Objective: We aimed to identify novel, functional, replicable and genome-wide significant risk regions specific for alcohol dependence using genome-wide association studies (GWASs). Methods: A discovery sample (1409 European-American cases with alcohol dependence and 1518 European-American controls) and a replication sample (6438 European-Australian family subjects with 1645 alcohol dependent probands) underwent association analysis. Nineteen other cohorts with 11 different neuropsychiatric disorders served as contrast groups. Additional eight samples underwent expression quantitative locus (eQTL) analysis. Results: A genome-wide significant risk gene region (NKAIN1-SERINC2) was identified in a meta-analysis of the discovery and replication samples. This region was enriched with 74 risk SNPs (unimputed); half of them had significant cis-acting regulatory effects. The distributions of -log(p) values for the SNP-disease associations or SNP-expression associations in this region were consistent throughout eight independent samples. Furthermore, imputing across the NKAIN1-SERINC2 region, we found that among all 795 SNPs in the discovery sample, 471 SNPs were nominally associated with alcohol dependence (1.7×10-7≤p≤0.047); 53 survived region- and cohort-wide correction for multiple testing; 92 SNPs were replicated in the replication sample (0.002≤p≤0.050). This region was neither significantly associated with alcohol dependence in African-Americans, nor with other non-alcoholism diseases. Finally, transcript expression of genes in NKAIN1-SERINC2 was significantly (p<3.4×10-7) associated with expression of numerous genes in the neurotransmitter systems or metabolic pathways previously associated with alcohol dependence. Conclusion: NKAIN1-SERINC2 may harbor a causal variant(s) for alcohol dependence. It may contribute to the disease risk by way of neurotransmitter systems or metabolic pathways.

Objective: We aimed to identify novel, functional, replicable and genome-wide significant risk regions specific for alcohol dependence using genome-wide association studies (GWASs). Methods: A discovery sample (1409 European-American cases with alcohol dependence and 1518 European-American controls) and a replication sample (6438 European-Australian family subjects with 1645 alcohol dependent probands) underwent association analysis. Nineteen other cohorts with 11 different neuropsychiatric disorders served as contrast groups. Additional eight samples underwent expression quantitative locus (eQTL) analysis. Results: A genome-wide significant risk gene region (NKAIN1-SERINC2) was identified in a meta-analysis of the discovery and replication samples. This region was enriched with 74 risk SNPs (unimputed); half of them had significant cis-acting regulatory effects. The distributions of -log(p) values for the SNP-disease associations or SNP-expression associations in this region were consistent throughout eight independent samples. Furthermore, imputing across the NKAIN1-SERINC2 region, we found that among all 795 SNPs in the discovery sample, 471 SNPs were nominally associated with alcohol dependence (1.7×10-7≤p≤0.047); 53 survived region- and cohort-wide correction for multiple testing; 92 SNPs were replicated in the replication sample (0.002≤p≤0.050). This region was neither significantly associated with alcohol dependence in African-Americans, nor with other non-alcoholism diseases. Finally, transcript expression of genes in NKAIN1-SERINC2 was significantly (p<3.4×10-7) associated with expression of numerous genes in the neurotransmitter systems or metabolic pathways previously associated with alcohol dependence. Conclusion: NKAIN1-SERINC2 may harbor a causal variant(s) for alcohol dependence. It may contribute to the disease risk by way of neurotransmitter systems or metabolic pathways.

Although multiple susceptibility loci for late-onset Alzheimer's disease (LOAD) have been identified, a large portion of the genetic risk for this disease remains unexplained. LOAD risk may be associated with single-nucleotide polymorphisms responsible for changes in gene expression (eSNPs). To detect eSNPs associated with LOAD, we integrated data from LOAD genome-wide association studies and expression quantitative trait loci using Sherlock (a Bayesian statistical method). We identified a cis-regulatory eSNP (rs2927438) located on chromosome 19q13.32, for which subsequent analyses confirmed the association with both LOAD risk and the expression level of several nearby genes. Importantly, rs2927438 may represent an APOE-independent LOAD eSNP according to the weak linkage disequilibrium of rs2927438 with the 2 polymorphisms (rs7412 and rs429358) defining the APOE-ε2, -ε3, and -ε4 alleles. Furthermore, rs2927438 does not influence chromatin interaction events at the APOE locus or cis-regulation of APOE expression. Further exploratory analysis revealed that rs2927438 is significantly associated with tau levels in the cerebrospinal fluid. Our findings suggest that rs2927438 may confer APOE-independent risk for LOAD.

No description available.

