg, M. J., Bartels, M., van Beijsterveldt, C. E. M., Jaddoe, V. W. V., Middeldorp, C. M., Rippe, R. C. A., et al. (2015) · Journal of the American Academy of Child & Adolescent Psychiatry · doi:10.1016/j.jaac.2015.06.004 · Cited by 50
Assumption-free estimation of the genetic contribution to refractive error across childhood.
Guggenheim, J. A., Pourcain, B. S., McMahon, G., Timpson, N. J., Evans, D. M., & Williams, C. (2015) · PubMed Central · Cited by 52
Wolfe, M. D., Kulakow, P., Rabbi, I. Y., & Jannink, J. (2016) · G3 Genes Genomes Genetics · doi:10.1534/g3.116.033332 · Cited by 54
The evolution of group differences in changing environments
Harpak, A., & Przeworski, M. (2021) · PLOS Biology · doi:10.1371/journal.pbio.3001072 · Cited by 50
Multikernel linear mixed models for complex phenotype predictionPreprint
Weissbrod, O., Geiger, D., & Rosset, S. (2016) · Genome Research · doi:10.1101/gr.201996.115 · Cited by 46
Hill, W. G., & Mäki‐Tanila, A. (2015) · Journal of Animal Breeding and Genetics · doi:10.1111/jbg.12140 · Cited by 47
Self-citationRaymond, B., Bouwman, A. C., Wientjes, Y. C. J., Schrooten, C., Houwing‐Duistermaat, J. J., & Veerkamp, R. F. (2018) · Genetics Selection Evolution · doi:10.1186/s12711-018-0419-5 · Cited by 47
Inferring the Nature of Missing Heritability in Human Traits Using Data from the GWAS Catalog
López‐Cortegano, E., & Caballero, A. (2019) · Genetics · doi:10.1534/genetics.119.302077 · Cited by 45
Heritability and genetic variance of dementia with Lewy bodies
Guerreiro, R. J., Escott‐Price, V., Hernandez, D. G., Kun‐Rodrigues, C., Ross, O. A., Orme, T., Neto, J. L., Carmona, S., Dehghani, N., Eicher, J. D., et al. (2019) · Neurobiology of Disease · doi:10.1016/j.nbd.2019.04.004 · Cited by 48
Guffanti, G., Gameroff, M. J., Warner, V., Talati, A., Glatt, C. E., WICKRAMARATNE, P. J., & Weissman, M. M. (2016) · American Journal of Medical Genetics Part B Neuropsychiatric Genetics · doi:10.1002/ajmg.b.32477 · Cited by 49
Duenk, P., Calus, M. P. L., Wientjes, Y. C. J., & Bijma, P. (2017) · G3 Genes Genomes Genetics · doi:10.1534/g3.117.300113 · Cited by 44
On Negative Heritability and Negative Estimates of Heritability
Steinsaltz, D., Dahl, A. W., & Wachter, K. W. (2020) · Genetics · doi:10.1534/genetics.120.303161 · Cited by 53
Current clinical use of polygenic scores will risk exacerbating health disparities
Martin, A. R., Kanai, M., Kamatani, Y., Okada, Y., Neale, B. M., & Daly, M. J. (2018) · doi:10.1101/441261 · Cited by 2,813
Genome-Wide Association Studies of Mineral Content in Common Bean
Gunjača, J., Carović‐Stanko, K., Lazarević, B., Vidak, M., Petek, M., Liber, Z., & Šatović, Z. (2021) · Frontiers in Plant Science · doi:10.3389/fpls.2021.636484 · Cited by 43
Pazokitoroudi, A., Chiu, A. M., Burch, K. S., Paşaniuc, B., & Sankararaman, S. (2021) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2021.03.018 · Cited by 43
Braz, C. U., Rowan, T. N., Schnabel, R. D., & Decker, J. E. (2021) · Scientific Reports · doi:10.1038/s41598-021-92455-x · Cited by 47
Heidaritabar, M., Wolc, A., Arango, J., Zeng, J., Settar, P., Fulton, J. E., O'Sullivan, N. P., Bastiaansen, J. W. M., Fernando, R. L., Garrick, D. J., et al. (2016) · Journal of Animal Breeding and Genetics · doi:10.1111/jbg.12225 · Cited by 39
Canalization and Robustness in Human Genetics and Disease
