Good health is vital to all of us, and finding sustainable solutions to the most pressing healthcare challenges of our world cannot wait. That’s why our pharmaceutical client is committed to applying science to improve health and well-being at every stage of life.
Genome-wide association studies (GWAS) analyze large sets of genetic markers across large cohorts of individuals to locate genetic variants contributing to the heritability of phenotypes (i.e., traits) of interest. GWAS analysis is computationally challenging because of the scale of the data involved and the modeling algorithms required. Our client was looking to speed up the logistic regression modeling that is the most computationally demanding component of many GWAS analyses that ask which markers help explain which phenotypes.
The Pharmaceutical client’s existing solution hampered researches due to the long ‘run time’ on each experiment they were seeking to administer. The algorithmic optimization delivered through our crowdsourcing challenge improved the CPU for our client 1200x. This extreme value solution provided a shift in how fast our client could perform their crucial GWAS research.