Ponsuksili Siriluck, Reyer Henry, Trakooljul Nares, Murani Eduard, Wimmers Klaus
Research Unit 'Functional Genome Analyses', Institute for Genome Biology, Leibniz Institute for Farm Animal Biology (FBN), Wilhelm-Stahl-Allee 2, D-18196, Dummerstorf, Germany.
Research Unit 'Genomics', Institute for Genome Biology, Leibniz Institute for Farm Animal Biology (FBN), Wilhelm-Stahl-Allee 2, D-18196, Dummerstorf, Germany.
PLoS One. 2016 Jul 19;11(7):e0159212. doi: 10.1371/journal.pone.0159212. eCollection 2016.
Haematological traits are important traits that show associations with immune and metabolic status, as well as diseases in humans and animals. Mapping genome regions that affect the blood cell traits can contribute to the identification of genomic features useable as biomarkers for immune, disease and metabolic status. A genome-wide association study (GWAS) was conducted using PorcineSNP60 BeadChips. Single-marker and Bayesian multi-marker approaches were integrated to identify genomic regions and corresponding genes overlapping for both methods. GWAS was performed for haematological traits of 591 German Landrace pig. Heritability estimates for haematological traits were medium to high. In total 252 single SNPs associated with 12 haematological traits were identified (NegLog10 of p-value > 5). The Bayesian multi-marker approach revealed 102 QTL regions across the genome, indicated by 1-Mb windows with contribution to additive genetic variance above 0.5%. The integration of both methods resulted in 24 overlapping QTL regions. This study identified overlapping QTL regions from single- and multi-marker approaches for haematological traits. Identifying candidate genes that affect blood cell traits provides the first step towards the understanding of the molecular basis of haematological phenotypes.
血液学性状是重要的性状,与人类和动物的免疫及代谢状态以及疾病相关。绘制影响血细胞性状的基因组区域有助于识别可用作免疫、疾病和代谢状态生物标志物的基因组特征。使用猪60K SNP芯片进行了全基因组关联研究(GWAS)。整合了单标记和贝叶斯多标记方法,以识别两种方法重叠的基因组区域和相应基因。对591头德国长白猪的血液学性状进行了GWAS。血液学性状的遗传力估计值为中到高。共鉴定出与12种血液学性状相关的252个单核苷酸多态性(SNP)(p值的负对数>5)。贝叶斯多标记方法揭示了全基因组102个数量性状位点(QTL)区域,以1兆碱基窗口表示,对加性遗传方差的贡献超过0.5%。两种方法的整合产生了24个重叠的QTL区域。本研究从单标记和多标记方法中鉴定出了血液学性状重叠的QTL区域。识别影响血细胞性状的候选基因是理解血液学表型分子基础的第一步。