Post‐GWAS Polygenic Risk Score: Utility and Challenges

Nguyen, Tuan V.; Eisman, John A.

doi:10.1002/jbm4.10411

Cited by 13 publications

(3 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Polygenic risk assessment is designed to improve the quality of predictive medicine [ 33 ], although its results should be interpreted with extreme caution, taking into account the ethnic origin of the sample and the variety of bioinformatic approaches to statistical analysis [ 34 ]. Thus, the results of PGS can be valuable in diagnostic medicine and have higher prognostic power at the individual level.…”

Section: Discussionmentioning

confidence: 99%

Using a Polygenic Score to Predict the Risk of Developing Primary Osteoporosis

Yalaev

Tyurin

Prokopenko

et al. 2022

IJMS

View full text Add to dashboard Cite

Osteoporosis (OP) is a multifactorial bone disease belonging to the metabolic osteopathies group. Using the polygenic score (PGS) approach, we combined the effects of bone mineral density (BMD) DNA loci, affecting osteoporosis pathogenesis, based on GEFOS/GENOMOS consortium GWAS meta-analysis. We developed models to predict the risk of low fractures in women from the Volga-Ural region of Russia with efficacy of 74% (AUC = 0.740; OR (95% CI) = 2.9 (2.353–3.536)), as well as the formation of low BMD with efficacy of 79% (AUC = 0.790; OR (95% CI) = 3.94 (2.993–5.337)). In addition, we propose a model that predicts fracture risk and low BMD in a comorbid condition with 85% accuracy (AUC = 0.850; OR (95% CI) = 6.6 (4.411–10.608)) in postmenopausal women.

show abstract

Section: Discussionmentioning

confidence: 99%

Using a Polygenic Score to Predict the Risk of Developing Primary Osteoporosis

Yalaev

Tyurin

Prokopenko

et al. 2022

IJMS

View full text Add to dashboard Cite

show abstract

“…Besides, rare variants (minor allele frequency <1%) or copy number variations are normally left out of a PRS as they are not included in the genetic data derived from GWAS. Thus, these inclusion criteria may restrict the genomic information included in the model leading to the inability of PRS to completely capture the genomic landscape of the selected trait [ 176 , 177 ]. Other important aspects of PRS calculation are subject to debate: the best way to model APOE within a PRS, the p-value threshold for including SNPs, and the comparison of PRSs for independent cohorts [ 173 , 178 , 179 ].…”

Section: Introductionmentioning

confidence: 99%

Step by step: towards a better understanding of the genetic architecture of Alzheimer’s disease

et al. 2023

View full text Add to dashboard Cite

Alzheimer’s disease (AD) is considered to have a large genetic component. Our knowledge of this component has progressed over the last 10 years, thanks notably to the advent of genome-wide association studies and the establishment of large consortia that make it possible to analyze hundreds of thousands of cases and controls. The characterization of dozens of chromosomal regions associated with the risk of developing AD and (in some loci) the causal genes responsible for the observed disease signal has confirmed the involvement of major pathophysiological pathways (such as amyloid precursor protein metabolism) and opened up new perspectives (such as the central role of microglia and inflammation). Furthermore, large-scale sequencing projects are starting to reveal the major impact of rare variants – even in genes like APOE – on the AD risk. This increasingly comprehensive knowledge is now being disseminated through translational research; in particular, the development of genetic risk/polygenic risk scores is helping to identify the subpopulations more at risk or less at risk of developing AD. Although it is difficult to assess the efforts still needed to comprehensively characterize the genetic component of AD, several lines of research can be improved or initiated. Ultimately, genetics (in combination with other biomarkers) might help to redefine the boundaries and relationships between various neurodegenerative diseases.

show abstract

“…The main goals of trait mapping studies, including genome-wide association studies (GWASs), are to understand the genetic architecture of diseases, pinpoint the number of loci associated with a particular trait, and approximate the underlying heritability rate ( Hirschhorn and Daly, 2003 ; Cantor et al, 2010 ; Wray et al, 2010 ; Garfield, 2020 ). Once the disease-causing variants and genes are identified, this information will help researchers who are working in clinical, medical, or public health fields to establish prevention strategies, predict risks, and adapt therapeutic measurements Hirschhorn and Daly (2003) ; Cantor et al (2010) ; Tam et al (2019) ; Nguyen and Eisman (2020) ; Legge et al (2021) . Regardless of the successes, GWASs are still confronting many challenges and limitations Hao et al, 2019 ); Zhu et al (2017) and have received considerable criticism ( Zuka et al, 2012 ; Cantor et al, 2010) .…”

Section: Introductionmentioning

confidence: 99%

Dissecting Meta-Analysis in GWAS Era: Bayesian Framework for Gene/Subnetwork-Specific Meta-Analysis

Chimusa

Defo

2022

Front. Genet.

View full text Add to dashboard Cite

Over the past decades, advanced high-throughput technologies have continuously contributed to genome-wide association studies (GWASs). GWAS meta-analysis has been increasingly adopted, has cross-ancestry replicability, and has power to illuminate the genetic architecture of complex traits, informing about the reliability of estimation effects and their variability across human ancestries. However, detecting genetic variants that have low disease risk still poses a challenge. Designing a meta-analysis approach that combines the effect of various SNPs within genes or genes within pathways from multiple independent population GWASs may be helpful in identifying associations with small effect sizes and increasing the association power. Here, we proposed ancMETA, a Bayesian graph-based framework, to perform the gene/pathway-specific meta-analysis by combining the effect size of multiple SNPs within genes, and genes within subnetwork/pathways across multiple independent population GWASs to deconvolute the interactions between genes underlying the pathogenesis of complex diseases across human populations. We assessed the proposed framework on simulated datasets, and the results show that the proposed model holds promise for increasing statistical power for meta-analysis of genetic variants underlying the pathogenesis of complex diseases. To illustrate the proposed meta-analysis framework, we leverage seven different European bipolar disorder (BD) cohorts, and we identify variants in the angiotensinogen (AGT) gene to be significantly associated with BD across all 7 studies. We detect a commonly significant BD-specific subnetwork with the ESR1 gene as the main hub of a subnetwork, associated with neurotrophin signaling (p = 4e−14) and myometrial relaxation and contraction (p = 3e−08) pathways. ancMETA provides a new contribution to post-GWAS methodologies and holds promise for comprehensively examining interactions between genes underlying the pathogenesis of genetic diseases and also underlying ethnic differences.

show abstract

Post‐GWAS Polygenic Risk Score: Utility and Challenges

Cited by 13 publications

References 46 publications

Using a Polygenic Score to Predict the Risk of Developing Primary Osteoporosis

Using a Polygenic Score to Predict the Risk of Developing Primary Osteoporosis

Step by step: towards a better understanding of the genetic architecture of Alzheimer’s disease

Dissecting Meta-Analysis in GWAS Era: Bayesian Framework for Gene/Subnetwork-Specific Meta-Analysis

Contact Info

Product

Resources

About