Development and validation of a trans-ancestry polygenic risk score for type 2 diabetes in diverse populations.

paper Cited Public

Authors: Ge, Tian; Irvin, Marguerite R; Patki, Amit; Srinivasasainagendra, Vinodh; Lin, Yen-Feng; Tiwari, Hemant K; Armstrong, Nicole D; Benoit, Barbara; Chen, Chia-Yen; Choi, Karmel W; Cimino, James J; Davis, Brittney H; Dikilitas, Ozan; Etheridge, Bethany; Feng, Yen-Chen Anne; Gainer, Vivian; Huang, Hailiang; Jarvik, Gail P; Kachulis, Christopher; Kenny, Eimear E; Khan, Atlas; Kiryluk, Krzysztof; Kottyan, Leah; Kullo, Iftikhar J; Lange, Christoph; Lennon, Niall; Leong, Aaron; Malolepsza, Edyta; Miles, Ayme D; Murphy, Shawn; Namjou, Bahram; Narayan, Renuka; O'Connor, Mark J; Pacheco, Jennifer A; Perez, Emma; Rasmussen-Torvik, Laura J; Rosenthal, Elisabeth A; Schaid, Daniel; Stamou, Maria; Udler, Miriam S; Wei, Wei-Qi; Weiss, Scott T; Ng, Maggie C Y; Smoller, Jordan W; Lebo, Matthew S; Meigs, James B; Limdi, Nita A; Karlson, Elizabeth W
Year: 2022
Journal: Genome medicine
PMID: 35765100
DOI: 10.1186/s13073-022-01074-2
PMCID: PMC9241245

BACKGROUND: Type 2 diabetes (T2D) is a worldwide scourge caused by both genetic and environmental risk factors that disproportionately afflicts communities of color. Leveraging existing large-scale genome-wide association studies (GWAS), polygenic risk scores (PRS) have shown promise to complement established clinical risk factors and intervention paradigms, and improve early diagnosis and prevention of T2D. However, to date, T2D PRS have been most widely developed and validated in individuals of European descent. Comprehensive assessment of T2D PRS in non-European populations is critical for equitable deployment of PRS to clinical practice that benefits global populations. METHODS: We integrated T2D GWAS in European, African, and East Asian populations to construct a trans-ancestry T2D PRS using a newly developed Bayesian polygenic modeling method, and assessed the prediction accuracy of the PRS in the multi-ethnic Electronic Medical Records and Genomics (eMERGE) study (11,945 cases; 57,694 controls), four Black cohorts (5137 cases; 9657 controls), and the Taiwan Biobank (4570 cases; 84,996 controls). We additionally evaluated a post hoc ancestry adjustment method that can express the polygenic risk on the same scale across ancestrally diverse individuals and facilitate the clinical implementation of the PRS in prospective cohorts. RESULTS: The trans-ancestry PRS was significantly associated with T2D status across the ancestral groups examined. The top 2% of the PRS distribution can identify individuals with an approximately 2.5-4.5-fold of increase in T2D risk, which corresponds to the increased risk of T2D for first-degree relatives. The post hoc ancestry adjustment method eliminated major distributional differences in the PRS across ancestries without compromising its predictive performance. CONCLUSIONS: By integrating T2D GWAS from multiple populations, we developed and validated a trans-ancestry PRS, and demonstrated its potential as a meaningful index of risk among diverse patients in clinical settings. Our efforts represent the first step towards the implementation of the T2D PRS into routine healthcare.

