Using LDpred2 to adapt polygenic risk score techniques for methylation score creation
Abstract Objective This study sought to determine if the R package LDpred2, designed for polygenic risk score creation for genome-wide association studies using summary statistics, could be adapted for deriving DNA methylation scores from methylome-wide association studies. Recognizing that linkage...
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BMC
2025-04-01
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| Series: | BMC Research Notes |
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| Online Access: | https://doi.org/10.1186/s13104-025-07222-2 |
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| author | Kristoffer Sandås Leticia Spindola Solveig Løkhammer Anne-Kristin Stavrum Ole Andreassen Markos Tesfaye Stéphanie Le Hellard |
| author_facet | Kristoffer Sandås Leticia Spindola Solveig Løkhammer Anne-Kristin Stavrum Ole Andreassen Markos Tesfaye Stéphanie Le Hellard |
| author_sort | Kristoffer Sandås |
| collection | DOAJ |
| description | Abstract Objective This study sought to determine if the R package LDpred2, designed for polygenic risk score creation for genome-wide association studies using summary statistics, could be adapted for deriving DNA methylation scores from methylome-wide association studies. Recognizing that linkage disequilibrium, used as prior in LDpred2, does not apply to methylation, we explored co-methylated regions and topologically associating domains as alternative structural priors for correlation between methylation sites. A genomic sliding-window approach was also tested. The performance of the LDpred2-based models was evaluated on methylation data from schizophrenia and control samples (N = 1,227). Results LDpred2 models employing topologically associating domains and sliding window clusters as priors performed similarly to existing methods, explaining approximately 3.6% of schizophrenia phenotypic variance. The co-methylated regions model underperformed due to insufficient clustering of probes. The similarity in performance between the model using topologically associating domains and a null model consisting of random clusters suggests that the structural information provided by these domains enhances performance only marginally. In conclusion, while LDpred2 can be adapted for methylation data, it does not substantially enhance methylation score performance over existing methods, and the choice of structural prior may not be a critical factor. |
| format | Article |
| id | doaj-art-e12c3fbcdf324326bdba52ff28342ef5 |
| institution | OA Journals |
| issn | 1756-0500 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Research Notes |
| spelling | doaj-art-e12c3fbcdf324326bdba52ff28342ef52025-08-20T02:20:03ZengBMCBMC Research Notes1756-05002025-04-011811610.1186/s13104-025-07222-2Using LDpred2 to adapt polygenic risk score techniques for methylation score creationKristoffer Sandås0Leticia Spindola1Solveig Løkhammer2Anne-Kristin Stavrum3Ole Andreassen4Markos Tesfaye5Stéphanie Le Hellard6Department of Clinical Science, University of BergenDr. Einar Martens Research Group for Biological Psychiatry, Department of Medical Genetics, Haukeland University HospitalDepartment of Clinical Science, University of BergenDepartment of Clinical Science, University of BergenCenter for Precision Psychiatry, Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of OsloDr. Einar Martens Research Group for Biological Psychiatry, Department of Medical Genetics, Haukeland University HospitalDepartment of Clinical Science, University of BergenAbstract Objective This study sought to determine if the R package LDpred2, designed for polygenic risk score creation for genome-wide association studies using summary statistics, could be adapted for deriving DNA methylation scores from methylome-wide association studies. Recognizing that linkage disequilibrium, used as prior in LDpred2, does not apply to methylation, we explored co-methylated regions and topologically associating domains as alternative structural priors for correlation between methylation sites. A genomic sliding-window approach was also tested. The performance of the LDpred2-based models was evaluated on methylation data from schizophrenia and control samples (N = 1,227). Results LDpred2 models employing topologically associating domains and sliding window clusters as priors performed similarly to existing methods, explaining approximately 3.6% of schizophrenia phenotypic variance. The co-methylated regions model underperformed due to insufficient clustering of probes. The similarity in performance between the model using topologically associating domains and a null model consisting of random clusters suggests that the structural information provided by these domains enhances performance only marginally. In conclusion, while LDpred2 can be adapted for methylation data, it does not substantially enhance methylation score performance over existing methods, and the choice of structural prior may not be a critical factor.https://doi.org/10.1186/s13104-025-07222-2Methylation scoresSchizophreniaMethylome-wide association studiesSummary statisticsLDpred2Polygenic risk score |
| spellingShingle | Kristoffer Sandås Leticia Spindola Solveig Løkhammer Anne-Kristin Stavrum Ole Andreassen Markos Tesfaye Stéphanie Le Hellard Using LDpred2 to adapt polygenic risk score techniques for methylation score creation BMC Research Notes Methylation scores Schizophrenia Methylome-wide association studies Summary statistics LDpred2 Polygenic risk score |
| title | Using LDpred2 to adapt polygenic risk score techniques for methylation score creation |
| title_full | Using LDpred2 to adapt polygenic risk score techniques for methylation score creation |
| title_fullStr | Using LDpred2 to adapt polygenic risk score techniques for methylation score creation |
| title_full_unstemmed | Using LDpred2 to adapt polygenic risk score techniques for methylation score creation |
| title_short | Using LDpred2 to adapt polygenic risk score techniques for methylation score creation |
| title_sort | using ldpred2 to adapt polygenic risk score techniques for methylation score creation |
| topic | Methylation scores Schizophrenia Methylome-wide association studies Summary statistics LDpred2 Polygenic risk score |
| url | https://doi.org/10.1186/s13104-025-07222-2 |
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