Neurobiology of Self-Regulation: Longitudinal Influence of FKBP5 and Intimate Partner Violence on Emotional and Cognitive Development in Childhood.

OBJECTIVE::Self-regulation includes the volitional and nonvolitional regulation of emotional, cognitive, and physiological responses to stimulation. It develops from infancy through individual characteristics and the environment, with the stress hormone system as a central player. Accordingly, the authors hypothesized that genes involved in regulating the stress system, such as FK506 binding protein 5 (FKBP5), interact with early-life stress exposure, such as exposure to intimate partner violence (IPV), to predict self-regulation indicators and associated outcomes, including behavioral and learning problems in school. METHODS::Study participants were a longitudinal birth cohort of 910 children for whom FKBP5 genotypes were available and who were assessed for exposure to IPV during the first 2 years of life as well as multiple measures of self-regulation: stress-induced cortisol reactivity and fear-elicited emotional reactivity at 7, 15, and 24 months, executive function at 36, 48, and 60 months, and emotional and behavioral difficulties and reading and math achievement in school grades 1, 2, and 5. Data were analyzed using longitudinal clustering and ordinal logistic regression procedures followed by mixed linear modeling. RESULTS::Children with two copies of a risk FKBP5 haplotype and IPV exposure were significantly more likely to have a developmental trajectory characterized by high, prolonged stress-induced cortisol reactivity and emotional reactivity in toddlerhood, followed by low executive function at school entry and high emotional and behavior problems and low reading ability in the primary school grades. CONCLUSIONS::The interaction of FKBP5 and IPV affects the physiological response to stress early in life, with consequences for emotional and cognitive self-regulation. Targeting self-regulation may present an early intervention strategy for children facing genetic and environmental risk

Characterisation of age and polarity at onset in bipolar disorder

BACKGROUND Studying phenotypic and genetic characteristics of age at onset (AAO) and polarity at onset (PAO) in bipolar disorder can provide new insights into disease pathology and facilitate the development of screening tools. AIMS To examine the genetic architecture of AAO and PAO and their association with bipolar disorder disease characteristics. METHOD Genome-wide association studies (GWASs) and polygenic score (PGS) analyses of AAO (n = 12 977) and PAO (n = 6773) were conducted in patients with bipolar disorder from 34 cohorts and a replication sample (n = 2237). The association of onset with disease characteristics was investigated in two of these cohorts. RESULTS Earlier AAO was associated with a higher probability of psychotic symptoms, suicidality, lower educational attainment, not living together and fewer episodes. Depressive onset correlated with suicidality and manic onset correlated with delusions and manic episodes. Systematic differences in AAO between cohorts and continents of origin were observed. This was also reflected in single-nucleotide variant-based heritability estimates, with higher heritabilities for stricter onset definitions. Increased PGS for autism spectrum disorder (β = -0.34 years, s.e. = 0.08), major depression (β = -0.34 years, s.e. = 0.08), schizophrenia (β = -0.39 years, s.e. = 0.08), and educational attainment (β = -0.31 years, s.e. = 0.08) were associated with an earlier AAO. The AAO GWAS identified one significant locus, but this finding did not replicate. Neither GWAS nor PGS analyses yielded significant associations with PAO. CONCLUSIONS AAO and PAO are associated with indicators of bipolar disorder severity. Individuals with an earlier onset show an increased polygenic liability for a broad spectrum of psychiatric traits. Systematic differences in AAO across cohorts, continents and phenotype definitions introduce significant heterogeneity, affecting analyses

A Novel Locus and Candidate Gene for Familial Developmental Dyslexia on Chromosome 4q

Objective: Developmental dyslexia is a highly heritable specific reading and writing disability. To identify a possible new locus and candidate gene for this disability, we investigated a four-generation pedigree where transmission of dyslexia is consistent with an autosomal dominant inheritance pattern. Methods: We performed genome wide array-based SNP genotyping and parametric linkage analysis and sequencing analysis of protein-coding exons, exon-intron boundaries and conserved extragenic regions within the haplotype cosegregating with dyslexia in DNA from one affected and one unaffected family member. Cosegregation was confirmed by sequencing all available family members. Additionally, we analyzed 96 dyslexic individuals who had previously shown positive LOD scores on chromosome 4q28 as well as an even larger sample (n = 2591). Results: We found a single prominent linkage interval on chromosome 4q, where sequence analysis revealed a nucleotide variant in the 3' UTR of brain expressed SPRY1 in the dyslexic family member that cosegregated with dyslexia. This sequence alteration might affect the binding efficiency of the IGF2BP1 RNA-binding protein and thus influence the expression level of the SPRY1 gene product. An analysis of 96 individuals from a cohort of dyslexic individuals revealed a second heterozygous variant in this gene, which was absent in the unaffected sister of the proband. An investigation of the region in a much larger sample further found a nominal p-value of 0.0016 for verbal short-term memory (digit span) in 2,591 individuals for a neighboring SNV. After correcting for the local number of analyzed SNVs, and after taking into account linkage disequilibrium, we found this corresponds to a p-value of 0.0678 for this phenotype. Conclusions: We describe a new locus for familial dyslexia and discuss the possibility that SPRY1 might play a role in the etiology of a monogenic form of dyslexia

