Thesis advisor: Gabor T. Marth / During the last few years, numerous new sequencing technologies have emerged that require tools that can process large amounts of read data quickly and accurately. Regardless of the downstream methods used, reference-guided aligners are at the heart of all next-generation analysis studies. I have developed a general reference-guided aligner, MOSAIK, to support all current sequencing technologies (Roche 454, Illumina, Applied Biosystems SOLiD, Helicos, and Sanger capillary). The calibrated alignment qualities calculated by MOSAIK allow the user to fine-tune the alignment accuracy for a given study. MOSAIK is a highly configurable and easy-to-use suite of alignment tools that is used in hundreds of labs worldwide. MOSAIK is an integral part of our genetic variant discovery pipeline. From SNP and short-INDEL discovery to structural variation discovery, alignment accuracy is an essential requirement and enables our downstream analyses to provide accurate calls. In this thesis, I present three major studies that were formative during the development of MOSAIK and our analysis pipeline. In addition, I present a novel algorithm that identifies mobile element insertions (non-LTR retrotransposons) in the human genome using split-read alignments in MOSAIK. This algorithm has a low false discovery rate (4.4 %) and enabled our group to be the first to determine the number of mobile elements that differentially occur between any two individuals. / Thesis (PhD) — Boston College, 2010. / Submitted to: Boston College. Graduate School of Arts and Sciences. / Discipline: Biology.

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Tous les individus ne répondent pas de la même façon à un même traitement médicamenteux, tant sur le plan pharmacologique (efficacité) que sur le plan toxicologique (effets indésirables). Des facteurs génétiques affectant la pharmacocinétique et la pharmacodynamie des médicaments jouent un rôle déterminant dans cette variabilité interindividuelle de réponse. Certains de ces facteurs sont distribués de manière hétérogène entre les populations humaines. Ces différences s’expliquent en partie par des phénomènes d’adaptation locale des populations à leur environnement. Au cours de son histoire, l’homme a dû en effet faire face à des changements de son environnement chimique, qui ont entraîné des pressions de sélection naturelle sur les gènes intervenant dans la réponse de l’organisme aux xénobiotiques. Ce sont ces mêmes gènes qui, aujourd’hui, influencent la réponse aux médicaments.La formidable accélération des progrès de la génétique donne accès aujourd’hui à la variabilité génétique des populations humaines sur l’ensemble du génome, facilitant la découverte et la compréhension des mécanismes génétiques à l’origine des traits complexes comme la réponse aux médicaments. Les outils de la génétique des populations permettent notamment d’identifier des variants affichant un niveau de différenciation génétique inhabituel entre les populations humaines et de déterminer dans quelle mesure la sélection naturelle a joué un rôle dans les profils atypiques observés.Dans cette thèse, nous avons appliqué ces outils à des données de génotypage et de séquençage pour analyser les profils de différenciation génétique des populations humaines pour les gènes de la réponse aux médicaments. Nous avons ainsi démontré qu’une sélection positive récente en Asie de l’Est dans la région génomique du gène VKORC1 était responsable d’une hétérogénéité de distribution du variant fonctionnel de VKORC1, à l’origine des différences de sensibilité génétique aux anticoagulant oraux de type antivitamine K entre les populations humaines. Puis, en étendant notre analyse à l’ensemble des pharmacogènes majeurs, nous avons identifié de nouveaux variants potentiellement intéressants en pharmacogénétique pour expliquer les différences de réponse aux médicaments entre les populations humaines et les individus. Enfin, l’étude approfondie du gène NAT2 nous a permis de révéler un processus de sélection homogénéisante ciblant un variant fonctionnel associé à un phénotype d’acétylation très lent. Ces résultats soulignent l’influence déterminante de la sélection naturelle dans la variabilité de réponse aux médicaments entre les populations et les individus. Ils montrent l’apport de la génétique des populations pour une meilleure compréhension de la composante génétique de la réponse aux médicaments et des traits complexes. / Response to drug treatment can be highly variable between individuals, both in terms of therapeutic effect (efficacy) and of adverse reactions (toxicity).Genetic factors affecting drug