Genetic analysis of quantitative phenotypes in AD and MCI: imaging, cognition and biomarkers

The Genetics Core of the Alzheimer’s Disease Neuroimaging Initiative (ADNI), formally established in 2009, aims to provide resources and facilitate research related to genetic predictors of multidimensional Alzheimer’s disease (AD)-related phenotypes. Here, we provide a systematic review of genetic studies published between 2009 and 2012 where either ADNI APOE genotype or genome-wide association study (GWAS) data were used. We review and synthesize ADNI genetic associations with disease status or quantitative disease endophenotypes including structural and functional neuroimaging, fluid biomarker assays, and cognitive performance. We also discuss the diverse analytical strategies used in these studies, including univariate and multivariate analysis, meta-analysis, pathway analysis, and interaction and network analysis. Finally, we perform pathway and network enrichment analyses of these ADNI genetic associations to highlight key mechanisms that may drive disease onset and trajectory. Major ADNI findings included all the top 10 AD genes and several of these (e.g., APOE, BIN1, CLU, CR1, and PICALM) were corroborated by ADNI imaging, fluid and cognitive phenotypes. ADNI imaging genetics studies discovered novel findings (e.g., FRMD6) that were later replicated on different data sets. Several other genes (e.g., APOC1, FTO, GRIN2B, MAGI2, and TOMM40) were associated with multiple ADNI phenotypes, warranting further investigation on other data sets. The broad availability and wide scope of ADNI genetic and phenotypic data has advanced our understanding of the genetic basis of AD and has nominated novel targets for future studies employing next-generation sequencing and convergent multi-omics approaches, and for clinical drug and biomarker development. Electronic supplementary material The online version of this article (doi:10.1007/s11682-013-9262-z) contains supplementary material, which is available to authorized users

Imaging genetics : Methodological approaches to overcoming high dimensional barriers

Imaging genetics is still a quite novel area of research which attempts to discover how genetic factors affect brain structures and functions. In this thesis, using a various methodological approaches I showed how it can contribute to our understanding of the complex genetic architecture of the human brain

Sparse reduced-rank regression for imaging genetics studies: models and applications

We present a novel statistical technique; the sparse reduced rank regression (sRRR) model which is a strategy for multivariate modelling of high-dimensional imaging responses and genetic predictors. By adopting penalisation techniques, the model is able to enforce sparsity in the regression coefficients, identifying subsets of genetic markers that best explain the variability observed in subsets of the phenotypes. To properly exploit the rich structure present in each of the imaging and genetics domains, we additionally propose the use of several structured penalties within the sRRR model. Using simulation procedures that accurately reflect realistic imaging genetics data, we present detailed evaluations of the sRRR method in comparison with the more traditional univariate linear modelling approach. In all settings considered, we show that sRRR possesses better power to detect the deleterious genetic variants. Moreover, using a simple genetic model, we demonstrate the potential benefits, in terms of statistical power, of carrying out voxel-wise searches as opposed to extracting averages over regions of interest in the brain. Since this entails the use of phenotypic vectors of enormous dimensionality, we suggest the use of a sparse classification model as a de-noising step, prior to the imaging genetics study. Finally, we present the application of a data re-sampling technique within the sRRR model for model selection. Using this approach we are able to rank the genetic markers in order of importance of association to the phenotypes, and similarly rank the phenotypes in order of importance to the genetic markers. In the very end, we illustrate the application perspective of the proposed statistical models in three real imaging genetics datasets and highlight some potential associations

ENIGMA and global neuroscience: A decade of large-scale studies of the brain in health and disease across more than 40 countries.

