Statistical Methods For Genetic Epidemiology

全基因组关联研究通常比较单个SNP常染色体变异的病例和对照，隐含的假设是遗传效应是次要于遗传的常染色体遗传变异。还可能涉及四种非标准的遗传机制：与性别相关的特征、母亲在怀孕期间影响胎儿发育的母系效应、这会影响以后的风险、线粒体DNA的突变以及父母的起源效应。这些非标准机制中的每一种都可能导致家族史数据的不对称，即使在没有任何基因数据的情况下也可以对其进行研究。在一个项目中，我们正在估计这种机制的存在将在家族史数据中产生的不对称程度。我们将这一策略应用于我们的大型女性研究中的家族史数据，这些女性的姐妹都被诊断出患有乳腺癌(NIEHS Sister研究)，并发现有证据表明，年轻(50岁以下)乳腺癌患者的祖母比她们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得到了实施(苏珊·G·科曼的部分资金用于治疗)，该研究正在评估遗传和环境风险因素在年轻(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。两姐妹研究完成了核心家庭的登记，其中一个女儿在50岁之前患上乳腺癌，另一个女儿没有受到影响。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。预计9月底将公布基因分型数据。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。我们还参与了游戏联盟，该联盟将从新开发的芯片中提供更多SNPs。 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。我们还在努力评估我们识别X染色体上风险相关变异的新方法的性能。我们的方法，PI-XLRT，除了传输失真外，还以一种稳健的方式利用父母信息，因此比现有方法更有效地利用数据。一篇关于识别X的风险相关变异的论文正在准备中。

Genome-wide association studies typically compare cases and controls for single SNP autosomal variants under the implicit assumption that heritable effects are secondary to inherited autosomal genetic variants. Four nonstandard genetic mechanisms could be involved as well: sex-linked traits, matermally-mediated effects where the mother influences the development of her fetus during gestation, and this influences later risk, mutations in the mitochondrial DNA, and parent-of-origin effects. Each of these nonstandard mechanisms can cause asymmetry in family history data, which can be studied even in the absence of any genotype data. In one project, we are estimating the extent of asymmetry that would be produced in family history data secondary to the existence of such mechanisms. We applied this strategy to family history data from our large study of women, each of whom had as sister diagnosed with breast cancer (the NIEHS Sister Study), and found evidence that maternal grandmothers of young-onset (under age 50) cases of breast cancer were more likely to have had breast cancer than were their 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 (funded in part by Susan G. Komen for the Cure), 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 completed enrollment of nuclear families where one daughter developed breast cancer before age 50 and the other daughter is unaffected. 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 genotype data are expected at the end of September. 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 also are participating in the GAME-ON consortium, which will provide additional SNPs from the newly developed oncochip. 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. We are also working on assessing the performance of our new method for identifying risk-related variants on the X chromosome. Our method, the PI-XLRT, makes use of parental information in a robust way in addition to the transmission distortion, and thus makes more efficient use of the data than do existing methods. A paper on identifying risk-related variants on the X is in preparation.