应用多因子降维法分析基因-基因交互作用

目的介绍在遗传流行病学病例对照研究中,应用多因子降维法（MDR）分析基因-基因交互作用.方法简述MDR的基本步骤、原理及其特点,并结合研究实例说明在病例对照研究中如何应用软件进行MDR分析.结果相对于传统的统计学方法,MDR是一种无参数、无遗传模式的分析交互作用的方法,理论和实例研究均表明其分析交互作用具有较好的效能,目前已成功应用于散发性乳腺癌、心房颤动和原发性高血压等疾病的研究.结论 MDR能够应用于病例对照研究进行基因-基因交互作用的分析,且具有较传统的统计学分析方法无法比拟的优势.     

易感基因多态性与孕期环境因素对先天性心脏病风险的交互作用分析

目的分析先天性心脏病中TBX5基因、MTHFR基因与环境因素的交互作用。方法回顾性分析2012年12月至2015年5月山东大学齐鲁儿童医院就诊的单纯先天性心脏病患儿150例。应用单因素和多因素非条件logistic回归模型分析环境因素与先天性心脏病的关系并计算OR(95%CI),利用多因子降维法(MDR)进行交互作用分析。结果妊娠高血压综合征和孕期情绪状态是先天性心脏病可能的环境危险因素,基因-基因交互作用MDR模型筛选中,1阶与2阶交互作用最佳预测模型交叉验证一致性均不高,基因-环境交互作用MDR模型筛选中,最优交互模型是孕母情绪状态、MTHFR基因rs1801133位点多态与TBX5基因rs883079位点多态,该模型的拟合均衡准确性为0.685,检测均衡准确性为0.613,交叉验证一致性为10/10。TBX5基因rs883079、MTHFR基因rs1801133和rs1801131位点可能与先天性心脏病存在关联,MTHFR和TBX5基因可能存在交互作用,但交互作用不明显。结论孕母情绪状态MTHFR基因和TBX5基因在先天性心脏病中具有显著交互效应,可增加先天性心脏病的发病风险。     

应用广义多因子降维法分析数量性状的交互作用

介绍广义多因子降维法(GMDR)在交互作用分析,尤其是数量性状的基因-基因交互作用分析中的应用.文中简述GMDR的原理、基本步骤及其特点,并结合实例说明如何在研究中对GMDR进行应用.GMDR是无模型的交互作用分析方法,能够处理连续型结局变量,还可纳入协变量改善预测准确率,目前已成功应用于尼古丁依赖等疾病的研究.GMDR能够处理多种样本类型和结局变量类型,与其他连续变量交互作用分析方法相比具有一定优势.     

多因子降维法分析基因与环境因素交互作用的研究

目的探讨多因子降维法（multifactor dimensionality reduction,MDR）在筛选糖尿病的基因与环境危险因素交互作用时的用法以及MDR的优缺点,并与传统的Logistic回归做比较。方法通过模拟研究确定MDR所适用的样本量大小,实例分析采用针对糖尿病的病例对照研究数据,分别用MDR方法、Logistic回归进行分析,将两种结果进行对比进而对两种方法作出评价。结果模拟实验结果表明MDR在分析1阶交互作用时效能不如Logistic回归,但随着样本量的增大,两种检测方法效能趋于一致;在分析高阶交互作用时,MDR所表现出的效能高于Logistic回归,而且在分析2阶交互作用中,样本量200时MDR已经表现良好,当样本量为300时,MDR显示了微弱的优势。实例分析中MDR的最优模型为174G/C和是否总胆固醇和总甘油三酯同时偏高之间的交互作用,预测准确率为0.5822,交叉验证一致性为8/10,置换检验有统计学意义（P=0.021）。结论 MDR在分析小样本、高维度数据中表现良好,可有效的分析糖尿病基因与环境的交互作用。     

