CAUSALdb: a database for disease/trait causal variants identified using summary statistics of genome-wide association studies

Abstract

Genome-wide association studies (GWASs) have revolutionized the field of complex trait genetics over the past decade, yet for most of the significant genotype-phenotype associations the true causal variants remain unknown. Identifying and interpreting how causal genetic variants confer disease susceptibility is still a big challenge. Herein we introduce a new database, CAUSALdb, to integrate the most comprehensive GWAS summary statistics to date and identify credible sets of potential causal variants using uniformly processed fine-mapping. The database has six major features: it (i) curates 3052 high-quality, fine-mappable GWAS summary statistics across five human super-populations and 2629 unique traits; (ii) estimates causal probabilities of all genetic variants in GWAS significant loci using three state-of-the-art fine-mapping tools; (iii) maps the reported traits to a powerful ontology MeSH, making it simple for users to browse studies on the trait tree; (iv) incorporates highly interactive Manhattan and LocusZoom-like plots to allow visualization of credible sets in a single web page more efficiently; (v) enables online comparison of causal relations on variant-, gene- and trait-levels among studies with different sample sizes or populations and (vi) offers comprehensive variant annotations by integrating massive base-wise and allele-specific functional annotations. CAUSALdb is freely available at http://mulinlab.org/causaldb.

Universal annotation of the human genome through integration of over a thousand epigenomic datasets

“…We also analyzed the full-stack state enrichments for fine-mapped variants previously generated from a large collection of GWAS studies from the UK Biobank database and other public databases [52].…”

Turning genome-wide association study findings into opportunities for drug repositioning

“…‘tag’ SNPs) are usually chosen [26] . Tools for fine-mapping include, for example, FINEMAP [39] , PAINTOR [40] , CAVIAR [41] , CAVIAR Bayes factor [42] and more recently CAUSALdb [43] . The second step involves in silico methods for annotation and characterization of the functional impact of identified SNPs.…”

“…The project employs a variety of techniques including RNA-seq, DNase-seq, FAIRE-seq and ChIP-seq etc., which provide very rich data for functional annotation. Other useful resources or tools for functional annotation include modENCODE ( modencode.org ), the NIH Roadmap Epigenomics Project, GWAS3D [44] and its successor GWAS4D [45] , HaploReg [46] , Mutation Enrichment Gene set Analysis of Variants (MEGA-V) [47] , SNPinfo [48] , FUMA [49] and CAUSALdb [43] . One point to note is that the genes closest to the associated SNP may not necessarily be the most functionally relevant gene; a recent study suggested that the likely causative genes are often >2Mbp from the index SNP [50] .…”

Addiction-associated genetic variants implicate brain cell type- and region-specific cis-regulatory elements in addiction neurobiology

“…We limited ourselves SNPs that overlapped Fullard et al NeuN+ OCRs (Fullard et al, 2018) since nucleotide variants in these peaks may disrupt epigenomic DNA sequences measured by ATAC-seq. We also limited ourselves SNPs that are fine-mapped and predicted to be causal by CAUSALdb using the European LD structure and an ensemble of statistical fine-mapping tools (FINEMAP, CAVIARBF, PAINTOR) (Chen et al, 2015;Benner et al, 2016;Kichaev et al, 2017;Wang et al, 2020). Combining these two filtering heuristics, being in NeuN+ OCRs and fine-mapped putatively causal, narrowed us down to 170 SNPs over 54 loci to be further refined for cell typespecific activity.…”

“…Addiction-associated genetic variants from the seven GWAS explored in this study further annotated by FUMA (Watanabe et al, 2017), CAUSALdb (Wang et al, 2020), overlap with NeuN+ OCRs (Fullard et al, 2018) 0 1 2 3 4 0 1 2 3 4 0 1 2 3 4 0 1 2 3 4 0 1 2 3 4 0 1 2 3 4 0 1 2 3…”