Polygenic scores for schizophrenia and major depression are associated with psychosocial risk factors in children: evidence of gene-environment correlation.

BACKGROUND: Whilst genetic and environmental risk factors for schizophrenia (SCZ) and major depressive disorder (MDD) have been established, it is unclear whether exposure to environmental risk factors is genetically confounded by passive, evocative or active gene-environment correlation (rGE). STUDY OBJECTIVE: This study aims to investigate: (a) whether the genetic risk for SCZ/MDD in children is correlated with established environmental and psychosocial risk factors in two British community samples, the 1958 National Child Development Study (NCDS) and the Millennium Cohort Study (MCS), (b) whether these associations vary between both psychopathologies, and (c) whether findings differ across the two cohorts which were born 42 years apart. METHODS: Polygenic risk scores (PRS) from existing large genome-wide associations studies (GWAS) were applied to test the correlation between the child genetic risk for SCZ/MDD and known environmental risk factors. In addition, parental and child genetic data from MCS were used to distinguish between passive and evocative rGE. RESULTS: The child polygenic risk for SCZ and MDD was correlated with single parenthood in MCS. Moreover, the lack of father's involvement in child care was associated with the genetic risk for SCZ in NCDS. However, we also found associations between several indicators of low socioeconomic status and heightened genetic risk for MDD in children in both cohorts. Further, the genetic risk for MDD was associated with parental lack of interest in the child's education in NCDS as well as more maternal smoking and less maternal alcohol consumption during childhood in MCS. According to sensitivity analyses in MCS (controlling for parental genotype), more than half of our significant correlations reflected passive rGE. CONCLUSIONS: Findings suggest that several established environmental and psychosocial risk factors for SCZ and MDD are at least partially associated with children's genetic risk for these psychiatric disorders.

Identifying the Common Genetic Basis of Antidepressant Response.

Background: Antidepressants are a first-line treatment for depression. However, only a third of individuals experience remission after the first treatment. Common genetic variation, in part, likely regulates antidepressant response, yet the success of previous genome-wide association studies has been limited by sample size. This study performs the largest genetic analysis of prospectively assessed antidepressant response in major depressive disorder to gain insight into the underlying biology and enable out-of-sample prediction. Methods: Genome-wide analysis of remission (n remit = 1852, n nonremit = 3299) and percentage improvement (n = 5218) was performed. Single nucleotide polymorphism-based heritability was estimated using genome-wide complex trait analysis. Genetic covariance with eight mental health phenotypes was estimated using polygenic scores/AVENGEME. Out-of-sample prediction of antidepressant response polygenic scores was assessed. Gene-level association analysis was performed using MAGMA and transcriptome-wide association study. Tissue, pathway, and drug binding enrichment were estimated using MAGMA. Results: Neither genome-wide association study identified genome-wide significant associations. Single nucleotide polymorphism-based heritability was significantly different from zero for remission (h 2 = 0.132, SE = 0.056) but not for percentage improvement (h 2 = -0.018, SE = 0.032). Better antidepressant response was negatively associated with genetic risk for schizophrenia and positively associated with genetic propensity for educational attainment. Leave-one-out validation of antidepressant response polygenic scores demonstrated significant evidence of out-of-sample prediction, though results varied in external cohorts. Gene-based analyses identified ETV4 and DHX8 as significantly associated with antidepressant response. Conclusions: This study demonstrates that antidepressant response is influenced by common genetic variation, has a genetic overlap schizophrenia and educational attainment, and provides a useful resource for future research. Larger sample sizes are required to attain the potential of genetics for understanding and predicting antidepressant response.

Mapping genomic loci implicates genes and synaptic biology in schizophrenia.

Schizophrenia has a heritability of 60-80%1, much of which is attributable to common risk alleles. Here, in a two-stage genome-wide association study of up to 76,755 individuals with schizophrenia and 243,649 control individuals, we report common variant associations at 287 distinct genomic loci. Associations were concentrated in genes that are expressed in excitatory and inhibitory neurons of the central nervous system, but not in other tissues or cell types. Using fine-mapping and functional genomic data, we identify 120 genes (106 protein-coding) that are likely to underpin associations at some of these loci, including 16 genes with credible causal non-synonymous or untranslated region variation. We also implicate fundamental processes related to neuronal function, including synaptic organization, differentiation and transmission. Fine-mapped candidates were enriched for genes associated with rare disruptive coding variants in people with schizophrenia, including the glutamate receptor subunit GRIN2A and transcription factor SP4, and were also enriched for genes implicated by such variants in neurodevelopmental disorders. We identify biological processes relevant to schizophrenia pathophysiology; show convergence of common and rare variant associations in schizophrenia and neurodevelopmental disorders; and provide a resource of prioritized genes and variants to advance mechanistic studies.

