October 5, 2015
Case Study Motivation
Case Study Overview
Goal: Reproduce the final pharmacogenomic regression model and create an interactive web dose calculator.
This study used clinical and genetic data from a broad population to estimate appropriate warfarin dose for patients newly starting warfarin.
The main data set for the study is available on PharmGKB.
Data Set
This data set is in an excel file and presents some challenges:
- Column names with odd symbols
- Data types change with different project sites
For the purpose of this tutorial I have reduced the complexity of the data set and converted it to a tab-delimited file available on my GitHub. We will use read.delim() to deal with the odd column names.
Live Demo
Data Manipulation - Which Variables?
They Used:
- Age in decades = 1 for 10-19, etc…
- VKORC1 G/A = 1 if heterozygous
- VKORC1 A/A = 1 if homozygous for A
- VKORC1 genotype unknown = 1
- CYP2C9 *1/*2 = 1 if *1/*2
- CYP2C9 *1/*3 = 1 if *1/*3
- CYP2C9 *2/*2 = 1 if homozygous *2
- CYP2C9 *2/*3 = 1 if *2/*3
- CYP2C9 *3/*3 = 1 if homozygous *3
- CYP2C9 genotype unknown = 1
- Asian Race = 1
- Black/African American = 1
- Missing or Mixed race = 1
- Amiodarone status = 1
- Enzyme inducer status = 1 if any of: rifampin, carbamazepine, phenytoin, rifampicin
We Have:
- Age: 10-19, 20-29, 30-39 etc.
- VKORC1: A/A, A/G, G/G
- CYP2C9: combinations of: *1, *2, *3, *5, *6, *8, *11, etc.
- Race: Asian, Black or African America, White, Other
- Medications: character list of medications, lack of medications, etc.