Composite selection signals can localize the trait specific genomic regions in multi-breed

M E THODO L OGY AR TIC L E Open Access

Composite selection signals can localize the trait

specific genomic regions in multi-breed

populations of cattle and sheep

Imtiaz Ahmed Sajid Randhawa*

, Mehar Singh Khatkar, Peter Campbell Thomson and Herman Willem Raadsma

Abstract

Background: Discerning the traits evolving under neutral conditions from those traits evolving rapidly because of

various selection pressures is a great challenge. We propose a new method, composite selection signals (CSS),

which unifies the multiple pieces of selection evidence from the rank distribution of its diverse constituent tests.

The extreme CSS scores capture highly differentiated loci and underlying common variants hauling excess

haplotype homozygosity in the samples of a target population.

Results: The data on high-density genotypes were analyzed for evidence of an association with either polledness or

double muscling in various cohorts of cattle and sheep. In cattle, extreme CSS scores were found in the candidate

regions on autosome BTA-1 and BTA-2, flanking the POLL locus and MSTN gene, for polledness and double muscling,

respectively. In sheep, the regions with extreme scores were localized on autosome OAR-2 harbouring the MSTN gene

for double muscling and on OAR-10 harbouring the RXFP2 gene for polledness. In comparison to the constituent tests,

there was a partial agreement between the signals at the four candidate loci; however, they consistently identified

additional genomic regions harbouring no known genes. Persuasively, our list of all the additional significant CSS

regions contains genes that have been successfully implicated to secondary phenotypic diversity among several

subpopulations in our data. For example, the method identified a strong selection signature for stature in cattle

capturing selective sweeps harbouring UQCC-GDF5 and PLAG1-CHCHD7 gene regions on BTA-13 and BTA-14,

respectively. Both gene pairs have been previously associated with height in humans, while PLAG1-CHCHD7 has

also been reported for stature in cattle. In the additional analysis, CSS identified significant regions harbouring

multiple genes for various traits under selection in European cattle including polledness, adaptation, metabolism,

growth rate, stature, immunity, reproduction traits and some other candidate genes for dairy and beef production.

Conclusions: CSS successfully localized the candidate regions in validation datasets as well as identified previously

known and novel regions for various traits experiencing selection pressure. Together, the results demonstrate the utility

of CSS by its improved power, reduced false positives and high-resolution of selection signals as compared to individual

constituent tests.

Keywords: Selection signatures, Selective sweeps, Polledness, Double muscle, Geographic origin, Cattle, Sheep

Background

Genetics research has increased rapidly with availability

of high throughput molecular biology tools and analytical

approaches [1]. Recent molecular genetics techniques

combined with large scale in silico analysis of genetic poly￾morphism data have provided insights to many questions

about the origin of species [2], evolution [3], co-evolution

and selection [4], domestication [5], genetic control of

adaptation and diseases [6-8], and genetic diversity [9,10]

for a wide range of species. More recently, identification

of chromosomal regions that contain signatures of selec￾tion has been helpful to understand various mechanisms

of adaptation, domestication and selection for important

traits of various domestic species [11-21].

Evidence of selection can be gained from the measures

of population differentiation, the allele frequency spectrum,

* Correspondence: [email protected]

ReproGen - Animal Bioscience Group, Faculty of Veterinary Science,

University of Sydney, 425 Werombi Road, Camden NSW 2570, Australia

© 2014 Randhawa et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the

Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), 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.

Randhawa et al. BMC Genetics 2014, 15:34

http://www.biomedcentral.com/1471-2156/15/34