Genome-wide association study of more than 40,000 bipolar disorder cases provides new insights into the underlying biology.

Bipolar disorder is a heritable mental illness with complex etiology. We performed a genome-wide association study of 41,917 bipolar disorder cases and 371,549 controls of European ancestry, which identified 64 associated genomic loci. Bipolar disorder risk alleles were enriched in genes in synaptic signaling pathways and brain-expressed genes, particularly those with high specificity of expression in neurons of the prefrontal cortex and hippocampus. Significant signal enrichment was found in genes encoding targets of antipsychotics, calcium channel blockers, antiepileptics and anesthetics. Integrating expression quantitative trait locus data implicated 15 genes robustly linked to bipolar disorder via gene expression, encoding druggable targets such as HTR6, MCHR1, DCLK3 and FURIN. Analyses of bipolar disorder subtypes indicated high but imperfect genetic correlation between bipolar disorder type I and II and identified additional associated loci. Together, these results advance our understanding of the biological etiology of bipolar disorder, identify novel therapeutic leads and prioritize genes for functional follow-up studies.

Clinical evaluation of germline polymorphisms associated with capecitabine toxicity in breast cancer: TBCRC-015.

PURPOSE: Capecitabine is important in breast cancer treatment but causes diarrhea and hand-foot syndrome (HFS), affecting adherence and quality of life. We sought to identify pharmacogenomic predictors of capecitabine toxicity using a novel monitoring tool. METHODS: Patients with metastatic breast cancer were prospectively treated with capecitabine (2000 mg/m2/day, 14 days on/7 off). Patients completed in-person toxicity questionnaires (day 1/cycle) and automated phone-in assessments (days 8, 15). Correlation of genotypes with early and overall toxicity was the primary endpoint. RESULTS: Two hundred and fifty-nine patients were enrolled (14 institutions). Diarrhea and HFS occurred in 52% (17% grade 3) and 69% (9% grade 3), respectively. Only 29% of patients completed four cycles without dose reduction/interruption. In 39%, the highest toxicity grade was captured via phone. Three single nucleotide polymorphisms (SNPs) associated with diarrhea-DPYD*5 (odds ratio [OR] 4.9; P = 0.0005), a MTHFR missense SNP (OR 3.3; P = 0.02), and a SNP upstream of MTRR (OR 3.0; P = 0.03). GWAS elucidated a novel HFS SNP (OR 3.0; P = 0.0007) near TNFSF4 (OX40L), a gene implicated in autoimmunity including autoimmune skin diseases never before implicated in HFS. Genotype-gene expression analyses of skin tissues identified rs11158568 (associated with HFS via GWAS) with expression of CHURC1, a transcriptional activator controlling fibroblast growth factor (beta = - 0.74; P = 1.46 × 10-23), representing a previously unidentified mechanism for HFS. CONCLUSIONS: This is the first cancer pharmacogenomic study to use phone-in self-reporting, permitting augmented toxicity characterization. Three germline toxicity SNPs were replicated, and several novel SNPs/genes having strong functional relevance were discovered. If further validated, these markers could permit personalized capecitabine dosing.

RICOPILI: Rapid Imputation for COnsortias PIpeLIne.

SUMMARY: Genome-wide association study (GWAS) analyses, at sufficient sample sizes and power, have successfully revealed biological insights for several complex traits. RICOPILI, an open-sourced Perl-based pipeline was developed to address the challenges of rapidly processing large-scale multi-cohort GWAS studies including quality control (QC), imputation and downstream analyses. The pipeline is computationally efficient with portability to a wide range of high-performance computing environments. RICOPILI was created as the Psychiatric Genomics Consortium pipeline for GWAS and adopted by other users. The pipeline features (i) technical and genomic QC in case-control and trio cohorts, (ii) genome-wide phasing and imputation, (iv) association analysis, (v) meta-analysis, (vi) polygenic risk scoring and (vii) replication analysis. Notably, a major differentiator from other GWAS pipelines, RICOPILI leverages on automated parallelization and cluster job management approaches for rapid production of imputed genome-wide data. A comprehensive meta-analysis of simulated GWAS data has been incorporated demonstrating each step of the pipeline. This includes all the associated visualization plots, to allow ease of data interpretation and manuscript preparation. Simulated GWAS datasets are also packaged with the pipeline for user training tutorials and developer work. AVAILABILITY AND IMPLEMENTATION: RICOPILI has a flexible architecture to allow for ongoing development and incorporation of newer available algorithms and is adaptable to various HPC environments (QSUB, BSUB, SLURM and others). Specific links for genomic resources are either directly provided in this paper or via tutorials and external links. The central location hosting scripts and tutorials is found at this URL: https://sites.google.com/a/broadinstitute.org/RICOPILI/home. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Cortical Surfaces Mediate the Relationship Between Polygenic Scores for Intelligence and General Intelligence.

