Dissecting closely linked association signals in combination with the mammalian phenotype database can identify candidate genes in dairy cattle

TY - JOUR

T1 - Dissecting closely linked association signals in combination with the mammalian phenotype database can identify candidate genes in dairy cattle

AU - Cai, Zexi

AU - Guldbrandtsen, Bernt

AU - Lund, Mogens Sandø

AU - Sahana, Goutam

PY - 2019/1/29

Y1 - 2019/1/29

N2 - Background: Genome-wide association studies (GWAS) have been successfully implemented in cattle research and breeding. However, moving from the associations to identify the causal variants and reveal underlying mechanisms have proven complicated. In dairy cattle populations, we face a challenge due to long-range linkage disequilibrium (LD) arising from close familial relationships in the studied individuals. Long range LD makes it difficult to distinguish if one or multiple quantitative trait loci (QTL) are segregating in a genomic region showing association with a phenotype. We had two objectives in this study: 1) to distinguish between multiple QTL segregating in a genomic region, and 2) use of external information to prioritize candidate genes for a QTL along with the candidate variants. Results: We observed fixing the lead SNP as a covariate can help to distinguish additional close association signal(s). Thereafter, using the mammalian phenotype database, we successfully found candidate genes, in concordance with previous studies, demonstrating the power of this strategy. Secondly, we used variant annotation information to search for causative variants in our candidate genes. The variant information successfully identified known causal mutations and showed the potential to pinpoint the causative mutation(s) which are located in coding regions. Conclusions: Our approach can distinguish multiple QTL segregating on the same chromosome in a single analysis without manual input. Moreover, utilizing information from the mammalian phenotype database and variant effect predictor as post-GWAS analysis could benefit in candidate genes and causative mutations finding in cattle. Our study not only identified additional candidate genes for milk traits, but also can serve as a routine method for GWAS in dairy cattle.

AB - Background: Genome-wide association studies (GWAS) have been successfully implemented in cattle research and breeding. However, moving from the associations to identify the causal variants and reveal underlying mechanisms have proven complicated. In dairy cattle populations, we face a challenge due to long-range linkage disequilibrium (LD) arising from close familial relationships in the studied individuals. Long range LD makes it difficult to distinguish if one or multiple quantitative trait loci (QTL) are segregating in a genomic region showing association with a phenotype. We had two objectives in this study: 1) to distinguish between multiple QTL segregating in a genomic region, and 2) use of external information to prioritize candidate genes for a QTL along with the candidate variants. Results: We observed fixing the lead SNP as a covariate can help to distinguish additional close association signal(s). Thereafter, using the mammalian phenotype database, we successfully found candidate genes, in concordance with previous studies, demonstrating the power of this strategy. Secondly, we used variant annotation information to search for causative variants in our candidate genes. The variant information successfully identified known causal mutations and showed the potential to pinpoint the causative mutation(s) which are located in coding regions. Conclusions: Our approach can distinguish multiple QTL segregating on the same chromosome in a single analysis without manual input. Moreover, utilizing information from the mammalian phenotype database and variant effect predictor as post-GWAS analysis could benefit in candidate genes and causative mutations finding in cattle. Our study not only identified additional candidate genes for milk traits, but also can serve as a routine method for GWAS in dairy cattle.

KW - ABCG2 GENE

KW - COMPLEX TRAITS

KW - Candidate genes

KW - Closely linked association signals

KW - Dairy cattle

KW - GENOME-WIDE ASSOCIATION

KW - GENOTYPE IMPUTATION

KW - GWAS

KW - HOLSTEIN CATTLE

KW - MAMMARY-GLAND DEVELOPMENT

KW - MILK-YIELD

KW - MISSENSE MUTATION

KW - Milk traits

KW - SEQUENCE VARIANTS

KW - WHOLE

UR - http://www.scopus.com/inward/record.url?scp=85060687364&partnerID=8YFLogxK

U2 - 10.1186/s12863-019-0717-0

DO - 10.1186/s12863-019-0717-0

M3 - Journal article

C2 - 29751743

AN - SCOPUS:85060687364

SN - 1471-2156

VL - 20

JO - BMC Genetics

JF - BMC Genetics

M1 - 15

ER -