CASE CONTROL BIAS & EFFICIENCY: CANDIDATE GENES & GENE ENVIRONMENT INTERACTION

近年来，对该病病因的认识已成为一个焦点 复杂的疾病，不遵循简单的孟德尔，单基因座 种族隔离 复杂的疾病被认为是由超过 一个场所和/或环境因素，或仅仅表现出连续的 （即数量）变化。 数量性状已经被研究 在植物和动物遗传学中有广泛的应用。 随着新的 确定候选人的综合办法 作为人类数量性状基础的基因是可用的。 候选基因对数量性状贡献的检验 随着更多的基因被识别，变异将变得司空见惯。 我们 考虑两种基于可能性的统计策略进行测试， 定量候选基因座基因型对定量基因型的影响， 性状与随机确定的系谱或家庭数据。 第一 策略估计平均表型，然后检验平均表型的相等性 值与每个基因型的关联。 这一战略可以参考 这就是“平均效应”策略。 第二个战略估计和 测试与按状态标识相关联的方差分量参数 从候选基因座的等位基因收集的信息。 这 策略测试等位基因变异和共变异是否在 相关个体之间的基因座解释了变异， 这些个体中感兴趣的表型的共变异。 这第二种策略可以被称为“方差分量” 战略，并形成了大多数连锁分析战略的基础， 数量性状 这两种战略都可以在以下背景下制定： 可以适应其他因素影响的线性模型（例如， 性别、年龄、性别等）对感兴趣的表型的影响。 我们认为 使用这些模型与同胞对数据和双等位基因位点，和 描述分析推导，比较和对比它们的力量 效能 我们的研究结果表明，平均效应模型是 上级优于方差分量模型在同胞对，双等位基因 轨迹设置

In recent years, there has been a focus to understand the etiology of complex diseases which do not follow simple Mendelian, single-locus segregation. Complex diseases are assumed to result from more than one locus and/or environmental factors, or merely exhibit continuous (i.e.quantitative) variation. Quantitative traits have been studied extensively in plant and animal genetics. With the advent of new tools and methods, comprehensive approaches to identify the candidate genes underlying quantitative traits for humans are available. Testing the contribution of candidate genes to quantitative trait variation will become commonplace as more genes are identified. We consider two likelihood-based statistical strategies for testing and quantifying the effect of candidate locus genotypes on a quantitative trait with randomly ascertained pedigree or family data. The first strategy estimates, and then tests the equality of, mean phenotype values association with each genotype. This strategy can be referred to as the "mean effects" strategy. The second strategy estimates and tests a variance component parameter associated with identity-by-state information gathered from alleles at the candidate locus. This strategy tests whether or not allelic variation and co-variation at the locus in question among related individuals explains variation and co-variation in the phenotype of interest among those individuals. This second strategy can be referred as the "variance component" strategy, and forms the basis of most linkage analysis strategies for quantitative traits. Both strategies can be framed in the context of linear models which can accommodate the effects of other factors (e.g. gender, age, sex, etc.) on the phenotype of interest. We consider the use of these models with sib-pair data and bi-allelic loci, and describe analytic derivations that compare and contrast their power and efficiency. Our results suggest that the mean effects model is superior to the variance component model in the sib-pair, di-allelic locus setting.