Polygenic score - Knowledge

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Moreover, the polygenic risk score may be informative across an individual's lifespan helping to quantify the genetic lifelong risk for certain diseases. For many diseases, having a strong genetic risk can results in an earlier onset of presentation (e.g. Familial Hypercholesterolemia). Recognizing an increased genetic burden earlier can allow clinicians to intervene earlier and avoid delayed diagnoses. Polygenic score can be combined with traditional risk factors to increase clinical utility. For example, polygenic risk scores may help improve diagnosis of diseases. This is especially evident in distinguishing Type 1 from Type 2 Diabetes. Likewise, a polygenic risk score based approach may reduce invasive diagnostic procedures as demonstrated in Celiac disease. Polygenic scores may also empower individuals to alter their lifestyles to reduce risk for diseases. While there is some evidence for behavior modification as a result of knowing one's genetic predisposition, more work is required to evaluate risk-modifying behaviors across a variety of different disease states. Population level screening is another use case for polygenic scores. The goal of population-level screening is to identify patients at high risk for a disease who would benefit from an existing treatment. Polygenic scores can identify a subset of the population at high risk that could benefit from screening. Several clinical studies are being done in breast cancer and heart disease is another area that could benefit from a polygenic score based screening program.

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genetics where, as of 2018, a majority of the studies to date have been done in Europeans. Other challenges that can arise include how precisely the polygenic risk score can be calculated and how precise it needs to be for clinical utility. Even if a polygenic score is accurately calculated and calibrated for a population, its interpretation must be approached with caution. First, it is important to realize that polygenic traits are different from monogenic traits; the latter stem from fewer genetic loci and can be detected more accurately. Genetic tests are often difficult to interpret and require genetic counseling. Currently, polygenic-score results are being shared with clinicians. Since monogenic genetic testing is far more mature than polygenic scores, we can look there for approximating the clinical impact of polygenic scores. While some studies have found negative effects of returning monogenic genetic results to patients, the majority of studies have that negative consequences are minor.

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GEBV is the same as a PGS: a linear function of genetic variants that are each weighted by the apparent effect of the variant. Despite this, polygenic prediction in livestock is useful for a fundamentally different reason than for humans. In humans, a PRS is used for the prediction of individual phenotype, while in livestock a GEBV is typically used to predict the offspring's average value of a phenotype of interest in terms of the genetic material it inherited from a parent. In this way, a GEBV can be understood as the average of the offspring of an individual or pair of individual animals. GEBVs are also typically communicated in the units of the trait of interest. For example, the expected increase in milk production of the offspring of a specific parent compared to the offspring from a reference population might be a typical way of using a GEBV in dairy cow breeding and selection.

531: 576: 722:, are all technically "related". In human genomic prediction, by contrast, unrelated individuals in large populations are selected to estimate the effects of common SNPs. Because of smaller effective population in livestock, the mean coefficient of relationship between any two individuals is likely high, and common SNPs will tag causal variants at greater physical distance than for humans; this is the major reason for lower SNP-based heritability estimates for humans compared to livestock. In both cases, however, sample size is key for maximizing the accuracy of genomic prediction. 714:, and humans alike. Although the same basic concepts underlie these areas of prediction, they face different challenges that require different methodologies. The ability to produce very large family size in nonhuman species, accompanied by deliberate selection, leads to a smaller effective population, higher degrees of linkage disequilibrium among individuals, and a higher average genetic relatedness among individuals within a population. For example, members of plant and animal breeds that humans have effectively created, such as modern 639:. The most frequently reported motivation for individuals to seek out PRS reports is general curiosity (98.2%), and the reactions are generally mixed with common misinterpretations. It is speculated that personal use of PRS could contribute to treatment choices, but that more data is needed. As of 2020 a more typical use was that clinicians face individuals with commercially derived disease-specific PRS in the expectation that the clinician will interpret them, something that may create extra burdens for the clinical care system. 201: 620:

comparable to those with rare genetic variants. This comparison is important because clinical practice can be influenced by knowing which individuals have this rare genetic cause of cardiovascular disease. Since this study, polygenic risk scores have shown promise for disease prediction across other traits. Polygenic risk scores have been studied heavily in obesity, coronary artery disease, diabetes, breast cancer, prostate cancer, Alzheimer's disease and psychiatric diseases.

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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.

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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.

86: 273:, (see top graphic). 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, where the number of alleles for each SNP is multiplied by the weight for the SNP. 161:. 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 36: 192:(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. 232:

to the variations in nucleotide bases in human populations. Improvements in methodology and studies with large cohorts have enabled the mapping of many traits—some of which are diseases—to the human genome. Learning which variations influence which specific traits and how strongly they do so, are the

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With the use of these growing biobanks, data from many thousands of individuals are used to detect the relevant variants for a specific trait. Exactly how many are required depends very much on the trait in question. Typically, increasing levels of prediction are observed until a plateau phase where

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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

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At a fundamental level, the use of polygenic scores in clinical context will have similar technical issues as existing tools. For example, if a tool is not validated in a diverse population, then it may exacerbate disparities with unequal efficacy across populations. This is especially important in

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of the specific trait. The sample size required to reach this performance level for a certain trait is determined by the complexity of the underlying genetic architecture and the distribution of genetic variance in the sampled population. This sample size dependence is illustrated in the figure for

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PGS predictor performance increases with the dataset sample size available for training. Here illustrated for hypertension, hypothyroidism and type 2 diabetes. The x-axis labels number of cases (i.e. individuals with the disease) present in the training data and uses a logarithmic scale. The entire

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can also be used to construct polygenic scores. From prior information penalized regression assigns probabilities 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

