This year at both IGES and ASHG, Golden Helix booth visitors filled out a short survey about their current and future work. In return, they were entered in a contest for a chance to win a one year SNP & Variation Suite server license with all modules (worth over $30,000!). We had over 230 people participate in the contest, and the excitement about what’s going on in the genetics community was infectious.
And without further ado… Our winners:
Congratulations to our IGES winner, Yi-Hsiang Hsu, an Assistant Scientist at the Hebrew SeniorLife Institute for Aging Research and Harvard Medical School. Dr. Hsu’s research focuses on genome-wide association studies (GWAS), copy number variation and deep re-sequencing on musculoskeletal traits in large consortia, such as CHARGE and GEFOS. He also works on the methodological development of multi-phenotype GWAS meta-analysis to identify pleiotropic genetic effects on reproductive phenotypes and musculoskeletal phenotypes as well as metabolic syndrome risk factors and the musculoskeletal phenotypes.
The winner from ASHG is Stephen Turner, a graduate student finishing a Human Genetics doctoral program at the Vanderbilt University Center for Human Genetics Research. He specializes in genetic epidemiology and development of novel statistical and bioinformatics methodology for understanding the complex relationship between genes, environment, and human disease. Stephen is also a registered patent agent, and blogs about genetic analysis on http://GettingGeneticsDone.blogspot.com.
We found that many participants, including Stephen and Yi-Hsiang, had survey responses that confirmed a key premise on which we designed SVS: that there is much to be gained by adding research capacity via software technology. Let’s be honest, one of the critical limiting constraints in genetics research is that not everyone has the skills necessary to sit down, analyze, and make sense of their data. SVS addresses this constraint by enabling researchers to do real scientific analysis instead of spending large amounts of time learning and actually writing custom code across multiple languages. It also allows bioinformaticians and statistical geneticists alike to offload some of the relatively standard projects to other team members, providing them with the opportunity to build upon our feature rich platform to study the more complicated problems that they are trained to pursue.
Both Yi-Hsiang and Stephen are part of groups that can harness the intuitive technology in our software to produce actionable scientific findings and measurable results. We look forward to seeing what they discover.
Congratulations to both of them!