Gibson, G. C., & Lacek, K. A. (2020) · Annual Review of Genetics · doi:10.1146/annurev-genet-022020-022327 · Cited by 34
Genomic prediction of the polled and horned phenotypes in Merino sheep
Duijvesteijn, N., Bolormaa, S., Daetwyler, H. D., & van der Werf, J. H. J. (2018) · Genetics Selection Evolution · doi:10.1186/s12711-018-0398-6 · Cited by 32
Sanjak, J. S., Long, A. D., & Thornton, K. (2017) · PLoS Genetics · doi:10.1371/journal.pgen.1006573 · Cited by 31
Sequential recruitment of study participants may inflate genetic heritability estimates
Noce, D., Gögele, M., Schwienbacher, C., Caprioli, G., Grandi, A. D., Foco, L., Platzgummer, S., Pramstaller, P. P., & Pattaro, C. (2017) · Human Genetics · doi:10.1007/s00439-017-1785-8 · Cited by 29
Genome-wide scan for commons SNPs affecting bovine leukemia virus infection level in dairy cattle
Carignano, H. A., Roldán, D. L., Beribe, M. J., Raschia, M. A., Amadío, A., Nani, J. P., Gutiérrez, G., Álvarez, I., Trono, K. G., Poli, M. A., et al. (2018) · BMC Genomics · doi:10.1186/s12864-018-4523-2 · Cited by 31
Menie, M. A. W. O., Luoto, S., Peñaherrera-Aguirre, M., & Sarraf, M. (2021) · Evolutionary Psychological Science · doi:10.1007/s40806-021-00280-2 · Cited by 29
Pittner, K., van IJzendoorn, M. H., Alink, L. R. A., Buisman, R. S. M., Block, L. C.-D., van den Berg, L. J. M., Elzinga, B. M., Lindenberg, J., Tollenaar, M. S., Diego, V. P., et al. (2019) · Development and Psychopathology · doi:10.1017/s0954579418001608 · Cited by 31
rTASSEL: An R interface to TASSEL for analyzing genomic diversity
Monier, B., Casstevens, T. M., Bradbury, P. J., & Buckler, E. S. (2022) · The Journal of Open Source Software · doi:10.21105/joss.04530 · Cited by 27
Maternal-by-environment but not genotype-by-environment interactions in a fish without parental care
Vega‐Trejo, R., Head, M. L., Jennions, M. D., & Kruuk, L. E. B. (2017) · Heredity · doi:10.1038/s41437-017-0029-y · Cited by 24
Guo, J., Qu, L., Dou, T., Shen, M., Hu, Y., Ma, M., & Wang, K. (2019) · Genome · doi:10.1139/gen-2019-0022 · Cited by 25
Human genetic analyses of organelles highlight the nucleus in age-related trait heritability
Gupta, R., Karczewski, K. J., Howrigan, D. P., Neale, B. M., & Mootha, V. K. (2021) · eLife · doi:10.7554/elife.68610 · Cited by 22
Mönkediek, B. (2020) · Journal of Family Research · doi:10.20377/jfr-381 · Cited by 23
Heidaritabar, M., Bink, M. C. A. M., Dervishi, E., Charagu, P., Huisman, A., & Plastow, G. S. (2023) · Journal of Animal Breeding and Genetics · doi:10.1111/jbg.12768 · Cited by 23
Social Science Genetics and FertilityPreprint
Tropf, F. C. (2018) · SocArXiv (OSF Preprints) · doi:10.31235/osf.io/gn9ve · Cited by 20
Non-additive QTL mapping of lactation traits in 124,000 cattle reveals novel recessive loci
Reynolds, E., Lopdell, T., Wang, Y., Tiplady, K., Harland, C., Johnson, T., Neeley, C., Carnie, K., Sherlock, R. G., Couldrey, C., et al. (2022) · Genetics Selection Evolution · doi:10.1186/s12711-021-00694-3 · Cited by 20
Pasanen, A., Sliz, E., Huilaja, L., Reimann, E., Mägi, R., Laisk, T., Tasanen, K., & Kettunen, J. (2024) · Journal of Investigative Dermatology · doi:10.1016/j.jid.2024.02.036 · Cited by 20
Self-citationGene action, genetic variation, and GWAS: A user-friendly web tool
Hivert, V., Wray, N. R., & Visscher, P. M. (2021) · PLoS Genetics · doi:10.1371/journal.pgen.1009548 · Cited by 20
