ORCID Profile
0000-0002-2365-386X
Current Organisation
Vanderbilt University Medical Center
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Publisher: Cold Spring Harbor Laboratory
Date: 21-11-2017
DOI: 10.1101/222786
Abstract: Bipolar disorder is a complex neuropsychiatric disorder presenting with episodic mood disturbances. In this study we use a transcriptomic imputation approach to identify novel genes and pathways associated with bipolar disorder, as well as three diagnostically and genetically distinct subtypes. Transcriptomic imputation approaches leverage well-curated and publicly available eQTL reference panels to create gene-expression prediction models, which may then be applied to “impute” genetically regulated gene expression (GREX) in large GWAS datasets. By testing for association between phenotype and GREX, rather than genotype, we hope to identify more biologically interpretable associations, and thus elucidate more of the genetic architecture of bipolar disorder. We applied GREX prediction models for 13 brain regions (derived from CommonMind Consortium and GTEx eQTL reference panels) to 21,488 bipolar cases and 54,303 matched controls, constituting the largest transcriptomic imputation study of bipolar disorder (BPD) to date. Additionally, we analyzed three specific BPD subtypes, including 14,938 in iduals with subtype 1 (BD-I), 3,543 in iduals with subtype 2 (BD-II), and 1,500 in iduals with schizoaffective subtype (SAB). We identified 125 gene-tissue associations with BPD, of which 53 represent independent associations after FINEMAP analysis. 29/53 associations were novel i.e., did not lie within 1Mb of a locus identified in the recent PGC-BD GWAS. We identified 37 independent BD-I gene-tissue associations (10 novel), 2 BD-II associations, and 2 SAB associations. Our BPD, BD-I and BD-II associations were significantly more likely to be differentially expressed in post-mortem brain tissue of BPD, BD-I and BD-II cases than we might expect by chance. Together with our pathway analysis, our results support long-standing hypotheses about bipolar disorder risk, including a role for oxidative stress and mitochondrial dysfunction, the post-synaptic density, and an enrichment of circadian rhythm and clock genes within our results.
Publisher: Springer Science and Business Media LLC
Date: 09-2015
DOI: 10.1038/NBT.3299
Publisher: Springer Science and Business Media LLC
Date: 02-2016
DOI: 10.1038/NN.4228
Publisher: Springer Science and Business Media LLC
Date: 30-07-2008
DOI: 10.1038/NATURE07239
Publisher: Springer Science and Business Media LLC
Date: 07-2009
DOI: 10.1038/NATURE08185
Publisher: Springer Science and Business Media LLC
Date: 27-01-2016
DOI: 10.1038/NATURE16549
Publisher: Elsevier BV
Date: 2013
Publisher: Public Library of Science (PLoS)
Date: 09-09-2010
Publisher: Public Library of Science (PLoS)
Date: 22-06-2012
Publisher: American Medical Association (AMA)
Date: 07-2014
Publisher: Springer Science and Business Media LLC
Date: 02-11-2015
DOI: 10.1038/NG.3431
Publisher: Springer Science and Business Media LLC
Date: 02-02-2015
DOI: 10.1038/NG.3211
Publisher: Springer Science and Business Media LLC
Date: 07-2014
DOI: 10.1038/NATURE13595
Publisher: Springer Science and Business Media LLC
Date: 22-02-2011
DOI: 10.1038/MP.2011.11
Publisher: Cold Spring Harbor Laboratory
Date: 21-11-2017
DOI: 10.1101/222596
Abstract: Transcriptomic imputation approaches offer an opportunity to test associations between disease and gene expression in otherwise inaccessible tissues, such as brain, by combining eQTL reference panels with large-scale genotype data. These genic associations could elucidate signals in complex GWAS loci and may disentangle the role of different tissues in disease development. Here, we use the largest eQTL reference panel for the dorso-lateral pre-frontal cortex (DLPFC), collected by the CommonMind Consortium, to create a set of gene expression predictors and demonstrate their utility. We applied these predictors to 40,299 schizophrenia cases and 65,264 matched controls, constituting the largest transcriptomic imputation study of schizophrenia to date. We also computed predicted gene expression levels for 12 additional brain regions, using publicly available predictor models from GTEx. We identified 413 genic associations across 13 brain regions. Stepwise conditioning across the genes and tissues identified 71 associated genes (67 outside the MHC), with the majority of associations found in the DLPFC, and of which 14/67 genes did not fall within previously genome-wide significant loci. We identified 36 significantly enriched pathways, including hexosaminidase-A deficiency, and multiple pathways associated with porphyric disorders. We investigated developmental expression patterns for all 67 non-MHC associated genes using BRAINSPAN, and identified groups of genes expressed specifically pre-natally or post-natally.
