Wang Xiao, Kadarmideen Haja N
Quantitative Genomics, Bioinformatics and Computational Biology Group, Department of AppliedMathematics and Computer Science, Technical University of Denmark, Richard Petersens Plads, Building324, 2800 Kongens Lyngby, Denmark.
Metabolites. 2020 May 15;10(5):201. doi: 10.3390/metabo10050201.
Metabolites represent the ultimate response of biological systems, so metabolomics is considered the link between genotypes and phenotypes. Feed efficiency is one of the most important phenotypes in sustainable pig production and is the main breeding goal trait. We utilized metabolic and genomic datasets from a total of 108 pigs from our own previously published studies that involved 59 Duroc and 49 Landrace pigs with data on feed efficiency (residual feed intake (RFI)), genotype (PorcineSNP80 BeadChip) data, and metabolomic data (45 final metabolite datasets derived from LC-MS system). Utilizing these datasets, our main aim was to identify genetic variants (single-nucleotide polymorphisms (SNPs)) that affect 45 different metabolite concentrations in plasma collected at the start and end of the performance testing of pigs categorized as high or low in their feed efficiency (based on RFI values). Genome-wide significant genetic variants could be then used as potential genetic or biomarkers in breeding programs for feed efficiency. The other objective was to reveal the biochemical mechanisms underlying genetic variation for pigs' feed efficiency. In order to achieve these objectives, we firstly conducted a metabolite genome-wide association study (mGWAS) based on mixed linear models and found 152 genome-wide significant SNPs (-value < 1.06 × 10) in association with 17 metabolites that included 90 significant SNPs annotated to 52 genes. On chromosome one alone, 51 significant SNPs associated with isovalerylcarnitine and propionylcarnitine were found to be in strong linkage disequilibrium (LD). SNPs in strong LD annotated to , and consisted of two haplotype blocks where three SNPs (ALGA0004000, ALGA0004041, and ALGA0004042) were in the intron regions of and . The interaction network revealed that and were linked by the hub gene that was associated with isovalerylcarnitine and propionylcarnitine. Moreover, three metabolites (i.e., isovalerylcarnitine, propionylcarnitine, and pyruvic acid) were clustered in one group based on the low-high RFI pigs. This study performed a comprehensive metabolite-based genome-wide association study (GWAS) analysis for pigs with differences in feed efficiency and provided significant metabolites for which there is significant genetic variation as well as biological interaction networks. The identified metabolite genetic variants, genes, and networks in high versus low feed efficient pigs could be considered as potential genetic or biomarkers for feed efficiency.
代谢物代表了生物系统的最终反应，因此代谢组学被认为是基因型和表型之间的纽带。饲料效率是可持续养猪生产中最重要的表型之一，也是主要的育种目标性状。我们利用了来自我们之前发表的研究中的总共108头猪的代谢和基因组数据集，这些研究涉及59头杜洛克猪和49头长白猪，包含饲料效率（剩余采食量（RFI））、基因型（猪SNP80芯片）数据以及代谢组学数据（45个源自液相色谱-质谱系统的最终代谢物数据集）。利用这些数据集，我们的主要目的是识别影响在性能测试开始和结束时采集的血浆中45种不同代谢物浓度的遗传变异（单核苷酸多态性（SNP）），这些猪根据饲料效率（基于RFI值）被分类为高或低。全基因组显著的遗传变异随后可作为饲料效率育种计划中的潜在遗传或生物标志物。另一个目标是揭示猪饲料效率遗传变异背后的生化机制。为了实现这些目标，我们首先基于混合线性模型进行了代谢物全基因组关联研究（mGWAS），并发现了152个全基因组显著的SNP（-值<1.06×10）与17种代谢物相关，其中包括注释到52个基因的90个显著SNP。仅在1号染色体上，就发现与异戊酰肉碱和丙酰肉碱相关的51个显著SNP处于强连锁不平衡（LD）状态。注释到、和的处于强LD状态的SNP由两个单倍型块组成，其中三个SNP（ALGA0004000、ALGA0004041和ALGA0004042）位于和的内含子区域。相互作用网络显示和通过与异戊酰肉碱和丙酰肉碱相关的枢纽基因相连。此外，基于低-高RFI猪，三种代谢物（即异戊酰肉碱、丙酰肉碱和丙酮酸）聚集在一组。本研究对饲料效率存在差异的猪进行了基于代谢物的全面全基因组关联研究（GWAS）分析，并提供了存在显著遗传变异的重要代谢物以及生物相互作用网络。在高饲料效率猪与低饲料效率猪中鉴定出的代谢物遗传变异、基因和网络可被视为饲料效率的潜在遗传或生物标志物。
