Deleterious Alleles in the Human Genome Are on Average Younger Than Neutral Alleles of the Same Frequency
Open accessAbstract
Large-scale population sequencing studies provide a complete picture of human genetic variation within the studied populations. A key challenge is to identify, among the myriad alleles, those variants that have an effect on molecular function, phenotypes, and reproductive fitness. Most non-neutral variation consists of deleterious alleles segregating at low population frequency due to incessant mutation. To date, studies characterizing selection against deleterious alleles have been based on allele frequency (testing for a relative excess of rare alleles) or ratio of polymorphism to divergence (testing for a relative increase in the number of polymorphic alleles). Here, starting from Maruyama's theoretical prediction (Maruyama T (1974), Am J Hum Genet USA 6:669-673) that a (slightly) deleterious allele is, on average, younger than a neutral allele segregating at the same frequency, we devised an approach to characterize selection based on allelic age. Unlike existing methods, it compares sets of neutral and deleterious sequence variants at the same allele frequency. When applied to human sequence data from the Genome of the Netherlands Project, our approach distinguishes low-frequency coding non-synonymous variants from synonymous and non-coding variants at the same allele frequency and discriminates between sets of variants independently predicted to be benign or damaging for protein structure and function. The results confirm the abundance of slightly deleterious coding variation in humans.
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
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?
Disorder-specific lead SNPs and pleiotropic SNPs did not differ in minor allele frequencies, average heterozygosity, or predicted allele ages.
“Neither did we find differences between disorder-specific lead SNPs and pleiotropic SNPs with respect to their minor allele frequencies, average heterozygosity, or predicted allele ages (Kiezun et al., 2013).”
From Genomic Relationships, Novel Loci, and Pleiotropic Mechanisms across Eight Psychiatric Disorders (2019) · cites this paper as Kiezun et al., 2013
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Fast and accurate short read alignment with Burrows-Wheeler transform
Li, H., & Durbin, R. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp324 · Cited by 58,607 · see citing works
The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data
McKenna, A., Hanna, M., Banks, E., Sivachenko, A., Cibulskis, K., Kernytsky, A., Garimella, K., Altshuler, D., Gabriel, S., Daly, M., et al. (2010) · Genome Research · doi:10.1101/gr.107524.110 · Cited by 27,895 · see citing works
A method and server for predicting damaging missense mutations.
Adzhubei, I. A., Schmidt, S., Peshkin, L., Ramensky, V. E., Gerasimova, A., Bork, P., Kondrashov, A. S., & Sunyaev, S. R. (2010) · Nature Methods · doi:10.1038/nmeth0410-248 · Cited by 13,194 · see citing works
Self-citationA framework for variation discovery and genotyping using next-generation DNA sequencing data
DePristo, M. A., Banks, E., Poplin, R. E., Garimella, K. V., Maguire, J., Hartl, C., Philippakis, A., del Angel, G., Rivas, M. A., Hanna, M., et al. (2011) · Nature Genetics · doi:10.1038/ng.806 · Cited by 11,216 · see citing works
A map of human genome variation from population-scale sequencing.
The 1000 Genomes Project Consortium (2010) · Nature · doi:10.1038/nature09534 · Cited by 7,877 · see citing works
Sequence Variations in <i>PCSK9,</i> Low LDL, and Protection against Coronary Heart Disease
Cohen, J. C., Boerwinkle, E., Mosley, T. H., & Hobbs, H. H. (2006) · New England Journal of Medicine · doi:10.1056/nejmoa054013 · Cited by 3,181 · see citing works
A Map of Recent Positive Selection in the Human Genome
Voight, B. F., Kudaravalli, S., Wen, X., & Pritchard, J. K. (2006) · PLoS Biology · doi:10.1371/journal.pbio.0040072 · Cited by 2,847 · see citing works
Genome-wide detection and characterization of positive selection in human populations
The International HapMap Consortium, Sabeti, P. C., Varilly, P., Fry, B., Lohmueller, J., Hostetter, E., Cotsapas, C., Xie, X., Byrne, E. H., McCarroll, S. A., et al. (2007) · Nature · doi:10.1038/nature06250 · Cited by 2,131 · see citing works
Detecting recent positive selection in the human genome from haplotype structure
Sabeti, P. C., Reich, D. E., Higgins, J. M., Levine, H. Z. P., Richter, D. J., Schaffner, S. F., Gabriel, S. B., Platko, J. V., Patterson, N. J., McDonald, G. J., et al. (2002) · Nature · doi:10.1038/nature01140 · Cited by 2,106 · see citing works
Browning, B. L., & Browning, S. R. (2009) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2009.01.005 · Cited by 1,640 · see citing works
Prediction of deleterious human alleles
Sunyaev, S. R. (2001) · Human Molecular Genetics · doi:10.1093/hmg/10.6.591 · Cited by 1,042 · see citing works
Self-citationNejentsev, S., Walker, N., Riches, D., Egholm, M., & Todd, J. A. (2009) · Science · doi:10.1126/science.1167728 · Cited by 958 · see citing works
Positive and Negative Selection on the Human Genome
Fay, J. C., Wyckoff, G. J., & Wu, C. (2001) · Genetics · doi:10.1093/genetics/158.3.1227 · Cited by 646 · see citing works
Assessing the evolutionary impact of amino acid mutations in the human genome.
