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Quantitative Trait Locus Analysis of Longitudinal Quantitative Trait Data in Complex Pedigrees

机译:复杂谱系纵向定量性状数据的定量性状基因座分析

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摘要

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.
机译:当前,人们对诸如血压和体重指数等定量特征的遗传分析非常感兴趣。尽管这些特征在整个生命中都会发生变化,但通常仅在单个时间点对其进行分析。通过收集和有效分析纵向数据,可以更好地理解此类性状的遗传基础。由于需要整合来自复杂血统结构和遗传标记的信息,因此对这些数据的分析变得非常复杂。我们建议通过使用灵活的随机回归估计技术对此类数据集进行纵向定量性状基因座(QTL)分析。使用协方差函数(CF)可以有效地模拟不同年龄的遗传效应之间的关系。使用模拟数据,我们可以证明,使用CF可以很好地表征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,包括对随着年龄变化的效应进行建模的参数可以大大提高检测QTL的能力。研究了所用方法的渐近分布,并讨论了克服拟合CF的实际困难的方法。基于CF的技术应允许对人类和自然种群遗传学中​​许多数据集进行有效的多变量分析。

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