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While modern genomic prediction scoring in humans is generally referred to as a "polygenic score" (PGS) or a "polygenic risk score" (PRS), in livestock the more common term is "genomic estimated breeding value", or GEBV (similar to the more familiar "EBV", but with genotypic data). Conceptually, a

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diseases, which are typically affected by many genetic variants that individually confer a small effect to overall risk. Additionally, a polygenic score can be used in several different ways: as a lower bound to test whether heritability estimates may be biased; as a measure of genetic overlap of

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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

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More approaches for developing polygenic risk scores continue to be described. For example, by incorporating effect sizes from populations of different ancestry, the predictive ability of scores can be improved. Incorporating knowledge of the functional roles of specific genomic chunks can improve

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for life. Although polygenic risk scores from study 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's—animal or plant—breeding value was

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Unlike many other clinical laboratory or imaging methods, an individual's germ-line genetic risk can be calculated at birth for a variety of diseases after sequencing their DNA once. Thus, polygenic scores may ultimately be a cost-effective measure that can be informative for clinical management.

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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

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Adeyemo, Adebowale; Balaconis, Mary K.; Darnes, Deanna R.; Fatumo, Segun; Granados Moreno, Palmira; Hodonsky, Chani J.; Inouye, Michael; Kanai, Masahiro; Kato, Kazuto; Knoppers, Bartha M.; Lewis, Anna C. F.; Martin, Alicia R.; McCarthy, Mark I.; Meyer, Michelle N.; Okada, Yukinori; Richards, J.

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An early (2006) example of a genetic risk score applied to Type 2 Diabetes in humans. The authors of the study concluded that, individually, risk alleles only moderately identify increase-of-risk of disease; but identifiable risk is "multiplicatively increased" when information is combined from

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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

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A landmark study examining the role of polygenic risk scores in cardiovascular disease invigorated interest the clinical potential of polygenic scores. This study demonstrated that an individual with the highest polygenic risk score (top 1%) had a lifetime cardiovascular risk >10% which was

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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

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Gregory, Gillian; Das Gupta, Kuheli; Meiser, Bettina; Barlow-Stewart, Kristine; Geelan-Small, Peter; Kaur, Rajneesh; Scheepers-Joynt, Maatje; McInerny, Simone; Taylor, Shelby; Antill, Yoland; Salmon, Lucinda; Smyth, Courtney; Young, Mary-Anne; James, Paul A; Yanes, Tatiane (February 2022).

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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

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Note again, that current methods to construct polygenic predictors are sensitive to the ancestries present in the data. As of 2021, most available data have been primarily of populations with European ancestry, which is the reason why PGS generally perform better within this ancestry. The

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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

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The methods were first considered for humans after the year 2000, and specifically by a proposal in 2007 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

101:(individuals without the disease, (blue)). The y-axis (vertical axis) indicates how many in each group are assigned a certain score. + At the right panel, the same population is divided into three groups according to their predicted risk, i.e., their assigned score, as 593:

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

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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.

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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

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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

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range is from 1,000 cases up to over 100,000 cases. The numbers of controls (i.e. individuals without the disease) in the training data were much larger than the numbers of cases. These particular predictors were trained using the

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Mbatchou, J; Barnard, L; Backman, J; Marcketta, A; Kosmicki, JA; Ziyatdinov, A; Benner, C; O'Dushlaine, C; Barber, M; Boutkov, B; Habegger, L; Ferreira, M; Baras, A; Reid, J; Abecasis, G; Maxwell, E; Marchini, J (July 2021).

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Ripke S, Walters JT, O'Donovan MC, et al. (Schizophrenia Working Group of the Psychiatric Genomics Consortium) (2020-09-12). "Mapping genomic loci prioritises genes and implicates synaptic biology in schizophrenia".

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Francesca Forzano, Olga Antonova, Angus Clarke, Guido de Wert, Sabine Hentze, Yalda Jamshidi, Yves Moreau, Markus Perola, Inga Prokopenko, Andrew Read, Alexandre Reymond, Vigdis Stefansdottir, Carla van El (2022).

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the performance levels off and does not change much when increasing the sample size even further. This is the limit of how accurate a polygenic predictor that only uses genetic information can be and is set by the

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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

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Brockman DG, Petronio L, Dron JS, Kwon BC, Vosburg T, Nip L, et al. (January 2021). "Design and user experience testing of a polygenic score report: a qualitative study of prospective users". p. 238.

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Brent; Richter, Lucas; Ripatti, Samuli; Rotimi, Charles N.; Sanderson, Saskia C.; Sturm, Amy C.; Verdugo, Ricardo A.; Widen, Elisabeth; Willer, Cristen J.; Wojcik, Genevieve L.; Zhou, Alicia (November 2021).

473:, first proposed in 2001 that directly incorporate genetic features of a given trait as well as genomic features like linkage disequilibrium. (One Bayesian method uses "linkage disequilibrium prediction" or 205:

several known risk polymorphisms. Using such combined information allows for identifying subgroups of a population with odds for disease that are significantly greater than when using a single polymorphism.

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Khera A, Chaggin M, Aragam KG, Emdin CA, Klarin D, Haas ME, Roselli C, Natarajan P, Kathiresan S (2017-11-15). "Genome-wide polygenic score to identify a monogenic risk-equivalent for coronary disease".

2657:"Validation of concurrent preimplantation genetic testing for polygenic and monogenic disorders, structural rearrangements, and whole and segmental chromosome aneuploidy with a single universal platform" 674:), which might indicate e.g. shared genetic bases for groups of mental disorders; as a means to assess group differences in a trait such as height, or to examine changes in a trait over time due to 623:

As of January 2021 providing PRS directly to individuals was undergoing research trials in health systems around the world, but is not yet offered as standard of care. Most use is therefore through

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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.

