Genetic variants associated with colorectal-cancer risk: comprehensive research synopsis, meta-analysis, and epidemiological evidence

Abstract

Objective

In the past two decades, approximately 1,000 reports have been published regarding associations between genetic variants in candidate genes and risk of colorectal cancer (CRC). Study results are inconsistent. We aim to provide a synopsis of the current understanding of genetic factors for CRC risk through systematically evaluating results from previous studies.

Design

We searched PubMed and Google Scholar to identify papers that investigated associations between genetic variants and CRC risk and published through December 25, 2012. With data from 950 papers, we conducted 910 meta-analyses for 267 genetic variants in 150 candidate genes with at least three data sources. We used Venice criteria and false-positive report probability tests to grade levels of cumulative epidemiological evidence of significant associations with CRC risk.

Results

Sixty-two variants in 50 candidate genes showed a nominally significant association with CRC risk (p<0.05). Cumulative epidemiological evidence for a significant association with CRC risk was graded strong for eight variants in five genes (APC, CHEK2, DNMT3B MLH1, and MUTYH), moderate for two variants in two genes (GSTM1 and TERT), and weak for 52 variants in 45 genes. In addition, 40 variants in 33 genes showed convincing evidence of no association with CRC risk in meta-analyses including at least 5,000 cases and 5,000 controls.

Conclusion

Approximately 4% of genetic variants evaluated to date in candidate-gene association studies showed moderate to strong cumulative epidemiological evidence of an association with CRC risk. These genetic variants, if confirmed, may explain approximately 5% of familial CRC risk.

Introduction

Colorectal cancer (CRC) is the third-most common cancer, and the second leading cause of cancer death worldwide (1). Genetic factors play an important role in CRC development (2-6). High-penetrance germline mutations in the APC, MUTYH, SMAD4, BMPR1A, STK11, and mismatch repair genes have been identified to account for about 6% of CRC cases (Table 1) (6-13). Since 2007, common genetic variants in approximately 21 loci have been identified through genome-wide association studies (GWAS) (Table 2) (14-24). GWAS-identified variants, however, are associated with weak to moderately elevated risk of CRC, and explain approximately 8% of the familial risk of CRC (20;21).

Table 1. Known high-penetrance mutations in genes contribute to familial colorectal cancer.

Gene	Variants	Hereditary syndrome	Population frequency	References
APC	Nonsense or frameshift mutations	Familial adenomatous polyposis	0.01-0.02%	6, 7
MLH1	Truncating and missense mutations	Lynch syndrome	0.10%	6, 8
MSH2	Truncating and missense mutations	Lynch syndrome	<0.1%	6, 8
MSH6	Truncating and missense mutations	Lynch syndrome	<0.05%	6, 8
PMS2	Truncating and missense mutations	Lynch syndrome	<0.05%	6, 8
STK11	Multiple mutations	Peutz-Jeghers syndrome	0.0005-0.01%	9, 10
BMPR1A	Multiple mutations	Juvenile polyposis syndrome	<0.0005%	10
SMAD4	Multiple mutations	Juvenile polyposis syndrome	<0.0005%	10
MUTYH	Nonsense and missense mutations	MUTYH-associated polyposis	<0.02%	11, 12

Table 2. Low-penetrance loci associated with colorectal-cancer risk, identified by genome-wide association studies with p<5×10^-8.

Loci	Genes *	Variants	Alleles †	MAF (%) ‡	OR (95% CI)	P value	Ethnicity	Reference
1q41	DUSP10	rs6691170	T/G	36	1.06 (1.03-1.09)	9.55 × 10-10	European	21
1q41	DUSP10	rs6687758	G/A	20	1.09 (1.06-1.12)	2.27 × 10-9	European	21
3q26.2	MYNN	rs10936599	T/C	25	0.93 (0.91-0.96)	3.39 × 10-8	European	21
5q31.1	PITX1	rs647161	A/C	31	1.11 (1.08-1.15)	1.22 × 10-10	Asian	24
6p21.31	CDKN1A	rs1321311	A/C	23	1.10 (1.07-1.13)	1.14 × 10-10	European	22
6q26-q27	SLC22A3	rs7758229	T/G	22	1.28 (1.18-1.39)	7.92 × 10-9	Asian	23
8q23.3	EIF3H	rs16892766	C/A	7	1.25 (1.19-1.32)	3.30 × 10-18	European	19
8q24.21	MYC	rs10505477	A/G	51	1.17 (1.12-1.23)	3.16 × 10-11	European	14
8q24.21	MYC	rs6983267	G/T	52	1.21 (1.15-1.27)	1.27 × 10-14	European	15
8q24.21	MYC	rs7014346	A/G	37	1.19 (1.14-1.24)	8.60 × 10-26	European	18
10p14	FLJ3802842	rs10795668	A/G	33	0.89 (0.86-0.91)	2.50 × 10-13	European	19
11q13.4	POLD3	rs3824999	A/C	50	0.93 (0.91-0.95)	3.65 × 10-10	European	22
11q23	Unknown	rs3802842	C/A	29	1.11 (1.08-1.15)	5.82 × 10-10	European	18
12p13.32	CCND2	rs10774214	T/C	35	1.09 (1.06-1.13)	3.06 × 10-8	Asian	24
12q13.13	LARP4, DIP2	rs7136702	T/C	35	1.06 (1.04-1.08)	4.02 × 10-8	European	21
12q13.3	DIP2B, ATF1	rs11169552	T/C	28	0.92 (0.90-0.95)	1.89 × 10-10	European	21
14q22.2	BMP4	rs4444235	C/T	46	1.11 (1.08-1.15)	8.10 × 10-10	European	20
15q13.3	GREM1	rs4779584	T/C	18	1.26 (1.19-1.34)	4.44 × 10-14	European	17
16q22.1	CDH1	rs9929218	A/G	29	0.91 (0.89-0.94)	1.20 × 10-8	European	20
18q21.1	SMAD7	rs4939827	C/T	48	0.85 (0.81-0.89)	1.00 × 10-12	European	16
19q13.1	RHPN2	rs10411210	T/C	10	0.87 (0.83-0.91)	4.60 × 10-9	European	20
20p12.3	HAO1	rs2423279	C/T	30	1.10 (1.06-1.14)	6.64 × 10-9	Asian	24
20p12.3	BMP2	rs961253	A/C	36	1.12 (1.08-1.16)	2.00 × 10-10	European	20
20q13.33	LAMA5	rs4925386	T/C	32	0.93 (0.91-0.95)	1.89 × 10-10	European	21
Xp22.2	SHROOM2	rs5934683	T/C	33	1.07 (1.04-1.10)	7.30 × 10-10	European	22