In the last years, Genome-Wide Associations Studies (GWAS) found many variants associated with complex diseases. However, the biological and molecular links between these variants and phenotypes are still mostly unknown. Also, even if sample sizes are constantly increasing, the associated variants do not explain all the heritability estimated for many traits. Many hypotheses have been proposed to explain the problem: from variant-variant interactions, the effect of rare and ultra-rare coding variants and also technical biases related to sequencing or statistic on sexual chromosomes. In this thesis, we mainly explore the hypothesis of variant-variant interaction and, briefly, the rare coding variants hypothesis while also considering possible molecular effects like allele-specific expression and the effects of variants on protein interfaces. Some parts of the thesis are also devoted to explore the implementation of efficient computational tools to explore these effects and to perform scalable genotyping of germline single nucleotide polymorphisms (SNPs) in huge datasets. The main part of the thesis regards the development of a new resource to identify putative variant-variant interactions. In particular, we integrated ChIP-seq data from ENCODE, transcription factor binding motifs from several resources and genotype and transcript level data from GTeX and TCGA. This new dataset allows us to formalize new models, to make hypothesis and to find putative novel associations and interactions between (mainly non-coding) germline variants and phenotypes, like cancer-specific phenotypes. In particular, we focused on the characterization of breast cancer and Alzheimer’s Disease GWAS risk variants, looking for putative variants’ interactions. Recently, the study of rare variants has become feasible thanks to the biobanks that made available genotypes and clinical data of thousands of patients. We characterize and explore the possible effects of rare coding inherited polymorphisms on protein interfaces in the UKBioBank trying to understand if the change in structure of protein can be one of the causes of complex diseases. Another part of the thesis explores variants as causal molecular effect for allele-specific expression. In particular, we describe UTRs variants that can alter the post-transcriptional regulation in mRNA leading to a phenomenon where an allele is more expressed than the other. Finally, we show those variants can have prognostic significance in breast cancer. This thesis work introduces results and computational tools that can be useful to a broad community of researcher studying human polymorphisms effects.

<p>  </p> <p>Forward genetics discovery of the molecular basis of induced mutants has fundamentally contributed to our understanding of basic biological processes such as metabolism, cell dynamics, growth, and development. Advances in Next-Generation Sequencing (NGS) technologies enabled rapid genome sequencing but also come with limitations such as sequencing errors, dependence on reference genome accuracy, and alignment errors. By incorporating pedigree information to help correct for some errors I optimized variant calling and filtering strategies to respond to experimental design. This led to the identification of multiple causative alleles, the detection of pedigree errors, and an ability to explore the mutational spectrum of multiple mutagens in Arabidopsis. Similar to the problems in forward genetic discovery of mutant alleles, variation in genomes complicates the analysis of gene expression affected by natural variation. The plant hypersensitive response (HR) is a highly localized and rapid form of programmed cell death that plants use to contain biotrophic pathogens. Substantial natural variation exists in the mechanisms that trigger and control HR, yet a complete understanding of the molecular mechanisms modulating HR is lacking. I explored the gene expression consequences of the plant HR in maize using a semi-dominant mutant encoding a constitutively active HR-inducing Nucleotide Binding Site Leucine Rich Repeat protein, <em>Rp1-D21,</em> derived from the receptor responsible for perceiving certain strains of the common rust <em>Puccinia sorghi</em>. Differentially expressed genes (DEG) in response to <em>Rp1-D21</em> were identified in different genetic backgrounds and hybrids that exhibit divergent enhancing (NC350) or suppressing (H95, B73) effects on the visual manifestations of HR. To enable this analysis, I created anonymized reference genomes for each comparison, so that the reference genome induced less bias in the mapping steps. Comprehensive identification of DEG corroborated the visual phenotypes and provided the identities of genes influential in plant hypersensitive response for further studies. The locations of expression quantitative trait loci (eQTL) that determined the differential response of NC350 and B73 were identified using 198 F1 families generated by crossing B73 x NC350 RIL population and <em>Rp1-D21</em>/+ in H95. This identified 3514 eQTL controlling the variability in differential expression between mutant versus wild-type. <em>Trans-</em>eQTL were dramatically arranged in the genome and identified 17 hotspots with more than 200 genes influenced by each locus. A single locus significantly affected expression variation in 5700 genes, 5396 (94.7%) of which were DGE. An allele specific expression analysis of NC350 x H95 and B73 x H95 F1 hybrids with and without <em>Rp1-D21</em> identified <em>cis-</em>eQTL and ASE at a subset of these genes. Bias in the confirmation of eQTL by ASE was still present despite the anonymized reference genomes indicating that additional efforts to improve signal processing in these experiments is needed.</p>