Boyko, A. R., Williamson, S. H., Indap, A. R., Degenhardt, J. D., Hernandez, R. D., Lohmueller, K. E., Adams, M. D., Schmidt, S., Sninsky, J. J., Sunyaev, S. R., et al. (2008) · PLoS Genetics · doi:10.1371/journal.pgen.1000083 · Cited by 628 · see citing works
Self-citationKryukov, G. V., Pennacchio, L. A., & Sunyaev, S. R. (2007) · The American Journal of Human Genetics · doi:10.1086/513473 · Cited by 598 · see citing works
Self-citationWang, N., Akey, J. M., Zhang, K., Chakraborty, R., & Li, J. (2002) · The American Journal of Human Genetics · doi:10.1086/344398 · Cited by 459 · see citing works
The distribution of fitness effects of new deleterious amino acid mutations in humans.
Eyre-Walker, A., Woolfit, M., & Phelps, T. (2006) · Genetics · doi:10.1534/genetics.106.057570 · Cited by 377 · see citing works
Williamson, S., Hernandez, R. D., Fledel-Alon, A., Zhu, L., Nielsen, R., & Bustamante, C. D. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0502300102 · Cited by 325 · see citing works
A flexible forward simulator for populations subject to selection and demography
Hernandez, R. D. (2008) · Bioinformatics · doi:10.1093/bioinformatics/btn522 · Cited by 208 · see citing works
The functional spectrum of low-frequency coding variation
Marth, G., Yu, F., Indap, A. R., Garimella, K. V., Gravel, S., Leong, W. F., Tyler‐Smith, C., Bainbridge, M. N., Blackwell, T. W., Zheng-Bradley, X., et al. (2011) · Genome biology · doi:10.1186/gb-2011-12-9-r84 · Cited by 190 · see citing works
Power of deep, all-exon resequencing for discovery of human trait genes
Kryukov, G. V., Shpunt, A., Stamatoyannopoulos, J. A., & Sunyaev, S. R. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0812824106 · Cited by 156 · see citing works
Self-citationComputational and statistical approaches to analyzing variants identified by exome sequencing
Stitziel, N. O., Kieżun, A., & Sunyaev, S. R. (2011) · Genome biology · doi:10.1186/gb-2011-12-9-227 · Cited by 137 · see citing works
Self-citationEstimating the age of rare disease mutations: the example of Triple-A syndrome
Génin, E., Pelet, A., Begeot, F., Lyonnet, S., & Abel, L. (2004) · Journal of Medical Genetics · doi:10.1136/jmg.2003.017962 · Cited by 125 · see citing works
(1997) · PubMed · Cited by 112 · see citing works
High-Resolution Multipoint Linkage-Disequilibrium Mapping in the Context of a Human Genome Sequence
Rannala, B. H., & REEVE, J. P. (2001) · The American Journal of Human Genetics · doi:10.1086/321279 · Cited by 110 · see citing works
Simulating genealogies of selected alleles in a population of variable size
Slatkin, M. (2001) · Genetics Research · doi:10.1017/s0016672301005183 · Cited by 82 · see citing works
The age of a rare mutant gene in a large population.