Recent large-scale, genome-wide association studies (GWAS) have identified hundreds of genetic loci associated with general intelligence. The cumulative influence of these loci on brain structure is unknown. We examined if cortical morphology mediates the relationship between GWAS-derived polygenic scores for intelligence (PSi) and g-factor. Using the effect sizes from one of the largest GWAS meta-analysis on general intelligence to date, PSi were calculated among 10 P value thresholds. PSi were assessed for the association with g-factor performance, cortical thickness (CT), and surface area (SA) in two large imaging-genetics samples (IMAGEN N = 1651; IntegraMooDS N = 742). PSi explained up to 5.1% of the variance of g-factor in IMAGEN (F1,1640 = 12.2-94.3; P < 0.005), and up to 3.0% in IntegraMooDS (F1,725 = 10.0-21.0; P < 0.005). The association between polygenic scores and g-factor was partially mediated by SA and CT in prefrontal, anterior cingulate, insula, and medial temporal cortices in both samples (PFWER-corrected < 0.005). The variance explained by mediation was up to 0.75% in IMAGEN and 0.77% in IntegraMooDS. Our results provide evidence that cumulative genetic load influences g-factor via cortical structure. The consistency of our results across samples suggests that cortex morphology could be a novel potential biomarker for neurocognitive dysfunction that is among the most intractable psychiatric symptoms.

Genome-wide association study identifies 30 loci associated with bipolar disorder.

Bipolar disorder is a highly heritable psychiatric disorder. We performed a genome-wide association study (GWAS) including 20,352 cases and 31,358 controls of European descent, with follow-up analysis of 822 variants with P < 1 × 10-4 in an additional 9,412 cases and 137,760 controls. Eight of the 19 variants that were genome-wide significant (P < 5 × 10-8) in the discovery GWAS were not genome-wide significant in the combined analysis, consistent with small effect sizes and limited power but also with genetic heterogeneity. In the combined analysis, 30 loci were genome-wide significant, including 20 newly identified loci. The significant loci contain genes encoding ion channels, neurotransmitter transporters and synaptic components. Pathway analysis revealed nine significantly enriched gene sets, including regulation of insulin secretion and endocannabinoid signaling. Bipolar I disorder is strongly genetically correlated with schizophrenia, driven by psychosis, whereas bipolar II disorder is more strongly correlated with major depressive disorder. These findings address key clinical questions and provide potential biological mechanisms for bipolar disorder.

Refining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetes.

We aggregated coding variant data for 81,412 type 2 diabetes cases and 370,832 controls of diverse ancestry, identifying 40 coding variant association signals (P < 2.2 × 10-7); of these, 16 map outside known risk-associated loci. We make two important observations. First, only five of these signals are driven by low-frequency variants: even for these, effect sizes are modest (odds ratio ≤1.29). Second, when we used large-scale genome-wide association data to fine-map the associated variants in their regional context, accounting for the global enrichment of complex trait associations in coding sequence, compelling evidence for coding variant causality was obtained for only 16 signals. At 13 others, the associated coding variants clearly represent 'false leads' with potential to generate erroneous mechanistic inference. Coding variant associations offer a direct route to biological insight for complex diseases and identification of validated therapeutic targets; however, appropriate mechanistic inference requires careful specification of their causal contribution to disease predisposition.

Consensus Genotyper for Exome Sequencing (CGES): improving the quality of exome variant genotypes.

