Development And Application Of Genomic Selection Signatures Strategies In Domestic Animals

Identifying signatures of selection can provide a straightforward insight into the mechanism of artificial selection and further uncover the causal genes related to the phenotypic variation. With the advent of high throughput and cost-effective genotyping techniques, a series of statistical tests have been developed to detect directional selection signatures based on different models. These methods can be grouped into three categories:site-frequecy spectrum based methods, haplotyped based methods and population differentiation based methods. Viewed from this perspective, statistics and P-values obtained with those methods should exhibit a certain degree of correlation if they reflect fully or partly the same underlying pattern caused by selection or if they are derived from the same basic statistics. Accordingly, it is becoming promising to use multiple methods or a compsite strategy to detect selection signatures to benefit from advantageous complementarities across methods.From a statistical perspective, determining a proper testing procedure and combining various test statistics is challenging. In this study, we discussed the statistical properties of eight different elementary selection signature statistics based on extensive simulations. In the considered scenario we show that a reasonable power to detect selection signatures is achieved with high marker density (>1SNP/kb) as obtained from sequencing, while rather small sample sizes (-15diploid individuals) appear to be sufficient. Most selection signature statistics such as CLR and XPEHH have the highest power when fixation of the selected allele is reached, while iHS has the highest power when selection is ongoing.Furthermore, we suggest a novel strategy, called de-correlated composite of multiple signals (DCMS) to combine different statistics for detecting selection signatures while accounting for the correlation between the different selection signature statistics. When examined with simulated data, DCMS consistently has a higher power than most of the single statistics and shows a reliable positional resolution. Compared to other combining strategies it has the advantage to be easily computable even in populations with not sufficiently known demography (compared to CMS), and to account for correlations of the elementary test statistics, which were found to be too large to be ignored. We illustrate the new statistic to the established selective sweep around the lactase gene in human HapMap data providing further evidence of the reliability of this new statistic.Based on Illumina Porcine60K SNP chip data, four complementary methods were implemented in this study to detect the selection signatures in the whole genome of four pig breeds. In this part, a total of159,127,179and159candidate selection regions with average length of0.80Mb,0.73Mb,0.78Mb and0.73Mb were identified in Landrace, Rongchang, Songliao and Yorkshire, respectively. Bioinformatics analysis showed that the genes/QTLs relevant to fertility, coat color, and ear morphology were found in those candidate selection regions and this analysis also demonstrated the diversity of breeds. Based on Affymetrix chicken600K SNP chip data, we employed eight different elementary selection signature statistics and applied DCMS strategy to scan selection signatures in two chicken samples with diverse skin color. Our analysis suggests that a set of well-known genes such as BCO2, MC1R, ASIP and TYR were involved in the divergent selection for this trait.Selection signature analysis is a relatively novel and highly promising approach in livestock population genomics, an accurate and comprehensive set of selection signatures will be the basis for a better understanding of the forces driving artificial selection and will help to design more efficient livestock breeding programs.