复杂疾病病因研究中基因问交互作用分析:基于基因型传递不平衡的多因子降维法

介绍复杂疾病病因研究中分析基因-基因交互作用的一种新方法：基于基因型传递不平衡的多因子降维法（MDR-PDT）。文中简述MDR-PDT的基本原理、步骤及特点，并结合研究实例说明其应用过程。MDR-PDT是原始MDR的扩展，可用于多种结构类型的核心家系资料分析基因-基因交互作用。结论MDR-PDT具有非参数、无需遗传模式的特点，并能充分利用家系中多个家庭成员的信息，在复杂疾病病因研究中分析基因-基因交互作用具有良好的效能。     

叉生分析在复杂疾病基因-基因、基因-环境交互作用研究中的应用

目的 探讨叉生分析在复杂疾病基因-基因、基因-环境交互作用研究中的应用及其意义.方法 应用叉生分析对病例对照研究的糖尿病数据进行基因-基因、基因-环境交互作用分析结果 交互作用的分析依赖于对相加或相乘模型的选择.本文中相加模型与相乘模型的交互作用结果均具有统计学意义,根据交互作用模型选择原则,本文结果适合应用相加模型进行解释.基于相加模型的ENPP1基因K121Q（rs1044498）与饮酒交互作用具有统计学意义（OR=4.01,S=17.22,AP=0.71,P〈0.05）,与家族史交互作用也具有统计学意义（OR=5.14,S=7.43,AP=0.69,P〈0.05）;174G/C与572C/G两基因交互作用具有统计学意义（OR=5.12,S=5.40,AP=0.65,P〈0.05）结论 叉生分析不仅可以分析基因-环境的交互作用,同样可以分析基因-基因的交互作用.而且叉生分析结果简单明了,通过SAS、R等统计软件都可实现叉生分析的分析过程和假设检验过程.并且可以非常直观地呈现绝大部分的流行病学中基本单元的信息,可为我们判断交互作用提供基本的理论依据因此叉生分析在复杂疾病的基因-基因、基因-环境交互作用研究中具有广阔的应用前景.     

多因子降维法在评价代谢酶基因-基因-环境交互作用中的应用

目的探讨如何用多因子降维法(multifactor dimensionality reduction,MDR)分析人群肺癌发病风险的基因-基因-环境间交互作用。方法收集500例肺癌确诊患者和517例非肺癌对照个体的16种代谢酶基因65个位点单核苷酸多态性(single nucleotide polymorphism,SNP)资料及主要的环境因素(性别、年龄、吸烟状况、烟龄、吸烟量和肿瘤家族史)暴露情况,在遗传平衡(Hardy-Weinberg equilibrium,HWE)的基础上,采用MDR分析其基因-基因及基因-环境间的交互效应。结果该资料符合HWE(P=0.238~0.937);多环芳烃受体基因(aryl hydrocarbon acceptor,AhR)rs2158041位点和乙醛脱氢酶2基因(aldehyde dehydrogenase 2,ALDH2)rs4646782位点杂合型及烟龄较长等交互组合的"高危"人群是非上述组合的"低危"人群肺癌发病风险的2.639倍(OR=2.639,95%CI:2.047~3.403)。结论 rs2158041、rs4646782、烟龄三因素在肺癌发病中具有显著的交互效应,可增加1.639倍发病风险。MDR法在分析疾病基因-基因-环境交互作用中切实可行,具有较好的应用前景。     

叉生分析在基因-环境交互作用研究中的应用与意义

目的　探讨叉生分析在基因 环境交互作用研究中的作用和意义。方法　以口服避孕药和Leiden因子Ⅴ基因突变与静脉血栓栓塞的病例对照研究为例 ,分析 2× 4叉生分析表核心信息 ,并与分层分析、单纯病例研究相比较。结果　对基因 环境交互作用的分析依赖于相加或相乘模型的选择。基于相乘模型交互作用的叉生分析OR值为 1.35 (P >0 .0 5 ) ,其结果与分层分析和单纯病例研究一致 ;叉生分析还可计算相加模型的各项指标 :交互作用指数为 3.90、交互作用归因比为72 .2 4 %、交互作用超额相对危险度为 2 5 .0 8(P >0 .0 5 )。结论　叉生分析在基因 环境交互作用分析中可进一步应用。     

