Polygenic score

In genetics, a polygenic score (PGS), also called a polygenic risk score (PRS), genetic risk score, or genome-wide score, is a number that summarises the estimated effect of many genetic variants on an individual's phenotype, typically calculated as a weighted sum of trait-associated alleles. It reflects an individual's estimated genetic predisposition for a given trait and can be used as a predictor for that trait. In other words, it gives an estimate of how likely an individual is to have a given trait only based on genetics, without taking environmental factors into account. Polygenic scores are widely used in animal breeding and plant breeding (usually termed "genomic prediction" or "genomic selection") due to their efficacy in improving livestock breeding and crops. In humans, polygenic scores are typically generated from genome-wide association study (GWAS) data.
Recent progress in machine learning (ML) analysis of large genomic datasets has enabled the creation of polygenic predictors of complex human traits, including risk for many important complex diseases, which are typically affected by many genetic variants that each confer a small effect on overall risk. In a polygenic risk predictor the lifetime (or age-range) risk for the disease is a numerical function captured by the score which depends on the states of thousands of individual genetic variants (i.e., single nucleotide polymorphisms, or SNPs).
Polygenic scores are an area of intense scientific investigation: hundreds of papers are written each year on topics such as learning algorithms for genomic prediction, new predictor training, validation testing of predictors, clinical application of PRS. In 2018, the American Heart Association named polygenic risk scores as one of the major breakthroughs in research in heart disease and stroke.
History.
One of the first precursors to the modern polygenic score was proposed under the term "marker-assisted selection" (MAS) in 1990.<ref>