Animal Genomics and Bioinformatics Division, National Institute of Animal Science, Rural Development Administration, Gwonsun-gu, Suwon, Korea 441-706.
J Anim Sci. 2012 Apr;90(4):1081-7. doi: 10.2527/jas.2011-4228. Epub 2011 Oct 7.
This study was carried out to identify SNP associated with fatness traits on pig chromosome 6. In total, 11,067 putative genomic variations were detected in 125 complete bacterial artificial chromosome sequences corresponding to the region between SW2098 and SW1881, which harbors multiple QTL affecting intramuscular fat content (IMF) and backfat thickness (BFT). Among 173 putative SNP validated by MassArray, 120 SNP were used in an association study on 541 offspring produced by a cross of Korean native pig and Landrace breeds. The significance level of each SNP was determined using single marker regression analysis. Further, significant threshold values were determined using a false discovery rate. Nine out of 120 SNP showed significant effects on BFT or IMF or both. Of the 9 significant SNP, 4 were significantly associated with IMF, 7 were significantly related to BFT, and 2 SNP (Kps8172 and Kps6413) showed significant effects on both traits. Moreover, multiple regression analysis considering all significant SNP was used to correct spurious false positives due to linkage disequilibrium. Consequently, only 1 SNP (Kps6413) was significant for IMF, whereas 4 SNP including Kps6413 showed significant effects on BFT. The significant SNP had generally additive effects and on average explained 1.72% of the genetic variation for IMF and 3.92% for BFT, respectively. These markers can potentially be applied in pig breeding programs for improving IMF and BFT traits after validation in other populations.
本研究旨在鉴定与猪 6 号染色体上肥胖性状相关的 SNP。在总共 125 个完全细菌人工染色体序列中,检测到了 11067 个推定的基因组变异,这些序列对应于 SW2098 和 SW1881 之间的区域,该区域包含多个影响肌内脂肪含量(IMF)和背膘厚(BFT)的 QTL。在通过 MassArray 验证的 173 个推定 SNP 中,有 120 个 SNP 用于韩国本地猪和长白猪杂交后代的 541 头后代的关联研究。使用单标记回归分析确定每个 SNP 的显著性水平。此外,使用错误发现率确定显著阈值值。在 120 个 SNP 中,有 9 个对 BFT 或 IMF 或两者都有显著影响。在这 9 个显著 SNP 中,有 4 个与 IMF 显著相关,7 个与 BFT 显著相关,2 个 SNP(Kps8172 和 Kps6413)与这两个性状都有显著影响。此外,考虑到所有显著 SNP 的多元回归分析用于校正由于连锁不平衡而产生的虚假阳性。结果,只有 1 个 SNP(Kps6413)对 IMF 显著,而包括 Kps6413 在内的 4 个 SNP 对 BFT 有显著影响。显著 SNP 具有一般的加性效应,平均分别解释 IMF 遗传变异的 1.72%和 BFT 的 3.92%。这些标记在经过其他群体验证后,可应用于猪育种计划,以改善 IMF 和 BFT 性状。