^*Candidate gene in the locus.

^†Minor (bold)/major alleles (per initial studies).

^‡MAF=minor allele frequency in controls.

In addition to GWAS, approximately 1,000 papers have been published over the past 25 years investigating genetic variants in candidate genes in relation to CRC risk. Because of the limitation of SNP arrays used in GWAS, many genetic variants evaluated in candidate gene association studies have not been adequately investigated in GWAS. Results from previous candidate gene studies have been inconsistent and are difficult to interpret. Most findings from candidate gene association studies cannot be replicated. Furthermore, sample size from most previous candidate gene association studies was small, so these studies often do not have adequate power to detect a true association. Meta-analysis is a useful tool to systematically evaluate available results published to date to assess evidence for a true association. By pooling data from multiple studies, meta-analysis can increase statistical power and evaluate consistency of association, a major criterion for determining causality. Recently, an interim guideline, named Venice criteria, has been used to systematically grade the cumulative evidence of genetic associations (25;26). Systematic field synopses and meta-analyses have been utilized to evaluate the association of genetic variations in candidate genes with several diseases, including Alzheimer's disease (27), schizophrenia (28), breast cancer (29), cutaneous melanoma (30), and Parkinson's disease (31). Herein, we sought to systematically collect and comprehensively evaluate all candidate-gene association studies of CRC risk, perform meta-analyses for variants with at least three independent datasets, and provide a systematic synopsis of our current understanding of the genetic basis of CRC risk.

Methods

Search strategy and selection criteria

Literature searches were conducted through a two-stage strategy (Figure 1). In Stage 1, we searched the PubMed database using key terms “(colorectal cancer OR colon cancer OR rectal cancer) AND association” before October 1, 2010. This search yielded 8,443 potentially relevant articles which were screened for eligibility by title, abstract, or full text, as necessary – 428 reports, which included 1,036 potential candidate genes, then met eligibility criteria. In Stage 2, conducted October 1, 2010 through December 25, 2012, we used four supplementary approaches to query PubMed and Google Scholar: 1) monthly database queries for “colorectal cancer” and the 1,036 gene names identified in Stage 1 such as “MTHFR”; 2) monthly queries using “colorectal cancer OR colon cancer OR rectum cancer”; 3) searching references and related articles of all gathered papers; and 4) checking previously published meta-analyses and reviews. These four searches identified 48,521 additional reports, of which 522 met our inclusion criteria, adding genetic variants in 342 additional candidate genes. In Stages 1 and 2 combined, we screened a total of 56,964 articles, identifying 945 which reported 3,603 variants in 1,378 independent candidate genes which met our criteria for further analysis.

Figure 1. Profiles of literature search, meta-analysis and evaluation of cumulative evidence.

Studies were eligible for inclusion in this meta-analysis if they met the following criteria: 1) data were published in a peer-reviewed journal in English; 2) the study used a case-control, cohort, or a cross-sectional design in human beings; 3) the study provided sufficient information for the genotypic or allelic distribution of individual variants for both CRC cases and controls, and 4) CRC cases were diagnosed by pathological and/or histological examination. We did not include in the meta-analyses the following two groups of variants: 1) high-penetrance germline mutations in known CRC susceptibility genes, and 2) risk variants identified and confirmed in recent GWAS (Table 2). When multiple publications reported on the same or overlapping data, we used the most informative or most recent publication. Only data from original published papers were included in the present analysis. All variants, regardless of their minor allele frequency (MAF), were considered for meta-analyses when genotype counts or allelic counts were provided in the original studies.

Data extraction and management

All data were extracted by two authors (XM and BZ), and disagreement was resolved by discussion. We recorded first author, year of publication, study name, geographic location of study, ethnicity, PubMed identification number, study design, sample size, mean ages of cases and controls, sample source, genes, variants, major and minor alleles, genotype counts or allelic counts for cases and controls, and Hardy-Weinberg equilibrium (HWE) in controls. Ethnicity was classified as African descendants, Asian (East Asian descent), White (European descent), or Other (including mixed), based on ethnicity of at least 80% of the study population (32). If ethnicity was not reported, we considered ethnicity of the source population where the study was conducted (32). Finally, if a report included several sources or study populations, data were extracted separately.

