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

Gene Set Analysis for improving genetic association studies

Curs 2012-2013Introduction. Genetic epidemiology is focused on the study of the genetic causes that determine health and diseases in populations. To achieve this goal a common strategy is to explore differences in genetic variability between diseased and nondiseased individuals. Usual markers of genetic variability are single nucleotide polymorphisms (SNPs) which are changes in just one base in the genome. The usual statistical approach in genetic epidemiology study is a marginal analysis, where each SNP is analyzed separately for association with the phenotype. Motivation. It has been observed, that for common diseases the single-SNP analysis is not very powerful for detecting genetic causing variants. In this work, we consider Gene Set Analysis (GSA) as an alternative to standard marginal association approaches. GSA aims to assess the overall association of a set of genetic variants with a phenotype and has the potential to detect subtle effects of variants in a gene or a pathway that might be missed when assessed individually. Objective. We present a new optimized implementation of a pair of gene set analysis methodologies for analyze the individual evidence of SNPs in biological pathways. We perform a simulation study for exploring the power of the proposed methodologies in a set of scenarios with different number of causal SNPs under different effect sizes. In addition, we compare the results with the usual single-SNP analysis method. Moreover, we show the advantage of using the proposed gene set approaches in the context of an Alzheimer disease case-control study where we explore the Reelin signal pathway.Director/a: M. Luz Call

Brain transcriptomic profiling reveals common alterations across neurodegenerative and psychiatric disorders

Neurodegenerative and neuropsychiatric disorders (ND-NPs) are multifactorial, polygenic and complex behavioral phenotypes caused by brain abnormalities. Large-scale collaborative efforts have tried to identify the genetic architecture of these conditions. However, the specific and shared underlying molecular pathobiology of brain illnesses is not clear. Here, we examine transcriptome-wide characterization of eight conditions, using a total of 2,633 post-mortem brain samples from patients with Alzheimer’s disease (AD), Parkinson’s disease (PD), Progressive Supranuclear Palsy (PSP), Pathological Aging (PA), Autism Spectrum Disorder (ASD), Schizophrenia (Scz), Major Depressive Disorder (MDD), and Bipolar Disorder (BP)–in comparison with 2,078 brain samples from matched control subjects. Similar transcriptome alterations were observed between NDs and NPs with the top correlations obtained between Scz-BP, ASD-PD, AD-PD, and Scz-ASD. Region-specific comparisons also revealed shared transcriptome alterations in frontal and temporal lobes across NPs and NDs. Co-expression network analysis identified coordinated dysregulations of cell-type-specific modules across NDs and NPs. This study provides a transcriptomic framework to understand the molecular alterations of NPs and NDs through their shared- and specific gene expression in the brain

Multi-ancestry genome-wide association study of 21,000 cases and 95,000 controls identifies new risk loci for atopic dermatitis

Genetic association studies have identified 21 loci associated with atopic dermatitis risk predominantly in populations of European ancestry. To identify further susceptibility loci for this common, complex skin disease, we performed a meta-analysis of >15 million genetic variants in 21,399 cases and 95,464 controls from populations of European, African, Japanese and Latino ancestry, followed by replication in 32,059 cases and 228,628 controls from 18 studies. We identified ten new risk loci, bringing the total number of known atopic dermatitis risk loci to 31 (with new secondary signals at four of these loci). Notably, the new loci include candidate genes with roles in the regulation of innate host defenses and T cell function, underscoring the important contribution of (auto)immune mechanisms to atopic dermatitis pathogenesis

Genetic association study of childhood aggression across raters, instruments, and age

