Coalescent tree imbalance and a simple test for selective sweeps based on microsatellite variation.
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ABSTRACT:
SUBMITTER: Li H
PROVIDER: S-EPMC3656098 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:MotivationSpecies trees provide insight into basic biology, including the mechanisms of evolution and how it modifies biomolecular function and structure, biodiversity and co-evolution between genes and species. Yet, gene trees often differ from species trees, creating challenges to species tree estimation. One of the most frequent causes for conflicting topologies between gene trees and species trees is incomplete lineage sorting (ILS), which is modelled by the multi-species coalescent. While many methods have been developed to estimate species trees from multiple genes, some which have statistical guarantees under the multi-species coalescent model, existing methods are too computationally intensive for use with genome-scale analyses or have been shown to have poor accuracy under some realistic conditions.ResultsWe present ASTRAL, a fast method for estimating species trees from multiple genes. ASTRAL is statistically consistent, can run on datasets with thousands of genes and has outstanding accuracy-improving on MP-EST and the population tree from BUCKy, two statistically consistent leading coalescent-based methods. ASTRAL is often more accurate than concatenation using maximum likelihood, except when ILS levels are low or there are too few gene trees.Availability and implementationASTRAL is available in open source form at https://github.com/smirarab/ASTRAL/. Datasets studied in this article are available at http://www.cs.utexas.edu/users/phylo/datasets/astral.Supplementary informationSupplementary data are available at Bioinformatics online.
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Project description:Many population genomic studies have been conducted in the past to search for traces of recent events of positive selection. These traces, however, can be obscured by temporal variation of population size or other demographic factors. To reduce the confounding impact of demography, the coalescent tree topology has been used as an additional source of information for detecting recent positive selection in a population or a species. Based on the branching pattern at the root, we partition the hypothetical coalescent tree, inferred from a sequence sample, into two subtrees. The reasoning is that positive selection could impose a strong impact on branch length in one of the two subtrees while demography has the same effect on average on both subtrees. Thus, positive selection should be detectable by comparing statistics calculated for the two subtrees. Simulations demonstrate that the proposed test based on these principles has high power to detect recent positive selection even when DNA polymorphism data from only one locus is available, and that it is robust to the confounding effect of demography. One feature is that all components in the summary statistics ([Formula: see text]) can be computed analytically. Moreover, misinference of derived and ancestral alleles is seen to have only a limited effect on the test, and it therefore avoids a notorious problem when searching for traces of recent positive selection.
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Project description:MotivationPopulation trees represent past population divergence histories. The inference of population trees can be useful for the study of population evolution. With the size of data increases in large-scale population genetic projects, such as the 1000 Genomes Project, there are new computational challenges for ancestral population inference, including population tree inference. Existing methods for population tree inference are mainly designed for unlinked genetic variants (e.g. single nucleotide polymorphisms or SNPs). There is a potential loss of information by not considering the haplotypes.ResultsIn this article, we propose a new population tree inference method (called STELLSH) based on coalescent likelihood. The likelihood is for haplotypes over multiple SNPs within a non-recombining region, not unlinked variants. Unlike many existing ancestral inference methods, STELLSH does not use Monte Carlo approaches when computing the likelihood. For efficient computation, the likelihood model is approximated but still retains much information about population divergence history. STELLSH can find the maximum likelihood population tree based on the approximate likelihood. We show through simulation data and the 1000 Genomes Project data that STELLSH gives reasonably accurate inference results. STELLSH is reasonably efficient for data of current interest and can scale to handle whole-genome data.Availability and implementationThe population tree inference method STELLSH has been implemented as part of the STELLS program: http://www.engr.uconn.edu/∼ywu/STELLS.html.
