Prioritization of therapeutic targets for cancers using integrative multi-omics analysis

Table 2 Druggable genes significantly associated with cancers (SMR significant and coloc > 0.8)

Type	Gene	SMR					Coloc
Type	Gene	topSNP	eQTL beta	SMR beta	OR (95% CI)	P val	PP.H4
Breast cancer	APOBEC3A	rs12628403	− 0.74	− 0.17	0.85 (0.80–0.89)	1.51E−09	1.00
Breast cancer	NEK10	rs62255653	− 0.06	− 1.55	0.21 (0.11–0.40)	1.69E−06	0.87
Lung cancer	GPX1	rs11130203	− 0.11	0.85	2.35 (1.63–3.39)	5.42E−06	0.92
Kidney cancer	CASP9	rs4233533	0.33	− 0.42	0.66 (0.54–0.79)	1.32E−05	0.94
Gastric cancer	THBS3	rs760077	− 0.56	0.41	1.50 (1.30–1.73)	3.30E−08	0.99

We perform SMR analysis and colocalization analysis to identify druggable genes significantly associated with cancers. The cis-eQTLs within 1 Mb on either side of the encoded gene extracted from the eQTLGen Consortium were used in SMR analysis. HEIDI tests and Bayesian colocalization were further conducted to assess the impact of pleiotropy. SMR analysis and colocalization analysis, eQTL expression quantitative trait loci, OR odds ratio, PP.H4 posterior probability that two traits are associated with a single causal variant, SNP single nucleotide polymorphism

ISSN: 1479-7364