SweeD: Likelihood-Based Detection of Selective Sweeps in Thousands of Genomes
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TLDR
It is shown that an increase of sample size results in more precise detection of positive selection and the ability to analyze substantially larger sample sizes by using SweeD leads to more accurate sweep detection.Abstract:
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.read more
Citations
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Sweeps in time: leveraging the joint distribution of branch lengths
TL;DR: A tractable method of describing the effect of positive selection on the genealogical histories in the surrounding genome, explicitly modeling both the timing and context of an adaptive event is developed.
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D. Ravi Kumar,M. Joel Devadasan,T. Surya,M R Vineeth,Anjali Choudhary,Jayakumar Sivalingam,Ranjit S. Kataria,Saket Kumar Niranjan,M S Tantia,Archana Verma +9 more
TL;DR: PCA and structure analysis revealed Manipuri swamp buffalo was genetically distinct and closely related to Nagaland swamp buffalo and least to Assamese swamp buffalo.
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Thinking too positive? Revisiting current methods of population-genetic selection inference
TL;DR: It is argued that the development and obligatory use of advanced simulation tools is necessary for improved identification of selected loci, that genomic information from multiple-time points will enhance the power of inference, and that results from experimental evolution should be utilized to better inform population-genomic studies.
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Identification of important genomic footprints using eight different selection signature statistics in domestic cattle breeds
Divya Rajawat,Manjit Panigrahi,Harshit Kumar,Sonali Sonejita Nayak,Subhashree Parida,Bharat Bhushan,Gyanendra Kumar Gaur,Triveni Dutt,B. P. Mishra +8 more
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References
More filters
Journal ArticleDOI
An integrated map of genetic variation from 1,092 human genomes
Gonçalo R. Abecasis,Adam Auton,Lisa D. Brooks,Mark A. DePristo,Richard Durbin,Robert E. Handsaker,Robert E. Handsaker,Hyun Min Kang,Gabor T. Marth,Gil McVean +9 more
TL;DR: It is shown that evolutionary conservation and coding consequence are key determinants of the strength of purifying selection, that rare-variant load varies substantially across biological pathways, and that each individual contains hundreds of rare non-coding variants at conserved sites, such as motif-disrupting changes in transcription-factor-binding sites.
Book
Practical Methods of Optimization
TL;DR: The aim of this book is to provide a Discussion of Constrained Optimization and its Applications to Linear Programming and Other Optimization Problems.
Journal ArticleDOI
The hitch-hiking effect of a favourable gene.
John Maynard Smith,John Haigh +1 more
TL;DR: If the selective coefficients at the linked locus are small compared to those at the substituted locus, it is shown that the probability of complete fixation at the links is approximately exp (− Nc), where c is the recombinant fraction and N the population size.
Journal ArticleDOI
Generating samples under a Wright-Fisher neutral model of genetic variation.
TL;DR: A Monte Carlo computer program is available to generate samples drawn from a population evolving according to a Wright-Fisher neutral model, and the samples produced can be used to investigate the sampling properties of any sample statistic under these neutral models.