Publications by authors named "Douglas M Ruderfer"

60 Publications

Phenotypic signatures in clinical data enable systematic identification of patients for genetic testing.

Nat Med 2021 Jun 3;27(6):1097-1104. Epub 2021 Jun 3.

Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.

Around 5% of the population is affected by a rare genetic disease, yet most endure years of uncertainty before receiving a genetic test. A common feature of genetic diseases is the presence of multiple rare phenotypes that often span organ systems. Here, we use diagnostic billing information from longitudinal clinical data in the electronic health records (EHRs) of 2,286 patients who received a chromosomal microarray test, and 9,144 matched controls, to build a model to predict who should receive a genetic test. The model achieved high prediction accuracies in a held-out test sample (area under the receiver operating characteristic curve (AUROC), 0.97; area under the precision-recall curve (AUPRC), 0.92), in an independent hospital system (AUROC, 0.95; AUPRC, 0.62), and in an independent set of 172,265 patients in which cases were broadly defined as having an interaction with a genetics provider (AUROC, 0.9; AUPRC, 0.63). Patients carrying a putative pathogenic copy number variant were also accurately identified by the model. Compared with current approaches for genetic test determination, our model could identify more patients for testing while also increasing the proportion of those tested who have a genetic disease. We demonstrate that phenotypic patterns representative of a wide range of genetic diseases can be captured from EHRs to systematize decision-making for genetic testing, with the potential to speed up diagnosis, improve care and reduce costs.
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http://dx.doi.org/10.1038/s41591-021-01356-zDOI Listing
June 2021

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

Nat Genet 2021 06 17;53(6):817-829. Epub 2021 May 17.

Department of Neuroscience, Istituto Di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy.

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.
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http://dx.doi.org/10.1038/s41588-021-00857-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8192451PMC
June 2021

Investigating rare pathogenic/likely pathogenic exonic variation in bipolar disorder.

Mol Psychiatry 2021 Jan 22. Epub 2021 Jan 22.

HudsonAlpha Institute for Biotechnology, Huntsville, AL, 35806, USA.

Bipolar disorder (BD) is a serious mental illness with substantial common variant heritability. However, the role of rare coding variation in BD is not well established. We examined the protein-coding (exonic) sequences of 3,987 unrelated individuals with BD and 5,322 controls of predominantly European ancestry across four cohorts from the Bipolar Sequencing Consortium (BSC). We assessed the burden of rare, protein-altering, single nucleotide variants classified as pathogenic or likely pathogenic (P-LP) both exome-wide and within several groups of genes with phenotypic or biologic plausibility in BD. While we observed an increased burden of rare coding P-LP variants within 165 genes identified as BD GWAS regions in 3,987 BD cases (meta-analysis OR = 1.9, 95% CI = 1.3-2.8, one-sided p = 6.0 × 10), this enrichment did not replicate in an additional 9,929 BD cases and 14,018 controls (OR = 0.9, one-side p = 0.70). Although BD shares common variant heritability with schizophrenia, in the BSC sample we did not observe a significant enrichment of P-LP variants in SCZ GWAS genes, in two classes of neuronal synaptic genes (RBFOX2 and FMRP) associated with SCZ or in loss-of-function intolerant genes. In this study, the largest analysis of exonic variation in BD, individuals with BD do not carry a replicable enrichment of rare P-LP variants across the exome or in any of several groups of genes with biologic plausibility. Moreover, despite a strong shared susceptibility between BD and SCZ through common genetic variation, we do not observe an association between BD risk and rare P-LP coding variants in genes known to modulate risk for SCZ.
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http://dx.doi.org/10.1038/s41380-020-01006-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8295400PMC
January 2021

Clinical laboratory test-wide association scan of polygenic scores identifies biomarkers of complex disease.

Genome Med 2021 Jan 13;13(1). Epub 2021 Jan 13.

Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, 37232, USA.

Background: Clinical laboratory (lab) tests are used in clinical practice to diagnose, treat, and monitor disease conditions. Test results are stored in electronic health records (EHRs), and a growing number of EHRs are linked to patient DNA, offering unprecedented opportunities to query relationships between genetic risk for complex disease and quantitative physiological measurements collected on large populations.

Methods: A total of 3075 quantitative lab tests were extracted from Vanderbilt University Medical Center's (VUMC) EHR system and cleaned for population-level analysis according to our QualityLab protocol. Lab values extracted from BioVU were compared with previous population studies using heritability and genetic correlation analyses. We then tested the hypothesis that polygenic risk scores for biomarkers and complex disease are associated with biomarkers of disease extracted from the EHR. In a proof of concept analyses, we focused on lipids and coronary artery disease (CAD). We cleaned lab traits extracted from the EHR performed lab-wide association scans (LabWAS) of the lipids and CAD polygenic risk scores across 315 heritable lab tests then replicated the pipeline and analyses in the Massachusetts General Brigham Biobank.

