Functional linear models for association analysis of quantitative traits.
Functional linear models for association analysis of quantitative traits.
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DOI：
10.1002/gepi.21757
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发表时间：
2013-11
影响因子：
2.1
通讯作者：
Xiong, Momiao
中科院分区：
文献类型：
--
作者：
Fan, Ruzong;Wang, Yifan;Mills, James L.;Wilson, Alexander F.;Bailey-Wilson, Joan E.;Xiong, Momiao
关键词：
本文提出了一种用于检验数量性状与遗传变异之间关联的函数线性模型，这些遗传变异可以是罕见变异、常见变异或两者的组合。通过将人类群体中个体的多个遗传变体视为随机过程的实现，染色体区域中个体的基因组是序列数据的连续体，而不是离散观测。个体的基因组被看作是一个随机函数，包含了遗传标记的连锁和连锁不平衡（LD）信息。通过使用功能数据分析技术，建立固定和混合效应的功能线性模型来检验数量性状与协变量调整后的遗传变异之间的关联。通过大量的模拟分析，表明在大多数情况下，固定效应函数线性模型的F分布检验比序列核关联检验（SKAT）及其最优统一检验（SKAT-O）具有更高的功效：（1）因果变量都是罕见的，（2）因果变量既罕见又常见，（3）因果变量常见。固定效应函数线性模型的上级性能最可能是由于其最佳利用了基因组中多个遗传变异的遗传连锁和LD信息以及不同个体之间的相似性，而SKAT和SKAT-O仅建模相似性和成对LD，但不能充分建模连锁和高阶LD信息。此外，所提出的固定效应模型在模拟研究中产生准确的I类错误率。我们还表明，所提出的混合效应函数线性模型的函数核得分测试是更好的候选基因分析和小样本问题。应用该方法对Trinity Students Study中的三个生化性状进行了分析。
Functional linear models are developed in this paper for testing associations between quantitative traits and genetic variants, which can be rare variants or common variants or the combination of the two. By treating multiple genetic variants of an individual in a human population as a realization of a stochastic process, the genome of an individual in a chromosome region is a continuum of sequence data rather than discrete observations. The genome of an individual is viewed as a stochastic function that contains both linkage and linkage disequilibrium (LD) information of the genetic markers. By using techniques of functional data analysis, both fixed and mixed effect functional linear models are built to test the association between quantitative traits and genetic variants adjusting for covariates. After extensive simulation analysis, it is shown that the F-distributed tests of the proposed fixed effect functional linear models have higher power than that of sequence kernel association test (SKAT) and its optimal unified test (SKAT-O) for three scenarios in most cases: (1) the causal variants are all rare, (2) the causal variants are both rare and common, and (3) the causal variants are common. The superior performance of the fixed effect functional linear models is most likely due to its optimal utilization of both genetic linkage and LD information of multiple genetic variants in a genome and similarity among different individuals, while SKAT and SKAT-O only model the similarities and pairwise LD but do not model linkage and higher order LD information sufficiently. In addition, the proposed fixed effect models generate accurate type I error rates in simulation studies. We also show that the functional kernel score tests of the proposed mixed effect functional linear models are preferable in candidate gene analysis and small sample problems. The methods are applied to analyze three biochemical traits in data from the Trinity Students Study.
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