Common variation near CDKN1A, POLD3 and SHROOM2 influences colorectal cancer risk

Dunlop, Malcolm G.; Dobbins, Sara E.; Farrington, Susan M.; Jones, Angela; Palles, Claire; Whiffin, Nicola; Tenesa, Albert; Spain, Sarah L.; Broderick, Peter; Ooi, Li Yin; Domingo, Enric; Smillie, Claire; Henrion, Marc; Frampton, Matthew; Martin, Lynn; Grimes, Graeme R.; Gorman, Maggie; Semple, Colin A.; Yusanne, P; Barclay, Ella; Prendergast, James; Cazier, Jean‐Baptiste; Olver, Bianca; Penegar, Steven; Lubbe, Steven; Chander, Ian; Carvajal‐Carmona, Luis G.; Ballereau, Stéphane; Lloyd, Amy; Vijayakrishnan, Jayaram; Zgaga, Lina; Rudan, Igor; Τheodoratou, Evropi; Starr, John M.; Deary, Ian J.; Kirac, Iva; Kovačević, Dujo; Aaltonen, Lauri A.; Renkonen–Sinisalo, Laura; Mecklin∥, Jukka–Pekka; Matsuda, Koichi; Nakamura, Yusuke; Okada, Yukinori; Gallinger, Steven; Duggan, David; Conti, David V.; Newcomb, Polly A.; Hopper, John L.; Jenkins, Mark A.; Schumacher, Fredrick; Casey, Graham; Easton, Douglas F.; Shah, Mitul; Pharoah, Paul D.P.; Lindblom, Annika; Liu, Tao; Smith, Christopher G.; West, Hannah; Cheadle, Jeremy P.; Midgley, Rachel; Kerr, David; Campbell, Harry; Tomlinson, Ian; Houlston, Richard S.

doi:10.1038/ng.2293

Cited by 209 publications

(167 citation statements)

References 39 publications

(43 reference statements)

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“…In a study of conventional risk factors and 10 low-penetrance risk loci, the AUC for both combined was 0.59 and for the low-penetrance markers alone, 0.57 (12). The practicality of identifying a subgroup of individuals with defined absolute risk was considered by the authors who nevertheless stated that genotype data in addition to conventional risk data are not currently good enough for individualized risk prediction.…”

Section: Discussionmentioning

confidence: 99%

“…This suggests that easy-to-find high-penetrance mutations probably do not exist or are rare. In contrast, accumulating evidence from other malignancies (10)(11)(12) and multifactorial diseases and traits (13)(14)(15)(16) suggests that the genetic predisposition often consists of a multitude of low-penetrance alleles (16,17). The first few years of GWASs appear to have amply confirmed this assumption.…”

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confidence: 87%

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Cumulative Risk Impact of Five Genetic Variants Associated with Papillary Thyroid Carcinoma

Liyanarachchi

Wójcicka

et al. 2013

Thyroid

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Background: Two recent genome-wide association studies (GWASs) identified five single nucleotide polymorphisms (SNPs; rs965513, rs944289, rs966423, rs2439302, and rs116909374) associated with papillary thyroid carcinoma (PTC). Each variant showed highly significant but moderate to low disease risk. Here we assessed the cumulative risk and predictive value of the five SNPs. Methods: We genotyped two cohorts of individuals, 747 PTC cases and 1047 controls from Ohio and 1795 PTC cases and 2090 controls from Poland. Cumulative genetic risk scores were calculated using unweighted and weighted approaches. Results: All five SNPs showed significant association with PTC. The average cumulative risk score in cases was significantly higher than in controls ( p < 2.2 · 10 Poland. An analysis was performed weighing risk alleles by effect size and assigning individuals to three weighted risk score groups, low ( £ 2), medium (2-5), and high (>5). Individuals in the high group were significantly more susceptible to PTC compared with individuals in the low group with an odds ratio of 8.7 [95% CI 5.8, 13.3] in Ohio and 4.24 [95% CI 3.10, 5.84] in Poland. Almost identical results were obtained when follicular variant PTCs and microPTCs were omitted. These five SNPs explained 11% of the familial risk of thyroid cancer in the Ohio cohort and 6% in the Polish cohort. Conclusion: As the genetic risk score increases, the risk of having PTC increases. However, the predictive power of the cumulative effect of these five variants is only moderately high and clinical use may not be feasible until more variants are detected.

