Exploring Case-Control Genetic Association Tests Using Phase Diagrams

Background: By a new concept called "phase diagram", we compare two commonly used genotype-based tests for case-control genetic analysis, one is a Cochran-Armitage trend test (CAT test at $x=0.5$, or CAT0.5) and another (called MAX2) is the maximization of two chi-square test results: one from the two-by-two genotype count table that combines the baseline homozygotes and heterozygotes, and another from the table that combines heterozygotes with risk homozygotes. CAT0.5 is more suitable for multiplicative disease models and MAX2 is better for dominant/recessive models. Methods: We define the CAT0.5-MAX2 phase diagram on the disease model space such that regions where MAX2 is more powerful than CAT0.5 are separated from regions where the CAT0.5 is more powerful, and the task is to choose the appropriate parameterization to make the separation possible. Results: We find that using the difference of allele frequencies ($\delta_p$) and the difference of Hardy-Weinberg disequilibrium coefficients ($\delta_\epsilon$) can separate the two phases well, and the phase boundaries are determined by the angle $tan^{-1}(\delta_p/\delta_\epsilon)$, which is an improvement over the disease model selection using $\delta_\epsilon$ only. Conclusions: We argue that phase diagrams similar to the one for CAT0.5-MAX2 have graphical appeals in understanding power performance of various tests, clarifying simulation schemes, summarizing case-control datasets, and guessing the possible mode of inheritance

A common gene drive language eases regulatory process and eco-evolutionary extensions

Background Synthetic gene drive technologies aim to spread transgenic constructs into wild populations even when they impose organismal fitness disadvantages. The extraordinary diversity of plausible drive mechanisms and the range of selective parameters they may encounter makes it very difficult to convey their relative predicted properties, particularly where multiple approaches are combined. The sheer number of published manuscripts in this field, experimental and theoretical, the numerous techniques resulting in an explosion in the gene drive vocabulary hinder the regulators' point of view. We address this concern by defining a simplified parameter based language of synthetic drives. Results Employing the classical population dynamics approach, we show that different drive construct (replacement) mechanisms can be condensed and evaluated on an equal footing even where they incorporate multiple replacement drives approaches. Using a common language, it is then possible to compare various model properties, a task desired by regulators and policymakers. The generalization allows us to extend the study of the invasion dynamics of replacement drives analytically and, in a spatial setting, the resilience of the released drive constructs. The derived framework is available as a standalone tool. Conclusion Besides comparing available drive constructs, our tool is also useful for educational purpose. Users can also explore the evolutionary dynamics of future hypothetical combination drive scenarios. Thus, our results appraise the properties and robustness of drives and provide an intuitive and objective way for risk assessment, informing policies, and enhancing public engagement with proposed and future gene drive approaches

A study of genetic polymorphism underlying idiosyncratic hepatotoxicity due to anti-tuberculosis medications

Anti-tuberculosis drug-induced liver injury is a rare but serious adverse drug reaction. This study aimed to identity specific genes conferring susceptibility to this serious adverse drug reaction, especially in relation to isoniazid treatment and to study the underlying mechanism for toxicity. Anti-tuberculosis drug-induced liver injury cases (n=26) and community controls (n=90) from Europe and South Asia were genotyped for polymorphisms in NAT2, GST genes, CYP2E1, PXR and SOD2. NAT2 slow acetylators were more susceptible to liver injury (OR=4.60; 95% CI=1.47-14.44). The GSTM1 null genotype was more common in cases than controls (OR=2.91; 95% CI=1.14-7.43). Risk of liver injury was significantly increased in subjects with combined NAT2 slow acetylator and GSTM1 null genotype (OR=3.71; 95% CI=1.48-9.31). No significant effects were seen for the other genotypes studied except that a GSTA4 haplotype was slightly more common in liver injury cases. The contribution of NAT2 genotype to isoniazid toxicity was examined using an in vitro overexpression approach. Stable expression of either NAT2*4 or NAT*5 constructs in HepG2 cells had small effects on reduced glutathione to oxidised glutathione ratio and apoptosis. These changes were consistent with higher NAT2 activity increasing isoniazid toxicity. In addition, overexpression and siRNA knockdown approaches showed protective roles for GSTA1 and A4 against isoniazid toxicity. The relevance of combinations of anti-tuberculosis drugs to overall toxicity was investigated by studies in human hepatocytes and LS180 cells. In the LS180 cells, rifampicin coadministation with isoniazid resulted in a small but significant decrease in both isoniazid and pyrazinamide toxicity. Studies on the isoniazid-rifampicin combination in human hepatocytes gave inconsistent findings but a decrease in cell toxicity due to isoniazid by pretreatment with rifampicin was seen in some donors. Increased expression of the carboxyesterase gene CES2 was seen in LS180 cells and in some hepatocytes and could represent a protective effect.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

A Latent Variable Approach to Study Gene–Environment Interactions in the Presence of Multiple Correlated Exposures

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/92103/1/j.1541-0420.2011.01677.x.pd

Biometrics

Many existing cohort studies initially designed to investigate disease risk as a function of environmental exposures have collected genomic data in recent years with the objective of testing for gene-environment interaction (G 7 E) effects. In environmental epidemiology, interest in G 7 E arises primarily after a significant effect of the environmental exposure has been documented. Cohort studies often collect rich exposure data; as a result, assessing G 7 E effects in the presence of multiple exposure markers further increases the burden of multiple testing, an issue already present in both genetic and environment health studies. Latent variable (LV) models have been used in environmental epidemiology to reduce dimensionality of the exposure data, gain power by reducing multiplicity issues via condensing exposure data, and avoid collinearity problems due to presence of multiple correlated exposures. We extend the LV framework to characterize gene-environment interaction in presence of multiple correlated exposures and genotype categories. Further, similar to what has been done in case-control G 7 E studies, we use the assumption of gene-environment (G-E) independence to boost the power of tests for interaction. The consequences of making this assumption, or the issue of how to explicitly model G-E association has not been previously investigated in LV models. We postulate a hierarchy of assumptions about the LV model regarding the different forms of G-E dependence and show that making such assumptions may influence inferential results on the G, E, and G 7 E parameters. We implement a class of shrinkage estimators to data adaptively trade-off between the most restrictive to most flexible form of G-E dependence assumption and note that such class of compromise estimators can serve as a benchmark of model adequacy in LV models. We demonstrate the methods with an example from the Early Life Exposures in Mexico City to Neuro-Toxicants Study of lead exposure, iron metabolism genes, and birth weight.P01 ES012874/ES/NIEHS NIH HHS/United StatesP20 ES018171/ES/NIEHS NIH HHS/United States1-P20-SE018171-01/SE/SEPDPO CDC HHS/United StatesK23ES000381/ES/NIEHS NIH HHS/United StatesR01 ES007821/ES/NIEHS NIH HHS/United StatesR01 ES016932/ES/NIEHS NIH HHS/United StatesR01 ES013744/ES/NIEHS NIH HHS/United StatesP42 ES005947/ES/NIEHS NIH HHS/United StatesR01 ES014930/ES/NIEHS NIH HHS/United StatesP30 ES017885/ES/NIEHS NIH HHS/United StatesK23 ES000381/ES/NIEHS NIH HHS/United StatesR01 ES017022/ES/NIEHS NIH HHS/United StatesP42 ES05947/ES/NIEHS NIH HHS/United States2015-04-22T00:00:00Z21955029PMC4405908vault:775

Genetic characterization of nucleoside analogue transporters ABCC4 and ABCC5 gene loci

Ph.DDOCTOR OF PHILOSOPH