Choi Jiin, Park Taesung
BMC Syst Biol. 2013;7 Suppl 6(Suppl 6):S15. doi: 10.1186/1752-0509-7-S6-S15. Epub 2013 Dec 13.
Recently, one of the greatest challenges in genome-wide association studies is to detect gene-gene and/or gene-environment interactions for common complex human diseases. Ritchie et al. (2001) proposed multifactor dimensionality reduction (MDR) method for interaction analysis. MDR is a combinatorial approach to reduce multi-locus genotypes into high-risk and low-risk groups. Although MDR has been widely used for case-control studies with binary phenotypes, several extensions have been proposed. One of these methods, a generalized MDR (GMDR) proposed by Lou et al. (2007), allows adjusting for covariates and applying to both dichotomous and continuous phenotypes. GMDR uses the residual score of a generalized linear model of phenotypes to assign either high-risk or low-risk group, while MDR uses the ratio of cases to controls.
In this study, we propose multivariate GMDR, an extension of GMDR for multivariate phenotypes. Jointly analysing correlated multivariate phenotypes may have more power to detect susceptible genes and gene-gene interactions. We construct generalized estimating equations (GEE) with multivariate phenotypes to extend generalized linear models. Using the score vectors from GEE we discriminate high-risk from low-risk groups. We applied the multivariate GMDR method to the blood pressure data of the 7,546 subjects from the Korean Association Resource study: systolic blood pressure (SBP) and diastolic blood pressure (DBP). We compare the results of multivariate GMDR for SBP and DBP to the results from separate univariate GMDR for SBP and DBP, respectively. We also applied the multivariate GMDR method to the repeatedly measured hypertension status from 5,466 subjects and compared its result with those of univariate GMDR at each time point.
Results from the univariate GMDR and multivariate GMDR in two-locus model with both blood pressures and hypertension phenotypes indicate best combinations of SNPs whose interaction has significant association with risk for high blood pressures or hypertension. Although the test balanced accuracy (BA) of multivariate analysis was not always greater than that of univariate analysis, the multivariate BAs were more stable with smaller standard deviations.
In this study, we have developed multivariate GMDR method using GEE approach. It is useful to use multivariate GMDR with correlated multiple phenotypes of interests.
最近，全基因组关联研究中最大的挑战之一是检测常见复杂人类疾病的基因-基因和/或基因-环境相互作用。Ritchie等人（2001年）提出了多因素降维（MDR）方法用于相互作用分析。MDR是一种将多位点基因型组合为高风险和低风险组的方法。尽管MDR已广泛用于二元表型的病例对照研究，但也有人提出了几种扩展方法。其中一种方法，由Lou等人（2007年）提出的广义MDR（GMDR），允许对协变量进行调整并应用于二分和连续表型。GMDR使用表型广义线性模型的残差分数来分配高风险或低风险组，而MDR使用病例与对照的比例。
在本研究中，我们提出了多变量GMDR，它是GMDR对多变量表型的扩展。联合分析相关的多变量表型可能更有能力检测易感基因和基因-基因相互作用。我们构建了具有多变量表型的广义估计方程（GEE）以扩展广义线性模型。使用来自GEE的得分向量，我们区分高风险组和低风险组。我们将多变量GMDR方法应用于韩国协会资源研究中7546名受试者的血压数据：收缩压（SBP）和舒张压（DBP）。我们分别将SBP和DBP的多变量GMDR结果与单独的SBP和DBP单变量GMDR结果进行比较。我们还将多变量GMDR方法应用于5466名受试者重复测量的高血压状态，并将其结果与每个时间点的单变量GMDR结果进行比较。
在双位点模型中，单变量GMDR和多变量GMDR对血压和高血压表型的结果表明，SNP的最佳组合，其相互作用与高血压或高血压风险有显著关联。虽然多变量分析的测试平衡准确率（BA）并不总是大于单变量分析，但多变量BA更稳定，标准差更小。
在本研究中，我们使用GEE方法开发了多变量GMDR方法。将多变量GMDR与相关的多个感兴趣表型一起使用是有用的。
