Methane emission by ruminants has contributed considerably to the global warming and understanding the genomic architecture of methane production may help livestock producers to reduce the methane emission from the livestock production system. The goal of our study was to identify genomic regions affecting the predicted methane emission (PME) from volatile fatty acids (VFAs) indicators and VFA traits using imputed whole-genome sequence data in Iranian Holstein cattle. Based on the significant-association threshold (p < 5 × 10− 8), 33 single nucleotide polymorphisms (SNPs) were detected for PME per kg milk (n = 2), PME per kg fat (n = 14), and valeric acid (n = 17). Besides, 69 genes were identified for valeric acid (n = 18), PME per kg milk (n = 4) and PME per kg fat (n = 47) that were located within 1 Mb of significant SNPs. Based on the gene ontology (GO) term analysis, six promising candidate genes were significantly clustered in organelle organization (GO:0004984, p = 3.9 × 10− 2) for valeric acid, and 17 candidate genes significantly clustered in olfactory receptors activity (GO:0004984, p = 4 × 10− 10) for PME traits. Annotation results revealed 31 quantitative trait loci (QTLs) for milk yield and its components, body weight, and residual feed intake within 1 Mb of significant SNPs. Our results identified 33 SNPs associated with PME and valeric acid traits, as well as 17 olfactory receptors activity genes for PME traits related to feed intake and preference. Identified SNPs were close to 31 QTLs for milk yield and its components, body weight, and residual feed intake traits. In addition, these traits had high correlations with PME trait. Overall, our findings suggest that marker-assisted and genomic selection could be used to improve the difficult and expensive-to-measure phenotypes such as PME. Moreover, prediction of methane emission by VFA indicators could be useful for increasing the size of reference population required in genome-wide association studies and genomic selection.

中文翻译：

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使用荷斯坦牛序列数据的甲烷排放和瘤胃挥发性脂肪酸的全基因组关联研究

反刍动物的甲烷排放量对全球变暖做出了很大贡献，并且了解甲烷生产的基因组结构可以帮助畜牧生产者减少畜牧生产系统中的甲烷排放。我们研究的目的是使用推算的伊朗荷斯坦牛全基因组序列数据，从挥发性脂肪酸（VFA）指标和VFA性状中确定影响预测甲烷排放（PME）的基因组区域。根据显着关联阈值（p <5×10-8），每公斤牛奶中的PME（n = 2），每公斤脂肪中的PME（n = 14）和戊酸检测到33个单核苷酸多态性（SNP）。 （n = 17）。此外，还确定了位于重要SNPs 1 Mb之内的戊酸（n = 18），每公斤牛奶PME（n = 4）和每公斤脂肪PME（n = 47）的69个基因。根据基因本体（GO）术语分析，​​六个有希望的候选基因在戊二酸的细胞器组织中显着聚集（GO：0004984，p = 3.9×10-2），而17个候选基因在嗅觉受体活性（GO）中明显聚集：0004984，p = 4×10-10）（对于PME特性）。注释结果显示了31个定量性状位点（QTL），用于牛奶产量，其组成，体重和1 Mb内的重要SNP内的剩余饲料摄入量。我们的结果确定了与PME和戊酸性状相关的33个SNP，以及与饲料摄入和偏好有关的PME性状的17个嗅觉受体活性基因。牛奶产量，其组成，体重和残留饲料摄入性状的鉴定出的SNP接近31个QTL。另外，这些性状与PME性状高度相关。总体，我们的发现表明，标记物辅助和基因组选择可用于改善难于测量且昂贵的表型，例如PME。此外，通过VFA指标预测甲烷排放量可能有助于增加全基因组关联研究和基因组选择所需的参考群体的规模。