Maruyama, T. (1974) · Europe PMC (PubMed Central) · Cited by 48 · see citing works
Maruyama, T., & Kimura, M. (1975) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.72.4.1602 · Cited by 15 · see citing works
Flagged first, then by citations
1 of the 29 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Genomic Relationships, Novel Loci, and Pleiotropic Mechanisms across Eight Psychiatric Disorders
Lee, P. H., Anttila, V., Won, H., Feng, Y.-C. A., Rosenthal, J., Zhu, Z., Tucker-Drob, E. M., Nivard, M. G., Grotzinger, A. D., Posthuma, D., et al. (2019) · Cell · doi:10.1016/j.cell.2019.11.020 · Cited by 1,381 · see citing works
Gazal, S., Finucane, H. K., Furlotte, N. A., Loh, P.-R., Palamara, P. F., Liu, X., Schoech, A., Bulik-Sullivan, B., Neale, B. M., Gusev, A., et al. (2017) · Nature Genetics · doi:10.1038/ng.3954 · Cited by 525 · see citing works
A method for genome-wide genealogy estimation for thousands of samples
Speidel, L., Forest, M., Shi, S., & Myers, S. R. (2019) · Nature Genetics · doi:10.1038/s41588-019-0484-x · Cited by 480 · see citing works
Gene hunting in autism spectrum disorder: on the path to precision medicine
Geschwind, D. H., & State, M. W. (2015) · The Lancet Neurology · doi:10.1016/s1474-4422(15)00044-7 · Cited by 481 · see citing works
Genome-Wide Inference of Ancestral Recombination Graphs
Rasmussen, M. D., Hubisz, M. J., Gronau, I., & Siepel, A. (2014) · PLoS Genetics · doi:10.1371/journal.pgen.1004342 · Cited by 371 · see citing works
Long Runs of Homozygosity Are Enriched for Deleterious Variation
Szpiech, Z. A., Xu, J., Pemberton, T. J., Peng, W., Zöllner, S. K., Rosenberg, N. A., & Li, J. Z. (2013) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2013.05.003 · Cited by 335 · see citing works
Thinking 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 263 · see citing works
Dating genomic variants and shared ancestry in population-scale sequencing data
Albers, P. K., & McVean, G. (2020) · PLoS Biology · doi:10.1371/journal.pbio.3000586 · Cited by 193 · see citing works
The Genetics of Microdeletion and Microduplication Syndromes: An Update
Watson, C. T., Marquès‐Bonet, T., Sharp, A. J., & Mefford, H. C. (2014) · Annual Review of Genomics and Human Genetics · doi:10.1146/annurev-genom-091212-153408 · Cited by 185 · see citing works
Demography and the Age of Rare Variants
Mathieson, I., & McVean, G. (2014) · PLoS Genetics · doi:10.1371/journal.pgen.1004528 · Cited by 146 · see citing works
Loss and Recovery of Genetic Diversity in Adapting Populations of HIV
Pennings, P. S., Kryazhimskiy, S., & Wakeley, J. (2014) · PLoS Genetics · doi:10.1371/journal.pgen.1004000 · Cited by 132 · see citing works
Saada, J. N., Kalantzis, G., Shyr, D., Cooper, F., Robinson, M. J., Gusev, A., & Palamara, P. F. (2020) · Nature Communications · doi:10.1038/s41467-020-19588-x · Cited by 122 · see citing works
Neutral genomic regions refine models of recent rapid human population growth
Gazave, É., Ma, L., Chang, D., Coventry, A., Gao, F., Muzny, D. M., Boerwinkle, E., Gibbs, R. A., Sing, C. F., Clark, A. G., et al. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1310398110 · Cited by 79 · see citing works
Genes with monoallelic expression contribute disproportionately to genetic diversity in humans
Savova, V., Chun, S., Sohail, M., McCole, R. B., Witwicki, R. M., Gai, L., Lenz, T. L., Wu, C.-T., Sunyaev, S. R., & Gimelbrant, A. A. (2016) · Nature Genetics · doi:10.1038/ng.3493 · Cited by 75 · see citing works
Self-citationGazave, É., Chang, D., Clark, A. G., & Keinan, A. (2013) · Genetics · doi:10.1534/genetics.113.153973 · Cited by 73 · see citing works
