Statistical Methods For Genetic Epidemiology

遗传流行病学统计方法

基本信息

批准号：
8553689
负责人：
Clarice Weinberg
金额：
$ 20.08万
依托单位：
NATIONAL INSTITUTE OF ENVIRONMENTAL HEALTH SCIENCES
依托单位国家：
美国
项目类别：
财政年份：
资助国家：
美国
起止时间：
至
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/8553689
关键词：
Affect Age Algorithms Alleles Biometry Characteristics Chromosome Mapping Clinical Complex Congenital Abnormality Consumption Contracts Critical Pathways Data Daughter Defect Detection Disease Drug Metabolic Detoxication Enrollment Environment Environmental Risk Factor Epigenetic Process Epistatic Gene Etiology Family Fetus Frequencies Genes Genetic Genetic Risk Genome Genotype Hand Haplotypes Human Individual Inherited Joints Learning Life Linkage Disequilibrium Machine Learning Maps Mediating Methods Mitochondria Modeling Mutation Nuclear Family Oral Paper Parents Performance Phase Play Population Predisposition Pregnancy Prevalence Procedures Publishing Qualitative Methods Recording of previous events Research Research Personnel Resistance Risk Role Sampling Secondary to Siblings Simulate Single Nucleotide Polymorphism Sister Specific qualifier value Statistical Methods Stratification Structure Testing Triad Acrylic Resin Variant Work base case control database of Genotypes and Phenotypes design disorder risk early onset exome gene environment interaction genetic epidemiology genetic risk factor genetic variant genome wide association study graduate student imprint improved malignant breast neoplasm offspring oral cleft prenatal influence simulation surrogacy trait tumor

项目摘要

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.

我们以前曾以一种称为TRIMM（Triad Multi-sarker）的方法为受影响的个体及其父母使用多种SNP基因型的定性性状开发了定性性状的方法。由于人口分层，测试方法对偏见是可靠的。我们进一步扩展了通过我们称为Gei-Trimm的方法来允许对单倍型进行测试的方法。今年发表了描述这种方法并通过模拟表征其性能的论文。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，该数据是存在于母体介导的遗传效应之后的。我们将该策略应用于姐妹研究的家族史数据，并发现证据表明，年轻乳腺癌的祖母（50岁以下）乳腺癌的可能性比父亲祖母更有可能患有乳腺癌。这表明乳腺癌可能存在母体介导的遗传危险因素，可能存在与危险有关的烙印基因或线粒体变体起作用。表观遗传学对乳腺癌也可能很重要。我们现在正在考虑的一个特别重要的设计涉及一个“四分法”结构，除两个父母外，还具有一个受影响的和一个未受影响的后代。这项设计已在两项姊妹研究中实施，该研究正在评估遗传和环境危险因素在年轻人（50岁以下）乳腺癌中的共同作用。不一致的SIB对允许估计暴露的影响，而嵌入式的病例三合会则允许检测赋予保护或风险的单倍型。分析的四四分之一应为评估逐个环境相互作用的基因提供强大的设计。我们一直在努力开发和评估与Tetrad设计一起使用的方法。这两个姐妹研究正在继续招募核心家庭，其中一个女儿在50岁之前患上乳腺癌，另一个女儿不受影响。我们目前已经招收了近1500个此类家庭。这在一个单独的项目下进行了描述。将需要探索遗传的基因型以及肿瘤特征，以调查预测治疗后临床过程的因素，并且在这种情况下还需要开发改进的统计方法。我们通过与约翰·霍普金斯（Johns Hopkins）的遗传性疾病研究中心的合同进行了基于这些数据的全基因组关联研究，并且将能够探索逐个环境对年轻乳腺癌风险的影响，并研究母体介导的影响以及可能对风险的父母影响对风险的影响。将要使用的Illumina平台是人类Omniexpress加上外观阵列，使用外显子键入将迫使需要开发适合稀有等位基因的进一步方法。我们已经开发了一种使用Tetrad结构来研究基因相互作用的方法，并进行了广泛的模拟，以记录其在各种情况下的性能，有些且有些没有暴露涉及的人口结构。令我们惊讶的是，如果人口曝光了涉及的人口结构，那么所有现有的逐环境相互作用方法都会遭受偏见。当有一些亚群在研究中的标记等位基因频率和暴露率的频率上有所不同时，就会发生这种情况。最佳的偏见可以理解为反映这样的事实，即通过这种结构，这种曝光可以作为链接不平衡程度的替代物（在研究标记和研究标记中的子群体和一种病因/单倍型之间的不同）。这种偏见可能是极端的。现在，我们已经开发了一些避免它的补救措施，同时保留了良好的统计能力，这些统计能力在最近发表的工作中。强大的过程使用纯粹的方法，但通过随机采样未受影响的兄弟姐妹的曝光数据来增强它。与来自UNC生物统计学的研究生Alison Wise一起，我们正在研究一种机器学习方法，以基于病例父母的三合会来寻找复杂的上皮和基因相互作用的相互作用。我们从口腔裂口上的DBGAP下载了病例 - 父母的三合会数据，对其进行了消毒以进行实际效果，并使用这些基因组生成模拟的案例 - 父母三合会数据，并具有已知的GXGXGXG Intervations。我们正在努力开发一种算法，该算法可以从GWAS数据中搜索SNP的3向选择的巨大搜索空间，并确定正确的多SNP模型，即使属性风险很小。这项工作正在进行中。