October 5, 2015

## 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.

## 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.