Efficient Methods for Genotype-Specific Distributions with Unobserved Genotypes.

未观察到的基因型的基因型特异性分布的有效方法。

• 批准号: 8083280
• 负责人: Yuanjia Wang
• 金额: $ 28.05万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-07-15 至 2015-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8083280
• 关键词: Accounting; Address; Binding; Charge; Clinical Research; Cohort Studies; Communities; Computer software; Data; Disease; Event; Family; Family Study; Family member; Gender; Genes; Genetic; Genotype; Height; Huntington Disease; Infection; Laws; Least-Squares Analysis; Listeria monocytogenes; Literature; Maps; Methodology; Methods; Metric; Modeling; Mus; Mutation; Outcome; Parkinson Disease; Penetrance; Performance; Phenotype; Plants; Probability; Quantitative Trait Loci; Rare Diseases; Recombination Fraction; Recording of previous events; Relative (related person); Research; Residual state; Rice; Risk Estimate; Sampling; Series; Specific qualifier value; Statistical Methods; Testing; Time; Universities; Weight; base; frailty; genetic epidemiology; indexing; interest; member; mutation carrier; proband; programs; research study; simulation; theories; tool; trait; user-friendly

DESCRIPTION (provided by applicant): This proposal develops a series of new semiparametric efficient methods for genetic data where subjects' genotypes are not observed therefore phenotype data arise from a mixture of genotype-specific subpopulations. One example is data collected in a kin-cohort study, where the scientific interest is in estimating the distribution function of a trait or time to developing a disease for deleterious mutation carriers (penetrance function). In a kin- cohort study, index subjects (probands) possibly enriched with mutation carriers are sampled and genotyped. Disease history in relatives of the probands is collected, but the relatives are not genotyped therefore it may be unknown whether they carry a mutation. However, one can calculate the probability of each relative being a mutation carrier using the proband's genotype and Mendelian laws. Another example is interval mapping of quantitative traits (QTL). In such studies, genotype at a QTL is unobserved therefore the trait distribution takes the form of a mixture of QTL-genotype specific distributions. The probability of the QTL having a specific geno- type is computed based on marker genotypes and recombination fractions between the marker and the QTL. Interest is on estimating the QTL genotype-specific distributions. A common feature of these examples is that the scientific interest is in inference of genotype-specific subpopulations but it is unknown which subpopulation each observation belongs to. The probability of each observation being in any subpopulation varies and can be estimated. Without making a prespecified, error prone parametric assumption on these genotype-specific distributions, the only available statistical methods in the literature are two distinct nonparametric maximum like- lihood estimators (NPMLE1, NPMLE2). However, we will show that NPMLE1 is not efficient, and NPMLE2 is not consistent. There is therefore great need to develop valid and efficient statistical tools for such data. We use modern semiparametric theory to carry out a formal semiparametric analysis where we define a rich class of estimators. We show that any least squares based estimator is a member of this estimation class. We construct an optimal member of this family which obtains the minimum estimation variance hence reaches the semipara- metric efficiency bound. For censored outcomes, we propose a semiparametric efficient estimator given an influence function of the complete uncensored data. We propose an inverse probability weighting estimator, and add an augmentation term to obtain optimal efficiency. We also construct an imputation estimator which is easy to implement and does not require additional model assumption for the imputation step. Furthermore we propose methods to handle other observed covariates such as gender and additional residual correlation among family members. We also develop a series of tests for equality of two distributions at single or multi- ple time points simultaneously and an overall test of two distributions being equal at all time points. We will apply apply developed methods to analyze a kin-cohort study on Parkinson's disease, a large family study on Huntington's disease and two QTL studies. PUBLIC HEALTH RELEVANCE: This proposal develops a series of new semiparametric efficient methods for genetic data where subjects' genotypes are not observed therefore trait data arise from a mixture of genotype-specific subpopulations. The methodologies can be applied to estimate risk of developing a disease for deleterious mutation carriers.

描述（由申请人提供）：该提案开发了一系列新的半参数有效遗传数据方法，其中未观察到受试者的基因型，因此表型数据来自基因型特异性亚群的混合物。一个例子是在亲属队列研究中收集的数据，其中的科学兴趣在于估计有害突变携带者的某种性状的分布函数或发展为疾病的时间（外显率函数）。在亲属队列研究中，对可能富含突变携带者的指标受试者（先证者）进行取样和基因分型。收集先证者亲属的疾病史，但未对亲属进行基因分型，因此可能未知他们是否携带突变。然而，我们可以使用先证者的基因型和孟德尔定律来计算每个亲属作为突变携带者的概率。另一个例子是数量性状（QTL）的区间作图。在此类研究中，QTL 的基因型未被观察到，因此性状分布采用 QTL 基因型特异性分布的混合形式。基于标记基因型和标记与QTL之间的重组分数来计算QTL具有特定基因型的概率。人们感兴趣的是估计 QTL 基因型特异性分布。这些例子的一个共同特征是，科学兴趣在于推断基因型特异性亚群，但不知道每个观察结果属于哪个亚群。每个观测值在任何子群体中的概率各不相同，并且可以进行估计。如果不对这些基因型特异性分布做出预先指定的、容易出错的参数假设，文献中唯一可用的统计方法是两种不同的非参数最大似然估计器（NPMLE1、NPMLE2）。然而，我们将证明 NPMLE1 效率不高，并且 NPMLE2 不一致。因此，非常需要为此类数据开发有效且高效的统计工具。我们使用现代半参数理论来进行正式的半参数分析，其中我们定义了丰富的估计量类别。我们证明任何基于最小二乘的估计器都是该估计类的成员。我们构造该族的一个最佳成员，它获得最小估计方差，从而达到半参数效率界限。对于审查结果，我们提出了一个半参数有效估计量，给出了完整未经审查数据的影响函数。我们提出了一种逆概率加权估计器，并添加了一个增强项以获得最佳效率。我们还构建了一个易于实现的插补估计器，并且不需要插补步骤的额外模型假设。此外，我们提出了处理其他观察到的协变量的方法，例如性别和家庭成员之间的额外残差相关性。我们还开发了一系列同时在单个或多个时间点上两个分布相等的测试，以及两个分布在所有时间点上相等的总体测试。我们将应用已开发的方法来分析帕金森病的亲属队列研究、亨廷顿病的大家族研究和两项 QTL 研究。 公共卫生相关性：该提案为遗传数据开发了一系列新的半参数有效方法，其中未观察到受试者的基因型，因此性状数据来自基因型特定亚群的混合物。该方法可用于估计有害突变携带者患疾病的风险。