title: Properties of different selection signature statistics and a new strategy for combining them.
abstract: Identifying signatures of recent or ongoing selection is of high relevance in livestock population genomics. From a statistical perspective, determining a proper testing procedure and combining various test statistics is challenging. On the basis of extensive simulations in this study, we discuss the statistical properties of eight different established selection signature statistics. In the considered scenario, we show that a reasonable power to detect selection signatures is achieved with high marker density (>1 SNP/kb) as obtained from sequencing, while rather small sample sizes (~15 diploid individuals) appear to be sufficient. Most selection signature statistics such as composite likelihood ratio and cross population extended haplotype homozogysity have the highest power when fixation of the selected allele is reached, while integrated haplotype score has the highest power when selection is ongoing. 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. 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. Then, we apply it 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.
authors.affiliation: Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture, National Engineering Laboratory for Animal Breeding, College Animal Science and Technology, China Agricultural University, Beijing, China.|Animal Breeding and Genetics Group, Department of Animal Sciences, Georg-August University, Goettingen, Germany.
authors.forename: Y
authors.initials: Y
authors.lastname: Ma
authors.affiliation: Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture, National Engineering Laboratory for Animal Breeding, College Animal Science and Technology, China Agricultural University, Beijing, China.
authors.forename: X
authors.initials: X
authors.lastname: Ding
authors.affiliation: Animal Breeding and Genetics Group, Department of Animal Sciences, Georg-August University, Goettingen, Germany.
authors.forename: S
authors.initials: S
authors.lastname: Qanbari
authors.affiliation: Institute for Animal Breeding, Federal Agricultural Research Centre, Mariensee, Germany.
authors.forename: S
authors.initials: S
authors.lastname: Weigend
authors.affiliation: Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture, National Engineering Laboratory for Animal Breeding, College Animal Science and Technology, China Agricultural University, Beijing, China.
authors.forename: Q
authors.initials: Q
authors.lastname: Zhang
authors.affiliation: Animal Breeding and Genetics Group, Department of Animal Sciences, Georg-August University, Goettingen, Germany.
authors.forename: H
authors.initials: H
authors.lastname: Simianer
mesh_terms: D000818:Animals; D001947:Breeding / Q000379:methods; D002645:Chickens / Q000235:genetics; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D005787:Gene Frequency; D060148:HapMap Project; D006239:Haplotypes; D006801:Humans; D016013:Likelihood Functions; D008957:Models, Genetic; D020641:Polymorphism, Single Nucleotide; D012641:Selection, Genetic