Publisher: Springer Science and Business Media LLC
Date: 25-08-2013
DOI: 10.1038/NG.2742
Publisher: Springer Science and Business Media LLC
Date: 17-08-2008
DOI: 10.1038/NG.209
Publisher: Springer Science and Business Media LLC
Date: 08-2016
DOI: 10.1038/NATURE19057
Publisher: Springer Science and Business Media LLC
Date: 17-08-2016
DOI: 10.1038/NG.3638
Publisher: Springer Science and Business Media LLC
Date: 16-02-2010
DOI: 10.1038/MP.2010.7
Abstract: 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 s le. Nominally significant pathways were tested in the validation s le, 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 s les (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.
Publisher: Oxford University Press (OUP)
Date: 18-08-2015
DOI: 10.1093/IJE/DYV136
Publisher: BMJ
Date: 20-12-2012
DOI: 10.1136/JMEDGENET-2011-100397
Abstract: After the recent successes of genome-wide association studies (GWAS), one key challenge is to identify genetic variants that might have a significant joint effect on complex diseases but have failed to be identified in idually due to weak to moderate marginal effect. One popular and effective approach is gene set based analysis, which investigates the joint effect of multiple functionally related genes (eg, pathways). However, a typical gene set analysis method is biased towards long genes, a problem that is especially severe in psychiatric diseases. A novel approach was proposed, namely generalised additive model (GAM) for GWAS (gamGWAS), for gene set enrichment analysis of GWAS data, specifically adjusting the gene length bias or the number of single-nucleotide polymorphisms per gene. GAM is applied to estimate the probability of a gene to be selected as significant given its gene length, followed by weighted res ling and computation of empirical p values for the rank of pathways. We demonstrated gamGWAS in two schizophrenia GWAS datasets from the International Schizophrenia Consortium and the Genetic Association Information Network. The gamGWAS results not only confirmed previous findings, but also highlighted several immune related pathways. Comparison with other methods indicated that gamGWAS could effectively reduce the correlation between pathway p values and its median gene length. gamGWAS can effectively relieve the long gene bias and generate reliable results for GWAS data analysis. It does not require genotype data or permutation of s le labels in the original GWAS data thus, it is computationally efficient.
