A Polygenic Risk Score to Predict Future Adult Short Stature Among Children

Lu, Tianyuan; Forgetta, Vincenzo; Wu, Haoyu; Perry, John R. B.; Ong, Ken K.; Greenwood, Celia; Timpson, Nicholas J.; Manousaki, Despoina; Richards, J. Brent

doi:10.1210/clinem/dgab215

Cited by 22 publications

(24 citation statements)

References 51 publications

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“…A polygenic risk score for age and sex-adjusted adult height z-score was recently developed using resources from the UK Biobank and the Genetic Investigation of ANthropometric Traits consortium 7,25,26 . On an out-of-sample test dataset in the UK Biobank, this score explained 36.7% of the total variance in height z-score 7 . To complement this score, an observational study found that the mid-parental height z-score was able to 3/27 explain 44.9% of the total variance in offspring height z-scores in the Erasmus Rucphen Family Study 27 .…”

Section: Improved Height Prediction Amongst Children By Incorporating Parental Height Measuresmentioning

confidence: 99%

“…From studies that did not involve the ALSPAC cohort, we obtained an empirical estimate for α based on the association between a polygenic risk score for height z-score and the measured height z-score 7 . We further obtained an empirical estimate for β based on the association between the mid-parental height z-score and the measured height z-score 32 based on Equation 11.…”

Section: /30mentioning

confidence: 99%

“…In recent years, large-scale genome-wide association studies (GWASs) have characterized the genetic architecture of many complex traits and diseases 3 . Developing polygenic risk scores aggregating the effects of well-profiled genetic determinants has become possible 4,5 , and polygenic risk scores have demonstrated the potential to improve risk stratification in large populations [6][7][8][9] , assist diagnosis and clinical differentiation 10,11 , and refine risk management and treatment strategies 12,13 .…”

Section: Introductionmentioning

confidence: 99%

“…Developing polygenic risk scores aggregating the effects of well-profiled genetic determinants has become possible 4,5 . Polygenic risk scores have demonstrated the potential to improve risk stratification in large populations [6][7][8][9] , assist diagnosis and clinical differentiation [10][11][12] , and refine risk management and treatment strategies [13][14][15] .…”

Section: Introductionmentioning

confidence: 99%

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Capturing additional genetic risk from family history for improved polygenic risk prediction

Forgetta

Richards

et al. 2022

Preprint

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Family history of complex traits may reflect transmitted rare pathogenic variants, intrafamilial shared exposures to environmental and lifestyle factors, as well as a common genetic predisposition. We developed a latent factor model to quantify trait heritability in excess of that captured by a common variant-based polygenic risk score, but inferable from family history. We applied our model to predict adult height for 941 children in the Avon Longitudinal Study of Parents and Children cohort as well as 11 complex diseases for ~400,000 European ancestry participants in the UK Biobank. Parental history brought consistent significant improvements in the predictive power of polygenic risk prediction. For instance, a joint predictor was able to explain ~55% of the total variance in sex-adjusted adult height z-scores, close to the estimated heritability. Our work showcases an innovative paradigm for risk calculation, and supports incorporation of family history into polygenic risk score-based genetic risk prediction models.

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Section: Improved Height Prediction Amongst Children By Incorporating Parental Height Measuresmentioning

confidence: 99%

Section: /30mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Capturing additional genetic risk from family history for improved polygenic risk prediction

Forgetta

Richards

et al. 2022

Preprint

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“…These polygenic risk scores aggregate multiple genetic variants associated with the target traits across the genome and may be able to capture a significant proportion of trait heritability [2,4]. It has been recognized that polygenic risk scores can contribute importantly both clinically and in research, by enabling risk stratification in large populations [2,[5][6][7], informing on risk factors [8,9], assisting diagnosis for complex diseases [10], and suggesting potential therapeutic targets [11].…”

