D9.2 Report, containing internal deliverable outcomes ID9.2-ID9.11

The aim of this deliverable is to report on TENCompetence training activities from the project month 13 to 30The work on this publication has been sponsored by the TENCompetence Integrated Project that is funded by the European Commission's 6th Framework Programme, priority IST/Technology Enhanced Learning. Contract 027087 [http://www.tencompetence.org

Genetic basis of neurocognitive decline and reduced white-matter integrity in normal human brain aging

Identification of genes associated with brain aging should markedly improve our understanding of the biological processes that govern normal age-related decline. However, challenges to identifying genes that facilitate successful brain aging are considerable, including a lack of established phenotypes and difficulties in modeling the effects of aging per se, rather than genes that influence the underlying trait. In a large cohort of randomly selected pedigrees (n = 1,129 subjects), we documented profound aging effects from young adulthood to old age (18-83 y) on neurocognitive ability and diffusion-based white-matter measures. Despite significant phenotypic correlation between white-matter integrity and tests of processing speed, working memory, declarative memory, and intelligence, no evidence for pleiotropy between these classes of phenotypes was observed. Applying an advanced quantitative gene-by-environment interaction analysis where age is treated as an environmental factor, we demonstrate a heritable basis for neurocognitive deterioration as a function of age. Furthermore, by decomposing gene-by-aging (G × A) interactions, we infer that different genes influence some neurocognitive traits as a function of age, whereas other neurocognitive traits are influenced by the same genes, but to differential levels, from young adulthood to old age. In contrast, increasing white-matter incoherence with age appears to be nongenetic. These results clearly demonstrate that traits sensitive to the genetic influences on brain aging can be identified, a critical first step in delineating the biological mechanisms of successful aging

Independent evidence for an association between general cognitive ability and a genetic locus for educational attainment

Cognitive deficits and reduced educational achievement are common in psychiatric illness; understanding the genetic basis of cognitive and educational deficits may be informative about the etiology of psychiatric disorders. A recent, large genome-wide association study (GWAS) reported a genome-wide significant locus for years of education, which subsequently demonstrated association to general cognitive ability (g) in overlapping cohorts. The current study was designed to test whether GWAS hits for educational attainment are involved in general cognitive ability in an independent, large-scale collection of cohorts. Using cohorts in the Cognitive Genomics Consortium (COGENT; up to 20,495 healthy individuals), we examined the relationship between g and variants associated with educational attainment. We next conducted meta-analyses with 24,189 individuals with neurocognitive data from the educational attainment studies, and then with 53,188 largely independent individuals from a recent GWAS of cognition. A SNP (rs1906252) located at chromosome 6q16.1, previously associated with years of schooling, was significantly associated with g (P=1.47x10(-4)) in COGENT. The first joint analysis of 43,381 non-overlapping individuals for this a priori-designated locus was strongly significant (P=4.94x10(-7)), and the second joint analysis of 68,159 non-overlapping individuals was even more robust (P=1.65x10(-9)). These results provide independent replication, in a large-scale dataset, of a genetic locus associated with cognitive function and education. As sample sizes grow, cognitive GWAS will identify increasing numbers of associated loci, as has been accomplished in other polygenic quantitative traits, which may be relevant to psychiatric illness. (c) 2015 Wiley Periodicals, Inc

Multi-site genetic analysis of diffusion images and voxelwise heritability analysis : a pilot project of the ENIGMA–DTI working group

The ENIGMA (Enhancing NeuroImaging Genetics through Meta-Analysis) Consortium was set up to analyze brain measures and genotypes from multiple sites across the world to improve the power to detect genetic variants that influence the brain. Diffusion tensor imaging (DTI) yields quantitative measures sensitive to brain development and degeneration, and some common genetic variants may be associated with white matter integrity or connectivity. DTI measures, such as the fractional anisotropy (FA) of water diffusion, may be useful for identifying genetic variants that influence brain microstructure. However, genome-wide association studies (GWAS) require large populations to obtain sufficient power to detect and replicate significant effects, motivating a multi-site consortium effort. As part of an ENIGMA–DTI working group, we analyzed high-resolution FA images from multiple imaging sites across North America, Australia, and Europe, to address the challenge of harmonizing imaging data collected at multiple sites. Four hundred images of healthy adults aged 18–85 from four sites were used to create a template and corresponding skeletonized FA image as a common reference space. Using twin and pedigree samples of different ethnicities, we used our common template to evaluate the heritability of tract-derived FA measures. We show that our template is reliable for integrating multiple datasets by combining results through meta-analysis and unifying the data through exploratory mega-analyses. Our results may help prioritize regions of the FA map that are consistently influenced by additive genetic factors for future genetic discovery studies. Protocols and templates are publicly available at (http://enigma.loni.ucla.edu/ongoing/dti-working-group/)

Genome- and epigenome-wide association study of hypertriglyceridemic waist in Mexican American families

