Department of Botany and Plant Sciences, University of California, Riverside, CA 92521, USA.
Department of Agronomy, Iowa State University, Ames, IW 50011, USA.
Genetics. 2021 Nov 5;219(3). doi: 10.1093/genetics/iyab115.
The Beavis effect in quantitative trait locus (QTL) mapping describes a phenomenon that the estimated effect size of a statistically significant QTL (measured by the QTL variance) is greater than the true effect size of the QTL if the sample size is not sufficiently large. This is a typical example of the Winners' curse applied to molecular quantitative genetics. Theoretical evaluation and correction for the Winners' curse have been studied for interval mapping. However, similar technologies have not been available for current models of QTL mapping and genome-wide association studies where a polygene is often included in the linear mixed models to control the genetic background effect. In this study, we developed the theory of the Beavis effect in a linear mixed model using a truncated noncentral Chi-square distribution. We equated the observed Wald test statistic of a significant QTL to the expectation of a truncated noncentral Chi-square distribution to obtain a bias-corrected estimate of the QTL variance. The results are validated from replicated Monte Carlo simulation experiments. We applied the new method to the grain width (GW) trait of a rice population consisting of 524 homozygous varieties with over 300 k single nucleotide polymorphism markers. Two loci were identified and the estimated QTL heritability were corrected for the Beavis effect. Bias correction for the larger QTL on chromosome 5 (GW5) with an estimated heritability of 12% did not change the QTL heritability due to the extremely large test score and estimated QTL effect. The smaller QTL on chromosome 9 (GW9) had an estimated QTL heritability of 9% reduced to 6% after the bias-correction.
Beavis 效应在数量性状基因座（QTL）作图中描述了一种现象，如果样本量不够大，统计上显著的 QTL（通过 QTL 方差衡量）的估计效应大小大于 QTL 的真实效应大小。这是分子数量遗传学中“赢家诅咒”的典型例子。已经对区间作图的“赢家诅咒”进行了理论评估和校正。然而，类似的技术尚未应用于当前的 QTL 作图模型和全基因组关联研究，其中多基因通常包含在线性混合模型中，以控制遗传背景效应。在这项研究中，我们使用截断非中心卡方分布在线性混合模型中发展了 Beavis 效应理论。我们将显著 QTL 的观察 Wald 检验统计量与截断非中心卡方分布的期望相等，以获得 QTL 方差的偏置校正估计。结果通过复制蒙特卡罗模拟实验进行了验证。我们将新方法应用于由 524 个纯合品种组成的水稻群体的粒宽（GW）性状，该群体包含超过 300k 个单核苷酸多态性标记。鉴定了两个位点，并校正了 Beavis 效应的估计 QTL 遗传力。对染色体 5（GW5）上较大的 QTL（估计遗传力为 12%）进行偏置校正并没有改变 QTL 遗传力，因为测试得分和估计的 QTL 效应非常大。染色体 9（GW9）上较小的 QTL 的估计 QTL 遗传力从校正前的 9%降低到校正后的 6%。