Section: Introductionmentioning

confidence: 99%

Genetic determinants of polygenic prediction accuracy within a population

Forgetta²,

Richards

et al. 2022

Preprint

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Genomic risk prediction is on the emerging path towards personalized medicine. However, the accuracy of polygenic prediction varies strongly in different individuals. In this study, based on up to 352,277 White British participants in the UK Biobank, we constructed polygenic risk scores for 15 physiological and biochemical quantitative traits after performing genome-wide association studies (GWASs). We identified 185 polygenic prediction variability quantitative trait loci (pvQTLs) for 11 traits by Levene’s test among 254,376 unrelated individuals. We validated the effects of pvQTLs using an independent test set of 58,927 individuals. A score aggregating 51 pvQTL SNPs for triglycerides had the strongest Spearman correlation of 0.185 (p-value < 1.0x10−300) with the squared prediction errors. We found a strong enrichment of complex genetic effects conferred by pvQTLs compared to risk loci identified in GWASs, including 89 pvQTLs exhibiting dominance effects. Incorporation of dominance effects into polygenic risk scores significantly improved polygenic prediction for triglycerides, low-density lipoprotein cholesterol, vitamin D, and platelet. After including 87 dominance effects for triglycerides, the adjusted R2 for the polygenic risk score had an 8.1% increase on the test set. In addition, 108 pvQTLs had significant interaction effects with measured environmental or lifestyle exposures. In conclusion, we have discovered and validated genetic determinants of polygenic prediction variability for 11 quantitative biomarkers, and partially profiled the underlying complex genetic effects. These findings may assist interpretation of genomic risk prediction in various contexts, and encourage novel approaches for constructing polygenic risk scores with complex genetic effects.

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Polygenic Risk in Families With Spontaneous Coronary Artery Dissection

Tarr,

Hesselson,

Troup

et al. 2024

JAMA Cardiol

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ImportanceSpontaneous coronary artery dissection (SCAD) is a poorly understood cause of acute coronary syndrome that predominantly affects women. Evidence to date suggests a complex genetic architecture, while a family history is reported for a minority of cases.ObjectiveTo determine the contribution of rare and common genetic variants to SCAD risk in familial cases, the latter via the comparison of a polygenic risk score (PRS) with those with sporadic SCAD and healthy controls.Design, Setting, and ParticipantsThis genetic association study analyzed families with SCAD, individuals with sporadic SCAD, and healthy controls. Genotyping was undertaken for all participants. Participants were recruited between 2017 and 2021. A PRS for SCAD was calculated for all participants. The presence of rare variants in genes associated with connective tissue disorders (CTD) was also assessed. Individuals with SCAD were recruited via social media or from a single medical center. A previously published control database of older healthy individuals was used. Data were analyzed from January 2022 to October 2023.ExposuresPRS for SCAD comprised of 7 single-nucleotide variants.Main Outcomes and MeasuresDisease status (familial SCAD, sporadic SCAD, or healthy control) associated with PRS.ResultsA total of 13 families with SCAD (27 affected and 12 unaffected individuals), 173 individuals with sporadic SCAD, and 1127 healthy controls were included. A total of 188 individuals with SCAD (94.0%) were female, including 25 of 27 with familial SCAD and 163 of 173 with sporadic SCAD; of 12 unaffected individuals from families with SCAD, 6 (50%) were female; and of 1127 healthy controls, 672 (59.6%) were female. Compared with healthy controls, the odds of being an affected family member or having sporadic SCAD was significantly associated with a SCAD PRS (where the odds ratio [OR] represents an increase in odds per 1-SD increase in PRS) (affected family member: OR, 2.14; 95% CI, 1.78-2.50; adjusted P = 1.96 × 10−4; sporadic SCAD: OR, 1.63; 95% CI, 1.37-1.89; adjusted P = 5.69 × 10−4). This association was not seen for unaffected family members (OR, 1.03; 95% CI, 0.46-1.61; adjusted P = .91) compared with controls. Further, those with familial SCAD were overrepresented in the top quintile of the control PRS distribution (OR, 3.70; 95% CI, 2.93-4.47; adjusted P = .001); those with sporadic SCAD showed a similar pattern (OR, 2.51; 95% CI, 1.98-3.04; adjusted P = .001). Affected individuals within a family did not share any rare deleterious variants in CTD-associated genes.Conclusions and RelevanceExtreme aggregation of common genetic risk appears to play a significant role in familial clustering of SCAD as well as in sporadic case predisposition, although further study is required.

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A Polygenic Risk Score to Predict Future Adult Short Stature Among Children

Cited by 22 publications

References 51 publications

Capturing additional genetic risk from family history for improved polygenic risk prediction

Capturing additional genetic risk from family history for improved polygenic risk prediction

Genetic determinants of polygenic prediction accuracy within a population

Polygenic Risk in Families With Spontaneous Coronary Artery Dissection

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