Doctor of Philosophy / Genetics Interdepartmental Program / James C. Nelson / QTL (quantitative-trait locus) analysis aims to locate and estimate the effects of genes that are responsible for quantitative traits, by means of statistical methods that evaluate the association of genetic variation with trait (phenotypic) variation. Quantitative traits are typically controlled by multiple genes with varying degrees of influence on the phenotype. I describe a new QTL analysis method based on shrinkage and a unifying framework based on the generalized linear model for non-normal data. I develop their extensions to multiple-trait QTL analysis. Expression QTL, or eQTL, analysis is QTL analysis applied to gene expression data to reveal the eQTLs controlling transcript-abundance variation, with the goal of elucidating gene regulatory networks. For exploiting eQTL data, I develop a novel extension of the graphical Gaussian model that produces an undirected graph of a gene regulatory network. To reduce the dimensionality, the extension constructs networks one cluster at a time. However, because Fuzzy-K, the clustering method of choice, relies on subjective visual cutoffs for cluster membership, I develop a bootstrap method to overcome this disadvantage. Finally, I describe QGene, an extensible QTL- and eQTL-analysis software platform written in Java and used for implementation of all analyses.

La present Tesi Doctoral s’emmarca dins d’una línia d’investigació dedicada a l’estudi de les bases genètiques del metabolisme dels greixos en relació a la producció de carn de porcí d’alta qualitat i saludable. L’objectiu final és la identificació de polimorfismes i mecanismes de regulació responsables de la variabilitat genètica d’aquests caràcters complexes en l’espècie porcina. En aquest context, s’han utilitzat mètodes de genòmica estructural (mapes de QTL d’expressió (eQTL); estudis de gens candidats) i funcional (estudis d’expressió gènica) a més a més d’anàlisis proteics i cel·lulars en mostres de múscul i greix de porcs seleccionats per les seves característiques de qualitat de carn. Així, mitjançant la tècnica de microarrays s’ha analitzat el patró d’expressió d’ARNm en mostres de múscul gluteus medius obtingudes a partir de porcs d’una població comercial Duroc amb fenotips divergents per diversos paràmetres relacionats amb la deposició dels lípids. Com a resultat, s’han observat nombrosos gens diferencialment expressats entre els animals amb perfils divergents d’engreixament. Un estudi ontològic/funcional va revelar que aquests gens estaven particularment relacionats amb el metabolisme lipídic, el creixement i la diferenciació muscular, la immunitat i la captació de glucosa en la ruta de la insulina. D’altra banda, les anàlisis d’eQTL han revelat l’existència de regions genòmiques responsables de la variació de l’expressió gènica en el múscul gluteus medius porcí; algunes de les quals mostren una concordança posicional amb varis QTL per caràcters de qualitat de cran i engreixament detectats prèviament a la mateixa població Duroc. Complementàriament, s’ha realitzat un estudi més exhaustiu de quatre gens candidats (ACACA, HMGCR, SCD i 6D), directament implicats en caràcters relacionats amb la qualitat de la carn al ser els principals responsables de la síntesis i dessaturació d’àcids grassos i colesterol. Combinant els resultats de l’anàlisi d’expressió gènica, mapes d’eQTL i els gens candidats estudiats s’ha elaborat una llista de gens candidats funcionals i posicionals que serà la base de futures investigacions cap a l’establiment de les xarxes gèniques i els mecanismes moleculars implicats en el metabolisme dels lípids musculars i els caràcters relacionats amb la qualitat de la carn en porcí. / La presente Tesis Doctoral se enmarca en una línea de investigación dedicada al estudio de las bases genéticas del metabolismo de las grasas en relación a la producción de carne de porcino de alta calidad y saludable. El objetivo final es la identificación de polimorfismos y mecanismos de regulación responsables de la variabilidad genética de estos caracteres complejos en porcino. Para ello se han utilizado métodos de genómica estructural (mapas de QTL de expresión (eQTL); estudio de genes candidatos) y funcional (estudios de expresión génica) y también de análisis proteico y celular en muestras de músculo y grasa de cerdos seleccionados por sus características de calidad de carne. Así, mediante la técnica de microarrays se ha analizado el patrón de expresión de ARNm en muestras de músculo gluteus medius obtenidas a partir de cerdos de una población comercial Duroc con fenotipos divergentes para varios parámetros relacionados con la deposición de los lípidos. Como resultado, se han observado numerosos genes diferencialmente expresados entre los animales con perfiles divergentes de engorde. Un estudio ontológico/funcional mostró que estos genes estaban particularmente relacionados con el metabolismo lipídico, el crecimiento y la diferenciación muscular, la inmunidad, y la captación de glucosa en la ruta de la insulina. Por otra parte, un análisis de eQTL ha revelado la existencia de regiones genómicas responsables de la variación de la expresión génica en el músculo gluteus medius porcino, algunas de las cuales muestran una concordancia posicional con varios QTL para caracteres de calidad de carne y engorde detectados previamente en la misma población Duroc. Complementariamente, se ha realizado un estudio más exhaustivo de cuatro genes candidatos (ACACA, HMGCR, SCD y 6D) directamente implicados en caracteres relacionados con la calidad de la carne al ser los principales responsables de la síntesis y desaturación de ácidos grasos y colesterol. Combinando los resultados del análisis de expresión génica, mapas de eQTL y los genes candidatos estudiados se ha elaborado una lista de genes candidatos funcionales y posicionales que será la base de futuras investigaciones hacia el establecimiento de las redes génicas y los mecanismos moleculares implicados en el metabolismo de los lípidos musculares y los caracteres relacionados con la calidad de la carne en porcino. / This PhD is part of a line of research devoted to studying the genetic basis of lipid metabolism and fat deposition in pigs with a view to producing healthy and high quality meat. The main objective is the identification of polymorphisms and regulatory mechanisms responsible for the genetic variability of these complex characters in pigs. In this sense, we have used several methods in the fields of structural (expression QTL (eQTL) maps; candidate genes studies) and functional (gene expression studies) genomics and also protein and cell studies in muscle and fat samples from pigs selected by meat quality parameters. In this context, using microarrays we analyzed the mRNA expression pattern in gluteus medius muscle samples obtained from a commercial Duroc pig population with divergent phenotypes for several parameters related to lipid deposition. As a result, we have obtained a list of genes differentially expressed between animals with divergent profiles related to lipid deposition. The ontological/functional study showed that these genes were particularly related to lipid metabolism, growth and muscle differentiation, immunity and glucose uptake in the insulin pathway. Moreover, analysis of eQTL has revealed the existence of genomic regions responsible for the variation of gene expression in porcine gluteus medius muscle. Some of these eQTL show positional concordance with several QTL related to meat quality and fat deposition previously identified in the same Duroc population. Additionally, we have performed a comprehensive study of four candidate genes (ACACA, HMGCR, SCD and 6D) directly involved in traits related to meat quality, playing an important role in fatty acids and cholesterol synthesis and desaturation. Combining the results of gene expression analysis, eQTL maps and candidate genes studied have resulted in a list of functional and positional candidate genes representing a valuable contribution to the understanding of the genetic regulation of skeletal muscle individual gene expression in swine species. This is a first step towards disentangling gene networks and molecular mechanisms involved in muscular lipid metabolism and meat quality traits in pigs.

