Aris Floratos

HLA-B*5701 genotype is a major determinant of drug-induced liver injury due to flucloxacillin

Drug-induced liver injury (DILI) is an important cause of serious liver disease. The antimicrobial agent flucloxacillin is a common cause of DILI, but the genetic basis for susceptibility remains unclear. We conducted a genome-wide association (GWA) study using 866,399 markers in 51 cases of flucloxacillin DILI and 282 controls matched for sex and ancestry. The GWA showed an association peak in the major histocompatibility complex (MHC) region with the strongest association (P = 8.7 × 10−33) seen for rs2395029[G], a marker in complete linkage disequilibrium (LD) with HLA-B*5701. Further MHC genotyping, which included 64 flucloxacillin-tolerant controls, confirmed the association with HLA-B*5701 (OR = 80.6, P = 9.0 × 10−19). The association was replicated in a second cohort of 23 cases. In HLA-B*5701 carrier cases, rs10937275 in ST6GAL1 on chromosome 3 also showed genome-wide significance (OR = 4.1, P = 1.4 × 10−8). These findings provide new insights into the mechanism of flucloxacillin DILI and have the potential to substantially improve diagnosis of this serious disease.

Combinatorial pattern discovery in biological sequences: The TEIRESIAS algorithm.

MOTIVATION The discovery of motifs in biological sequences is an important problem. RESULTS This paper presents a new algorithm for the discovery of rigid patterns (motifs) in biological sequences. Our method is combinatorial in nature and able to produce all patterns that appear in at least a (user-defined) minimum number of sequences, yet it manages to be very efficient by avoiding the enumeration of the entire pattern space. Furthermore, the reported patterns are maximal: any reported pattern cannot be made more specific and still keep on appearing at the exact same positions within the input sequences. The effectiveness of the proposed approach is showcased on a number of test cases which aim to: (i) validate the approach through the discovery of previously reported patterns; (ii) demonstrate the capability to identify automatically highly selective patterns particular to the sequences under consideration. Finally, experimental analysis indicates that the algorithm is output sensitive, i.e. its running time is quasi-linear to the size of the generated output.

Susceptibility to Amoxicillin-Clavulanate-Induced Liver Injury is Influenced by Multiple HLA Class I and II Alleles

BACKGROUND & AIMS Drug-induced liver injury (DILI), especially from antimicrobial agents, is an important cause of serious liver disease. Amoxicillin-clavulanate (AC) is a leading cause of idiosyncratic DILI, but little is understood about genetic susceptibility to this adverse reaction. METHODS We performed a genome-wide association study using 822,927 single nucleotide polymorphism (SNP) markers from 201 White European and US cases of DILI following AC administration (AC-DILI) and 532 population controls, matched for genetic background. RESULTS AC-DILI was associated with many loci in the major histocompatibility complex. The strongest effect was with an HLA class II SNP (rs9274407, P=4.8×10(-14)), which correlated with rs3135388, a tag SNP of HLA-DRB1*1501-DQB1*0602 that was previously associated with AC-DILI. Conditioned on rs3135388, rs9274407 is still significant (P=1.1×10(-4)). An independent association was observed in the class I region (rs2523822, P=1.8×10(-10)), related to HLA-A*0201. The most significant class I and II SNPs showed statistical interaction (P=.0015). High-resolution HLA genotyping (177 cases and 219 controls) confirmed associations of HLA-A*0201 (P=2×10(-6)) and HLA-DQB1*0602 (P=5×10(-10)) and their interaction (P=.005). Additional, population-dependent effects were observed in HLA alleles with nominal significance. In an analysis of autoimmune-related genes, rs2476601 in the gene PTPN22 was associated (P=1.3×10(-4)). CONCLUSIONS Class I and II HLA genotypes affect susceptibility to AC-DILI, indicating the importance of the adaptive immune response in pathogenesis. The HLA genotypes identified will be useful in studies of the pathogenesis of AC-DILI but have limited utility as predictive or diagnostic biomarkers because of the low positive predictive values.

