The application of genomic technologies has significantly impacted the livestock industry in recent years, with the advent of next-generation sequencing and high-throughput genotyping platforms. These technologies have enabled the identification of genetic variants associated with economically important traits in livestock. Among these techniques, Genome-wide Association Studies (GWAS) have emerged as a powerful tool for identifying the genetic basis of complex traits.
GWAS has been extensively used in animal breeding to improve animal performance, health,
and welfare. The traditional univariate GWAS approach identifies associations
between a single phenotype and single nucleotide polymorphisms (SNPs) or
genetic markers. However, the univariate approach does not account for the fact
that multiple traits may be controlled by the same genetic factors. Therefore,
the multivariate GWAS approach has gained popularity in livestock breeding as
it allows the identification of genomic regions that affect multiple traits
simultaneously.
Multivariate GWAS is a statistical method that simultaneously evaluates multiple traits and
identifies the genetic variants that influence them. This approach has the
advantage of detecting pleiotropic effects, which are genetic variants that
affect multiple traits. Additionally, multivariate GWAS improves the accuracy
of genomic predictions as it captures the genetic correlation among traits.
Several studies have applied the multivariate GWAS approach in livestock breeding to
identify genomic regions affecting multiple traits. One study in beef cattle used
a multivariate GWAS approach to identify genetic variants associated with both
growth and meat quality traits. The study identified several genomic regions
with pleiotropic effects on multiple traits. Another study in dairy cattle used
multivariate GWAS to identify genetic variants associated with milk production,
somatic cell count, and fertility. The study identified several regions with
pleiotropic effects on the three traits.
Multivariate GWAS has also been used to improve the accuracy of genomic predictions in
livestock. In dairy cattle, for instance, a multivariate GWAS approach was used
to identify genomic regions affecting milk yield and milk composition traits.
The identified regions were then used to construct a genomic prediction model,
which significantly improved the accuracy of genomic predictions for both
traits.
In conclusion, multivariate GWAS has become an important tool for identifying
genetic variants associated with multiple traits simultaneously in livestock
breeding. This approach has the potential to accelerate genetic improvement in
livestock by identifying genomic regions with pleiotropic effects, and by
improving the accuracy of genomic predictions. As genomic technologies continue
to evolve, multivariate GWAS is expected to play an increasingly important role
in livestock breeding.
Published by: Muhammad Basil
M.Sc.(Hons.) Animal Breeding and Genetics
Institute of Animal & Dairy Sciences, UAF
B.Sc.(Hons.) Dairy Sciences
Faculty of Animal Husbandry, UAF
University of Agriculture, Faisalabad
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