Statistical Methods For Genetic Epidemiology

我们以前已经开发了一种方法，用一种称为Trimm(三联体多标记)的方法，对受影响的个体及其父母使用多个SNP基因型来检测质量性状。该测试方法对由于人口分层而产生的偏差具有很强的鲁棒性。我们进一步扩展了这种方法，通过一种我们称为GEI-Trimm的方法，允许测试单倍型与环境的交互作用。描述这种方法并通过模拟来表征其性能的论文于今年发表。 在另一个项目中，我们正在估计家族史数据中存在的不对称性，这种不对称性是母亲中介的遗传效应存在的次要因素。我们将这一策略应用于Sister研究的家族史数据，发现有证据表明，年轻(50岁以下)乳腺癌患者的祖母比父亲的祖母更有可能患乳腺癌。这表明可能存在母体介导的乳腺癌遗传风险因素，可能存在与风险相关的印记基因，或者线粒体变异起到了一定作用。表观遗传学对乳腺癌也可能很重要。 我们现在考虑的一个特别重要的设计涉及一个“四分体”结构，除了父母之外，还有一个受影响的后代和一个未受影响的后代。这一设计已经在两姐妹研究中实施，该研究正在评估遗传和环境风险因素在年轻发病(50岁以下)乳腺癌中的联合作用。不一致的同胞对允许估计暴露的影响，而嵌入的病例-亲本三联体允许检测提供保护或风险的单倍型。共同分析的四分体应该为评估基因与环境的交互作用提供一个强有力的设计。我们一直致力于开发和评估用于四分体设计的方法。两姐妹研究继续纳入核心家庭，其中一个女儿在50岁之前患上乳腺癌，而另一个女儿没有受到影响。我们目前已经招收了近1500个这样的家庭。这在另一个单独的项目中进行了描述。将需要探索遗传基因类型以及肿瘤特征，以调查预测治疗后临床过程的因素，在这方面也需要开发改进的统计方法。通过与约翰·霍普金斯大学遗传病研究中心的合同，我们正在进行一项基于这些数据的全基因组关联研究，并将能够探索基因与环境对年轻乳腺癌风险的影响，还将研究母亲对风险的影响和可能的父母对风险的影响。将使用的Illumina平台是人类OmniExpress+Exome阵列，外显子组分型的使用将要求进一步开发适用于稀有等位基因的方法。 我们已经开发了一种使用四分体结构来研究基因与环境相互作用的方法，并进行了广泛的模拟来记录它在一系列场景下的表现，其中一些场景有暴露相关的种群结构，另一些没有暴露相关的种群结构。令我们惊讶的是，如果种群具有暴露涉及的种群结构，所有现有的基因与环境相互作用的方法都会受到偏差。当研究中的标记等位基因的频率和暴露流行率都不同时，就会发生这种情况。由此产生的偏差可以很好地理解为反映了这样一个事实，即在这种结构下，暴露可以替代被研究标记与致病SNP/单倍型之间的连锁不平衡程度(这在不同的亚群中也是不同的)。这种偏见可能是极端的。我们现在已经在最近发表的研究中找到了一些避免这种情况的补救措施，同时保留了良好的统计能力。稳健的程序使用仅限病例的方法，但使用来自随机抽样的未受影响兄弟姐妹的暴露数据来增强它。 与北卡罗来纳大学生物统计学的研究生艾莉森·怀斯一起，我们正在研究一种机器学习方法，以病例-父母三合一为基础，寻找复杂的上位性和基因与环境的相互作用。我们从DBGaP下载了关于口腔裂隙的病例-父母三联体数据，对其进行了消毒以获得真正的效果，并使用这些基因组来生成具有已知GxGxGxG迭代的模拟病例-父母三联体数据。我们正在努力开发一种算法，即使归因风险非常小，也可以从Gwas数据中搜索SNP的3向选择的巨大搜索空间，并识别正确的多SNP模型。这项工作正在进行中。

We had previously developed methods for qualitative traits using multiple-SNP genotypes for affected individuals and their parents in a method called TRIMM (triad multi-marker). The testing approach is robust against bias due to population stratification. We further extended the approach to allow testing for haplotype-by-environment interaction, via a method we call GEI-TRIMM. The paper describing this approach and characterizing its performance through simulations was published this year. In another project, we are estimating the asymmetry that would exist in family history data secondary to the existence of a maternally-mediated genetic effect. We applied this strategy to family history data from the Sister Study, and found evidence that maternal grandmothers of young-onset (under 50) cases of breast cancer were more likely to have had breast cancer than were paternal grandmothers. This suggests there may be maternally-mediated genetic risk factors for breast cancer, that there may be imprinted genes related to risk or that mitochondrial variants play a role. Epigenetics could also be important for breast cancer. A particularly important design we are now considering involves a "tetrad" structure, with one affected and one unaffected offspring, in addition to the two parents. This design has been implemented in the Two Sister Study, which is assessing the joint role of genetic and environmental risk factors in young-onset (under age 50) breast cancer. The discordant sib pair allows estimation of effects of exposures, while the embedded case-parent triad allows detection of haplotypes that confer either protection or risk. The tetrad analyzed together should provide a powerful design for assessing gene-by-environment interaction. We have been working on developing and evaluating methods for use with the tetrad design. The Two Sister Study is continuing to enroll nuclear families where one daughter developed breast cancer before age 50 and the other daughter is unaffected. We currently have enrolled almost 1500 such families. This is described under a separate project. Inherited genotypes, together with tumor characteristics, will need to be explored to investigate factors that predict the clinical course following treatment, and improved statistical methods will also need to be developed in that context. We are undertaking a genome-wide association study based on these data through a contract with the Center for Inherited Disease Research at Johns Hopkins and will be able to explore gene-by-environment effects on risk of young-onset breast cancer and also look at maternally-mediated effects and possible parent-of-origin effects on risk. The Illumina platform that will be used is the human OmniExpress plus Exome array, and the use of the exome typing will impose the need to develop further methods appropriate for rare alleles. We have developed a method for studying gene-by-environment interaction using the tetrad structure and we carried out extensive simulations to document its performance under a range of scenarios, some with and some without exposure-involved population structure. We learned to our surprise that all of the existing gene-by-environment interaction methods are subject to bias if the population has exposured-involved population structure. This happens when there are subpopulations that differ both in their frequency of the marker allele under study and in their exposure prevalence. The resulting bias can best be understood as reflecting the fact that with that kind of structure the exposure can serve as a surrogate for the degree of linkage disequilibrium (which also varies across subpopulations) between the marker under study and a causative SNP/haplotype. This bias can be extreme. We have now developed some remedies for avoiding it, while preserving good statistical power, in work that was recently published. A robust procedure uses a case-only approach but augments it with exposure data from a randomly sampled unaffected sibling. Together with a graduate student from UNC Biostatistics, Alison Wise, we are working on a machine-learning approach to finding complex epistatic and gene-by-environement interactions based on case-parent triads. We downloaded case-parent triad data from dbGaP on oral clefts, sanitized it for real effects and are using those genomes to generate simulated case-parent triad data with known GxGxGxG interations. We are working to develop an algorithm that can search through the enormous search space of 3-way choices of SNPs from the GWAS data and identify the right multi-SNP model, even when the attributable risk is very small. This work is in progress.