597 research outputs found
Supplemental Material for Bradburd, Coop, and Ralph, 2018
No full textContains all supplementary figures for the article "Inferring continuous and discrete population genetic structure across space," by GS Bradburd, GM Coop, and PL Ralph
Luck, lottery, or legacy? The problem of confounding. A reply to Harden
No full textA reply to Harden's response to Coop and Przeworski (2022)
mdedge/rhps_coalescent: release concurrent with submission of article
No full text<p>This release contains the code used to run simulations, analyze data, and make figures for the paper "Reconstructing the history of polygenic scores using coalescent trees" by M.D. Edge and G. Coop. (October 12, 2018)</p>
Supplemental Material for Edge and Coop, 2018
No full textR file of helper functions for the paper "Reconstructing the history of polygenic scores using coalescent trees"The Supplementary Material pdf contains Figures S1-S2 and Table S1.</p
A Model-Based Analysis of GC-Biased Gene Conversion in the Human and Chimpanzee Genomes
No full textGC-biased gene conversion (gBGC) is a recombination-associated process that favors the fixation of G/C alleles over A/T alleles. In mammals, gBGC is hypothesized to contribute to variation in GC content, rapidly evolving sequences, and the fixation of deleterious mutations, but its prevalence and general functional consequences remain poorly understood. gBGC is difficult to incorporate into models of molecular evolution and so far has primarily been studied using summary statistics from genomic comparisons. Here, we introduce a new probabilistic model that captures the joint effects of natural selection and gBGC on nucleotide substitution patterns, while allowing for correlations along the genome in these effects. We implemented our model in a computer program, called phastBias, that can accurately detect gBGC tracts about 1 kilobase or longer in simulated sequence alignments. When applied to real primate genome sequences, phastBias predicts gBGC tracts that cover roughly 0.3% of the human and chimpanzee genomes and account for 1.2% of human-chimpanzee nucleotide differences. These tracts fall in clusters, particularly in subtelomeric regions; they are enriched for recombination hotspots and fast-evolving sequences; and they display an ongoing fixation preference for G and C alleles. They are also significantly enriched for disease-associated polymorphisms, suggesting that they contribute to the fixation of deleterious alleles. The gBGC tracts provide a unique window into historical recombination processes along the human and chimpanzee lineages. They supply additional evidence of long-term conservation of megabase-scale recombination rates accompanied by rapid turnover of hotspots. Together, these findings shed new light on the evolutionary, functional, and disease implications of gBGC. The phastBias program and our predicted tracts are freely available. © 2013 Capra et al
The distribution of distant cousins of modern-day people in the UK, at three different levels of geneological distance (circle size proportional to numbers of cousins; units are numbers of shared ancestors).
No full text<p>Image credit: Peter Ralph and Graham Coop.</p
Multi-locus analysis of genomic time series data from experimental evolution.
No full textGenomic time series data generated by evolve-and-resequence (E&R) experiments offer a powerful window into the mechanisms that drive evolution. However, standard population genetic inference procedures do not account for sampling serially over time, and new methods are needed to make full use of modern experimental evolution data. To address this problem, we develop a Gaussian process approximation to the multi-locus Wright-Fisher process with selection over a time course of tens of generations. The mean and covariance structure of the Gaussian process are obtained by computing the corresponding moments in discrete-time Wright-Fisher models conditioned on the presence of a linked selected site. This enables our method to account for the effects of linkage and selection, both along the genome and across sampled time points, in an approximate but principled manner. We first use simulated data to demonstrate the power of our method to correctly detect, locate and estimate the fitness of a selected allele from among several linked sites. We study how this power changes for different values of selection strength, initial haplotypic diversity, population size, sampling frequency, experimental duration, number of replicates, and sequencing coverage depth. In addition to providing quantitative estimates of selection parameters from experimental evolution data, our model can be used by practitioners to design E&R experiments with requisite power. We also explore how our likelihood-based approach can be used to infer other model parameters, including effective population size and recombination rate. Then, we apply our method to analyze genome-wide data from a real E&R experiment designed to study the adaptation of D. melanogaster to a new laboratory environment with alternating cold and hot temperatures
Correction: Predicting Carriers of Ongoing Selective Sweeps without Knowledge of the Favored Allele.
No full text[This corrects the article DOI: 10.1371/journal.pgen.1005527.]
- …
