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Distribution of allele frequencies and effect sizes and their interrelationships for common genetic susceptibility variants
Open accessAbstract
Recent discoveries of hundreds of common susceptibility SNPs from genome-wide association studies provide a unique opportunity to examine population genetic models for complex traits. In this report, we investigate distributions of various population genetic parameters and their interrelationships using estimates of allele frequencies and effect-size parameters for about 400 susceptibility SNPs across a spectrum of qualitative and quantitative traits. We calibrate our analysis by statistical power for detection of SNPs to account for overrepresentation of variants with larger effect sizes in currently known SNPs that are expected due to statistical power for discovery. Across all qualitative disease traits, minor alleles conferred “risk” more often than “protection.” Across all traits, an inverse relationship existed between “regression effects” and allele frequencies. Both of these trends were remarkably strong for type I diabetes, a trait that is most likely to be influenced by selection, but were modest for other traits such as human height or late-onset diseases such as type II diabetes and cancers. Across all traits, the estimated effect-size distribution suggested the existence of increasingly large numbers of susceptibility SNPs with decreasingly small effects. For most traits, the set of SNPs with intermediate minor allele frequencies (5–20%) contained an unusually small number of susceptibility loci and explained a relatively small fraction of heritability compared with what would be expected from the distribution of SNPs in the general population. These trends could have several implications for future studies of common and uncommon variants.
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“analyses of the distribution of risk allele frequencies across 8 traits argues that if anything minor allele frequencies are skewed to be greater than 0.2, providing strong empirical evidence that rare alleles are not alone responsible53.”
From Rare and common variants: twenty arguments (2012) · cites this paper as [53]
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“No-one doubts that some fraction of the total risk for any complex disease is due to rare alleles, but these studies argue that it is not the majority.”
From Rare and common variants: twenty arguments (2012) · cites this paper as [53]
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“In several instances where SNP-wise GWAS has uncovered few loci, this approach has provided clear evidence for polygenic risk, but it must be recognized that the high variance in estimation of effects results in noisy scores that, although highly significant in the replication sample, only capture over an order of magnitude less of the variance and are far from predictive.”
From Rare and common variants: twenty arguments (2012) · cites this paper as [53]
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Citations
Cites
The genetical theory of natural selection
Fisher, R. A. (1930) · doi:10.5962/bhl.title.27468 · Cited by 17,617
The Neutral Theory of Molecular Evolution
Kimura, M. (1979) · Scientific American · doi:10.1038/scientificamerican1179-98 · Cited by 7,687
The Neutral Theory of Molecular Evolution
