Additive and epistatic genome‐wide association for growth and ultrasound scan measures of carcass‐related traits in Brahman cattle. (6th March 2015)

• Record Type: Journal Article
• Authors: Ali, A.A.; Khatkar, M.S.; Kadarmideen, H.N.; Thomson, P.C.
• Is Part Of: Journal of animal breeding and genetics. Volume 132:Number 2(2015:Apr.)
• Page Start: 187
• Page End: 197
• Publication Date: 2015-03-06
• Subjects: Livestock -- Breeding -- Periodicals; Livestock -- Genetics -- Periodicals; 636.0820
• DOI: 10.1111/jbg.12147
• Languages: English
• ISSNs: 0931-2668

Abstract

<abstract abstract-type="main" id="jbg12147-abs-0001"> <title>Summary</title> <p>Genome‐wide association studies are routinely used to identify genomic regions associated with traits of interest. However, this ignores an important class of genomic associations, that of epistatic interactions. A genome‐wide interaction analysis between single nucleotide polymorphisms (SNPs) using highly dense markers can detect epistatic interactions, but is a difficult task due to multiple testing and computational demand. However, It is important for revealing complex trait heredity. This study considers analytical methods that detect statistical interactions between pairs of loci. We investigated a three‐stage modelling procedure: (i) a model without the SNP to estimate the variance components; (ii) a model with the SNP using variance component estimates from (i), thus avoiding iteration; and (iii) using the significant SNPs from (ii) for genome‐wide epistasis analysis. We fitted these three‐stage models to field data for growth and ultrasound measures for subcutaneous fat thickness in Brahman cattle. The study demonstrated the usefulness of modelling epistasis in the analysis of complex traits as it revealed extra sources of genetic variation and identified potential candidate genes affecting the concentration of insulin‐like growth factor‐1 and ultrasound scan measure of fat depth traits. Information about epistasis can add to our understanding of the complex genetic networks that form the fundamental basis of biological systems.</p> </abstract>