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Section: Discussionmentioning

confidence: 99%

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confidence: 87%

Cumulative Risk Impact of Five Genetic Variants Associated with Papillary Thyroid Carcinoma

Liyanarachchi

Wójcicka

et al. 2013

Thyroid

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“…Two SNPs within 19q13.2 (rs1800469 and rs2241714) were reported to be perfectly correlated and could be represented by a common haplotype [17] (named here as the 19q13.2 haplotype). One SNP is on the X chromosome (rs5934683) [18] and was not included in our simulation of colorectal cancer risk for males and females combined. Two SNPs within 1q41 (rs6687758 and rs6691170) were shown to be in linkage disequilibrium with a single imputed SNP [19], and we therefore excluded rs6691170.…”

Section: Resultsmentioning

confidence: 99%

“…[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% …”

Section: Resultsmentioning

confidence: 99%

“…There was considerable overlap for the number of risk alleles for the simulated people with and without colorectal cancer (for those with colorectal cancer: median 42 [22] 1q25.3 LAMC1 rs10911251 1.05 0.54 1.0006 0.07% [23,24] 1q41 DUSP10; CICP13 rs6687758 1.09 0.2 1.0012 0.15% [25] 2q32.3 NABP1; MYO1B; SDPR rs11903757 1.06 0.36 1.003 0.37% [25,26] 3p14.1 LRIG1 rs812481 1.09 0.58 1.0018 0.22% [27] 3p22.1 RP11; CTNNB1 rs35360328 1.14 0.16 1.0023 0.29% [27] 3q26.2 MYNN; TERC rs10936599 1.08 0.75 1.0011 0.14% [25] 4q26 NDST3 rs3987 1.36 0.44 1.0235 2.87% [28] 4q32.2 FSTL5 rs35509282 1.53 0.09 1.0149 1.83% [29] 5q31.1 PITX1; H2AFY rs647161 1.11 0.67 1.0024 0.30% [30] 6p21.31 CDKN1A rs1321311 1.1 0.23 1.0016 0.20% [18] 8q23. [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% …”

Section: Resultsmentioning

confidence: 99%

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

et al. 2016

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Aim:To determine whether single nucleotide polymorphisms (SNPs) can be used to identify people who should be screened for colorectal cancer. Methods: We simulated one million people with and without colorectal cancer based on published SNP allele frequencies and strengths of colorectal cancer association. We estimated 5-year risks of colorectal cancer by number of risk alleles. Results: We identified 45 SNPs with an average 1.14-fold increase colorectal cancer risk per allele (range: 1.05-1.53). The colorectal cancer risk for people in the highest quintile of risk alleles was 1.81-times that for the average person. Conclusion: We have quantified the extent to which known susceptibility SNPs can stratify the population into clinically useful colorectal cancer risk categories.

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Mutation Frequencies in Patients With Early-Onset Colorectal Cancer

Beggs

2017

JAMA Oncol

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To the Editor The recent study by Pearlman et al 1 raises concerns. The hypothesis of the article is laudable, that is, genetic susceptibility forms a significant part of the risk in patients with early-onset (defined as younger than 50 years) colorectal cancer (CRC). However, the data reported in the article do not correlate with what is known as being relevant in the disease mechanism of CRC. 2 The authors report mutations in "nontraditional" CRC genes such as BRCA1/2, CDKN2A, PALB2, and CHEK2 in 32 of 72 patients, all of which are associated with hereditary breast cancer. 3 In fact, given that CRC is a phenomenon driven primarily by dysfunctional Wnt signaling, it is difficult to understand how these germline drivers in cell cycle and DNA repair genes (which have been extensively investigated previously, in vitro and in vivo, as potential driver genes in CRC) have any relevance to this topic; they merely inflate the relevant germline variation rate reported in this study. What the article should in fact report is that a detection rate of 10% for patients with Lynch syndrome was observed, which is above what has previously been reported in other population studies and is likely to be enriched by the selection for early-onset cancer. The observed APC mutation (APC c.3920T>A, p.I1307K mutation) is almost uniquely associated with Ashkenazi Jewish ancestry, 4 being associated with a multiple-tumor phenotype; it is not low penetrance and unlikely to be common outside the group of patients. In conclusion, this type of study is useful in that it highlights the role of genetic testing in early onset-cancer cases, but it should not be used to argue that there is a hidden pool of patients with relevant germline drivers. It is likely that further information from population-based whole-genome sequencing studies (eg, the UK 100 000 Genomes project) will reveal the complex landscape of rare, highly penetrant CRC driver genes.

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Common variation near CDKN1A, POLD3 and SHROOM2 influences colorectal cancer risk

Cited by 209 publications

References 39 publications

Cumulative Risk Impact of Five Genetic Variants Associated with Papillary Thyroid Carcinoma

Cumulative Risk Impact of Five Genetic Variants Associated with Papillary Thyroid Carcinoma

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

Mutation Frequencies in Patients With Early-Onset Colorectal Cancer

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