Results: Heritability estimates of lipid values (after cleaning with QualityLab) were comparable to previous reports and polygenic scores for lipids were strongly associated with their referent lipid in a LabWAS. LabWAS of the polygenic score for CAD recapitulated canonical heart disease biomarker profiles including decreased HDL, increased pre-medication LDL, triglycerides, blood glucose, and glycated hemoglobin (HgbA1C) in European and African descent populations. Notably, many of these associations remained even after adjusting for the presence of cardiovascular disease and were replicated in the MGBB.

Conclusions: Polygenic risk scores can be used to identify biomarkers of complex disease in large-scale EHR-based genomic analyses, providing new avenues for discovery of novel biomarkers and deeper understanding of disease trajectories in pre-symptomatic individuals. We present two methods and associated software, QualityLab and LabWAS, to clean and analyze EHR labs at scale and perform a Lab-Wide Association Scan.
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http://dx.doi.org/10.1186/s13073-020-00820-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7807864PMC
January 2021

Genetic variants and functional pathways associated with resilience to Alzheimer's disease.

Brain 2020 08;143(8):2561-2575

Department of Neurology, Columbia University, New York, NY, USA.

Approximately 30% of older adults exhibit the neuropathological features of Alzheimer's disease without signs of cognitive impairment. Yet, little is known about the genetic factors that allow these potentially resilient individuals to remain cognitively unimpaired in the face of substantial neuropathology. We performed a large, genome-wide association study (GWAS) of two previously validated metrics of cognitive resilience quantified using a latent variable modelling approach and representing better-than-predicted cognitive performance for a given level of neuropathology. Data were harmonized across 5108 participants from a clinical trial of Alzheimer's disease and three longitudinal cohort studies of cognitive ageing. All analyses were run across all participants and repeated restricting the sample to individuals with unimpaired cognition to identify variants at the earliest stages of disease. As expected, all resilience metrics were genetically correlated with cognitive performance and education attainment traits (P-values < 2.5 × 10-20), and we observed novel correlations with neuropsychiatric conditions (P-values < 7.9 × 10-4). Notably, neither resilience metric was genetically correlated with clinical Alzheimer's disease (P-values > 0.42) nor associated with APOE (P-values > 0.13). In single variant analyses, we observed a genome-wide significant locus among participants with unimpaired cognition on chromosome 18 upstream of ATP8B1 (index single nucleotide polymorphism rs2571244, minor allele frequency = 0.08, P = 2.3 × 10-8). The top variant at this locus (rs2571244) was significantly associated with methylation in prefrontal cortex tissue at multiple CpG sites, including one just upstream of ATPB81 (cg19596477; P = 2 × 10-13). Overall, this comprehensive genetic analysis of resilience implicates a putative role of vascular risk, metabolism, and mental health in protection from the cognitive consequences of neuropathology, while also providing evidence for a novel resilience gene along the bile acid metabolism pathway. Furthermore, the genetic architecture of resilience appears to be distinct from that of clinical Alzheimer's disease, suggesting that a shift in focus to molecular contributors to resilience may identify novel pathways for therapeutic targets.
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http://dx.doi.org/10.1093/brain/awaa209DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7447518PMC
August 2020

Functional annotation of rare structural variation in the human brain.

Nat Commun 2020 06 12;11(1):2990. Epub 2020 Jun 12.

Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.

Structural variants (SVs) contribute to many disorders, yet, functionally annotating them remains a major challenge. Here, we integrate SVs with RNA-sequencing from human post-mortem brains to quantify their dosage and regulatory effects. We show that genic and regulatory SVs exist at significantly lower frequencies than intergenic SVs. Functional impact of copy number variants (CNVs) stems from both the proportion of genic and regulatory content altered and loss-of-function intolerance of the gene. We train a linear model to predict expression effects of rare CNVs and use it to annotate regulatory disruption of CNVs from 14,891 independent genome-sequenced individuals. Pathogenic deletions implicated in neurodevelopmental disorders show significantly more extreme regulatory disruption scores and if rank ordered would be prioritized higher than using frequency or length alone. This work shows the deleteriousness of regulatory SVs, particularly those altering CTCF sites and provides a simple approach for functionally annotating the regulatory consequences of CNVs.
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http://dx.doi.org/10.1038/s41467-020-16736-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7293301PMC
June 2020

Expanding cultural and ancestral representation in psychiatric genetic studies.

