Exome sequencing (ES) has empowered genetic diagnosis and novel gene discovery, and is increasingly applied as a first-line test for a variety of disorders. Chronic kidney disease (CKD) affects more than in 1 in 10 persons worldwide, resulting in high morbidity, mortality, and healthcare costs. As CKD displays substantial genetic and phenotypic heterogeneity, the unbiased approach of ES can help to pinpoint a specific etiology and thereby support personalized care. However, the broader utility of ES for nephropathy and challenges associated with such expanded implementation have yet to be systematically assessed. Here, we investigate these questions through integrating ES and phenotype data from large CKD case and control cohorts. First, we survey the genetic and clinical disease spectrum of Mendelian forms of kidney and genitourinary disease, and generate a comprehensive curated list of gene-disease pairs. We then use ES data from 7,974 self-declared healthy adults to evaluate the population prevalence of candidate pathogenic variants for Mendelian nephropathy under different analytic filtering pipelines. We observe an appreciable frequency of putatively diagnostic variants for these conditions using stringent as well as standard filters, resulting in a considerable burden for both variant interpretation and clinical follow-up. Next, we perform ES and diagnostic analysis in a combined cohort of 3,315 all-cause CKD cases. We find diagnostic variants among patients spanning clinical disease categories, and that both the primary and secondary genetic findings resulting from ES have meaningful implications for medical management. We conclude by discussing the greater insights regarding the value of ES for kidney disease emerging from our investigations, and promising avenues for subsequent studies.

A subset of germline mutations that arise de novo each generation are deleterious and may cause severe genetic diseases. Predicting where in the genome and how often we expect to see deleterious mutations requires an understanding both of the distribution of mutation rates and the distribution of fitness effects in the genome. Both aspects are addressed in turn in the two projects described in this thesis. The distribution of mutations in the genome is poorly understood because germline mutations occur very rarely. In Chapter 1 of this work, we investigated the sources of mutations by using the spectrum of low-frequency variants in 13,860 human X chromosomes and autosomes as a proxy for the spectrum of germline de novo mutations. By comparing the mutation spectrum in multiple genomic compartments on the autosomes and between the X and autosomes that have unique biochemical and sex-specific properties, we ascribed specific mutation patterns to replication timing and recombination and identified differences in the types of mutations that accrue in males and females. Understanding mutational mechanisms provides a basis for modeling mutation rate variation in the genome, which is ultimately needed to infer the fitness effects of mutations. In Chapter 2, we used patterns of human genetic variation at methylated CpGsites, known to experience mutations at very high rates, to directly learn about the fitness effects of mutations at these sites. In whole exome sequences now available for 390,000 humans, 99% of putatively-neutral, synonymous CpG sites have experienced a C>T mutation; at current sample sizes, not seeing a C>T mutation at these sites indicates strong selection against that mutation. We leveraged the saturation of neutral C>T mutations and the similarity of mutation rates at methylated CpG sites across annotations to identify the subset of sites in a given functional annotation of interest that are likely to be under strong selection. One implication of this work is that for the vast majority of sites in the genome, there will be little information about strong selection even in samples that are many times larger than at present; the distribution of fitness effects at highly mutable CpG sites may then serve as an anchor for what to expect for other types of sites. Through the two specific cases described, this work illustrates the potential of large contemporary repositories of human genetic variation to inform human genetics and evolution, as well as their limitations in the absence of suitable models of mutation, selection, and other aspects of the evolutionary process.

