Abstract
Traditionally, genome-wide association studies (GWAS) require maximum numbers of genotyped and phenotyped animals to efficiently detect marker-trait associations. Under financial constraints, alternative solutions should be envisaged such that of performing GWAS with fractioned samples of the population. In the present study, we investigated the potential of using random and extreme phenotype samples of a population including 6,700 broilers in detecting significant markers and candidate genes for a typical complex trait (body weight at 35 days). We also explored the utility of using continuous vs. dichotomized phenotypes to detect marker-trait associations. Present results revealed that extreme phenotype samples were superior to random samples while detection efficacy was higher on the continuous over the dichotomous phenotype scale. Furthermore, the use of 50% extreme phenotype samples resulted in detection of 8 out of the 10 markers identified in whole population sampling. Putative causative variants identified in 50% extreme phenotype samples resided in genomic regions harboring 10 growth-related QTLs (e.g. breast muscle percentage, abdominal fat weight etc.) and 6 growth related genes (<i>CACNB1, MYOM2, SLC20A1, ANXA4, FBXO32, SLAIN2</i>). Current findings proposed the use of 50% extreme phenotype sampling as the optimal sampling strategy when performing a cost-effective GWAS.
Highlights
Quantitative Trait Loci (QTL) detection presumes the availability of both phenotypic trait values and marker genotypic data
The significant (FDR p-value< 0.05) SNPs detected in population sampling (PS) are shown in Table 1 along with estimated Proportion of Variance Explained (PVE), respective minor allele frequency (MAF) and allelic effects (β)
In case of rs15425131, PVE is the result of low MAF (0.091) and highest β (3.86 g) while in the case of rs315329074 is the product of higher MAF (0.171) with lower β (2.93 g)
Summary
Quantitative Trait Loci (QTL) detection presumes the availability of both phenotypic trait values and marker genotypic data. In livestock populations where extensive individual performance recording takes place, collection and availability of phenotypic data on large numbers of animals is an ongoing situation. When costs are not of primary concern, all individuals with phenotypic data are genotyped and included for QTL analysis. This is seldom the case and under a limited budget it is necessary to make an effective allocation of genotyping costs. The latter could be extremely high for large sized populations and the high-throughput genotyping technology [1]. A useful genotyping cost-saving strategy is selective genotyping (SG) in which only a selected fraction of the phenotyped individuals, are genotyped [2, 3]
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