Dataset Information

Gene-based mapping and pathway analysis of metabolic traits in dairy cows.

ABSTRACT: The metabolic adaptation of dairy cows during the transition period has been studied intensively in the last decades. However, until now, only few studies have paid attention to the genetic aspects of this process. Here, we present the results of a gene-based mapping and pathway analysis with the measurements of three key metabolites, (1) non-esterified fatty acids (NEFA), (2) beta-hydroxybutyrate (BHBA) and (3) glucose, characterizing the metabolic adaptability of dairy cows before and after calving. In contrast to the conventional single-marker approach, we identify 99 significant and biologically sensible genes associated with at least one of the considered phenotypes and thus giving evidence for a genetic basis of the metabolic adaptability. Moreover, our results strongly suggest three pathways involved in the metabolism of steroids and lipids are potential candidates for the adaptive regulation of dairy cows in their early lactation. From our perspective, a closer investigation of our findings will lead to a step forward in understanding the variability in the metabolic adaptability of dairy cows in their early lactation.

SUBMITTER: Ha NT

PROVIDER: S-EPMC4366076 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Gene-based mapping and pathway analysis of metabolic traits in dairy cows.

Ha Ngoc-Thuy NT Gross Josef Johann JJ van Dorland Annette A Tetens Jens J Thaller Georg G Schlather Martin M Bruckmaier Rupert R Simianer Henner H

PloS one 20150319 3

The metabolic adaptation of dairy cows during the transition period has been studied intensively in the last decades. However, until now, only few studies have paid attention to the genetic aspects of this process. Here, we present the results of a gene-based mapping and pathway analysis with the measurements of three key metabolites, (1) non-esterified fatty acids (NEFA), (2) beta-hydroxybutyrate (BHBA) and (3) glucose, characterizing the metabolic adaptability of dairy cows before and after ca ...[more]

PMID: 25789767

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Project description:BACKGROUND:An unfavorable genetic correlation between milk production and fertility makes simultaneous improvement of milk production and fertility difficult in cattle breeding. Rapid genetic improvement in milk production traits in dairy cattle has been accompanied by decline in cow fertility. The genetic basis of this correlation remains poorly understood. Expanded reference populations and large sets of sequenced animals make genome-wide association studies (GWAS) with imputed markers possible for large populations and thereby studying genetic architecture of complex traits. RESULTS:In this study, we associated 15,551,021 SNPs with female fertility index in 5038 Nordic Holstein cattle. We have identified seven quantitative trait loci (QTL) on six chromosomes in cattle. Along with nearest genes to GWAS hits, we used gene-based analysis and spread of linkage disequilibrium (LD) information to generate a list of potential candidate genes affecting fertility in cattle. Subsequently, we used prior knowledge on gene related to fertility from Gene Ontology terms, Kyoto Encyclopedia of Genes and Genomes pathway analysis, mammalian phenotype database, and public available RNA-seq data to refine the list of candidate genes for fertility. We used variant annotations to investigate candidate mutations within the prioritized candidate genes. Using multiple source of information, we proposed candidate genes with biological relevance underlying each of these seven QTL. On chromosome 1, we have identified ten candidate genes for two QTL. For the rest of chromosomes, we proposed one candidate gene for each QTL. In the candidate genes list, differentially expressed genes from different studies support FRAS1, ITGB5, ADCY5, and SEMA5B as candidate genes for cow fertility. CONCLUSION:The GWAS result not only confirmed previously mapped QTL, but also made new findings. Our findings contributes towards dissecting the genetics for female fertility in cattle. Moreover, this study shows the usefulness of adding independent information to pick candidate genes during post-GWAS analysis.

Project description:BackgroundFor years, multiple trait genetic evaluations have been used to increase the accuracy of estimated breeding values (EBV) using information from correlated traits. In France, accurate approximations of multiple trait evaluations were implemented for traits that are described by different models by combining the results of univariate best linear unbiased prediction (BLUP) evaluations. Functional longevity (FL) is the trait that has most benefited from this approach. Currently, with many single-step (SS) evaluations, only univariate FL evaluations can be run. The aim of this study was to implement a "combined" SS (CSS) evaluation that extends the "combined" BLUP evaluation to obtain more accurate genomic (G) EBV for FL when information from five correlated traits (somatic cell score, clinical mastitis, conception rate for heifers and cows, and udder depth) is added.ResultsGEBV obtained from univariate SS (USS) evaluations and from a CSS evaluation were compared. The correlations between these GEBV showed the benefits of including information from correlated traits. Indeed, a CSS evaluation run without any performances on FL showed that the indirect information from correlated traits to evaluate FL was substantial. USS and CSS evaluations that mimic SS evaluations with data available in 2016 were compared. For each evaluation separately, the GEBV were sorted and then split into 10 consecutive groups (deciles). Survival curves were calculated for each group, based on the observed productive life of these cows as known in 2021. Regardless of their genotyping status, the worst group of heifers based on their GEBV in 2016 was well identified in the CSS evaluation and they had a substantially shorter herd life, while those in the best heifer group had a longer herd life. The gaps between groups were more important for the genotyped than the ungenotyped heifers, which indicates better prediction of future survival.ConclusionsA CSS evaluation is an efficient tool to improve FL. It allows a proper combination of information on functional traits that influence culling. In contrast, because of the strong selection intensity on young bulls for functional traits, the benefit of such a "combined" evaluation of functional traits is more modest for these males.

Dataset Information

Gene-based mapping and pathway analysis of metabolic traits in dairy cows.

Publications

Gene-based mapping and pathway analysis of metabolic traits in dairy cows.

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