Neuropsychopharmacology 2020 09 15;45(10):1593-1594. Epub 2020 Apr 15.

Division of Genetic Medicine, Department of Medicine, Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA.

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http://dx.doi.org/10.1038/s41386-020-0676-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7609284PMC
September 2020

APOE ε4-specific associations of VEGF gene family expression with cognitive aging and Alzheimer's disease.

Neurobiol Aging 2020 03 5;87:18-25. Epub 2019 Nov 5.

Vanderbilt Memory and Alzheimer's Center, Vanderbilt University Medical Center, Nashville, TN, USA; Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA. Electronic address:

Literature suggests vascular endothelial growth factor A (VEGFA) is protective among those at highest risk for Alzheimer's disease (AD). Apolipoprotein E (APOE) ε4 allele carriers represent a highly susceptible population for cognitive decline, and VEGF may confer distinct protection among APOE-ε4 carriers. We evaluated interactions between cortical expression of 10 VEGF gene family members and APOE-ε4 genotype to clarify which VEGF genes modify the association between APOE-ε4 and cognitive decline. Data were obtained from the Religious Orders Study and Rush Memory and Aging Project (N = 531). Linear regression assessed interactions on global cognition. VEGF genes NRP1 and VEGFA interacted with APOE-ε4 on cognitive performance (p.fdr < 0.05). Higher NRP1 expression correlated with worse outcomes among ε4 carriers but better outcomes among ε4 noncarriers, suggesting NRP1 modifies the risk for poor cognitive scores based on APOE-ε4 status. NRP1 regulates angiogenesis, and literature suggests vessels in APOE-ε4 brains are more prone to leaking, perhaps placing young vessels at risk for ischemia. Results suggest that future therapeutics targeting brain angiogenesis should also consider ε4 allele status.
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http://dx.doi.org/10.1016/j.neurobiolaging.2019.10.021DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7064375PMC
March 2020

Characterization of Single Gene Copy Number Variants in Schizophrenia.

Biol Psychiatry 2020 04 4;87(8):736-744. Epub 2019 Oct 4.

Division of Genetic Medicine, Departments of Medicine, Psychiatry, and Biomedical Informatics, Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee. Electronic address:

Background: Genetic studies of schizophrenia have implicated numerous risk loci including several copy number variants (CNVs) of large effect and hundreds of loci of small effect. In only a few cases has a specific gene been clearly identified. Rare CNVs affecting a single gene offer a potential avenue to discovering schizophrenia risk genes.

Methods: CNVs were generated from exome sequencing of 4913 schizophrenia cases and 6188 control subjects from Sweden. We integrated two CNV calling methods (XHMM and ExomeDepth) to expand our set of single-gene CNVs and leveraged two different approaches for validating these variants (quantitative polymerase chain reaction and NanoString).

Results: We found a significant excess of all rare CNVs (deletions: p = .0004, duplications: p = .0006) and single-gene CNVs (deletions: p = .04, duplications: p = .03) in schizophrenia cases compared with control subjects. An expanded set of CNVs generated from integrating multiple approaches showed a significant burden of deletions in 11 of 21 gene sets previously implicated in schizophrenia and across all genes in those sets (p = .008), although no tests survived correction. We performed an extensive validation of all deletions in the significant set of voltage-gated calcium channels among CNVs called from both exome sequencing and genotyping arrays. In total, 4 exonic, single-gene deletions were validated in schizophrenia cases and none in control subjects (p = .039), of which all were identified by exome sequencing.

Conclusions: These results point to the potential contribution of single-gene CNVs to schizophrenia, indicate that the utility of exome sequencing for CNV calling has yet to be maximized, and note that single-gene CNVs should be included in gene-focused studies using other classes of variation.
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http://dx.doi.org/10.1016/j.biopsych.2019.09.023DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7103483PMC
April 2020

Publisher Correction: Gene expression imputation across multiple brain regions provides insights into schizophrenia risk.

Nat Genet 2019 Jun;51(6):1068

Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL, USA.

In the HTML version of the article originally published, the author group 'The Schizophrenia Working Group of the Psychiatric Genomics Consortium' was displayed incorrectly. The error has been corrected in the HTML version of the article.
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http://dx.doi.org/10.1038/s41588-019-0435-6DOI Listing
June 2019

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

Nat Genet 2019 05 1;51(5):793-803. Epub 2019 May 1.

Department of Psychiatry, Weill Cornell Medical College, New York, NY, USA.

