Modelling dependencies in genetic-marker data and its application to haplotype analysis

The objective of this thesis is to develop new methods to reconstruct haplotypes from phaseunknown genotypes. The need for new methodologies is motivated by the increasing avail¬ ability of high-resolution marker data for many species. Such markers typically exhibit correlations, a phenomenon known as Linkage Disequilibrium (LD). It is believed that re¬ constructed haplotypes for markers in high LD can be valuable for a variety of application areas in population genetics, including reconstructing population history and identifying genetic disease variantsTraditionally, haplotype reconstruction methods can be categorized according to whether they operate on a single pedigree or a collection of unrelated individuals. The thesis begins with a critical assessment of the limitations of existing methods, and then presents a uni¬ fied statistical framework that can accommodate pedigree data, unrelated individuals and tightly linked markers. The framework makes use of graphical models, where inference entails representing the relevant joint probability distribution as a graph and then using associated algorithms to facilitate computation. The graphical model formalism provides invaluable tools to facilitate model specification, visualization, and inference.Once the unified framework is developed, a broad range of simulation studies are conducted using previously published haplotype data. Important contributions include demonstrating the different ways in which the haplotype frequency distribution can impact the accuracy of both the phase assignments and haplotype frequency estimates; evaluating the effectiveness of using family data to improve accuracy for different frequency profiles; and, assessing the dangers of treating related individuals as unrelated in an association study

Joint analysis of sequence data and single-nucleotide polymorphism data using pedigree information for imputation and recombination inference

We developed a general framework for family-based imputation using single-nucleotide polymorphism data and sequence data distributed by Genetic Analysis Workshop 18. By using PedIBD, we first inferred haplotypes and inheritance patterns of each family from SNP data. Then new variants in unsequenced family members can be obtained from sequenced relatives through their shared haplotypes. We then compared the results of our method against the imputation results provided by Genetic Analysis Workshop organizers. The results showed that our strategy uncovered more variants for more unsequenced relatives. We also showed that recombination breakpoints inferred by PedIBD have much higher resolution than those inferred from previous studies

Quantifying the Fraction of Missing Information for Hypothesis Testing in Statistical and Genetic Studies

Many practical studies rely on hypothesis testing procedures applied to data sets with missing information. An important part of the analysis is to determine the impact of the missing data on the performance of the test, and this can be done by properly quantifying the relative (to complete data) amount of available information. The problem is directly motivated by applications to studies, such as linkage analyses and haplotype-based association projects, designed to identify genetic contributions to complex diseases. In the genetic studies the relative information measures are needed for the experimental design, technology comparison, interpretation of the data, and for understanding the behavior of some of the inference tools. The central difficulties in constructing such information measures arise from the multiple, and sometimes conflicting, aims in practice. For large samples, we show that a satisfactory, likelihood-based general solution exists by using appropriate forms of the relative Kullback--Leibler information, and that the proposed measures are computationally inexpensive given the maximized likelihoods with the observed data. Two measures are introduced, under the null and alternative hypothesis respectively. We exemplify the measures on data coming from mapping studies on the inflammatory bowel disease and diabetes. For small-sample problems, which appear rather frequently in practice and sometimes in disguised forms (e.g., measuring individual contributions to a large study), the robust Bayesian approach holds great promise, though the choice of a general-purpose "default prior" is a very challenging problem.Comment: Published in at http://dx.doi.org/10.1214/07-STS244 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Association analysis between binary traits and common or rare genetic variants on family-based data

Association studies test for genetic variation influencing disease risk. We explore here the application and development of statistics for binary traits on family data. There are two main areas of focus: the first on comparing existing single-variant tests, and the second on developing a gene-based test. In the first part, we carried out a comparative study by applying 42 family-based association test statistics on different family-based datasets, which are simulated under a variety of scenarios (varying levels of linkage disequilibrium; dominant, additive, and recessive disease models; a variety of family structures). We have compared the Type I error, power and robustness of all the statistics. The results show that, when testing the null hypothesis of no association and no linkage, among the statistics that have well-behaved Type I error, the More powerful Quasi-likelihood Score test has the highest power and high robustness. In the second part, motivated by a need for powerful gene-based association statistics on family-based data for binary traits, we have proposed a new test statistic, which is based on a mixed model framework, Laplace's method and a variance component score test. We have compared the Type I error rates and power of our new statistic and six existing statistics by simulating different scenarios (varying the number and effect size of risk and protective variants). Our proposed statistic shows well-behaved Type I error and high power in some scenarios. The insights gathered here may improve public health by providing information on how to effectively utilize association methods to detect genetic variants that are related to disease. Ultimately, they should help improve the understanding of disease etiology

