Abstract

The development of efficient methods for genome–wide 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 single–marker analyses. To accomplish this, simulated data were used in six scenarios, with SNPs allocated in non–overlapping 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. Single–marker analyses identified SNPs associated only in oligogenic inheritance scenarios and was lower than the other criteria.

Highlights

  • The development of new sequencing and genotyping technologies has promoted the growth of molecular genetics, enabling breeding programs to carry out genome–wide association studies (GWAS) between quantitative trait loci (QTL) and genetic values of individuals

  • Selection by the percentage of variance explained by genomic regions (%var), selection criteria of tag single nucleotide polymorphisms, and selection based on window posterior probability of association (WPPA) are the most notable

  • The posterior probability of the interval (PPint) and WPPA criteria can be widely used in GWAS for inheritance, especially considering that the inheritance of most agronomically important traits are controlled by many genes, which individually have small or rare alleles (Yang et al, 2010)

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Summary

Introduction

The development of new sequencing and genotyping technologies has promoted the growth of molecular genetics, enabling breeding programs to carry out genome–wide association studies (GWAS) between quantitative trait loci (QTL) and genetic values of individuals. Criteria using the Bayesian approach to select markers and associated regions that do not require major computational efforts are proposed. Selection by the percentage of variance explained by genomic regions (%var), selection criteria of tag single nucleotide polymorphisms (tag SNPs), and selection based on window posterior probability of association (WPPA) are the most notable. These approaches consider the genetic variance and differ in the criteria used for selecting the regions and thresholds determined for selection

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