Composite selection signals can localize the trait specific genomic regions in multi-breed populations of cattle and sheep

Abstract

BackgroundDiscerning 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.ResultsThe 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.ConclusionsCSS 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.

On the performance of tests for the detection of signatures of selection: a case study with the Spanish autochthonous beef cattle populations

“…Among the genes included in these regions, some of them may be good candidates for being affected by selection i.e. : (1) genes that are related to energy balance and homeostasis: R3HGM1 ( R3H domain containing 1 ) on BTA2 [48]; CAMK1D ( calcium/calmodulin - dependent protein kinase ID ) on BTA13 [49]; and SLC25A33 ( solute carrier family 25 ( pyrimidine nucleotide carrier ) , member 33 ) and SLC2A5 ( solute carrier family 2 ( facilitated glucose/fructose transporter ), member 5 ) on BTA16 [49, 50]; (2) PLIN5 ( perilipin 5 ) on BTA7, which is involved in the regulation of lipid metabolism in rat and pigs [51, 52]; (3) SCAPER ( s - phase cyclin A - associated protein in the endoplasmic reticulum ) on BTA21, which regulates cell cycle progression [53]; and (4) there are two other signatures of selection that are worth noting i.e. one detected for the RG population on BTA19 (between 27,941,270 and 28,571,032 bp) where ALOX15B ( arachidonate 15 - lipoxygenase, type B ) and ALOX12B ( arachidonate 12 - lipoxygenase, 12R type ) are located and are involved in immune response [54], and one for the ANI population on BTA20 (between 40,854,136 and 40,996,384 bp) where the NPR3 ( natriuretic peptide receptor 3 ) gene is located, which is related with cattle stature [55].…”

Selection signatures in Shetland ponies

“…In this study, we applied the composite selection score (CSS) method to detect selection signatures within our multibreed dataset (Randhawa et al 2014). The CSS method takes as input the results of different test statistics that are commonly applied to identify selection signatures.…”

“…S2). Finally, we combined the results of the four test statistics and calculated the CSS per SNP, as previously described (Randhawa et al 2014). To minimize the amount of false positives and the large standard variations, selection signatures are commonly averaged over windows.…”

Composite measures of selection can improve the signal‐to‐noise ratio in genome scans

“…This approach employs Stouffer's method for combining P-values, which assumes independence among the statistics being summarized (Stouffer et al 1949;Whitlock 2005). The CSS statistic is calculated from univariate measures as follows: (i) convert univariate statistics to fractional ranks between 1/(n + 1) and n/(n + 1), where n is the number of observations, (ii) convert fractional ranks to Z-values using the inverse normal cumulative distribution function, (iii) take the mean Z-score and convert to a P-value using the normal N(0, m À1 ) distribution, where m is the number of univariate statistics, (iv) the CSS statistic is defined as Àlog 10 of the P-value (Randhawa et al 2014(Randhawa et al , 2015. Note that this method does not account for covariance among signals in univariate statistics, nor directionality in the signal.…”

“…take the overlap among all univariate statistics), but this approach may miss loci affected by weak selection . Recently, several composite measures have been proposed based on combining P-values and these measures generally perform better than univariate single statistics, but evaluation of their performance has been limited to relatively simple scenarios (Grossman et al 2010;Evangelou & Ioannidis 2013;Utsunomiya et al 2013;Randhawa et al 2014Randhawa et al , 2015Franc ßois et al 2015;Ma et al 2015).…”