Distinguishing Between Selective Sweeps and Demography Using DNA Polymorphism Data

Jensen, Jeffrey D.; Kim, Yuseob; DuMont, Vanessa Bauer; Aquadro, Charles F.; Bustamante, Carlos D.

doi:10.1534/genetics.104.038224

Cited by 250 publications

(317 citation statements)

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“…22 In addition, several studies have shown that some demographic scenarios can also cause negative skews in Fay and Wu's H. 42,43 To distinguish between these two possible causes of the pattern, it is useful to consider the pattern of polymorphism in the Memphis genomic region in a wider variety of human populations. Examination of HapMap (phase 3) SNP data from our resequenced region shows five overlapping SNPs that have been genotyped in 10 world populations (excluding the Japanese), including three where the derived allele is at high frequency in our sample (Supplementary Table S2).…”

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confidence: 99%

Molecular population genetics of SLC4A1 and Southeast Asian Ovalocytosis

et al. 2009

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Southeast Asian ovalocytosis (SAO) is an erythrocyte abnormality that protects affected individuals from cerebral malaria. This trait is caused by a 27-bp deletion in the SLC4A1 gene, which is lethal when homozygous. We reseqeunced approximately 5 kb of SLC4A1 in an Indonesian population where SAO is prevalent to better understand the evolution of this clinically important trait. The four SAO chromosomes we resequenced share a single haplotype that differs from a sampled non-SAO haplotype only by the 27-bp deletion. Comparison of Indonesian sequence data to that from two other Asian populations (aboriginal Taiwanese and Japanese) shows Indonesian SLC4A1 to be strongly differentiated from the Taiwanese, but not the Japanese. Indeed, the Taiwanese sample contains only chromosomes that are highly divergent from all sampled SAO chromosomes. Because earlier studies have found an association between Austronesian-speakers (who most likely originated in Taiwan) and SAO, our failure to find SAO-like chromosomes in Taiwan is unexpected. Finally, our data find a strong excess of high-frequency derived alleles in all three populations. These alleles include the non-synonymous 'Memphis' variant, which is known to affect anion transport across the erythrocyte membrane. Our data suggest a role for recent natural selection acting on Memphis or a linked variant.

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Section: Discussionmentioning

confidence: 99%

Molecular population genetics of SLC4A1 and Southeast Asian Ovalocytosis

et al. 2009

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“…For example, positive selection driving a mutation to fixation (i.e., a selective sweep) (Maynard Smith and Haigh 1974) may resemble a population bottleneck (Simonsen et al 1995). Conversely, many demographic perturbations are well known to cause unacceptably high rates of false positives for many classical tests for selection (Simonsen et al 1995;Przeworski 2002;Akey et al 2004;Jensen et al 2005;Nielsen et al 2005). Thus, if natural selection has a substantial impact on genome-wide patterns of variation, then many demographic parameter estimates could be biased (Hahn 2008;Gazave et al 2014).…”

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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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“…We study a bottleneck model to explore the properties of genome scans using parameters that may be relevant for Drosophila melanogaster and also because population bottlenecks severely confound the inference of selection (e.g., Jensen et al 2005). We apply what is currently the state-of-the art method for ''subgenomic'' scans (i.e., less than whole-genome SNP data)-the composite-likelihood method of Kim and Stephan (2002) and the goodness-of-fit (GOF) test of Jensen et al (2005). The former method estimates both the strength and target of selection, assuming the demographic null model of a large, panmictic population, and gives a composite likelihood-ratio test (CLRT) comparing the selective sweep model to the standard neutral model.…”

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“…The former method estimates both the strength and target of selection, assuming the demographic null model of a large, panmictic population, and gives a composite likelihood-ratio test (CLRT) comparing the selective sweep model to the standard neutral model. Jensen et al (2005) proposed a GOF statistic intended to be applied to data sets that reject neutrality following the procedure of Kim and Stephan (2002). They showed that the GOF procedure substantially reduces the false-positive rate under nonequilibrium demographic models and also results in a test statistic with a uniform distribution of P-values when the true model is a single selective sweep occurring in a large, constant-size, panmictic population (the model assumed by the CLRT).…”

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“…They showed that the GOF procedure substantially reduces the false-positive rate under nonequilibrium demographic models and also results in a test statistic with a uniform distribution of P-values when the true model is a single selective sweep occurring in a large, constant-size, panmictic population (the model assumed by the CLRT). In Jensen et al (2005), the calculation of the GOF test was applied to simulated data assuming that loci are random draws from a population model. In practice, however, both the method of Kim and Stephan (2002) and the GOF tests are often applied to loci that are preselected by an investigator because some feature of the region is an outlier in a multilocus genome scan (e.g., Harr et al 2002;Bauer-DuMont and Aquadro 2005;Beisswanger et al 2006;Pool et al 2006).…”

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Controlling the False-Positive Rate in Multilocus Genome Scans for Selection

2007

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Rapid typing of genetic variation at many regions of the genome is an efficient way to survey variability in natural populations in an effort to identify segments of the genome that have experienced recent natural selection. Following such a genome scan, individual regions may be chosen for further sequencing and a more detailed analysis of patterns of variability, often to perform a parametric test for selection and to estimate the strength of a recent selective sweep. We show here that not accounting for the ascertainment of loci in such analyses leads to false inference of natural selection when the true model is selective neutrality, because the procedure of choosing unusual loci (in comparison to the rest of the genome-scan data) selects regions of the genome with genealogies similar to those expected under models of recent directional selection. We describe a simple and efficient correction for this ascertainment bias, which restores the falsepositive rate to near-nominal levels. For the parameters considered here, we find that obtaining a test with the expected distribution of P-values depends on accurately accounting both for ascertainment of regions and for demography. Finally, we use simulations to explore the utility of relying on outlier loci to detect recent selective sweeps. We find that measures of diversity and of population differentiation are more effective than summaries of the site-frequency spectrum and that sequencing larger regions (2.5 kbp) in genome-scan studies leads to more power to detect recent selective sweeps.

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Distinguishing Between Selective Sweeps and Demography Using DNA Polymorphism Data

Cited by 250 publications

References 31 publications

Molecular population genetics of SLC4A1 and Southeast Asian Ovalocytosis

Molecular population genetics of SLC4A1 and Southeast Asian Ovalocytosis

Effects of Linked Selective Sweeps on Demographic Inference and Model Selection

Controlling the False-Positive Rate in Multilocus Genome Scans for Selection

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