Abstract
Undoubtedly livestock is one of the major contributors to the economy of any country. The economic value of livestock includes meat, dairy products, fiber, fertilizer etc. Understanding and identifying the associations of quantitative trait loci (QTL) with the economically important traits is believed to substantially benefit the livestock industry. The past two decades have seen a flurry of interest in mapping the QTL associated with traits of economic importance on the genome. With the availability of single nucleotide polymorphism chip of various densities it is possible to identify regions, QTL and genes on the genome that explain the association and its effect on the phenotype under consideration. Remarkable advancement has been seen in genome wide association studies (GWAS) since its inception till the present day. In this review we describe the progress and challenges of GWAS in various livestock species.
Highlights
Identifying genes and quantitative trait locus (QTLs) in the genome, that are associated with any phenotype is like finding a needle in the haystack
In 90’s QTL mapping was largely based on microsatellite markers (Lipkin et al, 1998) whereas these days with the advent of whole genome sequencing technologies and availability of affordable whole genome single nucleotide polymorphisms (SNP) panels, SNP along with the phenotype and pedigree information are utilized for mapping
The first successful GWA study was published in 2005 by Klein et al (2005) where the group carried out a genome wide scan of polymorphisms, on humans, associated with agerelated macular degeneration and found two SNPs which had significantly altered allele frequency when comparing with healthy controls
Summary
Identifying genes and quantitative trait locus (QTLs) in the genome, that are associated with any phenotype is like finding a needle in the haystack. The v-kit Hardy-Zuckerman 4 feline sarcoma viral oncogene homolog (KIT) gene, which has been previously reported to relate to hematological parameters, was located within the region significantly associated with MCH and MCV and could be a candidate gene These results of this study may lead to a better understanding of the molecular mechanisms of hematological parameters in pigs. Results from a study by Wolc et al (2012) suggested some regions on chromosomes GGA 2, 3, 4, 9, 15, 18, and 21 to be associated with Marek’s disease resistance Many genes, such as serpins, BCL-2 proteins; tumor necrosis factor receptor superfamily, member 11a (TNFRSF11A), unc-13 homolog D (UNC13D), sphingosine, ArfGAP with FG repeats 1 (AGFG1), mitogen-activated protein kinase kinase 4 (MAP2K4), IGFBP7; receptor (TNFRSF)-interacting serine-threonine kinase 1 (RIPK1), were identified. This study paves the way for further research on the host immune response to NDV (Luo et al, 2013)
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