#	Section	Preview
0	Background	Type 2 diabetes (T2D) is a common, chronic disease caused by both genetic and environmental risk…
1	Background	Recent large-scale genome-wide association studies (GWAS) in diverse populations have identified…
2	Background	The Electronic Medical Records and Genomics (eMERGE) study is a consortium of US medical research…
3	Background	the opportunities and challenges in the clinical translation of T2D PRS. Specifically, we integrated…
4	Methods — Construction of trans-ancestry T2D PRS from published T2D GWAS	We identified three large-scale T2D GWAS conducted in different populations to derive a…
5	Methods — Construction of trans-ancestry T2D PRS from published T2D GWAS	To empirically examine the concordance of genetic effects on T2D across different populations, we…
6	Methods — Construction of trans-ancestry T2D PRS from published T2D GWAS	We used PRS-CSx, a recently developed Bayesian polygenic modeling method, to construct the…
7	Methods — Overview of the evaluation of the trans-ancestry PRS	We first evaluated the trans-ancestry PRS constructed by PRS-CSx among the European, African and…
8	Methods — Overview of the evaluation of the trans-ancestry PRS	lower in African individuals. We next evaluated the trans-ancestry PRS in four independent…
9	Methods — Overview of the evaluation of the trans-ancestry PRS	as cases in the PRS analysis. After removing 1,776 REGARDS samples that overlapped with the MEDIA…
10	Methods — Validation of EMR-based phenotyping algorithms to classify T2D in eMERGE	We adapted two prior phenotyping algorithms, one from eMERGE developed in 2011 [32] and one from…
11	Methods — Validation of EMR-based phenotyping algorithms to classify T2D in eMERGE	We tested the performance of the original eMERGE algorithm, modified eMERGE algorithm, and modified…
12	Methods — eMERGE genetic data	We used genetic data from 8 eMERGE sites in this work: Cincinnati Children's Hospital Medical Center…
13	Methods — eMERGE genetic data	super-populations—European [EUR], African [AFR], Admixed American [AMR], and East Asian [EAS]—by…
14	Methods — Reasons for Geographic and Racial Differences in Stroke Study (REGARDS)	REGARDS [28] is a national, population-based, longitudinal study of incident stroke and associated…
15	Methods — Reasons for Geographic and Racial Differences in Stroke Study (REGARDS)	performed on 8,916 Black participants using Illumina MEGA arrays. Imputation was conducted using…
16	Methods — The Genetics of Hypertension Associated Treatments (GenHAT) Study	GenHAT [29] is an ancillary study of the Antihypertensive and Lipid-Lowering Treatment to Prevent…
17	Methods — The Genetics of Hypertension Associated Treatments (GenHAT) Study	CVD outcomes. Genotyping was performed using Illumina Infinium Multi-Ethnic AMR/AFR BeadChip (MEGA)…
18	Methods — The Hypertension Genetic Epidemiology Network (HyperGEN)	HyperGEN [30] is a cross-sectional, population-based study and component of the NHLBI Family Blood…
19	Methods — The Hypertension Genetic Epidemiology Network (HyperGEN)	of the REGARDS, GenHAT, and Warfarin (see below) studies, we compiled a set of non-monomorphic and…

Name	Type
1000 Genomes Project	cohort
1KG phase 3 dataset local	cohort
adjusted PRS local	drug
Admixed American super-population local	cohort
African	cohort
African American	cohort
African samples	cohort
African super-population local	cohort
age	phenotype
Age >35 years local	phenotype
Alcohol Use	phenotype
ALLHAT local	cohort
amlodipine	drug
AMR	cohort
ancestry	phenotype
Antihypertensive medications local	drug
Asian	cohort
atrial fibrillation	phenotype
Batch 1 local	cohort
Batch 2 local	cohort
Batch 3 local	cohort
BBJ local	cohort
BBJ T2D GWAS local	cohort
Biobank Japan GWAS local	cohort
BioData Catalyst framework local	drug
Black	phenotype
Black cohorts local	cohort
black participants	cohort
blood pressure	phenotype
BMI	phenotype
body mass index	phenotype
cardiovascular disease outcomes local	phenotype
Children’s Hospital of Pennsylvania local	cohort
chlorthalidone local	drug
Cincinnati Children's Hospital Medical Center local	cohort