The support of human genetic evidence for approved drug indications

Over a quarter of drugs that enter clinical development fail because they are ineffective. Growing insight into genes that influence human disease may affect how drug targets and indications are selected. However, there is little guidance about how much weight should be given to genetic evidence in making these key decisions. To answer this question, we investigated how well the current archive of genetic evidence predicts drug mechanisms. We found that, among well-studied indications, the proportion of drug mechanisms with direct genetic support increases significantly across the drug development pipeline, from 2.0% at the preclinical stage to 8.2% among mechanisms for approved drugs, and varies dramatically among disease areas. We estimate that selecting genetically supported targets could double the success rate in clinical development. Therefore, using the growing wealth of human genetic data to select the best targets and indications should have a measurable impact on the successful development of new drugs.

Genomewide Pharmacogenetics of Bisphosphonate-Induced Osteonecrosis of the Jaw: The Role of RBMS3

UNLABELLED Bisphosphonate-related osteonecrosis of the jaw (BRONJ) is a serious adverse drug reaction. We conducted a genomewide association study to search for genetic variants with a large effect size that increase the risk for BRONJ. METHODS We ascertained BRONJ cases according to the diagnostic criteria of the American Association of Oral and Maxillofacial Surgeons. We genotyped cases and a set of treatment-matched controls using Illumina Human Omni Express 12v1 chip (733,202 markers). To maximize the power of the study, we expanded the initial control set by including population and treatment-tolerant controls from publicly available sources. Imputation at the whole-genome level was performed to increase the number of single nucleotide polymorphisms (SNPs) investigated. Tests of association were carried out by logistic regression, adjusting for population structure. We also examined a list of candidate genes comprising genes potentially involved in the pathogenesis of BRONJ and genes related to drug absorption, distribution, metabolism, and excretion. RESULTS Based on principal component analysis, we initially analyzed 30 white cases and 17 treatment-tolerant controls. We subsequently expanded the control set to include 60 genetically matched controls per case. Association testing identified a significant marker in the RBMS3 gene, rs17024608 (p-value < 7 × 10(-8)); individuals positive for the SNP were 5.8× more likely to develop BRONJ (odds ratio, 5.8; 95% confidence interval, 3.1-11.1). Candidate gene analysis further identified SNPs in IGFBP7 and ABCC4 as potentially implicated in BRONJ risk. CONCLUSION Our findings suggest that genetic susceptibility plays a role in the pathophysiology of BRONJ, with RBMS3 having a significant effect in the risk.

geWorkbench: an open source platform for integrative genomics

SUMMARY geWorkbench (genomics Workbench) is an open source Java desktop application that provides access to an integrated suite of tools for the analysis and visualization of data from a wide range of genomics domains (gene expression, sequence, protein structure and systems biology). More than 70 distinct plug-in modules are currently available implementing both classical analyses (several variants of clustering, classification, homology detection, etc.) as well as state of the art algorithms for the reverse engineering of regulatory networks and for protein structure prediction, among many others. geWorkbench leverages standards-based middleware technologies to provide seamless access to remote data, annotation and computational servers, thus, enabling researchers with limited local resources to benefit from available public infrastructure. AVAILABILITY The project site (http://www.geworkbench.org) includes links to self-extracting installers for most operating system (OS) platforms as well as instructions for building the application from scratch using the source code [which is freely available from the projects SVN (subversion) repository]. geWorkbench support is available through the end-user and developer forums of the caBIG Molecular Analysis Tools Knowledge Center, https://cabig-kc.nci.nih.gov/Molecular/forums/

Genome Wide Analysis of Drug-Induced Torsades de Pointes: Lack of Common Variants with Large Effect Sizes