Self-citationBalick, D. J., Jordan, D. M., Sunyaev, S. R., & Do, R. (2021) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2021.12.001 · Cited by 19
D’Ambrosio, E., Dahoun, T., Pardiñas, A. F., Veronese, M., Bloomfield, M., Jauhar, S., Bonoldi, I., Rogdaki, M., Froudist‐Walsh, S., Walters, J., et al. (2019) · Psychiatry Research Neuroimaging · doi:10.1016/j.pscychresns.2019.07.005 · Cited by 19
Yu, D., Wang, H., Gu, W., Qin, T., Sun, P., Lu, Y., Shi, B., & Zheng, H. (2021) · Genetic Resources and Crop Evolution · doi:10.1007/s10722-021-01137-0 · Cited by 19
Capturing additional genetic risk from family history for improved polygenic risk prediction
Lu, T., Forgetta, V., Richards, J. B., & Greenwood, C. M. T. (2022) · Communications Biology · doi:10.1038/s42003-022-03532-4 · Cited by 18
A polygenic score method boosted by non-additive models
Ohta, R., Tanigawa, Y., Suzuki, Y., Kellis, M., & Morishita, S. (2024) · Nature Communications · doi:10.1038/s41467-024-48654-x · Cited by 19
Genome-wide estimates of heritability and genetic correlations in essential tremor
Díez-Fairén, M., Bandrés‐Ciga, S., Houle, G., Nalls, M. A., Girard, S., Dion, P. A., Blauwendraat, C., Singleton, A., Rouleau, G. A., & Pástor, P. (2019) · Parkinsonism & Related Disorders · doi:10.1016/j.parkreldis.2019.05.002 · Cited by 17
Obesity Genomics and Metabolomics: a Nexus of Cardiometabolic Risk
Regan, J. A., & Shah, S. H. (2020) · Current Cardiology Reports · doi:10.1007/s11886-020-01422-x · Cited by 18
Non-additive (dominance) effects of genetic variants associated with refractive error and myopia
Pozarickij, A., Williams, C., & Guggenheim, J. A. (2020) · Molecular Genetics and Genomics · doi:10.1007/s00438-020-01666-w · Cited by 16
Genomic partitioning of inbreeding depression in humans
Yengo, L., Yang, J., Keller, M. C., Goddard, M. E., Wray, N. R., & Visscher, P. M. (2021) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2021.06.005 · Cited by 15
Self-citationChen, C., Powell, O. M., Dinglasan, E. G., Ross, E. M., Yadav, S., Wei, X., Atkin, F., Deomano, E. C., & Hayes, B. J. (2023) · The Plant Genome · doi:10.1002/tpg2.20390 · Cited by 18
Cui, L., Yang, B., Xiao, S., Gao, J., Baud, A., Graham, D., McBride, M., Dominiczak, A. F., Schäfer, S., López-Aumatell, R., et al. (2023) · Genome biology · doi:10.1186/s13059-023-03060-2 · Cited by 20
Genomic prediction for sugarcane diseases including hybrid Bayesian-machine learning approaches
Chen, C., Bhuiyan, S. A., Ross, E. M., Powell, O. M., Dinglasan, E. G., Wei, X., Atkin, F., Deomano, E. C., & Hayes, B. J. (2024) · Frontiers in Plant Science · doi:10.3389/fpls.2024.1398903 · Cited by 18
Feng, J., Chen, C., Cai, Y., Ye, Z., Feng, K., Liu, J., Zhang, L., Yang, Q., Li, A., Sheng, J., et al. (2020) · Human Brain Mapping · doi:10.1002/hbm.25018 · Cited by 17
Genetic architecture of four smoking behaviors using partitioned SNP heritability
Evans, L. M., Jang, S.-K., Hancock, D. B., Ehringer, M. A., Otto, J. M., Vrieze, S. I., & Keller, M. C. (2021) · Addiction · doi:10.1111/add.15450 · Cited by 15
Genetic determinants of polygenic prediction accuracy within a population
Lu, T., Forgetta, V., Richards, J. B., & Greenwood, C. M. T. (2022) · Genetics · doi:10.1093/genetics/iyac158 · Cited by 12
Cross-ancestry analyses identify new genetic loci associated with 25-hydroxyvitamin D
