| Title: | Gene-based burden scores identify rare variant associations for 28 blood biomarkers |
| Journal: | BMC Genomic Data |
| Published: | 4 Sep 2023 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/37667186/ |
| DOI: | https://doi.org/10.1186/s12863-023-01155-0 |
| URL: | https://bmcgenomdata.biomedcentral.com/counter/pdf/10.1186/s12863-023-01155-0 |
Publication 9643
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Abstract
BackgroundA relevant part of the genetic architecture of complex traits is still unknown; despite the discovery of many disease-associated common variants. Polygenic risk score (PRS) models are based on the evaluation of the additive effects attributable to common variants and have been successfully implemented to assess the genetic susceptibility for many phenotypes. In contrast, burden tests are often used to identify an enrichment of rare deleterious variants in specific genes. Both kinds of genetic contributions are typically analyzed independently. Many studies suggest that complex phenotypes are influenced by both low effect common variants and high effect rare deleterious variants. The aim of this paper is to integrate the effect of both common and rare functional variants for a more comprehensive genetic risk modeling.MethodsWe developed a framework combining gene-based scores based on the enrichment of rare functionally relevant variants with genome-wide PRS based on common variants for association analysis and prediction models. We applied our framework on UK Biobank dataset with genotyping and exome data and considered 28 blood biomarkers levels as target phenotypes. For each biomarker, an association analysis was performed on full cohort using gene-based scores (GBS). The cohort was then split into 3 subsets for PRS construction and feature selection, predictive model training, and independent evaluation, respectively. Prediction models were generated including either PRS, GBS or both (combined).ResultsAssociation analyses of the cohort were able to detect significant genes that were previously known to be associated with different biomarkers. Interestingly, the analyses also revealed heterogeneous effect sizes and directionality highlighting the complexity of the blood biomarkers regulation. However, the combined models for many biomarkers show little or no improvement in prediction accuracy compared to the PRS models.ConclusionThis study shows that rare variants play an important role in the genetic architecture of complex multifactorial traits such as blood biomarkers. However, while rare deleterious variants play a strong role at an individual level, our results indicate that classical common variant based PRS might be more informative to predict the genetic susceptibility at the population level.</p>
9 Authors
- Rana Aldisi
- Emadeldin Hassanin
- Sugirthan Sivalingam
- Andreas Buness
- Hannah Klinkhammer
- Andreas Mayr
- Holger Fröhlich
- Peter Krawitz
- Carlo Maj
1 Application
| Application ID | Title |
|---|---|
| 81202 | Association and prediction models for complex phenotypes characterized by different genetic architecture. |
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