HaploForge: a comprehensive pedigree drawing and haplotype visualization web application

Motivation: Haplotype reconstruction is an important tool for understanding the aetiology of human disease. Haplotyping infers the most likely phase of observed genotypes conditional on constraints imposed by the genotypes of other pedigree members. The results of haplotype reconstruction, when visualized appropriately, show which alleles are identical by descent despite the presence of untyped individuals. When used in concert with linkage analysis, haplotyping can help delineate a locus of interest and provide a succinct explanation for the transmission of the trait locus. Unfortunately, the design choices made by existing haplotype visualization programs do not scale to large numbers of markers. Indeed, following haplotypes from generation to generation requires excessive scrolling back and forth. In addition, the most widely used program for haplotype visualization produces inconsistent recombination artefacts for the X chromosome. / Results: To resolve these issues, we developed HaploForge, a novel web application for haplotype visualization and pedigree drawing. HaploForge takes advantage of HTML5 to be fast, portable and avoid the need for local installation. It can accurately visualize autosomal and X-linked haplotypes from both outbred and consanguineous pedigrees. Haplotypes are coloured based on identity by descent using a novel A* search algorithm and we provide a flexible viewing mode to aid visual inspection. HaploForge can currently process haplotype reconstruction output from Allegro, GeneHunter, Merlin and Simwalk. / Availability and implementation: HaploForge is licensed under GPLv3 and is hosted and maintained via GitHub. https://github.com/mtekman/haploforge / Contact: [email protected] / Supplementary information: Supplementary data are available at Bioinformatics online

Fast and Accurate Haplotype Inference with Hidden Markov Model

The genome of human and other diploid organisms consists of paired chromosomes. The haplotype information (DNA constellation on one single chromosome), which is crucial for disease association analysis and population genetic inference among many others, is however hidden in the data generated for diploid organisms (including human) by modern high-throughput technologies which cannot distinguish information from two homologous chromosomes. Here, I consider the haplotype inference problem in two common scenarios of genetic studies: 1. Model organisms (such as laboratory mice): Individuals are bred through prescribed pedigree design. 2. Out-bred organisms (such as human): Individuals (mostly unrelated) are drawn from one or more populations or continental groups. In the two scenarios, one individual may share short blocks of chromosomes with other individual(s) or with founder(s) if available. I have developed and implemented methods, by identifying the shared blocks statistically, to accurately and more rapidly reconstruct the haplotypes for individuals under study and to solve important related problems including genotype imputation and ancestry inference. My methods, based on hidden Markov model, can scale up to tens of thousands of individuals. Analysis based on my method leads to a new genetic map in mouse population which reveals important biological properties of the recombination process. I have also explored the study design and empirical quality control for imputation tasks with large scale datasets from admixed population.Doctor of Philosoph

GEVALT: An integrated software tool for genotype analysis

BACKGROUND: Genotype information generated by individual and international efforts carries the promise of revolutionizing disease studies and the association of phenotypes with alleles and haplotypes. Given the enormous amounts of public genotype data, tools for analyzing, interpreting and visualizing these data sets are of critical importance to researchers. In past works we have developed algorithms for genotypes phasing and tag SNP selection, which were shown to be quick and accurate. Both algorithms were available until now only as batch executables. RESULTS: Here we present GEVALT (GEnotype Visualization and ALgorithmic Tool), a software package designed to simplify and expedite the process of genotype analysis, by providing a common interface to several tasks relating to such analysis. GEVALT combines the strong visual abilities of Haploview with our quick and powerful algorithms for genotypes phasing (GERBIL), tag SNP selection (STAMPA) and permutation testing for evaluating significance of association. All of the above are provided in a visually appealing and interactive interface. CONCLUSION: GEVALT is an integrated viewer that uses state of the art phasing and tag SNP selection algorithms. By streamlining the application of GERBIL and STAMPA together with strong visualization for assessment of the results, GEVALT makes the algorithms accessible to the broad community of researchers in genetics