Le cancer de l’ovaire est le cancer gynécologique avec la plus grande mortalité due à un diagnostique tardif au stade de maladie extensive péritonéale. Malgré les progrès de la chirurgie radicale et de la chimiothérapie les récurrences abdominales demeurent la cause la plus fréquente de mortalité. Il existe peu d’études de la maladie métastatique péritonéale. Notre hypothèse de travail est que les différences entre la maladie métastatique et la tumeur primaire sont primordiales dans la survenue d’une maladie résiduelle ou récurrente. Nous avons utilisé une approche exhaustive comprenant des études du transcriptome, des variations du nombre de copie (VNC) et des sequençages des exomes pour caractériser les différences entre lésions primaires, métastases péritonéales et métastases lymphatiques.Résultats: Notre étude démontre que les VNC varient de façon significative entre la tumeur primaire et la métastase peritonéale. Les différences d’expressions géniques bien que mineures permettent de retrouver les voies de signalisation primordiales pour le développement des métastases. Le séquençage des exomes montre très peu de différences en terme de polymorphisme. Par ailleurs la majorité des polymorphismes présents dans les métastases se retrouvent à une faible fréquence dans la tumeur primaire de façon concordante avec la théorie clonale. Conclusion: L’ensemble des résultats montre la possibilité d’une origine clonale de la maladie métastatique des cancers de l’ovaire comportant la majorité des anomalies au niveau des variations du nombre de copie. L’intégration de ces données permettrait d’optimiser les thérapeutiques ciblées. / Ovarian cancer is the most deadly gynecological cancer. The high rate of mortality is due to the large tumor burden with extensive metastatic lesion of the abdominal cavity. There are few studies on genetic alterations and their consequences in peritoneal metastatic tumors when compared to their matched ovarian primary tumors. Our hypothesis is that differences between the metastatic and primary lesions might be the cause of residual disease and, most importantly may have a role in post-chemotherapeutic recurrences. Methods: We conducted integrated genomics analysis on matched primary and metastatic tumors from 9 patients. In the papers presented here we analyze genome-wide Copy Number Variations (CNVs) using SNP Arrays targeting peritoneal metastasis differences, Gene expression differences using Microarrays also targeting peritoneal metastasis differences, and for some patients, Single Nucleotide Polymorphisms (SNPs) in genes through Exome sequencing.Results: Here we show that CNVs vary significantly between primary and metastatic tumors and include genes that have been considered potential chemotherapeutic targets based on primary tumor only data. Gene expression differences, while minor, showed highly statistically significant enrichment of genes in ovarian cancer critical pathways. In agreement with findings in other cancers, exome sequencing data revealed very few SNP differences of which most metastasis enriched SNPs were present at very low levels in the primary tumor. The results presented here should allow better design of therapies to target residual ovarian cancer disease.

Au cours de la dernière décennie, la recherche biomédicale et la pratique médicale ont été révolutionné par l'ère post-génomique et l'émergence des « Big Data » en biologie. Il existe toutefois, le cas particulier des maladies rares caractérisées par la rareté, allant de l’effectif des patients jusqu'aux connaissances sur le domaine. Néanmoins, les maladies rares représentent un réel intérêt, car les connaissances fondamentales accumulées en temps que modèle d'études et les solutions thérapeutique qui en découlent peuvent également bénéficier à des maladies plus communes. Cette thèse porte sur le développement de nouvelles solutions bioinformatiques, intégrant des données Big Data et des approches guidées par la connaissance pour améliorer l'étude des maladies rares. En particulier, mon travail a permis (i) la création de PubAthena, un outil de criblage de la littérature pour la recommandation de nouvelles publications pertinentes, (ii) le développement d'un outil pour l'analyse de données exomique, VarScrut, qui combine des connaissance multiniveaux pour améliorer le taux de résolution. / Over the last decade, biomedical research and medical practice have been revolutionized by the post-genomic era and the emergence of Big Data in biology. The field of rare diseases, are characterized by scarcity from the patient to the domain knowledge. Nevertheless, rare diseases represent a real interest as the fundamental knowledge accumulated as well as the developed therapeutic solutions can also benefit to common underlying disorders. This thesis focuses on the development of new bioinformatics solutions, integrating Big Data and Big Data associated approaches to improve the study of rare diseases. In particular, my work resulted in (i) the creation of PubAthena, a tool for the recommendation of relevant literature updates, (ii) the development of a tool for the analysis of exome datasets, VarScrut, which combines multi-level knowledge to improve the resolution rate.