The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.
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.
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?
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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]
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
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
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Items 101–200 of 273
An Empirical Bayes Mixture Model for Effect Size Distributions in Genome-Wide Association Studies
Thompson, W. K., Wang, Y., Schork, A. J., Witoelar, A., Zuber, V., Xu, S., Werge, T. M., Holland, D., Andreassen, O. A., & Dale, A. M. (2015) · PLoS Genetics · doi:10.1371/journal.pgen.1005717 · Cited by 23
Epigenomic Mapping and Effect Sizes of Noncoding Variants Associated with Psychotropic Drug Response
Higgins, G. A., Allyn-Feuer, A., & Athey, B. D. (2015) · Pharmacogenomics · doi:10.2217/pgs.15.105 · Cited by 23
Zhu, B., ZHANG, J.-J., Niu, H., Guan, L., Guo, P., Xu, L., Chen, Y., Zhang, L., Gao, H., Gao, X., et al. (2017) · Journal of Integrative Agriculture · doi:10.1016/s2095-3119(16)61474-0 · Cited by 23
Cheung, Y. H., Wang, G., Leal, S. M., & Wang, S. (2012) · Genetic Epidemiology · doi:10.1002/gepi.21662 · Cited by 23
van Tilburg, M. A. L., Parisien, M., Boles, R. G., Drury, G. L., Smith-Voudouris, J., Verma, V., Khoury, S., Chabot‐Doré, A., Nackley, A. G., Smith, S. B., et al. (2020) · Pain · doi:10.1097/j.pain.0000000000001996 · Cited by 22
Basu, M., Das, T., Ghosh, A., Majumder, S., Maji, A. K., Kanjilal, S. D., Mukhopadhyay, I., Roychowdhury, S., Banerjee, S., & Sengupta, S. (2012) · PLoS ONE · doi:10.1371/journal.pone.0046441 · Cited by 22
Collective effects of common SNPs and risk prediction in lung cancer
Lei, X., Yuan, D., Zhu, Z., & Huang, S. (2018) · Heredity · doi:10.1038/s41437-018-0063-4 · Cited by 22
Rigor and reproducibility in genetic research on eating disorders
Hübel, C., Leppä, V., Breen, G. D., & Bulik, C. M. (2018) · International Journal of Eating Disorders · doi:10.1002/eat.22896 · Cited by 24
Genome-wide genetic variation discovery in Chinese Taihu pig breeds using next generation sequencing
Wang, Z., Chen, Q., Liao, R., Zhang, Z., Zhang, X., Liu, X., Zhu, M., Zhang, W., Xue, M., Yang, H., et al. (2016) · Animal Genetics · doi:10.1111/age.12465 · Cited by 21
Self-citationOliveira, M., Saraiva, D. P., Cavadas, B., Fernandes, V., Pedro, N., Casadémont, I., Koeth, F., Alshamali, F., Harich, N., Cherni, L., et al. (2018) · Infection Genetics and Evolution · doi:10.1016/j.meegid.2018.04.018 · Cited by 22
A Novel Approach to Detect Cumulative Genetic Effects and Genetic Interactions in Crohn’s Disease
Wang, M., Fiocchi, C., Ripke, S., Zhu, X., Duerr, R. H., & Achkar, J. (2013) · Inflammatory Bowel Diseases · doi:10.1097/mib.0b013e31828706a0 · Cited by 21
Sharafeldin, N., Richman, J., Bosworth, A., Chen, Y., Singh, P., Patel, S. K., Wang, X., Francisco, L., Forman, S. J., Wong, F. L., et al. (2020) · Journal of Clinical Oncology · doi:10.1200/jco.19.01085 · Cited by 20
Pavard, S., & Coste, C. F. D. (2021) · Nature Ecology & Evolution · doi:10.1038/s41559-020-01355-2 · Cited by 20
Walters, R. K., Laurin, C., & Lubke, G. H. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts483 · Cited by 20
Human genotype-to-phenotype predictions: Boosting accuracy with nonlinear models
Медведев, А. В., Sharma, S. M., Tsatsorin, E., Nabieva, E., & Yarotsky, D. (2022) · PLoS ONE · doi:10.1371/journal.pone.0273293 · Cited by 20
