Bioinformatics pipeline to guide late‐onset Alzheimer's disease (LOAD) post‐GWAS studies: Prioritizing transcription regulatory variants within LOAD‐associated regions
Abstract Introduction As new late‐onset Alzheimer's disease (LOAD) genetic risk loci are identified and brain cell–type specific omics data becomes available, there is an unmet need for a bioinformatics framework to prioritize genes and variants for testing in single‐cell molecular profiling ex...
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Wiley
2022-01-01
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| Series: | Alzheimer’s & Dementia: Translational Research & Clinical Interventions |
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| Online Access: | https://doi.org/10.1002/trc2.12244 |
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| author | Michael W. Lutz Ornit Chiba‐Falek |
| author_facet | Michael W. Lutz Ornit Chiba‐Falek |
| author_sort | Michael W. Lutz |
| collection | DOAJ |
| description | Abstract Introduction As new late‐onset Alzheimer's disease (LOAD) genetic risk loci are identified and brain cell–type specific omics data becomes available, there is an unmet need for a bioinformatics framework to prioritize genes and variants for testing in single‐cell molecular profiling experiments and validation using disease models and gene editing technologies. Prior work has characterized and prioritized active enhancers located in LOAD‐genome‐wide association study (GWAS) regions and their potential interactions with candidate genes. The current study extends this work by focusing on single nucleotide polymorphisms (SNPs) within these LOAD enhancers and their impact on altering transcription factor (TF) binding. The proposed bioinformatics pipeline progresses from SNPs located in LOAD‐GWAS regions to a filtered set of candidate regulatory SNPs that have a predicted strong effect on TF binding. Methods Active enhancers within LOAD‐associated regions were identified and SNPs located in the enhancers were catalogued. SNPs that disrupt TF binding sites were prioritized and the respective TFs were filtered to include only those that were expressed in brain tissues relevant to LOAD. The TFs binding to the corresponding sequence was further confirmed by ChIP‐seq signals. Finally, the high‐priority candidate SNPs were evaluated as expression quantitative trait loci (eQTLs) in disease‐relevant tissues. Results We catalogued 61 strong enhancers in LOAD‐GWAS regions encompassing 326 SNPs and 104 TF binding sites. Seventy‐seven and 78 of the TFs were expressed in brain and monocytes, respectively, out of which 19 TF‐binding sites showed ChIP‐seq signals. Eleven SNPs were found to interrupt with TF binding out of which three SNPs were also significant eQTL. Discussion This study provides a framework to catalogue noncoding variations in enhancers located in LOAD‐GWAS loci and characterize their likelihood to perturb TF binding. The approach integrates multiple data types to characterize and prioritize SNPs for putative regulatory function using single‐cell multi‐omics assays and gene editing. |
| format | Article |
| id | doaj-art-b7bb4b8b56164a25a5c68881f5114f63 |
| institution | Kabale University |
| issn | 2352-8737 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Alzheimer’s & Dementia: Translational Research & Clinical Interventions |
| spelling | doaj-art-b7bb4b8b56164a25a5c68881f5114f632024-12-03T12:37:31ZengWileyAlzheimer’s & Dementia: Translational Research & Clinical Interventions2352-87372022-01-0181n/an/a10.1002/trc2.12244Bioinformatics pipeline to guide late‐onset Alzheimer's disease (LOAD) post‐GWAS studies: Prioritizing transcription regulatory variants within LOAD‐associated regionsMichael W. Lutz0Ornit Chiba‐Falek1Division of Translational Brain Sciences Department of Neurology Duke University Medical Center Durham North Carolina USADivision of Translational Brain Sciences Department of Neurology Duke University Medical Center Durham North Carolina USAAbstract Introduction As new late‐onset Alzheimer's disease (LOAD) genetic risk loci are identified and brain cell–type specific omics data becomes available, there is an unmet need for a bioinformatics framework to prioritize genes and variants for testing in single‐cell molecular profiling experiments and validation using disease models and gene editing technologies. Prior work has characterized and prioritized active enhancers located in LOAD‐genome‐wide association study (GWAS) regions and their potential interactions with candidate genes. The current study extends this work by focusing on single nucleotide polymorphisms (SNPs) within these LOAD enhancers and their impact on altering transcription factor (TF) binding. The proposed bioinformatics pipeline progresses from SNPs located in LOAD‐GWAS regions to a filtered set of candidate regulatory SNPs that have a predicted strong effect on TF binding. Methods Active enhancers within LOAD‐associated regions were identified and SNPs located in the enhancers were catalogued. SNPs that disrupt TF binding sites were prioritized and the respective TFs were filtered to include only those that were expressed in brain tissues relevant to LOAD. The TFs binding to the corresponding sequence was further confirmed by ChIP‐seq signals. Finally, the high‐priority candidate SNPs were evaluated as expression quantitative trait loci (eQTLs) in disease‐relevant tissues. Results We catalogued 61 strong enhancers in LOAD‐GWAS regions encompassing 326 SNPs and 104 TF binding sites. Seventy‐seven and 78 of the TFs were expressed in brain and monocytes, respectively, out of which 19 TF‐binding sites showed ChIP‐seq signals. Eleven SNPs were found to interrupt with TF binding out of which three SNPs were also significant eQTL. Discussion This study provides a framework to catalogue noncoding variations in enhancers located in LOAD‐GWAS loci and characterize their likelihood to perturb TF binding. The approach integrates multiple data types to characterize and prioritize SNPs for putative regulatory function using single‐cell multi‐omics assays and gene editing.https://doi.org/10.1002/trc2.12244Alzheimer's disease geneticsexpression analysisgenetic variant annotation and prioritizationtranscription factor binding analysis |
| spellingShingle | Michael W. Lutz Ornit Chiba‐Falek Bioinformatics pipeline to guide late‐onset Alzheimer's disease (LOAD) post‐GWAS studies: Prioritizing transcription regulatory variants within LOAD‐associated regions Alzheimer’s & Dementia: Translational Research & Clinical Interventions Alzheimer's disease genetics expression analysis genetic variant annotation and prioritization transcription factor binding analysis |
| title | Bioinformatics pipeline to guide late‐onset Alzheimer's disease (LOAD) post‐GWAS studies: Prioritizing transcription regulatory variants within LOAD‐associated regions |
| title_full | Bioinformatics pipeline to guide late‐onset Alzheimer's disease (LOAD) post‐GWAS studies: Prioritizing transcription regulatory variants within LOAD‐associated regions |
| title_fullStr | Bioinformatics pipeline to guide late‐onset Alzheimer's disease (LOAD) post‐GWAS studies: Prioritizing transcription regulatory variants within LOAD‐associated regions |
| title_full_unstemmed | Bioinformatics pipeline to guide late‐onset Alzheimer's disease (LOAD) post‐GWAS studies: Prioritizing transcription regulatory variants within LOAD‐associated regions |
| title_short | Bioinformatics pipeline to guide late‐onset Alzheimer's disease (LOAD) post‐GWAS studies: Prioritizing transcription regulatory variants within LOAD‐associated regions |
| title_sort | bioinformatics pipeline to guide late onset alzheimer s disease load post gwas studies prioritizing transcription regulatory variants within load associated regions |
| topic | Alzheimer's disease genetics expression analysis genetic variant annotation and prioritization transcription factor binding analysis |
| url | https://doi.org/10.1002/trc2.12244 |
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