Ansar Jawaid

Genome-wide pharmacogenetic investigation of a hepatic adverse event without clinical signs of immunopathology suggests an underlying immune pathogenesis

One of the major goals of pharmacogenetics is to elucidate mechanisms and identify patients at increased risk of adverse events (AEs). To date, however, there have been only a few successful examples of this type of approach. In this paper, we describe a retrospective case–control pharmacogenetic study of an AE of unknown mechanism, characterized by elevated levels of serum alanine aminotransferase (ALAT) during long-term treatment with the oral direct thrombin inhibitor ximelagatran. The study was based on 74 cases and 130 treated controls and included both a genome-wide tag single nucleotide polymorphism and large-scale candidate gene analysis. A strong genetic association between elevated ALAT and the MHC alleles DRB1*07 and DQA1*02 was discovered and replicated, suggesting a possible immune pathogenesis. Consistent with this hypothesis, immunological studies suggest that ximelagatran may have the ability to act as a contact sensitizer, and hence be able to stimulate an adaptive immune response.

Voluntary exploratory data submissions to the US FDA and the EMA: experience and impact

Heterogeneity in the underlying mechanisms of disease processes and inter-patient variability in drug responses are major challenges in drug development. To address these challenges, biomarker strategies based on a range of platforms, such as microarray gene-expression technologies, are increasingly being applied to elucidate these sources of variability and thereby potentially increase drug development success rates. With the aim of enhancing understanding of the regulatory significance of such biomarker data by regulators and sponsors, the US Food and Drug Administration initiated a programme in 2004 to allow sponsors to submit exploratory genomic data voluntarily, without immediate regulatory impact. In this article, a selection of case studies from the first 5 years of this programme — which is now known as the voluntary exploratory data submission programme, and also involves collaboration with the European Medicines Agency — are discussed, and general lessons are highlighted.

Tacrine-induced liver damage: an analysis of 19 candidate genes

Objectives Tacrine, the first acetylcholinesterase inhibitor used in the treatment of Alzheimers disease, is associated with transaminase elevation in up to 50% of patients. The mechanism of tacrine-induced liver damage is not fully understood, but earlier studies have suggested that genetic factors may play a role. Our aim was to investigate whether single-nucleotide polymorphisms (SNPs) in 19 candidate genes were associated with tacrine-induced liver damage. Methods Sixty-nine patients of Caucasian origin treated with tacrine for Alzheimers disease were investigated by genotyping 241 SNPs in 19 candidate genes potentially related to hepatotoxicity. The association with ABCB4 [which encodes MultiDrug Resistance Protein 3 (MDR3)] was explored in transepithelial transport studies using the ABCB4-transfected pig kidney epithelial cell line (LLC-PK1). Results The strongest association between alanine aminotransferase levels and three SNPs within ATP-binding cassette, subfamily B (MDR/TAP), member 4 (ABCB4) (uncorrected P=0.0005) was not significant after adjusting for multiple testing. No association was demonstrated with ATP-binding cassette, subfamily B (MDR/TAP), member 1 (ABCB1) or carnitine O-octanoyltransferase (CROT) which are located adjacent to ABCB4. Using the transepithelial transport system we failed to show a difference in tacrine accumulation between ABCB4-transfected and parental cell lines. The association with ABCB4 warrants further testing using either another population and/or functional studies.

Classification of rheumatoid arthritis status with candidate gene and genome-wide single-nucleotide polymorphisms using random forests

Using the North American Rheumatoid Arthritis Consortium (NARAC) candidate gene and genome-wide single-nucleotide polymorphism (SNP) data sets, we applied regression methods and tree-based random forests to identify genetic associations with rheumatoid arthritis (RA) and to predict RA disease status. Several genes were consistently identified as weakly associated with RA without a significant interaction or combinatorial effect with other candidate genes. Using random forests, the tested candidate gene SNPs were not sufficient to predict RA patients and normal subjects with high accuracy. However, using the top 500 SNPs, ranked by the importance score, from the genome-wide linkage panel of 5742 SNPs, we were able to accurately predict RA patients and normal subjects with sensitivity of approximately 90% and specificity of approximately 80%, which was confirmed by five-fold cross-validation. However, in a complete training-testing framework, replication of genetic predictors was less satisfactory; thus, further evaluation of existing methodology and development of new methods are warranted.

