Statistical Methods For Gene/environment Interaction And Genetic Susceptibility

我们早期的方法来分析的情况下，父母的数据倍增的基因环境相互作用，导致有效的推理下的两个关键假设的致病SNP。第一个假设是，以父母的基因型为条件，子女的基因型分布反映孟德尔分类。第二，在父母基因型的条件下，孩子的基因型和暴露是独立的。 最近，我们一直在考虑一项研究设计，涉及一个受影响的和一个未受影响的后代和他们的父母。 我们称这种结构为四分体。 我们的建议是基因型的受影响的后代和父母，并收集暴露信息，从两个后代的想法，我们可以测试遗传和基因-环境的相互作用的影响，使用嵌入式的情况下，父母三重设计，我们可以研究暴露使用嵌入式同胞对设计。 在研究这种设计时，我们了解到，以前提出的基于家庭的基因-环境相互作用的测试可能会有偏见，当亚群在等位基因频率和暴露患病率方面存在差异时，以及当研究中的SNP是一种标记而不是其本身的致病SNP时。 这一发现既令人惊讶又令人不安，因为研究人员以前认为，即使在研究标记物时，基于家族的基因-环境相互作用研究也会受到群体结构偏差的影响。 我们已经发表了一份手稿，详细描述了这些问题，并提出了一个强大的四分体设计或不一致的同胞对，可以提供有效的推断标记的基因-环境相互作用时，暴露在研究中是二分的分析方法。 在相关的手稿中，我们提出了一个同胞增强的情况下，只有设计，并表明，通过适当的分析，它提供了相同的强大的推论基因-环境相互作用的分析，我们提出的四分体和疾病不一致的同胞对设计。 我们还发现，对于一个受影响的和两个或两个以上未受影响的兄弟姐妹的匹配设计，将兄弟姐妹关系视为缺失父母的核心家庭比传统的条件Logistic回归更有效，并且它也允许包含不匹配的受试者。 当基因分型检测计数合并标本中变异等位基因的数量时，我们正在研究将标本合并应用于来自病例-父母三联体的DNA。我们的程序将一个三联体样本分成几个小的子集，比如说，每个子集有两个三联体，并且，对于每个子集，构建三个合并的DNA样本：一个来自母亲，一个来自父亲，一个来自后代。将构成每个池的个体基因型视为缺失数据，我们的对数线性建模方法使用期望最大化算法来估计遗传等位基因、母体等位基因或起源父母效应的相对风险参数，这是其他DNA池方法无法做到的。当基因型测量没有错误时，我们看到与基因分型个体相比，功效几乎没有损失，但功效随着基因分型错误率的增加而下降。对于足够准确的检测，我们的方法有望以最小的功率损失降低基因分型成本。 (see也称为Z01 ES 040007 BB; PI克莱尔温伯格。

Our earlier approach to analyzing case-parents data for multiplicative gene-environment interaction leads to valid inference for a causative SNP under two crucial assumptions. The first assumption is that, conditional on parents' genotypes, the genotype distributions of children reflect Mendelian assortment. The second is that, conditional on parents' genotypes, a child's genotype and exposure are independent. Recently, we have been considering a study design that involves one affected and one unaffected offspring and their parents. We call this structure a tetrad. Our proposal is to genotype the affected offspring and the parents and to collect exposure information from both offspring under that idea that we could test genetic and gene-environment interaction effects using the embedded case-parent-triad design and we could study exposure using the embedded sibling-pair design. In studying this design, we learned that previously proposed family-based tests of gene-environment interaction can be biased when subpopulations differ in both allele frequency and exposure prevalence and when the SNP under study is a marker and not itself the causative SNP. This finding was both surprising and troubling, as researchers had previously believed that family-based studies of gene-environment interaction would robust to bias from population structure even when studying markers. We have published a manuscript that describes these issues in detail and proposes a robust method of analysis for tetrad designs or discordant sib-pairs that can provide valid inference about gene-environment interactions for markers when the exposure under study is dichotomous. In a related manuscript, we have proposed a sibling-augmented case-only design and showed that, with appropriate analysis, it provides the same robust inferences for gene-environment interactions as the analyses we proposed for the tetrad and disease-discordant sib-pair designs. We also found that, for matched designs with one affected and two or more unaffected sibling, treating the sibship as a nuclear family with missing parents is more efficient than traditional conditional logistic regression, and it allows inclusion of unmatched subjects as well. We are studying the application of specimen pooling to DNA from case-parent triads when the genotyping assay counts the number of variant alleles in a pooled specimen. Our procedure partitions a sample of triads into small subsets of, say, two triads each, and, for each subset, constructs three pooled DNA specimens: one each from mothers, from fathers, and from offspring. Treating the individual genotypes that comprise each pool as missing data, our log-linear-modeling approach uses the expectation-maximization algorithm to estimate relative risk parameters for inherited alleles, maternal alleles, or parent-of-origin effects, something other DNA pooling approaches cannot do. We see little loss of power compared to genotyping individuals when genotypes are measured without error, but power declines as genotyping error rates increase. For sufficiently accurate assays, our approach promises to reduce genotyping costs with minimal loss of power. (see also Z01 ES040007 BB; PI Clare Weinberg.)