No description available.

En 1975, Wilson et King ont proposé que l'évolution opère non seulement via des changements affectant la structure des protéines, mais aussi via des mutations qui modifient la régulation génétique. L'étude des éléments régulateurs de l'expression génétique a un rôle important dans la compréhension de l'expression de différentes maladies et de la réponse thérapeutique. Nous avons développé un algorithme bio- informatique qui nous permet rapidement de trouver des sites de régulation génétique à travers tout le génome et pour une grande quantité de gènes. Notre approche consiste à trouver des sites polymorphes (SNPs) qui sont en déséquilibre de liaison avec le débalancement allélique (AI) afin de cartographier la région régulatrice et le site responsable. Notre méthode est avantageuse par rapport à d'autres méthodes, car elle n'a pas besoin des données « phasées». De plus, les données de débalancement allélique ne sont pas affectées par des facteurs externes étant donné qu'ils sont mesurés dans la même cellule. Nous avons démontré que notre approche est fiable et qu'elle peut détecter des sites loin du gène. De plus, il peut être appliqué à des données de génotypage sans avoir besoin de les « phaser » . / Wilson and King (1975) proposed that evolution frequently operates through mutations affecting genetic regulation. Likewise, it is expected that genetic variation responsible for inter-individual differences will be due to variation in regulatory sites. Identifying such sites is thus important in the genetic and medical research. We have developed a new bioinformatics algorithm to find genome-wide regulatory sites for a big number of genes. Individuals carrying different alleles at a regulatory site will exhibit allelic imbalance(AI) due to differential expression of the two copies the same locus. Our approach consists of searching polymorphic sites (SNPs) in linkage disequilibrium with AI in order to map regulatory regions. We have detected many SNPs associated to the regulation of different genes pointed in previous studies. We have also found regulatory regions far from the transcription start site (TSS). The major advantage of this method is that phased data is not needed. In addition, AI data has the benefit of not being affected by external factors since it is measured in the same cell. The results show that our approach is reliable and it can detect sites far from the gene.