Clinical Risk Factors local	phenotype
Columbia University local	cohort
communities of color local	cohort
Communities of color local	cohort
coronary heart disease	phenotype
CVD outcomes local	phenotype
diabetes medications local	drug
discovery GWAS samples local	cohort
diverse samples local	cohort
doxazosin local	drug
East Asian	cohort
East Asian super-population local	cohort
eMERGE African ancestry cohort local	cohort
eMERGE African dataset local	cohort
eMERGE African samples local	cohort
eMERGE algorithm local	cohort
eMERGE algorithm local	drug
eMERGE dataset local	cohort
eMERGE European dataset local	cohort
eMERGE European samples local	cohort
eMERGE I-III genotyped dataset local	cohort
eMERGE I-III samples local	cohort
eMERGE IV study local	cohort
eMERGE network	cohort
eMERGE study local	cohort
End-organ damage local	phenotype
European ancestry	cohort
European descent GWAS local	cohort
European population	cohort
European super-population local	cohort
European T2D GWAS local	cohort
evaluation cohorts local	cohort
Family history of diabetes local	phenotype
family history positive	phenotype
fasting glucose	phenotype
Genetic Counseling/Lifestyle Change (GC/LC) study local	cohort
GenHAT local	cohort
Gibbs sampler local	drug
glucose	drug
glucose-lowering medications local	drug
GWAS	cohort
Haplotype Reference Consortium local	drug
HapMap3 variants local	variant
Healthcare Professionals local	cohort
high glucose local	phenotype
His	cohort
Hispanic	phenotype
Hispanic/Latino local	cohort
Hispanic/Latino ancestry local	cohort
Hispanic/Latino sample local	cohort
Hispanic/Latino samples local	cohort
HyperGen local	cohort
HyperGEN local	cohort
hypertension	phenotype
Icahn School of Medicine at Mount Sinai	cohort
Illumina 1M duo array local	drug
Illumina MEGA array local	drug
Illumina MEGA arrays local	drug
imputed SNPs	variant
incident stroke local	phenotype
INR local	phenotype
Integrated Risk Assessment local	phenotype
LDL cholesterol	phenotype
LDpred2 local	drug
LDpred2 Meta local	drug
lead variant local	variant
lead variants	variant
lisinopril local	drug
MAF >1% local	variant
Mass General Brigham local	cohort
Mayo Clinic	cohort
MEDIA local	cohort
MEDIA Consortium local	cohort
MEDIA Consortium GWAS local	cohort
MEDIA study local	cohort
MEDIA T2D GWAS local	cohort
MedSeq Project local	cohort
MGB local	cohort
MGB algorithm local	cohort
MGB chart review dataset local	cohort
MI-GENES study local	cohort
modified eMERGE algorithm local	cohort
modified eMERGE algorithm local	drug
modified MGB algorithm local	cohort
modified MGB algorithm local	drug
multi-ethnic meta-GWAS local	cohort
myocardial infarction	phenotype
Native Americans	cohort
NHLBI Family Blood Pressure Program local	cohort
NHLBI TOPMed reference panel local	cohort
NHLBI TOPMed reference panel local	drug
non-European ancestry	cohort
non-European GWAS local	cohort
Northwestern University local	cohort
outcome	phenotype
Overweight or obesity local	phenotype
patients	cohort
peripheral arterial disease	phenotype
physical activity	phenotype
physical inactivity	phenotype
PLINK 1.9 local	drug
polygenic risk score	cohort
principal components	drug
Prospective Study	cohort
PRS	drug
PRS_adj local	phenotype
PRS-CS	drug
PRS-CS EUR local	drug
PRS-CS Meta local	drug
PRS_raw local	phenotype
raw PRS local	drug
REGARDS local	cohort
screen negative subjects local	cohort
screen positive subjects local	cohort
smoking	phenotype
statins	drug
stroke	phenotype
study cohort	cohort
T2D meta-GWAS local	cohort
T2D PRS local	drug
tag variant local	variant
Taiwan Biobank local	cohort
Taiwan Biobank participants local	cohort
target population	cohort
tobacco use	phenotype
Trans-ancestry Polygenic Risk Score local	drug
trans-ancestry PRS local	variant
trans-ancestry T2D PRS local	drug
transient ischemic attack local	phenotype
TWB local	cohort
TWB batches local	cohort
TWBv1 array local	drug
TWBv2 array local	drug
type 1 diabetes	phenotype
type 2 diabetes	phenotype
UAB local	cohort
UAB Black cohorts local	cohort
UAB chart review dataset local	cohort
UAB WPC local	cohort
underrepresented populations local	cohort