Kimura, M. (1983) · Cambridge University Press eBooks · doi:10.1017/cbo9780511623486 · Cited by 6,300
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
A generalization of sampling without replacement from a finite universe
Horvitz, D. G., & Thompson, D. J. (1952) · Journal of the American Statistical Association · doi:10.1080/01621459.1952.10483446 · Cited by 4,293
Biological, clinical and population relevance of 95 loci for blood lipids
Teslovich, T. M., Musunuru, K., Smith, A. V., Edmondson, A. C., Stylianou, I. M., Koseki, M., Pirruccello, J. P., Ripatti, S., Chasman, D. I., Willer, C. J., et al. (2010) · Nature · doi:10.1038/nature09270 · Cited by 3,487
A Generalization of Sampling Without Replacement From a Finite Universe
Horvitz, D. G., & Thompson, D. J. (1952) · Journal of the American Statistical Association · doi:10.2307/2280784 · Cited by 3,011
Franke, A., McGovern, D. P. B., Barrett, J. C., Wang, K., Radford-Smith, G. L., Ahmad, T., Lees, C. W., Balschun, T., Lee, J., Roberts, R., et al. (2010) · Nature Genetics · doi:10.1038/ng.717 · Cited by 2,520
Hundreds of variants clustered in genomic loci and biological pathways affect human height
Lango Allen, H., Estrada, K., Lettre, G., Berndt, S. I., Weedon, M. N., Rivadeneira, F., Willer, C. J., Jackson, A. U., Vedantam, S., Raychaudhuri, S., et al. (2010) · Nature · doi:10.1038/nature09410 · Cited by 1,945
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Gibson, G. (2012) · Nature Reviews Genetics · doi:10.1038/nrg3118 · Cited by 1,278
The UK10K project identifies rare variants in health and disease
The UK10K Consortium, Writing group, Walter, K., Min, J. L., Huang, J., Crooks, L., Memari, Y., McCarthy, S., Perry, J. R. B., Xu, C., et al. (2015) · Nature · doi:10.1038/nature14962 · Cited by 1,040
Improved Heritability Estimation from Genome-wide SNPs
Speed, D., Hemani, G., Johnson, M. R., & Balding, D. J. (2012) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2012.10.010 · Cited by 719
Comprehensive genotyping of the USA national maize inbred seed bank
Romay, M. C., Millard, M. J., Glaubitz, J. C., Peiffer, J. A., Swarts, K., Casstevens, T. M., Elshire, R. J., Acharya, C. B., Mitchell, S. E., Flint-Garcia, S. A., et al. (2013) · Genome biology · doi:10.1186/gb-2013-14-6-r55 · Cited by 563
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Verma, A., Huffman, J. E., Rodriguez, A., Conery, M., Liu, M., Ho, Y.-L., Kim, Y., Heise, D. A., Guare, L., Panickan, V. A., et al. (2024) · Science · doi:10.1126/science.adj1182 · Cited by 463
Self-citationZhang, J., Dutta, D., Köttgen, A., Tin, A., Schlosser, P., Grams, M. E., Harvey, B., CKDGen Consortium, Yu, B., Boerwinkle, E., et al. (2022) · Nature Genetics · doi:10.1038/s41588-022-01051-w · Cited by 373
Self-citationSignatures of negative selection in the genetic architecture of human complex traits
Zeng, J., de Vlaming, R., Wu, Y., Robinson, M. R., Lloyd-Jones, L. R., Yengo, L., Yap, C. X., Xue, A., Sidorenko, J., McRae, A. F., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0101-4 · Cited by 391
Selecting instruments for Mendelian randomization in the wake of genome-wide association studies.
Swerdlow, D. I., Kuchenbaecker, K. B., Shah, S., Sofat, R., Holmes, M. V., White, J., Mindell, J. S., Kivimaki, M., Brunner, E. J., Whittaker, J. C., et al. (2016) · International Journal of Epidemiology · doi:10.1093/ije/dyw088 · Cited by 387