Statistical analysis and evaluation of cumulative evidence

Statistical analyses were performed by STATA, version 11.0. All tests were two-sided, and p<0.05 was considered statistically significant unless otherwise stated.

Summary odds ratios (ORs) with 95% confidence intervals (CIs) for alleles and genotypes, were used to assess strength of associations between genetic variants and CRC risk by the random-effects method (33). Genotype counts or allelic counts for cases and controls from each original study were used to estimate summary ORs. We did not use adjusted ORs to estimate summary ORs since inconsistent covariates were used for adjustment in original studies included in this meta-analysis. In the primary analyses, we evaluated common variants (MAF≥0.05) using additive model and rare variants (MAF<0.05) using dominant model. For some common variants, a few original studies did not provide sufficient data for analyses with additive model, and thus dominant/recessive model was applied in the primary analyses. For some specific variants, we used the conventional comparisons in original studies, like GSTM1 ‘Present/Null’, NAT2 phenotype (predicted by genetic variants) and MUTYH rs36053993 in the primary analyses. We also conducted subgroup analyses by ethnicities. Dominant and recessive models were also used to assess associations between genetic variants and CRC risk, if available. Meta-analyses were performed only for variants with at least three independent datasets. Because major and minor alleles can be reversed in populations of different ethnicities, averaged MAFs across studies might be greater than 50%. When this occurred, the minor allele among White populations was used as the minor allele in all analyses. For genetic variants other than SNPs, the less prevalent variant or trait was evaluated for associated effects unless otherwise stated. HWE among control groups in each study was assessed by Fisher's exact test to compare observed and expected genotype frequencies (34). We conducted power analysis to evaluate the statistical power of meta-analyses in detecting an association (i.e., OR=1.15) with certain allele frequency (i.e., MAF=0.10) under the additive genetic model, assuming an alpha of 0.05 (35). We calculated the proportion of the familial risk of CRC based on the formula provided by Houlston et al (20).

To determine heterogeneity, we performed Cochran's Q test (36) and calculated the I² statistic to quantify the proportion of total variation due to heterogeneity (37). Heterogeneity was considered significant if p<0.10. Generally, I² values <25% correspond to no or little heterogeneity, values 25% – 50% correspond to moderate heterogeneity, and values >50% correspond to strong heterogeneity between studies. Potential small-study bias was assessed with a modified Egger test by Harbord et al. (38). We also evaluated if there was any excess in studies with positive findings than expected using the method described by Ioannidis and Trikalinos (39). To evaluate small-study bias and excessive significant findings, we used p<0.10 as the significant level, as recommended (38;39). For variants showing statistically significant association with CRC risk, sensitivity analyses were performed to determine if the association would be lost when the first published or first positive report was excluded, or when all studies deviated from HWE in controls were excluded.

For statistically significant associations identified by meta-analyses, Venice criteria were applied to assess cumulative evidence (Webappendix notes for Venice criteria). Venice criteria details are published elsewhere (25). For amount of evidence, we did not apply this criterion for rare variants with frequency<1% since an A grade is virtually unobtainable (29). For protection from bias, we also considered GWAS results for all common SNPs (MAF≥5%). If a common variant that can be adequately tagged by GWAS chips was not identified by GWAS, that variant would be downgraded for its evidence of association with CRC risk. Cumulative epidemiological evidence of significant associations in meta-analyses were considered strong if all three grades were A, moderate if all three grades were A or B, and weak if any grade was C. We also performed false-positive report probability (FPRP) analysis to determine if a significant association can be excluded as a false-positive finding. We used the approach developed by Wacholder et al (40) to calculate FPRP for the 62 significant associations. We used prior probability of 0.05 to estimate FPRP value for each of the 62 associations based on p-value and OR obtained from meta-analysis. FPRP<0.05, 0.2≤ FPRP≤0.05, and FPRP>0.2 were considered strong, moderate, and weak evidence of true association, respectively. We upgraded cumulative evidence from moderate to strong, and from weak to moderate, if evidence of true association based on the FPRP analysis was strong. We downgraded cumulative evidence from strong to moderate, and from moderate to weak if evidence of true association was weak. For the 25 significant associations derived from subgroup analysis of different ethnicities or under dominant or recessive model, we also assessed significance based on Bonferroni corrected p-value (5.49×10^-5=0.05/910). Regardless of Venice criteria and FPRP grades, we assigned weak evidence of association credibility if p-value > 5.49×10^-5.

Results

A total of 945 articles reporting 3,603 variants in 1,378 independent genes were eligible for our analysis (Figure 1). Most of these reports (n=884, 93.5%) were published since 2000. We conducted 910 meta-analyses for 267 variants (241 common and 26 rare) in 150 genes that had at least three data sources (Figure 1). For the 267 main meta-analyses with the use of all available data, mean sample size was 9,633 (range: 519-76,991) from a mean of seven (range: 3-68) independent studies (Webappendix Table 1).