pharmacodynamics and pharmacokinetics play a major role in this inter-individual variability. Some of these factors are heterogeneously distributed among human populations. Local adaptation of populations to their environment partly explained those differences. Indeed,during human evolution, populations had to cope with changes in their chemical environment that triggered selective pressures on genes involved in xenobiotic response. Those genes are the same ones that influence drug response today.The tremendous recent advances in genotyping and sequencing technologies now provide access to the genome-wide patterns of genetic variation in a growing number of human populations, facilitating our understanding of the genetic mechanisms underlying complex traits such as drug response. Population genetic tools allow the identification of variants showing an unusual pattern of genetic differentiation among human populations and the determination of the role played by natural selection in shaping the atypical patterns observed.In this thesis, we have applied these tools on both SNP-chip genotyping data and Next Generation Sequencing data to analyze the genetic differentiation patterns of human populations for genes involved in drug response. We show that a nearly complete selective sweep in East Asia in the genomic region of the VKORC1 gene is responsible for an heterogeneous distribution of theVKORC1 functional variant and can explain the inter-population genetic differences in response to oral anti-vitamin K anticoagulants. Extending the analysis to all major pharmacogenes, we have identified new variants of potential relevance to pharmacogenetics which could explain inter-population and inter-individual differences in drug response. Finally, by a comprehensive analysis of the NAT2 gene, we evidence a homogenizing selection process targeting a functional variant associated with a very slow acetylation phenotype. These results emphasize the crucial role of natural selection in the inter-population and inter-individual drug response variability.They also illustrate the relevance of population genetics studies for a better understanding of the genetic component underlying drug response and complex traits.

Ankylosing spondylitis (AS) is a common inflammatory arthritis of the spine and other affected joints, which is highly heritable, being strongly influenced by the HLA-B27 status, as well as hundreds of mostly unknown genetic variants of smaller effect. The aim of my research was to confirm some of the previously observed genetic associations and to identify new associations, many of which are in biological pathways relevant to AS pathogenesis, most notably the IL-23/T_H17 axis (IL23R) and antigen presentation (ERAP1 and ERAP2). Studies presented in this thesis include replication and refinement of several potential associations initially identified by earlier GWAS (WTCCC-TASC, 2007 and TASC, 2010). I conducted an extended study of IL23R association with AS and undertook a meta-analysis, confirming the association between AS and IL23R (non-synonymous SNP rs11209026, p=1.5 x 10-9, OR=0.61). An extensive re-sequencing and fine mapping project, including a meta-analysis, to replicate and refine the association of TNFRSF1A with AS was also undertaken; a novel variant in intron 6 was identified and a weak association with a low frequency variant, rs4149584 (p=0.01, OR=1.58), was detected. Somewhat stronger associations were seen with rs4149577 (p=0.002, OR=0.91) and rs4149578 (p=0.015, OR=1.14) in the meta-analysis. Associations at several additional loci had been identified by a more recent GWAS (WTCCC2-TASC, 2011). I used in silico techniques, including imputation using a denser panel of variants from the 1000 Genomes Project, conditional analysis and rare/low frequency variant analysis, to refine these associations. Imputation analysis (1782 cases/5167 controls) revealed novel associations with ERAP2 (rs4869313, p=7.3 x 10-8, OR=0.79) and several additional candidate loci including IL6R, UBE2L3 and 2p16.3. Ten SNPs were then directly typed in an independent sample (1804 cases/1848 controls) to replicate selected associations and to determine the imputation accuracy. I established that imputation using the 1000 Genomes Project pilot data was largely reliable, specifically for common variants (genotype concordence~97%). However, more accurate imputation of low frequency variants may require larger reference populations, like the most recent 1000 Genomes reference panels. The results of my research provide a better understanding of the complex genetics of AS, and help identify future targets for genetic and functional studies.