Kuehn L A, Rohrer G A, Nonneman D J, Thallman R M, Leymaster K A
USDA, ARS, US Meat Animal Research Center, Clay Center, NE 68933-0166, USA.
J Anim Sci. 2007 May;85(5):1111-9. doi: 10.2527/jas.2006-704. Epub 2007 Jan 30.
Multiple genomic scans have identified QTL for backfat deposition across the porcine genome. The objective of this study was to detect SNP and genomic regions associated with ultrasonic backfat. A total of 74 SNP across 5 chromosomes (SSC 1, 3, 7, 8, and 10) were selected based on their proximity to backfat QTL or to QTL for other traits of interest in the experimental population. Gilts were also genotyped for a SNP thought to influence backfat in the thyroxine-binding globulin gene (TBG) on SSC X. Genotypic data were collected on 298 gilts, divided between the F8 and F10 generations of the US Meat Animal Research Center Meishan resource population (composition, one-quarter Meishan). Backfat depths were recorded by ultrasound from 3 locations along the back at approximately 210 and 235 d of age in the F8 and F10 generations, respectively. Ultrasound measures were averaged for association analyses. Regressors for additive, dominant, and parent-of-origin effects of each SNP were calculated using genotypic probabilities computed by allelic peeling algorithms in GenoProb. The association model included the fixed effects of scan date and TBG genotype, the covariates of weight and SNP regressors, and random additive polygenic effects to account for genetic similarities between animals not explained by known genotypes. Variance components for polygenic effects and error were estimated using MTDFREML. Initially, each SNP was fitted (once with and once without parent-of-origin effects) separately due to potential multi-collinearity between regressions of closely linked markers. To form a final model, all significant SNP across chromosomes were included in a common model and were individually removed in successive iterations based on their significance. Across all analyses, TBG was significant, with an additive effect of approximately 1.2 to 1.6 mm of backfat. Three SNP on SSC3 remained in the final model even though few studies have identified QTL for backfat on this chromosome. Two of these SNP exhibited irregular parent-of-origin effects and may not have been detected in other genome scans. One significant SNP on SSC7 remained in the final, backward-selected model; the estimated effect of this marker was similar in magnitude and direction to previously identified QTL. This SNP can potentially be used to introgress the leaner Meishan allele into commercial swine populations.
多项基因组扫描已在猪基因组中鉴定出与背膘沉积相关的数量性状基因座(QTL)。本研究的目的是检测与超声背膘相关的单核苷酸多态性(SNP)和基因组区域。基于所选SNP与背膘QTL或与实验群体中其他感兴趣性状的QTL的接近程度,在5条染色体(猪1号、3号、7号、8号和10号染色体)上共选择了74个SNP。还对猪X染色体上甲状腺素结合球蛋白基因(TBG)中一个被认为影响背膘的SNP进行了基因分型。收集了美国肉类动物研究中心梅山资源群体(组成:四分之一梅山猪)F8和F10代298头后备母猪的基因型数据。分别在F8和F10代大约210日龄和235日龄时,通过超声记录沿背部3个位置的背膘深度。对超声测量值进行平均以进行关联分析。使用GenoProb中的等位基因剥离算法计算的基因型概率,计算每个SNP的加性、显性和印记效应的回归系数。关联模型包括扫描日期和TBG基因型的固定效应、体重和SNP回归系数的协变量,以及随机加性多基因效应,以解释已知基因型未解释的动物之间的遗传相似性。使用MTDFREML估计多基因效应和误差的方差分量。最初,由于紧密连锁标记的回归之间存在潜在的多重共线性,每个SNP分别进行拟合(一次包含印记效应,一次不包含印记效应)。为了形成最终模型,将所有染色体上显著的SNP纳入一个共同模型,并根据其显著性在连续迭代中逐个剔除。在所有分析中,TBG均显著,背膘的加性效应约为1.2至1.6毫米。尽管很少有研究在3号染色体上鉴定出背膘QTL,但3号染色体上的3个SNP仍保留在最终模型中。其中两个SNP表现出不规则的印记效应,可能在其他基因组扫描中未被检测到。7号染色体上的一个显著SNP保留在最终的向后选择模型中;该标记的估计效应在大小和方向上与先前鉴定的QTL相似。这个SNP有可能用于将更瘦的梅山等位基因导入商业猪群。