Bioinformatics Laboratory, Department of Statistics, University of Rajshahi, Rajshahi, 6205, Bangladesh.
Division of Neonatology, Department of Pediatrics, John H. Stroger, Jr. Hospital of Cook County, 1969 Ogden Avenue, Chicago, IL, 60612, USA.
Mol Genet Genomics. 2021 Sep;296(5):1103-1119. doi: 10.1007/s00438-021-01801-1. Epub 2021 Jun 25.
In genome-wide quantitative trait locus (QTL) mapping studies, multiple quantitative traits are often measured along with the marker genotypes. Multi-trait QTL (MtQTL) analysis, which includes multiple quantitative traits together in a single model, is an efficient technique to increase the power of QTL identification. The two most widely used classical approaches for MtQTL mapping are Gaussian Mixture Model-based MtQTL (GMM-MtQTL) and Linear Regression Model-based MtQTL (LRM-MtQTL) analyses. There are two types of LRM-MtQTL approach known as least squares-based LRM-MtQTL (LS-LRM-MtQTL) and maximum likelihood-based LRM-MtQTL (ML-LRM-MtQTL). These three classical approaches are equivalent alternatives for QTL detection, but ML-LRM-MtQTL is computationally faster than GMM-MtQTL and LS-LRM-MtQTL. However, one major limitation common to all the above classical approaches is that they are very sensitive to outliers, which leads to misleading results. Therefore, in this study, we developed an LRM-based robust MtQTL approach, called LRM-RobMtQTL, for the backcross population based on the robust estimation of regression parameters by maximizing the β-likelihood function induced from the β-divergence with multivariate normal distribution. When β = 0, the proposed LRM-RobMtQTL method reduces to the classical ML-LRM-MtQTL approach. Simulation studies showed that both ML-LRM-MtQTL and LRM-RobMtQTL methods identified the same QTL positions in the absence of outliers. However, in the presence of outliers, only the proposed method was able to identify all the true QTL positions. Real data analysis results revealed that in the presence of outliers only our LRM-RobMtQTL approach can identify all the QTL positions as those identified in the absence of outliers by both methods. We conclude that our proposed LRM-RobMtQTL analysis approach outperforms the classical MtQTL analysis methods.
在全基因组数量性状基因座（QTL）定位研究中，通常会同时测量多个数量性状以及标记基因型。多性状 QTL（MtQTL）分析将多个数量性状一起纳入单个模型中，是一种提高 QTL 鉴定效率的有效技术。MtQTL 映射的两种最广泛使用的经典方法是基于高斯混合模型的 MtQTL（GMM-MtQTL）和基于线性回归模型的 MtQTL（LRM-MtQTL）分析。基于线性回归模型的 MtQTL 方法有两种，分别是基于最小二乘的 LRM-MtQTL（LS-LRM-MtQTL）和基于最大似然的 LRM-MtQTL（ML-LRM-MtQTL）。这三种经典方法在 QTL 检测方面是等效的替代方法，但 ML-LRM-MtQTL 比 GMM-MtQTL 和 LS-LRM-MtQTL 计算速度更快。然而，所有上述经典方法都存在一个共同的主要局限性，即它们对异常值非常敏感，这会导致误导性的结果。因此，在这项研究中，我们基于回归参数的稳健估计，通过最大化β-散度诱导的β-似然函数，针对基于回交群体的 LRM 开发了一种稳健的 MtQTL 方法，称为 LRM-RobMtQTL。当β=0 时，所提出的 LRM-RobMtQTL 方法简化为经典的 ML-LRM-MtQTL 方法。模拟研究表明，在不存在异常值的情况下，ML-LRM-MtQTL 和 LRM-RobMtQTL 方法都能识别出相同的 QTL 位置。然而，在存在异常值的情况下，只有所提出的方法才能识别出所有真实的 QTL 位置。实际数据分析结果表明，在存在异常值的情况下，只有我们的 LRM-RobMtQTL 方法能够识别出所有的 QTL 位置，这些位置与两种方法在不存在异常值的情况下识别出的位置相同。我们得出结论，我们提出的 LRM-RobMtQTL 分析方法优于经典的 MtQTL 分析方法。
