Genomic resources and their influence on the detection of the signal of positive selection in genome scans

Abstract

Genome scans represent powerful approaches to investigate the action of natural selection on the genetic variation of natural populations and to better understand local adaptation. This is very useful, for example, in the field of conservation biology and evolutionary biology. Thanks to Next Generation Sequencing, genomic resources are growing exponentially, improving genome scan analyses in non-model species. Thousands of SNPs called using Reduced Representation Sequencing are increasingly used in genome scans. Besides, genome sequences are also becoming increasingly available, allowing better processing of short-read data, offering physical localization of variants, and improving haplotype reconstruction and data imputation. Ultimately, genome sequences are also becoming the raw material for selection inferences. Here, we discuss how the increasing availability of such genomic resources, notably genome sequences, influences the detection of signals of selection. Mainly, increasing data density and having the information of physical linkage data expand genome scans by (i) improving the overall quality of the data, (ii) helping the reconstruction of demographic history for the population studied to decrease false-positive rates and (iii) improving the statistical power of methods to detect the signal of selection. Of particular importance, the availability of a high-quality reference genome can improve the detection of the signal of selection by (i) allowing matching the potential candidate loci to linked coding regions under selection, (ii) rapidly moving the investigation to the gene and function and (iii) ensuring that the highly variable regions of the genomes that include functional genes are also investigated. For all those reasons, using reference genomes in genome scan analyses is highly recommended.

Lineage divergence, local adaptation across a biogeographic break, and artificial transport, shape the genetic structure in the ascidian Pyura chilensis

“…For example, identified outlier SNP loci may be linked to loci that are the direct target of natural selection instead of natural selection directly operating on them45. Additionally, spatial autocorrelation can cause random associations between the environment and the genetic structure of a species as a result of dispersal following IBD or demographic processes46.…”

Integrative Approaches for Studying Mitochondrial and Nuclear Genome Co-evolution in Oxidative Phosphorylation

“…Here we highlight, in the context of mitonuclear co-evolution, the more general issues that are explored in depth elsewhere (Nielsen, 2005; Haasl and Payseur, 2016; Manel et al, 2016; Stephan, 2016). …”

“…The power to detect candidate loci that evolve under natural selection rests on the molecular tools available for a given system: reduced representation genomic scans (SNPs), sequence-based genomic scans (candidate genes, exome-sequencing, or RNA-sequencing), whole genome re-sequencing, and/or physical linkage maps (Manel et al, 2016; Stephan, 2016). The key limitation to detecting natural selection in the wild is that several ecological and evolutionary processes can leave a similar signature to selection and lead to a high rate of false positives.…”

Combining Genotype, Phenotype, and Environment to Infer Potential Candidate Genes

“…If the individual locus F ST values are significantly higher than values estimated under neutral demographic models, this is an indication that such loci may be located in a gene or physically linked to a gene under selection, and we consider such loci to be potential candidate genes. There are now many methods available to researchers to identify loci with non-neutral divergence from the screening of large spatial datasets of individuals or populations genotyped at a large number of markers (Manel et al 2016). Some recent applications include the identification of domestication and improvement genes in cultivated sunflower (Baute et al 2015) and loci responding to spatially varying selection in white poplar (Stölting et al 2015).…”

“…may produce a signal similar to selection, and thus lead to false positives (Schoville et al 2012). It is therefore imperative to account for these confounding effects using analyses specifically designed to do so (Lotterhos and Whitlock 2014), and to add additional resources and validation steps to genome scan analyses (Manel et al 2016). Specifically, adding phenotypic trait data to genomic and environmental data in such analyses can support and greatly improve the inference of potential candidate genes (Stinchcombe and Hoekstra 2008;Haasl and Payseur 2016).…”