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Analysis of DNA methylation associates the cystine-glutamate antiporter SLC7A11 with risk of Parkinson's disease.
An improved understanding of etiological mechanisms in Parkinson's disease (PD) is urgently needed because the number of affected individuals is projected to increase rapidly as populations age. We present results from a blood-based methylome-wide association study of PD involving meta-analysis of 229 K CpG probes in 1,132 cases and 999 controls from two independent cohorts. We identify two previously unreported epigenome-wide significant associations with PD, including cg06690548 on chromosome 4. We demonstrate that cg06690548 hypermethylation in PD is associated with down-regulation of the SLC7A11 gene and show this is consistent with an environmental exposure, as opposed to medications or genetic factors with effects on DNA methylation or gene expression. These findings are notable because SLC7A11 codes for a cysteine-glutamate anti-porter regulating levels of the antioxidant glutathione, and it is a known target of the environmental neurotoxin β-methylamino-L-alanine (BMAA). Our study identifies the SLC7A11 gene as a plausible biological target in PD.
Identification of common genetic risk variants for autism spectrum disorder.
Autism spectrum disorder (ASD) is a highly heritable and heterogeneous group of neurodevelopmental phenotypes diagnosed in more than 1% of children. Common genetic variants contribute substantially to ASD susceptibility, but to date no individual variants have been robustly associated with ASD. With a marked sample-size increase from a unique Danish population resource, we report a genome-wide association meta-analysis of 18,381 individuals with ASD and 27,969 controls that identified five genome-wide-significant loci. Leveraging GWAS results from three phenotypes with significantly overlapping genetic architectures (schizophrenia, major depression, and educational attainment), we identified seven additional loci shared with other traits at equally strict significance levels. Dissecting the polygenic architecture, we found both quantitative and qualitative polygenic heterogeneity across ASD subtypes. These results highlight biological insights, particularly relating to neuronal function and corticogenesis, and establish that GWAS performed at scale will be much more productive in the near term in ASD.
Cumulative influence of parity-related genomic changes in multiple sclerosis.
Pregnancy reduces the frequency of relapses in Multiple Sclerosis (MS) and parity also has a beneficial long term effect on disease outcome. We aimed to uncover the biological mechanisms underlying the beneficial long-term effects of parity in MS. Genome-wide gene expression revealed 574 genes associated with parity; 38.3% showed significant DNA methylation changes (enrichment p = 0.029). These genes overlapped with previous MS genes in humans and a rat MS model and were overrepresented within axon guidance (P = 1.6e-05), developmental biology (P = 0.0094) and cell-cell communication (P = 0.019) pathways. This gene regulation could provide a basis for a protective effect of parity on the long-term outcome of MS.
Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions.
Major depression is a debilitating psychiatric illness that is typically associated with low mood and anhedonia. Depression has a heritable component that has remained difficult to elucidate with current sample sizes due to the polygenic nature of the disorder. To maximize sample size, we meta-analyzed data on 807,553 individuals (246,363 cases and 561,190 controls) from the three largest genome-wide association studies of depression. We identified 102 independent variants, 269 genes, and 15 genesets associated with depression, including both genes and gene pathways associated with synaptic structure and neurotransmission. An enrichment analysis provided further evidence of the importance of prefrontal brain regions. In an independent replication sample of 1,306,354 individuals (414,055 cases and 892,299 controls), 87 of the 102 associated variants were significant after multiple testing correction. These findings advance our understanding of the complex genetic architecture of depression and provide several future avenues for understanding etiology and developing new treatment approaches.
A genome-wide association study of shared risk across psychiatric disorders implicates gene regulation during fetal neurodevelopment.
There is mounting evidence that seemingly diverse psychiatric disorders share genetic etiology, but the biological substrates mediating this overlap are not well characterized. Here we leverage the unique Integrative Psychiatric Research Consortium (iPSYCH) study, a nationally representative cohort ascertained through clinical psychiatric diagnoses indicated in Danish national health registers. We confirm previous reports of individual and cross-disorder single-nucleotide polymorphism heritability for major psychiatric disorders and perform a cross-disorder genome-wide association study. We identify four novel genome-wide significant loci encompassing variants predicted to regulate genes expressed in radial glia and interneurons in the developing neocortex during mid-gestation. This epoch is supported by partitioning cross-disorder single-nucleotide polymorphism heritability, which is enriched at regulatory chromatin active during fetal neurodevelopment. These findings suggest that dysregulation of genes that direct neurodevelopment by common genetic variants may result in general liability for many later psychiatric outcomes.
Improving genetic prediction by leveraging genetic correlations among human diseases and traits.