Tsoi, L. C., Stuart, P. E., Tian, C., Gudjonsson, J. E., Das, S., Zawistowski, M., Ellinghaus, E., Barker, J. N., Chandran, V., Dand, N., et al. (2017) · Nature Communications · doi:10.1038/ncomms15382 · Cited by 404
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
Chatterjee, N., Wheeler, B., Sampson, J. N., Hartge, P. A., Chanock, S. J., & Park, J.-H. (2013) · Nature Genetics · doi:10.1038/ng.2579 · Cited by 359
Self-citationThe impact of rare and low-frequency genetic variants in common disease
Bomba, L., Walter, K., & Soranzo, N. (2017) · Genome biology · doi:10.1186/s13059-017-1212-4 · Cited by 369
MacLeod, I. M., Bowman, P. J., Jagt, C. J. V., Haile‐Mariam, M., Kemper, K. E., Chamberlain, A. J., Schrooten, C., Hayes, B. J., & Goddard, M. E. (2016) · BMC Genomics · doi:10.1186/s12864-016-2443-6 · Cited by 340
Dapas, M., Lin, F. T. J., Nadkarni, G. N., Sisk, R., Legro, R. S., Urbanek, M., Hayes, M. G., & Dunaif, A. (2020) · PLOS Medicine · doi:10.1371/journal.pmed.1003132 · Cited by 297
Genetic basis of chronotype in humans: insights from three landmark GWAS
Kalmbach, D. A., Schneider, L. D., Cheung, J., Bertrand, S. J., Kariharan, T., Pack, A. I., & Gehrman, P. R. (2017) · Sleep · doi:10.1093/sleep/zsw048 · Cited by 243
Druet, T., MacLeod, I. M., & Hayes, B. J. (2013) · Heredity · doi:10.1038/hdy.2013.13 · Cited by 218
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Charney, E. (2012) · Behavioral and Brain Sciences · doi:10.1017/s0140525x11002226 · Cited by 215
Ni, G., Zeng, J., Revez, J. A., Wang, Y., Zheng, Z., Ge, T., Restuadi, R., Kiewa, J., Nyholt, D. R., Coleman, J. R. I., et al. (2021) · Biological Psychiatry · doi:10.1016/j.biopsych.2021.04.018 · Cited by 213
Shrine, N. R. G., Izquierdo, A. G., Chen, J., Packer, R. J., Hall, R. J., Guyatt, A. L., Batini, C., Thompson, R. J., Pavuluri, C. K., Malik, V., et al. (2023) · Nature Genetics · doi:10.1038/s41588-023-01314-0 · Cited by 191
Pirola, C. J., & Sookoian, S. C. (2015) · Hepatology · doi:10.1002/hep.28142 · Cited by 173
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Tyrmi, J. S., Kaartokallio, T., Lokki, A. I., Jääskeläinen, T. J., Kortelainen, E., Ruotsalainen, S., Karjalainen, J. M., Ripatti, S., Kivioja, A., Laisk, T., et al. (2023) · JAMA Cardiology · doi:10.1001/jamacardio.2023.1312 · Cited by 169
Heritability in the genome-wide association era
Zaitlen, N., & Kraft, P. L. (2012) · Human Genetics · doi:10.1007/s00439-012-1199-6 · Cited by 163
The Impact of Population Demography and Selection on the Genetic Architecture of Complex Traits
Lohmueller, K. E. (2014) · PLoS Genetics · doi:10.1371/journal.pgen.1004379 · Cited by 154
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Agrawal, S., Wang, M., Klarqvist, M. D. R., Smith, K., Shin, J. H., Dashti, H., Diamant, N., Choi, S. H., Jurgens, S. J., Ellinor, P. T., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-30931-2 · Cited by 154
Explaining additional genetic variation in complex traits
Robinson, M. R., Wray, N. R., & Visscher, P. M. (2014) · Trends in Genetics · doi:10.1016/j.tig.2014.02.003 · Cited by 152
Evaluating empirical bounds on complex disease genetic architecture
Agarwala, V., Flannick, J. A., Sunyaev, S. R., & Altshuler, D. (2013) · Nature Genetics · doi:10.1038/ng.2804 · Cited by 148
