From a genomic risk model to clinical trial implementation in a learning health system: the ProGRESS Study DOI Creative Commons
Jason L. Vassy,

Anna Dornisch,

Roshan Karunamuni

et al.

medRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 4, 2024

ABSTRACT Background As healthcare moves from a one-size-fits-all approach towards precision care, individual risk prediction is an important step in disease prevention and early detection. Biobank-linked systems can generate knowledge about genomic test the impact of implementing that care. Risk-stratified prostate cancer screening one clinical application might benefit such approach. Methods We developed translation pipeline for genomics-informed national system. used data 585,418 male participants Veterans Affairs (VA) Million Veteran Program (MVP), among whom 101,920 self-identify as Black/African-American, to develop validate Prostate CAncer integrated Risk Evaluation (P-CARE) model, model based on polygenic score, family history, genetic principal components. The was externally validated 18,457 PRACTICAL Consortium participants. A novel blended genome-exome (BGE) platform laboratory assay both P-CARE rare variants cancer-associated genes, including additional validation 74,331 samples All Us Research Program. Results In overall ancestry-stratified analyses, score 601 associated with any, metastatic, fatal MVP PRACTICAL. Values at ≥80th percentile multiancestry cohort were hazard ratios (HR) 2.75 (95% CI 2.66-2.84), 2.78 2.54-2.99), 2.59 2.22-2.97) MVP, respectively, compared median. When high– low-risk groups defined HR>1.5 HR<0.75 metastatic cancer, 220,062 (37.6%) high-risk vs.146,826 (25.1%) had 47.9% vs. 14.1%, 9.3% 2.0%, 3.6% 0.8% cumulative cause-specific incidence by age 90, respectively. reports are now being implemented trial VA system (Clinicaltrials.gov NCT05926102 ). Conclusions consisting components describes clinically gradient diverse patient population demonstrates potential learning health implement evaluate care approaches.

Language: Английский

Integrative polygenic risk score improves the prediction accuracy of complex traits and diseases DOI Creative Commons
Buu Truong, Leland E. Hull, Yunfeng Ruan

et al.

Cell Genomics, Journal Year: 2024, Volume and Issue: 4(4), P. 100523 - 100523

Published: March 19, 2024

Polygenic risk scores (PRSs) are an emerging tool to predict the clinical phenotypes and outcomes of individuals. We propose PRSmix, a framework that leverages PRS corpus target trait improve prediction accuracy, PRSmix+, which incorporates genetically correlated traits better capture human genetic architecture for 47 32 diseases/traits in European South Asian ancestries, respectively. PRSmix demonstrated mean accuracy improvement 1.20-fold (95% confidence interval [CI], [1.10; 1.3]; p = 9.17 × 10

Language: Английский

Citations

28
Causal interpretations of family GWAS in the presence of heterogeneous effects DOI Creative Commons
Carl Veller, Molly Przeworski, Graham Coop

et al.

Proceedings of the National Academy of Sciences, Journal Year: 2024, Volume and Issue: 121(38)

Published: Sept. 13, 2024

Family-based genome-wide association studies (GWASs) are often claimed to provide an unbiased estimate of the average causal effects (or treatment effects; ATEs) alleles, on basis analogy between random transmission alleles from parents children and a randomized controlled trial. We show that this claim does not hold in general. Because Mendelian segregation only randomizes among heterozygotes, homozygotes observable. This feature will matter if allele has different as can arise presence gene-by-environment interactions, gene-by-gene or differences linkage disequilibrium patterns. At single locus, family-based GWAS be thought providing effect heterozygotes (i.e., local effect; LATE). interpretation extend polygenic scores (PGSs), however, because sets SNPs heterozygous each family. Therefore, other than under specific conditions, within-family regression slope PGS cannot assumed LATE for any subset weighted families. In practice, potential biases likely smaller those confounding standard, population-based GWAS, so family remain important dissection genetic contributions phenotypic variation. Nonetheless, their is less straightforward been widely appreciated.

Language: Английский

Citations

4
Polygenic Risk Scores in Human Disease DOI
Dimitri J. Maamari, Roukoz Abou-Karam, Akl C. Fahed

et al.

Clinical Chemistry, Journal Year: 2025, Volume and Issue: 71(1), P. 69 - 76

Published: Jan. 1, 2025

Polygenic risk scores (PRS) are measures of genetic susceptibility to human health traits. With the advent large data repositories combining and phenotypic information, PRS providing valuable insights into architecture complex diseases transforming landscape precision medicine.

Language: Английский

Citations

0
Distinct explanations underlie gene-environment interactions in the UK Biobank DOI
Arun Durvasula, Alkes L. Price

The American Journal of Human Genetics, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 1, 2025

Language: Английский

Citations

0
Type 2 diabetes polygenic risk score demonstrates context-dependent effects and associations with type 2 diabetes-related risk factors and complications across diverse populations DOI Open Access
Boya Guo, Yanwei Cai, Daeeun Kim

et al.

medRxiv (Cold Spring Harbor Laboratory), Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 20, 2025

Abstract Polygenic risk scores (PRS) hold prognostic value for identifying individuals at higher of type 2 diabetes (T2D). However, further characterization is needed to understand the generalizability T2D PRS in diverse populations across various contexts. We characterized a multi-ancestry among 244,637 cases and 637,891 controls eight from Population Architecture Genomics Epidemiology (PAGE) Study 13 additional biobanks cohorts. performance was context dependent, with better those who were younger, male, family history T2D, without hypertension, not obese or overweight. Additionally, associated diabetes-related cardiometabolic traits complications, suggesting its utility stratifying complications shared genetic architecture between other diseases. These findings highlight need account when evaluating as tool prognostication potentially generalizable associations despite differential prediction populations.