Genomic prediction has the potential to contribute to precision medicine. However, to date, the utility of such predictors is limited due to low accuracy for most traits. Here theory and simulation study are used to demonstrate that widespread pleiotropy among phenotypes can be utilised to improve genomic risk prediction. We show how a genetic predictor can be created as a weighted index that combines published genome-wide association study (GWAS) summary statistics across many different traits. We apply this framework to predict risk of schizophrenia and bipolar disorder in the Psychiatric Genomics consortium data, finding substantial heterogeneity in prediction accuracy increases across cohorts. For six additional phenotypes in the UK Biobank data, we find increases in prediction accuracy ranging from 0.7% for height to 47% for type 2 diabetes, when using a multi-trait predictor that combines published summary statistics from multiple traits, as compared to a predictor based only on one trait.
Common schizophrenia alleles are enriched in mutation-intolerant genes and in regions under strong background selection.
Schizophrenia is a debilitating psychiatric condition often associated with poor quality of life and decreased life expectancy. Lack of progress in improving treatment outcomes has been attributed to limited knowledge of the underlying biology, although large-scale genomic studies have begun to provide insights. We report a new genome-wide association study of schizophrenia (11,260 cases and 24,542 controls), and through meta-analysis with existing data we identify 50 novel associated loci and 145 loci in total. Through integrating genomic fine-mapping with brain expression and chromosome conformation data, we identify candidate causal genes within 33 loci. We also show for the first time that the common variant association signal is highly enriched among genes that are under strong selective pressures. These findings provide new insights into the biology and genetic architecture of schizophrenia, highlight the importance of mutation-intolerant genes and suggest a mechanism by which common risk variants persist in the population.
Integrative analysis of omics summary data reveals putative mechanisms underlying complex traits.
The identification of genes and regulatory elements underlying the associations discovered by GWAS is essential to understanding the aetiology of complex traits (including diseases). Here, we demonstrate an analytical paradigm of prioritizing genes and regulatory elements at GWAS loci for follow-up functional studies. We perform an integrative analysis that uses summary-level SNP data from multi-omics studies to detect DNA methylation (DNAm) sites associated with gene expression and phenotype through shared genetic effects (i.e., pleiotropy). We identify pleiotropic associations between 7858 DNAm sites and 2733 genes. These DNAm sites are enriched in enhancers and promoters, and >40% of them are mapped to distal genes. Further pleiotropic association analyses, which link both the methylome and transcriptome to 12 complex traits, identify 149 DNAm sites and 66 genes, indicating a plausible mechanism whereby the effect of a genetic variant on phenotype is mediated by genetic regulation of transcription through DNAm.
Genetic effects influencing risk for major depressive disorder in China and Europe.
Major depressive disorder (MDD) is a common, complex psychiatric disorder and a leading cause of disability worldwide. Despite twin studies indicating its modest heritability (~30-40%), extensive heterogeneity and a complex genetic architecture have complicated efforts to detect associated genetic risk variants. We combined single-nucleotide polymorphism (SNP) summary statistics from the CONVERGE and PGC studies of MDD, representing 10 502 Chinese (5282 cases and 5220 controls) and 18 663 European (9447 cases and 9215 controls) subjects. We determined the fraction of SNPs displaying consistent directions of effect, assessed the significance of polygenic risk scores and estimated the genetic correlation of MDD across ancestries. Subsequent trans-ancestry meta-analyses combined SNP-level evidence of association. Sign tests and polygenic score profiling weakly support an overlap of SNP effects between East Asian and European populations. We estimated the trans-ancestry genetic correlation of lifetime MDD as 0.33; female-only and recurrent MDD yielded estimates of 0.40 and 0.41, respectively. Common variants downstream of GPHN achieved genome-wide significance by Bayesian trans-ancestry meta-analysis (rs9323497; log10 Bayes Factor=8.08) but failed to replicate in an independent European sample (P=0.911). Gene-set enrichment analyses indicate enrichment of genes involved in neuronal development and axonal trafficking. We successfully demonstrate a partially shared polygenic basis of MDD in East Asian and European populations. Taken together, these findings support a complex etiology for MDD and possible population differences in predisposing genetic factors, with important implications for future genetic studies.
Genetic correlation between amyotrophic lateral sclerosis and schizophrenia.
