GenRisk: a tool for comprehensive genetic risk modeling

Aldisi, Rana; Hassanin, Emadeldin; Sivalingam, Sugirthan; Buneß, Andreas; Klinkhammer, Hannah; Mayr, Andreas; Fröhlich, Holger; Krawitz, Peter; Maj, Carlo

doi:10.1093/bioinformatics/btac152

Cited by 6 publications

(7 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To assess the cumulative contribution of rare variants to MPHL, we performed gene-based association analyses using SKAT-O 20 and GenRisk 21 , a new burden association test which upweights rarer and more deleterious variants (based on CADD). We applied the GenRisk test to a data set of both coding and non-coding rare variants, as well as to coding rare variants identical to the variant set used in the SKAT-O analysis.…”

Section: Resultsmentioning

confidence: 99%

Analysis of 72,469 UK Biobank exomes links rare variants to male-pattern hair loss

Henne,

Aldisi,

Sivalingam

et al. 2023

Nat Commun

Self Cite

View full text Add to dashboard Cite

Male-pattern hair loss (MPHL) is common and highly heritable. While genome-wide association studies (GWAS) have generated insights into the contribution of common variants to MPHL etiology, the relevance of rare variants remains unclear. To determine the contribution of rare variants to MPHL etiology, we perform gene-based and single-variant analyses in exome-sequencing data from 72,469 male UK Biobank participants. While our population-level risk prediction suggests that rare variants make only a minor contribution to general MPHL risk, our rare variant collapsing tests identified a total of five significant gene associations. These findings provide additional evidence for previously implicated genes (EDA2R, WNT10A) and highlight novel risk genes at and beyond GWAS loci (HEPH, CEPT1, EIF3F). Furthermore, MPHL-associated genes are enriched for genes considered causal for monogenic trichoses. Together, our findings broaden the MPHL-associated allelic spectrum and provide insights into MPHL pathobiology and a shared basis with monogenic hair loss disorders.

show abstract

Section: Resultsmentioning

confidence: 99%

Analysis of 72,469 UK Biobank exomes links rare variants to male-pattern hair loss

Henne,

Aldisi,

Sivalingam

et al. 2023

Nat Commun

Self Cite

View full text Add to dashboard Cite

show abstract

“…PRS can be a precious tool for risk stratification, particularly in identifying groups of people with extremely high or low genetic risk of developing a specific disease or trait. Moreover, based on our recent work and others, it has become clear that for certain traits high PRS along with rare disease-causing variants can further increase the individuals' risk of developing a disease compared to carriers without a high PRS (4)(5)(6)(7).…”

Section: Introductionmentioning

confidence: 95%

Transferability of European-derived cardiometabolic polygenic risk scores in the South Asians and their interplay with family history

Hassanin

Maj

Krawitz

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

Background & Aims: We aimed to investigate the effect of polygenic risk scores (PRSs) derived from individuals of European (EUR) ancestry on common diseases among individuals of South Asian (SAS) ancestry in the UK Biobank (UKB). Additionally, we studied the interaction between PRS and family history (FH) in the same population. Methods: To calculate the PRS, we used a previously published panel of SNPs derived from the EUR population and applied it to the individuals of SAS ancestry from the UKB study. We applied the PRS using summary statistics from genome-wide association studies (GWAS) for cardiometabolic and lifestyle diseases such as coronary artery disease (CAD), obesity, and type 2 diabetes (T2D). Each PRS was adjusted according to an individual's predicted genetic ancestry to derive an adjusted PRS (aPRS). We calculated the percentiles based on aPRS and divided them according to the percentiles into three categories: low, intermediate, and high. Considering the intermediate-aPRS percentile as a reference, we compared the low and high aPRS categories and generated the odds ratio (OR) estimates. Results: The risk of developing severe obesity for individuals of SAS ancestry was almost threefold higher for individuals with high aPRS than for those with intermediate aPRS, with an OR of 3.67 (95% CI = 2.47-5.48, P < 0.01). While the risk of severe obesity was lower in the low-aPRS group (OR = 0.19, CI = 0.05-0.52, P < 0.01). Comparable results were found in the EUR data, where the low-PRS group had an OR of 0.26 (95% CI= 0.24-0.3, P < 0.01) and the high-PRS group had an OR of 3.2 (95% CI = 3.1-3.3, P < 0.01). We observed similar results for CAD and T2D. Further, we show that SAS individuals with a familial history of CAD and T2D with high-aPRS exhibit further higher risk to these diseases, thereby implying a greater genetic predisposition to these conditions. Conclusion: Our findings suggest that using CAD, obesity, and T2D GWAS summary statistics predominantly from the EUR population have sufficient power to identify SAS individuals with higher genetic risk. With future GWAS recruiting more SAS participants and tailoring the PRSs towards SAS ancestry, we believe that the predictive power of PRS would improve.

show abstract

“…GenRisk—GenRisk is a Python package that leverages various gene-based scoring schemes to analyze and identify significant genes associated with a phenotype in a population [ 76 ]. It enables the computation and integration of gene scores, considering both rare deleterious variants’ burden and common-variants-based PRS.…”

Section: State-of-the-art Grs and Prs Toolsmentioning

confidence: 99%

A perspective on genetic and polygenic risk scores—advances and limitations and overview of associated tools

Schwarzerova,

Hurta,

Barton

et al. 2024

Briefings in Bioinformatics

View full text Add to dashboard Cite

Polygenetic Risk Scores are used to evaluate an individual's vulnerability to developing specific diseases or conditions based on their genetic composition, by taking into account numerous genetic variations. This article provides an overview of the concept of Polygenic Risk Scores (PRS). We elucidate the historical advancements of PRS, their advantages and shortcomings in comparison with other predictive methods, and discuss their conceptual limitations in light of the complexity of biological systems. Furthermore, we provide a survey of published tools for computing PRS and associated resources. The various tools and software packages are categorized based on their technical utility for users or prospective developers. Understanding the array of available tools and their limitations is crucial for accurately assessing and predicting disease risks, facilitating early interventions, and guiding personalized healthcare decisions. Additionally, we also identify potential new avenues for future bioinformatic analyzes and advancements related to PRS.

show abstract

GenRisk: a tool for comprehensive genetic risk modeling

Cited by 6 publications

References 15 publications

Analysis of 72,469 UK Biobank exomes links rare variants to male-pattern hair loss

Analysis of 72,469 UK Biobank exomes links rare variants to male-pattern hair loss

Transferability of European-derived cardiometabolic polygenic risk scores in the South Asians and their interplay with family history

A perspective on genetic and polygenic risk scores—advances and limitations and overview of associated tools

Contact Info

Product

Resources

About