TNF, LTA, HSPA1L and HLA‑DR gene polymorphisms in HIV-positive patients with hypersensitivity to cotrimoxazole

AIMS Sulfamethoxazole in combination with trimethoprim (cotrimoxazole) is used for prophylaxis and treatment of several opportunistic infections in HIV-infected patients. It is associated with a high incidence of hypersensitivity reactions, which is thought to have an immune basis. Genetic polymorphisms in MHC are known to predispose to hypersensitivity reactions to a structurally diverse group of drugs in HIV-positive patients. The aim of the study was to determine whether functional polymorphisms in TNF, LTA, HSPA1L and HLA-DRB1 genes influence the risk of cotrimoxazole hypersensitivity in HIV-infected patients. METHODS We genotyped 136 HIV-positive patients with (n = 53) and without (n = 83) cotrimoxazole hypersensitivity using a combination of PCR-based techniques, including PCR-restriction fragment length polymorphisms, PCR-sequence specific oligonucleotides and real-time PCR. Genotypes and the haplotype frequencies were analyzed using the chi(2) test in the Haploview and CLUMP programs. RESULTS No statistically significant difference in SNP or haplotype frequencies were found in HIV-infected sulfamethoxazole hypersensitive patients compared with controls. CONCLUSION Our data show that MHC polymorphisms are not major predisposing factors for cotrimoxazole hypersensitivity, although we cannot exclude a minor contribution. An environmental factor (i.e., HIV infection) seems to predominate over any of the genetic factors so far investigated in increasing the risk of cotrimoxazole hypersensitivity.

Optimizing the Power of Genome-Wide Association Studies by Using Publicly Available Reference Samples to Expand the Control Group

Genome‐wide association (GWA) studies have proved extremely successful in identifying novel genetic loci contributing effects to complex human diseases. In doing so, they have highlighted the fact that many potential loci of modest effect remain undetected, partly due to the need for samples consisting of many thousands of individuals. Large‐scale international initiatives, such as the Wellcome Trust Case Control Consortium, the Genetic Association Information Network, and the database of genetic and phenotypic information, aim to facilitate discovery of modest‐effect genes by making genome‐wide data publicly available, allowing information to be combined for the purpose of pooled analysis. In principle, disease or control samples from these studies could be used to increase the power of any GWA study via judicious use as “genetically matched controls” for other traits. Here, we present the biological motivation for the problem and the theoretical potential for expanding the control group with publicly available disease or reference samples. We demonstrate that a naïve application of this strategy can greatly inflate the false‐positive error rate in the presence of population structure. As a remedy, we make use of genome‐wide data and model selection techniques to identify “axes” of genetic variation which are associated with disease. These axes are then included as covariates in association analysis to correct for population structure, which can result in increases in power over standard analysis of genetic information from the samples in the original GWA study. Genet. Epidemiol. 34: 319–326, 2010.

Novel technology and the development of pharmacogenetics within the pharmaceutical industry

This article focuses on the role of pharmacogenetics (PGx) technology across the drug development pipeline. Recent technology developments in three main areas are discussed: the discovery of polymorphisms or other variants in genes of interest; genotyping technologies used in PGx research (both for candidate gene analyses and for a whole-genome association approach); and the use of genotyping in patients prior to prescription (diagnostics). Finally, the associated issues of genetic data management and analysis are addressed, and the challenges facing the pharmaceutical industry in storing, manipulating and exploiting the large and complex data sets that will be generated from emerging PGx platforms are discussed. In conclusion, it is demonstrated that, despite the failures of some technology development programs and the slow rate of progress of others, there has, in fact, been steady progress toward the implementation of PGx within the pharmaceutical industry.

Selecting cases from nuclear families for case-control association analysis

We examine the efficiency of a number of schemes to select cases from nuclear families for case-control association analysis using the Genetic Analysis Workshop 14 simulated dataset. We show that with this simulated dataset comparing all affected siblings with unrelated controls is considerably more powerful than all of the other approaches considered. We find that the test statistic is increased by almost 3-fold compared to the next best sampling schemes of selecting all affected sibs only from families with affected parents (AFaff), one affected sib with most evidence of allele-sharing from each family (SF), and all affected sibs from families with evidence for linkage (AFL). We consider accounting for biological relatedness of samples in the association analysis to maintain the correct type I error. We also discuss the relative efficiencies of increasing the ratio of unrelated cases to controls, methods to confirm associations and issues to consider when applying our conclusions to other complex disease datasets.

Gene mapping strategies for complex disease and drug response.

The identification of genetic variants involved in disease susceptibility and response to drugs through the use of statistical and epidemiological approaches is a potentially powerful methodology for uncovering causal relationships in human disease and its treatment. Here we introduce and compare the application of genetics in these two fields of research.: