In genetics, a polygenic score (PGS) is a number that summarizes the estimated effect of many genetic variants on an individual's phenotype. The PGS is also called the polygenic index (PGI) or genome-wide score; in the context of disease risk, it is called a polygenic risk score (PRS or PR score) or genetic risk score. The score reflects an individual's estimated genetic predisposition for a given trait and can be used as a predictor for that trait. It gives an estimate of how likely an individual is to have a given trait based only on genetics, without taking environmental factors into account; and it is typically calculated as a weighted sum of trait-associated alleles.
Recent progress in genetics has developed polygenic predictors of complex human traits, including risk for many important complex diseases that are typically affected by many genetic variants, each of which confers 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 widely used in animal breeding and plant breeding due to their efficacy in improving livestock breeding and crops. In humans, polygenic scores are typically generated from data of genome-wide association study (GWAS). They are an active area of research spanning topics such as learning algorithms for genomic prediction; new predictor training; validation testing of predictors; and 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.
Background
DNA in living organisms is the molecular genetic code for life. Although polygenic risk scores from studies in humans have gained the most attention, the basic idea was first introduced for selective plant and animal breeding. Similar to the latter-day approaches of constructing a polygenic risk score, an individual'sanimal or plantbreeding value was calculated to be the combined weight of several single-nucleotide polymorphisms (SNPs) by their individual effects on a trait.
Human DNA contains about 3 billion bases. The human genome can be broadly separated into coding and non-coding sequences, where the coding genome encodes instructions for genes, including some of the sequence that codes for proteins. Genome-wide association studies (GWASes) enable mapping phenotypes to the variations in nucleotide bases in human populations. Improvements in methodology and studies with large cohorts have enabled mapping many traits—some of which are diseases—to the human genome. Learning which variations influence which specific traits and how strongly they do so is the key target for constructing polygenic scores in humans.
The methods were first considered for humans after the year 2000, and specifically by a 2007 proposal that such scores could be used in human genetics to identify individuals at high risk for disease. The concept was successfully applied in 2009 by researchers who organized a genome-wide association study regarding schizophrenia with the objective of constructing scores of risk propensity. That study was the first to use the term polygenic score for a prediction drawn from a linear combination of single-nucleotide polymorphism (SNP) genotypeswhich was able to explain 3% of the variance in schizophrenia.
Calculation with genome-wide association study A PRS is constructed from the estimated effect sizes derived from a genome-wide association study (GWAS). In a GWAS, single-nucleotide polymorphisms (SNPs) are tested for an association between cases and controls (see graphic above). The results from a GWAS estimate the strength of the association at each SNP (i.e., the effect size at the SNP) as well as a p-value for statistical significance. A typical score is then calculated by adding the number of risk-modifying alleles across a large number of SNPs, whereby the number of alleles for each SNP is multiplied by the weight for the given SNP.
In mathematical form, the estimated polygenic score is obtained as the sum across m number of SNPs with risk-increasing alleles weighted by their weights (i.e., ):
This idea can be generalized to the study of any trait and is an example of the more general mathematical term regression analysis.
Key considerations Methods for generating polygenic scores in humans are an active area of research. Two key considerations in developing polygenic scores are which SNPs and the number of SNPs to include. The simplest method—the so-called "pruning and thresholding" method—sets weights equal to the coefficient estimates from a regression of the trait on each genetic variant. The included SNPs may be selected using an algorithm that attempts to ensure that each marker is approximately independent.
The independence of each SNP is important for the score's predictive accuracy. SNPs that are physically close to each other are more likely to be in linkage disequilibrium, meaning they typically are inherited together and therefore do not provide independent predictive power. The removal of these SNPs is called "pruning". The "thresholding" refers to using only SNPs that meet a specific p-value threshold.
Penalized regression can also be used to construct polygenic scores. From prior information, penalized regression assigns probabilities based on: 1) how many genetic variants are expected to affect a trait and 2) the distribution of their effect sizes. These methods, in effect, "penalize" the large coefficients in a regression model and shrink them conservatively. One popular tool for this approach is "PRS-CS". Another is to use Bayesian methods, first proposed in 2001, that directly incorporate genetic features of a given trait and genomic features like linkage disequilibrium. One Bayesian method uses "linkage disequilibrium prediction" or LDpred.
