Statistical Methods For Genetic Epidemiology

全基因组关联研究通常在隐性假设下比较单SNP常染色体变异的病例和对照，即遗传效应继发于遗传性常染色体遗传变异。四种非标准的遗传机制也可能涉及：性别相关的特征，母亲介导的效应，母亲在怀孕期间影响胎儿的发育，这影响了后来的风险，线粒体DNA的突变，以及父母的起源效应。这些不标准的机制都可能导致家族史数据的不对称，即使没有任何基因型数据，也可以对家族史数据进行研究。在一个项目中，我们正在估计在家族史数据中产生的不对称程度，这种不对称是由于这种机制的存在而产生的。我们将这一策略应用于我们的大型女性研究的家族史数据，这些女性都有一个姐妹被诊断患有乳腺癌（NIEHS姐妹研究），并发现有证据表明，年轻发病（50岁以下）乳腺癌病例的外祖母比她们的外祖母更有可能患乳腺癌。这表明可能存在母亲介导的乳腺癌遗传风险因素，可能存在与风险相关的印迹基因或线粒体变异起作用。表观遗传学对乳腺癌也很重要。

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.