Marked prolongation of the QT interval on the electrocardiogram associated with the polymorphic ventricular tachycardia Torsades de Pointes is a serious adverse event during treatment with antiarrhythmic drugs and other culprit medications, and is a common cause for drug relabeling and withdrawal. Although clinical risk factors have been identified, the syndrome remains unpredictable in an individual patient. Here we used genome-wide association analysis to search for common predisposing genetic variants. Cases of drug-induced Torsades de Pointes (diTdP), treatment tolerant controls, and general population controls were ascertained across multiple sites using common definitions, and genotyped on the Illumina 610k or 1M-Duo BeadChips. Principal Components Analysis was used to select 216 Northwestern European diTdP cases and 771 ancestry-matched controls, including treatment-tolerant and general population subjects. With these sample sizes, there is 80% power to detect a variant at genome-wide significance with minor allele frequency of 10% and conferring an odds ratio of ≥2.7. Tests of association were carried out for each single nucleotide polymorphism (SNP) by logistic regression adjusting for gender and population structure. No SNP reached genome wide-significance; the variant with the lowest P value was rs2276314, a non-synonymous coding variant in C18orf21 (p  =  3×10−7, odds ratio = 2, 95% confidence intervals: 1.5–2.6). The haplotype formed by rs2276314 and a second SNP, rs767531, was significantly more frequent in controls than cases (p  =  3×10−9). Expanding the number of controls and a gene-based analysis did not yield significant associations. This study argues that common genomic variants do not contribute importantly to risk for drug-induced Torsades de Pointes across multiple drugs.

Genome-wide association study of serious blistering skin rash caused by drugs

Stevens–Johnson syndrome (SJS) and toxic epidermal necrolysis (TEN) are rare but severe, potentially life threatening adverse drug reactions characterized by skin blistering. Previous studies have identified drug-specific and population-specific genetic risk factors with large effects. In this study, we report the first genome-wide association study (GWAS) of SJS/TEN induced by a variety of drugs. Our aim was to identify common genetic risk factors with large effects on SJS/TEN risk. We conducted a genome-wide analysis of 96 retrospective cases and 198 controls with a panel of over one million single-nucleotide polymorphisms (SNPs). We further improved power with about 4000 additional controls from publicly available datasets. No genome-wide significant associations with SNPs or copy number variants were observed, although several genomic regions were suggested that may have a role in predisposing to drug-induced SJS/TEN. Our GWAS did not find common, highly penetrant genetic risk factors responsible for SJS/TEN events in the cases selected.

iTools: a framework for classification, categorization and integration of computational biology resources.

The advancement of the computational biology field hinges on progress in three fundamental directions – the development of new computational algorithms, the availability of informatics resource management infrastructures and the capability of tools to interoperate and synergize. There is an explosion in algorithms and tools for computational biology, which makes it difficult for biologists to find, compare and integrate such resources. We describe a new infrastructure, iTools, for managing the query, traversal and comparison of diverse computational biology resources. Specifically, iTools stores information about three types of resources–data, software tools and web-services. The iTools design, implementation and resource meta - data content reflect the broad research, computational, applied and scientific expertise available at the seven National Centers for Biomedical Computing. iTools provides a system for classification, categorization and integration of different computational biology resources across space-and-time scales, biomedical problems, computational infrastructures and mathematical foundations. A large number of resources are already iTools-accessible to the community and this infrastructure is rapidly growing. iTools includes human and machine interfaces to its resource meta-data repository. Investigators or computer programs may utilize these interfaces to search, compare, expand, revise and mine meta-data descriptions of existent computational biology resources. We propose two ways to browse and display the iTools dynamic collection of resources. The first one is based on an ontology of computational biology resources, and the second one is derived from hyperbolic projections of manifolds or complex structures onto planar discs. iTools is an open source project both in terms of the source code development as well as its meta-data content. iTools employs a decentralized, portable, scalable and lightweight framework for long-term resource management. We demonstrate several applications of iTools as a framework for integrated bioinformatics. iTools and the complete details about its specifications, usage and interfaces are available at the iTools web page http://iTools.ccb.ucla.edu.

DELPHI: a pattern-based method for detecting sequence similarity

We describe DELPHI, a new computational tool for identifying sequence similarity between a query sequence and a database of proteins. Use is made of a set of patterns obtained from the underlying database through a one-time computation. The patterns are subsequently matched against every query sequence presented to the system. A pattern matched by a region of the query pinpoints a potential local similarity between that region and all of the database sequences also matching that pattern. In a final step, all such local similarities are examined more closely by aligning and scoring the corresponding query and database regions. By prudently choosing a set of patterns, the method can be used to discover weak but biologically important similarities. We provide a number of examples using both classified and unclassified proteins that corroborate this claim.