Browsing by Author "Trudy F.C. Mackay, Committee Member"

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    Multiple Trait Multiple Interval Mapping of Quantitative Trait Loci from Inbred Line Crosses
    (2010-03-03) Silva, Luciano Da Costa E; Zhao-Bang Zeng, Committee Chair; Howard D. Bondell, Committee Co-Chair; Trudy F.C. Mackay, Committee Member; Sujit K. Ghosh, Committee Member
    Tremendous progress has been made in recent years on developing statistical methods for mapping quantitative trait loci (QTL) from crosses of inbred lines. Most of the recent research is focused on strategies for mapping multiple QTL and associated model selection procedures and criterion. In Chapter 1, we review the progress of research on QTL mapping on one and multiple trait by maximum likelihood and Bayesian methods. Although in many instances multiple trait are measured in the same subject, single traits analyses have been the main stream for the purpose of QTL identiﬠcation. However, single trait analyses do not take advantage of correlation between traits. Multiple trait analysis allows an investigator to assess the pattern of action of QTL on multiple trait, such as, testing the hypothesis of existence of pleiotropic QTL versus the hypothesis of close linked QTL affecting multiple trait, and testing the hypothesis of QTL by environment interaction. In Chapter 2, we proposed a statistical model for mapping multiple QTL affecting multiple trait, the multiple trait multiple interval mapping (MTMIM) model. We also developed a score-based threshold for assessing signiﬠcance level of QTL effects on multiple trait. Our MTMIM model provides a comprehensive framework for QTL inference in multiple trait, in which the score-based threshold is built in as an essential and elegant tool for computing the signiﬠcance level of effects of putative QTL in the genome-wide scan, therefore, allowing us to build a set of models containing multiple QTL. In Chapter 3, we empirically showed that the score-based threshold maintains the false discovery rate within acceptable levels and the multiple trait analysis can bring insights into the analysis of data for the purpose of QTL identiﬠcation. The analysis of data from an experiment with Drosophila showed the potential of our MTMIM model in delivering complementary information regarding the genetic architecture of complex traits, such as, estimating QTL effects on a set of traits simultaneously, testing for the presence of pleiotropic QTL, and estimating the genotypic covariance between traits. A generalized expectation maximization Newton-Raphson (GEM-NR) algorithm for maximizing the likelihood function and estimating parameters in the MTMIM model was compared to the expectation-conditional maximization (ECM) algorithm. Empirical comparison showed that GEM-NR speeded up the convergence of likelihood function considerably when compared to the ECM algorithm, while still delivering stable estimates of parameters. In Chapter 4, we proposed analytical formulae to predict the length of conﬠdence interval for position of QTL and to predict shape of the LRT around the position of QTL in highly saturate linkage maps and multiple trait analysis using large sample theory. Our results generalize the results of Visscher and Goddard (2004) and they can be used to predict the length of conﬠdence interval for position of QTL with a hypothesized effect on multiple trait, for any given coverage probability. Our analytical formulae can also be used to predict shape of LRT around the position of QTL. Furthermore, we proposed an alternative method for predicting the length of conﬠdence interval for position of QTL, the adjusted method. The adjusted method accounts for the length of the chromosome in which the QTL is located and can deliver more accurately predictions than the method with no adjustments, especially for QTL of low heritability. Our simulation results showed that for sample size of 300 and QTL with heritability levels of 5, 10 and 15%, there are good agreement between lengths of conﬠdence intervals empirically estimated and analytically predicted with the adjusted method.
