Department of Biostatistics and Bioinformatics, The George Washington University, Washington, District of Columbia, USA.
Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
BMC Genet. 2020 Sep 7;21(1):99. doi: 10.1186/s12863-020-00902-x.
Associations between haplotypes and quantitative traits provide valuable information about the genetic basis of complex human diseases. Haplotypes also provide an effective way to deal with untyped SNPs. Two major challenges arise in haplotype-based association analysis of family data. First, haplotypes may not be inferred with certainty from genotype data. Second, the trait values within a family tend to be correlated because of common genetic and environmental factors.
To address these challenges, we present an efficient likelihood-based approach to analyzing associations of quantitative traits with haplotypes or untyped SNPs. This approach properly accounts for within-family trait correlations and can handle general pedigrees with arbitrary patterns of missing genotypes. We characterize the genetic effects on the quantitative trait by a linear regression model with random effects and develop efficient likelihood-based inference procedures. Extensive simulation studies are conducted to examine the performance of the proposed methods. An application to family data from the Childhood Asthma Management Program Ancillary Genetic Study is provided. A computer program is freely available.
Results from extensive simulation studies show that the proposed methods for testing the haplotype effects on quantitative traits have correct type I error rates and are more powerful than some existing methods.
单倍型与数量性状之间的关联为复杂人类疾病的遗传基础提供了有价值的信息。单倍型也为处理未分型 SNP 提供了一种有效方法。在基于单倍型的家系数据分析中存在两个主要挑战。首先，从基因型数据中推断单倍型可能无法确定。其次，由于共同的遗传和环境因素，家系内的性状值往往相关。
为了解决这些挑战，我们提出了一种有效的基于似然的方法来分析数量性状与单倍型或未分型 SNP 的关联。该方法适当地考虑了家系内性状相关性，并可以处理具有任意缺失基因型模式的一般系谱。我们通过带有随机效应的线性回归模型来描述对数量性状的遗传效应，并开发了有效的基于似然的推断程序。进行了广泛的模拟研究来检验所提出方法的性能。提供了来自儿童哮喘管理计划辅助遗传研究的家系数据的应用。提供了一个免费的计算机程序。
广泛的模拟研究结果表明，用于检验单倍型对数量性状影响的拟议方法具有正确的Ⅰ型错误率，并且比一些现有方法更有效。
