Deep resequencing reveals excess rare recent variants consistent with explosive population growth

Coventry, Alex; Bull-Otterson, Lara; Liu, Xiaoming; Clark, Andrew G.; Maxwell, Taylor J.; Crosby, Jacy R.; Hixson, James E.; Rea, Thomas J.; Lewis, Lora; Wheeler, David A.; Sabo, Aniko; Lusk, Christine M.; Weiss, Kenneth; Akbar, Humeira; Cree, Andrew; Hawes, Alicia; Newsham, Irene; Varghese, Robin; Villasana, Donna; Gross, Shannon; Joshi, Vandita; Santibanez, Jireh; Morgan, Margaret; Chang, Kyle; Hale, Walker; Templeton, Alan R.; Boerwinkle, Eric; Gibbs, Richard A.; Sing, Charles F.

doi:10.1038/ncomms1130

Cited by 222 publications

(266 citation statements)

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“…Recent studies have made clear that relative to expectations for constantsized populations human sequence variation shows a strong excess of rare variants, most probably owing to population growth [64,65]. When designing mapping studies to discover novel resistance variants, we must keep these expectations in consideration.…”

Section: Synthesismentioning

confidence: 99%

Human population structure and the adaptive response to pathogen-induced selection pressures

Novembre

Han

2012

Phil. Trans. R. Soc. B

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The past few years of research in human evolutionary genetics have provided novel insights and questions regarding how human adaptations to recent selective pressures have taken place. Here, we review the advances most relevant to understanding human evolution in response to pathogen-induced selective pressures. Key insights come from theoretical models of adaptive evolution, particularly those that consider spatially structured populations, and from empirical population genomic studies of adaptive evolution in humans. We also review the CCR5-D32 HIV resistance allele as a case study of pathogen resistance in humans. Taken together, the results make clear that the human response to pathogen-induced selection pressures depends on a complex interplay between the age of the pathogen, the genetic basis of potential resistance phenotypes, and how population structure impacts the adaptive process in humans.

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Section: Synthesismentioning

confidence: 99%

Human population structure and the adaptive response to pathogen-induced selection pressures

Novembre

Han

2012

Phil. Trans. R. Soc. B

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“…Later studies refined the estimated timing of this bottleneck (Fagundes et al 2007;Gravel et al 2011;Lukić and Hey 2012), uncovered a second bottleneck associated with the divergence of European and Asian populations (Keinan et al 2007;Gutenkunst et al 2009;Gravel et al 2011), and inferred that the recovery from this bottleneck proceeded through continuous exponential growth (Fagundes et al 2007;Gutenkunst et al 2009). Recent studies of large population samples capable of observing very rare alleles found evidence that this growth has accelerated dramatically within the last several thousand years (Coventry et al 2010;Tennessen et al 2012;Gao and Keinan 2016). Similarly, recent studies in Drosophila melanogaster show evidence of a severe out-of-Africa bottleneck (Begun and Aquadro 1993), but occurring within the last 20,000 years (Li and Stephan 2006; Thornton and Andolfatto 2006).…”

mentioning

confidence: 97%

Effects of Linked Selective Sweeps on Demographic Inference and Model Selection

2016

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The availability of large-scale population genomic sequence data has resulted in an explosion in efforts to infer the demographic histories of natural populations across a broad range of organisms. As demographic events alter coalescent genealogies, they leave detectable signatures in patterns of genetic variation within and between populations. Accordingly, a variety of approaches have been designed to leverage population genetic data to uncover the footprints of demographic change in the genome. The vast majority of these methods make the simplifying assumption that the measures of genetic variation used as their input are unaffected by natural selection. However, natural selection can dramatically skew patterns of variation not only at selected sites, but at linked, neutral loci as well. Here we assess the impact of recent positive selection on demographic inference by characterizing the performance of three popular methods through extensive simulation of data sets with varying numbers of linked selective sweeps. In particular, we examined three different demographic models relevant to a number of species, finding that positive selection can bias parameter estimates of each of these models-often severely. We find that selection can lead to incorrect inferences of population size changes when none have occurred. Moreover, we show that linked selection can lead to incorrect demographic model selection, when multiple demographic scenarios are compared. We argue that natural populations may experience the amount of recent positive selection required to skew inferences. These results suggest that demographic studies conducted in many species to date may have exaggerated the extent and frequency of population size changes.KEYWORDS population genetics; positive selection; demographic inference T HE widespread availability of population genomic data has spurred a new generation of studies aimed at understanding the histories of natural populations from a host of model and nonmodel organisms alike. In particular, genomescale variation data allows for inference of demographic factors such as population size changes, the timing and ordering of population splits, migration rates between populations, and the founding of admixed populations (Pool et al. 2010;Pickrell and Pritchard 2012;Sousa and Hey 2013). Such efforts can refine our picture of demographic events inferred from the archaeological record (e.g., Fagundes et al. 2007;Goebel et al. 2008), or reveal such events in species where no archaeological data are available, and can aid conservation efforts by complementing census data (e.g., Hájková et al. 2007;Garrick et al. 2015).Population genomic data sets are well suited for this task simply because demographic changes leave their mark on patterns of genetic variation. Recent population growth, for example, will result in an excess of rare variation compared to equilibrium expectations (Fu 1997), while population contraction will result in an excess of intermediate frequency alleles (Maruyama and Fuerst 198...

