Population size in QTL detection using quantile regression in genome-wide association studies

Abstract

The aim of this study was to evaluate the performance of Quantile Regression (QR) in Genome-Wide Association Studies (GWAS) regarding the ability to detect QTLs (Quantitative Trait Locus) associated with phenotypic traits of interest, considering different population sizes. For this, simulated data was used, with traits of different levels of heritability (0.30 and 0.50), and controlled by 3 and 100 QTLs. Populations of 1,000 to 200 individuals were defined, with a random reduction of 100 individuals for each population. The power of detection of QTLs and the false positive rate were obtained by means of QR considering three different quantiles (0.10, 0.50 and 0.90) and also by means of the General Linear Model (GLM). In general, it was observed that the QR models showed greater power of detection of QTLs in all scenarios evaluated and a relatively low false positive rate in scenarios with a greater number of individuals. The models with the highest detection power of true QTLs at the extreme quantils (0.10 and 0.90) were the ones with the highest detection power of true QTLs. In contrast, the analysis based on the GLM detected few (scenarios with larger population size) or no QTLs in the evaluated scenarios. In the scenarios with low heritability, QR obtained a high detection power. Thus, it was verified that the use of QR in GWAS is effective, allowing the detection of QTLs associated with traits of interest even in scenarios with few genotyped and phenotyped individuals.

Association mapping in bambara groundnut [Vigna subterranea (L.) Verdc.] reveals loci associated with agro-morphological traits

“…The present study identified few markers trait associations but using higher number of landraces with diverse population and reference genome of the bambara groundnut would lead to detection of higher number of MTAs. The effect of population size on GWAS has been noted and the detection power of MTAs decreased according to reduction of population size [ 78 ]. Alqudah et al [ 22 ] reported that a range of 100 to 500 genotypes are suitable for performing GWAS and the selection genotype population should consider the genetic background, genotypic and phenotypic variation.…”

“…Alqudah et al [ 22 ] reported that a range of 100 to 500 genotypes are suitable for performing GWAS and the selection genotype population should consider the genetic background, genotypic and phenotypic variation. Although, population size affects power of GWAS [ 78 ], but the result of the present study should be considered early evidence about the genomic regions and markers associated with the study traits and should be further validated using higher number of genotypes in more diverse environments.…”