Evaluation of Bayesian methods of genomic association via chromosomic regions using simulated data

The development of efficient methods for genome–wide association studies (GWAS) between quantitative trait loci (QTL) and genetic values is extremely important to animal plant breeding programs. Bayesian approaches that aim select regions single nucleotide polymorphisms (SNPs) proved be efficient, indicating genes with effects. Among the selection criteria SNPs or regions, criterion by percentage variance can explained genomic (%var), tag SNPs, based on window posterior probability (WPPA). To also detect potentially associated we proposed measuring interval PPint), which aims markers greatest Therefore, objective this work was evaluate these approaches, in terms efficiency selecting identifying located within close traits. This study aimed compare methodologies single–marker analyses. accomplish this, simulated data were used six scenarios, allocated non–overlapping regions. Considering traits oligogenic inheritance, WPPA followed %var PPint shown superior, presenting higher detection power, capturing percentages larger areas. For polygenic considered superior. Single–marker analyses identified only inheritance scenarios lower than other criteria.