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dc.contributor.authorWang, Shuaien_US
dc.date.accessioned2016-05-23T13:51:48Z
dc.date.available2016-05-23T13:51:48Z
dc.date.issued2015
dc.identifier.urihttps://hdl.handle.net/2144/16359
dc.description.abstractStatistical association tests of quantitative traits have been widely used in the past decade, to locate loci associated with a disease trait. For instance, Genome Wide Association Studies (GWAS) have led to tremendous success in finding susceptible genes or associated loci. However, most of the past studies were based on unrelated samples focusing on quantitative or qualitative traits. The analysis of polychotomous traits in family samples is very challenging. This dissertation describes three projects related to methods to conduct association tests beyond continuous traits, such as multinomial traits, bivariate traits, and tests involving haplotypes. The first project focuses on developing a statistical approach to test the association between common or low-frequency variants with a multinomial trait in family samples. It is an important issue because there is no computer efficient software available for this type of question. We employ Laplace approximation in conjunction with an efficient grid-search strategy to obtain an approximate maximum log-likelihood function and the Maximum Likelihood Estimate (MLE) of the variance component. We also successfully incorporate the kinship matrix to adjust for the familial correlation, based on a regression framework. Extensive simulation studies are performed to evaluate the type-I error rate and power in scenarios with causal variant with different Minor Allele Frequency (MAF). In the second project, we propose an approach to test the association between a genetic variant and a bivariate trait arising from a combination of a quantitative and a binary trait in family samples, based on Extended Generalized Estimating Equations (EGEE). Multiple phenotype-genotype association tests are often reduced to univariate tests, decreasing efficiency and power. Our approach is shown to be much more powerful and efficient than univariate association tests adjusted for multiple testing. The third project involves the development of a general framework for meta-analysis of haplotype association tests, applicable to both unrelated and family samples. Although meta-analysis has been widely used in single-variant and gene-based tests, there are few existing methods to meta-analyze haplotype association tests. A predominant advantage of our novel approach is that it accommodates cohort-specific haplotypes as well as haplotypes common to all cohorts. The cohort participants may be either related or unrelated. Our approach consists of two stages: in the first stage, each cohort performs a haplotype association test, reports the estimates of effect size, variance, haplotypes, and their frequency. In the second stage, a generalized least square method is applied to combine the results of all the cohorts into one vector of meta-analysis coefficients. Our approach is shown to have the correct type-I error rate in scenarios with different between and within cohort variation. We also present an application to exome-chip data from a large consortium. Through the three projects, we are able to tackle the problem of conducting association tests for non-continuous traits in family samples. All the approaches achieve the correct type-I error rate and are computationally efficient. We hope these approaches will not only facilitate analyses of categorical traits in family samples, but will also provide a basis for future methodological development of statistical approaches for non-continuous traits.en_US
dc.language.isoen_US
dc.subjectBiostatisticsen_US
dc.subjectEGEEen_US
dc.subjectFamily samplesen_US
dc.subjectGenetic association studiesen_US
dc.subjectHaplotype association testen_US
dc.subjectMeta-analysisen_US
dc.subjectMultinomial traiten_US
dc.titleGenetic association methods for multiple types of traits in family samplesen_US
dc.typeThesis/Dissertationen_US
dc.date.updated2016-04-08T20:33:37Z
etd.degree.nameDoctor of Philosophyen_US
etd.degree.leveldoctoralen_US
etd.degree.disciplineBiostatisticsen_US
etd.degree.grantorBoston Universityen_US


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