Incorporating Concomitant Medications into Genome-Wide Analyses for the Study of Complex Disease and Drug Response

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

Common and rare genetic markers of lipid variation in subjects with type 2 diabetes from the ACCORD clinical trial

Background Individuals with type 2 diabetes are at an increased risk of cardiovascular disease. Alterations in circulating lipid levels, total cholesterol (TC), low-density lipoprotein (LDL), high-density lipoprotein (HDL), and triglycerides (TG) are heritable risk factors for cardiovascular disease. Here we conduct a genome-wide association study (GWAS) of common and rare variants to investigate associations with baseline lipid levels in 7,844 individuals with type 2 diabetes from the ACCORD clinical trial. Methods DNA extracted from stored blood samples from ACCORD participants were genotyped using the Affymetrix Axiom Biobank 1 Genotyping Array. After quality control and genotype imputation, association of common genetic variants (CV), defined as minor allele frequency (MAF) ≥ 3%, with baseline levels of TC, LDL, HDL, and TG was tested using a linear model. Rare variant (RV) associations (MAF < 3%) were conducted using a suite of methods that collapse multiple RV within individual genes. Results Many statistically significant CV ( p  < 1 × 10 −8 ) replicate findings in large meta-analyses in non-diabetic subjects. RV analyses also confirmed findings in other studies, whereas significant RV associations with CNOT2 , HPN-AS1 , and SIRPD appear to be novel ( q  < 0.1). Discussion Here we present findings for the largest GWAS of lipid levels in people with type 2 diabetes to date. We identified 17 statistically significant ( p  < 1 × 10 −8 ) associations of CV with lipid levels in 11 genes or chromosomal regions, all of which were previously identified in meta-analyses of mostly non-diabetic cohorts. We also identified 13 associations in 11 genes based on RV, several of which represent novel findings

Common and rare genetic markers of lipid variation in subjects with type 2 diabetes from the ACCORD clinical trial

Background Individuals with type 2 diabetes are at an increased risk of cardiovascular disease. Alterations in circulating lipid levels, total cholesterol (TC), low-density lipoprotein (LDL), high-density lipoprotein (HDL), and triglycerides (TG) are heritable risk factors for cardiovascular disease. Here we conduct a genome-wide association study (GWAS) of common and rare variants to investigate associations with baseline lipid levels in 7,844 individuals with type 2 diabetes from the ACCORD clinical trial. Methods DNA extracted from stored blood samples from ACCORD participants were genotyped using the Affymetrix Axiom Biobank 1 Genotyping Array. After quality control and genotype imputation, association of common genetic variants (CV), defined as minor allele frequency (MAF) ≥ 3%, with baseline levels of TC, LDL, HDL, and TG was tested using a linear model. Rare variant (RV) associations (MAF < 3%) were conducted using a suite of methods that collapse multiple RV within individual genes. Results Many statistically significant CV (p < 1 × 10−8) replicate findings in large meta-analyses in non-diabetic subjects. RV analyses also confirmed findings in other studies, whereas significant RV associations with CNOT2, HPN-AS1, and SIRPD appear to be novel (q < 0.1). Discussion Here we present findings for the largest GWAS of lipid levels in people with type 2 diabetes to date. We identified 17 statistically significant (p < 1 × 10−8) associations of CV with lipid levels in 11 genes or chromosomal regions, all of which were previously identified in meta-analyses of mostly non-diabetic cohorts. We also identified 13 associations in 11 genes based on RV, several of which represent novel findings

Genetic Variants in \u3cem\u3eHSD17B3\u3c/em\u3e, \u3cem\u3eSMAD3\u3c/em\u3e, and \u3cem\u3eIPO11\u3c/em\u3e Impact Circulating Lipids in Response to Fenofibrate in Individuals With Type 2 Diabetes