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“…While changes in population size do affect the relationship between effect size and mutation frequency [48][49][50][51][52][53][54][55][56][57][58][59][60][61] (Fig 1 and S5 Fig), different mappings of genotype to trait value do this in radically different ways for the same demographic history (Fig 1). From an empirical perspective, our findings suggest that re-sequencing in large samples is likely the best way forward in the face of the allelic heterogeneity imposed by the presence of rare alleles of large effect.…”

Section: Resultsmentioning

confidence: 99%

“…The increase in the number of rare alleles due to population growth is a well established theoretical and empirical result [48][49][50][51][52][53][54][55][56][57][58][59][60][61]. The exact relationship between rare alleles [4,17,26,62,63], and the demographic and/or selective scenarios from which they arose [21,22,64], and the genetic architecture of common complex diseases in humans is an active area of research.…”

Section: Additive and Dominance Genetic Variance In The Populationmentioning

confidence: 99%

“…At which point, the genetic variance is much less dependent on λ (S1 Fig). When λ is large, however, the average allele frequency does continue to decrease (S5 Fig) which drives power down. Across all genetic models, the single marker logistic regression has less power under population expansion (Fig 3A). The loss of power is attributable to a combination of rapid growth resulting in an excess of rare variants overall [48][49][50][51][52][53][54][55][56][57][58][59][60][61], and the increasing efficacy of selection against causal variants in growing populations [21]. While complete resequencing is more powerful than a gene-chip design, the relative power gained is modest under growth (Fig 3A).…”

Section: The Genetic Model Affects the Outcomes Of Gwasmentioning

confidence: 99%

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A model of compound heterozygous, loss-of-function alleles is broadly consistent with observations from complex-disease GWAS datasets

Sanjak

Long

Thornton

2016

Preprint

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The genetic component of complex disease risk in humans remains largely unexplained. A corollary is that the allelic spectrum of genetic variants contributing to complex disease risk is unknown. Theoretical models that relate population genetic processes to the maintenance of genetic variation for quantitative traits may suggest profitable avenues for future experimental design. Here we use forward simulation to model a genomic region evolving under a balance between recurrent deleterious mutation and Gaussian stabilizing selection. We consider multiple genetic and demographic models, and several different methods for identifying genomic regions harboring variants associated with complex disease risk. We demonstrate that the model of gene action, relating genotype to phenotype, has a qualitative effect on several relevant aspects of the population genetic architecture of a complex trait. In particular, the genetic model impacts genetic variance component partitioning across the allele frequency spectrum and the power of statistical tests. Models with partial recessivity closely match the minor allele frequency distribution of significant hits from empirical genome-wide association studies without requiring homozygous effect sizes to be small. We highlight a particular gene-based model of incomplete recessivity that is appealing from first principles. Under that model, deleterious mutations in a genomic region partially fail to complement one another. This model of gene-based recessivity predicts the empirically observed inconsistency between twin and SNP based estimated of dominance heritability. Furthermore, this model predicts considerable levels of unexplained variance associated with intralocus epistasis. Our results suggest a need for improved statistical tools for region based genetic association and heritability estimation. Author SummaryGene action determines how mutations affect phenotype. When placed in an evolutionary context, the details of the genotype-to-phenotype model can impact the maintenance of genetic variation for complex traits. Likewise, non-equilibrium demographic history may affect patterns of genetic variation. Here, we explore the impact of genetic model and population growth on distribution of genetic variance across the allele frequency spectrum underlying risk for a complex disease. Using forward-in-time population genetic simulations, we show that the genetic model has important impacts on the composition of variation for complex disease risk in a population. We explicitly simulate genome-wide association studies (GWAS) and perform heritability estimation on population samples. A particular model of gene-based partial recessivity, based on allelic non-complementation, aligns well with empirical results. This model is congruent with the dominance variance estimates from both SNPs and twins, and the minor allele frequency distribution of GWAS hits.

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Deep resequencing reveals excess rare recent variants consistent with explosive population growth

Cited by 222 publications

References 29 publications

Human population structure and the adaptive response to pathogen-induced selection pressures

Human population structure and the adaptive response to pathogen-induced selection pressures

Effects of Linked Selective Sweeps on Demographic Inference and Model Selection

A model of compound heterozygous, loss-of-function alleles is broadly consistent with observations from complex-disease GWAS datasets

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