Suitability of GWAS as a Tool to Discover SNPs Associated with Tick Resistance in Cattle: A Review

Abstract

Understanding the biological mechanisms underlying tick resistance in cattle holds the potential to facilitate genetic improvement through selective breeding. Genome wide association studies (GWAS) are popular in research on unraveling genetic determinants underlying complex traits such as tick resistance. To date, various studies have been published on single nucleotide polymorphisms (SNPs) associated with tick resistance in cattle. The discovery of SNPs related to tick resistance has led to the mapping of associated candidate genes. Despite the success of these studies, information on genetic determinants associated with tick resistance in cattle is still limited. This warrants the need for more studies to be conducted. In Africa, the cost of genotyping is still relatively expensive; thus, conducting GWAS is a challenge, as the minimum number of animals recommended cannot be genotyped. These population size and genotype cost challenges may be overcome through the establishment of collaborations. Thus, the current review discusses GWAS as a tool to uncover SNPs associated with tick resistance, by focusing on the study design, association analysis, factors influencing the success of GWAS, and the progress on cattle tick resistance studies.

Integrative QTL mapping and selection signatures in Groningen White Headed cattle inferred from whole-genome sequences

“…This confounding effect should either be eliminated through a better study design (e.g. F2 crosses with another white face breed that does not show ACOP) [ 60 – 62 ] or by reducing the number of false positives using a combined approach in a downstream analysis [ 17 , 63 ]. For example, the application of complementary methods to investigate whether loci significantly associated were recently selected in the population [ 16 ], the description of functions of the genes in candidate regions, and finally the experimental validation.…”

“…This confounding effect should either be eliminated through a better study design (e.g. F2 crosses with another white face breed that does not show ACOP) [60][61][62] or by reducing the number of false positives using a combined approach in a downstream analysis [17,63]. For example, the…”

Vetinformatics from functional genomics to drug discovery: Insights into decoding complex molecular mechanisms of livestock systems in veterinary science

“…Once such genetic variants have been discovered, they are often utilized to look for neighboring variants that directly contribute to the disease or trait (84, 85). An analysis of GWAS can be conducted using single-locus, and multilocus models (86). The General linear model (GLM), Mixed linear model (MLM), Logistic mixed model (LMM), and Compressed mixed linear model (CMLM) are the single locus models (87-89), and multi-locus model includes Multilocus random SNP effect mixed linear models (mrMLM) and Fast multilocus random SNP effect efficient mixed-model association (FASTmrEMMA) (86,(90)(91)(92).…”

“…An analysis of GWAS can be conducted using single-locus, and multilocus models (86). The General linear model (GLM), Mixed linear model (MLM), Logistic mixed model (LMM), and Compressed mixed linear model (CMLM) are the single locus models (87-89), and multi-locus model includes Multilocus random SNP effect mixed linear models (mrMLM) and Fast multilocus random SNP effect efficient mixed-model association (FASTmrEMMA) (86,(90)(91)(92). The computer programs commonly used for GWAS include PLINK (93), GenABEL (94), GenAMap (95), and GEMMA (96).…”

“…animalgenome.org/cgi-bin/QTLdb/index), NAGRP (https:// www.animalgenome.org/), Ensembl (https://asia.ensembl. org/index.html), and UCSC (https://genome.ucsc.edu/) are valuable resources (86). Besides, the Genome Analysis Toolkit (GATK) pipeline is an important platform for high-throughput genomics data analysis (97).…”