Wang, X., Hivert, V., Groot, S., Wang, Y., Yengo, L., McGrath, J. J., Kemper, K. E., Visscher, P. M., Wray, N. R., & Revez, J. A. (2023) · PLoS Genetics · doi:10.1371/journal.pgen.1011033 · Cited by 15
Self-citationDiscovering non-additive heritability using additive GWAS summary statistics
Smith, S. P., Darnell, G., Udwin, D., Stamp, J., Harpak, A., Ramachandran, S., & Crawford, L. A. (2024) · eLife · doi:10.7554/elife.90459 · Cited by 14
Genomic dominance variance analysis of health and milk production traits in German Holstein cattle
Schneider, H., Heise, J., Tetens, J., Thaller, G., Wellmann, R., & Bennewitz, J. (2023) · Journal of Animal Breeding and Genetics · doi:10.1111/jbg.12765 · Cited by 14
Feature co-localization landscape of the human genome
Ng, S.-K., Hu, T., Long, X., Chan, C.-H., Tsang, S., & Xue, H. (2016) · Scientific Reports · doi:10.1038/srep20650 · Cited by 14
Best Prediction of the Additive Genomic Variance in Random-Effects Models
Schreck, N., Piepho, H., & Schlather, M. (2019) · Genetics · doi:10.1534/genetics.119.302324 · Cited by 13
Hao, R.-H., Yang, T.-L., Rong, Y., Yao, S., Dong, S.-S., Chen, H., & Guo, Y. (2017) · International Journal of Obesity · doi:10.1038/ijo.2017.283 · Cited by 13
Metadata
- Authors
- Zhu, Z., Bakshi, A., Vinkhuyzen, A. A. E., Hemani, G., Lee, S. H., Nolte, I. M., van Vliet-Ostaptchouk, J. V., Snieder, H., Esko, T., Milani, L., Mägi, R., Metspalu, A., Hill, W. G., Weir, B. S., Goddard, M. E., Visscher, P. M., & Yang, J.
- Affiliations
- The University of Queensland (AU)
- University Medical Center Groningen (NL)
- University of Tartu (EE)
- University of Edinburgh (GB)
- University of Washington (US)
- The University of Melbourne (AU)
- Corresponding author
- Yang, Jian
- ORCID
- Venue
- The American Journal of Human Genetics
- Publisher
- Elsevier BV
- Published
- 2015-03
- Volume
- 96
- Issue
- 3
- Pages
- 377-385
- ISSN
- 0002-9297 (ISSN-L)
- PMC
- PMC4375616
- OpenAlex
- W2022100988
- Cited by
- 224
- References
- 41
- Citations by year
- 2026: 6 · 2025: 11 · 2024: 18 · 2023: 23 · 2022: 22 · 2021: 22 · 2020: 25 · 2019: 21 · 2018: 24 · 2017: 17 · 2016: 23 · 2015: 9
- FWCI
- 16.73
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- The American Journal of Human Genetics (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (publishedVersion)
- EPubDev Repository (submittedVersion)
- Adelaide Research & Scholarship (AR&S) (University of Adelaide) (submittedVersion)
- Explore Bristol Research (submittedVersion)
- RUNE (Research UNE) (submittedVersion)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (submittedVersion)
- Funders
- National Institute of Mental Health (MH100141, R01MH100141)
- National Institute of General Medical Sciences (GM 099568, P01GM099568, GM-057091, R01GM075091)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic and phenotypic traits in livestock
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
- Keywords
- missing heritability, non-additive genetic effects, additive genetic variance, narrow-sense heritability, quantitative traits, single nucleotide polymorphisms, orthogonal models, ABO blood group system, factor VIII, von Willebrand factor, European Americans
- MeSH terms
- Evaluation Studies as Topic, Female, Humans, Male, Models, Genetic, Phenotype, Cohort Studies, Linear Models, Quantitative Trait, Heritable, Polymorphism, Single Nucleotide, White People, Genome-Wide Association Study
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
