Detection of selection signatures in dairy and beef cattle using high-density genomic information

Zhao et al. Genetics Selection Evolution (2015) 47:49 DOI 10.1186/s12711-015-0127-3 Ge n e t i c s Se l e c t i o n Ev o l u t i o n RESEARCH ARTICLE Open Access Detection of selection signatures in dairy and beef cattle using high-density genomic information Fuping Zhao1, Sinead McParland2, Francis Kearney3, Lixin Du1* and Donagh P Berry2* Abstract Background: Artificial selection for economically important traits in cattle is expected to have left distinctive selection signatures on the genome. Access to high-density genotypes facilitates the accurate identification of genomic regions that have undergone positive selection. These findings help to better elucidate the mechanisms of selection and to identify candidate genes of interest to breeding programs. Results: Information on 705 243 autosomal single nucleotide polymorphisms (SNPs) in 3122 dairy and beef male animals from seven cattle breeds (Angus, Belgian Blue, Charolais, Hereford, Holstein-Friesian, Limousin and Simmental) were used to detect selection signatures by applying two complementary methods, integrated haplotype score (iHS) and global fixation index (FST). To control for false positive results, we used false discovery rate (FDR) adjustment to calculate adjusted iHS within each breed and the genome-wide significance level was about 0.003. Using the iHS method, 83, 92, 91, 101, 85, 101 and 86 significant genomic regions were detected for Angus, Belgian Blue, Charolais, Hereford, Holstein-Friesian, Limousin and Simmental cattle, respectively. None of these regions was common to all seven breeds. Using the FST approach, 704 individual SNPs were detected across breeds. Annotation of the regions of the genome that showed selection signatures revealed several interesting candidate genes i.e. DGAT1, ABCG2, MSTN, CAPN3, FABP3, CHCHD7, PLAG1, JAZF1, PRKG2, ACTC1, TBC1D1, GHR, BMP2, TSG1, LYN, KIT and MC1R that play a role in milk production, reproduction, body size, muscle formation or coat color. Fifty-seven common candidate genes were found by both the iHS and global FST methods across the seven breeds. Moreover, many novel genomic regions and genes were detected within the regions that showed selection signatures; for some candidate genes, signatures of positive selection exist in the human genome. Multilevel bioinformatic analyses of the detected candidate genes suggested that the PPAR pathway may have been subjected to positive selection. Conclusions: This study provides a high-resolution bovine genomic map of positive selection signatures that are either specific to one breed or common to a subset of the seven breeds analyzed. Our results will contribute to the detection of functional candidate genes that have undergone positive selection in future studies. Introduction Artificial selection in cattle has resulted in divergent breeds that are specialized for either milk or meat production or raised as dual-purpose breeds. Such selection strategies are likely to have imposed selection pressures on particular regions of the genome that control these * Correspondence: ; 1 National Center for Molecular Genetics and Breeding of Animal, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China 2 Animal and Grassland Research and Innovation Centre, Teagasc, Moorpark, Co. Cork, Ireland Full list of author information is available at the end of the article traits as well as other important animal characteristics such as disease resistance or general immune competence. Under positive selection pressure, the frequency of favorable alleles in the genome will rapidly increase. If intensive selection pressure occurred only over a few generations, it is unlikely that recombination had an impact on haplotype structure, and thus it resulted in (extended) linkage disequilibrium (LD) patterns between the mutation and neighboring loci [1]. Analysis of these selection signatures can reveal genomic regions of interest for selection and provide insights into the mechanisms of evolution [2, 3]. © 2015 Zhao et al. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http:// creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Zhao et al. Genetics Selection Evolution (2015) 47:49 Various statistical approaches have been proposed for the detection of selection signatures. Such tests include Tajima’s D-statistic [4], Fay and Wu’s H-statistic [5], extended haplotype homozygosity (EHH) [1], integrated haplotype score (iHS) [6], the Ka/Ks test [7], and the McDonald and Kreitman test [8]. The EHH test is particularly useful to detect signatures of positive selection within a population using single nucleotide polymorphism (SNP) data [9–11]. This method that was first developed by Sabeti et al. [1] exploits knowledge on the relationship between the frequency of an allele and the measures of LD with neighboring alleles. An EHH is defined as the probability that two randomly chosen chromosomes that carry the core haplotype of interest are identical by descent for the entire interval between the core region and a certain locus [1]. To overcome the influence of heterogeneous recombination rates across the genome, Voight et al. [6] developed the iHS approach, which is an extension of the EHH method and is based on the comparison of EHH between derived and ancestral alleles within a population. The iHS achieves maximal power when a selected allele segregates at intermediate frequencies in the population. An alternative approach to the detection of selection signatures is based on the measure of population differentiation due to locus-specific allele frequencies between populations, which is quantified using the FST statistic [12]. The fixation index, FST was first defined by Wright [13] to quantify the degree of genetic differentiation among populations based on differences in allele frequencies. FST provides information on the genomic variation at a locus among populations relative to that within populations. Thus, FST is also a test for evidence of selection i.e. high FST values indicate local positive adaptation while low FST values suggest negative or neutral selection [14]. Both iHS and FST statistics are useful to detect selection signatures [15]. Previous analyses suggested that they are largely complementary; iHS has good power to detect selection signatures within breeds, while global FST is useful to detect selection signatures (i.e., loci that were differentially fixed in different breeds) across breeds [16]. Global FST is also used to determine how divergent selection has impacted the genome of these breeds. The objective of our study was to dete (...truncated)