Publisher: Springer Science and Business Media LLC
Date: 18-09-2011
DOI: 10.1038/NG.940
Publisher: Massachusetts Medical Society
Date: 14-02-2008
DOI: 10.1056/NEJMOA075974
Publisher: Cambridge University Press (CUP)
Date: 15-02-2013
DOI: 10.1017/S0033291713000196
Abstract: Schizophrenia is associated with lower pre-morbid intelligence (IQ) in addition to (pre-morbid) cognitive decline. Both schizophrenia and IQ are highly heritable traits. Therefore, we hypothesized that genetic variants associated with schizophrenia, including copy number variants (CNVs) and a polygenic schizophrenia (risk) score (PSS), may influence intelligence. IQ was estimated with the Wechsler Adult Intelligence Scale (WAIS). CNVs were determined from single nucleotide polymorphism (SNP) data using the QuantiSNP and PennCNV algorithms. For the PSS, odds ratios for genome-wide SNP data were calculated in a s le collected by the Psychiatric Genome-Wide Association Study (GWAS) Consortium (8690 schizophrenia patients and 11 831 controls). These were used to calculate in idual PSSs in our independent s le of 350 schizophrenia patients and 322 healthy controls. Although significantly more genes were disrupted by deletions in schizophrenia patients compared to controls ( p = 0.009), there was no effect of CNV measures on IQ. The PSS was associated with disease status ( R 2 = 0.055, p = 2.1 × 10 −7 ) and with IQ in the entire s le ( R 2 = 0.018, p = 0.0008) but the effect on IQ disappeared after correction for disease status. Our data suggest that rare and common schizophrenia-associated variants do not explain the variation in IQ in healthy subjects or in schizophrenia patients. Thus, reductions in IQ in schizophrenia patients may be secondary to other processes related to schizophrenia risk.
Publisher: Cold Spring Harbor Laboratory
Date: 16-04-2021
DOI: 10.1101/2021.04.16.21251163
Abstract: Studying the phenotypic and genetic characteristics of age and polarity at onset (AAO, PAO) in bipolar disorder (BD) can provide new insights into disease pathology and facilitate the development of screening tools. To examine the genetic architecture of AAO and PAO and their association with BD disease characteristics. Genome-wide association studies (GWASs) and polygenic score (PGS) analyses of AAO (N=12977) and PAO (N=6773) were conducted in BD patients of 34 cohorts and a replication s le (N=2237). The association of onset with disease characteristics was investigated in two of these cohorts. Earlier AAO was associated with an increased risk of psychotic symptoms, suicidality, and fewer episodes. A depressive onset correlated with lifetime suicidality and a manic onset with delusions and manic episodes. Systematic differences in AAO between cohorts and continents of origin were observed. This was also reflected in SNV-based heritability estimates, with higher heritabilities for stricter onset definitions. Increased polygenic scores for autism spectrum disorder (β=-0.34 years, SE=0.08), major depression (β=-0.34 years, SE=0.08), schizophrenia (β=-0.39 years, SE=0.08), and educational attainment (β=-0.31 years, SE=0.08) were associated with an earlier AAO. The AAO GWAS identified one significant locus, but this finding did not replicate. Neither GWAS nor PGS analyses yielded significant associations with PAO. AAO and PAO are associated with indicators of BD severity. In iduals with an earlier onset show an increased polygenic liability for a broad spectrum of psychiatric traits. Systematic differences in AAO across cohorts, continents, and phenotype definitions introduce significant heterogeneity, affecting analyses. In the largest study to systematically characterize age at onset (N=12977) and polarity at onset (N=6773) in bipolar disorder, we describe an association between illness onset characteristics and indicators of severity, confirming their clinical relevance. Our study shows that that early illness onset is associated with genetic liability for a broad range of psychiatric disorders. However, we also highlight systematic differences in age at onset across cohorts, continents, and phenotype definitions. This heterogeneity results in reduced heritability and affects genetic analyses, underscoring the need for the development of standardized phenotype definitions.
Publisher: American Association for the Advancement of Science (AAAS)
Date: 20-01-1970
DOI: 10.1126/SCITRANSLMED.AAD5169
Abstract: Large genomic reference data sets reveal a spectrum of pathogenicity in the prion protein gene and provide genetic validation for a therapeutic strategy in prion disease.
