Table 2.

Commonly employed computational and analytical tools in genome-wide association studies (GWAS) and sc/snRNA-seq studies.

Tool	Full Name	Analysis	Feature	Ref.
ALIGATOR	Association List Go Annotator	Pathway analysis tool for GWAS data	Adjust for common genomic confounding factors using well-controlled type I error	[93]
CytoScape	CytoScape	Visualization tool for network and pathway findings	Visualize results for network structure analyses, network clustering, hotspot detection, and functional enrichment	[31,94]
DAPPLE	Disease Association Protein-Protein Link Evaluator	Network-assisted analysis tool for prioritizing GWAS results	Find physical connectivity among proteins encoded by genes in loci associated with disease	[95]
DAVID	Database for Annotation, Visualization, and Integrated Discovery	Pathway analysis tool high-throughput gene-based data	Facilitate functional annotation and analysis of any given list of genes	[96]
DEPICT	Data-Driven Expression-Prioritized Integration for Complex Traits	Integrative GWAS analysis tool	Prioritize most likely causal genes using both established annotations and gene expression data	[97]
GCTA	Genome-Wide Complex Trait Analysis	SNP-based heritability analysis	Estimate the proportion of phenotypic variance explained by whole-genome genotype data	[101]
INRICH	Interval Enrichment Analysis	Pathway analysis tool for GWAS data	Detect enriched association signals of LD-independent genomic regions within biologically relevant gene sets	[98]
LDAK	Linkage Disequilibrium Adjusted Kinships	SNP-based heritability analysis	Create kinship matrices take into account LD between genotype markers	[102]
LDregress	LDregress 1	SNP-based heritability analysis	Adjust for LD between genotype markers using regression	[103]
LDSC	LD Score Regression	SNP-based heritability analysis	Use association summary statistics instead of genotype data	[104]
MAGMA	Multi-Marker Analysis of Genomic Annotation	Gene- and generalized gene-set analysis for GWAS data	Analyze both raw genotype data and summary SNP p-values from a previous GWAS or meta-analysis	[99]
MEGHA	Massively Expedited Genome-Wide Heritability Analysis	SNP-based heritability analysis	Estimate measures of heritability with several orders of magnitude less time than existing methods	[105]
WGCNA	Weighted Gene Co-Expression Network Analysis	Gene-expression data analysis	Find clusters of highly correlated genes and enriched biology or functions using module eigengenes or intramodular hub genes	[100]

¹ It is implemented in the EIGENSOFT software.