Literature & web-database research: PubMed, Google Scholar, GWAS Catalog, PhenoScanner, Open Targets.

Fine-mapping: FINEMAP, SuSiE, SuSiE derivatives.

Trans-ethnic and cross-population studies: mvSuSiE, meSuSiE, SuSiEx.

• bioinformatics tools: VEP, ANNOVAR,
• databases original: ENCODE, Roadmap Epigenomics, GTEx, FANTOM5, BLUEPRINT
• derived: CADD, DeepSEA, enformer, ExPecto, DanQ, Basenji, DeepCpG.

xQTL analysis for annotation: Open-Targets-Genetics, BloodeQTL, GTEx, BiosQTL, NBDC, Blueprint, FunGen-xQTL, and xQTL derived (EMS predictions).

• Enrichment analysis: MAGMA, FUMA, GREGOR
• Colocalization analysis: COLOC, fastENLOC, ColocBoost, PICCOLO.
• Transcriptome-wide association studies (TWAS): FUSION, UTMOST, pecotmr.
• Mendelian randomization (MR): SMR, pecotmr.

• Allele-specific expression (ASE) analysis: Assessing allele-specific expression of candidate variants, which can be considered as targets of ASE-QTLs, using databases such as AlleleDB and Blueprint to investigate the regulatory effects of the variants on gene expression. Tools: ASEP, WASP, QuASAR, MBASED, Cyp2d-ASE.
• Methylation peak analysis: Examining the effects of methylation QTLs (meQTLs) on DNA methylation peaks and patterns.
• Chromatin accessibility analysis: Assessing the effects of chromatin accessibility QTLs (caQTLs) on chromatin accessibility and open chromatin regions using eg ATAC-seq, DNase-seq, FAIRE-seq, ChiP-seq
• Splice site target analysis.

Chromatin interaction analysis: Hi-C, Promoter Capture Hi-C (PCHi-C), and Chromosome Conformation Capture (3C) data to identify potential target genes and regulatory elements.

In silico analysis of transcription factor binding: Assessing the allele-specific effects of candidate SNPs on transcription factor binding. eg msCENTIPEDE, DeepBind.

Deep learning-based variant function prediction: DeepSEA, ExPecto, DanQ, Basenji, DeepCpG, enformer.

SNP-to-Gene linking strategies: Utilizing SNP-to-Gene linking strategies such as Locus-to-Gene (L2G) and cis-expression Structural Gene (cS2G) to prioritize candidate causal variants at GWAS loci and identify potential target genes. Tools: FUMA, SMR, TWAS, COLOC, DEPICT, NetWAS, EpiMap.

Functional validation using CRISPR-based gene editing: Employing CRISPR/Cas9-based gene editing techniques, such as deletion and single-base mutation, in isogenic cell lines to investigate the functional consequences of the candidate variants on gene expression and cellular phenotypes. Tools: CRISPR-Cas9, CRISPRi, CRISPRa, base editors (ABE, CBE), prime editing, CRISPR screening (GeCKO, CRISPRko, CRISPRa), CRISPR-SURF.

Experimental validation using primary cells from patients: Assessing the functional effects of the candidate variants in primary cells, such as B-cells, T-cells, and monocytes, collected from individuals with the disease (e.g., SLE) and healthy controls, representing diverse sexes and ethnicities, to validate the findings from cell line experiments. Methods: FACS, magnetic cell separation, single-cell RNA-seq, ATAC-seq, ChIP-seq, bisulfite sequencing, proteomics, metabolomics.

Cellular phenotype analysis: Characterizing the effects of the candidate variants on cellular phenotypes related to the disease, such as autophagy, apoptosis, proliferation, and endosomal pH, using various experimental techniques, including qRT-PCR, Western blot, immunofluorescence, flow cytometry, and transmission electron microscopy. Tools: ImageJ, CellProfiler, FlowJo, GraphPad Prism, R, Python.