University of Alabama at Birmingham local	cohort
Vanderbilt University Medical Center local	cohort
venous thromboembolism local	phenotype
warfarin	drug
Warfarin local	cohort
White	phenotype
WPC local	cohort

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Predictive Models for Type 2 Diabetes Mellitus in Han Chinese with Insights into Cross-Population Applicability and Demographic Specific Risk Factors.	Chen YE et al.	—	2025	→
Predictive performance for alcohol use disorder of polygenic scores based on the general addiction risk factor and problematic alcohol use.	Pérez-Gutiérrez AM et al.	—	2025	→
Race, diabetes, and cognitive function: a cross-sectional analysis of intersecting disparities in the NHANES cohort.	Wang L et al.	—	2025	→
Real-time dynamic polygenic prediction for streaming data.	Tubbs JD et al.	—	2025	→
The role of genomic-informed risk assessments in predicting dementia outcomes.	Andrews SJ et al.	—	2025	→
The Taiwan Precision Medicine Initiative provides a cohort for large-scale studies.	Yang HC et al.	—	2025	→
Transferability of European-derived Alzheimer's disease polygenic risk scores across multiancestry populations.	Nicolas A et al.	—	2025	→
Type 1 Diabetes Genetic Risk Scores: History, Application and Future Directions.	Tosur M et al.	—	2025	→
Type 2 diabetes genetic risk and incident diabetes across diabetes risk enhancers.	Moura FA et al.	—	2025	→
Type 2 Diabetes Polygenic Risk Score Interactions with Lifestyle Risk Factors in Black Americans.	Scadden AW et al.	—	2025	→
Validation of GenProb-T1D and its clinical utility for differentiating types of diabetes in a biobank from a US healthcare system.	Billings LK et al.	—	2025	→
2024 Heart Disease and Stroke Statistics: A Report of US and Global Data From the American Heart Association.	Martin SS et al.	—	2024	→
AI-enhanced integration of genetic and medical imaging data for risk assessment of Type 2 diabetes.	Huang YJ et al.	—	2024	→
Algorithms for the identification of prevalent diabetes in the All of Us Research Program validated using polygenic scores.	Szczerbinski L et al.	—	2024	→
Can Open Science Advance Health Justice? Genomic Research Dissemination in the Evolving Data-Sharing Landscape.	Kraft SA et al.	—	2024	→
Combined polygenic scores for ischemic stroke risk factors aid risk assessment of ischemic stroke.	Huang S et al.	—	2024	→
Cross-population applications of genomics to understand the risk of multifactorial traits involving inflammation and immunity.	Alamad B et al.	—	2024	→
Evaluating the Efficacy of Type 2 Diabetes Polygenic Risk Scores in an Independent European Population.	Brīvība M et al.	—	2024	→
Genetic Risk, Healthy Lifestyle Adherence, and Risk of Developing Diabetes in the Japanese Population.	Takase M et al.	—	2024	→
Genomes in clinical care.	Riess O et al.	—	2024	→
Genome-wide association study and trans-ethnic meta-analysis identify novel susceptibility loci for type 2 diabetes mellitus.	Elashi AA et al.	—	2024	→
Genomic insights for personalised care in lung cancer and smoking cessation: motivating at-risk individuals toward evidence-based health practices.	Chen T et al.	—	2024	→
Impact of the trans-ancestry polygenic risk score on type 2 diabetes risk, onset age and progression among population in Taiwan.	Wang SH et al.	—	2024	→
Integrative common and rare variant analyses provide insights into the genetic architecture of liver cirrhosis.	Ghouse J et al.	—	2024	→
Lessons and Applications of Omics Research in Diabetes Epidemiology.	Yu G et al.	—	2024	→
Multi-Ancestry Polygenic Risk Score for Coronary Heart Disease Based on an Ancestrally Diverse Genome-Wide Association Study and Population-Specific Optimization.	Smith JL et al.	—	2024	→
Multi-ancestry polygenic risk scores for venous thromboembolism.	Jee YH et al.	—	2024	→
Optimizing clinico-genomic disease prediction across ancestries: a machine learning strategy with Pareto improvement.	Gao Y et al.	—	2024	→
Parkinson Disease Genetics Extended to African and Hispanic Ancestries in the VA Million Veteran Program.	Pankratz N et al.	—	2024	→