Among the main meta-analyses, 37 (13.9%) variants within 28 genes showed nominally significant association (p<0.05) for CRC risk (Table 3; Webappendix Table 2: references used; Webappendix Table 3). The 37 variants are not in linkage disequilibrium (r² < 0.1). Mean pooled sample size in the 37 meta-analyses that showed significant association was 15,912 (range: 1,730-51,971), drawn from an average of 11 independent studies (range: 3-56). Approximately 10-fold elevated risk of CRC risk showed association with MUTYH biallelic mutations. Strong associations with CRC (ORs 2.0-10.0) were detected for four rare variants (MLH1 rs121912963, OR=2.74; MLH1 rs63750447, OR=2.14; MUTYH rs34612342, OR=3.32; MUTYH rs36053993, OR=6.49). Moderate associations with CRC (ORs 1.5-2.0 or 0.50-0.67) were found for three rare variants (APC rs1801155, OR=1.96; CHEK2 rs17879961, OR=1.56; CHEK2 1100delC, OR=1.88) and two common variants (DNMT3B rs1569686, OR=0.57; MLH1 rs1800734, OR=1.51). Associations with CRC risk, ORs 0.67-1.50, were observed for the remaining 27 variants, of which most are common. Four of the 37 positive variants (MLH1 rs1800734; MUTYH biallelic mutations; CHEK2 rs17879961; DNMT3B rs1569686) showed highly significant association with CRC risk at p<5×10^-7; 13 showed association with CRC risk at p<0.01, and the remaining 20 had p<0.05 (Table 3).

Table 3. Genetic variants nominally significantly associated with colorectal-cancer risk in meta-analyses of all available data.

						Number evaluated			Colorectal-cancer risk meta-analysis					Venice criteria garde‡	False-positive report probability§	Cumulative evidence of association
Genes	Variants	Alleles *	Chromosome	Frequency (%)†	Ethinicity	Studies	Cases	Controls	Genetic models	OR (95% CI)	P value	I2 (%)	Pheterogeneity
APC	rs1801155	A/T	5	6.80	Jewish	3	804	6,188	Dominant	1.96 (1.37-2.79)	1.99×10-4	0	0.84	BAA	0.007	Strong
CHEK2	1100delC	1100delC/–	22	0.71	White	7	3,874	11,630	Dominant	1.88 (1.29-2.73)	0.001	0	0.50	×AA	0.036	Strong
CHEK2	rs17879961	C/T	22	3.91	White	6	6,042	17,051	Dominant	1.56 (1.32-1.84)	1.22×10-7	0	0.76	BAA	<0.001	Strong
CYP1A1	rs1048943	G/A	15	10.33	All	16	6,704	8,009	Additive	1.24 (1.05-1.47)	0.014	74	0.00	ACC	0.338	Weak
CYP2E1	96-bp insertion	96-bp ins/–	10	16.98	All	4	1,412	1,781	Additive	1.24 (1.03-1.49)	0.023	35	0.20	ABA	0.462	Weak
DNMT3B	rs1569686	G/T	20	16.99	All	4	1,054	1,224	Additive	0.57 (0.47-0.68)	1.86×10-9	0	0.99	BAA	<0.001	Strong
GH1	rs2665802	A/T	17	45.39	All	7	3,275	3,848	Additive	0.89 (0.80-0.99)	0.025	49	0.07	ABC	0.508	Weak
GSTM1	Present/Null	NA	1	50.64	All	56	20,552	31,419	null vs present	1.10 (1.04-1.17)	0.001	48	0.00	ABC	0.046	Moderate
GSTT1	Present/Null	NA	22	29.53	All	43	15,144	23,847	null vs present	1.15 (1.05-1.27)	0.004	68	0.00	ACC	0.144	Weak
IGFBP3	rs2854746	G/C	7	46.08	All	5	4,282	7,365	Additive	1.07 (1.01-1.14)	0.016	0	0.07	AAC	0.447	Weak
MLH1	rs121912963	C/G	3	0.43	All	3	1,412	1,508	Additive	2.74 (1.31-5.75)	0.008	15	0.31	×AC	0.231	Weak
MLH1	rs1800734	A/G	3	21.11	White	5	801	10,890	Additive	1.51 (1.34-1.69)	6.74×10-12	46	0.11	AAA	<0.001	Strong
MLH1	rs63750447	A/T	3	1.96	Asian	3	937	919	Additive	2.14 (1.12-4.12)	0.022	41	0.18	BBC	0.458	Weak
MMP1	rs1799750	2G/1G	11	39.63	All	8	1,477	1,751	Additive	0.76 (0.64-0.92)	0.004	61	0.01	ACC	0.138	Weak
MSH3	rs184967	A/G	5	15.25	White	3	5,085	7,136	Additive	1.11 (1.03-1.20)	0.005	0	0.38	AAC	0.182	Weak
MSH3	rs26279	G/A	5	28.13	White	4	5,691	7,665	Additive	1.1 (1.03-1.17)	0.006	17	0.31	AAC	0.157	Weak
MTHFD1	rs1950902	A/G	14	18.78	White	3	3,822	5,452	Additive	0.90 (0.84-0.98)	0.010	0	0.79	AAC	0.275	Weak
MUTYH	Monoallelic mutation	NA	1	1.69	White	17	25,981	18,811	Carriers vs wild homozygotes	1.17 (1.01-1.34)	0.036	0	0.84	BAC	0.546	Weak
MUTYH	Biallelic mutation	NA	1	0.01	White	17	25,981	18,811	Carriers vs wild homozygotes	10.19 (5.00-22.04)	5.30×10-10	0	0.88	×AA	<0.001	Strong
MUTYH	rs34612342	G/A	1	0.01	White	17	27,041	19,641	GG vs AA	3.32 (1.13-9.81)	0.030	0	1.00	×AA	0.533	Strong
MUTYH	rs36053993	A/G	1	0.00	White	17	26,957	19,870	AA vs GG	6.49 (2.57-1.35)	7.49×10-5	0	0.85	×AA	0.003	Strong
NAT2	Fast/slow	NA	8	47.39	All	35	11,684	15,348	Slow vs fast	0.94 (0.89-0.99)	0.023	1	0.45	AAC	0.47	Weak
NOD2	rs2066844	T/C	16	6.15	White	9	3,297	3,088	Dominant	1.35 (1.02-1.78)	0.038	34	0.14	BBC	0.581	Weak
NOD2	rs2066847	C/–	16	6.21	White	11	4,337	5,395	Dominant	1.30 (1.02-1.65)	0.032	33	0.13	BBC	0.546	Weak
PTGS1	rs5788	A/C	9	13.35	White	4	3,989	6,659	Additive	1.13 (1.04-1.22)	0.004	0	0.64	AAC	0.113	Weak
PTGS2	rs689466	G/A	1	30.34	All	9	4,076	7,610	Additive	0.88 (0.80-0.98)	0.018	56	0.02	ACC	0.405	Weak
SCD	rs7849	G/A	10	18.49	All	3	2,011	2,580	Additive	0.85 (0.73-0.98)	0.025	29	0.25	ABC	0.488	Weak
TERT	rs2736100	T/G	5	49.34	White	8	16,176	18,135	Additive	1.07 (1.04-1.1)	2.92×10-5	0	0.53	AAC	0.001	Moderate
TGFB1	rs1800469	T/C	19	38.74	All	10	4,405	5,383	Additive	0.88 (0.79-0.97)	0.013	55	0.02	ACC	0.33	Weak
TNF	rs1800629	A/G	6	13.78	All	11	2,296	2,283	Additive	1.28 (1-1.62)	0.046	71	0.00	ACC	0.625	Weak
TP73	G4C14/A4T14	NA	1	24.02	All	4	858	1,168	Additive	1.20 (1.04-1.40)	0.015	6	0.36	AAC	0.363	Weak
UBD	rs2076485	C/T	6	26.07	White	3	4,281	6,157	Additive	1.07 (1.01-1.14)	0.034	0	0.77	AAC	0.563	Weak
VDR	rs11568820	A/G	12	36.61	White	4	3,228	3,455	Dominant	1.15 (1.04-1.27)	0.005	0	0.93	AAB	0.165	Weak
VDR	rs1544410	A/G	12	38.96	All	17	11,687	12,301	Additive	0.85 (0.72-0.99)	0.040	93	93.40	ACC	0.87	Weak
VEGF	rs3025039	T/C	6	19.43	All	6	1,925	1,884	Additive	1.19 (1.04-1.37)	0.014	29	0.22	ABC	0.347	Weak
XPA	rs1800975	A/G	9	36.46	White	3	593	1,137	Additive	0.82 (0.70-0.96)	0.016	4	0.36	AAC	0.379	Weak
XPC	rs2228001	C/A	3	37.38	All	9	2,978	5,204	Additive	1.08 (1.01-1.16)	0.021	0	0.83	AAC	0.486	Weak