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Project description:An outstanding question in human genetics has been the degree to which adaptation occurs from standing genetic variation or from de novo mutations. Here, we combine several common statistics used to detect selection in an Approximate Bayesian Computation (ABC) framework, with the goal of discriminating between models of selection and providing estimates of the age of selected alleles and the selection coefficients acting on them. We use simulations to assess the power and accuracy of our method and apply it to seven of the strongest sweeps currently known in humans. We identify two genes, ASPM and PSCA, that are most likely affected by selection on standing variation; and we find three genes, ADH1B, LCT, and EDAR, in which the adaptive alleles seem to have swept from a new mutation. We also confirm evidence of selection for one further gene, TRPV6. In one gene, G6PD, neither neutral models nor models of selective sweeps fit the data, presumably because this locus has been subject to balancing selection.
| S-EPMC3469416 | biostudies-literature
Project description:Population bottlenecks, inbreeding, and artificial selection can all, in principle, influence levels of deleterious genetic variation. However, the relative importance of each of these effects on genome-wide patterns of deleterious variation remains controversial. Domestic and wild canids offer a powerful system to address the role of these factors in influencing deleterious variation because their history is dominated by known bottlenecks and intense artificial selection. Here, we assess genome-wide patterns of deleterious variation in 90 whole-genome sequences from breed dogs, village dogs, and gray wolves. We find that the ratio of amino acid changing heterozygosity to silent heterozygosity is higher in dogs than in wolves and, on average, dogs have 2-3% higher genetic load than gray wolves. Multiple lines of evidence indicate this pattern is driven by less efficient natural selection due to bottlenecks associated with domestication and breed formation, rather than recent inbreeding. Further, we find regions of the genome implicated in selective sweeps are enriched for amino acid changing variants and Mendelian disease genes. To our knowledge, these results provide the first quantitative estimates of the increased burden of deleterious variants directly associated with domestication and have important implications for selective breeding programs and the conservation of rare and endangered species. Specifically, they highlight the costs associated with selective breeding and question the practice favoring the breeding of individuals that best fit breed standards. Our results also suggest that maintaining a large population size, rather than just avoiding inbreeding, is a critical factor for preventing the accumulation of deleterious variants.
| S-EPMC4711855 | biostudies-literature
Project description:We introduce a new method to detect ancient selective sweeps centered on a candidate site. We explored different patterns produced by sweeps around a fixed beneficial mutation, and found that a particularly informative statistic measures the consistency between majority haplotypes near the mutation and genotypic data from a closely related population. We incorporated this statistic into an approximate Bayesian computation (ABC) method that tests for sweeps at a candidate site. We applied this method to simulated data and show that it has some power to detect sweeps that occurred more than 10,000 generations in the past. We also applied it to 1,000 Genomes and Complete Genomics data combined with high-coverage Denisovan and Neanderthal genomes to test for sweeps in modern humans since the separation from the Neanderthal-Denisovan ancestor. We tested sites at which humans are fixed for the derived (i.e., nonchimpanzee allele) whereas the Neanderthal and Denisovan genomes are homozygous for the ancestral allele. We observe only weak differences in statistics indicative of selection between functional categories. When we compare patterns of scaled diversity or use our ABC approach, we fail to find a significant difference in signals of classic selective sweeps between regions surrounding nonsynonymous and synonymous changes, but we detect a slight enrichment for reduced scaled diversity around splice site changes. We also present a list of candidate sites that show high probability of having undergone a classic sweep in the modern human lineage since the split from Neanderthals and Denisovans.
| S-EPMC4245817 | biostudies-literature
Project description:Our genetic diversity study uses microsatellites of known map position to estimate genome level population structure and linkage disequilibrium, and to identify genomic regions that have undergone selection during watermelon domestication and improvement. Thirty regions that showed evidence of selective sweep were scanned for the presence of candidate genes using the watermelon genome browser (www.icugi.org). We localized selective sweeps in intergenic regions, close to the promoters, and within the exons and introns of various genes. This study provided an evidence of convergent evolution for the presence of diverse ecotypes with special reference to American and European ecotypes. Our search for location of linked markers in the whole-genome draft sequence revealed that BVWS00358, a GA repeat microsatellite, is the GAGA type transcription factor located in the 5' untranslated regions of a structure and insertion element that expresses a Cys2His2 Zinc finger motif, with presumed biological processes related to chitin response and transcriptional regulation. In addition, BVWS01708, an ATT repeat microsatellite, located in the promoter of a DTW domain-containing protein (Cla002761); and 2 other simple sequence repeats that association mapping link to fruit length and rind thickness.
