Predictive genomics has been a promising field for a long time, but what does it take to make it deliver? This is the question that Dr. Samuli Ripatti, Vice Director at the Institute for Molecular Medicine Finland (FIMM) and member of the Department of Public Health at the University of Helsinki and the Broad Institute of the Massachusetts Institute of Technology and Harvard University, asks in our on-demand webinar.
Polygenic Risk Scores & Predictive Genomics
Bringing Predictive Genomics Into Therapeutic Practice
Polygenic risk scores are the primary vehicle of predictive genomics, hoping to provide for complex conditions the level of insight that single genes can provide for conditions like sickle-cell anemia. These scores combine the effects of numerous genetic variants into a single measure of the lifetime risk of developing a certain medical condition. These scores are the simplest way to bring predictive genomics into therapeutic practice, but deriving them is no small matter. It requires detailed measures, including genome-wide association studies (GWAS), of the effects of a large number of genes and additional disambiguation to make sure that the contributions of genes that are often inherited together are accurately rated when they occur separately. The ability to perform this level of examination has increased rapidly in recent years, with scores going from based on 12 single-nucleotide polymorphisms (SNPs) in 2010 to six million in 2018. Similarly, the ability to connect single-nucleotide polymorphisms and other gene variants to disease risk depends on detailed longitudinal studies of individuals with those variants, and that is where biobanks come in.
Biobanks Provide Invaluable Data
Biobanks like FinnGen and its counterparts in other European countries combine genomic information on large numbers of individuals with extremely detailed medical records throughout those individuals’ lives, providing an invaluable treasure trove of longitudinal data that can untangle the complicated relationship between genomes, life experiences, and disease.
Combining the Information
It is combining this kind of information with ordinary clinical practice where predictive genomics in general and polygenic risk scores specifically become valuable. Patient-specific clinical examinations are already valuable and often reasonably effective, and polygenic risk scores are at their best when they can augment this effectiveness. Polygenic risk scores especially excel at identifying people who should receive enhanced testing frequency or sensitivity from an earlier age than the general population, which in turn helps catch more serious and chronic illnesses earlier.
Webinar: Predictive Genomics Insights from Experts
Sign up here to watch the webinar and hear the rest of Dr. Ripatti’s thoughts on what it takes to build a predictive genomics program.