^*Minor alleles/major alleles (Per Caucasian); majors alleles were treated as reference alleles in the analyses;

Allelic ORs were estimated under the additive model. For dominant or recessive models, ORs were estimated for subjects who carry one or two minor alleles or subjects homozygous for the minor alleles,respectively.

^†Frequency of minor allele or effect genotype(s) in controls in primary meta-analysis

^‡Venice criteria grades are for amount of evidence, replication of the association, and protection from bias.

^§False-positive report probability (FPRP) was determined based on OR and P value of each variant from meta-analysis and a prior probability of 0.05.

Cumulative epidemiological evidence as graded by combination of results from Venice criteria and FPRP for association with colorectal-cancer risk.

Of the 267 meta-analyses of all available data, 120 (44.9%) had little or no heterogeneity, 43 (16.1%) had moderate heterogeneity, and 104 (39.0%) had strong heterogeneity. The proportion of studies with strong heterogeneity was significantly lower for the 37 positive variants (Table 3) than the remaining 230 variants (19% vs 42%, Fisher's exact p < 0·01). Small-study bias was detected for 36 variants (13.5%), of which seven were positive variants. Of the 267 variants, 38 (14.2%) showed evidence of excess studies with significant findings including four positive variants. When considering all studies included in 267 meta-analyses as a whole, the number of studies with significant findings was also greater than that expected (666 vs 301, p < 0.0001).

In sensitivity analyses, nine SNPs (rs7849, rs1800469, rs3025039, rs1048943, rs689466, rs1544410, rs2854746, rs1800629, G4C14/A4T14) became non-significant after exclusion of HWE-violating studies, and 13 variants (rs2854746, rs121912963, rs63750447, rs26279, rs1950902, MUTYH monoallelic mutation, NAT2 Fast/slow, rs2066844, rs2066847, rs1800629, G4C14/A4T14, rs2076485, rs1544410) became non-significant after exclusion of the first positive or first published report.

