Multiple Common Susceptibility Variants near BMP Pathway Loci GREM1, BMP4, and BMP2 Explain Part of the Missing Heritability of Colorectal Cancer

Abstract

Genome-wide association studies (GWAS) have identified 14 tagging single nucleotide polymorphisms (tagSNPs) that are associated with the risk of colorectal cancer (CRC), and several of these tagSNPs are near bone morphogenetic protein (BMP) pathway loci. The penalty of multiple testing implicit in GWAS increases the attraction of complementary approaches for disease gene discovery, including candidate gene- or pathway-based analyses. The strongest candidate loci for additional predisposition SNPs are arguably those already known both to have functional relevance and to be involved in disease risk. To investigate this proposition, we searched for novel CRC susceptibility variants close to the BMP pathway genes GREM1 (15q13.3), BMP4 (14q22.2), and BMP2 (20p12.3) using sample sets totalling 24,910 CRC cases and 26,275 controls. We identified new, independent CRC predisposition SNPs close to BMP4 (rs1957636, P = 3.93×10−10) and BMP2 (rs4813802, P = 4.65×10−11). Near GREM1, we found using fine-mapping that the previously-identified association between tagSNP rs4779584 and CRC actually resulted from two independent signals represented by rs16969681 (P = 5.33×10−8) and rs11632715 (P = 2.30×10−10). As low-penetrance predisposition variants become harder to identify—owing to small effect sizes and/or low risk allele frequencies—approaches based on informed candidate gene selection may become increasingly attractive. Our data emphasise that genetic fine-mapping studies can deconvolute associations that have arisen owing to independent correlation of a tagSNP with more than one functional SNP, thus explaining some of the apparently missing heritability of common diseases.

Quantifying the Utility of Single Nucleotide Polymorphisms to Guide Colorectal Cancer Screening

“…Three SNPs within 8q24.21 (rs10505477, rs6983267 and rs7014346) had a D prime of 1.0, and based on the results of a meta-analysis [20], we excluded rs10505477 and rs7014346. Three of the SNPs are within 15q31 (rs11632715, rs16969681 and rs4779584), but because only two were associated with colorectal cancer in a multivariable model [21], we excluded rs4779584. Two of the SNPs are within 10q24.2 (rs1035209, rs11190164) with a D prime of 0.9, therefore, we excluded rs1035209.…”

“…[32,34] 10p13 CUBN rs10904849 1.14 0.68 1.0037 0.46% [22] 10p14 GATA3 rs10795668 1.12 0.67 1.0028 0.35% [31] 10q22.3 ZMIZ1; AS1 rs704017 1.06 0.57 1.0008 0.10% [17] 10q24.2 SLC25A28; ENTPD7; COX15; CUTC; ABCC2 rs11190164 1.09 0.29 1.0015 0.19% [27] 10q25 VTI1A rs12241008 1.13 0.09 1.0012 0.15% [17] 11q12.2 FADS1; FEN1 11qhap ‡ 1.4 0.57 1.0281 3.41% [17] 11q13.4 POLD3 rs3824999 1.08 0.5 1.0015 0.18% [18] 11q23.1 COLCA2 rs3802842 1.11 0.29 1.0022 0.28% [35] 12p13.32 CCND2 rs3217810 1.2 0.16 1.0045 0.55% [23,24] 12p13.32 CCND2 rs3217901 1.1 0.41 1.0022 0.27% [23,24] 12p13.32 CCND2 rs10774214 1.09 0.38 1.0018 0.22% [30] 12q13.13 DIP2B; ATF1 rs11169552 1.09 0.72 1.0015 0.18% [25] 12q13.13 LARP4; DIP2B rs7136702 1.06 0.35 1.0008 0.10% [25] 12q24.12 SH2B3 rs3184504 1.09 0.53 1.0019 0.23% [27] 12q24.21 TBX3 rs59336 1.09 0.48 1.0019 0.23% [26] 12q24.22 NOS1 rs73208120 1.16 0.11 1.0021 0.26% [27] 14q22.2 BMP4 rs1957636 1.08 0.4 1.0014 0.18% [36] 14q22.2 BMP4 rs4444235 1.11 0.46 1.0027 0.33% [36,37] 15q13.3 SCG5; GREM1 rs11632715 1.12 0.47 1.0032 0.39% [36] 15q13.3 SCG5; GREM1 rs16969681 1.18 0.09 1.0022 0.28% [36] 16q22.1 CDH1 rs9929218 1.1 0.71 1.0019 0.23% [37] 16q24.1 FOXL1 rs16941835 1.15 0.21 1.0032 0.40% [22] 17q21 STAT3 rs744166 1.27 0.55 1.0142 1.74% [38] 18q21.1 SMAD7 rs4939827 1.18 0.52 1.0069 0.84% [35,39] 19q13.11 RHPN2 rs10411210 1.15 0.9 1.0018 0.22% [37] 19q13.2 TMEM91; TGFB1 19qhap ‡ 1.16 0.49 1.0055 0.68% [17] 20p12.3 FERMT1; BMP2 rs2423279, 1.14 0.3 1.0036 0.44% [24,30] 20p12.3 FERMT1; BMP2 rs4813802 1.09 0.36 1.0017 0.21% …”

“…On average, males reached the 1% risk threshold 3-5 years earlier, and females reached the threshold on average 1-3 years later than for males and females combined (Table 2). [36,37] 20q13.1 PREX1 rs6066825 1.09 0.64 1.0017 0.21% [27] 20q13.33 LAMA5 rs4925386 1.08 0.68 1.0013 0.16% [25] SNPs reported to be associated with colorectal cancer (independent of other SNPs) in European populations, including the SNP nomenclature, the gene(s) closest to or within the likely regulatory target of the SNPs, the reported risk allele genotype, the reported risk allele frequency in controls, the reported association with colorectal cancer per risk allele (odds ratio), the FRR attributable to the SNPs and the proportion of the log FRR due to the SNPs. † Gene/s closest to or likely regulatory target of SNP.…”

From Markers to Molecular Mechanisms: Type 1 Diabetes in the Post-GWAS Era

“…High throughput sequencing has facilitated complex-trait rare-variant association studies. In some cases, fine mapping identified multiple independent rare variants that contributed to the GWAS association between the tagging SNP and disease [144]. Ehret et al (2012) have developed a method for improving the tagging of unobserved causal variants by using a combination of SNPs to define a haplotype [145].…”

Polymorphisms in bone morphogenetic protein 3 and the risk of papillary thyroid cancer

“…BMPs have been identified to utilize the SMAD signaling pathway for their growth suppressive effects, and also to affect PTEN and p21 (WAF1) expression via RAS-ERK signaling in cancer (10)(11)(12). Additionally, previous studies have revealed that genetic variations in BMP genes are associated with a number of types of cancer (13)(14)(15). Genome-wide association studies have demonstrated associations between polymorphisms in BMP2 (13) and BMP4 (13,14) suggested that a frame-shift mutation in BMP receptor type II (BMPR2) contributes to the pathogenesis of gastric and colorectal cancers by inactivating BMPR2-mediated BMP signaling.…”