This review summarizes the last decade of work by the ENIGMA (Enhancing NeuroImaging Genetics through Meta Analysis) Consortium, a global alliance of over 1400 scientists across 43 countries, studying the human brain in health and disease. Building on large-scale genetic studies that discovered the first robustly replicated genetic loci associated with brain metrics, ENIGMA has diversified into over 50 working groups (WGs), pooling worldwide data and expertise to answer fundamental questions in neuroscience, psychiatry, neurology, and genetics. Most ENIGMA WGs focus on specific psychiatric and neurological conditions, other WGs study normal variation due to sex and gender differences, or development and aging; still other WGs develop methodological pipelines and tools to facilitate harmonized analyses of "big data" (i.e., genetic and epigenetic data, multimodal MRI, and electroencephalography data). These international efforts have yielded the largest neuroimaging studies to date in schizophrenia, bipolar disorder, major depressive disorder, post-traumatic stress disorder, substance use disorders, obsessive-compulsive disorder, attention-deficit/hyperactivity disorder, autism spectrum disorders, epilepsy, and 22q11.2 deletion syndrome. More recent ENIGMA WGs have formed to study anxiety disorders, suicidal thoughts and behavior, sleep and insomnia, eating disorders, irritability, brain injury, antisocial personality and conduct disorder, and dissociative identity disorder. Here, we summarize the first decade of ENIGMA's activities and ongoing projects, and describe the successes and challenges encountered along the way. We highlight the advantages of collaborative large-scale coordinated data analyses for testing reproducibility and robustness of findings, offering the opportunity to identify brain systems involved in clinical syndromes across diverse samples and associated genetic, environmental, demographic, cognitive, and psychosocial factors

Effect of 16P11.2 copy number variants on cognitive traits and brain structures

The 600kb 16p11.2 CNVs (breakpoints 4–5, 29.6-30.2 Mb-Hg19) are among the most frequent genetic risk factors for neurodevelopmental and psychiatric conditions: A 10-fold enrichment of deletions and duplications is observed in autism cohorts and a 10-fold enrichment of duplications in schizophrenia cohorts. Previous studies demonstrated “mirror” effects of both CNVs on body mass index and head circumference (deletion>control>duplication). However, the large global effect of brain size and the sample size of the two previous neuroimaging studies limited the interpretation of the analyses on regional brain structures, any estimate of the effect size, and the generalizability of the results across different ascertainments of the patients. In the first part of my Ph.D., I analyze structural magnetic resonance imaging (MRI) on 78 deletion carriers, 71 duplication carriers, and 212 controls. I show that both CNVs affect in a “mirror” way the volume and the cortical surface of the insula (Cohen’s d>1), whilst other brain regions are preferentially altered in either the deletion carriers (calcarine cortex and superior, middle, transverse temporal gyri, Cohen’s d>1) or the duplication carriers (caudate and hippocampus, Cohen’s d of 0.5 to 1). Results are generalizable across scanning sites, computational methods, age, sex, ascertainment for psychiatric disorders. They partially overlap with results of meta-analyses performed across psychiatric disorders. In the second part, I characterize the developmental trajectory of global brain metrics and regional brain structures in the 16p11.2 CNV carriers. I adapt a previously published longitudinal pipeline and normalizing method, derived from 339 typically developing individuals aged from 4.5 to 20 years old. From this population of reference, I Z-score our cross-sectional 16p11.2 dataset and show that all the brain alterations in the 16p11.2 carriers are already present at 4.5 years old and follow parallel trajectories to the controls. In summary, my results suggest that brain alterations, present in childhood and stable across adolescence and adulthood, are related to the risk conferred by the 16p11.2 CNVs, regardless of the carriers’ symptoms. Additional factors are therefore likely required for the development of psychiatric disorders. I highlight the relevance of studying genetic risk factors and mechanisms as a complement to groups defined by behavioral criteria. Further studies comparing multiple CNVs and monogenic conditions, from the earliest age, are required to understand the onset of neuroanatomical alterations and their overlap between different genetic risk factors for neurodevelopmental disorders. -- Les variations en nombre de copies (CNV), au locus 16p11.2 et d’une taille d’600kb (points de cassure 4–5, 29.6-30.2 Mb-Hg19) représentent un des facteurs de risque génétique les plus fréquents parmi les troubles psychiatriques : 10% d’enrichissement en délétion et duplication pour les troubles du spectre autistique, 10% d’enrichissement en duplication pour la schizophrénie. Les effets « miroirs » des deux CNVs sur l’indice de masse corporelle et le périmètre cranien ont déjà été démontrés (délétion>contrôle>duplication). Cependant, les différences en taille de cerveau et les échantillons des deux précédentes études de neuro- imagerie ont limité les analyses des régions cérébrales, l’estimation de la taille des effets, et la généralisation des résultats selon les modes de recrutement des patients. Dans cette thèse, j’analyse les images par résonance magnétique (IRM) de 78 porteurs de la délétion, 71 porteurs de la duplication et 212 participants contrôles. Je montre que les deux CNVs sont associées à des différences « en miroir » du volume et de la surface corticale de l’insula (Cohen’s d>1), tandis que le cortex calcarin, les gyri temporaux supérieur, moyen et transverse sont préférentiellement altérés par la délétion (Cohen’s d>1), les noyaux caudés et l’hippocampe sont préférentiellement altérés par la duplication (0.5<Cohen’s d<1). Les résultats sont généralisables à travers les differents sites d’IRM, les méthodes d’analyse computationnelle, les âges, les sexes et les divers diagnostiques psychiatriques des patients. Les résultats chevauchent partiellement ceux d’une méta-analyse sur plusieurs diagnostiques psychiatriques. Dans un second temps, je caractérise la trajectoire développementale de ces différences cérébrales. J’adapte un pipeline longitunal et une méthode de normalisation déjà publiés, construits à partir de 339 participants contrôles de 4.5 à 20 ans. Je calcule des Z-scores pour nos données transversales et montre que les différences cérébrales liées aux CNVs sont déjà présentes à 4.5 ans, avec les mêmes tailles d’effet et une trajectoire parallèle aux contrôles. En résumé, mes résultats suggèrent que les différences cérébrales, présentes dans la jeune enfance et stables à l’adolescence et l’âge adulte, sont liées au risque conféré par les CNVs en 16p11.2, quelque soient les symptômes. Des facteurs additionnels sont probablement nécessaires pour le développement de maladies psychiatriques. Je montre la pertinence d’étudier les facteurs de risque génétiques en complément des groupes de patients définis sur des critères comportementaux. Des études comparant diverses conditions génétiques, dès la naissance, sont nécessaires pour comprendre le début et le chevauchement des différences neuro-anatomiques observées pour différents facteurs de risque génétiques