We next calculated FPRP value at the prior probability, 0.05, to evaluate the probability of true association with CRC risk for the 37 positive variants from the main analyses. Associations with CRC risk had a FPRP value <0.05 for nine variants in seven genes (APC rs1801155, CHEK2 1100delC and rs17879961, DNMT3B rs1569686, GSTM1 deletion, MLH1 rs1800734, MUTYH biallelic mutations, rs36053993, TERT rs2736100), FPRP 0.05-0.2 for 6 variants in 5 genes (GSTT1 deletion, MMP1 rs1799750, MSH3 rs184967 and rs26279, PTGS1 rs5788, VDR rs11568820), and FPRP > 0.2 for the remaining 22 variants (Table 3).

Epidemiological credibility of significant associations was graded for the 37 positive variants identified through the main analyses (Table 3 and Webappendix Table 3). We first applied Venice criteria. Grades of A were given to 25, 22, and 9 meta-analyses for amount of evidence, replication of association, and protection from bias, respectively. Grades of B were given to 7, 8, and 1 meta-analyses for amount of evidence, replication of association, and protection from bias, respectively. Grades of C were given to 0, 7, and 27 meta-analyses for these three criteria, respectively. Next, strong, moderate, and weak for evidence of true association with CRC risk were assigned to 9, 6, and 22 variants, respectively, based on FPRP. For MUTYH rs34612342, we disregarded FPRP value (FPRP=0.533) when evaluating cumulative evidence because this mutation is pathogenic and has strong evidence to increase the risk of developing multiple adenomatous polyps and colorectal cancer (41). Altogether, eight variants in five genes (APC rs1801155, CHEK2 1100delC and rs17879961, DNMT3B rs1569686, MLH1 rs1800734, MUTYH biallelic mutations, rs34612342, rs36053993), were graded strong for evidence of association with CRC risk using combined Venice criteria and FPRP results. Two variants (GSTM1 Present/Null, TERT rs2736100) scored moderate for evidence of association with CRC risk. The remaining 27 variants scored C in one or more Venice criteria or were downgraded due to high FPRP. These variants were graded weak for cumulative evidence of association with CRC risk, based on combined Venice criteria and FRPR results.

Next, we performed stratified meta-analyses by ethnicity for 207 variants among Whites and 34 variants among Asians (Webappendix Table 5) and identified eight additional variants from eight genes to be nominally associated with CRC risk (p<0.05, Table 4 and Webappendix Table 3). Six of them (rs16260, rs28362491, rs1800566, rs1052133, rs1801394, rs7903146) were associated with CRC risk only in Whites; the other two (rs20417, rs1042522) were associated with CRC risk only in Asians. We also performed meta-analyses using dominant and recessive models to evaluate associations of genetic variants with CRC risk, identifying 17 additional variants across 17 genes showing significant association, although none were statistically significant in additive model (Table 5, and Webappendix Table 4). Similar to the 37 positive variants identified in the main analyses, we applied Venice criteria and FRRP to evaluate these 25 variants. We also considered Bonferroni corrected p-value. All were graded weak for cumulative evidence of association with CRC risk.

Table 4. Genetic variants nominally significantly associated with colorectal-cancer risk identified from additional analyses by ethnic group in additive model.

						Number evaluated			Colorectal-cancer risk meta-analysis				Venice criteria garde‡	False-positive report probability§	Cumulative evidence of association
Genes	Variants	Alleles *	Chromosome	MAF (%)†	Ethinicity	Studies	Cases	Controls	OR (95% CI)	P value	I2 (%)	Pheterogeneity
CDH1	rs16260	A/C	16	28.02	White	6	6,761	6,646	0.93 (0.87-1.00)	0.048	23	0.26	AAC	0.642	Weak
MTRR	rs1801394	A/G	5	44.65	White	10	6,430	9,746	0.98 (0.93-1.02)	0.030	4	0.41	AAC	0.535	Weak
NQO1	rs1800566	T/C	16	17.88	White	8	6,293	6,566	1.09 (1.03-1.16)	0.006	0	0.50	AAC	0.183	Weak
OGG1	rs1052133	G/C	3	21.59	White	14	5,908	7,355	1.15 (1.01-1.32)	0.033	74	0.00	ACC	0.558	Weak
PTGS2	rs20417	C/G	1	2.76	Asian	4	1,285	3,040	1.44 (1.06-1.95)	0.019	28	0.25	BBC	0.420	Weak
NFKB1	rs28362491	–/ATTG	4	41.05	White	6	1,199	3,134	1.29 (1.11-1.50)	0.001	55	0.05	ACB	0.036	Weak
TCF7L2	rs7903146	T/C	10	29.08	White	3	1,960	14,290	1.12 (1.02-1.22)	0.015	0	0.47	AAC	0.335	Weak
TP53	rs1042522	C/G	17	37.15	Asian	8	3,993	4,943	1.14 (1.02-1.27)	0.021	60	0.02	ACC	0.430	Weak

^*Minor alleles/major alleles (Per Caucasian); majors alleles were treated as reference alleles in the analyses

Allelic ORs were estimated under the additive model. For dominant or recessive models, ORs were estimated for subjects who carry one or two minor alleles or subjects homozygous for the minor alleles,respectively.

