Deciphering ADME genetic data using an automated haplotype approach.

OBJECTIVE: To investigate the utility of statistical tools in translating Affymetrix Drug Metabolizing Enzyme and Transporter (DMET) Assay single-nucleotide polymorphisms (SNPs) into common consensus star alleles. METHODS: DMET SNP data from clinical trials in different ethnicities were pooled for analyses. Three different statistical methods, PHASE, Bayesian, and expectation-maximization (EM), were first assessed by comparing the consistency of calling CYP2D6 alleles among 1108 Asians and 55 Caucasians. Subsequently, the performance of EM in deriving haplotype calls was evaluated against the Affymetrix Translation Table for CYPs 2B6, 2C19, 2C9, and 3A4/5 in 582 Asians, 296 Caucasians, and 369 Africans. Selected DNA samples were sequenced to verify the EM-predicted haplotype calls. RESULTS: PHASE, Bayesian, and EM methods showed a similar CYP2D6 star allele call rate. The EM method, with a 0.99 posterior probability cutoff, was chosen for further evaluation because of its low false-positive call rate. Haplotype calls obtained with the EM method were consistent with the Affymetrix Translation Table more than 95% of the time for all five CYPs, except for the CYP2B6 calls in the African descents (83%). In addition, the EM method was superior to the Translation Table-only approach in resolving complex haplotype patterns, identifying novel haplotypes in CYP2B6 and CYP3A5, and determining genotype calls in the presence of missing SNP data. CONCLUSION: A statistical method such as EM could be used to augment the translation of DMET assay SNP data into star alleles, especially for complex genes, to facilitate full utilization and interpretation of clinical pharmacogenetics data.

OmicsView: Omics data analysis through interactive visual analytics.

With advances in NGS technologies, transcriptional profiling of human tissue across many diseases is becoming more routine, leading to the generation of petabytes of data deposited in public repositories. There is a need for bench scientists with little computational expertise to be able to access and mine this data to understand disease pathology, identify robust biomarkers of disease and the effect of interventions (in vivo or in vitro). To this end we release an open source analytics and visualization platform for expression data called OmicsView, http://omicsview.org. This platform comes preloaded with 1000 s of samples across many disease areas and normal tissue, including the GTEx database, all processed with a harmonized pipeline. We demonstrate the power and ease-of-use of the platform by means of a Crohn's disease data mining exercise where we can quickly uncover disease pathology and identify strong biomarkers of disease and response to treatment.

Integrating Rules for Genomic Research, Clinical Care, Public Health Screening and DTC Testing: Creating Translational Law for Translational Genomics.

Human genomics is a translational field spanning research, clinical care, public health, and direct-to-consumer testing. However, law differs across these domains on issues including liability, consent, promoting quality of analysis and interpretation, and safeguarding privacy. Genomic activities crossing domains can thus encounter confusion and conflicts among these approaches. This paper suggests how to resolve these conflicts while protecting the rights and interests of individuals sequenced. Translational genomics requires this more translational approach to law.

Genetic Study of Severe Prolonged Lymphopenia in Multiple Sclerosis Patients Treated With Dimethyl Fumarate.

In delayed-release dimethyl fumarate (DMF)-treated patients, absolute lymphocyte count (ALC) often declines in the first year and stabilizes thereafter; early declines have been associated with development of severe prolonged lymphopenia (SPL). Prolonged moderate or severe lymphopenia is a known risk factor for progressive multifocal leukoencephalopathy (PML); DMF-associated PML is very rare. It is unknown whether genetic predictors of SPL secondary to DMF treatment exist. We aimed to identify genetic predictors of reduced white blood cell (WBC) counts in DMF-treated multiple sclerosis (MS) patients. Genotyping (N = 1,258) and blood transcriptional profiling (N = 1,133) were performed on MS patients from DEFINE/CONFIRM. ALCs were categorized as: SPL, < 500 cells/µL for ≥6 months; moderate prolonged lymphopenia (MPL), < 800 cells/µL for ≥6 months, excluding SPL; mildly reduced lymphocytes, < 910 cells/µL at any point, excluding SPL and MPL; no lymphopenia, ≥910 cells/µL. Genome-wide association, HLA, and cross-sectional gene expression studies were performed. No common variants, HLA alleles, or expression profiles clinically useful for predicting SPL or MPL were identified. There was no overlap between genetic peaks and genetic loci known to be associated with WBC. Gene expression profiles were not associated with lymphopenia status. A classification model including gene expression features was not more predictive of lymphopenia status than standard covariates. There were no genetic predictors of SPL (or MPL) secondary to DMF treatment. Our results support ALC monitoring during DMF treatment as the most effective way to identify patients at risk of SPL.

Pharmacogenomics and the drug discovery pipeline: when should it be implemented?

One of the key factors in developing improved medicines lies in understanding the molecular basis of the complex diseases we treat. Investigation of genetic associations with disease utilizing advances in linkage disequilibrium-based whole genome association strategies will provide novel targets for therapy and define relevant pathways contributing to disease pathogenesis. Genetic studies in conjunction with gene expression, proteomic, and metabonomic analyses provide a powerful tool to identify molecular subtypes of disease. Using these molecular data, pharmacogenomics has the potential to impact on the drug discovery and development process at many stages of the pipeline, contributing to both target identification and increased confidence in the therapeutic rationale. This is exemplified by the identified association of 5-lipoxygenase-activating protein (ALOX5AP/FLAP) with increased risk of myocardial infarction, and of the chemokine receptor 5 (CCR5) with HIV infection and therapy. Pharmacogenomics has already been used in oncology to demonstrate that molecular data facilitates assessment of disease heterogeneity, and thus identification of molecular markers of response to drugs such as imatinib mesylate (Gleevec) and trastuzumab (Herceptin). Knowledge of genetic variation in a target allows early assessment of the clinical significance of polymorphism through the appropriate design of preclinical studies and use of relevant animal models. A focussed pharmacogenomic strategy at the preclinical phase of drug development will produce data to inform the pharmacogenomic plan for exploratory and full development of compounds. Opportunities post-approval show the value of large well-characterized data sets for a systematic assessment of the contribution of genetic determinants to adverse drug reactions and efficacy. The availability of genomic samples in large phase IV trials also provides a valuable resource for further understanding the molecular basis of disease heterogeneity, providing data that feeds back into the drug discovery process in target identification and validation for the next generation of improved medicines.