Gene-environment interaction vs quantile-dependent penetrance of established SNPs

描述（由申请人提供）：基因-环境相互作用可以定义为基因型的表型表达被环境改变，例如，FTO多态性对锻炼者的体重指数（BMI）的影响较弱。然而，我们的初步分析表明，FTO多态性的表型表达减少瘦相对于超重的个人。这表明了另一种解释（图1）。基于锻炼者比不锻炼者更瘦的事实，我们假设身体活动影响BMI，进而影响FTO基因表达，而不是运动直接影响FTO基因表达。我们对1800名受试者的脂蛋白和BMI的初步分析表明，基因型的表型表达通常随表型的分位数而增加，即，当表型的值相对于其在群体中的分布较高时。我们把这种依赖性称为分位数依赖性.这种依赖性与标准回归模型不同，标准回归模型假设因变量和自变量之间的关系相同（例如，表型与基因型）适用于因变量的所有分位数。 我们建议将分位数回归应用于通过NHLBI数据-基因关联资源（CARe），DBGaP和其他研究获得的数据，以评估分位数依赖的遗传率是否适用于大多数其他基因型-表型关系。虽然我们的初步分析缺乏统计能力来评估单个SNP的这种现象，但其在遗传风险评分中的表现表明，大多数SNP效应也必须依赖于分位数。我们还将测试基因-环境相互作用的先前断言是否可归因于分位数依赖的遗传变异，允许基因型表达随性状分布的百分位数变化是否显著增加了所解释的表型方差，以及分位数依赖的遗传变异是否可以扩展到SNP-SNP相互作用。 该建议是假设驱动的;即，我们假设大多数基因型-表型相关性随着表型的百分位数而显著增加。这一假说是基于这样一个前提，即最重要的基因-环境相互作用涉及个体自身的生理环境，基因在该环境中表达。性状分布的最低到最高的分布代表了一系列生理参数、遗传组成和基因-基因相互作用，这些参数的存在可能对遗传变异的表达至关重要。据我们所知，分位数依赖的遗传率尚未被提出作为基因型-表型关系的主要基础，或作为基因-环境相互作用的替代。 公共卫生相关性：我们先前已经表明，基因型对表型的影响随着总胆固醇、甘油三酯、高密度脂蛋白胆固醇和体重指数的遗传风险评分中性状分布的百分位数而增加。使用1800名受试者的研究的基线数据证明了这种现象。本提案的目的是在更大、更多样化的数据集中展示这一现象，并将研究结果扩展到其他变量。我们认为，这种现象可能是表型-基因型关系的一般原则。这一结果将基因与环境的相互作用更明确地定义为：a）环境对基因型的影响，影响其表型表达; B）环境对表型的影响，影响基因型的表型表达。

DESCRIPTION (provided by applicant): Gene-environment interactions may be defined as a genotype's phenotypic expression being altered by the environment, e.g., the weaker effect of FTO polymorphisms on body mass index (BMI) in exercisers compared to non-exercisers. However, our preliminary analyses suggest that the phenotypic expressions of FTO polymorphisms are diminished in lean vis-a-vis overweight individuals. This suggests an alternative interpretation (Figure 1). Based on the fact that exercisers are leaner than non-exercisers, we hypothesize that physical activity affects BMI, which in turn affects FTO gene expression, rather than exercise affecting FTO gene expression directly. Our preliminary analyses of lipoproteins and BMI in 1800 subjects suggest that a genotype's phenotypic expression often increases with the quantile of the phenotype, i.e., when the value of the phenotype is high relative to its distribution in the population. We refer to this dependence as quantile- dependent penetrance. This dependence differs from the standard regression model, which assumes that the same relationship between the dependent and independent variables (e.g., phenotype vs. genotype) applies to all quantiles of the dependent variable. We propose to apply quantile regression to data available through the NHLBI Candidate-Gene Association Resource (CARe), DBGaP, and other studies to assess whether quantile-dependent penetrance applies to most other genotype-phenotype relationships. Although our preliminary analyses lacked the statistical power to assess this phenomenon for individual SNPs, its demonstration in genetic risk scores suggests that the majority of SNP effects must also be quantile dependent. We will also test whether prior assertions of gene- environment interactions are attributable to quantile-dependent penetrance, whether allowing the genotypic expression to vary with the percentile of the trait distribution significantly increases the phenotypic variances explained, and whether quantile-dependent penetrance can be extended to SNP-SNP interactions. This proposal is hypothesis driven; i.e., we hypothesize that most genotype-phenotype associations increase substantially with the percentile of the phenotype. This hypothesis is based upon the premise that the most important gene-environment interaction involves an individual's own physiological environment within which the genes are expressed. The lowest to highest percentiles of a trait's distribution represent a range of physiologic parameters, genetic make-ups, and gene-gene interactions whose presence may be essential for the genetic variant to be expressed. To our knowledge, quantile-dependent penetrance has not been proposed as a primary basis for genotype-phenotype relationships, or as an alternative to gene-environment interactions. PUBLIC HEALTH RELEVANCE: We have previously shown that the effect of the genotype on a phenotype increases with the percentile of the trait distribution in genetic risk scores for total cholesterol, triglycerides, high-density lipoprotein cholesterol, and body mass index. This phenomenon was demonstrated using baseline data for a study of 1800 subjects. The purpose of this proposal is to demonstrate this phenomenon in much larger, more diverse data sets and to extend the findings to other variables. We believe that this phenomenon could be a general principal of phenotype-genotype relationships. The results could more sharply define gene-environment interactions into: a) effects of the environment on the genotype, which affects its phenotype expression, and b) effects of the environment on the phenotype which affects the penetrance of the genotype.