Any-way and Sparse Analyses for Multimodal Fusion and Imaging Genomics

This dissertation aims to develop new algorithms that leverage sparsity and mutual information across data modalities built upon the independent component analysis (ICA) framework to improve the performance of current ICA-based multimodal fusion approaches. These algorithms are further applied to both simulated data and real neuroimaging and genomic data to examine their performance. The identified neuroimaging and genomic patterns can help better delineate the pathology of mental disorders or brain development. To alleviate the signal-background separation difficulties in infomax-decomposed sources for genomic data, we propose a sparse infomax by enhancing a robust sparsity measure, the Hoyer index. Hoyer index is scale-invariant and well suited for ICA frameworks since the scale of decomposed sources is arbitrary. Simulation results demonstrate that sparse infomax increases the component detection accuracy for situations where the source signal-to-background (SBR) ratio is low, particularly for single nucleotide polymorphism (SNP) data. The proposed sparse infomax is further extended into two data modalities as a sparse parallel ICA for applications to imaging genomics in order to investigate the associations between brain imaging and genomics. Simulation results show that sparse parallel ICA outperforms parallel ICA with improved accuracy for structural magnetic resonance imaging (sMRI)-SNP association detection and component spatial map recovery, as well as with enhanced sparsity for sMRI and SNP components under noisy cases. Applying the proposed sparse parallel ICA to fuse the whole-brain sMRI and whole-genome SNP data of 24985 participants in the UK biobank, we identify three stable and replicable sMRI-SNP pairs. The identified sMRI components highlight frontal, parietal, and temporal regions and associate with multiple cognitive measures (with different association strengths in different age groups for the temporal component). Top SNPs in the identified SNP factor are enriched in inflammatory disease and inflammatory response pathways, which also regulate gene expression, isoform percentage, transcription expression, or methylation level in the frontal region, and the regulation effects are significantly enriched. Applying the proposed sparse parallel ICA to imaging genomics in attention-deficit/hyperactivity disorder (ADHD), we identify and replicate one SNP component related to gray matter volume (GMV) alterations in superior and middle frontal gyri underlying working memory deficit in adults and adolescents with ADHD. The association is more significant in ADHD families than controls and stronger in adults and older adolescents than younger ones. The identified SNP component highlights SNPs in long non-coding RNAs (lncRNAs) in chromosome 5 and in several protein-coding genes that are involved in ADHD, such as MEF2C, CADM2, and CADPS2. Top SNPs are enriched in human brain neuron cells and regulate gene expression, isoform percentage, transcription expression, or methylation level in the frontal region. Moreover, to increase the flexibility and robustness in mining multimodal data, we propose aNy-way ICA, which optimizes the entire correlation structure of linked components across any number of modalities via the Gaussian independent vector analysis and simultaneously optimizes independence via separate (parallel) ICAs. Simulation results demonstrate that aNy-way ICA recover sources and loadings, as well as the true covariance patterns with improved accuracy compared to existing multimodal fusion approaches, especially under noisy conditions. Applying the proposed aNy-way ICA to integrate structural MRI, fractal n-back, and emotion identification task functional MRIs collected in the Philadelphia Neurodevelopmental Cohort (PNC), we identify and replicate one linked GMV-threat-2-back component, and the threat and 2-back components are related to intelligence quotient (IQ) score in both discovery and replication samples. Lastly, we extend the proposed aNy-way ICA with a reference constraint to enable prior-guided multimodal fusion. Simulation results show that aNy-way ICA with reference recovers the designed linkages between reference and modalities, cross-modality correlations, as well as loading and component matrices with improved accuracy compared to multi-site canonical correlation analysis with reference (MCCAR)+joint ICA under noisy conditions. Applying aNy-way ICA with reference to supervise structural MRI, fractal n-back, and emotion identification task functional MRIs fusion in PNC with IQ as the reference, we identify and replicate one IQ-related GMV-threat-2-back component, and this component is significantly correlated across modalities in both discovery and replication samples.Ph.D