炎性基因与环境交互作用及胃癌易感性分析

目的 探讨胃癌患病风险的炎性基因白介素1(IL-1)、肿瘤坏死因子(TNF)及巨噬细胞转移抑制因子(MIF)与环境间交互作用.方法 用多因子降维法(MDR)模型分析基因-环境的交互作用对胃癌易感性影响,结合logistic回归分析进行补充验证.结果 最佳交互作用模型是IL-1B-511、IL-1RN、TNF-A-308和肿瘤家族史的联合作用(P＜0.01),交叉验证一致性10/10,检验样本准确度为0.72;根据模型将研究对象划分为高危、低危人群,胃癌发病风险交互效应的危险度估计OR＝ 13.49,95％CI＝8.62～21.10,且交互作用有统计学意义(P＜0.01);将危险因素按结合数量进行分析,结果显示含有1、2、3个危险因素组在病例和对照中分布差异均有统计学意义(P＜0.05),OR值分别为2.17、11.61、27.19.结论 IL-1B-511、IL-1RN、TNF-A-308和肿瘤家族史的交互作用可能增加胃癌患病风险.     

CYP4A11、CYP4F2基因多态性及其交互作用与代谢综合征的相关性研究

目的分析CYP4A11基因(rs9332978,rs1126742)、CYP4F2基因(rs1558139,rs2108622)多态性与浙江省社区居民代谢综合征(MS)易感性的关系。方法运用病例对照研究方法,确定277例MS人群和304名正常对照进行研究,应用TaqMan荧光探针方法检测4个位点多态性,使用SAS 9.2统计软件分析数据,并结合MDR 2.0分析软件分析基因-基因、基因-环境的交互作用,最后用多因素非条件logistic回归模型估计MS危险因素及MDR模型中危险因素的危险度。结果 rs9332978 GG与AA+AG基因型的分布频率在病例对照组中差异有统计学意义(P=0.04)。连锁不平衡分析显示:rs9332978和rs1126742两位点呈现较强的连锁不平衡(病例组D′=0.783,对照组D′=0.836);但rs2108622和rs1558139两位点并不存在明显的连锁不平衡;连锁不平衡的两两位点组成的单体型在两组中分布差异均无统计学意义。MDR分析结果显示rs1558139、rs2108622两位点多态性存在交互作用。多因素logistic回归分析显示:rs9332978GG基因型、轻度体力活动与MS之间存在负相关,OR及95%CI值分别为0.32(0.10～1.00)、0.70(0.49～0.99);但未发现rs1558139、rs2108622交互作用对MS的影响差异有统计学意义。结论 rs9332978位点的GG基因型及轻度体力活动与MS的发生具有相关关系,可降低MS发生的危险性,可能是MS的保护因素;但rs9332978和rs1126742两个位点多态性在MS发生中可能不存在交互作用。     

Power of multifactor dimensionality reduction for detecting gene-gene interactions in the presence of genotyping error,missing data,phenocopy, and genetic heterogeneity

