Hadjipavlou Georgia, Hemani Gib, Leach Richard, Louro Bruno, Nadaf Javad, Rowe Suzanne, de Koning Dirk-Jan
Division of Genetics and Genomics, Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Roslin, Midlothian, EH25 9PS, UK.
BMC Proc. 2010 Mar 31;4 Suppl 1(Suppl 1):S11. doi: 10.1186/1753-6561-4-s1-s11.
We applied a range of genome-wide association (GWA) methods to map quantitative trait loci (QTL) in the simulated dataset provided by the QTLMAS2009 workshop to derive a comprehensive set of results. A Gompertz curve was modelled on the yield data and showed good predictive properties. QTL analyses were done on the raw measurements and on the individual parameters of the Gompertz curve and its predicted growth for each interval. Half-sib and variance component linkage analysis revealed QTL with different modes of inheritance but with low resolution. This was complemented by association studies using single markers or haplotypes, and additive, dominance, parent-of-origin and epistatic QTL effects. All association analyses were done on phenotypes pre-corrected for pedigree effects. These methods detected QTL positions with high concordance to each other and with greater refinement of the linkage signals. Two-locus interaction analysis detected no epistatic pairs of QTL. Overall, using stringent thresholds we identified QTL regions using linkage analyses, corroborated by 6 individual SNPs with significant effects as well as two putatively imprinted SNPs.
We obtained consistent results across a combination of intra- and inter- family based methods using flexible linear models to evaluate a variety of models. The Gompertz curve fitted the data really well, and provided complementary information on the detected QTL. Retrospective comparisons of the results with actual data simulated showed that best results were obtained by including both yield and the parameters from the Gompertz curve despite the data being simulated using a logistic function.
我们应用了一系列全基因组关联（GWA）方法，对QTLMAS2009研讨会提供的模拟数据集中的数量性状基因座（QTL）进行定位，以得出一套全面的结果。对产量数据建立了Gompertz曲线模型，该模型显示出良好的预测特性。对原始测量数据、Gompertz曲线的各个参数及其每个区间的预测生长情况进行了QTL分析。半同胞和方差成分连锁分析揭示了具有不同遗传模式但分辨率较低的QTL。使用单标记或单倍型以及加性、显性、亲本来源和上位性QTL效应的关联研究对其进行了补充。所有关联分析均针对经谱系效应校正的表型进行。这些方法检测到的QTL位置彼此高度一致，并且对连锁信号有更精细的定位。两位点相互作用分析未检测到QTL的上位性对。总体而言，使用严格的阈值，我们通过连锁分析确定了QTL区域，并得到了6个具有显著效应的个体单核苷酸多态性（SNP）以及两个推定的印记SNP的证实。
我们使用灵活的线性模型评估各种模型，通过基于家系内和家系间的方法组合获得了一致的结果。Gompertz曲线对数据拟合得非常好，并提供了有关检测到的QTL的补充信息。将结果与实际模拟数据进行回顾性比较表明，尽管数据是使用逻辑函数模拟的，但同时纳入产量和Gompertz曲线的参数可获得最佳结果。
