Biology subjects

Andersson, L.

Publications and source records attributed to Andersson, L..

3 recordsLinked to original sources

bioRxiv2017

Moderate Nucleotide Diversity In The Atlantic Herring Is Associated With A Low Mutation Rate

The Atlantic herring (Clupea harengus) is one of the most abundant vertebrates on earth but its nucleotide diversity is moderate ({pi}=0.3%), only three-fold higher than in human. The expected nucleotide diversity for selectively neutral alleles is a function of population size and the mutation rate, and it is strongly affected by demographic history. Here, we present a pedigree-based estimation of the mutation rate in the Atlantic herring. Based on whole-genome sequencing of four parents and 12 offspring, the estimated mutation rate is 1.7 x 10-9 per base per generation. There was no significant difference in the frequency of paternal and maternal mutations (8 and 7, respectively). Furthermore, we observed a high degree of parental mosaicism indicating that a large fraction of these de novo mutations occurred during early germ cell development when we do not expect a strong gender effect. The now estimated mutation rate - the lowest among vertebrates analyzed to date - partially explains the discrepancy between the rather low nucleotide diversity in herring and its huge census population size (>1011). But our analysis indicates that a species like the herring will never reach its expected nucleotide diversity for selectively neutral alleles primarily because of fluctuations in population size due to climate variation during the millions of years it takes to build up a high nucleotide diversity. In addition, background selection and selective sweeps lead to reductions in nucleotide diversity at linked neutral sites.

genomics↗

bioRxiv2017

A Model-Free Approach For Detecting Genomic Regions Of Deep Divergence Using The Distribution Of Haplotype Distances

Recent advances in comparative genomics have revealed that divergence between populations is not necessarily uniform across all parts of the genome. There are examples of regions with divergent haplotypes that are substantially more different from each other that the genomic average.\n\nTypically, these regions are of interest, as their persistence over long periods of time may reflect balancing selection. However, they are hard to detect unless the divergent sub-populations are known prior to analysis.\n\nHere, we introduce HaploDistScan, an R-package implementing model-free detection of deep-divergence genomic regions based on the distribution of pair-wise haplotype distances, and show that it can detect such regions without use of a priori information about population sub-division. We apply the method to real-world data sets, from ruff and Darwins finches, and show that we are able to recover known instances of balancing selection - originally identified in studies reliant on detailed phenotyping - using only genotype data. Furthermore, in addition to replicating previously known divergent haplotypes as a proof-of-concept, we identify novel regions of interest in the Darwins finch genome and propose a plausible, data-driven evolutionary history for each novel locus individually.\n\nIn conclusion, HaploDistScan requires neither phenotypic nor demographic input data, thus filling a gap in the existing set of methods for genome scanning, and provides a useful tool for identification of regions under balancing selection or similar evolutionary processes.

genomics↗

bioRxiv2016

A computational method for detection of structural variants using Deviant Reads and read pair Orientation: DevRO

BackgroundNext generation sequencing (NGS) technology has made it possible to perform high-resolution screens for structural variants. Computational methods for detection of structural variants utilize paired-end mapping information, depth of coverage, split reads, or some combination of such data. The available methods are particularly designed to detect structural variants in single genomes or multiple genomes in a pairwise manner. The aim of this study was to develop a bioinformatics pipeline for detection of large structural variants using multiple pooled populations.\n\nResultsHere we describe the method \"DevRO\", developed to enable identification of structural variants using short insert paired-ends and long-range mate-pairs. DevRO uses paired-end mapping information from both types of libraries for identification of inversions, deletions and duplications followed by read depth information to screen for copy number variants. DevRO can detect structural variants in multiple populations without the need for pairwise comparisons. It uses a combined approach based on (i) paired-end mapping and (ii) depth of coverage that gives power to the study as compared to traditional methods that are based on either of these. DevRO is also designed to detect deletions in the reference assembly, which is an added functionality as compared to available methods.\n\nConclusionWe report a bioinformatics pipeline \"DevRO\" for detection of structural variants using paired-end mapping and depth of coverage methods tested on sequencing reads from multiple pooled rabbit populations. This method is useful when large numbers of populations have been re-sequenced as compared to traditional methods that can detect structural variants in a pairwise manner.

bioinformatics↗