Made up of computational biologists, statistical geneticists, and bioinformaticians, this group is responsible for maintaining and improving our core methods for making genetic discoveries. These methods are the basis for all scientific efforts across the company, from supporting new features in our product to identifying drug target candidates for our Therapeutics division. Our statistical expertise, combined with what is one of the largest genomic datasets in the world, has made Uturn9 genetic testing a leader in studying human traits and diseases.
Using genome-wide association studies (GWAS) with sample sizes that can exceed one million participants, we have identified genetic associations with Parkinson’s, spontaneous preterm birth, susceptibility for common infections, and schizophrenia — just to name a few. By computing associations across a large number of traits, we have contributed to one of the largest phenome-wide association studies (PheWAS) to date, which investigated phenotypes associated with variants in the autoimmune TH17 pathway.
We continue to innovate genomics research and apply novel methods to our mega-cohort. We are developing better methods for detecting shared identity-by-descent (IBD) segments and IBD association mapping. We are building trait prediction models that leverage correlations in phenotypic data, and we are applying machine learning methods to impute markers in complex regions of the genome. Our team is investigating the action of negative selection within linkage disequilibrium-dependent architecture, and developing methods for identifying pairs of traits that show evidence of a causal relationship. To aid our fine-mapping efforts, we combine our database with public or internally generated high-throughput genomic or functional data. We also perform whole-genome sequencing in populations of interest, such as Ashkenazi Jews and African-Americans, to further diversity in genomics research. Finally, we work behind the scenes to convert answers to survey questions into phenotypes and develop phasing methods and imputation panels.