Individuals with type 2 diabetes (T2D) and dyslipidemia are at an increased risk of cardiovascular disease. Fibrates are a class of drugs prescribed to treat dyslipidemia, but variation in response has been observed. To evaluate common and rare genetic variants that impact lipid responses to fenofibrate in statin‐treated patients with T2D, we examined lipid changes in response to fenofibrate therapy using a genomewide association study (GWAS). Associations were followed‐up using gene expression studies in mice. Common variants in SMAD3 and IPO11 were marginally associated with lipid changes in black subjects (P \u3c 5 × 10‐6). Rare variant and gene expression changes were assessed using a false discovery rate approach. AKR7A3 and HSD17B13 were associated with lipid changes in white subjects (q \u3c 0.2). Mice fed fenofibrate displayed reductions in Hsd17b13 gene expression (q \u3c 0.1). Associations of variants in SMAD3, IPO11, and HSD17B13, with gene expression changes in mice indicate that transforming growth factor‐beta (TGF‐β) and NRF2 signaling pathways may influence fenofibrate effects on dyslipidemia in patients with T2D

Genetic Variants in <i>CPA6</i> and <i>PRPF31</i> are Associated with Variation in Response to Metformin in Individuals with Type 2 Diabetes

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 &lt; 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 &lt;0.1). STAT3 is a ubiquitously expressed pleiotropic transcriptional activator that participates in the regulation of metabolism and feeding behavior. Here, we provide novel evidence for associations of common and rare variants in PRPF31, CPA6, and STAT3 with metformin response that may provide insight into mechanisms important for metformin efficacy in T2D

Set membership experimental design for biological systems

BACKGROUND: Experimental design approaches for biological systems are needed to help conserve the limited resources that are allocated for performing experiments. The assumptions used when assigning probability density functions to characterize uncertainty in biological systems are unwarranted when only a small number of measurements can be obtained. In these situations, the uncertainty in biological systems is more appropriately characterized in a bounded-error context. Additionally, effort must be made to improve the connection between modelers and experimentalists by relating design metrics to biologically relevant information. Bounded-error experimental design approaches that can assess the impact of additional measurements on model uncertainty are needed to identify the most appropriate balance between the collection of data and the availability of resources. RESULTS: In this work we develop a bounded-error experimental design framework for nonlinear continuous-time systems when few data measurements are available. This approach leverages many of the recent advances in bounded-error parameter and state estimation methods that use interval analysis to generate parameter sets and state bounds consistent with uncertain data measurements. We devise a novel approach using set-based uncertainty propagation to estimate measurement ranges at candidate time points. We then use these estimated measurements at the candidate time points to evaluate which candidate measurements furthest reduce model uncertainty. A method for quickly combining multiple candidate time points is presented and allows for determining the effect of adding multiple measurements. Biologically relevant metrics are developed and used to predict when new data measurements should be acquired, which system components should be measured and how many additional measurements should be obtained. CONCLUSIONS: The practicability of our approach is illustrated with a case study. This study shows that our approach is able to 1) identify candidate measurement time points that maximize information corresponding to biologically relevant metrics and 2) determine the number at which additional measurements begin to provide insignificant information. This framework can be used to balance the availability of resources with the addition of one or more measurement time points to improve the predictability of resulting models

ToxPi Graphical User Interface 2.0: Dynamic exploration, visualization, and sharing of integrated data models

Abstract Background Drawing integrated conclusions from diverse source data requires synthesis across multiple types of information. The ToxPi (Toxicological Prioritization Index) is an analytical framework that was developed to enable integration of multiple sources of evidence by transforming data into integrated, visual profiles. Methodological improvements have advanced ToxPi and expanded its applicability, necessitating a new, consolidated software platform to provide functionality, while preserving flexibility for future updates. Results We detail the implementation of a new graphical user interface for ToxPi (Toxicological Prioritization Index) that provides interactive visualization, analysis, reporting, and portability. The interface is deployed as a stand-alone, platform-independent Java application, with a modular design to accommodate inclusion of future analytics. The new ToxPi interface introduces several features, from flexible data import formats (including legacy formats that permit backward compatibility) to similarity-based clustering to options for high-resolution graphical output. Conclusions We present the new ToxPi interface for dynamic exploration, visualization, and sharing of integrated data models. The ToxPi interface is freely-available as a single compressed download that includes the main Java executable, all libraries, example data files, and a complete user manual from http://toxpi.org

HGBEnviroScreen: Enabling Community Action through Data Integration in the Houston–Galveston–Brazoria Region