Leveraging Distant Relatedness to Quantify Human Mutation and Gene-Conversion Rates
Palamara, P. F., Francioli, L. C., Wilton, P., Genovese, G., Gusev, A., Finucane, H. K., Sankararaman, S., Sunyaev, S. R., de Bakker, P. I. W., Wakeley, J., et al. (2015) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2015.10.006 · Cited by 69 · see citing works
Self-citationExcess of Deleterious Mutations around HLA Genes Reveals Evolutionary Cost of Balancing Selection
Lenz, T. L., Spirin, V., Jordan, D. M., & Sunyaev, S. R. (2016) · Molecular Biology and Evolution · doi:10.1093/molbev/msw127 · Cited by 72 · see citing works
Self-citationHaplotype Sharing Provides Insights into Fine-Scale Population History and Disease in Finland
Martin, A. R., Karczewski, K. J., Kerminen, S., Kurki, M., Sarin, A., Artomov, M., Eriksson, J. G., Esko, T., Genovese, G., Havulinna, A. S., et al. (2018) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2018.03.003 · Cited by 65 · see citing works
A variant-centric perspective on geographic patterns of human allele frequency variation
Biddanda, A., Rice, D. P., & Novembre, J. (2020) · eLife · doi:10.7554/elife.60107 · Cited by 64 · see citing works
Relaxed Selection During a Recent Human Expansion
Peischl, S., Dupanloup, I., Foucal, A., Jomphe, M., Bruat, V., Grenier, J., Gouy, A., Gilbert, K. J., Gbeha, E., Bosshard, L., et al. (2017) · Genetics · doi:10.1534/genetics.117.300551 · Cited by 62 · see citing works
Ramos, Y. F., Bos, S. D., van der Breggen, R., Kloppenburg, M., Ye, K., Lameijer, E.-W., Nelissen, R. G. H. H., Slagboom, P. E., & Meulenbelt, I. (2014) · Annals of the Rheumatic Diseases · doi:10.1136/annrheumdis-2013-205149 · Cited by 61 · see citing works
Self-citationQuantifying realized inbreeding in wild and captive animal populations
Knief, U., Hemmrich‐Stanisak, G., Wittig, M., Franke, A., Griffith, S. C., Kempenaers, B., & Forstmeier, W. (2015) · Heredity · doi:10.1038/hdy.2014.116 · Cited by 59 · see citing works
Lee, P. H., Anttila, V., Won, H., Feng, Y. A., Rosenthal, J., Zhu, Z., Tucker–Drob, E. M., Nivard, M. G., Grotzinger, A. D., Posthuma, D., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/528117 · Cited by 51 · see citing works
Evolutionary Perspectives on Genetic and Environmental Risk Factors for Psychiatric Disorders
Keller, M. C. (2018) · Annual Review of Clinical Psychology · doi:10.1146/annurev-clinpsy-050817-084854 · Cited by 46 · see citing works
van Hoolwerff, M., Ruiz, A. R., Bouma, M. J., Suchiman, H. E. D., Koning, R. I., Jost, C. R., Mulder, A. A., Freund, C. M. A. H., Guilak, F., Ramos, Y. F., et al. (2021) · Science Advances · doi:10.1126/sciadv.abg8583 · Cited by 41 · see citing works
The Standing Pool of Genomic Structural Variation in a Natural Population of Mimulus guttatus
Flagel, L., Willis, J. H., & Vision, T. (2013) · Genome Biology and Evolution · doi:10.1093/gbe/evt199 · Cited by 37 · see citing works
Heaton, M. P., Smith, T. P. L., Carnahan, J. K., Basnayake, V. R., Qiu, J., Simpson, B., & Kalbfleisch, T. (2016) · F1000Research · doi:10.12688/f1000research.9254.2 · Cited by 38 · see citing works
Forces driving transposable element load variation during Arabidopsis range expansion
Jiang, J., Xu, Y., Zhang, Z., Chen, J., Niu, X., Hou, X.-H., Li, X.-T., Wang, L., Zhang, Y. E., Ge, S., et al. (2023) · The Plant Cell · doi:10.1093/plcell/koad296 · Cited by 36 · see citing works
Simple scaling laws control the genetic architectures of human complex traitsPreprint
Simons, Y. B., Mostafavi, H., Smith, C. J., Pritchard, J. K., & Sella, G. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.10.04.509926 · Cited by 35 · see citing works
