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