Trans‐ethnic polygenic risk scores for body mass index: An international hundred K+ cohorts consortium study

Qu, Hui‐Qi; Connolly, John J.; Kraft, Peter; Long, Jirong; Pereira, Alexandre C.; Flatley, Christopher; Turman, Constance; Prins, Bram P.; Mentch, Frank; Lotufo, Paulo A.; Magnus, Per; Stampfer, Meir J.; Tamimi, Rulla M.; Eliassen, A. Heather; Wang, Zheng; Knudsen, Gun Peggy; Helgeland, Øyvind; Butterworth, Adam S.; Hákonarson, Hákon; Sleiman, Patrick

doi:10.1002/ctm2.1291

Cited by 4 publications

(6 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our data showed that the TE PRS outperformed the ancestry‐specific models in the Chinese and Brazilian populations, whereas there is no appreciable loss in predictive power in European ancestry individuals when using a TE score. However, as shown in our study, the TE PRS for BMI has a poor performance in the admixed Brazilian population, although it outperforms the European PRS 42 . Increased genetic diversity and unique genetic drift and selection pressures contribute different LD patterns.…”

Section: Te Prs For Bmisupporting

confidence: 48%

“…However, as shown in our study, the TE PRS for BMI has a poor performance in the admixed Brazilian population, although it outperforms the European PRS. 42 Increased genetic diversity and unique genetic drift and selection pressures contribute different LD patterns. In particular, genetic admixture generates LD blocks inherited from each ancestral population.…”

Section: Positive Selectionmentioning

confidence: 99%

“…The PRS for BMI was facilitated by the comprehensive identification of SNPs associated with BMI by the previous large‐scale GWAS by the GIANT consortium 34 . The GWAS meta‐analysis by the GIANT consortium, including 339 224 individuals of European ancestry, 34 was the basis of our PRS model 42 . The updated GWAS by the GIANT consortium and the UK Biobank included approximately 700 000 individuals of European ancestry 35 .…”

Section: Potential Health Disparitiesmentioning

confidence: 99%

“…Our recent PRS model for extreme BMI, that is, the top 1% and 5% has a moderate AUC of 0.703-0.736 with an acceptable predictive power in two large European populations, the Nurses' Health Study (NHS) and Nurses' Health Study II (NHSII). 42 As further research and advancements continue, the BMI-PRS model is expected to evolve and enhance its predictive power, ultimately…”

Section: Polygenic Risk Score (Prs) For Bmimentioning

confidence: 99%

See 3 more Smart Citations

Trans‐ethnic polygenic risk scores for body mass index

Connolly

Hákonarson

2023

Clinical and Translational Dis

Self Cite

View full text Add to dashboard Cite

BackgroundObesity is a complex trait caused by a combination of genetic, environmental and lifestyle factors that contributes to the risks of numerous serious diseases. Predictive measures of body mass index (BMI) hold significant promise, with implications for the prevention and early intervention of obesity, promoting overall improvement in health.Main bodyAn effective BMI polygenic risk score (PRS) model can assist with the prediction and early detection of obesity at the individual level, aligning with the objectives of precision medicine in the management of obesity. However, potential health disparities may emerge among under‐represented populations with a high prevalence of obesity, primarily due to the lack of genomic data available for these populations. The development of a trans‐ethnic (TE) PRS for BMI necessitates collective action from the research community, and requires genomic data from diverse populations.ConclusionThe current BMI‐PRS model exhibits moderate performance, which could be improved in several key areas: integrating genomic information through TE GWAS studies, including admixed populations, considering gene–environment interactions, implementing advanced machine learning techniques, and incorporating genomic‐informed risk assessment (GIRA) with the inclusion of family history, environmental and lifestyle factors for the risk prediction.