Kelly, T. N., Sun, X., He, K., Brown, M. R., Taliun, S. A. G., Hellwege, J. N., Irvin, M. R., Mi, X., Brody, J. A., Franceschini, N., et al. (2022) · Hypertension · doi:10.1161/hypertensionaha.122.19324 · Cited by 32 · see citing works
Heaton, M. P., Smith, T. P. L., Carnahan, J. K., Basnayake, V. R., Qiu, J., Simpson, B., & Kalbfleisch, T. (2016) · F1000Research · doi:10.12688/f1000research.9254.1 · Cited by 28 · see citing works
Platt, A., Pivirotto, A. M., Knoblauch, J. G., & Hey, J. (2019) · PLoS Genetics · doi:10.1371/journal.pgen.1008340 · Cited by 28 · see citing works
Increased Substitution Rates Surrounding Low-Complexity Regions within Primate Proteins
Lenz, C., Haerty, W., & Golding, G. B. (2014) · Genome Biology and Evolution · doi:10.1093/gbe/evu042 · Cited by 25 · see citing works
Genomic findings and their implications for the evolutionary social sciences
Zietsch, B. P. (2024) · Evolution and Human Behavior · doi:10.1016/j.evolhumbehav.2024.106596 · Cited by 26 · see citing works
No Gene in the Genome Makes Sense Except in the Light of Evolution
Haerty, W., & Ponting, C. P. (2014) · Annual Review of Genomics and Human Genetics · doi:10.1146/annurev-genom-090413-025621 · Cited by 21 · see citing works
Liu, S. M., Huang, S., Rao, J., Ye, W., Krogh, A., & Wang, J. (2015) · GigaScience · doi:10.1186/s13742-015-0103-4 · Cited by 22 · see citing works
Mafessoni, F., & Lachmann, M. (2015) · Genetics · doi:10.1534/genetics.115.178160 · Cited by 20 · see citing works
Simple scaling laws control the genetic architectures of human complex traits
Simons, Y. B., Mostafavi, H., Zhu, H., Smith, C. J., Pritchard, J. K., & Sella, G. (2025) · PLoS Biology · doi:10.1371/journal.pbio.3003402 · Cited by 19 · see citing works
Bloks, N. G. C., Harissa, Z., Mazzini, G., Adkar, S. S., Dicks, A. R., Hajmousa, G., Steward, N. S., Koning, R. I., Mulder, A. A., de Koning, B. B. R., et al. (2024) · Advanced Science · doi:10.1002/advs.202400720 · Cited by 19 · see citing works
Maintenance of Complex Trait Variation: Classic Theory and Modern Data
Koch, E. M., & Sunyaev, S. R. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.763363 · Cited by 17 · see citing works
Self-citationEuropeans have a higher proportion of high-frequency deleterious variants than Africans
Subramanian, S. (2015) · Human Genetics · doi:10.1007/s00439-015-1604-z · Cited by 16 · see citing works
Sams, A. J., Hawks, J. D., & Keinan, A. (2014) · Journal of Human Evolution · doi:10.1016/j.jhevol.2014.10.009 · Cited by 14 · see citing works
Crossing-Over in a Hypervariable Species Preferentially Occurs in Regions of High Local Similarity
Seplyarskiy, V. B., Logacheva, M. D., Penin, A. A., Baranova, M. A., Leushkin, E. V., Demidenko, N. V., Klepikova, A. V., Kondrashov, F. A., Kondrashov, A. S., & James, T. Y. (2014) · Molecular Biology and Evolution · doi:10.1093/molbev/msu242 · Cited by 13 · see citing works
Jiang, J., Chen, J., Li, X.-T., Wang, L., Mao, J., Wang, B., & Guo, Y. (2025) · Nature Communications · doi:10.1038/s41467-025-58021-z · Cited by 12 · see citing works
Naji, M. M., Duarte, J. L. G., Forneris, N. S., & Druet, T. (2024) · Genetics Selection Evolution · doi:10.1186/s12711-024-00878-7 · Cited by 11 · see citing works
Relaxed selection during a recent human expansionPreprint
Peischl, S., Dupanloup, I., Foucal, A., Jomphe, M., Bruat, V., Grenier, J., Gouy, A., Gbeha, E., Bosshard, L., Hip-Ki, E., et al. (2016) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/064691 · Cited by 11 · see citing works
Inference of Coalescence Times and Variant Ages Using Convolutional Neural Networks