We have previously shown higher-than-expected rates of schizophrenia in relatives of patients with amyotrophic lateral sclerosis (ALS), suggesting an aetiological relationship between the diseases. Here, we investigate the genetic relationship between ALS and schizophrenia using genome-wide association study data from over 100,000 unique individuals. Using linkage disequilibrium score regression, we estimate the genetic correlation between ALS and schizophrenia to be 14.3% (7.05-21.6; P=1 × 10-4) with schizophrenia polygenic risk scores explaining up to 0.12% of the variance in ALS (P=8.4 × 10-7). A modest increase in comorbidity of ALS and schizophrenia is expected given these findings (odds ratio 1.08-1.26) but this would require very large studies to observe epidemiologically. We identify five potential novel ALS-associated loci using conditional false discovery rate analysis. It is likely that shared neurobiological mechanisms between these two disorders will engender novel hypotheses in future preclinical and clinical studies.
Genetic variants associated with response to lithium treatment in bipolar disorder: a genome-wide association study.
BackgroundLithium is a first-line treatment in bipolar disorder, but individual response is variable. Previous studies have suggested that lithium response is a heritable trait. However, no genetic markers of treatment response have been reproducibly identified.MethodsHere, we report the results of a genome-wide association study of lithium response in 2563 patients collected by 22 participating sites from the International Consortium on Lithium Genetics (ConLiGen). Data from common single nucleotide polymorphisms (SNPs) were tested for association with categorical and continuous ratings of lithium response. Lithium response was measured using a well established scale (Alda scale). Genotyped SNPs were used to generate data at more than 6 million sites, using standard genomic imputation methods. Traits were regressed against genotype dosage. Results were combined across two batches by meta-analysis.FindingsA single locus of four linked SNPs on chromosome 21 met genome-wide significance criteria for association with lithium response (rs79663003, p=1·37 × 10(-8); rs78015114, p=1·31 × 10(-8); rs74795342, p=3·31 × 10(-9); and rs75222709, p=3·50 × 10(-9)). In an independent, prospective study of 73 patients treated with lithium monotherapy for a period of up to 2 years, carriers of the response-associated alleles had a significantly lower rate of relapse than carriers of the alternate alleles (p=0·03268, hazard ratio 3·8, 95% CI 1·1-13·0).InterpretationThe response-associated region contains two genes for long, non-coding RNAs (lncRNAs), AL157359.3 and AL157359.4. LncRNAs are increasingly appreciated as important regulators of gene expression, particularly in the CNS. Confirmed biomarkers of lithium response would constitute an important step forward in the clinical management of bipolar disorder. Further studies are needed to establish the biological context and potential clinical utility of these findings.FundingDeutsche Forschungsgemeinschaft, National Institute of Mental Health Intramural Research Program.
Genome-wide association study reveals greater polygenic loading for schizophrenia in cases with a family history of illness.
Genome-wide association studies (GWAS) of schizophrenia have yielded more than 100 common susceptibility variants, and strongly support a substantial polygenic contribution of a large number of small allelic effects. It has been hypothesized that familial schizophrenia is largely a consequence of inherited rather than environmental factors. We investigated the extent to which familiality of schizophrenia is associated with enrichment for common risk variants detectable in a large GWAS. We analyzed single nucleotide polymorphism (SNP) data for cases reporting a family history of psychotic illness (N = 978), cases reporting no such family history (N = 4,503), and unscreened controls (N = 8,285) from the Psychiatric Genomics Consortium (PGC1) study of schizophrenia. We used a multinomial logistic regression approach with model-fitting to detect allelic effects specific to either family history subgroup. We also considered a polygenic model, in which we tested whether family history positive subjects carried more schizophrenia risk alleles than family history negative subjects, on average. Several individual SNPs attained suggestive but not genome-wide significant association with either family history subgroup. Comparison of genome-wide polygenic risk scores based on GWAS summary statistics indicated a significant enrichment for SNP effects among family history positive compared to family history negative cases (Nagelkerke's R(2 ) = 0.0021; P = 0.00331; P-value threshold <0.4). Estimates of variability in disease liability attributable to the aggregate effect of genome-wide SNPs were significantly greater for family history positive compared to family history negative cases (0.32 and 0.22, respectively; P = 0.031). We found suggestive evidence of allelic effects detectable in large GWAS of schizophrenia that might be specific to particular family history subgroups. However, consideration of a polygenic risk score indicated a significant enrichment among family history positive cases for common allelic effects. Familial illness might, therefore, represent a more heritable form of schizophrenia, as suggested by previous epidemiological studies.
A recessive genetic model and runs of homozygosity in major depressive disorder.