Childhood aggressive behavior (AGG) has a substantial heritability of around 50%. Here we present a genome-wide association metaanalysis (GWAMA) of childhood AGG, in which all phenotype measures across childhood ages from multiple assessors were included. We analyzed phenotype assessments for a total of 328 935 observations from 87 485 children aged between 1.5 and 18 years, while accounting for sample overlap. We also meta-analyzed within subsets of the data, i.e., within rater, instrument and age. SNP-heritability for the overall meta-analysis AGGoverall was 3.31% (SE= 0.0038). We found no genome-wide significant SNPs for AGGoverall. The gene-based analysis returned three significant genes: ST3GAL3 (P= 1.6E-06), PCDH7 (P= 2.0E-06), and IPO13 (P= 2.5E-06). All three genes have previously been associated with educational traits. Polygenic scores based on our GWAMA significantly predicted aggression in a holdout sample of children (variance explained = 0.44%) and in retrospectively assessed childhood aggression (variance explained = 0.20%). Genetic correlations rg among rater-specific assessment of AGG ranged from rg= 0.46 between self- and teacher-assessment to rg= 0.81 between mother- and teacher-assessment. We obtained moderate-to-strong rgs with selected phenotypes from multiple domains, but hardly with any of the classical biomarkers thought to be associated with AGG. Significant genetic correlations were observed with most psychiatric and psychological traits (range |rg|: 0.19-1.00), except for obsessive-compulsive disorder. Aggression had a negative genetic correlation (rg=∼-0.5) with cognitive traits and age at first birth. Aggression was strongly genetically correlated with smoking phenotypes (range |rg| : 0.46-0.60). The genetic correlations between aggression and psychiatric disorders were weaker for teacher-reported AGG than for mother- and self-reported AGG. The current GWAMA of childhood aggression provides a powerful tool to interrogate the rater-specific genetic etiology of AGG.</p

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

A genome-wide association study of total child psychiatric problems scores

Substantial genetic correlations have been reported across psychiatric disorders and numerous cross-disorder genetic variants have been detected. To identify the genetic variants underlying general psychopathology in childhood, we performed a genome-wide association study using a total psychiatric problem score. We analyzed 6,844,199 common SNPs in 38,418 school-aged children from 20 population-based cohorts participating in the EAGLE consortium. The SNP heritability of total psychiatric problems was 5.4% (SE = 0.01) and two loci reached genome-wide significance: rs10767094 and rs202005905. We also observed an association of SBF2, a gene associated with neuroticism in previous GWAS, with total psychiatric problems. The genetic effects underlying the total score were shared with common psychiatric disorders only (attention-deficit/hyperactivity disorder, anxiety, depression, insomnia) (rG > 0.49), but not with autism or the less common adult disorders (schizophrenia, bipolar disorder, or eating disorders) (rG 0.29). The results suggest that many common genetic variants are associated with childhood psychiatric symptoms and related phenotypes in general instead of with specific symptoms. Further research is needed to establish causality and pleiotropic mechanisms between related traits.The work of H. Tiemeier is further supported by a European Union’s Horizon 2020 research and innovation program (Contract grant number: 633595, DynaHealth) and a NWO-VICI grant (NWO-ZonMW: 016.VICI.170.200). https://www.nwo.nl/ The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

A genome-wide association study of total child psychiatric problems scores

Substantial genetic correlations have been reported across psychiatric disorders and numerous cross-disorder genetic variants have been detected. To identify the genetic variants underlying general psychopathology in childhood, we performed a genome-wide association study using a total psychiatric problem score. We analyzed 6,844,199 common SNPs in 38,418 school-aged children from 20 population-based cohorts participating in the EAGLE consortium. The SNP heritability of total psychiatric problems was 5.4% (SE = 0.01) and two loci reached genome-wide significance: rs10767094 and rs202005905. We also observed an association of SBF2, a gene associated with neuroticism in previous GWAS, with total psychiatric problems. The genetic effects underlying the total score were shared with common psychiatric disorders only (attention-deficit/hyperactivity disorder, anxiety, depression, insomnia) (rG > 0.49), but not with autism or the less common adult disorders (schizophrenia, bipolar disorder, or eating disorders) (rG 0.29). The results suggest that many common genetic variants are associated with childhood psychiatric symptoms and related phenotypes in general instead of with specific symptoms. Further research is needed to establish causality and pleiotropic mechanisms between related traits.The work of H. Tiemeier is further supported by a European Union’s Horizon 2020 research and innovation program (Contract grant number: 633595, DynaHealth) and a NWO-VICI grant (NWO-ZonMW: 016.VICI.170.200). https://www.nwo.nl/ The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript