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Metformin is the first-line treatment for type 2 diabetes (T2D). Although widely prescribed, the glucose-lowering mechanism for metformin is incompletely understood. Here, we used a genome-wide association approach in a diverse group of individuals with T2D from the Action to Control Cardiovascular Risk in Diabetes (ACCORD) clinical trial to identify common and rare variants associated with HbA1c response to metformin treatment and followed up these findings in four replication cohorts. Common variants in PRPF31 and CPA6 were associated with worse and better metformin response, respectively (P < 5 × 10-6), and meta-analysis in independent cohorts displayed similar associations with metformin response (P = 1.2 × 10-8 and P = 0.005, respectively). Previous studies have shown that PRPF31(+/-) knockout mice have increased total body fat (P = 1.78 × 10-6) and increased fasted circulating glucose (P = 5.73 × 10-6). Furthermore, rare variants in STAT3 associated with worse metformin response (q

AimAssociation mapping with lymphoblastoid cell lines (LCLs) is a promising approach in pharmacogenomics research, and in the current study we utilized LCLs to perform association mapping for 29 chemotherapy drugs.Materials & methodsCurrently, we use LCLs to perform genome-wide association mapping of the cytotoxic response of 520 European-Americans to 29 different anticancer drugs; the largest LCL study to date. A novel association approach using a multivariate analysis of covariance design was employed with the software program MAGWAS, testing for differences in the dose-response profiles between genotypes without making assumptions about the response curve or the biologic mode of association. Additionally, by classifying 25 of the 29 drugs into eight families according to structural and mechanistic relationships, MAGWAS was used to test for associations that were shared across each drug family. Finally, a unique algorithm using multivariate responses and multiple linear regressions across pairs of response curves was used for unsupervised clustering of drugs.ResultsAmong the single-drug studies, suggestive associations were obtained for 18 loci, 12 within/near genes. Three of these, MED12L, CHN2 and MGMT, have been previously implicated in cancer pharmacogenomics. The drug family associations resulted in four additional suggestive loci (three contained within/near genes). One of these genes, HDAC4, associated with the DNA alkylating agents, shows possible clinical interactions with temozolomide. For the drug clustering analysis, 18 of 25 drugs clustered into the appropriate family.ConclusionThis study demonstrates the utility of LCLs in identifying genes that have clinical importance in drug response and for assigning unclassified agents to specific drug families, and proposes new candidate genes for follow-up in a large number of chemotherapy drugs.

BackgroundThe large sample sizes, freedom of ethical restrictions and ease of repeated measurements make cytotoxicity assays of immortalized lymphoblastoid cell lines a powerful new in vitro method in pharmacogenomics research. However, previous studies may have over-simplified the complex differences in dose-response profiles between genotypes, resulting in a loss of power.MethodsThe current study investigates four previously studied methods, plus one new method based on a multivariate analysis of variance (MANOVA) design. A simulation study was performed using differences in cancer drug response between genotypes for biologically meaningful loci. These loci also showed significance in separate genome-wide association studies. This manuscript builds upon a previous study, where differences in dose-response curves between genotypes were constructed using the hill slope equation.ConclusionOverall, MANOVA was found to be the most powerful method for detecting real signals, and was also the most robust method for detection using alternatives generated with the previous simulation study. This method is also attractive because test statistics follow their expected distributions under the null hypothesis for both simulated and real data. The success of this method inspired the creation of the software program MAGWAS. MAGWAS is a computationally efficient, user-friendly, open source software tool that works on most platforms and performs GWASs for individuals having multivariate responses using standard file formats.

ObjectiveRecently, lymphoblastoid cell lines (LCLs) have emerged as an innovative model system for mapping gene variants that predict the dose response to chemotherapy drugs.MethodsIn the current study, this strategy was expanded to the in-vitro genome-wide association approach, using 516 LCLs derived from a White cohort to assess the cytotoxic response to temozolomide.ResultsGenome-wide association analysis using ∼2.1 million quality-controlled single-nucleotide polymorphisms (SNPs) identified a statistically significant association (P

Given the high costs of conducting a drug-response trial, researchers are now aiming to use retrospective analyses to conduct genome-wide association studies (GWAS) to identify underlying genetic contributions to drug-response variation. To prevent confounding results from a GWAS to investigate drug response, it is necessary to account for concomitant medications, defined as any medication taken concurrently with the primary medication being investigated. We use data from the Action to Control Cardiovascular Disease (ACCORD) trial in order to implement a novel scoring procedure for incorporating concomitant medication information into a linear regression model in preparation for GWAS. In order to accomplish this, two primary medications were selected: thiazolidinediones and metformin because of the wide-spread use of these medications and large sample sizes available within the ACCORD trial. A third medication, fenofibrate, along with a known confounding medication, statin, were chosen as a proof-of-principle for the scoring procedure. Previous studies have identified SNP rs7412 as being associated with statin response. Here we hypothesize that including the score for statin as a covariate in the GWAS model will correct for confounding of statin and yield a change in association at rs7412. The response of the confounded signal was successfully diminished from p = 3.19 × 10−7 to p = 1.76 × 10−5, by accounting for statin using the scoring procedure presented here. This approach provides the ability for researchers to account for concomitant medications in complex trial designs where monotherapy treatment regimens are not available. [ABSTRACT FROM AUTHOR]