Power of Variance Component Linkage Analysis to Identify Quantitative Trait Locus in Chickens

Abstract

A crucial first step in the planning of any scientific experiment is to evaluate an appropriate sample size to permit sufficient statistical power to detect the desired effect. In this study, we investigated the optimal sample size of quantitative trait locus (QTL) linkage analysis for simple random sibship samples in pedigreed chicken populations, under the variance component framework implemented in the genetic power calculator program. Using the program, we could compute the statistical power required to achieve given sample sizes in variance component linkage analysis in random sibship data. For simplicity, an additive model was taken into account. Power calculations were performed to relate sample size to heritability attributable to a QTL. Under the various assumptions, comparative power curves indicated that the power to detect QTL with the variance component method is highly affected by a function of the effect size of QTL. Hence, more power can be achievable for QTL with a larger effect. In addition, a marked improvement in power could be obtained by increasing the sibship size. Thus, the use of chickens is advantageous regarding the sampling unit issue, since desirable sibship size can be easily obtained compared with other domestic species.

Genome-wide association scan for QTL and their positional candidate genes associated with internal organ traits in chickens

“…The low PPA values observed in our study can be due to the reduced sample size used herein, that can reduce the power to detect QTL [43]. However, the overlaps observed with known QTL available in the Chicken QTL database – release 35 [29], and also the detection of PCGs within the QTL associated with internal organ weights and percentage corroborate our findings.…”

“…A total of 20 QTL were detected and together, they explained 2.08, 11.19, 8.71, 0.57 and 8.68% of genetic variance for LIVP, GIZZWT, GIZZP, LUNGP and INTES, respectively. A low heritability can reduce the power to detect QTL [43], also explaining these greater number of QTL detected for the traits with high heritability estimates.…”

“…A dataset of approximately 13 million reliable SNPs were initially identified in the whole genome as a result of sequencing 28 grandparental chickens from the Embrapa F 2 Chicken Resource Population, and after filtration, approximately 14 million SNPs were further utilized for the search of SNPs annotated in PCGs. A detailed description of the sequencing, SNP calling, and filtering criteria are available in Boschiero et al [43].…”