Using Mata to import Illumina SNP chip data for genome-wide association studies

Chuck Huber (), Michael Hallman, Victoria Friedel, Melissa Richard and Huandong Sun
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Chuck Huber: Texas A&M Health Science Center, School of Rural Public Health
Michael Hallman: University of Texas School of Public Health
Victoria Friedel: University of Texas School of Public Health
Melissa Richard: University of Texas School of Public Health
Huandong Sun: University of Texas School of Public Health

CHI11 Stata Conference from Stata Users Group

Abstract: Modern genetic genome-wide association studies (GWAS) typically rely on single nucleotide polymorphism (SNP) chip technology to determine hundreds of thousands of genotypes for an individual sample. Once these genotypes are ascertained, each SNP alone or combination is tested for association outcomes of interest such as disease status or severity. Project Heartbeat! was a longitudinal study conducted in the 1990’s that explored changes in lipids, hormones and morphological changes in children from age 8 to 18 years of age. A GWAS study is currently being conducted to look for SNPs that are associated with these developmental changes. While there are specialty programs available for the analysis of hundreds of thousands of SNPs they are not capable of modeling longitudinal data. Stata is well equipped for modeling longitudinal data but cannot load hundreds of thousands of variables into memory simultaneously. This talk will briefly describe the use of Mata to import hundreds of thousands of SNPs from the Illumina SNP chip platform and how to load that data into Stata for longitudinal modeling.

Date: 2011-07-20
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