TABLE 4.

Top GC-related protein-coding genes predicted by Sherlock integrative analysis.

Region	Gene	LBFa	Pb	Supporting SNPc	PGWASd	PeQTLe
Known region
1q22	THBS3	7.31	2.45 × 10–5	rs2049805	2.82 × 10–8	1.85 × 10–9
10q23.33	NOC3L	7.18	2.45 × 10–5	rs12220125	2.09 × 10–9	2.79 × 10–9
1q22	GBA	6.87	3.43 × 10–5	rs12034326	1.38 × 10–5	2.90 × 10–6
Unknown region
8q22.2	FBXO43	5.79	9.31 × 10–5	rs2453641	9.39 × 10–5	3.45 × 10–6
7p22.1	DAGLB	5.60	1.32 × 10–4	rs4724806	1.08 × 10–5	3.44 × 10–18
19p13.11	HAPLN4	4.18	7.99 × 10–4	rs2905421	4.48 × 10–5	4.62 × 10–8
19q13.43	ZNF329	4.17	8.08 × 10–4	rs157375	3.34 × 10–4	4.53 × 10–6

^aLBF (logarithm of Bayes factor) is to assess whether a gene is associated with GC through integrating the GWAS signal and eQTL. The larger LBF score represents the higher probability that the gene is associated with GC. For example, a LBF of 7.31 means that a gene is more likely [1495 times, (exp(7.31) = 1495] to be associated with GC than no association. ^bP-value from Sherlock integrative analysis. ^ceSNP with the highest LBF. ^dP-value from expression quantitative trait analysis. ^eP-value from meta-analysis of three GC GWAS datasets.