The Study On Methodology Of Familial Aggregation On Complex Traits

The analysis of familial aggregation is the first step in the genetic analysis of anytrait. The strong empirical evidence of familial aggregation should be a prerequisitefor further investigation on the underlying disease genes by segregation and linkageanalysis. The estimation of magnitude and pattern of familial correlation and thedetection of the unmeasured genetic and environmental factors may provide importantclues for further etiological study of diseases, but it is necessary to developecorresponding statistical methods on family data or case-control family data. Thispaper mainly studied the analysis methods of familial aggregation on complex traitsincluding continuous traits, binary traits and censored traits and our main purpose wasto provide a series of applied, convenient and effective statistical tools to studyfamilial aggregation of diseases. There were two main parts: measurement of familial correlation and geneticvariance components models. 1. Measurement of familial correlation Pearson's correlation coefficient was used to measure familial correlation ofquantitative traits. We fitted multivariate mean and correlation coefficient marginalregression models. Many kinds of hypothesis on the pattern of familial correlation canbe tested through changing association design matrix. The second-order generalizedestimating equation (GEE2) provides the robust parameter estimation. This methodwas illustrated by an example of 327 nuclear families data on height. For binary traits, we presented an analysis method for case-control family data.Conditional and marginal model were combined under the frame of logistic regressionmodel. The marginal mean model for the proband's phenotype and for the relative'sphenotype conditional on the proband's phenotype, and the marginal associationmodel of the relative were together modeled. Conditional odds ratio and marginalodds ratio/correlation coefficient quantified the familial correlation on phenotypebetween the proband and relative, and among relatives, alternatively. Alternativelogistic regression (ALR) was used to estimate parameters. The examples ofcase-control family data of ovarian cancer and liver cancer showed several advantages 32004 年复旦大学博士学位论文 Abstractof this method in practical application. This method have higher efficiency whenanalyzing the association between disease phenotype and risk factors due to makingfully use of information, and can flexibly test different hypothesis on the pattern offamilial correlation, and can deal with pedigrees of arbitrary size and structure, andcan be implemented using standard GEE2 software such as SAS, MAREG. Theresults from case-control family data of liver cancer showed that the relatives of caseshad a three-fold increase in the risk of developing liver cancer compared with therelatives of controls. Furthermore, our findings suggested that HBV infection waslikely a main reason for the familial aggregation of liver cancer in South China. Cross ratio, i.e. conditional hazard ratio, function had been used to measure thedependence between bivariate failure times. Age at onset may be thought of as thefailure time data. For case-control family data a connection was established betweenthe cross ratio and the relative risk in a stratified proportional hazards model, andmaximum partial likelihood estimation was used. Marginal model of multivariatefailure times adjusted the dependence among ages at onset of multiple relatives. Weanalyzed the case-control family data of liver cancer using this method and found thatthe familial correlation between ages at onset of mother-offspring was higher than thatof father-offspring and siblings. 2. Genetic variance component model Genetic variance component models of quantitative and qualitative traits wereestablished using generalized linear mixed model (G...