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 in an additional 9,412 cases and 137,760 controls. Eight of the 19 variants that were genome-wide significant (P < 5 × 10) 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.
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http://dx.doi.org/10.1038/s41588-019-0397-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6956732PMC
May 2019

Gene expression imputation across multiple brain regions provides insights into schizophrenia risk.

Nat Genet 2019 04 25;51(4):659-674. Epub 2019 Mar 25.

Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL, USA.

Transcriptomic imputation approaches combine eQTL reference panels with large-scale genotype data in order to test associations between disease and gene expression. These genic associations could elucidate signals in complex genome-wide association study (GWAS) loci and may disentangle the role of different tissues in disease development. We used the largest eQTL reference panel for the dorso-lateral prefrontal cortex (DLPFC) to create a set of gene expression predictors and demonstrate their utility. We applied DLPFC and 12 GTEx-brain predictors to 40,299 schizophrenia cases and 65,264 matched controls for a large transcriptomic imputation study of schizophrenia. We identified 413 genic associations across 13 brain regions. Stepwise conditioning identified 67 non-MHC genes, of which 14 did not fall within previous GWAS loci. We identified 36 significantly enriched pathways, including hexosaminidase-A deficiency, and multiple porphyric disorder pathways. We investigated developmental expression patterns among the 67 non-MHC genes and identified specific groups of pre- and postnatal expression.
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http://dx.doi.org/10.1038/s41588-019-0364-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7034316PMC
April 2019

Contribution of Rare Copy Number Variants to Bipolar Disorder Risk Is Limited to Schizoaffective Cases.

Biol Psychiatry 2019 07 20;86(2):110-119. Epub 2018 Dec 20.

Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.

Background: Genetic risk for bipolar disorder (BD) is conferred through many common alleles, while a role for rare copy number variants (CNVs) is less clear. Subtypes of BD including schizoaffective disorder bipolar type (SAB), bipolar I disorder (BD I), and bipolar II disorder (BD II) differ according to the prominence and timing of psychosis, mania, and depression. The genetic factors contributing to the combination of symptoms among these subtypes are poorly understood.

Methods: Rare large CNVs were analyzed in 6353 BD cases (3833 BD I [2676 with psychosis, 850 without psychosis, and 307 with unknown psychosis history], 1436 BD II, 579 SAB, and 505 BD not otherwise specified) and 8656 controls. CNV burden and a polygenic risk score (PRS) for schizophrenia were used to evaluate the relative contributions of rare and common variants to risk of BD, BD subtypes, and psychosis.

Results: CNV burden did not differ between BD and controls when treated as a single diagnostic entity. However, burden in SAB was increased relative to controls (p = .001), BD I (p = .0003), and BD II (p = .0007). Burden and schizophrenia PRSs were increased in SAB compared with BD I with psychosis (CNV p = .0007, PRS p = .004), and BD I without psychosis (CNV p = .0004, PRS p = 3.9 × 10). Within BD I, psychosis was associated with increased schizophrenia PRSs (p = .005) but not CNV burden.

Conclusions: CNV burden in BD is limited to SAB. Rare and common genetic variants may contribute differently to risk for psychosis and perhaps other classes of psychiatric symptoms.
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http://dx.doi.org/10.1016/j.biopsych.2018.12.009DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6586545PMC
July 2019

Contribution of Rare Copy Number Variants to Bipolar Disorder Risk Is Limited to Schizoaffective Cases.

Biol Psychiatry 2019 07 20;86(2):110-119. Epub 2018 Dec 20.

Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.

Background: Genetic risk for bipolar disorder (BD) is conferred through many common alleles, while a role for rare copy number variants (CNVs) is less clear. Subtypes of BD including schizoaffective disorder bipolar type (SAB), bipolar I disorder (BD I), and bipolar II disorder (BD II) differ according to the prominence and timing of psychosis, mania, and depression. The genetic factors contributing to the combination of symptoms among these subtypes are poorly understood.

Methods: Rare large CNVs were analyzed in 6353 BD cases (3833 BD I [2676 with psychosis, 850 without psychosis, and 307 with unknown psychosis history], 1436 BD II, 579 SAB, and 505 BD not otherwise specified) and 8656 controls. CNV burden and a polygenic risk score (PRS) for schizophrenia were used to evaluate the relative contributions of rare and common variants to risk of BD, BD subtypes, and psychosis.

Results: CNV burden did not differ between BD and controls when treated as a single diagnostic entity. However, burden in SAB was increased relative to controls (p = .001), BD I (p = .0003), and BD II (p = .0007). Burden and schizophrenia PRSs were increased in SAB compared with BD I with psychosis (CNV p = .0007, PRS p = .004), and BD I without psychosis (CNV p = .0004, PRS p = 3.9 × 10). Within BD I, psychosis was associated with increased schizophrenia PRSs (p = .005) but not CNV burden.

