Genome wide signatures of positive selection: The comparison of independent samples and the identification of regions associated to traits

Abstract

<p>Abstract</p> <p>Background</p> <p>The goal of genome wide analyses of polymorphisms is to achieve a better understanding of the link between genotype and phenotype. Part of that goal is to understand the selective forces that have operated on a population.</p> <p>Results</p> <p>In this study we compared the signals of selection, identified through population divergence in the Bovine HapMap project, to those found in an independent sample of cattle from Australia. Evidence for population differentiation across the genome, as measured by F_ST, was highly correlated in the two data sets. Nevertheless, 40% of the variance in F_STbetween the two studies was attributed to the differences in breed composition. Seventy six percent of the variance in F_STwas attributed to differences in SNP composition and density when the same breeds were compared. The difference between F_STof adjacent loci increased rapidly with the increase in distance between SNP, reaching an asymptote after 20 kb. Using 129 SNP that have highly divergent F_STvalues in both data sets, we identified 12 regions that had additive effects on the traits residual feed intake, beef yield or intramuscular fatness measured in the Australian sample. Four of these regions had effects on more than one trait. One of these regions includes the <it>R3HDM1 </it>gene, which is under selection in European humans.</p> <p>Conclusion</p> <p>Firstly, many different populations will be necessary for a full description of selective signatures across the genome, not just a small set of highly divergent populations. Secondly, it is necessary to use the same SNP when comparing the signatures of selection from one study to another. Thirdly, useful signatures of selection can be obtained where many of the groups have only minor genetic differences and may not be clearly separated in a principal component analysis. Fourthly, combining analyses of genome wide selection signatures and genome wide associations to traits helps to define the trait under selection or the population group in which the QTL is likely to be segregating. Finally, the F_STdifference between adjacent loci suggests that 150,000 evenly spaced SNP will be required to study selective signatures in all parts of the bovine genome.</p

A genome-wide association study of tick burden and milk composition in cattle

“…First, this could be a chance event, partly due to the small sample size used in the GWAS, although it is not clear how one would evaluate the likelihood of such a chance event -the sampling distribution of number of successful hits in a GWAS is not well understood when analysed over a range of traits in the same experiment. Second, the SNP in this panel are not uniformly distributed across the genome and show large gaps of more than 1 Mb between adjacent SNP in some areas (Barendse et al 2009b). If the QTL for milk composition were located where SNP are less dense then fewer milk composition QTL would be found.…”

“…Ethics approval for the use of these animals was obtained as noted previously (Barendse et al 2009b). The animals were selected to represent the northern Australian dairy industry, which has a broad range of breeds and farmer initiated taurine composites to deal with the hot, dry, tick-infested conditions.…”

“…Animals selected for genotyping using the SNP chip were chosen in approximately equal numbers from five properties to a total of 189 plus three repeated animals, to generate two plates of 96 samples (Barendse et al 2009b). The animals of this subset were chosen so that they were not closely related and were the offspring of 138 of the sires and 174 of the dams.…”

“…Further details of the SNP can be found at the link ftp://ftp.hgsc.bcm.tmc.edu/pub/data/Btaurus/snp/Btau20050310/ (verified 22 March 2010). All samples with more than 10% missing data were excluded and then all loci with more than 10% of missing data were excluded, as previously described (Barendse et al 2009b). …”

Next generation sequencing of African and Indicine cattle to identify single nucleotide polymorphisms

“…Other usages include the estimation of pairwise relatedness between individuals in the absence of pedigree data (Oliehoek et al 2006;Lee et al 2010) and high resolution studies of breed relationships and diversity (Pritchard et al 2000;Patterson et al 2006). Lastly, these polymorphisms can be used to study the genetic history of a species and identify regions of the bovine genome that have been under population genetic selection (Barendse et al 2009). …”