^†MAF=minor allele frequency in controls.

^‡Venice criteria grades are for amount of evidence, replication of the association, and protection from bias.

^§False-positive report probability (FPRP) was determined based on OR and P value of each variant from meta-analysis and a prior probability of 0.05.

Cumulative epidemiological evidence as graded by combination of results from Venice criteria and FPRP for association with colorectal-cancer risk.

Table 5. Additional genetic variants nominally significantly associated with colorectal-cancer risk in meta-analyses using dominant or recessive models.

						Number evaluated			Colorectal-cancer risk meta-analysis					Venice criteria grade‡	False-positive report probability§	Cumulative evidence of association
Genes	Variants	Alleles *	Chromosome	MAF (%)†	Ethnicity	Studies	Cases	Controls	Genetic models	OR (95% CI)	P value	I2 (%)	Pheterogeneity
SELS	rs34713741	T/C	15	33.22	All	3	1,442	2,071	Dominant	1.21 (1.05-1.39)	0.008	0	0.40	AAC	0.235	Weak
SERPINE1/PAI-1	rs1799889	5G/4G	7	44.94	White	4	2,241	4,534	Dominant	0.87 (0.78-0.97)	0.014	0	0.56	AAC	0.337	Weak
EPHX1	rs2234922	G/A	1	19.38	All	13	5,329	6,700	Dominant	0.91 (0.85-0.99)	0.020	0	0.50	AAC	0.46	Weak
ERCC5/XPG	rs17655	C/G	13	24.73	All	9	6,322	7,537	Dominant	1.13 (1.01-1.25)	0.027	38	0.12	ABC	0.48	Weak
RAD18	rs373572	C/T	3	29.22	All	3	3,174	3,397	Dominant	1.18 (1.01-1.37)	0.033	27	0.25	ABC	0.55	Weak
CCND1	rs9344	A/G	11	48.15	All	22	6,316	8,272	Dominant	1.13 (1.01-1.26)	0.035	43	0.00	ABC	0.569	Weak
IGF1	rs35767	T/C	12	24.75	All	3	2,717	4,880	Recessive	0.75 (0.62-0.91)	0.003	0	0.57	BAC	0.11	Weak
MGMT	rs12917	T/C	10	12.99	All	7	4,127	7,284	Recessive	1.54 (1.14-2.08)	0.005	0	0.47	BAA	0.158	Weak
CRP	rs1800947	C/G	1	5.70	All	4	2,916	3,544	Recessive	3.84 (1.38-10.74)	0.010	0	0.47	CAC	0.277	Weak
HPGD	rs2612656	G/A	4	22.75	White	3	2,979	5,575	Recessive	1.31 (1.05-1.64)	0.016	21	0.28	BAC	0.380	Weak
FRZB	rs7775	G/C	2	8.77	White	3	1,256	3,000	Recessive	3.20 (1.17-8.73)	0.023	64	0.06	CCC	0.468	Weak
TGFBR1	rs334354	A/G	9	26.71	All	4	1,226	2,776	Recessive	1.38 (1.04-1.84)	0.029	8	0.35	BAC	0.516	Weak
TGFB1	rs4803455	A/C	19	47.48	All	3	2,786	3,516	Recessive	1.14 (1.01-1.28)	0.030	0	0.37	AAC	0.536	Weak
LIPC	rs6083	A/G	15	36.52	All	3	4,702	4,914	Recessive	0.85 (0.74-0.99)	0.032	25	0.27	AAA	0.56	Weak
MTHFR	rs1801133	T/C	1	33.50	All	68	32,608	44,383	Recessive	0.92 (0.85-1)	0.036	52	0.00	ACC	0.61	Weak
CYP2C9	rs1799853	T/C	10	13.31	White	6	4,915	5,237	Recessive	1.36 (1.02-1.83)	0.038	0	0.76	BAA	0.60	Weak
MTRR	rs10380	T/C	5	9.31	White	4	3,869	5,141	Recessive	1.61 (1.02-2.52)	0.039	6	0.36	BAA	0.597	Weak

^*Minor alleles/Major alleles (Per Caucasian); majors alleles were treated as reference alleles in the analyses

Allelic ORs were estimated under the additive model. For dominant or recessive models, ORs were estimated for subjects who carry one or two minor alleles or subjects homozygous for the minor alleles, respectively.

^†MAF=minor allele frequency in controls.

OR=odds ratio; CI=confidence interval.

^‡Venice criteria grades are for amount of evidence, replication of the association, and protection from bias.

^§False-positive report probability (FPRP) was determined based on OR and P value of each variant from meta-analysis and a prior probability of 0.05.

Cumulative epidemiological evidence as graded by combination of results from Venice criteria and FPRP for association with colorectal-cancer risk.

The vast majority of meta-analyses performed in this project (205 variants in 130 genes) did not yield any evidence of significant association. These meta-analyses included a mean of six studies (range 3-34) and 7,916 participants (range 519-36,982). Table 6 shows results for 40 variants from 33 genes that showed no evidence of association with CRC risk in meta-analyses with a minimum of 5,000 cases and 5,000 controls.