Conclusions: CNV burden in BD is limited to SAB. Rare and common genetic variants may contribute differently to risk for psychosis and perhaps other classes of psychiatric symptoms.
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http://dx.doi.org/10.1016/j.biopsych.2018.12.009DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6586545PMC
July 2019

Learning Opportunities for Drug Repositioning via GWAS and PheWAS Findings.

AMIA Jt Summits Transl Sci Proc 2018 18;2017:237-246. Epub 2018 May 18.

Vanderbilt University, Nashville, TN.

Drug repositioning for available medications can be preferred over traditional drug development, which requires substantially more effort to uncover new insights into medications and diseases. Genome-Wide Association Studies (GWAS) and Phenome-Wide Association Studies (PheWAS) are two complimentary methods for finding novel associations between genes and diseases. We hypothesize that discoveries from these studies could be leveraged to find new targets for existing drugs. Thus, we propose a framework to learn opportunities for inferring such relationships via overlapped genes between disease-associated genes (e.g. GWAS and PheWAS findings) and drugtargeted genes. We use drug indications found in Medication Indication Resource (MEDI) as a gold standard to evaluate if drug indications learned from GWAS and PheWAS findings have clinical indications. We examined 151,011 pairs from 987 drugs across 153 diseases and 763 pairs were statistically significant. Out of these 763 pairs, 16 of them were found to have clinical indications.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5961790PMC
May 2018

Landscape of Conditional eQTL in Dorsolateral Prefrontal Cortex and Co-localization with Schizophrenia GWAS.

Am J Hum Genet 2018 06 24;102(6):1169-1184. Epub 2018 May 24.

Systems Biology, Sage Bionetworks, Seattle, WA 98109, USA. Electronic address:

Causal genes and variants within genome-wide association study (GWAS) loci can be identified by integrating GWAS statistics with expression quantitative trait loci (eQTL) and determining which variants underlie both GWAS and eQTL signals. Most analyses, however, consider only the marginal eQTL signal, rather than dissect this signal into multiple conditionally independent signals for each gene. Here we show that analyzing conditional eQTL signatures, which could be important under specific cellular or temporal contexts, leads to improved fine mapping of GWAS associations. Using genotypes and gene expression levels from post-mortem human brain samples (n = 467) reported by the CommonMind Consortium (CMC), we find that conditional eQTL are widespread; 63% of genes with primary eQTL also have conditional eQTL. In addition, genomic features associated with conditional eQTL are consistent with context-specific (e.g., tissue-, cell type-, or developmental time point-specific) regulation of gene expression. Integrating the 2014 Psychiatric Genomics Consortium schizophrenia (SCZ) GWAS and CMC primary and conditional eQTL data reveals 40 loci with strong evidence for co-localization (posterior probability > 0.8), including six loci with co-localization of conditional eQTL. Our co-localization analyses support previously reported genes, identify novel genes associated with schizophrenia risk, and provide specific hypotheses for their functional follow-up.
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http://dx.doi.org/10.1016/j.ajhg.2018.04.011DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5993513PMC
June 2018

Genetic validation of bipolar disorder identified by automated phenotyping using electronic health records.

Transl Psychiatry 2018 04 18;8(1):86. Epub 2018 Apr 18.

Psychiatric and Neurodevelopmental Genetics Unit, Massachusetts General Hospital, 185 Cambridge St., Boston, MA, 02114, USA.