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Raghavan, N. S., Brickman, A. M., Andrews, H., Manly, J. J., Schupf, N., Lantigua, R., Wolock, C. J., Kamalakaran, S., Petrovski, S., Tosto, G., et al. (2018) · Annals of Clinical and Translational Neurology · doi:10.1002/acn3.582 · Cited by 141
Genetic–epigenetic interactions in cis: a major focus in the post-GWAS era
Do, C., Shearer, A., Suzuki, M., Terry, M. B., Gelernter, J. E., Greally, J. M., & Tycko, B. (2017) · Genome biology · doi:10.1186/s13059-017-1250-y · Cited by 145
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Do, C., Lang, C. F., Lin, J. P., Darbary, H. K., Krupska, I., Gaba, A., Petukhova, L., Vonsattel, J., GALLAGHER, M. P., Goland, R., et al. (2016) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2016.03.027 · Cited by 130
Sinclair‐Waters, M., Ødegård, J., Korsvoll, S. A., Moen, T., Lien, S., Primmer, C. R., & Barson, N. J. (2020) · Genetics Selection Evolution · doi:10.1186/s12711-020-0529-8 · Cited by 124
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Yu, B., Zheng, Y., Alexander, D. C., Morrison, A. C., Coresh, J., & Boerwinkle, E. (2014) · PLoS Genetics · doi:10.1371/journal.pgen.1004212 · Cited by 104
APLP2 Regulates Refractive Error and Myopia Development in Mice and Humans
Tkatchenko, A. V., Tkatchenko, T. V., Guggenheim, J. A., Verhoeven, V. J. M., Hysi, P. G., Wojciechowski, R., Singh, P. K., Kumar, A., Thinakaran, G., & Williams, C. (2015) · PLoS Genetics · doi:10.1371/journal.pgen.1005432 · Cited by 102
MacLeod, I. M., Hayes, B. J., & Goddard, M. E. (2014) · Genetics · doi:10.1534/genetics.114.168344 · Cited by 99
TEC Monitoring by Using Satellite Positioning Systems
Jakowski, N. (1996) · elib (German Aerospace Center) · Cited by 97
Rare variants contribute disproportionately to quantitative trait variation in yeast
Bloom, J. S., Boocock, J., Treusch, S., Sadhu, M. J., Day, L., Oates-Barker, H., & Kruglyak, L. (2019) · eLife · doi:10.7554/elife.49212 · Cited by 101
Are minor alleles more likely to be risk alleles?
隆, 城., Sikora-Wohlfeld, W., Kawashima, M., Kikuchi, S., Kamatani, N., Patwardhan, A. J., Chen, R., Sirota, M., Kodama, K., Hadley, D., et al. (2018) · BMC Medical Genomics · doi:10.1186/s12920-018-0322-5 · Cited by 88
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Viding, E. M., Price, T. S., Jaffee, S. R., Trzaskowski, M., Davis, O. S. P., Meaburn, E. L., Haworth, C. M. A., & Plomin, R. J. (2013) · PLoS ONE · doi:10.1371/journal.pone.0065789 · Cited by 85
Personalized medicine: Genetic risk prediction of drug response
Zhang, G., & Nebert, D. W. (2017) · Pharmacology & Therapeutics · doi:10.1016/j.pharmthera.2017.02.036 · Cited by 81
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Wen, J., Tian, Y., Skampardoni, I., Yang, Z., Cui, Y., Anagnostakis, F., Mamourian, E., Zhao, B., Toga, A. W., Zalesky, A., et al. (2024) · Nature Aging · doi:10.1038/s43587-024-00662-8 · Cited by 77
Squires, K. E., Montañez‐Miranda, C., Pandya, R. R., Torres, M. P., & Hepler, J. R. (2018) · Pharmacological Reviews · doi:10.1124/pr.117.015354 · Cited by 72
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Zheng, Z., Hey, S., Jubery, T. Z., Liu, H., Yu, Y., Coffey, L., Miao, C., Sigmon, B., Schnable, J. C., Hochholdinger, F., et al. (2019) · PLANT PHYSIOLOGY · doi:10.1104/pp.19.00752 · Cited by 70
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Uricchio, L. H. (2019) · Human Genetics · doi:10.1007/s00439-019-02040-6 · Cited by 68
