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）：该提案为未观察到受试者基因型的遗传数据开发了一系列新的半参数有效方法，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Kin-Cohort研究中收集的数据，其中科学的兴趣在于估计特征或时间为有害突变载体开发疾病的分布功能（渗透函数）。在一项Kin-COHORT研究中，对可能富含突变载体的指数受试者（概率）进行了采样和基因分型。收集了概率亲戚的疾病史，但亲戚没有基因分型，因此可能未知它们是否具有突变。但是，可以使用Proband的基因型和Mendelian定律来计算每个相对是突变载体的概率。另一个示例是定量性状（QTL）的间隔映射。在此类研究中，QTL处的基因型未观察到，因此性状分布以QTL基因型特定分布的混合物形式。根据标记基因型和标记和QTL之间的重组分数计算具有特定Geno类型的QTL的概率。兴趣是估计QTL基因型特异性分布。这些例子的一个共同特征是，科学的兴趣是推断基因型特异性亚群，但尚不清楚每个观测值属于哪个亚群。每个观测值在任何亚种群中的概率都有所不同，可以估计。在这些基因型特异性分布上的预先指定的，易于误解的参数假设，文献中唯一可用的统计方法是两个不同的非参数最大值类似估计器（NPMLE1，NPMLE2）。但是，我们将证明NPMLE1不是有效的，并且NPMLE2不一致。因此，非常需要为此类数据开发有效有效的统计工具。我们使用现代的半参数理论来进行正式的半参数分析，在其中定义了丰富的估计量。我们表明，基于最小二乘的任何估计器都是此估计类别的成员。我们构建了该家族的最佳成员，该家族获得了最小估计方差，因此达到了半乳制效率结合。对于审查结果，我们提出了一个半参数有效估计器，鉴于完整未经审查的数据的影响函数。我们提出了一个反概率加权估计器，并添加增强项以获得最佳效率。我们还构建了一个易于实现的估算器，并且不需要为插图步骤进行其他模型假设。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留相关性。我们还开发了一系列的测试，以同时在单个或多个时间点上以两个分布的平等性以及对两个分布在所有时间点相等的总体测试。我们将应用开发的方法来分析一项有关帕金森氏病的亲属研究，一项关于亨廷顿氏病的大型家庭研究和两项QTL研究。 公共卫生相关性：该提案为未观察到受试者基因型的遗传数据开发了一系列新的半参数有效方法，因此特征数据来自基因型特异性亚群的混合物。该方法可以应用于估计有害突变携带者疾病的风险。