Bipolar disorder (BD) is a heritable mood disorder characterized by episodes of mania and depression. Although genomewide association studies (GWAS) have successfully identified genetic loci contributing to BD risk, sample size has become a rate-limiting obstacle to genetic discovery. Electronic health records (EHRs) represent a vast but relatively untapped resource for high-throughput phenotyping. As part of the International Cohort Collection for Bipolar Disorder (ICCBD), we previously validated automated EHR-based phenotyping algorithms for BD against in-person diagnostic interviews (Castro et al. Am J Psychiatry 172:363-372, 2015). Here, we establish the genetic validity of these phenotypes by determining their genetic correlation with traditionally ascertained samples. Case and control algorithms were derived from structured and narrative text in the Partners Healthcare system comprising more than 4.6 million patients over 20 years. Genomewide genotype data for 3330 BD cases and 3952 controls of European ancestry were used to estimate SNP-based heritability (h) and genetic correlation (r) between EHR-based phenotype definitions and traditionally ascertained BD cases in GWAS by the ICCBD and Psychiatric Genomics Consortium (PGC) using LD score regression. We evaluated BD cases identified using 4 EHR-based algorithms: an NLP-based algorithm (95-NLP) and three rule-based algorithms using codified EHR with decreasing levels of stringency-"coded-strict", "coded-broad", and "coded-broad based on a single clinical encounter" (coded-broad-SV). The analytic sample comprised 862 95-NLP, 1968 coded-strict, 2581 coded-broad, 408 coded-broad-SV BD cases, and 3 952 controls. The estimated h were 0.24 (p = 0.015), 0.09 (p = 0.064), 0.13 (p = 0.003), 0.00 (p = 0.591) for 95-NLP, coded-strict, coded-broad and coded-broad-SV BD, respectively. The h for all EHR-based cases combined except coded-broad-SV (excluded due to 0 h) was 0.12 (p = 0.004). These h were lower or similar to the h observed by the ICCBD + PGCBD (0.23, p = 3.17E-80, total N = 33,181). However, the r between ICCBD + PGCBD and the EHR-based cases were high for 95-NLP (0.66, p = 3.69 × 10), coded-strict (1.00, p = 2.40 × 10), and coded-broad (0.74, p = 8.11 × 10). The r between EHR-based BD definitions ranged from 0.90 to 0.98. These results provide the first genetic validation of automated EHR-based phenotyping for BD and suggest that this approach identifies cases that are highly genetically correlated with those ascertained through conventional methods. High throughput phenotyping using the large data resources available in EHRs represents a viable method for accelerating psychiatric genetic research.
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http://dx.doi.org/10.1038/s41398-018-0133-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5904248PMC
April 2018

Improving genetic prediction by leveraging genetic correlations among human diseases and traits.

Nat Commun 2018 03 7;9(1):989. Epub 2018 Mar 7.

Institute for Molecular Bioscience, University of Queensland, Queensland, QLD, 4072, Australia.

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.
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http://dx.doi.org/10.1038/s41467-017-02769-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5841449PMC
March 2018

Common schizophrenia alleles are enriched in mutation-intolerant genes and in regions under strong background selection.

Nat Genet 2018 03 26;50(3):381-389. Epub 2018 Feb 26.

Department of Neurology, Center for Autism Research and Treatment, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.

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.
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http://dx.doi.org/10.1038/s41588-018-0059-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5918692PMC
March 2018

Transcriptional signatures of schizophrenia in hiPSC-derived NPCs and neurons are concordant with post-mortem adult brains.

Nat Commun 2017 12 20;8(1):2225. Epub 2017 Dec 20.

Department of Genetics and Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.

The power of human induced pluripotent stem cell (hiPSC)-based studies to resolve the smaller effects of common variants within the size of cohorts that can be realistically assembled remains uncertain. We identified and accounted for a variety of technical and biological sources of variation in a large case/control schizophrenia (SZ) hiPSC-derived cohort of neural progenitor cells and neurons. Reducing the stochastic effects of the differentiation process by correcting for cell type composition boosted the SZ signal and increased the concordance with post-mortem data sets. We predict a growing convergence between hiPSC and post-mortem studies as both approaches expand to larger cohort sizes. For studies of complex genetic disorders, to maximize the power of hiPSC cohorts currently feasible, in most cases and whenever possible, we recommend expanding the number of individuals even at the expense of the number of replicate hiPSC clones.
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http://dx.doi.org/10.1038/s41467-017-02330-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5738408PMC
December 2017

Integrated Bayesian analysis of rare exonic variants to identify risk genes for schizophrenia and neurodevelopmental disorders.

Genome Med 2017 Dec 20;9(1):114. Epub 2017 Dec 20.

Division of Psychiatric Genomics, Department of Genetics and Genomic Sciences, Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, 10029, NY, USA.

Background: Integrating rare variation from trio family and case-control studies has successfully implicated specific genes contributing to risk of neurodevelopmental disorders (NDDs) including autism spectrum disorders (ASD), intellectual disability (ID), developmental disorders (DDs), and epilepsy (EPI). For schizophrenia (SCZ), however, while sets of genes have been implicated through the study of rare variation, only two risk genes have been identified.

Methods: We used hierarchical Bayesian modeling of rare-variant genetic architecture to estimate mean effect sizes and risk-gene proportions, analyzing the largest available collection of whole exome sequence data for SCZ (1,077 trios, 6,699 cases, and 13,028 controls), and data for four NDDs (ASD, ID, DD, and EPI; total 10,792 trios, and 4,058 cases and controls).

