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      The deleterious mutation load is insensitive to recent population history

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          Abstract

          Human populations have undergone dramatic changes in population size in the past 100,000 years, including recent rapid growth. How these demographic events have affected the burden of deleterious mutations in individuals and the frequencies of disease mutations in populations remains unclear. We use population genetic models to show that recent human demography has likely had little impact on the average burden of deleterious mutations. This prediction is supported by two exome sequence datasets showing that individuals of west African and European ancestry carry very similar burdens of damaging mutations. We further show that for many diseases, rare alleles are unlikely to contribute a large fraction of the heritable variation, and therefore the impact of recent growth is likely to be modest. However, for those diseases that have a direct impact on fitness, strongly deleterious rare mutations likely do play an important role, and recent growth will have increased their impact.

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          Most cited references20

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          Rare and common variants: twenty arguments.

          Genome-wide association studies have greatly improved our understanding of the genetic basis of disease risk. The fact that they tend not to identify more than a fraction of the specific causal loci has led to divergence of opinion over whether most of the variance is hidden as numerous rare variants of large effect or as common variants of very small effect. Here I review 20 arguments for and against each of these models of the genetic basis of complex traits and conclude that both classes of effect can be readily reconciled.
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            An abundance of rare functional variants in 202 drug target genes sequenced in 14,002 people.

            Rare genetic variants contribute to complex disease risk; however, the abundance of rare variants in human populations remains unknown. We explored this spectrum of variation by sequencing 202 genes encoding drug targets in 14,002 individuals. We find rare variants are abundant (1 every 17 bases) and geographically localized, so that even with large sample sizes, rare variant catalogs will be largely incomplete. We used the observed patterns of variation to estimate population growth parameters, the proportion of variants in a given frequency class that are putatively deleterious, and mutation rates for each gene. We conclude that because of rapid population growth and weak purifying selection, human populations harbor an abundance of rare variants, many of which are deleterious and have relevance to understanding disease risk.
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              Calibrating a coalescent simulation of human genome sequence variation.

              Population genetic models play an important role in human genetic research, connecting empirical observations about sequence variation with hypotheses about underlying historical and biological causes. More specifically, models are used to compare empirical measures of sequence variation, linkage disequilibrium (LD), and selection to expectations under a "null" distribution. In the absence of detailed information about human demographic history, and about variation in mutation and recombination rates, simulations have of necessity used arbitrary models, usually simple ones. With the advent of large empirical data sets, it is now possible to calibrate population genetic models with genome-wide data, permitting for the first time the generation of data that are consistent with empirical data across a wide range of characteristics. We present here the first such calibrated model and show that, while still arbitrary, it successfully generates simulated data (for three populations) that closely resemble empirical data in allele frequency, linkage disequilibrium, and population differentiation. No assertion is made about the accuracy of the proposed historical and recombination model, but its ability to generate realistic data meets a long-standing need among geneticists. We anticipate that this model, for which software is publicly available, and others like it will have numerous applications in empirical studies of human genetics.
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                Author and article information

                Journal
                9216904
                2419
                Nat Genet
                Nat. Genet.
                Nature genetics
                1061-4036
                1546-1718
                27 February 2014
                09 February 2014
                March 2014
                01 September 2014
                : 46
                : 3
                : 220-224
                Affiliations
                [1 ]Department of Ecology, Evolution, and Behavior, The Hebrew University of Jerusalem
                [2 ]Department of Human Genetics, The University of Chicago
                [3 ]Howard Hughes Medical Institute, Stanford University
                [4 ]Departments of Biology and Genetics, Stanford University
                [5 ]Department of Ecology and Evolution, The University of Chicago
                Author notes
                []To whom correspondence should be addressed: pritch@ 123456stanford.edu , gsella@ 123456math.huji.ac.il
                [6]

                current address: Department of Biological Sciences, Columbia University

                [*]

                These authors contributed equally.

                Article
                NIHMS557905
                10.1038/ng.2896
                3953611
                24509481
                0b3a8010-ae37-4b81-9029-ce4821476769

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                Genetics
                Genetics

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