Department of Genetics and Biotechnology, Faculty of Agricultural Sciences, University of Aarhus, Tjele, Denmark.
J Anim Breed Genet. 2009 Dec;126(6):443-54. doi: 10.1111/j.1439-0388.2009.00813.x.
In fine mapping of a large-scale experimental population where collection of phenotypes are very expensive, difficult to record or time-demanding, selective phenotyping could be used to phenotype the most informative individuals. Linkage analyses based sampling criteria (LAC) and linkage disequilibrium-based sampling criteria (LDC) for selecting individuals to phenotype are compared to random phenotyping in a quantitative trait loci (QTL) verification experiment using stochastic simulation. Several strategies based on LAC and LDC for selecting the most informative 30%, 40% or 50% of individuals for phenotyping to extract maximum power and precision in a QTL fine mapping experiment were developed and assessed. Linkage analyses for the mapping was performed for individuals sampled on LAC within families and combined linkage disequilibrium and linkage analyses was performed for individuals sampled across the whole population based on LDC. The results showed that selecting individuals with similar haplotypes to the paternal haplotypes (minimum recombination criterion) using LAC compared to random phenotyping gave at least the same power to detect a QTL but decreased the accuracy of the QTL position. However, in order to estimate unbiased QTL parameters based on LAC in a large half-sib family, prior information on QTL position was required. The LDC improved the accuracy to estimate the QTL position but not significantly compared to random phenotyping with the same sample size. When applying LDC (all phenotyping levels), the estimated QTL effect were closer to the true value in comparison to LAC. The results showed that the LDC were better than the LAC to select individuals for phenotyping and contributed to detection of the QTL.
在大规模实验群体的精细定位中，如果表型收集非常昂贵、难以记录或耗时，那么可以选择表型分析来对最具信息量的个体进行表型分析。基于连锁分析的抽样标准（LAC）和基于连锁不平衡的抽样标准（LDC）用于选择个体进行表型分析，并与随机表型分析在使用随机模拟的数量性状基因座（QTL）验证实验中进行比较。基于 LAC 和 LDC 开发了几种选择最具信息量的 30%、40%或 50%个体进行表型分析的策略，以在 QTL 精细定位实验中提取最大的功率和精度。在基于 LAC 的家族内个体的连锁分析和基于 LDC 的整个群体的个体连锁和连锁不平衡分析进行了图谱绘制的连锁分析。结果表明，与随机表型分析相比，使用 LAC 选择与父系单倍型相似的个体（最小重组标准）至少具有相同的检测 QTL 的能力，但降低了 QTL 位置的准确性。然而，为了在大型半同胞群体中基于 LAC 估计无偏 QTL 参数，需要 QTL 位置的先验信息。LDC 提高了估计 QTL 位置的准确性，但与相同样本量的随机表型分析相比，并没有显著提高。当应用 LDC（所有表型水平）时，与 LAC 相比，估计的 QTL 效应更接近真实值。结果表明，LDC 比 LAC 更适合选择个体进行表型分析，并有助于检测 QTL。