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    Statistical Methods for Family-Based Association Studies for Complex Human Diseases: Single-Locus and Haplotype Methods
    (2006-12-15) Chung, Ren-Hua; Bruce S. Weir, Committee Co-Chair; Eden R. Martin, Committee Co-Chair; Trudy F.C. Mackay, Committee Member; Dahlia M. Nielsen, Committee Member; Jung-Ying Tzeng, Committee Member
    Disease-gene fine-mapping is an important task in human genetics. Linkage and association analyses are the two main approaches for exploring disease susceptibility genes. In Chapter 1, we introduce the development of methods for disease-gene mapping in the past decades and present the rationale behind our new method development. Family-based association analyses have provided powerful tools for disease-gene mapping. The Association in the Presence of Linkage test (APL), a family-based association method, can use nuclear families with multiple affected siblings and infer missing parental genotypes properly in the linkage region. In Chapter 2, we generalized and extended APL so that it can be applied to general nuclear family structures using a bootstrap variance estimator. Unlike the original APL that can handle at most two affected siblings, the new APL can handle up to three affected siblings. We also extended APL from a single-marker test to a multiple-marker haplotype analysis. According to our simulations, the new APL has a correct type I error rate and more power than other family-based association methods such as PDT, FBAT⁄HBAT, and PDTPHASE in nuclear families with missing parents. The robustness of APL when there are rare alleles or haplotypes and when there is population substructure such that the allele frequencies in the population deviated from the Hardy-Weinberg Equilibrium (HWE) assumption was also examined in Chapter 2. Genes on the X chromosome play a role in many common diseases. Linkage analyses have identified regions on the X chromosome with high linkage peaks for several diseases. Currently there are few family-based association methods available for X-chromosome markers. In order to fill in this gap, we proposed a novel family-based association method, X-APL, in Chapter 3. X-APL is a modification of APL and shares some important properties with APL. X-APL can also perform haplotype analyses, which is the only family-based test of association we are aware of for testing haplotypes for the X-chromosome markers. Our simulation results showed that X-APL has a correct type I error rate and has more power than other family-based association methods for X chromosome such as XS-TDT, XPDT and XMCPDT for single-marker analysis in nuclear families. The robustness of X-APL when there are deviations of genotype frequencies from HWE was also examined in Chapter 3. Linkage and family-based association analyses are often applied simultaneously in the same data in order to maximize use of family data sets. However, it is not intuitively clear under what conditions association and linkage tests performed in the same data set may be correlated. In Chapter 4, we used computer simulations and theoretical statements to estimate the correlation between linkage statistics (affected sib pair maximum LOD scores) and family-based association statistics (PDT and APL) under various hypotheses. Different types of pedigrees were studied: nuclear families with affected sib pairs, extended pedigrees and incomplete pedigrees. Both simulation and theoretical results showed that when there is either no linkage or no association, the linkage and association statistics are not correlated. When there is linkage and association in the data, the two tests have a positive correlation.
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    Statistical Methods for Identifying X-linked Genes Associated with Complex Phenotypes
    (2008-11-05) Zhang, Li; Eden R. Martin, Committee Co-Chair; Richard W. Morris, Committee Member; Jeffrey L. Thorne, Committee Co-Chair; Trudy F.C. Mackay, Committee Member; Jung-Ying Tzeng, Committee Member
    Genetic association studies aim to detect association between one or more genetic polymorphisms and complex traits, which might be some quantitative characteristic or a qualitative attribute of disease. In Chapter 1, we introduce the development of methods for association mapping in the past decades and present the rationale behind our X-linked method development. Family-based association methods have been well developed for autosomes, but unique features of X-linked markers have received little attention. In Chapter 2, we propose a likelihood approach (X-LRT) to estimate genetic risks and test association using a case-parents design. The method uses nuclear families with a single affected proband, and allows additional siblings and missing parental genotypes. We also extend X-LRT from a single-marker test to a multiple-marker haplotype analysis. Our X-LRT offers great flexibility for testing different penetrance relationships within and between sexes. In addition, estimation of relative risks provides a measure of the magnitude of X-linked genetic effects on complex disorders. In Chapter 3 and 4, we fill the methodological gaps by developing two approaches (X-QTL and X-HQTL) to test association between X-linked marker alleles/haplotypes and quantitative traits in nuclear family design. We adopt the orthogonal decomposition which provides consistent estimates of the additive genetic values of marker alleles/haplotypes. Joint estimation of the linkage variance component in the association model reduces type I errors to nominal expectations. Dosage compensation models provide a simple relationship of X-linked additive effects between sexes. In Chapter 2, 3, and 4, our simulation results demonstrate the validity and substantially higher power of our approaches compared with other existing programs. We also apply our methods to MAOA & MAOB candidate-gene studies of family data with Parkinson disease. In Chapter 5, we discuss some issues relevant to the design and execution of our X-linked family-based association studies.
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    Transcriptional Regulatory Pattern in Yeast Revealed through Expression Quantitative Trait Locus Mapping
    (2007-08-04) Zou, Wei; Bruce Weir, Committee Member; Sujit K. Ghosh, Committee Member; Zhaobang Zeng, Committee Chair; Trudy F.C. Mackay, Committee Member