The impact of population expansion and mutation rate heterogeneity on DNA sequence polymorphism

Aris‐Brosou, Stéphane; Excoffier, Laurent

doi:10.1093/oxfordjournals.molbev.a025610

Cited by 377 publications

(243 citation statements)

References 33 publications

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“…The results demonstrated that S. crosnieri populations had different demographic histories in vent and cold seep environments. Significant D values may be due to factors such as population expansion and bottleneck (ArisBrosou and Excoffier 1996). The significant negative D values for the entire vents dataset also could support the results.…”

Section: Discussionmentioning

confidence: 99%

Comparative population structure of two dominant species, Shinkaia crosnieri (Munidopsidae: Shinkaia) and Bathymodiolus platifrons (Mytilidae: Bathymodiolus), inhabiting both deep‐sea vent and cold seep inferred from mitochondrial multi‐genes

Shen

Kou

Chen

et al. 2016

Ecology and Evolution

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Deep‐sea hydrothermal vents and cold seeps, limited environments without sunlight, are two types of extreme habitat for marine organisms. The differences between vents and cold seeps may facilitate genetic isolation and produce population heterogeneity. However, information on such chemosynthetic fauna taxa is rare, especially regarding the population diversity of species inhabiting both vents and cold seeps. In this study, three mitochondrial DNA fragments (the cytochrome c oxidase submit I (COI), cytochrome b gene (Cytb), and 16S) were concatenated as a mitochondrial concatenated dataset (MCD) to examine the genetic diversity, population structure, and demographic history of Shinkaia crosnieri and Bathymodiolus platifrons. The genetic diversity differences between vent and seep populations were statistically significant for S. crosnieri but not for B. platifrons. S. crosnieri showed less gene flow and higher levels of genetic differentiation between the vent and seep populations than B. platifrons. In addition, the results suggest that all the B. platifrons populations, but only the S. crosnieri vent populations, passed through a recent expansion or bottleneck. Therefore, different population distribution patterns for the two dominant species were detected; a pattern of population differentiation for S. crosnieri and a homogeneity pattern for B. platifrons. These different population distribution patterns were related to both extrinsic restrictive factors and intrinsic factors. Based on the fact that the two species were collected in almost identical or adjacent sampling sites, we speculated that the primary factors underlying the differences in the population distribution patterns were intrinsic. The historical demographics, dispersal ability, and the tolerance level of environmental heterogeneity are most likely responsible for the different distribution patterns.

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Section: Discussionmentioning

confidence: 99%

Comparative population structure of two dominant species, Shinkaia crosnieri (Munidopsidae: Shinkaia) and Bathymodiolus platifrons (Mytilidae: Bathymodiolus), inhabiting both deep‐sea vent and cold seep inferred from mitochondrial multi‐genes

Shen

Kou

Chen

et al. 2016

Ecology and Evolution

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“…A low genomic evolutionary rate and elevated inbreeding frequency may have contributed to the low genetic variation observed in this population. Demographic analyses (Tajima's D) using mtDNA sequence polymorphism showed a signal of population expansion in the Kuchi population characterized by an excess of rare variants consistent with population growth (Tajima, 1989;Aris-Brosou & Excoffier, 1996;Schmidt & Pool, 2002;Johnson et al, 2007).…”

Section: Discussionmentioning

confidence: 99%

Assessing the genetic diversity of five Tanzanian chicken ecotypes using molecular tools

Lyimo¹,

Weigend²,

Janssen-Tapken³

et al. 2014

SA J. An. Sci.

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The study aimed to evaluate the genetic diversity of Tanzanian chicken populations through phylogenetic relationship, and to trace the history of Tanzanian indigenous chickens. Five ecotypes of Tanzanian local chickens (Ching'wekwe, Kuchi, Morogoro-medium, Pemba and Unguja) from eight regions were studied. Diversity was assessed based on morphological measurements and 29 microsatellite markers recommended by ISAG/FAO advisory group on animal genetic diversity. A principal component analysis (PCA) of morphological measures distinguished individuals most by body sizes and body weight. Morogoro Medium, Pemba and Unguja were grouped together, while Ching'wekwe stood out because of their disproportionate short shanks and ulna bones. Kuchi formed an independent group owing to their comparably long body sizes. Microsatellite analysis revealed three clusters of Tanzanian chicken populations. These clusters encompassed i) Morogoro-medium and Ching'wekwe from Eastern and Central Zones ii) Unguja and Pemba from Zanzibar Islands and iii) Kuchi from Lake Zone regions, which formed an independent cluster. Sequence polymorphism of D-loop region was analysed to disclose the likely maternal origin of Tanzanian chickens. According to reference mtDNA haplotypes, the Tanzanian chickens that were sampled encompass two haplogroups of different genealogical origin. From haplotype network analysis, Tanzanian chickens probably originated on the Indian subcontinent and in Southeast Asia. The majority of Kuchi chickens clustered in a single haplogroup, which was previously found in Shamo game birds sampled from Shikoku Island of Japan in the Kōchi Prefecture. Analysis of phenotypic and molecular data, as well as the linguistic similarity of the breed names, suggests a recent introduction of the Kuchi breed to Tanzania.

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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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The impact of population expansion and mutation rate heterogeneity on DNA sequence polymorphism

Cited by 377 publications

References 33 publications

Comparative population structure of two dominant species, Shinkaia crosnieri (Munidopsidae: Shinkaia) and Bathymodiolus platifrons (Mytilidae: Bathymodiolus), inhabiting both deep‐sea vent and cold seep inferred from mitochondrial multi‐genes

Comparative population structure of two dominant species, Shinkaia crosnieri (Munidopsidae: Shinkaia) and Bathymodiolus platifrons (Mytilidae: Bathymodiolus), inhabiting both deep‐sea vent and cold seep inferred from mitochondrial multi‐genes

Assessing the genetic diversity of five Tanzanian chicken ecotypes using molecular tools

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

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