Anderson, M. R., Miller, L., WICKRAMARATNE, P. J., Svob, C., Odgerel, Z., Zhao, R., & Weissman, M. M. (2017) · Spirituality in Clinical Practice · doi:10.1037/scp0000125 · Cited by 66
Noh, H. J., Tang, R., Flannick, J. A., O'Dushlaine, C. T., Swofford, R., Howrigan, D. P., Genereux, D. P., Johnson, J., van Grootheest, G., Grünblatt, E., et al. (2017) · Nature Communications · doi:10.1038/s41467-017-00831-x · Cited by 69
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Valluru, R., Gazave, É., Fernandes, S. B., Ferguson, J. N., Lozano, R. D., Hirannaiah, P., Zuo, T., Brown, P. J., Leakey, A. D. B., Gore, M. A., et al. (2019) · Genetics · doi:10.1534/genetics.118.301742 · Cited by 64
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Cooper, R. (2013) · Cold Spring Harbor Perspectives in Medicine · doi:10.1101/cshperspect.a008573 · Cited by 62
Yoshida, G., & Yañez, J. M. (2021) · Evolutionary Applications · doi:10.1111/eva.13240 · Cited by 60
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Chatrath, A., Ratan, A., & Dutta, A. (2020) · Trends in Genetics · doi:10.1016/j.tig.2020.10.005 · Cited by 57
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Yao, Y., & Ochoa, A. (2023) · eLife · doi:10.7554/elife.79238 · Cited by 58
Uricchio, L. H., Zaitlen, N., Ye, C., Witte, J. S., & Hernandez, R. D. (2016) · Genome Research · doi:10.1101/gr.202440.115 · Cited by 57
Eckert, A. J., Wegrzyn, J., Liechty, J. D., Lee, J. M., Cumbie, W. P., Davis, J. M., Goldfarb, B., Loopstra, C. A., Palle, S. R., Quesada, T., et al. (2013) · Genetics · doi:10.1534/genetics.113.157198 · Cited by 53
Zou, J., Valiant, G. J., Valiant, P., Karczewski, K. J., Chan, S. O., Samocha, K. E., Lek, M., Sunyaev, S. R., Daly, M. J., & MacArthur, D. G. (2016) · Nature Communications · doi:10.1038/ncomms13293 · Cited by 55
Li, Y., Wang, D. W., Chen, Y., Chen, C., Guo, J., Zhang, S., Sun, Z., Ding, H., Yao, Y., Zhou, L., et al. (2018) · Arteriosclerosis Thrombosis and Vascular Biology · doi:10.1161/atvbaha.117.310594 · Cited by 50
Evolutionary dynamics and adaptive benefits of deleterious mutations in crop gene pools
Dwivedi, S. L., Heslop-Harrison, J. S., Spillane, C., McKeown, P. C., Edwards, D., Goldman, I. L., & Ortiz, R. O. (2023) · Trends in Plant Science · doi:10.1016/j.tplants.2023.01.006 · Cited by 49
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Holland, D., Wang, Y., Thompson, W. K., Schork, A. J., Chen, C., Lo, M., Witoelar, A., Werge, T. M., O’Donovan, M. C., Andreassen, O. A., et al. (2016) · Frontiers in Genetics · doi:10.3389/fgene.2016.00015 · Cited by 49
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Kamweru, I., Bruce, A. Y., Beyene, Y., Makumbi, D., Adetimirin, V. O., Prasanna, B. M., & Gowda, M. (2022) · Genes · doi:10.3390/genes13020251 · Cited by 43
Venkatesh, S. S., Wittemans, L. B. L., Palmer, D. S., Baya, N. A., Ferreira, T. C., Hill, B., Lassen, F. H., Parker, M. J., Reibe, S., Elhakeem, A., et al. (2025) · Nature Genetics · doi:10.1038/s41588-025-02156-8 · Cited by 48
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Chatrath, A., Przanowska, R. K., Kiran, S., Su, Z., Saha, S., Wilson, B., Tsunematsu, T., Ahn, J., Lee, K. Y., Paulsen, T., et al. (2020) · Genome Medicine · doi:10.1186/s13073-020-0718-7 · Cited by 43
