样本量测定
计算机科学
线性模型
生物
计算生物学
人口
遗传关联
联想(心理学)
计量经济学
统计
机器学习
数学
遗传学
医学
基因型
单核苷酸多态性
心理学
环境卫生
基因
心理治疗师
作者
Jian Yang,Noah Zaitlen,Michael E. Goddard,Peter M. Visscher,Valentina Escott‐Price
出处
期刊:Nature Genetics
[Springer Nature]
日期:2014-01-29
卷期号:46 (2): 100-106
被引量:913
摘要
Alkes Price, Peter Visscher and colleagues provide recommendations on the application of mixed-linear-model association methods across a range of study designs. Mixed linear models are emerging as a method of choice for conducting genetic association studies in humans and other organisms. The advantages of the mixed-linear-model association (MLMA) method include the prevention of false positive associations due to population or relatedness structure and an increase in power obtained through the application of a correction that is specific to this structure. An underappreciated point is that MLMA can also increase power in studies without sample structure by implicitly conditioning on associated loci other than the candidate locus. Numerous variations on the standard MLMA approach have recently been published, with a focus on reducing computational cost. These advances provide researchers applying MLMA methods with many options to choose from, but we caution that MLMA methods are still subject to potential pitfalls. Here we describe and quantify the advantages and pitfalls of MLMA methods as a function of study design and provide recommendations for the application of these methods in practical settings.
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