| S-EPMC4323065 | biostudies-literature
Project description:The advent of modern DNA sequencing technology is the driving force in obtaining complete intra-specific genomes that can be used to detect loci that have been subject to positive selection in the recent past. Based on selective sweep theory, beneficial loci can be detected by examining the single nucleotide polymorphism patterns in intraspecific genome alignments. In the last decade, a plethora of algorithms for identifying selective sweeps have been developed. However, the majority of these algorithms have not been designed for analyzing whole-genome data. We present SweeD (Sweep Detector), an open-source tool for the rapid detection of selective sweeps in whole genomes. It analyzes site frequency spectra and represents a substantial extension of the widely used SweepFinder program. The sequential version of SweeD is up to 22 times faster than SweepFinder and, more importantly, is able to analyze thousands of sequences. We also provide a parallel implementation of SweeD for multi-core processors. Furthermore, we implemented a checkpointing mechanism that allows to deploy SweeD on cluster systems with queue execution time restrictions, as well as to resume long-running analyses after processor failures. In addition, the user can specify various demographic models via the command-line to calculate their theoretically expected site frequency spectra. Therefore, (in contrast to SweepFinder) the neutral site frequencies can optionally be directly calculated from a given demographic model. We show that an increase of sample size results in more precise detection of positive selection. Thus, the ability to analyze substantially larger sample sizes by using SweeD leads to more accurate sweep detection. We validate SweeD via simulations and by scanning the first chromosome from the 1000 human Genomes project for selective sweeps. We compare SweeD results with results from a linkage-disequilibrium-based approach and identify common outliers.
| S-EPMC3748355 | biostudies-literature
Project description:MotivationSelective sweeps can successfully be distinguished from neutral genetic data using summary statistics and likelihood-based methods that analyze single nucleotide polymorphisms (SNPs). However, these methods are sensitive to confounding factors, such as severe population bottlenecks and old migration. By virtue of machine learning, and specifically convolutional neural networks (CNNs), new accurate classification models that are robust to confounding factors have been recently proposed. However, such methods are more computationally expensive than summary-statistic-based ones, yielding them impractical for processing large-scale genomic data. Moreover, SNP data are frequently preprocessed to improve classification accuracy, further exacerbating the long analysis times.ResultsTo this end, we propose a 1D CNN-based model, dubbed FAST-NN, that does not require any preprocessing while using only derived allele frequencies instead of summary statistics or raw SNP data, thereby yielding a sample-size-invariant, scalable solution. We evaluated several data fusion approaches to account for the variance of the density of genetic diversity across genomic regions (a selective sweep signature), and performed an extensive neural architecture search based on a state-of-the-art reference network architecture (SweepNet). The resulting model, FAST-NN, outperforms the reference architecture by up to 12% inference accuracy over all challenging evolutionary scenarios with confounding factors that were evaluated. Moreover, FAST-NN is between 30× and 259× faster on a single CPU core, and between 2.0× and 6.2× faster on a GPU, when processing sample sizes between 128 and 1000 samples. Our work paves the way for the practical use of CNNs in large-scale selective sweep detection.Availability and implementationhttps://github.com/SjoerdvandenBelt/FAST-NN.
| S-EPMC11373383 | biostudies-literature
Project description:Natural selection leaves distinct signatures in the genome that can reveal the targets and history of adaptive evolution. By analysing high-coverage genome sequence data from 4 major colour pattern loci sampled from nearly 600 individuals in 53 populations, we show pervasive selection on wing patterns in the Heliconius adaptive radiation. The strongest signatures correspond to loci with the greatest phenotypic effects, consistent with visual selection by predators, and are found in colour patterns with geographically restricted distributions. These recent sweeps are similar between co-mimics and indicate colour pattern turn-over events despite strong stabilising selection. Using simulations, we compare sweep signatures expected under classic hard sweeps with those resulting from adaptive introgression, an important aspect of mimicry evolution in Heliconius butterflies. Simulated recipient populations show a distinct 'volcano' pattern with peaks of increased genetic diversity around the selected target, characteristic of sweeps of introgressed variation and consistent with diversity patterns found in some populations. Our genomic data reveal a surprisingly dynamic history of colour pattern selection and co-evolution in this adaptive radiation.
| S-EPMC7029882 | biostudies-literature