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Genome-wide meta-analysis, fine-mapping and integrative prioritization implicate new Alzheimer’s disease risk genes

An Author Correction to this article was published on 26 February 2021

This article has been updated

Abstract

Genome-wide association studies have discovered numerous genomic loci associated with Alzheimer’s disease (AD); yet the causal genes and variants are incompletely identified. We performed an updated genome-wide AD meta-analysis, which identified 37 risk loci, including new associations near CCDC6, TSPAN14, NCK2 and SPRED2. Using three SNP-level fine-mapping methods, we identified 21 SNPs with >50% probability each of being causally involved in AD risk and others strongly suggested by functional annotation. We followed this with colocalization analyses across 109 gene expression quantitative trait loci datasets and prioritization of genes by using protein interaction networks and tissue-specific expression. Combining this information into a quantitative score, we found that evidence converged on likely causal genes, including the above four genes, and those at previously discovered AD loci, including BIN1, APH1B, PTK2B, PILRA and CASS4.

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Fig. 1: Analysis overview.
Fig. 2: Colocalization with eQTLs.
Fig. 3: Fine-mapping summary.
Fig. 4: Fine-mapped variants.
Fig. 5: Genome-wide network and gene expression enrichments.
Fig. 6: Gene evidence summary.

Data availability

Summary statistics from the meta-analysis are available through the National Human Genome Research Institute-European Bioinformatics Institute GWAS catalog under accession nos. GCST90012877 and GCST90012878 (https://www.ebi.ac.uk/gwas/downloads/summary-statistics). The eQTL Catalogue is available at http://www.ebi.ac.uk/eqtl/. The GTEx Portal is available at https://www.gtexportal.org/home/. The Roadmap Epigenomics is available at http://www.roadmapepigenomics.org/. DeepSEA is available at http://deepsea.princeton.edu/job/analysis/create/. SpliceAI is available at https://github.com/Illumina/SpliceAI. FANTOM5 enhancers are available at https://fantom.gsc.riken.jp/5/data/. GERP is available at hgdownload.cse.ucsc.edu/gbdb/hg19/bbi/All_hg19_RS.bw. PhyloP is available at hgdownload.cse.ucsc.edu/goldenpath/hg19/phyloP100way. PhastCons is available at hgdownload.cse.ucsc.edu/goldenpath/hg19/phastCons100way. The brain eQTL meta-analysis summary statistics are avilable at https://www.synapse.org/#!Synapse:syn16984815. Primary microglia eQTL summary statistics are available under EGA accession no. EGAD00001005736. Primary microglia ATAC-seq data are available under dbGaP accession no. phs001373.v1.p1. The Allen Brain Institute is available at http://portal.brain-map.org/atlases-and-data/rnaseq. The IntAct molecular interaction database is available at https://www.ebi.ac.uk/intact/. The BioGRID database is available at https://thebiogrid.org/. The STRING database is available at https://string-db.org/.

Code availability

The code for the analyses described in this article can be found at https://github.com/jeremy37/AD_finemap.

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Acknowledgements

This work was funded by Open Targets (OTAR037). We thank J. Barrett for guidance during the initiation of the project and K. Alasoo for early access to the eQTL Catalogue. We thank A. Ruiz for support in using the summary results from the GR@ACE study. We thank the participants and investigators of the FinnGen study and UK Biobank. R.J.M.F. is supported by grants from the UK Multiple Sclerosis Society (MS 50), the Adelson Medical Research Foundation and a core support grant from the Wellcome Trust and Medical Research Council (MRC) to the Wellcome-MRC Cambridge Stem Cell Institute (no. 203151/Z/16/Z). A.M.H.Y. is supported by a Wellcome Trust PhD for Clinicians fellowship.

Author information

Authors and Affiliations

Authors

Contributions

J.S. planned and conducted the analyses and wrote the paper. J.Z.L. performed the GWAX and meta-analysis. S.C. and E.B. assisted with fine-mapping, variant and gene prioritization. I.B.-H. and P.B. performed and supervised the gene network analysis. R.J.M.F. designed and A.M.H.Y. performed the isolation of human microglia from brain biopsies. N.K. performed the microglia eQTL mapping. A.B., T.J., D.J.G. and K.E. conceived and supervised the study.