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Saya S, McIntosh JG, Winship IM, Clendenning M, Milton S, Oberoi J, et al. (2020). "A Genomic Test for Colorectal Cancer Risk: Is This Acceptable and Feasible in Primary Care?".

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Millward M, Tiller J, Bogwitz M, Kincaid H, Taylor S, Trainer AH, Lacaze P (September 2020). "Impact of direct-to-consumer genetic testing on Australian clinical genetics services".

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Yanes T, Meiser B, Kaur R, Scheepers-Joynt M, McInerny S, Taylor S, et al. (March 2020). "Uptake of polygenic risk information among women at increased risk of breast cancer".

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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

343: 1230: 698:. Polygenic scores also have useful statistical properties in (genomic) association testing, for instance to account for outcome-specific background effects and/or improve 665:

A variety of applications exists for polygenic scores. In humans, polygenic scores were originally computed in an effort to predict the prevalence and etiology of complex,

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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

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Aurea appears to be the first child born after a new type of DNA testing that gave her a "polygenic risk score." ... Her parents underwent fertility treatment in 2019 ...

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Wray NR, Lin T, Austin J, McGrath JJ, Hickie IB, Murray GK, Visscher PM (January 2021). "From Basic Science to Clinical Application of Polygenic Risk Scores: A Primer".

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Wray NR, Lin T, Austin J, McGrath JJ, Hickie IB, Murray GK, Visscher PM (January 2021). "From Basic Science to Clinical Application of Polygenic Risk Scores: A Primer".

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with relevant traits); to detect & control for the presence of genetic confounds in outcomes (e.g. the correlation of schizophrenia with poverty); or to investigate

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construction of more diverse biobanks with successful recruitment from all ancestries is required to rectify this skewed access to and benefits from PGS-based medicine.

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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.

113:(blue). The y-axis shows the observed risk amounts, where the x-axis shows the groups separating in risk as they age—corresponding with the predicted risk scores. 1468: 4069:"The behavioral response to personalized genetic information: will genetic risk profiles motivate individuals and families to choose more healthful behaviors?" 682:(as e.g. for intelligence where the changes in frequency would be too small to detect on each individual hit but not on the overall polygenic score); in 740:

Preprint lists AUC for pure PRS while the published version of the paper only lists AUC for PGS combined with age, sex and genotyping array information.

2849:"Preimplantation Genetic Testing for Polygenic Disease Relative Risk Reduction: Evaluation of Genomic Index Performance in 11,883 Adult Sibling Pairs" 2707: 458:

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

3934:"Faculty Opinions recommendation of Genomic risk prediction of coronary artery disease in 480,000 adults: implications for primary prevention" 1493: 4301: 3072: 443:

Methods for generating polygenic scores in humans are an active area of research. Two key considerations in developing polygenic scores are

552:(AUC). Some example results of PGS performance, as measured in AUC (0 ≤ AUC ≤ 1 where a larger number implies better prediction), include: 350: 3873: 549: 506: 72: 695: 691: 530: 451:

to include. The simplest, the so-called "pruning and thresholding" method, sets weights equal to the coefficient estimates from a

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due to their efficacy in improving livestock breeding and crops. In humans, polygenic scores are typically generated from data of

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calculated to be the combined weight of several single-nucleotide polymorphisms (SNPs) by their individual effects on a trait.

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on polygenic risk score with increasing age. + The left panel shows how risk—(the standardized PRS on the x-axis)—can separate

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Raben TG, Lello L, Widen E, Hsu SD (2021-01-14). "From Genotype to Phenotype: polygenic prediction of complex human traits".

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Recent progress in genetics has developed polygenic predictors of complex human traits, including risk for many important

4426:"Controlling for background genetic effects using polygenic scores improves the power of genome-wide association studies" 4020:"A single nucleotide polymorphism genetic risk score to aid diagnosis of coeliac disease: a pilot study in clinical care" 4589: 760: 4367:

Jurgens, SJ; Pirruccello, JP; Choi, SH; Morrill, VN; Chaffin, M; Lubitz, SA; Lunetta, KL; Ellinor, PT (23 March 2023).

2725: 3430:"Why do people seek out polygenic risk scores for complex disorders, and how do they understand and react to results?" 559:

In 2019, AUC ≈ 0.63 for breast cancer, developed from ~95,000 case subjects and ~75,000 controls of European ancestry.

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Torkamani A, Wineinger NE, Topol EJ (September 2018). "The personal and clinical utility of polygenic risk scores".

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Singh Y (November 2016). "Effectiveness of Screening Programmes for Detection of Major Congenital Heart Diseases".

3126:"An atlas of polygenic risk score associations to highlight putative causal relationships across the human phenome" 930:"Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations" 4594: 3971:"A Type 1 Diabetes Genetic Risk Score Can Aid Discrimination Between Type 1 and Type 2 Diabetes in Young Adults" 3051:

Savulescu J (October 2001). Francis L (ed.). "Procreative beneficence: why we should select the best children".

2424:"Theoretical and empirical quantification of the accuracy of polygenic scores in ancestry divergent populations" 469:

model and shrink them conservatively. One popular tool for this approach is "PRS-CS". Another is to use certain

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This idea can be generalized to the study of any trait, and is an example of the more general mathematical term

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Nadir MA, Struthers AD (April 2011). "Family history of premature coronary heart disease and risk prediction".

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An open database of polygenic scores and the relevant metadata required for accurate application and evaluation

4486:"Complex Trait Prediction from Genome Data: Contrasting EBV in Livestock to PRS in Humans: Genomic Prediction" 200: 3650:"Effect of knowledge of APOE genotype on subjective and objective memory performance in healthy older adults" 1265:"Genomic Prediction of 16 Complex Disease Risks Including Heart Attack, Diabetes, Breast and Prostate Cancer" 4584: 4554: 683: 562:

In 2019, AUC ≈ 0.71 for hypothyroidism for ~24,000 case subjects and ~463,00 controls of European ancestry.

3379:"Design and user experience testing of a polygenic score report: a qualitative study of prospective users" 2800:"Screening embryos for polygenic conditions and traits: ethical considerations for an emerging technology" 459: 312: 4539:"The use of polygenic risk scores in pre-implantation genetic testing: an unproven, unethical practice." 1376:"Genomic prediction of complex human traits: relatedness, trait architecture and predictive meta-models" 545: 514: 481:

the utility of scores. Studies have examined the performances of these methods on standardized dataset.

2318:"A Comparison of Ten Polygenic Score Methods for Psychiatric Disorders Applied Across Multiple Cohorts" 245:

with the objective of constructing scores of risk propensity. That study was the first to use the term

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The two graphics illustrate sampling distributions of polygenic scores and the predictive ability of

4369:"Adjusting for common variant polygenic scores improves yield in rare variant association analyses" 3699:"On What We Have Learned and Still Need to Learn about the Psychosocial Impacts of Genetic Testing" 671: 470: 452: 432: 90: 3805:

Ganna A, Magnusson PK, Pedersen NL, de Faire U, Reilly M, Arnlöv J, et al. (September 2013).

2900:"Utility and First Clinical Application of Screening Embryos for Polygenic Disease Risk Reduction" 2749:

Karavani E, Zuk O, Zeevi D, Barzilai N, Stefanis NC, Hatzimanolis A, et al. (November 2019).

1569:"Combining information from common type 2 diabetes risk polymorphisms improves disease prediction" 4406: 4349: 4266: 4213: 4133: 4049: 3951: 3914: 3836: 3736: 3608: 3467: 3359: 3303: 2637: 2511: 1871: 1542: 1522: 1374:

Spiliopoulou A, Nagy R, Bermingham ML, Huffman JE, Hayward C, Vitart V, et al. (July 2015).

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Vilhjálmsson BJ, Yang J, Finucane HK, Gusev A, Lindström S, Ripke S, et al. (October 2015).

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279: 265:(GWAS). In a GWAS, single-nucleotide polymorphisms (SNPs) are tested for an association between 2606:

Zeevi DA, Backenroth D, Hakam-Spector E, Renbaum P, Mann T, Zahdeh F, et al. (July 2021).

761:"Polygenic risk in familial breast cancer: Changing the dynamics of communicating genetic risk" 129:) is a number that summarizes the estimated effect of many genetic variants on an individual's 4515: 4463: 4398: 4341: 4297: 4258: 4182: 4125: 4090: 4041: 4000: 3906: 3869: 3828: 3787: 3728: 3679: 3633: 3600: 3551: 3502: 3459: 3410: 3351: 3343: 3295: 3287: 3255: 3230: 3157: 3078: 3068: 3033: 2982: 2931: 2880: 2829: 2780: 2678: 2629: 2563: 2503: 2461: 2404: 2347: 2298: 2239: 2190: 2141: 2092: 2035: 1984: 1928: 1863: 1814: 1775:

Purcell SM, Wray NR, Stone JL, Visscher PM, O'Donovan MC, Sullivan PF, Sklar P (August 2009).

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Weedon MN, McCarthy MI, Hitman G, Walker M, Groves CJ, Zeggini E, et al. (October 2006).

1450: 1405: 1353: 1304: 1211: 1159: 1115: 1064: 1004: 959: 903: 852: 790: 699: 675: 4085: 4068: 2608:"Expanded clinical validation of Haploseek for comprehensive preimplantation genetic testing" 4505: 4497: 4453: 4445: 4388: 4380: 4331: 4289: 4248: 4240: 4209: 4172: 4164: 4117: 4080: 4031: 3990: 3982: 3941: 3898: 3859: 3818: 3777: 3767: 3718: 3710: 3669: 3661: 3590: 3579:"Responsible use of polygenic risk scores in the clinic: potential benefits, risks and gaps" 3541: 3533: 3494: 3449: 3441: 3400: 3390: 3335: 3279: 3220: 3210: 3147: 3137: 3064: 3060: 3023: 3013: 2972: 2962: 2921: 2911: 2870: 2860: 2819: 2811: 2770: 2762: 2668: 2619: 2553: 2545: 2495: 2451: 2443: 2394: 2386: 2337: 2329: 2288: 2278: 2229: 2221: 2180: 2172: 2131: 2123: 2082: 2074: 2025: 2015: 1974: 1964: 1918: 1910: 1853: 1845: 1804: 1796: 1747: 1739: 1698: 1688: 1669:"Polygenic inheritance, GWAS, polygenic risk scores, and the search for functional variants" 1639: 1631: 1590: 1580: 1440: 1395: 1387: 1343: 1335: 1294: 1284: 1201: 1151: 1105: 1095: 1054: 1046: 994: 986: 949: 941: 893: 883: 842: 832: 780: 772: 595: 519: 169: 4284:

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Sharp SA, Jones SE, Kimmitt RA, Weedon MN, Halpin AM, Wood AR, et al. (October 2020).

1231:"23andMe thinks polygenic risk scores are ready for the masses, but experts aren't so sure" 928:

Khera AV, Chaffin M, Aragam KG, Haas ME, Roselli C, Choi SH, et al. (September 2018).

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Mavaddat N, Michailidou K, Dennis J, Lush M, Fachal L, Lee A, et al. (January 2019).

2481:"Genome-wide association analysis identifies 30 new susceptibility loci for schizophrenia" 711: 710:

The benefit of polygenic scores is that they can be used to predict the future for crops,

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Brockman DG, Petronio L, Dron JS, Kwon BC, Vosburg T, Nip L, et al. (October 2021).

2259:"Leveraging functional annotations in genetic risk prediction for human complex diseases" 2257:

Hu Y, Lu Q, Powles R, Yao X, Yang C, Fang F, et al. (June 2017). Rigoutsos I (ed.).

1429:"When more is better: how data sharing would accelerate genomic selection of crop plants" 4441: 4393: 4368: 3856:

Coronary heart disease in the Netherlands : incidence, etiology and risk prediction

2581: 2439: 2382: 2274: 2070: 1906: 1792: 1684: 1280: 821:"The Current and Future Use of Ridge Regression for Prediction in Quantitative Genetics" 4510: 4485: 4458: 4425: 4253: 4228: 4177: 4152: 3995: 3970: 3782: 3755: 3723: 3698: 3674: 3649: 3546: 3521: 3454: 3429: 3405: 3378: 3225: 3198: 3152: 3125: 3028: 3001: 2977: 2950: 2926: 2899: 2875: 2848: 2824: 2799: 2775: 2750: 2702: 2558: 2533: 2456: 2423: 2399: 2366: 2342: 2317: 2293: 2258: 2234: 2209: 2185: 2160: 2136: 2111: 2087: 2054: 2030: 2003: 1979: 1952: 1923: 1890: 1858: 1833: 1809: 1776: 1752: 1728:"Prediction of individual genetic risk to disease from genome-wide association studies" 1727: 1703: 1668: 1644: 1619: 1595: 1568: 1400: 1375: 1348: 1323: 1299: 1264: 1110: 1083: 1059: 1034: 954: 929: 898: 871: 847: 820: 636: 628: 185: 27:

Numerical score aimed at predicting a trait based on variation in multiple genetic loci

4320:"Computationally efficient whole-genome regression for quantitative and binary traits" 2365:

Duncan L, Shen H, Gelaye B, Meijsen J, Ressler K, Feldman M, et al. (July 2019).

2176: 1777:"Common polygenic variation contributes to risk of schizophrenia and bipolar disorder" 4573: 4538: 4410: 4353: 4137: 4053: 3969:

Oram RA, Patel K, Hill A, Shields B, McDonald TJ, Jones A, et al. (March 2016).

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Treff NR, Zimmerman R, Bechor E, Hsu J, Rana B, Jensen J, et al. (August 2019).

2641: 2515: 2367:"Analysis of polygenic risk score usage and performance in diverse human populations" 1875: 1424: 1016: 802: 242: 3918: 2898:

Treff NR, Eccles J, Lello L, Bechor E, Hsu J, Plunkett K, et al. (2019-12-04).

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Treff NR, Eccles J, Marin D, Messick E, Lello L, Gerber J, et al. (June 2020).

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research not only within genomics but also within clinical applications and ethics.

17: 4270: 4153:"Learning one's genetic risk changes physiology independent of actual genetic risk" 3665: 2333: 1322:

Visscher PM, Wray NR, Zhang Q, Sklar P, McCarthy MI, Brown MA, Yang J (July 2017).

603: 213: 3946: 3933: 3840: 3199:"Polygenic Risk Scores for Prediction of Breast Cancer and Breast Cancer Subtypes" 2967: 2210:"Multiethnic polygenic risk scores improve risk prediction in diverse populations" 2127: 1635: 2534:"Clinical use of current polygenic risk scores may exacerbate health disparities" 2283: 1585: 1100: 785: 4501: 4449: 4384: 4336: 4319: 4229:"The WISDOM Study: breaking the deadlock in the breast cancer screening debate" 3823: 3806: 3595: 3578: 3537: 3498: 3445: 3395: 3215: 3018: 2815: 2766: 2673: 2656: 2624: 2607: 2447: 2390: 2078: 1969: 1914: 1891:"Improving reporting standards for polygenic scores in risk prediction studies" 1673:

Proceedings of the National Academy of Sciences of the United States of America

1339: 1289: 1050: 253:(SNP) genotypes—which was able to explain 3% of the variance in schizophrenia. 85: 4244: 4168: 2549: 2055:"Polygenic prediction via Bayesian regression and continuous shrinkage priors" 1849: 1155: 945: 888: 687: 510: 3772: 3347: 3291: 2916: 1035:"Modeling Linkage Disequilibrium Increases Accuracy of Polygenic Risk Scores" 309:

number of SNPs with risk-increasing alleles weighted by their weights, i.e.,

4424:

Bennett, D; O'Shea, D; Ferguson, J; Morris, D; Seoighe, C (1 October 2021).

4151:

Turnwald BP, Goyer JP, Boles DZ, Silder A, Delp SL, Crum AJ (January 2019).

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Ni G, Zeng J, Revez JA, Wang Y, Zheng Z, Ge T, et al. (November 2021).

1693: 666: 632: 556:

In 2018, AUC ≈ 0.64 for coronary disease using ~120,000 British individuals.

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Martin AR, Kanai M, Kamatani Y, Okada Y, Neale BM, Daly MJ (April 2019).

1391: 1206: 1189: 118: 3142: 1800: 999: 2225: 2112:"Prediction of total genetic value using genome-wide dense marker maps" 1743: 1620:"Prediction of total genetic value using genome-wide dense marker maps" 590: 225: 4121: 4036: 4019: 3986: 3807:"Multilocus genetic risk scores for coronary heart disease prediction" 3714: 2479:

Li Z, Chen J, Yu H, He L, Xu Y, Zhang D, et al. (November 2017).