Genetic risk prediction and neurobiological understanding of alcoholism

We have used a translational Convergent Functional Genomics (CFG) approach to discover genes involved in alcoholism, by gene-level integration of genome-wide association study (GWAS) data from a German alcohol dependence cohort with other genetic and gene expression data, from human and animal model studies, similar to our previous work in bipolar disorder and schizophrenia. A panel of all the nominally significant P-value single-nucleotide length polymorphisms (SNPs) in the top candidate genes discovered by CFG (n = 135 genes, 713 SNPs) was used to generate a genetic risk prediction score (GRPS), which showed a trend towards significance (P = 0.053) in separating alcohol dependent individuals from controls in an independent German test cohort. We then validated and prioritized our top findings from this discovery work, and subsequently tested them in three independent cohorts, from two continents. In order to validate and prioritize the key genes that drive behavior without some of the pleiotropic environmental confounds present in humans, we used a stress-reactive animal model of alcoholism developed by our group, the D-box binding protein (DBP) knockout mouse, consistent with the surfeit of stress theory of addiction proposed by Koob and colleagues. A much smaller panel (n = 11 genes, 66 SNPs) of the top CFG-discovered genes for alcoholism, cross-validated and prioritized by this stress-reactive animal model showed better predictive ability in the independent German test cohort (P = 0.041). The top CFG scoring gene for alcoholism from the initial discovery step, synuclein alpha (SNCA) remained the top gene after the stress-reactive animal model cross-validation. We also tested this small panel of genes in two other independent test cohorts from the United States, one with alcohol dependence (P = 0.00012) and one with alcohol abuse (a less severe form of alcoholism; P = 0.0094). SNCA by itself was able to separate alcoholics from controls in the alcohol-dependent cohort (P = 0.000013) and the alcohol abuse cohort (P = 0.023). So did eight other genes from the panel of 11 genes taken individually, albeit to a lesser extent and/or less broadly across cohorts. SNCA, GRM3 and MBP survived strict Bonferroni correction for multiple comparisons. Taken together, these results suggest that our stress-reactive DBP animal model helped to validate and prioritize from the CFG-discovered genes some of the key behaviorally relevant genes for alcoholism. These genes fall into a series of biological pathways involved in signal transduction, transmission of nerve impulse (including myelination) and cocaine addiction. Overall, our work provides leads towards a better understanding of illness, diagnostics and therapeutics, including treatment with omega-3 fatty acids. We also examined the overlap between the top candidate genes for alcoholism from this work and the top candidate genes for bipolar disorder, schizophrenia, anxiety from previous CFG analyses conducted by us, as well as cross-tested genetic risk predictions. This revealed the significant genetic overlap with other major psychiatric disorder domains, providing a basis for comorbidity and dual diagnosis, and placing alcohol use in the broader context of modulating the mental landscape

DeepWAS: multivariate genotype-phenotype associations by directly integrating regulatory information using deep learning

Genome-wide association studies (GWAS) identify genetic variants associated with traits or diseases. GWAS never directly link variants to regulatory mechanisms. Instead, the functional annotation of variants is typically inferred by post hoc analyses. A specific class of deep learning-based methods allows for the prediction of regulatory effects per variant on several cell type-specific chromatin features. We here describe "DeepWAS", a new approach that integrates these regulatory effect predictions of single variants into a multivariate GWAS setting. Thereby, single variants associated with a trait or disease are directly coupled to their impact on a chromatin feature in a cell type. Up to 61 regulatory SNPs, called dSNPs, were associated with multiple sclerosis (MS, 4,888 cases and 10,395 controls), major depressive disorder (MDD, 1,475 cases and 2,144 controls), and height (5,974 individuals). These variants were mainly non-coding and reached at least nominal significance in classical GWAS. The prediction accuracy was higher for DeepWAS than for classical GWAS models for 91% of the genome-wide significant, MS-specific dSNPs. DSNPs were enriched in public or cohort-matched expression and methylation quantitative trait loci and we demonstrated the potential of DeepWAS to generate testable functional hypotheses based on genotype data alone. DeepWAS is available at https://github.com/cellmapslab/DeepWAS

Novel multiple sclerosis susceptibility loci implicated in epigenetic regulation

We conducted a genome-wide association study (GWAS) on multiple sclerosis (MS) susceptibility in German cohorts with 4888 cases and 10,395 controls. In addition to associations within the major histocompatibility complex (MHC) region, 15 non-MHC loci reached genome-wide significance. Four of these loci are novel MS susceptibility loci. They map to the genes L3MBTL3, MAZ, ERG, and SHMT1. The lead variant at SHMT1 was replicated in an independent Sardinian cohort. Products of the genes L3MBTL3, MAZ, and ERG play important roles in immune cell regulation. SHMT1 encodes a serine hydroxymethyltransferase catalyzing the transfer of a carbon unit to the folate cycle. This reaction is required for regulation of methylation homeostasis, which is important for establishment and maintenance of epigenetic signatures. Our GWAS approach in a defined population with limited genetic substructure detected associations not found in larger, more heterogeneous cohorts, thus providing new clues regarding MS pathogenesis

