Multivariate methods and software for association mapping in dose¿response genome¿wide association studies

Abstract Background The 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. Methods The 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. Conclusion Overall, 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

Race and smoking status associated with paclitaxel drug response in patient-derived lymphoblastoid cell lines

The use of ex-vivo model systems to provide a level of forecasting for in-vivo characteristics remains an important need for cancer therapeutics. The use of lymphoblastoid cell lines (LCLs) is an attractive approach for pharmacogenomics and toxicogenomics, due to their scalability, efficiency, and cost-effectiveness. There is little data on the impact of demographic or clinical covariates on LCL response to chemotherapy. Paclitaxel sensitivity was determined in LCLs from 93 breast cancer patients from the University of North Carolina Lineberger Comprehensive Cancer Center Breast Cancer Database to test for potential associations and/or confounders in paclitaxel dose-response assays. Measures of paclitaxel cell viability were associated with patient data included treatment regimens, cancer status, demographic and environmental variables, and clinical outcomes. We used multivariate analysis of variance to identify the in-vivo variables associated with ex-vivo dose-response. In this unique dataset that includes both in-vivo and ex-vivo data from breast cancer patients, race (P = 0.0049) and smoking status (P = 0.0050) were found to be significantly associated with ex-vivo dose-response in LCLs. Racial differences in clinical dose-response have been previously described, but the smoking association has not been reported. Our results indicate that in-vivo smoking status can influence ex-vivo dose-response in LCLs, and more precise measures of covariates may allow for more precise forecasting of clinical effect. In addition, understanding the mechanism by which exposure to smoking in-vivo effects ex-vivo dose-response in LCLs may open up new avenues in the quest for better therapeutic prediction

SCOPA and META-SCOPA: software for the analysis and aggregation of genome-wide association studies of multiple correlated phenotypes

Abstract Background: Genome-wide association studies (GWAS) of single nucleotide polymorphisms (SNPs) have been successful in identifying loci contributing genetic effects to a wide range of complex human diseases and quantitative traits. The traditional approach to GWAS analysis is to consider each phenotype separately, despite the fact that many diseases and quantitative traits are correlated with each other, and often measured in the same sample of individuals. Multivariate analyses of correlated phenotypes have been demonstrated, by simulation, to increase power to detect association with SNPs, and thus may enable improved detection of novel loci contributing to diseases and quantitative traits

In vitro screening for population variability in toxicity of pesticide-containing mixtures

Population-based human in vitro models offer exceptional opportunities for evaluating the potential hazard and mode of action of chemicals, as well as variability in responses to toxic insults among individuals. This study was designed to test the hypothesis that comparative population genomics with efficient in vitro experimental design can be used for evaluation of the potential for hazard, mode of action, and the extent of population variability in responses to chemical mixtures. We selected 146 lymphoblast cell lines from 4 ancestrally and geographically diverse human populations based on the availability of genome sequence and basal RNA-seq data. Cells were exposed to two pesticide mixtures – an environmental surface water sample comprised primarily of organochlorine pesticides and a laboratory-prepared mixture of 36 currently used pesticides – in concentration response and evaluated for cytotoxicity. On average, the two mixtures exhibited a similar range of in vitro cytotoxicity and showed considerable inter-individual variability across screened cell lines. However, when in vitroto-in vivo extrapolation (IVIVE) coupled with reverse dosimetry was employed to convert the in vitro cytotoxic concentrations to oral equivalent doses and compared to the upper bound of predicted human exposure, we found that a nominally more cytotoxic chlorinated pesticide mixture is expected to have greater margin of safety (more than 5 orders of magnitude) as compared to the current use pesticide mixture (less than 2 orders of magnitude) due primarily to differences in exposure predictions. Multivariate genome-wide association mapping revealed an association between the toxicity of current use pesticide mixture and a polymorphism in rs1947825 in C17orf54. We conclude that a combination of in vitro human population-based cytotoxicity screening followed by dosimetric adjustment and comparative population genomics analyses enables quantitative evaluation of human health hazard from complex environmental mixtures. Additionally, such an approach yields testable hypotheses regarding potential toxicity mechanisms

ONE DRUG, TWO DRUGS: SYNERGISM OR ANTAGONISM? A PHARMACOGENETIC INVESTIGATION OF THE GENETIC ETIOLOGY OF DIFFERENTIAL CHEMOTHERAPY RESPONSES

This dissertation used genome-wide association studies (GWAS) to identify genetic variants associated with cancer treatment response. The study performed the largest screening of anticancer drugs on lymphoblastoid cell lines (LCLs) to date, testing 680 cell lines from the 1000 Genomes Project with 44 different anticancer drugs, resulting in a total of 179,520 drug-dose-cell line combinations. The study identified 40 distinct SNPs at suggestive genome-wide significant associations, as well as 10 SNP-drug associations that were significant at a genome-wide level.We considered biological relevance, statistical significance, and functional impact to identify the SNPs implicated through GWAS for further functional characterization. Firstly, we selected SNPs associated with temozolomide (TMZ) drug response used to treat gliomas based on the integration of molecular characterizations commonly used in the diagnosis and treatment decision-making process outlined in current guidelines. Secondly, SNPs associated with oxaliplatin (OXAL) drug response in colorectal cancer were chosen due to their high statistical significance and the clinical challenge of platinum therapy resistance in treating the disease. Lastly, SNPs associated with drug response to the combination of gemcitabine (GEM) and docetaxel (DOC), as well as the sequence of drug administration, were studied for their potential for drug synergy. After careful consideration, these associations were also considered highly likely to be confirmed.Functional analysis was used to uncover the biological mechanisms underlying the genetic associations identified. We utilized cell line model systems, specifically LCL and cancer cell lines from a diverse range of genetic backgrounds to study drug response.The dissertation research presented that Chapter 2 established key functional effects of rs4470517 on RYK expression on differences in TMZ response and survival outcomes of glioma patients. Chapter 3 determined functional effects of rs11006706 in MKX-AS1 and its association with differences in OXAL response and survival outcomes of colorectal cancer patients. Chapter 4 investigated the synergistic effects of an asynchronous combination of DOC and GEM chemotherapy in various types of cancer, highlighting the importance of personalized treatment strategies for different cancer types. Overall, the findings contribute to our understanding of the genetic causes of chemotherapy response variability and emphasize the need for precision medicine in cancer therapeutics.Doctor of Philosoph

Multivariate methods and software for association mapping in dose-response genome-wide association studies.

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