MOTIVATION: The development of cost-effective next-generation sequencing methods has spurred the development of high-throughput bioinformatics tools for detection of sequence variation. With many disparate variant-calling algorithms available, investigators must ask, 'Which method is best for my data?' Machine learning research has shown that so-called ensemble methods that combine the output of multiple models can dramatically improve classifier performance. Here we describe a novel variant-calling approach based on an ensemble of variant-calling algorithms, which we term the Consensus Genotyper for Exome Sequencing (CGES). CGES uses a two-stage voting scheme among four algorithm implementations. While our ensemble method can accept variants generated by any variant-calling algorithm, we used GATK2.8, SAMtools, FreeBayes and Atlas-SNP2 in building CGES because of their performance, widespread adoption and diverse but complementary algorithms. RESULTS: We apply CGES to 132 samples sequenced at the Hudson Alpha Institute for Biotechnology (HAIB, Huntsville, AL) using the Nimblegen Exome Capture and Illumina sequencing technology. Our sample set consisted of 40 complete trios, two families of four, one parent-child duo and two unrelated individuals. CGES yielded the fewest total variant calls (N(CGES) = 139° 897), the highest Ts/Tv ratio (3.02), the lowest Mendelian error rate across all genotypes (0.028%), the highest rediscovery rate from the Exome Variant Server (EVS; 89.3%) and 1000 Genomes (1KG; 84.1%) and the highest positive predictive value (PPV; 96.1%) for a random sample of previously validated de novo variants. We describe these and other quality control (QC) metrics from consensus data and explain how the CGES pipeline can be used to generate call sets of varying quality stringency, including consensus calls present across all four algorithms, calls that are consistent across any three out of four algorithms, calls that are consistent across any two out of four algorithms or a more liberal set of all calls made by any algorithm. AVAILABILITY AND IMPLEMENTATION: To enable accessible, efficient and reproducible analysis, we implement CGES both as a stand-alone command line tool available for download in GitHub and as a set of Galaxy tools and workflows configured to execute on parallel computers. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A novel mechanism of inherited TBG deficiency: mutation in a liver-specific enhancer.

CONTEXT: T4-binding globulin (TBG), a protein secreted by the liver, is the main thyroid hormone (TH) transporter in human serum. TBG deficiency is characterized by reduced serum TH levels, but normal free TH and TSH and absent clinical manifestations. The inherited form of TBG deficiency is usually due to a mutation in the TBG gene located on the X-chromosome. OBJECTIVE: Among the 75 families with X-chromosome-linked TBG deficiency identified in our laboratory, no mutations in the TBG gene were found in four families. The aim of the study was to identify the mechanism of TBG deficiency in these four families using biochemical and genetic studies. DESIGN: Observational cohort, prospective. SETTING: University research center. PATIENTS: Four families with inherited TBG deficiency and no mutations in the TBG gene. INTERVENTION: Clinical evaluation, thyroid function tests, and targeted resequencing of 1 Mb of the X-chromosome. RESULTS: Next-generation sequencing identified a novel G to A variant 20 kb downstream of the TBG gene in all four families. In silico analysis predicted that the variant resides within a liver-specific enhancer. In vitro studies confirmed the enhancer activity of a 2.2-kb fragment of genomic DNA containing the novel variant and showed that the mutation reduces the activity of this enhancer. The affected subjects share a haplotype of 8 Mb surrounding the mutation, and the most recent common ancestor among the four families was estimated to be 19.5 generations ago (95% confidence intervals, 10.4-37). CONCLUSIONS: To our knowledge, the present study is the first report of an inherited endocrine disorder caused by a mutation in an enhancer region.

Poly-omic prediction of complex traits: OmicKriging.

High-confidence prediction of complex traits such as disease risk or drug response is an ultimate goal of personalized medicine. Although genome-wide association studies have discovered thousands of well-replicated polymorphisms associated with a broad spectrum of complex traits, the combined predictive power of these associations for any given trait is generally too low to be of clinical relevance. We propose a novel systems approach to complex trait prediction, which leverages and integrates similarity in genetic, transcriptomic, or other omics-level data. We translate the omic similarity into phenotypic similarity using a method called Kriging, commonly used in geostatistics and machine learning. Our method called OmicKriging emphasizes the use of a wide variety of systems-level data, such as those increasingly made available by comprehensive surveys of the genome, transcriptome, and epigenome, for complex trait prediction. Furthermore, our OmicKriging framework allows easy integration of prior information on the function of subsets of omics-level data from heterogeneous sources without the sometimes heavy computational burden of Bayesian approaches. Using seven disease datasets from the Wellcome Trust Case Control Consortium (WTCCC), we show that OmicKriging allows simple integration of sparse and highly polygenic components yielding comparable performance at a fraction of the computing time of a recently published Bayesian sparse linear mixed model method. Using a cellular growth phenotype, we show that integrating mRNA and microRNA expression data substantially increases performance over either dataset alone. Using clinical statin response, we show improved prediction over existing methods. We provide an R package to implement OmicKriging (http://www.scandb.org/newinterface/tools/OmicKriging.html).