This file contains Supplementary Table 1. (XLSX 782 MB

The genetic basis of the comorbidity between cannabis use and major depression

Background and aims—While the prevalence of major depression is elevated amongst cannabis users, the role of genetics in this pattern of comorbidity is not clear. This study aimed to estimate the heritability of cannabis use and major depression, quantify the genetic overlap between these two traits, and localize regions of the genome that segregate in families with cannabis use and major depression. Design—Family-based univariate and bivariate genetic analysis. Setting—San Antonio, Texas, USA Participants—Genetics of Brain Structure and Function study (GOBS) participants: 1,284 Mexican-Americans from 75 large multi-generation families and an additional 57 genetically unrelated spouses. Measurements—Phenotypes of lifetime history of cannabis use and major depression, measured using the semi-structured MINI-Plus interview. Genotypes measured using ~1M single nucleotide polymorphisms (SNPs) on Illumina BeadChips. A sub-selection of these SNPs were used to build multipoint identity-by-descent matrices for linkage analysis. Findings—Both cannabis use (h2=0.614, p=1.00×10−6, SE=0.151) and major depression (h2=0.349, p=1.06×10−5, SE=0.100) are heritable traits, and there is significant genetic correlation between the two (ρg=0.424, p=0.0364, SE=0.195). Genome-wide linkage scans identify a significant univariate linkage peak for major depression on chromosome 22 (LOD=3.144 at 2cM), with a suggestive peak for cannabis use on chromosome 21 (LOD=2.123 at 37cM). A significant pleiotropic linkage peak influencing both cannabis use and major depression was identified on chromosome 11, using a bivariate model (LOD=3.229 at 112cM). Follow-up of this pleiotropic signal identified a SNP 20kb upstream of NCAM1 (rs7932341) that shows significant bivariate association (p=3.10×10−5). However this SNP is rare (7 minor allele carriers) and does not drive the linkage signal observed. Conclusions—There appears to be significant genetic overlap between cannabis use and major depression among Mexican-Americans, a pleiotropy that appears to be localized to a region on chromosome 11q23 that has been previously linked to these phenotypes

High dimensional endophenotype ranking in the search for major depression risk genes.

BACKGROUND: Despite overwhelming evidence that major depression is highly heritable, recent studies have localized only a single depression-related locus reaching genome-wide significance and have yet to identify a causal gene. Focusing on family-based studies of quantitative intermediate phenotypes or endophenotypes, in tandem with studies of unrelated individuals using categorical diagnoses, should improve the likelihood of identifying major depression genes. However, there is currently no empirically derived statistically rigorous method for selecting optimal endophentypes for mental illnesses. Here, we describe the endophenotype ranking value, a new objective index of the genetic utility of endophenotypes for any heritable illness. METHODS: Applying endophenotype ranking value analysis to a high-dimensional set of over 11,000 traits drawn from behavioral/neurocognitive, neuroanatomic, and transcriptomic phenotypic domains, we identified a set of objective endophenotypes for recurrent major depression in a sample of Mexican American individuals (n = 1122) from large randomly selected extended pedigrees. RESULTS: Top-ranked endophenotypes included the Beck Depression Inventory, bilateral ventral diencephalon volume, and expression levels of the RNF123 transcript. To illustrate the utility of endophentypes in this context, each of these traits were utlized along with disease status in bivariate linkage analysis. A genome-wide significant quantitative trait locus was localized on chromsome 4p15 (logarithm of odds = 3.5) exhibiting pleiotropic effects on both the endophenotype (lymphocyte-derived expression levels of the RNF123 gene) and disease risk. CONCLUSIONS: The wider use of quantitative endophenotypes, combined with unbiased methods for selecting among these measures, should spur new insights into the biological mechanisms that influence mental illnesses like major depression

Genome-wide significant loci for addiction and anxiety

Background Psychiatric comorbidity is common among individuals with addictive disorders, with patients frequently suffering from anxiety disorders. While the genetic architecture of comorbid addictive and anxiety disorders remains unclear, elucidating the genes involved could provide important insights into the underlying etiology. Methods Here we examine a sample of 1284 Mexican-Americans from randomly selected extended pedigrees. Variance decomposition methods were used to examine the role of genetics in addiction phenotypes (lifetime history of alcohol dependence, drug dependence or chronic smoking) and various forms of clinically relevant anxiety. Genome-wide univariate and bivariate linkage scans were conducted to localize the chromosomal regions influencing these traits. Results Addiction phenotypes and anxiety were shown to be heritable and univariate genome-wide linkage scans revealed significant quantitative trait loci for drug dependence (14q13.2-q21.2, LOD = 3.322) and a broad anxiety phenotype (12q24.32-q24.33, LOD = 2.918). Significant positive genetic correlations were observed between anxiety and each of the addiction subtypes (ρg = 0.550–0.655) and further investigation with bivariate linkage analyses identified significant pleiotropic signals for alcohol dependence-anxiety (9q33.1-q33.2, LOD = 3.054) and drug dependence-anxiety (18p11.23-p11.22, LOD = 3.425). Conclusions This study confirms the shared genetic underpinnings of addiction and anxiety and identifies genomic loci involved in the etiology of these comorbid disorders. The linkage signal for anxiety on 12q24 spans the location of TMEM132D, an emerging gene of interest from previous GWAS of anxiety traits, whilst the bivariate linkage signal identified for anxiety-alcohol on 9q33 peak coincides with a region where rare CNVs have been associated with psychiatric disorders. Other signals identified implicate novel regions of the genome in addiction genetics

Neurocognitive and Neuroimaging Predictors of Clinical Outcome in Bipolar Disorder

Historically, bipolar disorder has been conceptualized as a disease involving episodic rather than chronic dysfunction. However, increasing evidence indicates that bipolar disorder is associated with substantial inter-episode psychosocial and vocational impairment. Here we review the contributions of neurocognitive deficits and structural and functional neuroanatomic alterations to the observed functional impairments. In particular, compelling evidence now suggests that neurocognitive impairments, particularly in the areas of attention, processing speed, and memory, are associated with functional outcome. Although investigation of the neural correlates of functional disability in bipolar disorder is only in its nascent stages, preliminary evidence suggests that white matter abnormalities may be predictive of poor outcome. A better understanding of the relationship between neurocognitive and neuroimaging assays and functional outcome has the potential to improve current treatment options and provide targets for new treatment strategies in bipolar disorder