Evaluation of Bayesian Linear Regression derived gene set test methods
Comparing our method with other approaches, such as the gold standard MAGMA (Multi-marker Analysis of Genomic Annotation) approach, our BLR gene set test showed superior performance. Combining performance of our method in simulated and real phenotypes, this suggests that our BLR-based approach could more accurately identify genes and biological pathways underlying complex diseases.
Authors
- Mads Kjølby (ORCID: https://orcid.org/0000-0002-1043-6137)
- Zhonghao Bai (ORCID: https://orcid.org/0009-0005-8697-1813)
- Tahereh Gholipourshahraki (ORCID: https://orcid.org/0000-0003-0228-4177)
- Sile Hu (ORCID: https://orcid.org/0000-0002-8437-7387)
- Palle Duun Rohde (ORCID: https://orcid.org/0000-0003-4347-8656)
- Merina Shrestha (ORCID: https://orcid.org/0009-0003-2045-9564)
- Astrid Johannesson Hjelholt (ORCID: https://orcid.org/0000-0002-3114-4176)
- Peter Sørensen (ORCID: https://orcid.org/0000-0003-1213-2660)
Institutions
- Steno Diabetes Centers (DK)
- Aarhus University (DK)
- Aarhus University Hospital (DK)
- Novo Nordisk (United Kingdom) (GB)
- Aalborg University (DK)
Publication Details
- Journal
- BMC Genomics
- Published
- 2024-12-23
- DOI
- https://doi.org/10.1186/s12864-024-11026-2
- Citations
- 2
- Primary Topic
- Bioinformatics and Genomic Networks
- Type
- article
- Field-Weighted Citation Impact
- 0.47
Funders
- Novo Nordisk Fonden