A scalable Bayesian functional GWAS method accounting for multivariate quantitative functional annotations with applications for studying Alzheimer disease
Summary: Existing methods for integrating functional annotations in genome-wide association studies (GWASs) to fine-map and prioritize potential causal variants are limited to using non-overlapped categorical annotations or limited by the computation burden of modeling genome-wide variants. To overc...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-10-01
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| Series: | HGG Advances |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666247722000604 |
| _version_ | 1811254998284107776 |
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| author | Junyu Chen Lei Wang Philip L. De Jager David A. Bennett Aron S. Buchman Jingjing Yang |
| author_facet | Junyu Chen Lei Wang Philip L. De Jager David A. Bennett Aron S. Buchman Jingjing Yang |
| author_sort | Junyu Chen |
| collection | DOAJ |
| description | Summary: Existing methods for integrating functional annotations in genome-wide association studies (GWASs) to fine-map and prioritize potential causal variants are limited to using non-overlapped categorical annotations or limited by the computation burden of modeling genome-wide variants. To overcome these limitations, we propose a scalable Bayesian functional GWAS method to account for multivariate quantitative functional annotations (BFGWAS_QUANT), accompanied by a scalable computation algorithm enabling joint modeling of genome-wide variants. Simulation studies validated the performance of BFGWAS_QUANT for accurately quantifying annotation enrichment and improving GWAS power. Applying BFGWAS_QUANT to study five Alzheimer disease (AD)-related phenotypes using individual-level GWAS data (n = ∼1,000), we found that histone modification annotations have higher enrichment than expression quantitative trait locus (eQTL) annotations for all considered phenotypes, with the highest enrichment in H3K27me3 (polycomb regression). We also found that cis-eQTLs in microglia had higher enrichment than eQTLs of bulk brain frontal cortex tissue for all considered phenotypes. A similar enrichment pattern was also identified using the International Genomics of Alzheimer’s Project (IGAP) summary-level GWAS data of AD (n = ∼54,000). The strongest known APOE E4 risk allele was identified for all five phenotypes, and the APOE locus was validated using the IGAP data. BFGWAS_QUANT fine-mapped 32 significant variants from 1,073 genome-wide significant variants in the IGAP data. We also demonstrated that the polygenic risk scores (PRSs) using effect size estimates by BFGWAS_QUANT had a similar prediction accuracy as other methods assuming a sparse causal model. Overall, BFGWAS_QUANT is a useful GWAS tool for quantifying annotation enrichment and prioritizing potential causal variants. |
| first_indexed | 2024-04-12T17:17:06Z |
| format | Article |
| id | doaj.art-a5c6de13fd654a4487fd2c319b48e6e9 |
| institution | Directory Open Access Journal |
| issn | 2666-2477 |
| language | English |
| last_indexed | 2024-04-12T17:17:06Z |
| publishDate | 2022-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | HGG Advances |
| spelling | doaj.art-a5c6de13fd654a4487fd2c319b48e6e92022-12-22T03:23:37ZengElsevierHGG Advances2666-24772022-10-0134100143A scalable Bayesian functional GWAS method accounting for multivariate quantitative functional annotations with applications for studying Alzheimer diseaseJunyu Chen0Lei Wang1Philip L. De Jager2David A. Bennett3Aron S. Buchman4Jingjing Yang5Department of Epidemiology, Emory University School of Public Health, Atlanta, GA 30322, USA; Center for Computational and Quantitative Genetics, Department of Human Genetics, Emory University School of Medicine, Atlanta, GA 30322, USACenter for Computational and Quantitative Genetics, Department of Human Genetics, Emory University School of Medicine, Atlanta, GA 30322, USA; Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USACenter for Translational and Computational Neuroimmunology, Department of Neurology and Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Columbia University Irving Medical Center, New York, NY 10032, USARush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL 60612, USARush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL 60612, USACenter for Computational and Quantitative Genetics, Department of Human Genetics, Emory University School of Medicine, Atlanta, GA 30322, USA; Corresponding authorSummary: Existing methods for integrating functional annotations in genome-wide association studies (GWASs) to fine-map and prioritize potential causal variants are limited to using non-overlapped categorical annotations or limited by the computation burden of modeling genome-wide variants. To overcome these limitations, we propose a scalable Bayesian functional GWAS method to account for multivariate quantitative functional annotations (BFGWAS_QUANT), accompanied by a scalable computation algorithm enabling joint modeling of genome-wide variants. Simulation studies validated the performance of BFGWAS_QUANT for accurately quantifying annotation enrichment and improving GWAS power. Applying BFGWAS_QUANT to study five Alzheimer disease (AD)-related phenotypes using individual-level GWAS data (n = ∼1,000), we found that histone modification annotations have higher enrichment than expression quantitative trait locus (eQTL) annotations for all considered phenotypes, with the highest enrichment in H3K27me3 (polycomb regression). We also found that cis-eQTLs in microglia had higher enrichment than eQTLs of bulk brain frontal cortex tissue for all considered phenotypes. A similar enrichment pattern was also identified using the International Genomics of Alzheimer’s Project (IGAP) summary-level GWAS data of AD (n = ∼54,000). The strongest known APOE E4 risk allele was identified for all five phenotypes, and the APOE locus was validated using the IGAP data. BFGWAS_QUANT fine-mapped 32 significant variants from 1,073 genome-wide significant variants in the IGAP data. We also demonstrated that the polygenic risk scores (PRSs) using effect size estimates by BFGWAS_QUANT had a similar prediction accuracy as other methods assuming a sparse causal model. Overall, BFGWAS_QUANT is a useful GWAS tool for quantifying annotation enrichment and prioritizing potential causal variants.http://www.sciencedirect.com/science/article/pii/S2666247722000604Bayesian hierarchical variable selection regressionquantitative functional annotationgenome-wide association studymolecular quantitative trait locipolygenic risk scoreAlzheimer disease |
| spellingShingle | Junyu Chen Lei Wang Philip L. De Jager David A. Bennett Aron S. Buchman Jingjing Yang A scalable Bayesian functional GWAS method accounting for multivariate quantitative functional annotations with applications for studying Alzheimer disease HGG Advances Bayesian hierarchical variable selection regression quantitative functional annotation genome-wide association study molecular quantitative trait loci polygenic risk score Alzheimer disease |
| title | A scalable Bayesian functional GWAS method accounting for multivariate quantitative functional annotations with applications for studying Alzheimer disease |
| title_full | A scalable Bayesian functional GWAS method accounting for multivariate quantitative functional annotations with applications for studying Alzheimer disease |
| title_fullStr | A scalable Bayesian functional GWAS method accounting for multivariate quantitative functional annotations with applications for studying Alzheimer disease |
| title_full_unstemmed | A scalable Bayesian functional GWAS method accounting for multivariate quantitative functional annotations with applications for studying Alzheimer disease |
| title_short | A scalable Bayesian functional GWAS method accounting for multivariate quantitative functional annotations with applications for studying Alzheimer disease |
| title_sort | scalable bayesian functional gwas method accounting for multivariate quantitative functional annotations with applications for studying alzheimer disease |
| topic | Bayesian hierarchical variable selection regression quantitative functional annotation genome-wide association study molecular quantitative trait loci polygenic risk score Alzheimer disease |
| url | http://www.sciencedirect.com/science/article/pii/S2666247722000604 |
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