The identification and characterization of genes that influence the risk of common, complex multifactorial diseases, primarily through interactions with other genes and other environmental factors, remains a statistical and computational challenge in genetic epidemiology. This challenge is partly due to the limitations of parametric statistical methods for detecting genetic effects that are dependent solely or partially on interactions with other genes and environmental exposures. We previously introduced multifactor dimensionality reduction (MDR) as a method for reducing the dimensionality of multilocus genotype information to improve the identification of polymorphism combinations associated with disease risk. The MDR approach is nonparametric (i.e., no hypothesis about the value of a statistical parameter is made), is model-free (i.e., assumes no particular inheritance model), and is directly applicable to case-control and discordant sib-pair study designs. Both empirical and theoretical studies suggest that MDR has excellent power for identifying high-order gene-gene interactions. However, the power of MDR for identifying gene-gene interactions in the presence of common sources of noise is not currently known. The goal of this study was to evaluate the power of MDR for identifying gene-gene interactions in the presence of noise due to genotyping error, missing data, phenocopy, and genetic or locus heterogeneity. Using simulated data, we show that MDR has high power to identify gene-gene interactions in the presence of 5% genotyping error, 5% missing data, or a combination of both. However, MDR has reduced power for some models in the presence of 50% phenocopy, and very limited power in the presence of 50% genetic heterogeneity. Extending MDR to address genetic heterogeneity should be a priority for the continued methodological development of this new approach.     

Synergistic effect of the genetic polymorphisms of the renin–angiotensin–aldosterone system on high-altitude pulmonary edema: a study from Qinghai-Tibet altitude

The pathogenesis of high-altitude pulmonary edema (HAPE) has been at least partially attributed to the local dysregulation of the renin–angiotensin–aldosterone system (RAAS) cascade. To address this issue, we conducted the largest nested case-control study to-date to explore the association between variations in RAAS genes and HAPE in Chinese population. We recruited 140 HAPE patients and 144 controls during the construction of Qinghai-Tibet railway and genotyped 10 gene polymorphisms evenly interspersed in 5 RAAS candidate genes. The data were analyzed by haplotype and multifactor dimensionality reduction (MDR). The single-locus analysis showed that CYP11B2 C-344T and K173R and ACE A-240T polymorphisms were significantly associated with HAPE after Bonferroni correction (P < 0.005). The linkage analysis constructed a linkage block including C-344T and K173R polymorphisms in complete linkage disequilibrium with each other, while occurred with significantly different frequencies between HAPE and control groups. The gene-gene interaction analysis found the overall best model including ACE A-240T and A2350G and CYP11B2 C-344T polymorphisms with strong synergistic effect. This model had a maximum testing accuracy of 68.61% and a maximum cross validation consistency of 9 out of 10 (P = 0.004). The homozygous genotype combination of −240AA, 2350GG and −344TT conferred high genetic susceptibility to HAPE, which was further strengthened by haplotype analysis. Our results add evidence for synergistic effect of RAAS gene polymorphisms on HAPE susceptibility. Moreover, we proposed a promising data-mining analytical approach (MDR) for detecting and characterizing gene-gene interactions.     

Detecting genetic interactions for quantitative traits with U-statistics

The genetic etiology of complex human diseases has been commonly viewed as a process that involves multiple genetic variants, environmental factors, as well as their interactions. Statistical approaches, such as the multifactor dimensionality reduction (MDR) and generalized MDR (GMDR), have recently been proposed to test the joint association of multiple genetic variants with either dichotomous or continuous traits. In this study, we propose a novel Forward U-Test to evaluate the combined effect of multiple loci on quantitative traits with consideration of gene-gene/gene-environment interactions. In this new approach, a U-Statistic-based forward algorithm is first used to select potential disease-susceptibility loci and then a weighted U-statistic is used to test the joint association of the selected loci with the disease. Through a simulation study, we found the Forward U-Test outperformed GMDR in terms of greater power. Aside from that, our approach is less computationally intensive, making it feasible for high-dimensional gene-gene/gene-environment research. We illustrate our method with a real data application to nicotine dependence (ND), using three independent datasets from the Study of Addiction: Genetics and Environment. Our gene-gene interaction analysis of 155 SNPs in 67 candidate genes identified two SNPs, rs16969968 within gene CHRNA5 and rs1122530 within gene NTRK2, jointly associated with the level of ND (P-value = 5.31e-7). The association, which involves essential interaction, is replicated in two independent datasets with P-values of 1.08e-5 and 0.02, respectively. Our finding suggests that joint action may exist between the two gene products.     