Polygenic Risk for Type 2 Diabetes in African Americans.	Irvin MR et al.	—	2024	→
Polygenic risk score predicts all-cause death in East Asian patients with prior coronary artery disease.	Qin M et al.	—	2024	→
Principles and methods for transferring polygenic risk scores across global populations.	Kachuri L et al.	—	2024	→
Recent advances in polygenic scores: translation, equitability, methods and FAIR tools.	Xiang R et al.	—	2024	→
Screening embryos for polygenic disease risk: a review of epidemiological, clinical, and ethical considerations.	Capalbo A et al.	—	2024	→
Selection, optimization and validation of ten chronic disease polygenic risk scores for clinical implementation in diverse US populations.	Lennon NJ et al.	—	2024	→
The association between air pollutant exposure and cerebral small vessel disease imaging markers with modifying effects of PRS-defined genetic susceptibility.	Sun X et al.	—	2024	→
Trans-Ancestral Genetic Risk Factors for Treatment-Related Type 2 Diabetes Mellitus in Survivors of Childhood Cancer.	Im C et al.	—	2024	→
Variant level heritability estimates of type 2 diabetes in African Americans.	Armstrong ND et al.	—	2024	→
Adjusting for population stratification in polygenic risk score analyses: a guide for model specifications in the UK Biobank.	Lin BD et al.	—	2023	→
A Type 1 Diabetes Polygenic Score Is Not Associated With Prevalent Type 2 Diabetes in Large Population Studies.	Srinivasan S et al.	—	2023	→
Combining Asian and European genome-wide association studies of colorectal cancer improves risk prediction across racial and ethnic populations.	Thomas M et al.	—	2023	→
Comparing Pruning and Thresholding with Continuous Shrinkage Polygenic Score Methods in a Large Sample of Ancestrally Diverse Adolescents from the ABCD Study<sup>®</sup>.	Ahern J et al.	—	2023	→
Earlier Age at Type 2 Diabetes Diagnosis Is Associated With Increased Genetic Risk of Cardiovascular Disease.	Lee H et al.	—	2023	→
Examining interactions between polygenic scores and interpersonal trauma exposure on alcohol consumption and use disorder in an ancestrally diverse college cohort.	Sheerin CM et al.	—	2023	→
Explainable multi-task learning improves the parallel estimation of polygenic risk scores for many diseases through shared genetic basis.	Badré A et al.	—	2023	→
Genetic heterogeneity in cardiovascular disease across ancestries: Insights for mechanisms and therapeutic intervention.	Soremekun O et al.	—	2023	→
Influence of Diabetes Family History on the Associations of Combined Genetic and Lifestyle Risks with Diabetes in the Tohoku Medical Megabank Community-Based Cohort Study.	Takase M et al.	—	2023	→
Multi-Omics Studies in Historically Excluded Populations: The Road to Equity.	Yang G et al.	—	2023	→
Polygenic Background Modifies Risk of Coronary Artery Disease Among Individuals With Heterozygous Familial Hypercholesterolemia.	Reeskamp LF et al.	—	2023	→
Polygenic Scores in the Direct-to-Consumer Setting: Challenges and Opportunities for a New Era in Consumer Genetic Testing.	Park JK et al.	—	2023	→
Predicting Alcohol-Related Memory Problems in Older Adults: A Machine Learning Study with Multi-Domain Features.	Kamarajan C et al.	—	2023	→
Primary care physician use of patient race and polygenic risk scores in medical decision-making.	Kerman BJ et al.	—	2023	→
The GenoVA study: Equitable implementation of a pragmatic randomized trial of polygenic-risk scoring in primary care.	Vassy JL et al.	—	2023	→
Trans-ancestry polygenic models for the prediction of LDL blood levels: an analysis of the United Kingdom Biobank and Taiwan Biobank.	Hassanin E et al.	—	2023	→
Variation in <i>VEGFA</i> and risk of cardiovascular disease in the UK Biobank.	Chen H et al.	—	2023	→
Development of a clinical polygenic risk score assay and reporting workflow.	Hao L et al.	—	2022	→
Improving polygenic prediction in ancestrally diverse populations.	Ruan Y et al.	—	2022	→
Polygenic Risk Score in African populations: progress and challenges.	Adam Y et al.	—	2022	→
The Genetics of Type 2 Diabetes in Youth: Where We Are and the Road Ahead.	Srinivasan S et al.	—	2022	→