Translating Polygenic Analysis for Prevention
From Who to How
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There are no easy answers in complex disease genetics. Common, chronic health conditions such as obesity and heart disease are influenced by many genetic variants scattered across the genome, with each variant making small contributions to risk. Ever-larger genome-wide association studies (GWAS) are uncovering more and more of these variants. For many diseases and disease-related traits, discovered loci now number in triple digits. As GWAS sample sizes grow, the volume of discovered variants is expected to increase further.1,2 Translation of this growing volume of genetic discoveries to improve population health is needed.
See Articles by Seyednasrollah et al and Nuotio et al
The classic model of translation is to work from the bottom up, from a discovered DNA sequence variant through RNA transcription, protein production, and so on to disease pathogenesis. Such bottom-up translation is critical but can be time and resource intensive. For example, the FTO locus associated with obesity and the 9p21 locus associated with coronary artery disease were discovered a decade ago; understanding their molecular mechanisms remains a work in progress.3–6 With genetic discoveries already numbering in the hundreds and new findings expected to have ever-smaller individual effects on risk, complementary translational approaches are needed to generate return on investments in GWAS discovery.
An alternative to bottom-up translation of discovered variants one at a time is polygenic score analysis. Polygenic score analysis models genetic influence quantitatively, as the combined effects of many independent variants.7 Typically, each locus is assigned a weight based on the effect size estimated in GWAS. The count of risk alleles at that locus is then multiplied by the weight, and weighted counts are summed across an individual’s genome to compute their polygenic score. That polygenic score provides an overall summary of an individual’s genetic liability to …