The Houston&ndash;Galveston&ndash;Brazoria (HGB) region faces numerous environmental and public health challenges from both natural disasters and industrial activity, but the historically disadvantaged communities most often impacted by such risks have limited ability to access and utilize big data for advocacy efforts. We developed HGBEnviroScreen to identify and prioritize regions of heightened vulnerability, in part to assist communities in understanding risk factors and developing environmental justice action plans. While similar in objectives to existing environmental justice tools, HGBEnviroScreen is unique in its ability to integrate and visualize national and local data to address regional concerns. For the 1090 census tracts in the HGB region, we accrued data into five domains: (i) social vulnerability, (ii) baseline health, (iii) environmental exposures and risks, (iv) environmental sources, and (v) flooding. We then integrated and visualized these data using the Toxicological Prioritization Index (ToxPi). We found that the highest vulnerability census tracts have multifactorial risk factors, with common drivers being flooding, social vulnerability, and proximity to environmental sources. Thus, HGBEnviroScreen is not only helping identify communities of greatest overall vulnerability but is also providing insights into which domains would most benefit from improved planning, policy, and action in order to reduce future vulnerability

Maternal smoking impacts key biological pathways in newborns through epigenetic modification in Utero

Background: Children exposed to maternal smoking during pregnancy exhibit increased risk for many adverse health effects. Maternal smoking influences methylation in newborns at specific CpG sites (CpGs). Here, we extend evaluation of individual CpGs to gene-level and pathway-level analyses among 1062 participants in the Norwegian Mother and Child Cohort Study (MoBa) using the Illumina 450 K platform to measure methylation in newborn DNA and maternal smoking in pregnancy, assessed using the biomarker, plasma cotinine. We used novel implementations of bioinformatics tools to collapse epigenome-wide methylation data into gene- and pathway-level effects to test whether exposure to maternal smoking in utero differentially methylated CpGs in genes enriched in biologic pathways. Unlike most pathway analysis applications, our approach allows replication in an independent cohort. Results: Data on 485,577 CpGs, mapping to a total of 20,199 genes, were used to create gene scores that were tested for association with maternal plasma cotinine levels using Sequence Kernel Association Test (SKAT), and 15 genes were found to be associated (q 1.06 × 10−7). Pathway analyses using gene scores resulted in 51 significantly associated pathways, which we tested for replication in an independent cohort (q < 0.05). Of those 32 replicated in an independent cohort, which clustered into six groups. The largest cluster consisted of pathways related to cancer, cell cycle, ERα receptor signaling, and angiogenesis. The second cluster, organized into five smaller pathway groups, related to immune system function, such as T-cell regulation and other white blood cell related pathways. Conclusions: Here we use novel implementations of bioinformatics tools to determine biological pathways impacted through epigenetic changes in utero by maternal smoking in 1062 participants in the MoBa, and successfully replicate these findings in an independent cohort. The results provide new insight into biological mechanisms that may contribute to adverse health effects from exposure to tobacco smoke in utero

Genetic Variants in HSD17B3

Individuals with type 2 diabetes (T2D) and dyslipidemia are at an increased risk of cardiovascular disease. Fibrates are a class of drugs prescribed to treat dyslipidemia, but variation in response has been observed. To evaluate common and rare genetic variants that impact lipid responses to fenofibrate in statin‐treated patients with T2D, we examined lipid changes in response to fenofibrate therapy using a genomewide association study (GWAS). Associations were followed‐up using gene expression studies in mice. Common variants in SMAD3 and IPO11 were marginally associated with lipid changes in black subjects (P \u3c 5 × 10‐6). Rare variant and gene expression changes were assessed using a false discovery rate approach. AKR7A3 and HSD17B13 were associated with lipid changes in white subjects (q \u3c 0.2). Mice fed fenofibrate displayed reductions in Hsd17b13 gene expression (q \u3c 0.1). Associations of variants in SMAD3, IPO11, and HSD17B13, with gene expression changes in mice indicate that transforming growth factor‐beta (TGF‐β) and NRF2 signaling pathways may influence fenofibrate effects on dyslipidemia in patients with T2D