TY - JOUR
T1 - Invited review: Good practices in genome-wide association studies to identify candidate sequence variants in dairy cattle
AU - Sahana, G
AU - Cai, Z
AU - Sanchez, M P
AU - Bouwman, A C
AU - Boichard, D
N1 - Publisher Copyright:
© 2023 American Dairy Association
PY - 2023/8
Y1 - 2023/8
N2 - Genotype data from dairy cattle selection programs have greatly facilitated GWAS to identify variants related to economic traits. Results can enhance the accuracy of genomic prediction, analyze more complex models that go beyond additive effects, elucidate the genetic architecture of a trait, and finally, decipher the underlying biology of traits. The entire process, comprising data generation, quality control, statistical analyses, interpretation of association results, and linking results to biology should be designed and executed to minimize the generation of false-positive and false-negative associations and misleading links to biological processes. This review aims to provide general guidelines for data analysis that address data quality control, association tests, adjustment for population stratification, and significance evaluation to improve the reliability of conclusions. We also provide guidance on post-GWAS strategy and the interpretation of results. These guidelines are tailored to dairy cattle, which are characterized by long-range linkage disequilibrium, large half-sib families, and routinely collected phenotypes, requiring different approaches than those applied in human GWAS. We discuss common limitations and challenges that have been overlooked in the analysis and interpretation of GWAS to identify candidate sequence variants in dairy cattle.
AB - Genotype data from dairy cattle selection programs have greatly facilitated GWAS to identify variants related to economic traits. Results can enhance the accuracy of genomic prediction, analyze more complex models that go beyond additive effects, elucidate the genetic architecture of a trait, and finally, decipher the underlying biology of traits. The entire process, comprising data generation, quality control, statistical analyses, interpretation of association results, and linking results to biology should be designed and executed to minimize the generation of false-positive and false-negative associations and misleading links to biological processes. This review aims to provide general guidelines for data analysis that address data quality control, association tests, adjustment for population stratification, and significance evaluation to improve the reliability of conclusions. We also provide guidance on post-GWAS strategy and the interpretation of results. These guidelines are tailored to dairy cattle, which are characterized by long-range linkage disequilibrium, large half-sib families, and routinely collected phenotypes, requiring different approaches than those applied in human GWAS. We discuss common limitations and challenges that have been overlooked in the analysis and interpretation of GWAS to identify candidate sequence variants in dairy cattle.
KW - dairy cattle
KW - gene mapping
KW - genome-wide association study
KW - Cattle/genetics
KW - Phenotype
KW - Reproducibility of Results
KW - Animals
KW - Humans
KW - Genotype
KW - Genome-Wide Association Study/veterinary
KW - Polymorphism, Single Nucleotide
KW - Quantitative Trait Loci
UR - http://www.scopus.com/inward/record.url?scp=85165521561&partnerID=8YFLogxK
U2 - 10.3168/jds.2022-22694
DO - 10.3168/jds.2022-22694
M3 - Review
C2 - 37349208
SN - 0022-0302
VL - 106
SP - 5218
EP - 5241
JO - Journal of Dairy Science
JF - Journal of Dairy Science
IS - 8
ER -