1543:"Big picture genetic scoring approach reliably predicts heart disease" 1469:"Modern genetics will improve health and usher in "designer" children" 1445: 1428: 990: 776: 2951:"Three models for the regulation of polygenic scores in reproduction" 1183: 1181: 1137: 1135: 1133: 1131: 1129: 719: 162: 4484:

Wray NR, Kemper KE, Hayes BJ, Goddard ME, Visscher PM (April 2019).

2499: 1953:"Polygenic risk scores: from research tools to clinical instruments" 261:

A PRS is constructed from the estimated effect sizes derived from a

3182: 1527: 189: 2751:"Screening Human Embryos for Polygenic Traits Has Limited Utility" 715: 529: 422:{\displaystyle {\hat {S}}=\sum _{j=1}^{m}X_{j}{\hat {\beta }}_{j}} 3124:

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2004:"Applying compressed sensing to genome-wide association studies" 1324:"10 Years of GWAS Discovery: Biology, Function, and Translation" 221: 3648:

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3002:"Subsidizing PGD: The Moral Case for Funding Genetic Selection" 2422:

Wang Y, Guo J, Ni G, Yang J, Visscher PM, Yengo L (July 2020).

1834:"Tutorial: a guide to performing polygenic risk score analyses" 1263:

Lello L, Raben TG, Yong SY, Tellier LC, Hsu SD (October 2019).

1494:"Test could predict risk of future heart disease for just £40" 209: 29: 2698:"Picking Embryos With Best Health Odds Sparks New DNA Debate" 4564: 3096: 97:(i.e., individuals with a certain disease, (red)) from the 2798:

Lázaro-Muñoz G, Pereira S, Carmi S, Lencz T (March 2021).

233:

key targets for constructing polygenic scores in humans.

1084:"Power and predictive accuracy of polygenic risk scores" 2053:

Ge T, Chen CY, Ni Y, Feng YA, Smoller JW (April 2019).

2002:

Vattikuti S, Lee JJ, Chang CC, Hsu SD, Chow CC (2014).

872:"Prospects for using risk scores in polygenic medicine" 534:

Predicted vs actual height using a polygenic risk score

50: 353: 315: 282: 3428:

Peck L, Borle K, Folkersen L, Austin J (July 2021).

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Kemper JM, Gyngell C, Savulescu J (September 2019).

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In mathematical form, the estimated polygenic score

249:

for a prediction drawn from a linear combination of

2726:"Why using genetic risk scores on embryos is wrong" 2159:Privé F, Arbel J, Vilhjálmsson BJ (December 2020). 1190:"Towards clinical utility of polygenic risk scores" 45:

may be too technical for most readers to understand

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University of Maastricht. 3487:European Journal of Medical Genetics 2661:European Journal of Medical Genetics 1667:Crouch DJ, Bodmer WF (August 2020). 870:Lewis CM, Vassos E (November 2017). 180:Polygenic scores are widely used in 3697:Parens E, Appelbaum PS (May 2019). 3097:"The Polygenic Score (PGS) Catalog" 2161:"LDpred2: better, faster, stronger" 338:{\displaystyle {\hat {\beta }}_{j}} 4294:10.1093/med/9780198784906.003.0335 3654:The American Journal of Psychiatry 3434:European Journal of Human Genetics 3203:American Journal of Human Genetics 1328:American Journal of Human Genetics 1039:American Journal of Human Genetics 615:Clinical utility and current usage 25: 819:de Vlaming R, Groenen PJ (2015). 706:Applications in non-human species 3340:10.1001/jamapsychiatry.2020.3049 3284:10.1001/jamapsychiatry.2020.3049 2586:All of Us Research Program | NIH 494:Predictive performance in humans 34: 2177:10.1093/bioinformatics/btaa1029 1951:Lewis CM, Vassos E (May 2020). 175:single-nucleotide polymorphisms 4202:European Journal of Pediatrics 4073:Annual Review of Public Health 3760:Annals of Pediatric Cardiology 3666:10.1176/appi.ajp.2013.12121590 2334:10.1016/j.biopsych.2021.04.018 407: 360: 323: 305:is obtained as the sum across 289: 251:single-nucleotide polymorphism 1: 4565:Polygenic Score (PGS) Catalog 3947:10.3410/f.734171897.793567857 3101:Polygenic Score (PGS) Catalog 3006:Journal of Bioethical Inquiry 2968:10.1136/medethics-2020-106588 2724:Birney E (11 November 2019). 2696:Carey Goldberg (2021-09-17). 825:BioMed Research International 765:Journal of Genetic Counseling 692:gene–environment interactions 263:genome-wide association study 239:genome-wide association study 190:genome-wide association study 133:. The PGS is also called the 3897:(8): 684, author reply 684. 2284:10.1371/journal.pcbi.1005589 1586:10.1371/journal.pmed.0030374 1101:10.1371/journal.pgen.1003348 4502:10.1534/genetics.119.301859 3634:10.1101/2021.04.14.21255397 3256:10.1101/2020.09.12.20192922 2128:10.1093/genetics/157.4.1819 1636:10.1093/genetics/157.4.1819 1229:Regalado A (8 March 2019). 661:Non-predictive applications 4621: 4450:10.1038/s41598-021-99031-3 4385:10.1038/s41588-023-01342-w 4337:10.1038/s41588-021-00870-7 4227:Esserman LJ (2017-09-13). 3824:10.1161/atvbaha.113.301218 3703:The Hastings Center Report 3596:10.1038/s41591-021-01549-6 3538:10.1016/j.cell.2019.02.048 3499:10.1016/j.ejmg.2020.103968 3446:10.1038/s41431-021-00929-3 3396:10.1186/s12920-021-01056-0 3216:10.1016/j.ajhg.2018.11.002 3019:10.1007/s11673-019-09932-2 2904:Frontiers in Endocrinology 2816:10.1038/s41436-020-01019-3 2767:10.1016/j.cell.2019.10.033 2674:10.1016/j.ejmg.2019.04.004 2625:10.1038/s41436-021-01145-6 2448:10.1038/s41467-020-17719-y 2391:10.1038/s41467-019-11112-0 2263:PLOS Computational Biology 2079:10.1038/s41467-019-09718-5 1970:10.1186/s13073-020-00742-5 1915:10.1038/s41586-021-03243-6 1340:10.1016/j.ajhg.2017.06.005 1290:10.1038/s41598-019-51258-x 1082:Dudbridge F (March 2013). 1051:10.1016/j.ajhg.2015.09.001 298:{\displaystyle {\hat {S}}} 4245:10.1038/s41523-017-0035-5 4169:10.1038/s41562-018-0483-4 2955:Journal of Medical Ethics 2582:"Diversity and Inclusion" 2550:10.1038/s41588-019-0379-x 1850:10.1038/s41596-020-0353-1 1156:10.1038/s41576-018-0018-x 946:10.1038/s41588-018-0183-z 889:10.1186/s13073-017-0489-y 571:Importance of sample size 3773:10.4103/0974-2069.132478 3754:Varghese MJ (May 2014). 2917:10.3389/fendo.2019.00845 1380:Human Molecular Genetics 1194:Human Molecular Genetics 1144:Nature Reviews. Genetics 625:consumer genetic testing 550:area under the ROC curve 507:Embryo genetic screening 3903:10.1136/hrt.2011.222265 3865:10.26481/dis.20101210am 1694:10.1073/pnas.2005634117 684:Mendelian randomization 4157:Nature Human Behaviour 4110:Public Health Genomics 3932:Neale B (2019-11-26). 2021:10.1186/2047-217X-3-10 585: 535: 527:treatment/medication. 460:linkage disequilibrium 423: 389: 339: 299: 206: 114: 4605:Personalized medicine 4560:Polygenic Score Atlas 4555:Polygenic Risk Scores 2866:10.3390/genes11060648 2428:Nature Communications 2371:Nature Communications 2322:Biological Psychiatry 2059:Nature Communications 1235:MIT Technology Review 786:1959.4/unsworks_78566 578: 546:Binary classification 533: 485:Application to humans 424: 369: 340: 300: 203: 88: 4600:Statistical genetics 3383:BMC Medical Genomics 2804:Genetics in Medicine 2612:Genetics in Medicine 2214:Genetic Epidemiology 2171:(22–23): 5424–5431. 680:soft selective sweep 515:monogenetic diseases 466:Penalized regression 351: 313: 280: 147:polygenic risk score 18:Polygenic risk score 4590:Regression analysis 4442:2021NatSR..1119571B 3143:10.7554/eLife.43657 2761:(6): 1424–1435.e8. 2440:2020NatCo..11.3865W 2383:2019NatCo..10.3328D 2275:2017PLSCB..13E5589H 2071:2019NatCo..10.1776G 1907:2021Natur.591..211W 1801:10.1038/nature08185 1793:2009Natur.460..748P 1685:2020PNAS..11718924C 1679:(32): 18924–18933. 1433:The New Phytologist 1281:2019NatSR...915286L 838:10.1155/2015/143712 672:genetic correlation 433:regression analysis 91:stratified sampling 4544:30, pages 493–495. 