Adams, H. H. H., Verlinden, V. J. A., Callisaya, M. L., van Duijn, C. M., Albert Hofman, D., Thomson, R. J., Uitterlinden, A. G., Vernooij, M. W., van der Geest, J. N., Srikanth, V. K., et al. (2015) · The Journals of Gerontology Series A · doi:10.1093/gerona/glv081 · Cited by 19
Derkach, A., Zhang, H., & Chatterjee, N. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx770 · Cited by 21
Self-citationEvaluation of Existing Methods for High-Order Epistasis Detection
Ponte-Fernández, C., González‐Domínguez, J., Carvajal‐Rodríguez, A., & Martín, M. J. (2020) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2020.3030312 · Cited by 18
Zhang, W., Margines, J. B., Jacobs, D. S., Rabinowitz, Y. S., Hanser, E. M., Chauhan, T., Chung, D. D., Bykhovskaya, Y., Gaster, R. N., & Aldave, A. J. (2019) · Cornea · doi:10.1097/ico.0000000000002002 · Cited by 18
Zheng, X., Wang, L., Zhu, Y., Guan, Q., Li, H., Xiong, Z., Deng, L., Lü, J., Miao, X., & Cheng, L. (2012) · PLoS ONE · doi:10.1371/journal.pone.0034625 · Cited by 17
Zhao, J., Zhang, M., & Li, Z. (2023) · American Journal of Rhinology and Allergy · doi:10.1177/19458924231207131 · Cited by 17
Abdollahi-Arpanahi, R., Nejati‐Javaremi, A., Pakdel, A., MORADI-SHAHRBABAK, M., Morota, G., Valente, B. D., Kranis, A., Rosa, G. J. M., & Daniel Gianola, B. (2014) · Journal of Animal Breeding and Genetics · doi:10.1111/jbg.12075 · Cited by 17
Nakata, I., Yamashiro, K., Kawaguchi, T., Nakanishi, H., Akagi-Kurashige, Y., Miyake, M., Tsujikawa, A., Yamada, R., Matsuda, F., Yoshimura, N., et al. (2015) · Scientific Reports · doi:10.1038/srep09345 · Cited by 16
Efficient inference for genetic association studies with multiple outcomes
Ruffieux, H., Davison, A. C., Hager, J., & Irincheeva, I. (2017) · Biostatistics · doi:10.1093/biostatistics/kxx007 · Cited by 18
Weighted kernels improve multi-environment genomic prediction
Hu, X., Carver, B. F., El-Kassaby, Y. A., Zhu, L., & Chen, C. (2022) · Heredity · doi:10.1038/s41437-022-00582-6 · Cited by 16
Exploring the role of low-frequency and rare exonic variants in alcohol and tobacco use
Marees, A. T., Hammerschlag, A. R., Bastarache, L. A., de Kluiver, H., Vorspan, F., van den Brink, W., Smit, D. J. A., Denys, D. A. J. P., Gamazon, E. R., Li‐Gao, R., et al. (2018) · Drug and Alcohol Dependence · doi:10.1016/j.drugalcdep.2018.03.026 · Cited by 16
Oliynyk, R. T. (2019) · PeerJ · doi:10.7717/peerj.7168 · Cited by 15
PAX7nucleotide variants and the risk of non‐syndromic orofacial clefts in the Polish population
Gaczkowska, A. D., Biedziak, B., Budner, M., Zadurska, M., Lasota, A., Hozyasz, K. K., Dąbrowska, J., Wójcicki, P., Szponar‐Żurowska, A., Żukowski, K., et al. (2019) · Oral Diseases · doi:10.1111/odi.13139 · Cited by 15
Veyssière, M., Perea, J., Michou, L., Boland, A., Caloustian, C., Olaso, R., Olaso, R., Deleuze, J., Cornélis, F., Petit, É., et al. (2019) · PLoS ONE · doi:10.1371/journal.pone.0213387 · Cited by 15
Zhang, Z., Wang, Y., Zhang, Q.-M., Zhao, W., Chen, X., Zhai, J., Chen, M., Du, B., Deng, X., Ji, F., et al. (2018) · Schizophrenia Research · doi:10.1016/j.schres.2018.09.007 · Cited by 14
Holland, D., Frei, O., Desikan, R. S., Fan, C.-C., Shadrin, A. A., Smeland, O. B., Andreassen, O. A., & Dale, A. M. (2021) · Genetics · doi:10.1093/genetics/iyaa046 · Cited by 14
Human genetic variants disrupt RGS14 nuclear shuttling and regulation of LTP in hippocampal neurons