Chan, Y., Lim, E. T., Sandholm, N., Wang, S. R., McKnight, A. J., Ripke, S., Daly, M. J., Neale, B. M., Salem, R. M., & Hirschhorn, J. N. (2014) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2014.02.006 · Cited by 45
Emdin, C. A., Khera, A. V., Aragam, K. G., Haas, M. E., Chaffin, M. D., Klarin, D. M., Natarajan, P., Bick, A. G., Zekavat, S. M., Nomura, A., et al. (2018) · Diabetes · doi:10.2337/db18-0857 · Cited by 42
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Allegrini, A. G., Baldwin, J. R., Barkhuizen, W., & Pingault, J. (2022) · Journal of Child Psychology and Psychiatry · doi:10.1111/jcpp.13611 · Cited by 45
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Laurynenka, V., & Harley, J. B. (2024) · Frontiers in Lupus · doi:10.3389/flupu.2024.1398035 · Cited by 41
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Alex, A. M., Buß, C., Davis, E. P., Campos, G. D. L., Donald, K. A., Fair, D. A., Gaab, N., Gao, W., Gilmore, J. H., Girault, J. B., et al. (2023) · Biological Psychiatry · doi:10.1016/j.biopsych.2023.01.013 · Cited by 45
The use of vector bootstrapping to improve variable selection precision in Lasso models
Laurin, C., Boomsma, D. I., & Lubke, G. H. (2016) · Statistical Applications in Genetics and Molecular Biology · doi:10.1515/sagmb-2015-0043 · Cited by 40
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Hanson, A. A., Lorenz, A. J., Hesler, L. S., Bhusal, S. J., Bansal, R., Michel, A. P., Jiang, G., & Koch, R. L. (2018) · The Plant Genome · doi:10.3835/plantgenome2018.02.0011 · Cited by 39
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Politi, C., Roumeliotis, S. K., Tripepi, G., & Spoto, B. (2023) · Life · doi:10.3390/life13010235 · Cited by 38
Applying compressed sensing to genome-wide association studies
Vattikuti, S., Lee, J. J., Chang, C. C., Hsu, S., & Chow, C. C. (2014) · GigaScience · doi:10.1186/2047-217x-3-10 · Cited by 38
Schraiber, J. G., Edge, M. D., & Pennell, M. (2024) · PLoS Biology · doi:10.1371/journal.pbio.3002847 · Cited by 38
Dissection of additive genetic variability for quantitative traits in chickens using SNP markers
Abdollahi-Arpanahi, R., Pakdel, A., Nejati‐Javaremi, A., Shahrbabak, M. M., Morota, G., Valente, B. D., Kranis, A., Rosa, G. J. M., & Gianola, D. (2014) · Journal of Animal Breeding and Genetics · doi:10.1111/jbg.12079 · Cited by 38
Wollstein, A., Walsh, S., Liu, F., Chakravarthy, U., Rahu, M., Seland, J. H., Soubrane, G., Tomazzoli, L., Topouzis, F., Vingerling, J. R., et al. (2017) · Scientific Reports · doi:10.1038/srep43359 · Cited by 36
Choi, J., Kim, S., Kim, J., Son, H., Yoo, S., Kim, C.-U., Park, Y. J., Moon, S., Cha, B., Jeon, M. C., et al. (2023) · Science Advances · doi:10.1126/sciadv.adg6319 · Cited by 34
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Zhu, Z., Yuan, D., Luo, D., Lu, X., & Huang, S. (2015) · PLoS ONE · doi:10.1371/journal.pone.0133421 · Cited by 34
Conditions for the validity of SNP-based heritability estimation
Lee, J. J., & Chow, C. C. (2014) · Human Genetics · doi:10.1007/s00439-014-1441-5 · Cited by 35
Tran, T. T. H., Nguyen, H. T., Le, B. T. N., Tran, P. H., Nguyen, S. V., & Kim, O. T. P. (2021) · Aquaculture · doi:10.1016/j.aquaculture.2021.736542 · Cited by 33
Cormier, H., Vigneault, J., Garneau, V., Tchernof, A., Vohl, M., Weisnagel, S. J., & Robitaille, J. (2014) · BJOG An International Journal of Obstetrics & Gynaecology · doi:10.1111/1471-0528.12937 · Cited by 35