4430:Scientific Reports 3709:(Suppl 1): S2–S9. 2226:10.1002/gepi.22083 1744:10.1101/gr.6665407 1392:10.1093/hmg/ddv145 1269:Scientific Reports 1207:10.1093/hmg/ddz187 652:Benefits in humans 586: 536: 449:the number of SNPs 439:Key considerations 419: 335: 295: 207: 159:genetic risk score 115: 4303:978-0-19-878490-6 4233:npj Breast Cancer 4122:10.1159/000508963 4037:10.1111/apt.15826 3987:10.2337/dc15-1111 3854:Merry AH (2010). 3715:10.1002/hast.1011 3589:(11): 1876–1884. 3074:978-0-19-998187-8 2494:(11): 1576–1583. 1901:(7849): 211–219. 1787:(7256): 748–752. 1738:(10): 1520–1528. 1446:10.1111/nph.14174 1427:(December 2016). 1386:(14): 4167–4182. 1200:(R2): R133–R142. 991:10.1111/cge.13687 979:Clinical Genetics 777:10.1002/jgc4.1458 700:statistical power 676:natural selection 410: 363: 326: 292: 241:(GWAS) regarding 143:genome-wide score 83: 82: 75: 16:(Redirected from 4612: 4595:Genetics studies 4524: 4523: 4513: 4496:(4): 1131–1141. 4481: 4472: 4471: 4461: 4421: 4415: 4414: 4396: 4364: 4358: 4357: 4339: 4330:(7): 1097–1103. 4314: 4308: 4307: 4281: 4275: 4274: 4256: 4224: 4218: 4217: 4197: 4191: 4190: 4180: 4148: 4142: 4141: 4116:(3–4): 110–121. 4105: 4099: 4098: 4088: 4064: 4058: 4057: 4039: 4030:(7): 1165–1173. 4015: 4009: 4008: 3998: 3966: 3960: 3959: 3949: 3929: 3923: 3922: 3886: 3880: 3879: 3867: 3851: 3845: 3844: 3826: 3817:(9): 2267–2272. 3802: 3796: 3795: 3785: 3775: 3751: 3745: 3744: 3726: 3694: 3688: 3687: 3677: 3645: 3639: 3638: 3636: 3623: 3617: 3616: 3598: 3573: 3560: 3559: 3549: 3517: 3511: 3510: 3482: 3476: 3475: 3457: 3425: 3419: 3418: 3408: 3398: 3374: 3368: 3367: 3323: 3312: 3311: 3267: 3261: 3260: 3258: 3245: 3239: 3238: 3228: 3218: 3194: 3188: 3187: 3185: 3172: 3166: 3165: 3155: 3145: 3121: 3115: 3114: 3109: 3107: 3093: 3087: 3086: 3059:(5–6): 413–426. 3048: 3042: 3041: 3031: 3021: 2997: 2991: 2990: 2980: 2970: 2946: 2940: 2939: 2929: 2919: 2895: 2889: 2888: 2878: 2868: 2844: 2838: 2837: 2827: 2795: 2789: 2788: 2778: 2746: 2740: 2739: 2737: 2736: 2721: 2715: 2714: 2706:. 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1103: 1079: 1073: 1072: 1062: 1030: 1021: 1020: 1002: 974: 968: 967: 957: 940:(9): 1219–1224. 925: 912: 911: 901: 891: 867: 861: 860: 850: 840: 816: 807: 806: 788: 755: 739: 678:indicative of a 596:list of biobanks 520:embryo selection 471:Bayesian methods 428: 426: 425: 420: 418: 417: 412: 411: 403: 399: 398: 388: 383: 365: 364: 356: 344: 342: 341: 336: 334: 333: 328: 327: 319: 304: 302: 301: 296: 294: 293: 285: 173:variants (i.e., 170:complex diseases 78: 71: 67: 64: 58: 38: 37: 30: 21: 4620: 4619: 4615: 4614: 4613: 4611: 4610: 4609: 4580:Animal breeding 4570: 4569: 4551: 4533: 4531:Further reading 4528: 4527: 4483: 4482: 4475: 4423: 4422: 4418: 4373:Nature Genetics 4366: 4365: 4361: 4324:Nature Genetics 4316: 4315: 4311: 4304: 4283: 4282: 4278: 4226: 4225: 4221: 4199: 4198: 4194: 4150: 4149: 4145: 4107: 4106: 4102: 4066: 4065: 4061: 4017: 4016: 4012: 3968: 3967: 3963: 3931: 3930: 3926: 3888: 3887: 3883: 3876: 3853: 3852: 3848: 3804: 3803: 3799: 3753: 3752: 3748: 3696: 3695: 3691: 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537: 495: 492: 486: 483: 440: 437: 416: 409: 406: 397: 393: 387: 382: 379: 376: 372: 368: 362: 359: 332: 325: 322: 291: 288: 258: 255: 197: 194: 186:plant breeding 81: 80: 42: 40: 33: 26: 24: 14: 13: 10: 9: 6: 4: 3: 2: 4617: 4606: 4603: 4601: 4598: 4596: 4593: 4591: 4588: 4586: 4583: 4581: 4578: 4577: 4575: 4566: 4563: 4561: 4558: 4556: 4553: 4552: 4548: 4543: 4540: 4535: 4534: 4530: 4521: 4517: 4512: 4507: 4503: 4499: 4495: 4491: 4487: 4480: 4478: 4474: 4469: 4465: 4460: 4455: 4451: 4447: 4443: 4439: 4435: 4431: 4427: 4420: 4417: 4412: 4408: 4404: 4400: 4395: 4390: 4386: 4382: 4378: 4374: 4370: 4363: 4360: 4355: 4351: 4347: 4343: 4338: 4333: 4329: 4325: 4321: 4313: 4310: 4305: 4299: 4295: 4291: 4287: 4286:ESC CardioMed 4280: 4277: 4272: 4268: 4264: 4260: 4255: 4250: 4246: 4242: 4238: 4234: 4230: 4223: 4220: 4215: 4211: 4207: 4203: 4196: 4193: 4188: 4184: 4179: 4174: 4170: 4166: 4162: 4158: 4154: 4147: 4144: 4139: 4135: 4131: 4127: 4123: 4119: 4115: 4111: 4104: 4101: 4096: 4092: 4087: 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4354:220044465 4239:(1): 34. 4138:220669421 4054:221127365 3956:212766426 3741:195786850 3613:244131258 3472:236090477 3364:222169651 3348:2168-622X 3308:222169651 3292:2168-622X 3053:Bioethics 2642:232377300 2516:205355668 2014:(1): 10. 1963:(1): 44. 1876:220732490 1017:209342044 882:(1): 96. 803:235732957 667:heritable 633:Impute.me 548:) is the 408:^ 405:β 371:∑ 361:^ 324:^ 321:β 290:^ 131:phenotype 4520:30967442 4490:Genetics 4468:34599249 4403:36959364 4394:11078202 4346:34017140 4263:28944288 4187:30932047 4130:32688362 4095:20070198 4046:32790217 4005:26577414 3919:36394971 3911:21367743 3833:23685553 3792:24987256 3733:31268574 3684:24170170 3605:34782789 3556:30901543 3507:32502649 3464:34276054 3415:34598685 3356:32997097 3300:32997097 3235:30554720 3162:30835202 3106:29 April 3083:12058767 3038:31418161 2987:33462079 2936:31920964 2885:32545548 2834:33106616 2785:31761530 2683:31026593 2634:33772222 2568:30926966 2508:28991256 2466:32737319 2409:31346163 2352:34304866 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