Genome-wide association studies (GWASs) of major depressive disorder (MDD) have yet to identify variants that surpass the threshold for genome-wide significance. A recent study reported that runs of homozygosity (ROH) are associated with schizophrenia, reflecting a novel genetic risk factor resulting from increased parental relatedness and recessive genetic effects. Here, we explore the possibility of such a recessive model in MDD. In a sample of 9,238 cases and 9,521 controls reported in a recent mega-analysis of 9 GWAS we perform an analysis of ROH and common variants under a recessive model. Since evidence for association with ROH could reflect a recessive mode of action at loci, we also conducted a genome-wide association analyses under a recessive model. The genome-wide association analysis using a recessive model found no significant associations. Our analysis of ROH suggested that there was significant heterogeneity of effect across studies in effect (P = 0.001), and it was associated with genotyping platform and country of origin. The results of the ROH analysis show that differences across studies can lead to conflicting systematic genome-wide differences between cases and controls that are unaccounted for by traditional covariates. They highlight the sensitivity of the ROH method to spurious associations, and the need to carefully control for potential confounds in such analyses. We found no strong evidence for a recessive model underlying MDD.
ANKK1, TTC12, and NCAM1 polymorphisms and heroin dependence: importance of considering drug exposure.
ContextThe genetic contribution to liability for opioid dependence is well established; identification of the responsible genes has proved challenging.ObjectiveTo examine association of 1430 candidate gene single-nucleotide polymorphisms (SNPs) with heroin dependence, reporting here only the 71 SNPs in the chromosome 11 gene cluster (NCAM1, TTC12, ANKK1, DRD2) that include the strongest observed associations.DesignCase-control genetic association study that included 2 control groups (lacking an established optimal control group).SettingSemistructured psychiatric interviews.ParticipantsA total of 1459 Australian cases ascertained from opioid replacement therapy clinics, 531 neighborhood controls ascertained from economically disadvantaged areas near opioid replacement therapy clinics, and 1495 unrelated Australian Twin Registry controls not dependent on alcohol or illicit drugs selected from a twin and family sample.Main outcome measureLifetime heroin dependence.ResultsComparison of cases with Australian Twin Registry controls found minimal evidence of association for all chromosome 11 cluster SNPs (P ≥ .01); a similar comparison with neighborhood controls revealed greater differences (P ≥ 1.8 × 10(-4)). Comparing cases (n = 1459) with the subgroup of neighborhood controls not dependent on illicit drugs (n = 340), 3 SNPs were significantly associated (correcting for multiple testing): ANKK1 SNP rs877138 (most strongly associated; odds ratio = 1.59; 95% CI, 1.32-1.92; P = 9.7 × 10(-7)), ANKK1 SNP rs4938013, and TTC12 SNP rs7130431. A similar pattern of association was observed when comparing illicit drug-dependent (n = 191) and nondependent (n = 340) neighborhood controls, suggesting that liability likely extends to nonopioid illicit drug dependence. Aggregate heroin dependence risk associated with 2 SNPs, rs877138 and rs4492854 (located in NCAM1), varied more than 4-fold (P = 2.7 × 10(-9) for the risk-associated linear trend).ConclusionsOur results provide further evidence of association for chromosome 11 gene cluster SNPs with substance dependence, including extension of liability to illicit drug dependence. Our findings highlight the necessity of considering drug exposure history when selecting control groups for genetic investigations of illicit drug dependence.
Interpreting the role of de novo protein-coding mutations in neuropsychiatric disease.
Pedigree, linkage and association studies are consistent with heritable variation for complex disease due to the segregation of genetic factors in families and in the population. In contrast, de novo mutations make only minor contributions to heritability estimates for complex traits. Nonetheless, some de novo variants are known to be important in disease etiology. The identification of risk-conferring de novo variants will contribute to the discovery of etiologically relevant genes and pathways and may help in genetic counseling. There is considerable interest in the role of such mutations in complex neuropsychiatric disease, largely driven by new genotyping and sequencing technologies. An important role for large de novo copy number variations has been established. Recently, whole-exome sequencing has been used to extend the investigation of de novo variation to point mutations in protein-coding regions. Here, we consider several challenges for the interpretation of such mutations in the context of their role in neuropsychiatric disease.
Molecular pathways involved in neuronal cell adhesion and membrane scaffolding contribute to schizophrenia and bipolar disorder susceptibility.