More approaches for developing polygenic risk scores continue to be described. For example, by incorporating effect sizes from populations of different ancestral backgrounds, the predictive ability of scores can be improved. Incorporating knowledge of the functional roles of specific genomic chunks can improve the utility of scores. Studies have examined the performances of these methods on standardized dataset.
Application to humans As the number of genome-wide association studies has exploded, along with rapid advances in methods for calculating polygenic scores, its most obvious application is in clinical settings for disease prediction or risk stratification. It is important not to over- or under-state the value of polygenic scores. A key advantage of quantifying polygenic contribution for each individual is that the genetic liability does not change over an individual's lifespan. However, while a disease may have strong genetic contributions, the risk arising from one's genetics has to be interpreted in the context of environmental factors. For example, even if an individual has a high genetic risk for alcoholism, that risk is lessened if that individual is never exposed to alcohol.
Predictive performance in humans For humans, while most polygenic scores are not predictive enough to diagnose disease, they could be used in addition to other covariates (such as age, BMI, smoking status) to improve estimates of disease susceptibility. However, even if a polygenic score might not make reliable diagnostic predictions across an entire population, it may still make very accurate predictions for outliers at extreme high or low risk. The clinical utility may therefore still be large even if average measures of prediction performance are moderate.
Although issues such as poorer predictive performance in individuals of non-European ancestry limit widespread use, several authors have noted that some causal variants for some conditions, but not others, are shared between Europeans and other groups across different continents for (e.g.) BMI and type 2 diabetes in African populations as well as schizophrenia in Chinese populations. Other researchers recognize that polygenic under-prediction in non-European population should galvanize new GWAS that prioritize greater genetic diversity in order to maximize the potential health benefits brought about by predictive polygenic scores. Significant scientific efforts are being made to this end.
Embryo genetic screening is common with millions biopsied and tested each year worldwide. Genotyping methods have been developed so that the embryo genotype can be determined to high precision. Testing for aneuploidy and monogenetic diseases has increasingly become established over decades, whereas tests for polygenic diseases have begun to be employed more recently, having been first used in embryo selection in 2019. The use of polygenic scores for embryo selection has been criticised due to alleged ethical and safety issues as well as limited practical utility. However, trait-specific evaluations claiming the contrary have been put forth and ethical arguments for PGS-based embryo selection have also been made. The topic continues to be an active area of research not only within genomics but also within clinical applications and ethics.
As of 2019, polygenic scores from well over a hundred phenotypes have been developed from genome-wide association statistics. These include scores that can be categorized as anthropometric, behavioural, cardiovascular, non-cancer illness, psychiatric/neurological, and response to treatment/medication.
Examples of disease prediction performance When predicting disease risk, a PGS gives a continuous score that estimates the risk of having or getting the disease, within some pre-defined time span. A common metric for evaluating such continuous estimates of yes/no questions (see Binary classification) is the area under the ROC curve (AUC). Some example results of PGS performance, as measured in AUC (0 ≤ AUC ≤ 1 where a larger number implies better prediction), include: In 2018, AUC ≈ 0.64 for coronary disease using ~120,000 British individuals. In 2019, AUC ≈ 0.63 for breast cancer, developed from ~95,000 case subjects and ~75,000 controls of European ancestry. In 2019, AUC ≈ 0.71 for hypothyroidism for ~24,000 case subjects and ~463,00 controls of European ancestry. In 2020, AUC ≈ 0.71 for schizophrenia, using 90 cohorts including ~67,000 case subjects and ~94,000 controls with ~80% of European ancestry and ~20% of East Asian ancestry. Note that these results use purely genetic information as input; including additional information such as age and sex often greatly improves the predictions. The coronary disease predictor and the hypothyroidism predictor above achieve AUCs of ~ 0.80 and ~0.78, respectively, when also including age and sex.
Importance of sample size
The performance of a polygenic predictor is highly dependent on the size of the dataset that is available for analysis and ML training. Recent scientific progress in prediction power relies heavily on the creation and expansion of large biobanks containing data for both genotypes and phenotypes of very many individuals. As of 2021, there exist several biobanks with hundreds of thousands samples, i.e., data entries with both genetic and trait information for each individual (see for instance the incomplete list of biobanks).