Saada, J. N., Tsangalidou, Z., Stricker, M., & Palamara, P. F. (2023) · Molecular Biology and Evolution · doi:10.1093/molbev/msad211 · Cited by 9 · see citing works
Leveraging ancestral recombination graphs for scalable mixed-model analysis of complex traits
Zhu, J., Kalantzis, G., Pazokitoroudi, A., Gunnarsson, Á. F., Loya, H., Chen, H., Sankararaman, S., & Palamara, P. F. (2025) · Cell Genomics · doi:10.1016/j.xgen.2025.101072 · Cited by 9 · see citing works
Genome Evolution by Matrix Algorithms: Cellular Automata Approach to Population Genetics
Qiu, S., McSweeny, A. J., Choulet, S., Saha-Mandal, A., Fedorova, L. V., & Fedorov, A. (2014) · Genome Biology and Evolution · doi:10.1093/gbe/evu075 · Cited by 8 · see citing works
Sanctis, B. D., Krukov, I., & de Koning, A. P. J. (2017) · Scientific Reports · doi:10.1038/s41598-017-12239-0 · Cited by 8 · see citing works
Benefits and Challenges of Rare Genetic Variation in Alzheimer’s Disease
Grozeva, D., Saad, S., Menzies, G. E., & Sims, R. (2019) · Current Genetic Medicine Reports · doi:10.1007/s40142-019-0161-5 · Cited by 7 · see citing works
Haplotype-based inference of the distribution of fitness effects
Vecchyo, D. O., Lohmueller, K. E., & Novembre, J. (2022) · Genetics · doi:10.1093/genetics/iyac002 · Cited by 5 · see citing works
Maruyama's allelic age revised by whole-genome GEMA simulations
Qiu, S., & Fedorov, A. (2015) · Genomics · doi:10.1016/j.ygeno.2015.02.005 · Cited by 3 · see citing works
Allele ages provide limited information about the strength of negative selection
Shastry, V., & Berg, J. J. (2024) · Genetics · doi:10.1093/genetics/iyae211 · Cited by 2 · see citing works
Elevated incidence of somatic mutations at prevalent genetic sites
Wang, M., Li, S. C., & Shen, B. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae065 · Cited by 2 · see citing works
Genome-wide inference of ancestral recombination graphsPreprint
Rasmussen, M. D., Hubisz, M. J., Gronau, I., & Siepel, A. C. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1306.5110 · Cited by 2 · see citing works
Bloks, N. G. C., Harissa, Z., Adkar, S. S., Dicks, A. R., Hajmousa, G., Steward, N. S., Koning, R. I., Mulder, A. A., de Koning, B. B. R., Kloppenburg, M., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.12.19.520461 · Cited by 2 · see citing works
Pivirotto, A. M., Platt, A., Patel, R., Kumar, S., & Hey, J. (2024) · eLife · doi:10.7554/elife.93258 · Cited by 2 · see citing works
Reflections on the Field of Human Genetics: A Call for Increased Disease Genetics Theory
Schrodi, S. J. (2016) · Frontiers in Genetics · doi:10.3389/fgene.2016.00106 · Cited by 1 · see citing works
Identifying Single-Origin Rare Variants in Population Genomic Data
Reynolds, J. J., Koufopanou, V., & Burt, A. (2025) · Molecular Biology and Evolution · doi:10.1093/molbev/msaf283 · Cited by 1 · see citing works
Toncheva, D. I., Marinova, M., Borovskа, P., & Serbezov, D. (2022) · PLoS ONE · doi:10.1371/journal.pone.0269628 · Cited by 1 · see citing works
Identification of Genes under Purifying Selection in Human CancersPreprint
Mathis, R. A., Sokol, E. S., & Gupta, P. B. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/129205 · Cited by 1 · see citing works
Pivirotto, A. M., Platt, A., Patel, R., Kumar, S., & Hey, J. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.10.09.561569 · Cited by 1 · see citing works
Inference of elevated mutation rates and variant effects using 700k exomesPreprint
Kar, P., Moldovan, M. A., Guez, J., Nazeen, S., Goodrich, J. K., Karani, T., Samocha, K. E., Karczewski, K. J., Koch, E. M., Seplyarskiy, V. B., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.06.09.730991 · Cited by 1 · see citing works