Squires, K. E., Gerber, K. J., Tillman, M. C., Lustberg, D. J., Montañez‐Miranda, C., Zhao, M., Ramineni, S., Scharer, C. D., Saha, R. N., Shu, F., et al. (2020) · Journal of Biological Chemistry · doi:10.1074/jbc.ra120.016009 · Cited by 15
Genetic influences of the effect of circulating inflammatory cytokines on osteoarthritis in humans
Huang, G., Li, W., Kan, H., Lü, X., Liao, W., & Zhao, X. (2022) · Osteoarthritis and Cartilage · doi:10.1016/j.joca.2022.12.007 · Cited by 13
Innate and Adaptive Immune Genes Associated with MERS-CoV Infection in Dromedaries
Lado, S., Elbers, J. P., Plášil, M., Loney, T., Weidinger, P., Camp, J. V., Kolodziejek, J., Futas, J., Kannan, D. A., Orozco‐terWengel, P., et al. (2021) · Cells · doi:10.3390/cells10061291 · Cited by 13
González-Casanova, I., Schoen, M. S., Tandon, S., Stein, A. D., Barraza‐Villarreal, A., DiGirolamo, A. M., Demmelmair, H., Ramírez‐Silva, C. I., García‐Feregrino, R., Rzehak, P., et al. (2021) · Clinical Nutrition · doi:10.1016/j.clnu.2021.08.026 · Cited by 13
Credible Mendelian Randomization Studies in the Presence of Selection Bias Using Control Exposures
Yang, Z., Schooling, C. M., & Kwok, M. K. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.729326 · Cited by 13
Genetic susceptibility to hydroxychloroquine retinal toxicity
Mack, H. G., Kowalski, T., Lucattini, A., Symons, R. C. A., Wicks, I., & Hall, A. (2020) · Ophthalmic Genetics · doi:10.1080/13816810.2020.1747093 · Cited by 14
Precision Proteogenomics Reveals Pan-Cancer Impact of Germline Variants
Rodrigues, F. M., Terekhanova, N. V., Imbach, K., Clauser, K. R., Selvan, M. E., Mendizabal, I., Geffen, Y., Akiyama, Y., Maynard, M., Yaron, T., et al. (2025) · Cell · doi:10.1016/j.cell.2025.03.026 · Cited by 9
Progress in genetic association studies of plasma lipids
Asselbergs, F. W., Lovering, R. C., & Drenos, F. (2013) · Current Opinion in Lipidology · doi:10.1097/mol.0b013e32835df2d6 · Cited by 12
Mosaic patterns of selection in genomic regions associated with diverse human traits
Abraham, A., LaBella, A. L., Capra, J. A., & Rokas, A. (2022) · PLoS Genetics · doi:10.1371/journal.pgen.1010494 · Cited by 12
Guzmán, M. G., Berenguer, J., Jiménez‐Sousa, M. Á., Tenor, D. P., Aldámiz‐Echevarría, T., García‐Broncano, P., Carrero, A., García–Álvarez, M., Tejerina, F., Díez, C., et al. (2015) · Journal of Translational Medicine · doi:10.1186/s12967-015-0577-y · Cited by 12
GLP1R Gene Expression and Kidney Disease Progression
Triozzi, J. L., Yu, Z., Giri, A., Chen, H., Wilson, O. D., Ferolito, B. R., Ikizler, T. A., Akwo, E. A., Robinson‐Cohen, C., Gaziano, J. M., et al. (2024) · JAMA Network Open · doi:10.1001/jamanetworkopen.2024.40286 · Cited by 11
Moore, S. R., & Thoemmes, F. J. (2016) · Journal of Child Psychology and Psychiatry · doi:10.1111/jcpp.12579 · Cited by 11
Nishino, J., Kochi, Y., Shigemizu, D., Kato, M., Ikari, K., Ochi, H., Noma, H., Matsui, K., Morizono, T., Boroevich, K. A., et al. (2018) · Frontiers in Genetics · doi:10.3389/fgene.2018.00115 · Cited by 11
Park, T.-J., Kim, J., Kim, H. J., Bae, J. S., Cheong, H. S., Park, B. L., & Shin, H. D. (2013) · Gene · doi:10.1016/j.gene.2013.12.007 · Cited by 11
Sex-specific Trans-regulatory Variation on the Drosophila melanogaster X Chromosome
Stocks, M., Dean, R., Rogell, B., & Friberg, U. (2015) · PLoS Genetics · doi:10.1371/journal.pgen.1005015 · Cited by 11
Correction: Genetics of Callous-Unemotional Behavior in Children