ENIGMA and global neuroscience: A decade of large-scale studies of the brain in health and disease across more than 40 countries

This review summarizes the last decade of work by the ENIGMA (Enhancing NeuroImaging Genetics through Meta Analysis) Consortium, a global alliance of over 1400 scientists across 43 countries, studying the human brain in health and disease. Building on large-scale genetic studies that discovered the first robustly replicated genetic loci associated with brain metrics, ENIGMA has diversified into over 50 working groups (WGs), pooling worldwide data and expertise to answer fundamental questions in neuroscience, psychiatry, neurology, and genetics. Most ENIGMA WGs focus on specific psychiatric and neurological conditions, other WGs study normal variation due to sex and gender differences, or development and aging; still other WGs develop methodological pipelines and tools to facilitate harmonized analyses of "big data" (i.e., genetic and epigenetic data, multimodal MRI, and electroencephalography data). These international efforts have yielded the largest neuroimaging studies to date in schizophrenia, bipolar disorder, major depressive disorder, post-traumatic stress disorder, substance use disorders, obsessive-compulsive disorder, attention-deficit/hyperactivity disorder, autism spectrum disorders, epilepsy, and 22q11.2 deletion syndrome. More recent ENIGMA WGs have formed to study anxiety disorders, suicidal thoughts and behavior, sleep and insomnia, eating disorders, irritability, brain injury, antisocial personality and conduct disorder, and dissociative identity disorder. Here, we summarize the first decade of ENIGMA's activities and ongoing projects, and describe the successes and challenges encountered along the way. We highlight the advantages of collaborative large-scale coordinated data analyses for testing reproducibility and robustness of findings, offering the opportunity to identify brain systems involved in clinical syndromes across diverse samples and associated genetic, environmental, demographic, cognitive, and psychosocial factors

Genetic Analysis of Quantitative Phenotypes in AD and MCI: Imaging, Cognition and Biomarkers

The Genetics Core of the Alzheimer’s Disease Neuroimaging Initiative (ADNI), formally established in 2009, aims to provide resources and facilitate research related to genetic predictors of multidimensional Alzheimer’s disease (AD)-related phenotypes. Here, we provide a systematic review of genetic studies published between 2009 and 2012 where either ADNI APOE genotype or genome-wide association study (GWAS) data were used. We review and synthesize ADNI genetic associations with disease status or quantitative disease endophenotypes including structural and functional neuroimaging, fluid biomarker assays, and cognitive performance. We also discuss the diverse analytical strategies used in these studies, including univariate and multivariate analysis, meta-analysis, pathway analysis, and interaction and network analysis. Finally, we perform pathway and network enrichment analyses of these ADNI genetic associations to highlight key mechanisms that may drive disease onset and trajectory. Major ADNI findings included all the top 10 AD genes and several of these (e.g., APOE, BIN1, CLU, CR1, and PICALM) were corroborated by ADNI imaging, fluid and cognitive phenotypes. ADNI imaging genetics studies discovered novel findings (e.g., FRMD6) that were later replicated on different data sets. Several other genes (e.g., APOC1, FTO, GRIN2B, MAGI2, and TOMM40) were associated with multiple ADNI phenotypes, warranting further investigation on other data sets. The broad availability and wide scope of ADNI genetic and phenotypic data has advanced our understanding of the genetic basis of AD and has nominated novel targets for future studies employing next-generation sequencing and convergent multi-omics approaches, and for clinical drug and biomarker development

