Analytical strategies in imaging genetics : assessment of potential risk factors for neurodevelopmental domains

Imaging Genetics (IG) aims to test how genetic information influences brain structure and function, cognitive processes and complex neurodevelopmental domains, combining magnetic resonance imaging-based brain features and genetic data from the same individual. IG studies represent an opportunity to deepen our knowledge of the biological mechanisms of neurodevelopmental domains and complex brain disorders. Most studies focus on individual correlation and association tests between a subset of genetic variants (usually single nucleotide polymorphisms, SNPs) and a single measurement of the brain. Despite the great success of univariate approaches, given the current focus of imaging genetic studies in which genome-wide, whole-brain studies should be analyzed, the development of novel statistical methods becomes crucial. The main aim of this thesis consists of investigating genetic determinants of structural brain change, which in turn affect neurodevelopmental domains. We propose the application and development of statistical strategies to improve the assessment of significant relationships associated with neurodevelopmental domains. Specifically, we focus our research efforts on understanding what genomic changes in the cerebral structure allow improvements in the assessment of risk factors associated with Attention-Deficit/Hyperactivity disorder domains, and related cognitive processes such as attention function.Els estudis que combinen la informació genètica i de neuroimatge (IG) pretenen provar com la informació genètica influeix en l'estructura i funció cerebral, en el comportament, i en els dominis del neurodesenvolupament, combinant la informació extreta de ressonàncies magnètiques del cervell i de la informació genètica d'un mateix individu. Els estudis d'IG representen una oportunitat per aprofundir en el coneixement dels mecanismes biològics dels dominis del desenvolupament neurològic. La majoria dels estudis es centren en la correlació individual i en proves d'associació entre un subconjunt de variants genètiques (en general polimorfismes d'un únic nucleòtid, SNPs) i una única mesura d'una regió cerebral. Però, malgrat el gran èxit en l'enfocament univariat, donades les perspectives actuals dels estudis d'IG, en els quals es pretenen analitzar les relacions cerebrals de tot el genoma envers tota la informació del cervell, el desenvolupament de nous mètodes estadístics específics esdevé crucial. L'objectiu principal d'aquesta tesi consisteix a investigar els determinants genètics relacionats amb els canvis estructurals del cervell, que a la vegada, afecten els dominis del neurodesenvolupament. Proposem l'aplicació i el desenvolupament d'estratègies estadístiques per millorar l’avaluació de les relacions biològiques associades als dominis del neurodesenvolupament. Específicament, centrem els nostres esforços de recerca en comprendre quins canvis genètics que influeixen l'estructura cerebral permeten millorar l'avaluació dels factors de risc associats als dominis del trastorn per dèficit d'atenció i hiperactivitat, i a processos cognitius relacionats, com la funció d'atenció

Efficient and powerful method for combining p-values in Genome-wide Association Studies

The goal of Genome-wide Association Studies (GWAS) is the identification of genetic variants, usually Single Nucleotide Polymorphisms (SNPs), that are associated with disease risk. However, SNPs detected so far with GWAS for most common diseases only explain a small proportion of their total heritability. Gene Set Analysis (GSA) has been proposed as an alternative to single-SNP analysis with the aim of improving the power of genetic association studies. Nevertheless, most GSA methods rely on expensive computational procedures that make unfeasible their implementation in GWAS. We propose a new GSA method, referred as globalEVT, which uses the extreme value theory to derive gene-level p-values. GlobalEVT reduces dramatically the computational requirements compared to other GSA approaches. In addition, this new approach improves the power by allowing different inheritance models for each genetic variant as illustrated in the simulation study performed and allows the existence of correlation between the SNPs. Real data analysis of an Attention-deficit/hyperactivity disorder (ADHD) study illustrates the importance of using GSA approaches for exploring new susceptibility genes. Specifically, the globalEVT method is able to detect genes related to Cyclophilin A like domain proteins which is known to play an important role in the mechanisms of ADHD development.The work of N. Vilor-Tejedor was supported by a pre-doctoral grant from the Agència de Gestió d'Ajuts, Universitaris i de Recerca (2015 FI_B 00636), Generalitat de Catalunya. This work was also supported by grants MTM2011-26515 and MTM2012-38067-C02-02 from the Ministerio de Economía e Innovación (Spain) and the European Research Council under the ERC Grant Agreement number 26847

Efficient and powerful method for combining p-values in Genome-wide Association Studies

The goal of Genome-wide Association Studies (GWAS) is the identification of genetic variants, usually Single Nucleotide Polymorphisms (SNPs), that are associated with disease risk. However, SNPs detected so far with GWAS for most common diseases only explain a small proportion of their total heritability. Gene Set Analysis (GSA) has been proposed as an alternative to single-SNP analysis with the aim of improving the power of genetic association studies. Nevertheless, most GSA methods rely on expensive computational procedures that make unfeasible their implementation in GWAS. We propose a new GSA method, referred as globalEVT, which uses the extreme value theory to derive gene-level p-values. GlobalEVT reduces dramatically the computational requirements compared to other GSA approaches. In addition, this new approach improves the power by allowing different inheritance models for each genetic variant as illustrated in the simulation study performed and allows the existence of correlation between the SNPs. Real data analysis of an Attention-deficit/hyperactivity disorder (ADHD) study illustrates the importance of using GSA approaches for exploring new susceptibility genes. Specifically, the globalEVT method is able to detect genes related to Cyclophilin A like domain proteins which is known to play an important role in the mechanisms of ADHD development.The work of N. Vilor-Tejedor was supported by a pre-doctoral grant from the Agència de Gestió d'Ajuts, Universitaris i de Recerca (2015 FI_B 00636), Generalitat de Catalunya. This work was also supported by grants MTM2011-26515 and MTM2012-38067-C02-02 from the Ministerio de Economía e Innovación (Spain) and the European Research Council under the ERC Grant Agreement number 26847