Using expression data to fine map QTL associated with fertility in dairy cattle

IF 3.6 1区 农林科学 Q1 AGRICULTURE, DAIRY & ANIMAL SCIENCE
Irene van den Berg, Amanda J. Chamberlain, Iona M. MacLeod, Tuan V. Nguyen, Mike E. Goddard, Ruidong Xiang, Brett Mason, Susanne Meier, Claire V. C. Phyn, Chris R. Burke, Jennie E. Pryce
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Abstract

Female fertility is an important trait in dairy cattle. Identifying putative causal variants associated with fertility may help to improve the accuracy of genomic prediction of fertility. Combining expression data (eQTL) of genes, exons, gene splicing and allele specific expression is a promising approach to fine map QTL to get closer to the causal mutations. Another approach is to identify genomic differences between cows selected for high and low fertility and a selection experiment in New Zealand has created exactly this resource. Our objective was to combine multiple types of expression data, fertility traits and allele frequency in high- (POS) and low-fertility (NEG) cows with a genome-wide association study (GWAS) on calving interval in Australian cows to fine-map QTL associated with fertility in both Australia and New Zealand dairy cattle populations. Variants that were significantly associated with calving interval (CI) were strongly enriched for variants associated with gene, exon, gene splicing and allele-specific expression, indicating that there is substantial overlap between QTL associated with CI and eQTL. We identified 671 genes with significant differential expression between POS and NEG cows, with the largest fold change detected for the CCDC196 gene on chromosome 10. Our results provide numerous candidate genes associated with female fertility in dairy cattle, including GYS2 and TIGAR on chromosome 5 and SYT3 and HSD17B14 on chromosome 18. Multiple QTL regions were located in regions with large numbers of copy number variants (CNV). To identify the causal mutations for these variants, long read sequencing may be useful. Variants that were significantly associated with CI were highly enriched for eQTL. We detected 671 genes that were differentially expressed between POS and NEG cows. Several QTL detected for CI overlapped with eQTL, providing candidate genes for fertility in dairy cattle.
利用表达数据精细绘制与奶牛繁殖力相关的 QTL 图谱
雌性繁殖力是奶牛的一个重要性状。确定与繁殖力相关的假定因果变异可能有助于提高繁殖力基因组预测的准确性。结合基因、外显子、基因拼接和等位基因特异性表达的表达数据(eQTL)是一种很有前景的方法,可用于精细绘制 QTL 图谱,从而更接近因果变异。另一种方法是识别高繁殖力奶牛和低繁殖力奶牛之间的基因组差异,新西兰的一项选育实验正是创造了这种资源。我们的目标是将高繁殖力(POS)和低繁殖力(NEG)奶牛的多种表达数据、繁殖力性状和等位基因频率与澳大利亚奶牛产犊间隔的全基因组关联研究(GWAS)结合起来,精细绘制与澳大利亚和新西兰奶牛种群繁殖力相关的QTL图。与产犊间隔(CI)显著相关的变体强烈富集于与基因、外显子、基因剪接和等位基因特异性表达相关的变体,这表明与CI相关的QTL和eQTL之间存在大量重叠。我们确定了 671 个在 POS 和 NEG 奶牛之间有显著表达差异的基因,其中 10 号染色体上的 CCDC196 基因的折叠变化最大。我们的研究结果提供了许多与奶牛雌性繁殖力相关的候选基因,包括 5 号染色体上的 GYS2 和 TIGAR 以及 18 号染色体上的 SYT3 和 HSD17B14。多个 QTL 区域位于存在大量拷贝数变异(CNV)的区域。要确定这些变异的致病突变,长读数测序可能很有用。与 CI 显著相关的变异高度富集于 eQTL。我们检测到 671 个基因在 POS 和 NEG 奶牛之间有差异表达。检测到的几个CI QTL与eQTL重叠,为奶牛的繁殖力提供了候选基因。
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来源期刊
Genetics Selection Evolution
Genetics Selection Evolution 生物-奶制品与动物科学
CiteScore
6.50
自引率
9.80%
发文量
74
审稿时长
1 months
期刊介绍: Genetics Selection Evolution invites basic, applied and methodological content that will aid the current understanding and the utilization of genetic variability in domestic animal species. Although the focus is on domestic animal species, research on other species is invited if it contributes to the understanding of the use of genetic variability in domestic animals. Genetics Selection Evolution publishes results from all levels of study, from the gene to the quantitative trait, from the individual to the population, the breed or the species. Contributions concerning both the biological approach, from molecular genetics to quantitative genetics, as well as the mathematical approach, from population genetics to statistics, are welcome. Specific areas of interest include but are not limited to: gene and QTL identification, mapping and characterization, analysis of new phenotypes, high-throughput SNP data analysis, functional genomics, cytogenetics, genetic diversity of populations and breeds, genetic evaluation, applied and experimental selection, genomic selection, selection efficiency, and statistical methodology for the genetic analysis of phenotypes with quantitative and mixed inheritance.
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