Macgregor Stuart, Knott Sara A, White Ian, Visscher Peter M
Institute of Evolutionary Biology, University of Edinburgh, Edinburgh EH9 3JT, United Kingdom.
Genetics. 2005 Nov;171(3):1365-76. doi: 10.1534/genetics.105.043828. Epub 2005 Jul 14.
There is currently considerable interest in genetic analysis of quantitative traits such as blood pressure and body mass index. Despite the fact that these traits change throughout life they are commonly analyzed only at a single time point. The genetic basis of such traits can be better understood by collecting and effectively analyzing longitudinal data. Analyses of these data are complicated by the need to incorporate information from complex pedigree structures and genetic markers. We propose conducting longitudinal quantitative trait locus (QTL) analyses on such data sets by using a flexible random regression estimation technique. The relationship between genetic effects at different ages is efficiently modeled using covariance functions (CFs). Using simulated data we show that the change in genetic effects over time can be well characterized using CFs and that including parameters to model the change in effect with age can provide substantial increases in power to detect QTL compared with repeated measure or univariate techniques. The asymptotic distributions of the methods used are investigated and methods for overcoming the practical difficulties in fitting CFs are discussed. The CF-based techniques should allow efficient multivariate analyses of many data sets in human and natural population genetics.
目前，人们对血压和体重指数等数量性状的遗传分析有着浓厚的兴趣。尽管这些性状在整个生命过程中都会发生变化，但通常只在单个时间点进行分析。通过收集和有效分析纵向数据，可以更好地理解这些性状的遗传基础。由于需要纳入来自复杂家系结构和遗传标记的信息，对这些数据的分析变得复杂。我们建议使用灵活的随机回归估计技术对这类数据集进行纵向数量性状基因座（QTL）分析。利用协方差函数（CFs）可以有效地对不同年龄的遗传效应之间的关系进行建模。使用模拟数据，我们表明，利用CFs可以很好地描述遗传效应随时间的变化，并且与重复测量或单变量技术相比，纳入随年龄变化的效应建模参数可以显著提高检测QTL的能力。研究了所用方法的渐近分布，并讨论了克服拟合CFs实际困难的方法。基于CFs的技术应该能够对人类和自然群体遗传学中的许多数据集进行有效的多变量分析。