Corresponding authors

Correspondence to Jeremy Schwartzentruber or Andrew Bassett.

Ethics declarations

Competing interests

J.Z.L. was an employee of Biogen at the time of the study and is now an employee of GSK. D.J.G. is an employee of Genomics PLC. T.J. is an employee of GSK. K.E. is an employee of BioMarin Pharmaceutical.

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Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Association of AD loci in discovery + replication (‘global’) meta-analysis.

Association of AD loci in discovery + replication dataset (‘global’) meta-analysis. For most loci, association significance is increased in the global meta-analysis (blue bars) relative to the discovery analysis (grey bars). The dashed vertical line shows P = 5 × 10−8. P-values were computed by inverse variance weighted meta-analysis, and bars show the -log10(P) for the SNP with minimum P value at the locus in either the discovery or global meta-analysis.

Extended Data Fig. 2 Comparison of fine-mapping in the meta-analysis vs. Kunkle et al.

Comparison of fine-mapping in the meta-analysis vs. Kunkle et al. Scatterplots showing, for each locus, SNP probabilities from FINEMAP applied to either the Kunkle et al. + UK Biobank meta-analysis (x-axis), or to only Kunkle et al. The number of causal variants at each locus was set to the number detected by GCTA in the meta-analysis. For most of the 36 loci, SNP probabilities are well correlated. For a few loci that are well powered in Kunkle et al., this is not the case, namely ABCA7, EPHA1, ECHDC3, and HLA. For these loci, fine-mapping results should be interpreted with caution. Six other loci are not well correlated (ADAMTS4, APH1B, IKZF1, PLCG2, TMEM163, and VKORC1), but these loci are poorly powered in Kunkle et al. (lead P values 2.1 × 10−6 to 2.1 × 10−3).

Extended Data Fig. 3 Network enrichment.

a, The Pagerank percentile of all genes (within 500 kb) at each AD GWAS locus containing a seed gene is shown, with seed genes highlighted in blue. b, A violin/boxplot shows that seed genes have a markedly higher network Pagerank percentile than remaining genes (P = 2.4 × 10−9, one-tailed Wilcoxon rank sum test). c, Log odds ratio enrichment of AD risk among SNPs nearest to genes with network Pagerank percentile in different bins, determined using fgwas (whiskers represent 95% confidence intervals).

Extended Data Fig. 4 Gene expression enrichments.

Expression enrichments for GTEx + microglia. Shown are the log odds ratio enrichments of AD risk among SNPs with relative gene expression in each tissue above the 80th (or 90th) percentile across tissues. Whiskers represent 95% confidence intervals determined by fgwas.

Extended Data Fig. 5 Colocalization scores.

a, Genes with maximum colocalization H4 probability >0.9 have higher Pagerank percentile (left boxplot) and higher total score (sum of the four non-coloc predictors, right boxplot) than do genes without colocalisation (<0.5). Genes with intermediate colocalisation evidence (bins 0.5 - 0.8 and 0.8 - 0.9) show little evidence of having higher scores by the other metrics. Based on this, we chose a maxColoc probability of 0.9 as the lower bound for our colocalization score. b, Boxplot of the total score (excluding coloc) for genes that have a colocalisation probability > 0.9 in at least one QTL dataset within each tissue group. The most significant difference is between totalScore for genes with microglial colocalizations vs. the genes with colocalization in ‘other’ tissues (non-immune GTEx tissues), but the difference is weak (P = 0.041, Wilcoxon rank sum test). In all cases, boxplots show the 25th, median, and 75th percentile of the distribution, with whiskers extending to the largest (and smallest) value no further than 1.5 times the interquartile range from the boxplot hinge.

Extended Data Fig. 6 Gene distance score.

The distance score assigned to genes near an AD GWAS peak, which decreases approximately linearly (past a distance of 1 kb) with increasing log-scaled distance up to 500 kb.

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Schwartzentruber, J., Cooper, S., Liu, J.Z. et al. Genome-wide meta-analysis, fine-mapping and integrative prioritization implicate new Alzheimer’s disease risk genes. Nat Genet 53, 392–402 (2021). https://doi.org/10.1038/s41588-020-00776-w

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