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/annotation/0b16418f-ceb5-41b2-be2a-a20f0c56f9a6 · Cited by 11
Rare and low-frequency coding genetic variants contribute to pediatric-onset multiple sclerosis
Horton, M. K., Shim, J. E., Wallace, A. D., Graves, J. S., Aaen, G. S., Greenberg, B. M., Mar, S., Wheeler, Y., Weinstock‐Guttman, B., Waldman, A. T., et al. (2023) · Multiple Sclerosis Journal · doi:10.1177/13524585221150736 · Cited by 10
Nutrition, Other Environmental Influences, and Genetics in the Determination of Human Stature
Lui, J. C. K., Palmer, A., & Christian, P. S. (2024) · Annual Review of Nutrition · doi:10.1146/annurev-nutr-061121-091112 · Cited by 10
Zhu, B., Wang, T., Niu, Q., Wang, Z., Hay, E. H., Xu, L., Chen, Y., Zhang, L., Gao, H., Gao, H., et al. (2025) · Communications Biology · doi:10.1038/s42003-025-07604-z · Cited by 10
Self-citationHall, M. D., & Ebert, D. (2013) · BMC Biology · doi:10.1186/1741-7007-11-79 · Cited by 12
Goel, A., Raghupathy, V., Amirtharaj, G. J., Chapla, A., Venkatraman, A., Ramakrishna, B., Ramachandran, A., Thomas, N., Balasubramanian, K. A., Mackie, I. J., et al. (2017) · Indian Journal of Gastroenterology · doi:10.1007/s12664-017-0786-9 · Cited by 10
Lu, H., Hung, K., Chu, H., Wong, H. S.-C., Kim, J., Kim, M. K., Choi, B. Y., Tai, Y., Ikegawa, S., Cho, E.-C., et al. (2019) · Journal of Bone and Mineral Research · doi:10.1002/jbmr.3703 · Cited by 10
Khera, A. V., Wang, M., Chaffin, M. D., Emdin, C. A., Samani, N. J., Schunkert, H., Watkins, H. C., McPherson, R., Erdmann, J., Elosúa, R., et al. (2022) · Circulation Genomic and Precision Medicine · doi:10.1161/circgen.121.003598 · Cited by 10
A regression framework to uncover pleiotropy in large-scale electronic health record data
Li, R., Duan, R., Kember, R. L., Rader, D. J., Damrauer, S. M., Moore, J. H., & Chen, Y. (2019) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocz084 · Cited by 10
Ruffieux, H., Fairfax, B. P., Nassiri, I., Vigorito, E., Wallace, C., Richardson, S. T., & Bottolo, L. (2021) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2021.04.010 · Cited by 10
Hybrid breeding for fall armyworm resistance: Combining ability and hybrid prediction
Kamweru, I., Beyene, Y., Bruce, A. Y., Adetimirin, V. O., Prasanna, B. M., & Gowda, M. (2023) · Plant Breeding · doi:10.1111/pbr.13129 · Cited by 9
On the Fourier transform of a quantitative trait: Implications for compressive sensing
Doro, S., & Herman, M. A. (2021) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2021.110985 · Cited by 11
Harpur, B. A., & Rehan, S. M. (2021) · Biological Journal of the Linnean Society · doi:10.1093/biolinnean/blab003 · Cited by 9
Zheng, R., Qiao, S., Chen, Y., Jin, C., Fang, Y., Lin, Z., Xue, N., Yan, Y., Gu, L., Gao, T., et al. (2021) · Neuroscience Letters · doi:10.1016/j.neulet.2021.136150 · Cited by 8
Schneider, M. C., Ballvora, A., & Léon, J. (2024) · Agronomy for Sustainable Development · doi:10.1007/s13593-024-00962-8 · Cited by 8
A multilocus genetic risk score is associated with arterial stiffness in hypertensive patients
Polonis, K., Hoffmann, M., Szyndler, A., Wolf, J., Nowak, R., Becari, C., Laurent, S., Boutouyrie, P., Melander, O., & Narkiewicz, K. (2018) · Journal of Hypertension · doi:10.1097/hjh.0000000000001773 · Cited by 8
Rare missense variants in the human cytosolic antibody receptor preserve antiviral function
Zeng, J., Slodkowicz, G., & James, L. C. (2019) · eLife · doi:10.7554/elife.48339 · Cited by 8
