Linear mixed models for association analysis of quantitative traits with next-generation sequencing data
Linear mixed models for association analysis of quantitative traits with next-generation sequencing data
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DOI：
10.1002/gepi.22177
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发表时间：
2019-03-01
影响因子：
2.1
通讯作者：
Fan, Ruzong
中科院分区：
文献类型：
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作者：
Chiu, Chi-yang;Yuan, Fang;Fan, Ruzong
我们开发了线性混合模型（Linear mixed models，LMRM）和功能线性混合模型（Functional linear mixed models，FLMRM），用于基于基因的数量性状和家系遗传变异之间关联的检验。主基因的影响被建模为固定效应，多基因的贡献被建模为随机效应，系谱成员的相关性通过近交/亲属系数建模。F统计量和。2似然比检验（LRT）统计量的基础上的LRDT和FLRDT的构造来测试的关联。我们的经验表明，F-分布的统计提供了一个很好的控制I型错误率。L检验的F检验统计量具有与FL检验、基于核的famSKAT（基于家族的序列核关联检验）和负担检验famBT（基于家族的负担检验）相似或更高的功效。当分析罕见和常见变体的组合时，FLRisk的F统计量表现良好。对于小样本，FLQs的LRT统计量在标称水平α = 0.01和0.05下很好地控制了I类错误率。对于中等/大样本，FLQs的LRT统计量很好地控制了I类错误率。LRT的统计数据会导致I类错误率的膨胀。该模型可用于复杂性状的全基因组和全外显子组关联研究。
We develop linear mixed models (LMMs) and functional linear mixed models (FLMMs) for gene-based tests of association between a quantitative trait and genetic variants on pedigrees. The effects of a major gene are modeled as a fixed effect, the contributions of polygenes are modeled as a random effect, and the correlations of pedigree members are modeled via inbreeding/ kinship coefficients. F-statistics and. 2 likelihood ratio test (LRT) statistics based on the LMMs and FLMMs are constructed to test for association. We show empirically that the F-distributed statistics provide a good control of the type I error rate. The F-test statistics of the LMMs have similar or higher power than the FLMMs, kernel-based famSKAT (family-based sequence kernel association test), and burden test famBT (family-based burden test). The F-statistics of the FLMMs perform well when analyzing a combination of rare and common variants. For small samples, the LRT statistics of the FLMMs control the type I error rate well at the nominal levels alpha = 0.01 and 0.05. For moderate/ large samples, the LRT statistics of the FLMMs control the type I error rates well. The LRT statistics of the LMMs can lead to inflated type I error rates. The proposed models are useful in whole genome and whole exome association studies of complex traits.