The use of genomic data and imputation methods in dairy cattle breeding

Abstract

The inclusion of animal genotype data has contributed to the development of genomic selection. Animals are selected not only based on pedigree and phenotypic data but also on the basis of information about their genotypes. Genomic information helps to increase the accuracy of selection of young animals and thus enables a reduction of the generation interval. Obtaining information about genotypes in the form of SNPs (single nucleotide polymorphisms) has led to the development of new chips for genotyping. Several methods of genomic comparison have been developed as a result. One of the methods is data imputation, which allows the missing SNPs to be calculated using low-density chips to high-density chips. Through imputations, it is possible to combine information from diverse sets of chips and thus obtain more information about genotypes at a lower cost. Increasing the amount of data helps increase the reliability of predicting genomic breeding values. Imputation methods are increasingly used in genome-wide association studies. When classical genotyping and genome-wide sequencing data are combined, this option helps to increase the chances of identifying loci that are associated with economically significant traits.

Exploiting morphobiometric and genomic variability of African indigenous camel populations-A review

“…SNP genotyping of animals can be done using biochips. These chips vary from lower density to higher density ( Klímová et al, 2020 ). Linkage disequilibrium (LD) between markers and QTL can better be captured using denser chips or panels ( Aliloo et al, 2018 ).…”

Associations among Farm, Breed, Lactation Stage and Parity, Gene Polymorphisms and the Fatty Acid Profile of Milk from Holstein, Simmental and Their Crosses

“…Their findings provide evidence for the possible selection of dairy cows with a healthier milk FA composition by genomic selection schemes. Similarly, other authors have suggested a genomic approach to a complex description of the genetic background; this option helps to increase the chances of identifying loci associated with economically important traits [ 8 , 9 ]. The significant effect of FASN gene polymorphisms was also confirmed by other authors [ 10 ].…”