Nebert, D. W., & Zhang, G. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-812537-3.00016-0 · Cited by 10
Groenewoud, D., Shye, A., & Elkon, R. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009908 · Cited by 8
Weinberg, C. R., Shi, M., DeRoo, L. A., Taylor, J. A., Sandler, D. P., & Umbach, D. M. (2014) · PLoS Genetics · doi:10.1371/journal.pgen.1004174 · Cited by 8
Self-citationvarCADD: large sets of standing genetic variation enable genome-wide pathogenicity prediction
Nazaretyan, L., Rentzsch, P., & Kircher, M. (2025) · Genome Medicine · doi:10.1186/s13073-025-01517-6 · Cited by 7
Polymorphisms in the IRF ‐4 gene, asthma and recurrent bronchitis in children
Pinto, L. A., Michel, S., Klopp, N., Vogelberg, C., Berg, A. V., Bufe, A., Heinzmann, A., Laub, O., Simma, B., Frischer, T., et al. (2013) · Clinical & Experimental Allergy · doi:10.1111/cea.12175 · Cited by 7
An Accurate Genetic Colocalisation Method for the HLA Locus
Butler‐Laporte, G., Lu, T., Morris, S., Zhang, W., Band, G., Hamilton, F., Chong, A. Y.-Y., Lin, K., Nanjala, R., Richards, J. B., et al. (2026) · HLA · doi:10.1111/tan.70759 · Cited by 7
Fast and scalable ensemble learning method for versatile polygenic risk prediction
Chen, T., Zhang, H., Mazumder, R., & Lin, X. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2403210121 · Cited by 7
A-to-I RNA editing contributes to the persistence of predicted damaging mutations in populationsPreprint
Mai, T., & Chuang, T. (2019) · Genome Research · doi:10.1101/gr.246033.118 · Cited by 7
GIFT: new method for the genetic analysis of small gene effects involving small sample sizes
Rauch, C., Kyratzi, P., Blott, S. C., Bray, S., & Wattis, J. A. D. (2022) · Physical Biology · doi:10.1088/1478-3975/ac99b3 · Cited by 6
Simulated nonlinear genetic and environmental dynamics of complex traits
Hunter, M. D., McKee, K. L., & Turkheimer, E. (2022) · Development and Psychopathology · doi:10.1017/s0954579421001796 · Cited by 6
Identification of novel rare variants for anxiety: an exome-wide association study in the UK Biobank
Pan, C., Cheng, S., Liu, L., Chen, Y., Meng, P., Yang, X., Li, C., Zhang, J., Zhang, Z., Zhang, H., et al. (2023) · Progress in Neuro-Psychopharmacology and Biological Psychiatry · doi:10.1016/j.pnpbp.2023.110928 · Cited by 6
Genomic Risk Models Improve Prediction of Longitudinal Lipid Levels in Children and Young Adults
Wineinger, N. E., Harper, A. R., Libiger, O., Srinivasan, S. R., Chen, W., Berenson, G. S., & Schork, N. J. (2013) · Frontiers in Genetics · doi:10.3389/fgene.2013.00086 · Cited by 7
Oliveira, M., Ferreira, J. S. P. D. M., Fernandes, V., Sakuntabhai, A., & Pereira, L. (2018) · Future Virology · doi:10.2217/fvl-2018-0073 · Cited by 6
Discovering weaker genetic associations guided by known associations
Wang, H., Vanyukov, M. M., Xing, E. P., & Wu, W. (2020) · BMC Medical Genomics · doi:10.1186/s12920-020-0667-4 · Cited by 6
Oliynyk, R. T. (2019) · Journal of Personalized Medicine · doi:10.3390/jpm9030038 · Cited by 6
Im, S.-W., Chae, J., Son, H., Cho, B., Kim, J., & Park, J. (2020) · PLoS ONE · doi:10.1371/journal.pone.0231336 · Cited by 5
Self-citationPopulation-tailored mock genome enables genomic studies in species without a reference genome
Sabadin, F., Carvalho, H. F., Galli, G., & Fritsche‐Neto, R. (2021) · Molecular Genetics and Genomics · doi:10.1007/s00438-021-01831-9 · Cited by 5
Andreoli, M. F., Gentreau, M., Rukh, G., Perelló, M., & Schiöth, H. B. (2024) · Diabetes Obesity and Metabolism · doi:10.1111/dom.15695 · Cited by 5
