DETECTING GENES OBSCURED BY HETEROGENEITY AND PLEIOTROPY

任何已被确定为风险因素的定量表型， 一种常见的疾病可能比一种疾病更能反映基因型。 疾病状态。 因此，风险因素可以提供信息 需要将疾病分类为亚型，第一阶段 剖析一种常见疾病的遗传异质性。 然而，风险因素本身往往是许多步骤删除 从影响其水平的基因，提供多个 表达遗传变异的机会。 因此，在这方面， 危险因素也反映了遗传异质性;多效性结果 当一个基因影响一个以上的风险因素。 用于检测潜在基因的可用统计方法 风险因素水平或定量表型，通常会产生模棱两可的 结果 似然分析通常假设遗传同质性;作为一个 因此，分离分析的似然法经常 产生不确定的结果和联系的可能性方法 分析高估了重组分数。 定量 表型扩展的同胞对连锁方法缺乏权力， 可能性分析 该应用程序建议开发有效的统计方法 在检测一组数量表型的基因时， 反映三种数据情况的遗传异质性和多效性： 1)只有数量表型可用; 2）连锁内的基因型 除了数量表型外，还可以获得; 3）基因型， 候选基因座除了数量表型之外。 新 结合标准统计方法和谱系的方法 将开发可能性分析以检测基因。 该方法正确检测潜在基因的能力 将通过比较特征来评估定量表型 从模拟系谱数据分析推断的基因， 模拟基因的特征。 模拟数据将包括 连锁图谱内的标记基因型、候选基因型，以及 反映多效性和遗传异质性的数量表型。

Any quantitative phenotype which has been identified as a risk factor for a common disease may reflect the genotype more closely than does the disease state. Therefore, the risk factor may provide the information needed to classify the disease into subtypes, the first stage in dissecting the genetic heterogeneity underlying a common disease. Nevertheless, the risk factors themselves are often many steps removed from the genes which affect their levels, providing multiple opportunities for the expression of genetic variation. As a consequence, the risk factors also reflect genetic heterogeneity; pleiotropy results when one gene affects more than one risk factor. The available statistical methods used to detect the genes underlying risk factor levels, or quantitative phenotypes, often produce equivocal results. Likelihood analysis usually assumes genetic homogeneity; as a consequence the likelihood method of segregation analysis frequently yields inconclusive findings and the likelihood method of linkage analysis overestimates the recombination fraction. Quantitative phenotype extensions of sib pair linkage methods lack the power of likelihood analysis. This application proposes to develop statistical methodology effective at detecting the genes underlying a set of quantitative phenotypes which reflect genetic heterogeneity and pleiotropy for three data situations: 1) only quantitative phenotypes available; 2) genotypes within a linkage may available in addition to quantitative phenotypes; 3) genotypes at candidate loci available in addition to quantitative phenotypes. New approaches combining standard statistical methodology and pedigree likelihood analysis will be developed to detect the genes. The capability of the methodology to correctly detect genes underlying quantitative phenotypes will be assessed by comparing the characteristics of genes inferred from analysis of simulated pedigree data to the characteristics of the simulated genes. The simulated data will include marker genotypes within a linkage map, candidate genotypes, and quantitative phenotypes reflecting pleiotropy and genetic heterogeneity.