Browsing by Subject "genotype"

Now showing 1 - 3 of 3
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    CMAX3: A Robust Statistical Test for Genetic Association Accounting for Covariates
    (MDPI, 2021) Chen, Zhongxue; Zang, Yong; Biostatistics, School of Public Health
    The additive genetic model as implemented in logistic regression has been widely used in genome-wide association studies (GWASs) for binary outcomes. Unfortunately, for many complex diseases, the underlying genetic models are generally unknown and a mis-specification of the genetic model can result in a substantial loss of power. To address this issue, the MAX3 test (the maximum of three separate test statistics) has been proposed as a robust test that performs plausibly regardless of the underlying genetic model. However, the original implementation of MAX3 utilizes the trend test so it cannot adjust for any covariates such as age and gender. This drawback has significantly limited the application of the MAX3 in GWASs, as covariates account for a considerable amount of variability in these disorders. In this paper, we extended the MAX3 and proposed the CMAX3 (covariate-adjusted MAX3) based on logistic regression. The proposed test yielded a similar robust efficiency as the original MAX3 while easily adjusting for any covariate based on the likelihood framework. The asymptotic formula to calculate the p-value of the proposed test was also developed in this paper. The simulation results showed that the proposed test performed desirably under both the null and alternative hypotheses. For the purpose of illustration, we applied the proposed test to re-analyze a case-control GWAS dataset from the Collaborative Studies on Genetics of Alcoholism (COGA). The R code to implement the proposed test is also introduced in this paper and is available for free download
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    ESR1 and PGR polymorphisms are associated with estrogen and progesterone receptor expression in breast tumors
    (American Physiological Society, 2016-09-01) Hertz, Daniel L.; Henry, N. Lynn; Kidwell, Kelley M.; Thomas, Dafydd; Goddard, Audrey; Azzouz, Faouzi; Speth, Kelly; Li, Lang; Banerjee, Mousumi; Thibert, Jacklyn N.; Kleer, Celina G.; Stearns, Vered; Hayes, Daniel F.; Skaar, Todd C.; Rae, James M.; Medicine, School of Medicine
    Hormone receptor-positive (HR+) breast cancers express the estrogen (ERα) and/or progesterone (PgR) receptors. Inherited single nucleotide polymorphisms (SNPs) in ESR1, the gene encoding ERα, have been reported to predict tamoxifen effectiveness. We hypothesized that these associations could be attributed to altered tumor gene/protein expression of ESR1/ERα and that SNPs in the PGR gene predict tumor PGR/PgR expression. Formalin-fixed paraffin-embedded breast cancer tumor specimens were analyzed for ESR1 and PGR gene transcript expression by the reverse transcription polymerase chain reaction based Oncotype DX assay and for ERα and PgR protein expression by immunohistochemistry (IHC) and an automated quantitative immunofluorescence assay (AQUA). Germline genotypes for SNPs in ESR1 (n = 41) and PGR (n = 8) were determined by allele-specific TaqMan assays. One SNP in ESR1 (rs9322336) was significantly associated with ESR1 gene transcript expression (P = 0.006) but not ERα protein expression (P > 0.05). A PGR SNP (rs518162) was associated with decreased PGR gene transcript expression (P = 0.003) and PgR protein expression measured by IHC (P = 0.016), but not AQUA (P = 0.054). There were modest, but statistically significant correlations between gene and protein expression for ESR1/ERα and PGR/PgR and for protein expression measured by IHC and AQUA (Pearson correlation = 0.32–0.64, all P < 0.001). Inherited ESR1 and PGR genotypes may affect tumor ESR1/ERα and PGR/PgR expression, respectively, which are moderately correlated. This work supports further research into germline predictors of tumor characteristics and treatment effectiveness, which may someday inform selection of hormonal treatments for patients with HR+ breast cancer.
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    Pharmacogenomically actionable medications in a safety net health care system
    (2016) Carpenter, Janet S.; Rosenman, Marc B.; Knisely, Mitchell R.; Decker, Brian S.; Levy, Kenneth D.; Flockhart, David A.; IU School of Nursing
    OBJECTIVE: Prior to implementing a trial to evaluate the economic costs and clinical outcomes of pharmacogenetic testing in a large safety net health care system, we determined the number of patients taking targeted medications and their clinical care encounter sites. METHODS: Using 1-year electronic medical record data, we evaluated the number of patients who had started one or more of 30 known pharmacogenomically actionable medications and the number of care encounter sites the patients had visited. RESULTS: Results showed 7039 unique patients who started one or more of the target medications within a 12-month period with visits to 73 care sites within the system. CONCLUSION: Findings suggest that the type of large-scale, multi-drug, multi-gene approach to pharmacogenetic testing we are planning is widely relevant, and successful implementation will require wide-scale education of prescribers and other personnel involved in medication dispensing and handling.