Sallam, A., Amro, A., Mourad, A. M. I., Rafeek, A., Boerner, A., & Eltaher, S. (2024) · BMC Genomics · doi:10.1186/s12864-024-10245-x · Cited by 5
Cohen’s h for detection of disease association with rare genetic variants
Wen, S., & Yeh, J. (2014) · BMC Genomics · doi:10.1186/1471-2164-15-875 · Cited by 7
A guide on gene prioritization in studies of psychiatric disorders
Stringer, S., Cerrone, K. C., van den Brink, W., van den Berg, J. F., Denys, D. A. J. P., Kahn, R. S., & Derks, E. M. (2015) · International Journal of Methods in Psychiatric Research · doi:10.1002/mpr.1482 · Cited by 5
No association of AQP4 polymorphisms with neuromyelitis optica and multiple sclerosis
Yang, T., He, Y., Xiang, Y., Ao, D., Wang, Y., Zhang, Q., He, X., Zhong, S., Wu, J., & Liu, G.-Z. (2016) · Translational Neuroscience · doi:10.1515/tnsci-2016-0012 · Cited by 5
Defining the extent of gene function using ROC curvature
Fischer, S., & Gillis, J. A. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac692 · Cited by 5
Zhang, L., Pozsgai, É., Song, Y., Macharia, J., Alfatafta, H., Zheng, J., Li, Z., Liu, H., & Kiss, I. (2023) · Frontiers in Oncology · doi:10.3389/fonc.2023.1094309 · Cited by 5
Tissue-Specific Enrichment of Lymphoma Risk Loci in Regulatory Elements
Hayes, J. E., Trynka, G., Vijai, J., Offit, K., Raychaudhuri, S., & Klein, R. J. (2015) · PLoS ONE · doi:10.1371/journal.pone.0139360 · Cited by 5
Genomic analysis of maize lines introduced in the early stages of a breeding programme
Cantelmo, N. F., Pinho, R. G. V., & Balestre, M. (2017) · Plant Breeding · doi:10.1111/pbr.12543 · Cited by 6
Zhang, M., Gelfman, S., McCarthy, J., Harms, M. B., Moreno, C. A. M., Goldstein, D. B., & Allen, A. S. (2020) · Genetic Epidemiology · doi:10.1002/gepi.22283 · Cited by 6
Ruzicka, F., Connallon, T., & Reuter, M. (2021) · Genetics · doi:10.1093/genetics/iyab143 · Cited by 5
Xu, W., Liu, X., Liao, M., Xiao, S., Zheng, M., Yao, T., Chen, Z., Huang, L., & Zhang, Z. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.721600 · Cited by 5
Yu, X., Hu, J., Tan, Y., Pan, M., Zhang, H., & Li, B. (2025) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1013090 · Cited by 4
Bose, D., Fuchsberger, C., & Boehnke, M. (2025) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2025.07.002 · Cited by 4
Yamada, Y., Wang, Y., Liu, H., Gerongano, G. R., Tseng, C., Liu, S., Liao, G., Chang, C., Liao, J., Wang, M., et al. (2025) · Virology Journal · doi:10.1186/s12985-025-02672-3 · Cited by 4
Utility of AhTE Markers for Genetic and Genomic Studies in Groundnut (Arachis hypogaea L.)
Hake, A. A., & Bhat, R. (2017) · International Journal of Current Microbiology and Applied Sciences · doi:10.20546/ijcmas.2017.608.244 · Cited by 4
Juodakis, J., Bačelis, J., Zhang, G., Muglia, L. J., & Jacobsson, B. (2017) · G3 Genes Genomes Genetics · doi:10.1534/g3.116.038612 · Cited by 4
Gompert, Z. (2021) · Molecular Ecology Resources · doi:10.1111/1755-0998.13371 · Cited by 4
The Bayesian lens and Bayesian blinkers
Stephens, M. (2023) · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rsta.2022.0144 · Cited by 4
Detection and clinical significance of DNA repair gene ERCC8 tag SNPs in gastric cancer
Lu, X., Chen, F., Liu, X., Yuan, D., Zi, Y., He, X. F., & Zhu, D. (2018) · The Turkish Journal of Gastroenterology · doi:10.5152/tjg.2018.17662 · Cited by 4
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.
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
