Inferring the selective history of CNVs using a maximum likelihood model.

IF 3.2 2区 生物学 Q2 EVOLUTIONARY BIOLOGY
Seyed Amir Malekpour, Ata Kalirad, Sina Majidian
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引用次数: 0

Abstract

Copy number variations (CNVs) - structural variations generated by deletion and/or duplication that result in a change in DNA dosage - are prevalent in nature. CNVs can drastically affect the phenotype of an organism and have been shown to be both involved in genetic disorders and be used as raw material in adaptive evolution. Unlike single-nucleotide variations, the often large and varied effects of CNVs on phenotype hinders our ability to infer their selective advantage based on the population genetics data. Here, we present a likelihood-based approach, dubbed PoMoCNV (POlymorphism-aware phylogenetic MOdel for CNVs), that estimates the evolutionary parameters such as mutation rates among different copy numbers and relative fitness loss per copy deletion at a genomic locus based on population genetics data. As a case study, we analyze the genomics data of 40 strains of Caenorhabditis elegans, representing four different populations. We take advantage of the data on chromatin accessibility to interpret the mutation rate and fitness of copy numbers, as inferred by PoMoCNV, specifically in open or closed chromatin loci. We further test the reliability of PoMoCNV by estimating the evolutionary parameters of CNVs for mutation-accumulation experiments in C. elegans with varying levels of genetic drift.

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来源期刊
Genome Biology and Evolution
Genome Biology and Evolution EVOLUTIONARY BIOLOGY-GENETICS & HEREDITY
CiteScore
5.80
自引率
6.10%
发文量
169
审稿时长
1 months
期刊介绍: About the journal Genome Biology and Evolution (GBE) publishes leading original research at the interface between evolutionary biology and genomics. Papers considered for publication report novel evolutionary findings that concern natural genome diversity, population genomics, the structure, function, organisation and expression of genomes, comparative genomics, proteomics, and environmental genomic interactions. Major evolutionary insights from the fields of computational biology, structural biology, developmental biology, and cell biology are also considered, as are theoretical advances in the field of genome evolution. GBE’s scope embraces genome-wide evolutionary investigations at all taxonomic levels and for all forms of life — within populations or across domains. Its aims are to further the understanding of genomes in their evolutionary context and further the understanding of evolution from a genome-wide perspective.
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