Improving strategies for detecting genetic patterns of disease susceptibility in association studies

The analysis of gene interactions and epistatic patterns of susceptibility is especially important for investigating complex diseases such as cancer characterized by the joint action of several genes. This work is motivated by a case-control study of bladder cancer, aimed at evaluating the role of both genetic and environmental factors in bladder carcinogenesis. In particular, the analysis of the inflammation pathway is of interest, for which information on a total of 282 SNPs in 108 genes involved in the inflammatory response is available. Detecting and interpreting interactions with such a large number of polymorphisms is a great challenge from both the statistical and the computational perspectives. In this paper we propose a two-stage strategy for identifying relevant interactions: (1) the use of a synergy measure among interacting genes and (2) the use of the model-based multifactor dimensionality reduction method (MB-MDR), a model-based version of the MDR method, which allows adjustment for confounders.     

A novel method to identify gene-gene effects in nuclear families: the MDR-PDT

It is now well recognized that gene-gene and gene-environment interactions are important in complex diseases, and statistical methods to detect interactions are becoming widespread. Traditional parametric approaches are limited in their ability to detect high-order interactions and handle sparse data, and standard stepwise procedures may miss interactions that occur in the absence of detectable main effects. To address these limitations, the multifactor dimensionality reduction (MDR) method [Ritchie et al., 2001: Am J Hum Genet 69:138-147] was developed. The MDR is well-suited for examining high-order interactions and detecting interactions without main effects. The MDR was originally designed to analyze balanced case-control data. The analysis can use family data, but requires a single matched pair be selected from each family. This may be a discordant sib pair, or may be constructed from triad data when parents are available. To take advantage of additional affected and unaffected siblings requires a test statistic that measures the association of genotype with disease in general nuclear families. We have developed a novel test, the MDR-PDT, by merging the MDR method with the genotype-Pedigree Disequilibrium Test (geno-PDT)[Martin et al., 2003: Genet Epidemiol 25:203-213]. MDR-PDT allows identification of single-locus effects or joint effects of multiple loci in families of diverse structure. We present simulations to demonstrate the validity of the test and evaluate its power. To examine its applicability to real data, we applied the MDR-PDT to data from candidate genes for Alzheimer disease (AD) in a large family dataset. These results show the utility of the MDR-PDT for understanding the genetics of complex diseases.     

A computationally efficient hypothesis testing method for epistasis analysis using multifactor dimensionality reduction

Multifactor dimensionality reduction (MDR) was developed as a nonparametric and model‐free data mining method for detecting, characterizing, and interpreting epistasis in the absence of significant main effects in genetic and epidemiologic studies of complex traits such as disease susceptibility. The goal of MDR is to change the representation of the data using a constructive induction algorithm to make nonadditive interactions easier to detect using any classification method such as naïve Bayes or logistic regression. Traditionally, MDR constructed variables have been evaluated with a naïve Bayes classifier that is combined with 10‐fold cross validation to obtain an estimate of predictive accuracy or generalizability of epistasis models. Traditionally, we have used permutation testing to statistically evaluate the significance of models obtained through MDR. The advantage of permutation testing is that it controls for false positives due to multiple testing. The disadvantage is that permutation testing is computationally expensive. This is an important issue that arises in the context of detecting epistasis on a genome‐wide scale. The goal of the present study was to develop and evaluate several alternatives to large‐scale permutation testing for assessing the statistical significance of MDR models. Using data simulated from 70 different epistasis models, we compared the power and type I error rate of MDR using a 1,000‐fold permutation test with hypothesis testing using an extreme value distribution (EVD). We find that this new hypothesis testing method provides a reasonable alternative to the computationally expensive 1,000‐fold permutation test and is 50 times faster. We then demonstrate this new method by applying it to a genetic epidemiology study of bladder cancer susceptibility that was previously analyzed using MDR and assessed using a 1,000‐fold permutation test. Genet. Epidemiol. 2008. © 2008 Wiley‐Liss, Inc.