Results: For SCZ, we estimate there are 1,551 risk genes. There are more risk genes and they have weaker effects than for NDDs. We provide power analyses to predict the number of risk-gene discoveries as more data become available. We confirm and augment prior risk gene and gene set enrichment results for SCZ and NDDs. In particular, we detected 98 new DD risk genes at FDR < 0.05. Correlations of risk-gene posterior probabilities are high across four NDDs (ρ>0.55), but low between SCZ and the NDDs (ρ<0.3). An in-depth analysis of 288 NDD genes shows there is highly significant protein-protein interaction (PPI) network connectivity, and functionally distinct PPI subnetworks based on pathway enrichment, single-cell RNA-seq cell types, and multi-region developmental brain RNA-seq.

Conclusions: We have extended a pipeline used in ASD studies and applied it to infer rare genetic parameters for SCZ and four NDDs ( https://github.com/hoangtn/extTADA ). We find many new DD risk genes, supported by gene set enrichment and PPI network connectivity analyses. We find greater similarity among NDDs than between NDDs and SCZ. NDD gene subnetworks are implicated in postnatally expressed presynaptic and postsynaptic genes, and for transcriptional and post-transcriptional gene regulation in prenatal neural progenitor and stem cells.
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http://dx.doi.org/10.1186/s13073-017-0497-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5738153PMC
December 2017

The ExAC browser: displaying reference data information from over 60 000 exomes.

Nucleic Acids Res 2017 01 28;45(D1):D840-D845. Epub 2016 Nov 28.

Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, 02114, USA.

Worldwide, hundreds of thousands of humans have had their genomes or exomes sequenced, and access to the resulting data sets can provide valuable information for variant interpretation and understanding gene function. Here, we present a lightweight, flexible browser framework to display large population datasets of genetic variation. We demonstrate its use for exome sequence data from 60 706 individuals in the Exome Aggregation Consortium (ExAC). The ExAC browser provides gene- and transcript-centric displays of variation, a critical view for clinical applications. Additionally, we provide a variant display, which includes population frequency and functional annotation data as well as short read support for the called variant. This browser is open-source, freely available at http://exac.broadinstitute.org, and has already been used extensively by clinical laboratories worldwide.
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http://dx.doi.org/10.1093/nar/gkw971DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5210650PMC
January 2017

Contribution of copy number variants to schizophrenia from a genome-wide study of 41,321 subjects.

Nat Genet 2017 01 21;49(1):27-35. Epub 2016 Nov 21.

Department of Psychiatry, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands.

Copy number variants (CNVs) have been strongly implicated in the genetic etiology of schizophrenia (SCZ). However, genome-wide investigation of the contribution of CNV to risk has been hampered by limited sample sizes. We sought to address this obstacle by applying a centralized analysis pipeline to a SCZ cohort of 21,094 cases and 20,227 controls. A global enrichment of CNV burden was observed in cases (odds ratio (OR) = 1.11, P = 5.7 × 10), which persisted after excluding loci implicated in previous studies (OR = 1.07, P = 1.7 × 10). CNV burden was enriched for genes associated with synaptic function (OR = 1.68, P = 2.8 × 10) and neurobehavioral phenotypes in mouse (OR = 1.18, P = 7.3 × 10). Genome-wide significant evidence was obtained for eight loci, including 1q21.1, 2p16.3 (NRXN1), 3q29, 7q11.2, 15q13.3, distal 16p11.2, proximal 16p11.2 and 22q11.2. Suggestive support was found for eight additional candidate susceptibility and protective loci, which consisted predominantly of CNVs mediated by nonallelic homologous recombination.
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http://dx.doi.org/10.1038/ng.3725DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5737772PMC
January 2017

No Reliable Association between Runs of Homozygosity and Schizophrenia in a Well-Powered Replication Study.

PLoS Genet 2016 Oct 28;12(10):e1006343. Epub 2016 Oct 28.

Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim / Heidelberg University, Mannheim, Germany.

