Kim Jong-Joo, Zhao Honghua, Thomsen Hauke, Rothschild Max F, Dekkers Jack C M
Department of Animal Science, Center for Integrated Animal Genomics, Iowa State University, Ames, IA 50011, USA.
Genet Res. 2005 Jun;85(3):235-48. doi: 10.1017/S0016672305007597.
Data from an F 2 cross between breeds of livestock are typically analysed by least squares line-cross or half-sib models to detect quantitative trait loci (QTL) that differ between or segregate within breeds. These models can also be combined to increase power to detect QTL, while maintaining the computational efficiency of least squares. Tests between models allow QTL to be characterized into those that are fixed (LC QTL), or segregating at similar (HS QTL) or different (CB QTL) frequencies in parental breeds. To evaluate power of the combined model, data wih various differences in QTL allele frequencies (FD) between parental breeds were simulated. Use of all models increased power to detect QTL. The line-cross model was the most powerful model to detect QTL for FD>0.6. The combined and half-sib models had similar power for FD<0.4. The proportion of detected QTL declared as LC QTL decreased with FD. The opposite was observed for HS QTL. The proportion of CB QTL decreased as FD deviated from 0.5. Accuracy of map position tended to be greatest for CB QTL. Models were applied to a cross of Berkshire and Yorkshire pig breeds and revealed 160 (40) QTL at the 5% chromosome (genome)-wise level for the 39 growth, carcass composition and quality traits, of which 72, 54, and 34 were declared as LC, HS and CB QTL. Fourteen CB QTL were detected only by the combined model. Thus, the combined model can increase power to detect QTL and mapping accuracy and enable characterization of QTL that segregate within breeds.
来自家畜品种间F2杂交的数据通常通过最小二乘线交或半同胞模型进行分析,以检测品种间存在差异或在品种内分离的数量性状基因座(QTL)。这些模型也可以结合起来,在保持最小二乘计算效率的同时,提高检测QTL的能力。模型间的检验可将QTL分为在亲本品种中固定的(LC QTL)、以相似(HS QTL)或不同(CB QTL)频率分离的QTL。为了评估组合模型的能力,模拟了亲本品种间QTL等位基因频率(FD)存在各种差异的数据。使用所有模型都提高了检测QTL的能力。对于FD>0.6,线交模型是检测QTL最有效的模型。对于FD<0.4,组合模型和半同胞模型的能力相似。被判定为LC QTL的检测到的QTL比例随FD降低。HS QTL则相反。随着FD偏离0.5,CB QTL的比例降低。CB QTL的图谱位置准确性往往最高。将这些模型应用于伯克希尔猪和约克夏猪品种的杂交,在5%染色体(基因组)水平上,针对39个生长、胴体组成和品质性状揭示了160个(40个)QTL,其中72个、54个和34个分别被判定为LC、HS和CB QTL。14个CB QTL仅通过组合模型检测到。因此,组合模型可以提高检测QTL的能力和图谱准确性,并能够对品种内分离的QTL进行特征描述。