Yuan, D., Zhu, Z., Tan, X., Liang, J., Zeng, C., Zhang, J., Chen, J., Ma, L., Doğan, A., Brockmann, G. A., et al. (2014) · Science China Life Sciences · doi:10.1007/s11427-014-4704-4 · Cited by 33
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Ganna, A., Lee, D., Ingelsson, E., & Yudi Pawitan, Y. (2014) · Briefings in Bioinformatics · doi:10.1093/bib/bbu033 · Cited by 32
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Xie, K., Chen, M., Zhu, M., Wang, C., Qin, N., Liang, C., Song, C., Dai, J., Jin, G., Shen, H., et al. (2017) · International Journal of Cancer · doi:10.1002/ijc.30788 · Cited by 30
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Karamperis, K., Katz, S., Melograna, F., Ganau, F. P., Steen, K. V., Patrinos, G. P., & Lao, Ó. (2024) · iScience · doi:10.1016/j.isci.2024.110916 · Cited by 28
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Choi, E. H., Lee, H. J., & Chanock, S. J. (2013) · Current topics in microbiology and immunology · doi:10.1007/978-3-642-38919-1_6 · Cited by 28
Self-citationMa, G., Liu, H., Du, M., Zhang, G., Lin, Y., Ge, Y., Wang, M., Jin, G., Zhao, Q., Chu, H., et al. (2019) · Cancer · doi:10.1002/cncr.32081 · Cited by 28
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He, P., Lei, X., Yuan, D., Zhu, Z., & Huang, S. (2017) · Scientific Reports · doi:10.1038/s41598-017-12104-0 · Cited by 26
Lin, W., Yi, N., Lou, X., Zhi, D., Zhang, K., Gao, G., Tiwari, H. K., & Liu, N. (2013) · Genetic Epidemiology · doi:10.1002/gepi.21740 · Cited by 27
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Self-citationLyra, D. H., Griffiths, C. A., Watson, A., Joynson, R., Molero, G., Igna, A., Hassani‐Pak, K., Reynolds, M. P., Hall, A. J. W., & Paul, M. J. (2021) · Food and Energy Security · doi:10.1002/fes3.292 · Cited by 24
Metadata
- Authors
- Park, J.-H., Gail, M. H., Weinberg, C. R., Carroll, R. J., Chung, C. C., Wang, Z., Chanock, S. J., Fraumeni, J. F., & Chatterjee, N.
- Affiliations
- National Institutes of Health (US)
- Texas A&M University (US)
- ORCID
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2011-10-14
- Volume
- 108
- Issue
- 44
- Pages
- 18026-18031
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC3207674
- OpenAlex
- W2074498980
- Cited by
- 280
- References
- 31
- Citations by year
- 2026: 10 · 2025: 14 · 2024: 21 · 2023: 23 · 2022: 16 · 2021: 26 · 2020: 21 · 2019: 22 · 2018: 22 · 2017: 24 · 2016: 13 · 2015: 17 · 2014: 19 · 2013: 22 · 2012: 9
- FWCI
- 9.84
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- OakTrust (Texas A&M University Libraries) (submittedVersion)
- UTS ePRESS (University of Technology Sydney) (submittedVersion)
- Funders
- National Institutes of Health (5r37ca057030-23)
- National Cancer Institute ((R37-CA057030), U01CA057030, R27-CA057030, R01CA057030)
- 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 › Forensic and Genetic Research
- Keywords
- allele frequency distribution, effect size distribution, genetic risk variants, single nucleotide polymorphisms, statistical power, minor allele frequency, regression effect, type 1 diabetes, type 2 diabetes, human height, cancer susceptibility, heritability, complex traits, population genetics, natural selection
- MeSH terms
- Calibration, Gene Frequency, Humans, Genetic Variation, Polymorphism, Single Nucleotide
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.
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