Publications by authors named "Vassily Trubetskoy"

10 Publications

  • Page 1 of 1

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.
View Article and Find Full Text PDF

Download full-text PDF

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

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

Breast Cancer Res Treat 2020 Jun 6;181(3):623-633. Epub 2020 May 6.

The University of Chicago, 5841 S. Maryland Avenue, MC 2115, Chicago, IL, 60637, USA.

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/m/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), 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.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1007/s10549-020-05603-8DOI Listing
June 2020

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

Cereb Cortex 2020 04;30(4):2707-2718

Holland Bloorview Kids Rehabilitation Hospital and Departments of Psychology and Psychiatry, Bloorview Research Institute, University of Toronto, Toronto, Ontario, M6A 2E1, Canada.

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.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1093/cercor/bhz270DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7175009PMC
April 2020

RICOPILI: Rapid Imputation for COnsortias PIpeLIne.

Bioinformatics 2020 02;36(3):930-933

Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA.

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.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1093/bioinformatics/btz633DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7868045PMC
February 2020

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.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s41588-019-0397-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6956732PMC
May 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.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s41588-019-0397-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6956732PMC
May 2019

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

Nat Genet 2018 04 9;50(4):559-571. Epub 2018 Apr 9.

Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands.

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); 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.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s41588-018-0084-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5898373PMC
April 2018

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

J Clin Endocrinol Metab 2015 Jan;100(1):E173-81

Departments of Medicine (A.M.F., T.P., J.F., A.P., V.T., R.E.W., A.M.D., S.R.), Pediatrics (R.E.W., S.R.), and Human Genetics (C.D.B., A.P., K.P.W., M.N.), and Committee on Genetics (S.R.), The University of Chicago, Chicago, Illinois 60637; Department of Endocrinology and Metabolic Diseases (L.C.M.), University of Duisburg-Essen, Essen 45147, Germany; and Department of Medicine (K.W.), Weill Cornell Medical College, The Methodist Hospital, Houston, Texas 77030.

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.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1210/jc.2014-3490DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4283011PMC
January 2015

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

Bioinformatics 2015 Jan 29;31(2):187-93. Epub 2014 Sep 29.

Department of Medicine, Section of Genetic Medicine, Computation Institute, University of Chicago, Chicago, IL 60637, Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, Tennessee 37232, USA, Vanderbilt Brain Institute, Vanderbilt University School of Medicine, Nashville, TN 37232 and Department of Psychiatry, University of Illinois at Chicago, Chicago, IL 60608, USA.

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.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1093/bioinformatics/btu591DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4287941PMC
January 2015

Poly-omic prediction of complex traits: OmicKriging.

Genet Epidemiol 2014 Jul 2;38(5):402-15. Epub 2014 May 2.

Section of Hematology/Oncology, Department of Medicine, University of Chicago, Chicago, Illinois, United States of America.

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).
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1002/gepi.21808DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4072756PMC
July 2014
-->