Li Mingyao, Boehnke Michael, Abecasis Gonçalo R, Song Peter X-K
Department of Biostatistics and Epidemiology, University of Pennsylvania School of Medicine, Philadelphia 19104, USA.
Genetics. 2006 Aug;173(4):2317-27. doi: 10.1534/genetics.105.054650. Epub 2006 Jun 4.
Mapping and identifying variants that influence quantitative traits is an important problem for genetic studies. Traditional QTL mapping relies on a variance-components (VC) approach with the key assumption that the trait values in a family follow a multivariate normal distribution. Violation of this assumption can lead to inflated type I error, reduced power, and biased parameter estimates. To accommodate nonnormally distributed data, we developed and implemented a modified VC method, which we call the "copula VC method," that directly models the nonnormal distribution using Gaussian copulas. The copula VC method allows the analysis of continuous, discrete, and censored trait data, and the standard VC method is a special case when the data are distributed as multivariate normal. Through the use of link functions, the copula VC method can easily incorporate covariates. We use computer simulations to show that the proposed method yields unbiased parameter estimates, correct type I error rates, and improved power for testing linkage with a variety of nonnormal traits as compared with the standard VC and the regression-based methods.
对影响数量性状的变异进行定位和鉴定是遗传学研究中的一个重要问题。传统的数量性状基因座（QTL）定位依赖于方差成分（VC）方法，其关键假设是一个家系中的性状值遵循多元正态分布。违反这一假设可能导致第一类错误率膨胀、检验效能降低以及参数估计有偏差。为了适应非正态分布的数据，我们开发并实施了一种改进的VC方法，我们称之为“copula VC方法”，该方法使用高斯copula直接对非正态分布进行建模。copula VC方法允许分析连续、离散和删失的性状数据，并且当数据呈多元正态分布时，标准VC方法是其一种特殊情况。通过使用连接函数，copula VC方法可以轻松纳入协变量。我们通过计算机模拟表明，与标准VC方法和基于回归的方法相比，所提出的方法能产生无偏参数估计、正确的第一类错误率，并且在检验与各种非正态性状的连锁关系时具有更高的检验效能。