How complex analyses of large multidimensional datasets advance psychology – examples from large-scale studies on behavior, brain imaging, and genetics

Psychology investigates the interplay of human mind, body, and its environment in health and disease. Fully understanding these complex interrelations requires comprehensive analyses across multiple modalities and multidimensional datasets. Large-scale analyses on complex datasets are the exception rather than the rule in current psychological research. At the same time, large and complex datasets are becoming increasingly available. This thesis points out benefits, challenges and adequate approaches for analyzing complex multidimensional datasets in psychology. We applied these approaches and analysis strategies in two studies. In the first publication, we reduced the dimensionality of brain activation during a working memory task based on data from a very large sample. We observed that a mainly parietally-centered brain network was associated with working memory performance and global measures of white matter integrity. In the second publication, we exhaustively assessed pairwise interaction effects of genetic markers onto epigenetic modifications of the genome. Such modifications are complex traits that can be influenced by the environment and in turn affect development and behavior. The lack of observed strong interaction effects in our study suggested that focusing on additive effects is a suitable approach for investigating the link between genetic markers and epigenetic modifications. Both studies demonstrate how psychological scientists can exploit large complex datasets by applying adequate research practices and methodologies. Further adopting these approaches will prepare psychological research for harnessing large and complex datasets, leading towards a better understanding of mental health and disease

Recent publications from the Alzheimer's Disease Neuroimaging Initiative: Reviewing progress toward improved AD clinical trials

INTRODUCTION: The Alzheimer's Disease Neuroimaging Initiative (ADNI) has continued development and standardization of methodologies for biomarkers and has provided an increased depth and breadth of data available to qualified researchers. This review summarizes the over 400 publications using ADNI data during 2014 and 2015. METHODS: We used standard searches to find publications using ADNI data. RESULTS: (1) Structural and functional changes, including subtle changes to hippocampal shape and texture, atrophy in areas outside of hippocampus, and disruption to functional networks, are detectable in presymptomatic subjects before hippocampal atrophy; (2) In subjects with abnormal β-amyloid deposition (Aβ+), biomarkers become abnormal in the order predicted by the amyloid cascade hypothesis; (3) Cognitive decline is more closely linked to tau than Aβ deposition; (4) Cerebrovascular risk factors may interact with Aβ to increase white-matter (WM) abnormalities which may accelerate Alzheimer's disease (AD) progression in conjunction with tau abnormalities; (5) Different patterns of atrophy are associated with impairment of memory and executive function and may underlie psychiatric symptoms; (6) Structural, functional, and metabolic network connectivities are disrupted as AD progresses. Models of prion-like spreading of Aβ pathology along WM tracts predict known patterns of cortical Aβ deposition and declines in glucose metabolism; (7) New AD risk and protective gene loci have been identified using biologically informed approaches; (8) Cognitively normal and mild cognitive impairment (MCI) subjects are heterogeneous and include groups typified not only by "classic" AD pathology but also by normal biomarkers, accelerated decline, and suspected non-Alzheimer's pathology; (9) Selection of subjects at risk of imminent decline on the basis of one or more pathologies improves the power of clinical trials; (10) Sensitivity of cognitive outcome measures to early changes in cognition has been improved and surrogate outcome measures using longitudinal structural magnetic resonance imaging may further reduce clinical trial cost and duration; (11) Advances in machine learning techniques such as neural networks have improved diagnostic and prognostic accuracy especially in challenges involving MCI subjects; and (12) Network connectivity measures and genetic variants show promise in multimodal classification and some classifiers using single modalities are rivaling multimodal classifiers. DISCUSSION: Taken together, these studies fundamentally deepen our understanding of AD progression and its underlying genetic basis, which in turn informs and improves clinical trial desig