show abstract

Section: Te Prs For Bmisupporting

confidence: 48%

Section: Positive Selectionmentioning

confidence: 99%

Section: Potential Health Disparitiesmentioning

confidence: 99%

Section: Polygenic Risk Score (Prs) For Bmimentioning

confidence: 99%

See 2 more Smart Citations

Trans‐ethnic polygenic risk scores for body mass index

Connolly

Hákonarson

2023

Clinical and Translational Dis

Self Cite

View full text Add to dashboard Cite

show abstract

“…Most recently, Qu et al 5 . addressed this limitation in the application of PRS for body mass index (BMI) in non‐European populations.…”

mentioning

confidence: 99%

Transferability of polygenic risk score among diverse ancestries

Cheng,

Zhao

2023

Clinical and Translational Dis

View full text Add to dashboard Cite

Polygenic risk score (PRS) has recently emerged as a powerful tool for predicting the risk of complex traits by summarizing weighted single nucleotide polymorphisms (SNPs) associated with the trait(s). The data required for PRS construction, that is, SNPs and effect sizes, can be accessed from in-house association results or previously published association results as deposited in the PGS catalogue (https://www.pgscatalog.org/). PRS has been applicated in risk prediction and stratification for cancers, cardiometabolic diseases, and psychiatric disorders. 1,2 Despite the promising prospect of PRS in clinical application, the application of PRS in the general population is limited by ethnic inequality with respect to the training data. As of 2021, about 86% of genome-wide association study (GWAS) participants are European descendants, 3 mainly due to the scarcity of comprehensive genotyping data for non-European populations. Therefore, the prediction accuracy across non-European populations, i.e., the transferability of PRS, is often poor due to the difference in allele frequencies and linkage disequilibrium (LD) 4 patterns across populations. The insufficient transferability limits global clinical applicability and may increase disparities in PRS implementation among regions.Most recently, Qu et al. 5 addressed this limitation in the application of PRS for body mass index (BMI) in non-European populations. In this study, they developed a globally applicable trans-ethnic PRS for BMI from European ancestry GWAS by generating ethnic-specific LD reference panels and combining them into a BayesianThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

show abstract

Trans‐ethnic polygenic risk scores for body mass index: An international hundred K+ cohorts consortium study

Connolly

Kraft

et al. 2023

Clinical & Translational Med

Self Cite

View full text Add to dashboard Cite

Background While polygenic risk scores hold significant promise in estimating an individual's risk of developing a complex trait such as obesity, their application in the clinic has, to date, been limited by a lack of data from non‐European populations. As a collaboration model of the International Hundred K+ Cohorts Consortium (IHCC), we endeavored to develop a globally applicable trans‐ethnic PRS for body mass index (BMI) through this relatively new international effort. Methods The polygenic risk score (PRS) model was developed, trained and tested at the Center for Applied Genomics (CAG) of The Children's Hospital of Philadelphia (CHOP) based on a BMI meta‐analysis from the GIANT consortium. The validated PRS models were subsequently disseminated to the participating sites. Scores were generated by each site locally on their cohorts and summary statistics returned to CAG for final analysis. Results We show that in the absence of a well powered trans‐ethnic GWAS from which to derive marker SNPs and effect estimates for PRS, trans‐ethnic scores can be generated from European ancestry GWAS using Bayesian approaches such as LDpred, by adjusting the summary statistics using trans‐ethnic linkage disequilibrium reference panels. The ported trans‐ethnic scores outperform population specific‐PRS across all non‐European ancestry populations investigated including East Asians and three‐way admixed Brazilian cohort. Conclusions Here we show that for a truly polygenic trait such as BMI adjusting the summary statistics of a well powered European ancestry study using trans‐ethnic LD reference results in a score that is predictive across a range of ancestries including East Asians and three‐way admixed Brazilians.

show abstract

Trans‐ethnic polygenic risk scores for body mass index: An international hundred K+ cohorts consortium study

Cited by 4 publications

References 41 publications

Trans‐ethnic polygenic risk scores for body mass index

Trans‐ethnic polygenic risk scores for body mass index

Transferability of polygenic risk score among diverse ancestries

Trans‐ethnic polygenic risk scores for body mass index: An international hundred K+ cohorts consortium study

Contact Info

Product

Resources

About