Susceptibility to schizophrenia and bipolar disorder may involve a substantial, shared contribution from thousands of common genetic variants, each of small effect. Identifying whether risk variants map to specific molecular pathways is potentially biologically informative. We report a molecular pathway analysis using the single-nucleotide polymorphism (SNP) ratio test, which compares the ratio of nominally significant (P<0.05) to nonsignificant SNPs in a given pathway to identify the 'enrichment' for association signals. We applied this approach to the discovery (the International Schizophrenia Consortium (n=6909)) and validation (Genetic Association Information Network (n=2729)) of schizophrenia genome-wide association study (GWAS) data sets. We investigated each of the 212 experimentally validated pathways described in the Kyoto Encyclopaedia of Genes and Genomes in the discovery sample. Nominally significant pathways were tested in the validation sample, and five pathways were found to be significant (P=0.03-0.001); only the cell adhesion molecule (CAM) pathway withstood conservative correction for multiple testing. Interestingly, this pathway was also significantly associated with bipolar disorder (Wellcome Trust Case Control Consortium (n=4847)) (P=0.01). At a gene level, CAM genes associated in all three samples (NRXN1 and CNTNAP2), which were previously implicated in specific language disorder, autism and schizophrenia. The CAM pathway functions in neuronal cell adhesion, which is critical for synaptic formation and normal cell signaling. Similar pathways have also emerged from a pathway analysis of autism, suggesting that mechanisms involved in neuronal cell adhesion may contribute broadly to neurodevelopmental psychiatric phenotypes.
Estimating missing heritability for disease from genome-wide association studies.
Genome-wide association studies are designed to discover SNPs that are associated with a complex trait. Employing strict significance thresholds when testing individual SNPs avoids false positives at the expense of increasing false negatives. Recently, we developed a method for quantitative traits that estimates the variation accounted for when fitting all SNPs simultaneously. Here we develop this method further for case-control studies. We use a linear mixed model for analysis of binary traits and transform the estimates to a liability scale by adjusting both for scale and for ascertainment of the case samples. We show by theory and simulation that the method is unbiased. We apply the method to data from the Wellcome Trust Case Control Consortium and show that a substantial proportion of variation in liability for Crohn disease, bipolar disorder, and type I diabetes is tagged by common SNPs.
A whole genome association study of neuroticism using DNA pooling.
We describe a multistage approach to identify single nucleotide polymorphisms (SNPs) associated with neuroticism, a personality trait that shares genetic determinants with major depression and anxiety disorders. Whole genome association with 452 574 SNPs was performed on DNA pools from approximately 2000 individuals selected on extremes of neuroticism scores from a cohort of 88 142 people from southwest England. The most significant SNPs were then genotyped on independent samples to replicate findings. We were able to replicate association of one SNP within the PDE4D gene in a second sample collected by our laboratory and in a family-based test in an independent sample; however, the SNP was not significantly associated with neuroticism in two other independent samples. We also observed an enrichment of low P-values in known regions of copy number variations. Simulation indicates that our study had approximately 80% power to identify neuroticism loci in the genome with odds ratio (OR)>2, and approximately 50% power to identify small effects (OR=1.5). Since we failed to find any loci accounting for more than 1% of the variance, the heritability of neuroticism probably arises from many loci each explaining much less than 1%. Our findings argue the need for much larger samples than anticipated in genetic association studies and that the biological basis of emotional disorders is extremely complex.
COPD education and cognitive behavioral therapy group treatment for clinically significant symptoms of depression and anxiety in COPD patients: a randomized controlled trial.
BackgroundChronic obstructive pulmonary disease (COPD) affects 14 to 20 million Americans and is associated with increased prevalence of affective disorders, contributing significantly to disability. This study compared cognitive behavioral therapy (CBT) group treatment for anxiety and depression with COPD education for COPD patients with moderate-to-severe anxiety and/or depressive symptoms.MethodA randomized controlled trial (RCT) was conducted between 11 July 2002 and 30 April 2005 at the Michael E. DeBakey VA Medical Center, Houston, TX. Participants were 238 patients treated for COPD the year before, with forced expiratory value in 1 second (FEV)1/forced vital capacity (FVC)<70% and FEV1<70% predicted, and symptoms of moderate anxiety and/or moderate depression, who were being treated by a primary care provider or pulmonologist. Participants attended eight sessions of CBT or COPD education. Assessments were at baseline, at 4 and 8 weeks, and 4, 8 and 12 months. Primary outcomes were disease-specific and generic quality of life (QoL) [Chronic Respiratory Questionnaire (CRQ) and Medical Outcomes Survey Short Form-36 (SF-36) respectively]. Secondary outcomes were anxiety [Beck Anxiety Inventory (BAI)], depressive symptoms [Beck Depression Inventory-II (BDI-II)], 6-minute walk distance (6MWD) and use of health services.ResultsBoth treatments significantly improved QoL, anxiety and depression (p<0.005) over 8 weeks; the rate of change did not differ between groups. Improvements were maintained with no significant change during follow-up. Ratios of post- to pretreatment use of health services were equal to 1 for both groups.ConclusionsCBT group treatment and COPD education can achieve sustainable improvements in QoL for COPD patients experiencing moderate-to-severe symptoms of depression or anxiety.