Footprints of sustained selection in livestock are generally diverse and complex. It has been a great challenge to differentiate between the neutrally (random drift) and rapidly (selection) evolving traits (Hohenlohe et al. 2010). In general, signatures of selection are identified from the patterns of DNA variation without recorded phenotypic information on populations undergoing investigation. Nonetheless, the signatures of selection detected between populations are often indicative of segregating regions of functional mutations underlying divergently selected quantitative traits (Hayes et al. 2008, 2009). In complex genomes such as mammalian species, the functional mutations and their systematic patterns of polymorphism exist in multiple locations. Identification of the trait-specific genomic regions requires comprehensive phenotypic and genomic data of large populations to detect causal effects of selection (Decker et al. 2012). Despite using known mapping approaches, it has often been difficult to localize historical selection events for complex traits (Kemper et al. 2014). A number of tests are available to detect candidate regions under selection, which often provide differing results from the same genomic dataset (Qanbari et al. 2011; Crisci et al. 2012). Combining several tests with complementary characteristics can improve the ability to localize the candidate regions under selection (Utsunomiya et al. 2013; Grossman et al. 2010).