It is well known that inbreeding increases the risk of recessive monogenic diseases, but it is less certain whether it contributes to the etiology of complex diseases such as schizophrenia. One way to estimate the effects of inbreeding is to examine the association between disease diagnosis and genome-wide autozygosity estimated using runs of homozygosity (ROH) in genome-wide single nucleotide polymorphism arrays. Using data for schizophrenia from the Psychiatric Genomics Consortium (n = 21,868), Keller et al. (2012) estimated that the odds of developing schizophrenia increased by approximately 17% for every additional percent of the genome that is autozygous (β = 16.1, CI(β) = [6.93, 25.7], Z = 3.44, p = 0.0006). Here we describe replication results from 22 independent schizophrenia case-control datasets from the Psychiatric Genomics Consortium (n = 39,830). Using the same ROH calling thresholds and procedures as Keller et al. (2012), we were unable to replicate the significant association between ROH burden and schizophrenia in the independent PGC phase II data, although the effect was in the predicted direction, and the combined (original + replication) dataset yielded an attenuated but significant relationship between Froh and schizophrenia (β = 4.86,CI(β) = [0.90,8.83],Z = 2.40,p = 0.02). Since Keller et al. (2012), several studies reported inconsistent association of ROH burden with complex traits, particularly in case-control data. These conflicting results might suggest that the effects of autozygosity are confounded by various factors, such as socioeconomic status, education, urbanicity, and religiosity, which may be associated with both real inbreeding and the outcome measures of interest.
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http://dx.doi.org/10.1371/journal.pgen.1006343DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5085024PMC
October 2016

Increased burden of ultra-rare protein-altering variants among 4,877 individuals with schizophrenia.

Nat Neurosci 2016 11 3;19(11):1433-1441. Epub 2016 Oct 3.

Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA.

By analyzing the exomes of 12,332 unrelated Swedish individuals, including 4,877 individuals affected with schizophrenia, in ways informed by exome sequences from 45,376 other individuals, we identified 244,246 coding-sequence and splice-site ultra-rare variants (URVs) that were unique to individual Swedes. We found that gene-disruptive and putatively protein-damaging URVs (but not synonymous URVs) were more abundant among individuals with schizophrenia than among controls (P = 1.3 × 10). This elevation of protein-compromising URVs was several times larger than an analogously elevated rate for de novo mutations, suggesting that most rare-variant effects on schizophrenia risk are inherited. Among individuals with schizophrenia, the elevated frequency of protein-compromising URVs was concentrated in brain-expressed genes, particularly in neuronally expressed genes; most of this elevation arose from large sets of genes whose RNAs have been found to interact with synaptically localized proteins. Our results suggest that synaptic dysfunction may mediate a large fraction of strong, individually rare genetic influences on schizophrenia risk.
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http://dx.doi.org/10.1038/nn.4402DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5104192PMC
November 2016

Gene expression elucidates functional impact of polygenic risk for schizophrenia.

Nat Neurosci 2016 11 26;19(11):1442-1453. Epub 2016 Sep 26.

Department of Basic Pharmaceutical Sciences, Fred Wilson School of Pharmacy, High Point University, High Point, North Carolina, USA.

Over 100 genetic loci harbor schizophrenia-associated variants, yet how these variants confer liability is uncertain. The CommonMind Consortium sequenced RNA from dorsolateral prefrontal cortex of people with schizophrenia (N = 258) and control subjects (N = 279), creating a resource of gene expression and its genetic regulation. Using this resource, ∼20% of schizophrenia loci have variants that could contribute to altered gene expression and liability. In five loci, only a single gene was involved: FURIN, TSNARE1, CNTN4, CLCN3 or SNAP91. Altering expression of FURIN, TSNARE1 or CNTN4 changed neurodevelopment in zebrafish; knockdown of FURIN in human neural progenitor cells yielded abnormal migration. Of 693 genes showing significant case-versus-control differential expression, their fold changes were ≤ 1.33, and an independent cohort yielded similar results. Gene co-expression implicates a network relevant for schizophrenia. Our findings show that schizophrenia is polygenic and highlight the utility of this resource for mechanistic interpretations of genetic liability for brain diseases.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5083142PMC
http://dx.doi.org/10.1038/nn.4399DOI Listing
November 2016

Analysis of protein-coding genetic variation in 60,706 humans.

Nature 2016 08;536(7616):285-91

Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA.

Large-scale reference data sets of human genetic variation are critical for the medical and functional interpretation of DNA sequence changes. Here we describe the aggregation and analysis of high-quality exome (protein-coding region) DNA sequence data for 60,706 individuals of diverse ancestries generated as part of the Exome Aggregation Consortium (ExAC). This catalogue of human genetic diversity contains an average of one variant every eight bases of the exome, and provides direct evidence for the presence of widespread mutational recurrence. We have used this catalogue to calculate objective metrics of pathogenicity for sequence variants, and to identify genes subject to strong selection against various classes of mutation; identifying 3,230 genes with near-complete depletion of predicted protein-truncating variants, with 72% of these genes having no currently established human disease phenotype. Finally, we demonstrate that these data can be used for the efficient filtering of candidate disease-causing variants, and for the discovery of human 'knockout' variants in protein-coding genes.
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http://dx.doi.org/10.1038/nature19057DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5018207PMC
August 2016
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