Genome-wide assocation studies typically utlize only one phenotype measurement for each individual in the study. However, by modeling multiple phenotype measurements jointly in a longitudinal GWAS, it is possible to increase statistical power. In this project, we introduce a method and software for doing this.

The software can be downloaded here: longGWAS.beta.0.2.tar.gz. . Please refer to R/example.R for a quick start guide, noting that the examples used in this file are only toy examples (the results are in no way interesting). For more detail about how to use the sofware, please see the documentation contained in R/longGWAS.R. A more formal documentation is soon to come.
Contact: Nick Furlotte for details. (nfurlott at cs dot ucla dot edu).

Funding Information:
National Institute of Health training grant T32-HG002536 (to N.F.). National Science Foundation (No. 0513612, No. 0731455 and No. 0729049) (to N.F., E.E.). S.E. is supported by FONDECYT grant 11085012. This research sponsored in part by National Institute for Mental Health NIMH No. NH084698, and GlaxoSmithKline (in part). UCLA subcontract of contract N01-ES-45530 from the National Toxicology Program/National Institute of Environmental Health Sciences to Perlegen Sciences (in part).