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. 2015 Sep 10;36(11):1314–1326. doi: 10.1093/carcin/bgv128

Table 3.

Annotation of possible ‘functional’ SNPs using lead SNP approach in (A) RegulomeDB (61) and (B) HaploReg (60)

Lead SNP chr Position (hg19) Associated gene Highest SNP in LD RegulomeDB score Position (hg19) Distance to lead SNP (bp)
(A)
rs6448050 (discovery) 4 21407759 KCNIP4 rs59708864 3a 21368138 39621
rs9799795 (fine mapping) 4 20831806 KCNIP4 rs923673 1f 20791197 40609
rs11662168 (discovery) 18 30026392 GAREM rs7237059 2b 29958797 67595
rs3786309 (fine mapping) 18 29972774 GAREM rs73956883 2b 29913973 58801
Lead SNP chr Position (hg19) Associated gene Highest SNP in LD HaploReg results Position (hg19) Distance to lead SNP (bp)
(B)
rs6448050 (discovery) 4 21407759 KCNIP4 rs17462464 16 regulatory motifs altered 21383391 24368
rs9799795 (fine mapping) 4 20831806 KCNIP4 rs11727733 Foxp3 regulatory motif altered 20837717 5911
rs11662168 (discovery) 18 30026392 GAREM rs10468834 p53 regulatory motif altered 30029959 3567
rs3786309 (fine mapping) 18 29972774 GAREM rs3786309 RXRA motif altered

Regulome scores correspond to 1f, eQTL + TF binding/DNase peak; 2a, TF binding + matched TF motif + matched DNase Footprint + DNase peak; 2b, TF binding + any motif + DNase Footprint + DNase peak; 3a, TF binding + any motif + DNase peak. Table 3B highlights the variants that may impact the function of genes known to be relevant in lung cancer development and/or progression. For rs3786309, HaploReg did not yield any functional SNPs in their LD algorithm.