Genome-wide association study identifies a variant in HDAC9 associated with large vessel ischemic stroke

Genetic factors have been implicated in stroke risk but few replicated associations have been reported. We conducted a genome-wide association study (GWAS) in ischemic stroke and its subtypes in 3,548 cases and 5,972 controls, all of European ancestry. Replication of potential signals was performed in 5,859 cases and 6,281 controls. We replicated reported associations between variants close to PITX2 and ZFHX3 with cardioembolic stroke, and a 9p21 locus with large vessel stroke. We identified a novel association for a SNP within the histone deacetylase 9(HDAC9) gene on chromosome 7p21.1 which was associated with large vessel stroke including additional replication in a further 735 cases and 28583 controls (rs11984041, combined P = 1.87×10−11, OR=1.42 (95% CI) 1.28-1.57). All four loci exhibit evidence for heterogeneity of effect across the stroke subtypes, with some, and possibly all, affecting risk for only one subtype. This suggests differing genetic architectures for different stroke subtypes

Effect of HLA-DRB1 alleles and genetic variants on the development of neutralizing antibodies to interferon beta in the BEYOND and BENEFIT trials

BACKGROUND: Treatment of multiple sclerosis (MS) with interferon β can lead to the development of antibodies directed against interferon β that interfere with treatment efficacy. Several observational studies have proposed different HLA alleles and genetic variants associated with the development of antibodies against interferon β. OBJECTIVE: To validate the proposed genetic markers and to identify new markers. METHODS: Associations of genetic candidate markers with antibody presence and development were examined in a post hoc analysis in 941 patients treated with interferon β-1b in the Betaferon® Efficacy Yielding Outcomes of a New Dose (BEYOND) and BEtaseron®/BEtaferon® in Newly Emerging multiple sclerosis For Initial Treatment (BENEFIT) prospective phase III trials. All patients were treated with interferon β-1b for at least 6 months. In addition, a genome-wide association study was conducted to identify new genetic variants. RESULTS: We confirmed an increased risk for carriers of HLA-DRB1*04:01 (odds ratio (OR) = 3.3, p = 6.9 × 10-4) and HLA-DRB1*07:01 (OR = 1.8, p = 3.5 × 10-3) for developing neutralizing antibodies (NAbs). Several additional, previously proposed HLA alleles and genetic variants showed nominally significant associations. In the exploratory analysis, variants in the HLA region were associated with NAb development at genome-wide significance (OR = 2.6, p = 2.30 × 10-15). CONCLUSION: The contribution of HLA alleles and HLA-associated single-nucleotide polymorphisms (SNPs) to the development and titer of antibodies against interferon β was confirmed in the combined analysis of two multi-national, multi-center studies

Shared Genetic Etiology Between Alcohol Dependence and Major Depressive Disorder

The clinical comorbidity of alcohol dependence (AD) and major depressive disorder (MDD) is well established, whereas genetic factors influencing co-occurrence remain unclear. A recent study using polygenic risk scores (PRS) calculated based on the first-wave Psychiatric Genomics Consortium MDD meta-analysis (PGC-MDD1) suggests a modest shared genetic contribution to MDD and AD. Using a (∼10 fold) larger discovery sample, we calculated PRS based on the second wave (PGC-MDD2) of results, in a severe AD case–control target sample. We found significant associations between AD disease status and MDD-PRS derived from both PGC-MDD2 (most informative P-threshold=1.0, P=0.00063, R2=0.533%) and PGCMDD1 (P-threshold=0.2, P=0.00014, R2=0.663%) metaanalyses; the larger discovery sample did not yield additional predictive power. In contrast, calculating PRS in a MDD target sample yielded increased power when using PGC-MDD2 (P-threshold=1.0, P=0.000038, R2=1.34%) versus PGC-MDD1 (P-threshold=1.0, P=0.0013, R2=0.81%). Furthermore, when calculating PGC-MDD2 PRS in a subsample of patients with AD recruited explicitly excluding comorbid MDD, significant associations were still found (n=331; P-threshold=1.0, P=0.042, R2=0.398%). Meanwhile, in the subset of patients in which MDD was not the explicit exclusion criteria, PRS predicted more variance (n=999; P-threshold=1.0, P=0.0003, R2=0.693%). Our findings replicate the reported genetic overlap between AD and MDD and also suggest the need for improved, rigorous phenotyping to identify true shared cross-disorder genetic factors. Larger target samples are needed to reduce noise and take advantage of increasing discovery sample size