Statistical Methods For Genetic Epidemiology

遗传流行病学统计方法

• 批准号: 8734062
• 负责人: Clarice Weinberg
• 金额: $ 55.29万
• 依托单位: NATIONAL INSTITUTE OF ENVIRONMENTAL HEALTH SCIENCES
• 依托单位国家: 美国
• 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/8734062
• 关键词: Affect; Age; Algorithms; Alleles; Autistic Disorder; Biometry; Characteristics; Clinical; Complex; Congenital Abnormality; Contracts; Critical Pathways; Data; Daughter; Defect; Detection; Development; Diagnosis; Disease; Drug Metabolic Detoxication; Enrollment; Entropy; Environment; Environmental Risk Factor; Epigenetic Process; Epistatic Gene; Etiology; Family; Fetus; Funding; Genes; Genetic; Genetic Risk; Genome; Genotype; Hand; Haplotypes; Human; Individual; Inherited; Joints; Life; Machine Learning; Maps; Mediating; Methods; Mitochondria; Mitochondrial DNA; Modeling; Mothers; Mutation; National Institute of Environmental Health Sciences; Nuclear Family; Oral; Paper; Parents; Performance; Phase; Play; Population; Pregnancy; Preparation; Recording of previous events; Research; Resistance; Risk; Role; Secondary to; Siblings; Simulate; Single Nucleotide Polymorphism; Sister; Specific qualifier value; Statistical Methods; Stratification; Structure; Testing; Triad Acrylic Resin; Variant; Woman; Work; X Chromosome; 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; sex linked trait; tool; transmission process; tumor

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.

全基因组关联研究通常会比较单个SNP常染色体变体的病例和对照在隐式假设下，即遗传效应是继承遗传常染色体遗传变异的继发效应。 也可能涉及四种非标准的遗传机制：与性别相关的性状，母亲介导的作用，母亲在妊娠期间影响胎儿的发展，这会影响后来的风险，线粒体DNA的突变和父母的胎儿影响。这些非标准机制中的每一个都会在家族史数据中引起不对称性，即使没有任何基因型数据，也可以研究。 在一个项目中，我们正在估计在存在此类机制之后的家族史数据中将产生的不对称程度。我们将该策略应用于我们对妇女的大型研究的家族史数据，每个妇女都被诊断为乳腺癌（NIEHS姐妹研究），并发现证据表明，年轻的祖母（50岁以下）的乳腺癌病例比其父亲祖母更可能患有乳腺癌。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（部分由Susan G. Komen用于治疗），该研究正在评估遗传和环境危险因素在年轻乳腺癌（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。这两项姐妹研究完成了核心家庭的入学率，其中一个女儿在50岁之前就患上了乳腺癌，另一个女儿不受影响。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。预计基因型数据将在9月底。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。 我们还参加了游戏财团，该财团将提供新开发的Oncochip的额外SNP。 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。我们还致力于评估我们的新方法的性能，以识别X染色体上与风险相关的变体。 我们的方法是PI-XLRT，除了传输失真外，还以强大的方式利用了父母信息，因此比现有方法更有效地利用了数据。一份有关识别X上与风险相关的变体的论文正在制备中。