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Hunting for Genes with Longitudinal Phenotype Data Using Stata

Author

Listed:
  • Chuck Huber

    () (Texas A&M Health Science Center School of Rural Public Health)

  • Michael Hallman

    (University of Texas School of Public Health)

  • Ron Harrist

    (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)

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 1500 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 the 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 as 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," BOS10 Stata Conference 10, Stata Users Group.
  • Handle: RePEc:boc:bost10:10
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    File URL: http://repec.org/bost10/Huber2010.ppt
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    References listed on IDEAS

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    2. LaLonde, Robert J, 1986. "Evaluating the Econometric Evaluations of Training Programs with Experimental Data," American Economic Review, American Economic Association, vol. 76(4), pages 604-620, September.
    3. James J. Heckman & Hidehiko Ichimura & Petra E. Todd, 1997. "Matching As An Econometric Evaluation Estimator: Evidence from Evaluating a Job Training Programme," Review of Economic Studies, Oxford University Press, vol. 64(4), pages 605-654.
    4. Giuseppe Porro & Stefano Maria Iacus, 2009. "Random Recursive Partitioning: a matching method for the estimation of the average treatment effect," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(1), pages 163-185.
    5. Stefano Iacus & Gary King & Giuseppe Porro, 2008. "Matching for Causal Inference Without Balance Checking," UNIMI - Research Papers in Economics, Business, and Statistics unimi-1073, Universit√° degli Studi di Milano.
    6. Kosuke Imai & Gary King & Elizabeth A. Stuart, 2008. "Misunderstandings between experimentalists and observationalists about causal inference," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 171(2), pages 481-502.
    7. Ho, Daniel E. & Imai, Kosuke & King, Gary & Stuart, Elizabeth A., 2007. "Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference," Political Analysis, Cambridge University Press, vol. 15(03), pages 199-236, June.
    8. repec:cup:apsrev:v:95:y:2001:i:01:p:49-69_00 is not listed on IDEAS
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