Establishing the age of each mutation segregating in contemporary human populations is important to fully understand our evolutionary history1,2 and will help to facilitate the development of new approaches for disease-gene discovery3. Large-scale surveys of human genetic variation have reported signatures of recent explosive population growth4,5,6, notable for an excess of rare genetic variants, suggesting that many mutations arose recently. To more quantitatively assess the distribution of mutation ages, we resequenced 15,336 genes in 6,515 individuals of European American and African American ancestry and inferred the age of 1,146,401 autosomal single nucleotide variants (SNVs). We estimate that approximately 73% of all protein-coding SNVs and approximately 86% of SNVs predicted to be deleterious arose in the past 5,000–10,000 years. The average age of deleterious SNVs varied significantly across molecular pathways, and disease genes contained a significantly higher proportion of recently arisen deleterious SNVs than other genes. Furthermore, European Americans had an excess of deleterious variants in essential and Mendelian disease genes compared to African Americans, consistent with weaker purifying selection due to the Out-of-Africa dispersal. Our results better delimit the historical details of human protein-coding variation, show the profound effect of recent human history on the burden of deleterious SNVs segregating in contemporary populations, and provide important practical information that can be used to prioritize variants in disease-gene discovery.
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Filtered sets of annotated variants and their allele frequencies are available at (http://evs.gs.washington.edu/EVS/) and genotypes and phenotypes from a large subset of individuals are also available through dbGaP (http://www.ncbi.nlm.nih.gov/gap) using the following accession information: NHLBI GO-ESP: Women’s Health Initiative Exome Sequencing Project (WHI) – WHISP, WHISP_Subject_Phenotypes, pht002246.v2.p2, phs000281.v2.p2; NHLBI GO-ESP: Heart Cohorts Exome Sequencing Project (JHS), ESP_HeartGO_JHS_LDLandEOMI_Subject_Phenotypes, pht002539.v1.p1, phs000402.v1.p1; NHLBI GO-ESP: Heart Cohorts Exome Sequencing Project (FHS), HeartGO_FHS_LDLandEOMI_PhenotypeDataFile, pht002476.v1.p1, phs000401.v1.p1; NHLBI GO-ESP: Heart Cohorts Exome Sequencing Project (CHS), HeartGO_CHS_LDL_PhenotypeDataFile, pht002536.v1.p1, phs000400.v1.p1; NHLBI GO-ESP: Heart Cohorts Exome Sequencing Project (ARIC), ESP_ARIC_LDLandEOMI_Sample, pht002466.v1.p1, phs000398.v1.p1;NHLBIGO-ESP: Lung Cohorts Exome Sequencing Project (Cystic Fibrosis), ESP_LungGO_CF_PA_Culture_Data, pht002227.v1.p1, phs000254.v1.p1; NHLBI GO-ESP: Early-Onset Myocardial Infarction (Broad EOMI), ESP_Broad_EOMI_Subject_Phenotypes, pht001437.v1.p1, phs000279.v1.p1; NHLBI GO-ESP: Lung Cohorts Exome Sequencing Project (Pulmonary Arterial Hypertension), PAH_Subject_Phenotypes_Baseline_Measures, pht002277.v1.p1, phs000290.v1.p1; NHLBI GO-ESP: Lung Cohorts Exome Sequencing Project (Lung Health Study of Chronic Obstructive Pulmonary Disease), LHS_COPD_Subject_Phenotypes_Baseline_Measures, pht002272.v1.p1, phs000291.v1.p1.
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We acknowledge the support of the National Heart, Lung and Blood Institute (NHLBI), the contributions of the research institutions that participated in this study, the study investigators, field staff and study participants who created this resource for biomedical research, and the Population Genetics Project Team of the NHLBI. We thank J. Wilson and R. Do for critical feedback on the manuscript. Funding for the GO (Grand Opportunity) Exome Sequencing Project was provided by NHLBI grants RC2 HL-103010 (Heart GO), RC2 HL-102923 (Lung GO) and RC2 HL-102924 (WHISP). The exome sequencing was was supported by NHLBI grants RC2 HL-102925 (Broad GO) and RC2 HL-102926 (Seattle GO).
The authors declare no competing financial interests.
This file contains Supplementary Text and Data, Supplementary References, Supplementary Tables 1-4 and Supplementary Figures 1-15 (see Table of Contents for more details). (PDF 3066 kb)
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Fu, W., O’Connor, T., Jun, G. et al. Analysis of 6,515 exomes reveals the recent origin of most human protein-coding variants. Nature 493, 216–220 (2013) doi:10.1038/nature11690
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