Language: Английский

Citations

0
Evolution, genetic diversity, and health DOI Creative Commons
María J. Palma-Martínez, Yuridia S. Posadas‐García, Amara Shaukat

et al.

Nature Medicine, Journal Year: 2025, Volume and Issue: unknown

Published: March 7, 2025

Language: Английский

Citations

0
fastGxE: Powering genome-wide detection of genotype-environment interactions in biobank studies DOI Creative Commons
Xiang Zhou, Chao Ning

Research Square (Research Square), Journal Year: 2025, Volume and Issue: unknown

Published: March 20, 2025

Abstract Traditional genome-wide association studies (GWAS) have primarily focused on detecting main genotype effects, often overlooking genotype-environment interactions (GxE), which are essential for understanding context-specific genetic effects and refining disease etiology. Here, we present fastGxE, a scalable effective GxE method designed to identify variants that interact with environmental factors influence traits of interest. fastGxE controls both polygenic interaction is robust the number involved in interactions, ensures scalability analysis large biobank studies, achieving speed improvements 32.98-126.49 times over existing approaches. We illustrate benefits through extensive simulations an in-depth 32 physical 67 blood biomarkers from UK Biobank. In real data applications, identifies nine genomic loci associated traits, including six novel ones, 26 biomarkers, 19 novel. The new discoveries highlight dynamic interplay between genetics environment, uncovering potentially clinically significant pathways could inform personalized interventions treatment strategies.

Language: Английский

Citations

0
All of Us diversity and scale improve polygenic prediction contextually with greatest improvements for under-represented populations DOI Creative Commons
Kristin Tsuo,

Zhuozheng Shi,

Tian Ge

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 6, 2024

Abstract Recent studies have demonstrated that polygenic risk scores (PRS) trained on multi-ancestry data can improve prediction accuracy in groups historically underrepresented genomic studies, but the availability of linked health and genetic from large-scale diverse cohorts representative a wide spectrum human diversity remains limited. To address this need, All Us research program (AoU) generated whole-genome sequences 245,388 individuals who collectively reflect USA. Leveraging resource another widely-used population-scale biobank, UK Biobank (UKB) with half million participants, we developed PRS multi-biobank up to ∼750,000 participants for 32 common, complex traits diseases across range architectures. We then compared effects ancestry, methodology, architecture held out subset ancestrally AoU participants. Due more heterogeneous study design AoU, found lower heritability average UKB (0.075 vs 0.165), which limited maximal achievable AoU. Overall, increased significantly improved performance some especially individuals, multiple phenotypes. Notably, maximizing sample size by combining discovery is not optimal approach predicting phenotypes African ancestry populations; rather, using only these resulted greatest accuracy. This was true less large ancestry-enriched effects, such as neutrophil count ( R 2 : 0.055 vs. 0.035 cross-biobank meta-analysis, respectively, because e.g. DARC ). Lastly, calculated individual-level accuracies rather than grouping continental critical step towards interpretability precision medicine. Individualized decays linearly function divergence, slope smaller GWAS European GWAS. Our results highlight potential biobanks balanced representations facilitate accurate least represented studies.

Language: Английский

Citations

3
Three Open Questions in Polygenic Score Portability DOI Creative Commons
Joyce Y. Wang,

Neeka Lin,

Michael Zietz

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 21, 2024

A major obstacle hindering the broad adoption of polygenic scores (PGS) is their lack "portability" to people that differ-in genetic ancestry or other characteristics-from GWAS samples in which effects were estimated. Here, we use UK Biobank measure change PGS prediction accuracy as a continuous function individuals' genome-wide dissimilarity sample ("genetic distance"). Our results highlight three gaps our understanding portability. First, extremely noisy at individual level and not well predicted by distance. In fact, variance explained comparably socioeconomic measures. Second, trends portability vary across traits. For several immunity-related traits, drops near zero quickly even intermediate levels This quick drop may reflect associations being more ancestry-specific traits than Third, show qualitative can depend on used. instance, for white blood cell count, (reduction mean squared error) increases with Together, cannot be understood through global groupings alone. There are other, understudied factors influencing portability, such specifics evolution trait its architecture, social context, construction score. Addressing these aid development application inform equitable genomic research.

Language: Английский

Citations

3
The PRIMED Consortium: Reducing disparities in polygenic risk assessment DOI Creative Commons
Iftikhar J. Kullo, Matthew P. Conomos, Sarah C. Nelson

et al.

The American Journal of Human Genetics, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 1, 2024

Language: Английский

Citations

3