Statistical Problems in Livestock Population Genomics

Monday, August 18, 2014: 4:00 PM
Bayshore Grand Ballroom E-F (The Westin Bayshore)
Henner Simianer , Georg-August-University, Göttingen, Germany
Yunlong Ma , China Agricultural University, Beijing, China
Saber Qanbari , Georg-August University, Göttingen, Germany
Abstract Text: 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. Based on an extensive simulation study we discuss the statistical properties of eight different selection signature statistics. It is demonstrated, that a reasonable power to detect selection signatures requires high density marker information as obtained from sequencing, while small sample sizes are acceptable. We suggest a novel principal component based combination of different statistics, which yields a statistic with similar power as the best single statistic but with an improved positional resolution. 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.


selection signatures

statistical testing

statistical power