Current Projects
The GSLL is pursuing a number of lines of research related to validating the use of statistical learning methods with genome-wide data. Projects include
- developing corrections for bias in variable importance measures
- testing the power of statistical learning algorithms to detect functional SNPs
- assessing methods for analyzing multivariate phenotypes
- validating candidate SNPs discovered in applied analyses
Collaborators
The GSLL works closely with collarborators from around the world, including close partnerships with
- Dorret Boomsma, Netherlands Twin Registry
- Nick Martin, Queensland Institute of Medical Research
- Sarah Medland, Queensland Institute of Medical Research
- Notre Dame Center for Research Computing