Self-citationNatural Selection at Rare Variants
Xue, Y., & Tyler‐Smith, C. (2015) · Springer eBooks · doi:10.1007/978-1-4939-2824-8_9
Inherited and de novo variation in human genomes
Francioli, L. C. (2015) · Utrecht University Repository (Utrecht University)
Self-citationThe Standing Pool of Genomic Structural Variation in a Natural Population of Mimulus guttatusPreprint
Flagel, L., Willis, J. H., & Vision, T. (2013) · arXiv (Cornell University) · doi:10.17615/w0kv-qw58
Pivirotto, A. M., Platt, A., Patel, R., Kumar, S., & Hey, J. (2024) · eLife · doi:10.7554/elife.93258.1
Naji, M. M., Duarte, J. L. G., Forneris, N. S., & Druet, T. (2024) · Figshare · doi:10.6084/m9.figshare.25124098
Naji, M. M., Duarte, J. L. G., Forneris, N. S., & Druet, T. (2024) · Figshare · doi:10.6084/m9.figshare.25123831
Pope, N. S., Tallman, S., Jeffery, B., Mbuli-Robertson, D., Wong, Y., Karthikeyan, S., Ralph, P. L., & Kelleher, J. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.01.07.698223
Lopez, M. (2018) · theses.fr (ABES) · doi:10.70675/df71047dz87f5z482cz96b0zf7bc6df59e0f
- Authors
- Kiezun, A., Pulit, S. L., Francioli, L. C., van Dijk, F., Swertz, M., Boomsma, D. I., van Duijn, C. M., Slagboom, P. E., van Ommen, G. J. B., Wijmenga, C., Genome of the Netherlands Consortium, de Bakker, P. I. W., & Sunyaev, S. R.
- Affiliations
- Broad Institute (US)
- University Medical Center Utrecht (NL)
- University Medical Center Groningen (NL)
- Vrije Universiteit Amsterdam (NL)
- Leiden University Medical Center (NL)
- Corresponding author
- Sunyaev, Shamil R.
- ORCID
- Venue
- PLoS Genetics
- Publisher
- Public Library of Science (PLoS)
- Published
- 2013-02-28
- Volume
- 9
- Issue
- 2
- Pages
- e1003301
- ISSN
- 1553-7390 (ISSN-L)
- 1553-7404
- PMC
- PMC3585140
- OpenAlex
- W2110857221
- Cited by
- 72
- References
- 29
- Citations by year
- 2026: 2 · 2025: 4 · 2024: 9 · 2023: 3 · 2022: 5 · 2021: 2 · 2020: 3 · 2019: 6 · 2018: 3 · 2017: 4 · 2016: 6 · 2015: 9 · 2014: 10 · 2013: 6
- FWCI
- 6.86
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- PLoS Genetics (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- EUR Research Repository (Erasmus University Rotterdam) (publishedVersion, other-oa)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (publishedVersion, cc-by)
- VU Research Portal (publishedVersion, other-oa)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
- EUR Research Repository (Erasmus University Rotterdam) (submittedVersion, other-oa)
- RePub (Erasmus University Rotterdam) (submittedVersion, other-oa)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Leiden Repository (Leiden University) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (submittedVersion, other-oa)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- Funders
- Nederlandse Organisatie voor Wetenschappelijk Onderzoek
- National Institutes of Health
- National Institute of Mental Health (R01MH084676)
- National Institute of General Medical Sciences (R01GM078598)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Evolution and Genetic Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Keywords
- deleterious alleles, natural selection, neutral alleles, allele frequency, non-synonymous variants, synonymous mutations, non-coding variants, human genetic variation, protein structure-function relationship
- MeSH terms
- Alleles, Gene Frequency, Genetics, Population, Humans, Models, Theoretical, Selection, Genetic, Genetic Variation, Genome, Human, Sequence Deletion, Evolution, Molecular, Genetic Drift
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