|Title||A penalized likelihood approach for investigating gene-drug interactions in pharmacogenetic studies.|
|Publication Type||Journal Article|
|Year of Publication||2015|
|Authors||Neely, Megan L., Howard D. Bondell, and Jung-Ying Tzeng|
|Date Published||2015 Jun|
|Keywords||Antineoplastic Agents, Biometry, Computer Simulation, Female, Genes, bcl-2, Haplotypes, Humans, Likelihood Functions, Models, Statistical, Ovarian Neoplasms, Pharmacogenetics, Regression Analysis|
Pharmacogenetics investigates the relationship between heritable genetic variation and the variation in how individuals respond to drug therapies. Often, gene-drug interactions play a primary role in this response, and identifying these effects can aid in the development of individualized treatment regimes. Haplotypes can hold key information in understanding the association between genetic variation and drug response. However, the standard approach for haplotype-based association analysis does not directly address the research questions dictated by individualized medicine. A complementary post-hoc analysis is required, and this post-hoc analysis is usually under powered after adjusting for multiple comparisons and may lead to seemingly contradictory conclusions. In this work, we propose a penalized likelihood approach that is able to overcome the drawbacks of the standard approach and yield the desired personalized output. We demonstrate the utility of our method by applying it to the Scottish Randomized Trial in Ovarian Cancer. We also conducted simulation studies and showed that the proposed penalized method has comparable or more power than the standard approach and maintains low Type I error rates for both binary and quantitative drug responses. The largest performance gains are seen when the haplotype frequency is low, the difference in effect sizes are small, or the true relationship among the drugs is more complex.
|Original Publication||A penalized likelihood approach for investigating gene-drug interactions in pharmacogenetic studies.|
|PubMed Central ID||PMC4480191|
|Grant List||R01 MH084022 / MH / NIMH NIH HHS / United States |
P01-CA142538 / CA / NCI NIH HHS / United States
T32 GM081057 / GM / NIGMS NIH HHS / United States
P01 CA142538 / CA / NCI NIH HHS / United States
T32GM081057 / GM / NIGMS NIH HHS / United States
A penalized likelihood approach for investigating gene-drug interactions in pharmacogenetic studies.