Publisher: American Association for the Advancement of Science (AAAS)
Date: 08-05-2015
Abstract: Human genomes show extensive genetic variation across in iduals, but we have only just started documenting the effects of this variation on the regulation of gene expression. Furthermore, only a few tissues have been examined per genetic variant. In order to examine how genetic expression varies among tissues within in iduals, the Genotype-Tissue Expression (GTEx) Consortium collected 1641 postmortem s les covering 54 body sites from 175 in iduals. They identified quantitative genetic traits that affect gene expression and determined which of these exhibit tissue-specific expression patterns. Melé et al. measured how transcription varies among tissues, and Rivas et al. looked at how truncated protein variants affect expression across tissues. Science , this issue p. 648 , p. 660 , p. 666 see also p. 640
Publisher: Cold Spring Harbor Laboratory
Date: 25-04-2022
DOI: 10.1101/2022.04.20.22273895
Abstract: Genome-wide association studies (GWAS) identify genetic variants underlying complex traits but are limited by stringent genome-wide significance thresholds. Here we dramatically relax GWAS stringency by orders of magnitude and apply GRIN (Gene set Refinement through Interacting Networks), which increases confidence in the expanded gene set by retaining genes strongly connected by biological networks from erse lines of evidence. From multiple GWAS summary statistics of suicide attempt, a complex psychiatric phenotype, GRIN identified additional genes that replicated across independent cohorts and retained genes that were more biologically interrelated despite a relaxed significance threshold. We present a conceptual model of how these retained genes interact through neurobiological pathways to influence suicidal behavior and identify existing drugs associated with these pathways that would not have been identified under traditional GWAS thresholds. We demonstrate that GRIN is a useful community resource for improving the signal to noise ratio of GWAS results.
Publisher: Elsevier BV
Date: 02-2013
Publisher: Wiley
Date: 11-12-2015
DOI: 10.1002/AJMG.B.32402
Publisher: Cold Spring Harbor Laboratory
Date: 08-08-2017
DOI: 10.1101/173435
Abstract: Schizophrenia (SCZ) and bipolar disorder (BD) are highly heritable disorders that share a significant proportion of common risk variation. Understanding the genetic factors underlying the specific symptoms of these disorders will be crucial for improving diagnosis, intervention and treatment. In case-control data consisting of 53,555 cases (20,129 BD, 33,426 SCZ) and 54,065 controls, we identified 114 genome-wide significant loci (GWS) when comparing all cases to controls, of which 41 represented novel findings. Two genome-wide significant loci were identified when comparing SCZ to BD and a third was found when directly incorporating functional information. Regional joint association identified a genomic region of overlapping association in BD and SCZ with disease-independent causal variants indicating a fourth region contributing to differences between these disorders. Regional SNP-heritability analyses demonstrated that the estimated heritability of BD based on the SCZ GWS regions was significantly higher than that based on the average genomic region (91 regions, p = 1.2×10 −6 ) while the inverse was not significant (19 regions, p=0.89). Using our BD and SCZ GWAS we calculated polygenic risk scores and identified several significant correlations with: 1) SCZ subphenotypes: negative symptoms (SCZ, p=3.6×10 −6 ) and manic symptoms (BD, p=2×10 −5 ), 2) BD subphenotypes: psychotic features (SCZ p=1.2×10 −10 , BD p=5.3×10 −5 ) and age of onset (SCZ p=7.9×10 −4 ). Finally, we show that psychotic features in BD has significant SNP-heritability (h 2 snp =0.15, SE=0.06), and a significant genetic correlation with SCZ (r g =0.34) in addition there is a significant sign test result between SCZ GWAS and a GWAS of BD cases contrasting those with and without psychotic features (p=0.0038, one-side binomial test). For the first time, we have identified specific loci pointing to a potential role of 4 genes ( DARS2 , ARFGEF2 , DCAKD and GATAD2A ) that distinguish between BD and SCZ, providing an opportunity to understand the biology contributing to clinical differences of these disorders. Our results provide the best evidence so far of genomic components distinguishing between BD and SCZ that contribute directly to specific symptom dimensions.
Publisher: Public Library of Science (PLoS)
Date: 12-05-2011
Publisher: Springer Science and Business Media LLC
Date: 11-08-2202
DOI: 10.1038/NG.2711
Location: United States of America
No related grants have been discovered for Douglas Ruderfer.