Abstract：
The aims of this study were (1) to identify genomic regions associated with a N efficiency index (NEI) and its composition traits and (2) to analyze the functional annotation of identified genomic regions. The NEI included N intake (NINT1), milk true protein N (MTPN1), milk urea N yield (MUNY1) in primiparous cattle, and N intake (NINT2+), milk true protein N (MTPN2+), and milk urea N yield (MUNY2+) in multiparous cattle (2 to 5 parities). The edited data included 1,043,171 records on 342,847 cows distributed in 1,931 herds. The pedigree consisted of 505,125 animals (17,797 males). Data of 565,049 SNPs were available for 6,998 animals included in the pedigree (5,251 females and 1,747 males). The SNP effects were estimated using a single-step genomic BLUP approach. The proportion of the total additive genetic variance explained by windows of 50 consecutive SNPs (with an average size of about 240 kb) was calculated. The top 3 genomic regions explaining the largest rate of the total additive genetic variance of the NEI and its composition traits were selected for candidate gene identification and quantitative trait loci (QTL) annotation. The selected genomic regions explained from 0.17% (MTPN2+) to 0.58% (NEI) of the total additive genetic variance. The largest explanatory genomic regions of NEI, NINT1, NINT2+, MTPN1, MTPN2+, MUNY1, and MUNY2+ were Bos taurus autosome 14 (1.52-2.09 Mb), 26 (9.24-9.66 Mb), 16 (75.41-75.51 Mb), 6 (8.73-88.92 Mb), 6 (8.73-88.92 Mb), 11 (103.26-103.41 Mb), 11 (103.26-103.41 Mb). Based on the literature, gene ontology, Kyoto Encyclopedia of Genes and Genomes, and protein-protein interaction, 16 key candidate genes were identified for NEI and its composition traits, which are mainly expressed in the milk cell, mammary, and liver tissues. The number of enriched QTL related to NEI, NINT1, NINT2+, MTPN1, and MTPN2+ were 41, 6, 4, 11, 36, 32, and 32, respectively, and most of them were related to the milk, health, and production classes. In conclusion, this study identified genomic regions associated with NEI and its composition traits, and identified key candidate genes describing the genetic mechanisms of N use efficiency-related traits. Furthermore, the NEI reflects not only its composition traits but also the interactions among them.
摘要：
这项研究的目的是（1）鉴定与N效率指数（NEI）及其组成性状相关的基因组区域，以及（2）分析已鉴定基因组区域的功能注释。NEI包括N摄入量(NINT1)，牛奶真蛋白N（MTPN1），初产牛的牛奶尿素N产量（MUNY1），和N摄入量(NINT2+)，牛奶真蛋白N（MTPN2+），多胎牛（2至5胎）的牛奶尿素N产量（MUNY2）。编辑的数据包括分布在1,931群中的342,847头母牛的1,043,171条记录。谱系由505,125只动物(17,797只雄性)组成。565,049个SNP的数据可用于包括在谱系中的6,998只动物(5,251只雌性和1,747只雄性)。使用单步基因组BLUP方法估计SNP效应。计算了由50个连续SNP（平均大小约为240kb）的窗口解释的总加性遗传变异的比例。选择解释NEI及其组成性状的总加性遗传变异率最大的前3个基因组区域进行候选基因鉴定和数量性状位点（QTL）注释。选定的基因组区域占总加性遗传变异的0.17％（MTPN2）至0.58％（NEI）。NEI最大的解释基因组区域，NINT1,NINT2+,MTPN1,MTPN2+,MUNY1和MUNY2是Bostaurusautosome14（1.52-2.09Mb），26（9.24-9.66Mb），16（75.41-75.51Mb），6（8.73-88.92Mb），6（8.73-88.92Mb），11（103.26-103.41Mb），11（103.26-103.41Mb）。根据文献,基因本体论，京都基因和基因组百科全书，和蛋白质-蛋白质相互作用，确定了16个关键候选基因的NEI及其组成性状，主要在牛奶细胞中表达，mammary,和肝脏组织。与NEI相关的丰富QTL的数量，NINT1,NINT2+,MTPN1和MTPN2+分别为41、6、4、11、36、32和32,其中大多数与牛奶有关，健康,和生产类。总之,这项研究确定了与NEI及其组成性状相关的基因组区域，并确定了描述N利用效率相关性状遗传机制的关键候选基因。此外，NEI不仅反映了其组成特征，还反映了它们之间的相互作用。
