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Hunting for genes with longitudinal phenotype data using Stata

Author

Listed:
  • Chuck Huber

    (Texas A&M University)

  • Michael Hallman

    (Texas A&M University)

  • Ron Harrist

    (Texas A&M University)

  • Victoria Friedel

    (Texas A&M University)

  • Melissa Richard

    (Texas A&M University)

  • Huandong Sun

    (Texas A&M University)

Abstract

Project Heartbeat! was a longitudinal study of metabolic and morphological changes in adolescents aged 8–18 years and was conducted in the 1990s. A study is currently being conducted to consider the relationship between a collection of phenotypes (including BMI, blood pressure, and blood lipids) and a panel of 1,500 candidate SNPs (single nucleotide polymorphisms). Traditional genetics software such as PLINK and HelixTree lacks the ability to model longitudinal phenotype data. This talk will describe the use of Stata for a longitudinal genetic association study from the early stages of data checking (allele frequencies and Hardy-Weinberg equilibrium), modeling of individual SNPs, the use of false discovery rates to control for the large number of comparisons, exporting and importing data through PHASE for haplotype reconstruction, selection of tagSNPs in Stata, and the analysis of haplotypes. We will also discuss strategies for scaling up to an Illumina 100k SNP chip using Stata. All SNP and gene names will be de-identified, because this is a work in progress.

Suggested Citation

  • Chuck Huber & Michael Hallman & Ron Harrist & Victoria Friedel & Melissa Richard & Huandong Sun, 2010. "Hunting for genes with longitudinal phenotype data using Stata," United Kingdom Stata Users' Group Meetings 2010 07, Stata Users Group.
    • Handle: RePEc:boc:usug10:07
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