Table 6. Genetic variants showing no relation to colorectal-cancer risk in meta-analyses with at least 5,000 cases and 5,000 controls in additive model.

					Number assessed			Colorectal cancer risk		Heterogeneity
Genes	Variants	Comparisons*	Frequency (%)†	Ethnicity	Studies	Cases	Controls	OR (95% CI)	P value	I2 (%)	Pheterogeneity
ABCB1	rs1202168	T vs C	41.25	White	4	6,318	5,805	1.05 (0.97-1.14)	0.191	55	0.08
ABCB1	rs9282564	G vs A	8.89	White	4	5,792	5,234	1.21 (0.78-1.88)	0.400	96	0.00
ABCB1/MDR1	rs1045642	C vs T	47.52	All	13	6,312	7,128	0.98 (0.89-1.07)	0.611	58	0.00
APC	rs459552	A vs T	22.27	All	8	6,654	7,117	0.96 (0.91-1.02)	0.205	0	0.66
CASP8	rs3834129	6 bp ins vs del	41.14	All	10	6,922	10,750	1.05 (0.92-1.20)	0.441	82	0.00
CASR	rs1042636	G vs A	8.52	White	4	6,298	7,839	1.00 (0.92-1.09)	0.936	0	0.66
CDH1	rs16260	A vs C	28.09	All	9	7,220	7,045	0.94 (0.88-1.01)	0.116	21	0.26
COMT	rs4680	A vs G	48.89	White	5	5,074	5,239	1.05 (0.94-1.16)	0.390	56	0.06
CYP1A1	rs4646903	C vs T	14.42	All	15	7,258	8,154	1.05 (0.92-1.19)	0.500	65	0.00
CYP1A2	rs762551	C vs A	30.45	All	11	7,667	8,242	1.02 (0.94-1.10)	0.664	55	0.01
CYP1B1	rs1056836	G vs C	43.27	White	9	8,709	9,097	1.02 (0.97-1.06)	0.488	0	0.44
CYP1B1	rs1800440	G vs A	18.36	White	6	6,679	6,923	0.97 (0.88-1.07)	0.580	53	0.06
CYP2C9	rs1057910	G vs A	7.16	All	8	8,538	9,182	1.00 (0.86-1.16)	0.994	62	0.01
EPHX1	rs1051740	C vs T	30.95	All	18	10,478	12,372	1.02 (0.97-1.08)	0.447	36	0.06
ERCC2/XPD	rs13181	C vs A	30.25	All	17	6,039	8,749	0.99 (0.92-1.05)	0.649	24	0.17
ERCC2/XPD	rs1799793	A vs G	29.71	All	7	5,470	7,135	1.01 (0.96-1.07)	0.674	0	0.68
GSTP1	rs1138272	T vs C	9.84	All	10	7,160	7,789	0.92 (0.80-1.06)	0.234	56	0.02
GSTP1	rs1695	G vs A	27.54	All	33	9,986	15,562	0.98 (0.93-1.03)	0.487	25	0.10
IGF1	(CA)n	non R19 vs R19	36.71	All	8	5,493	6,827	0.99 (0.89-1.09)	0.769	70	0.00
IGFBP3	rs2854744	C vs A	46.30	All	9	6,872	10,606	1.03 (0.98-1.07)	0.284	0	0.98
IL6	rs1800795	C vs G	38.55	All	14	6,952	8,657	1.01 (0.93-1.11)	0.749	65	0.00
IRS1	rs1801278	A vs G	6.92	White	7	7,048	7,533	1.08 (0.96-1.22)	0.219	39	0.13
MLH1	rs1799977	G vs A	29.56	All	10	6,384	8,972	1.01 (0.92-1.11)	0.904	58	0.01
MTHFD1	rs2236225	A vs G	45.19	White	6	6,535	9,347	0.98 (0.90-1.07)	0.603	67	0.01
MTHFR	rs1801131	C vs A	30.14	All	34	14,965	22,017	0.99 (0.94-1.03)	0.514	32	0.04
MTR	rs1805087	G vs A	19.42	All	19	12,945	17,655	0.99 (0.94-1.05)	0.717	33	0.09
MTRR	rs1801394	A vs G	48.33	All	16	7,674	11,593	0.96 (0.91-1.01)	0.110	37	0.12
MUTYH	rs3219484	A vs G	8.20	White	3	5,391	5,222	0.95 (0.68-1.34)	0.787	91	0.00
MUTYH	rs3219489	C vs G	28.44	All	4	5,082	5,280	1.09 (0.92-1.28)	0.317	81	0.00
NAT1	Phenotype	Fast vs slow	44.37	All	15	7,336	9,825	1.03 (0.92-1.16)	0.596	61	0.00
NQO1	rs1800566	T vs C	22.90	All	12	7,209	8,783	1.07 (0.99-1.15)	0.090	32	0.13
OGG1	rs1052133	G vs C	25.79	All	18	6,654	8,599	1.10 (0.99-1.23)	0.085	71	0.00
PPARG	rs1801282	G vs C	9.10	All	18	13,758	20,300	0.97 (0.91-1.03)	0.339	18	0.24
PPARG	rs3856806	T vs C	11.39	All	10	6,189	8,707	1.03 (0.96-1.11)	0.412	2	0.42
PTGS2/COX2	rs5275	C vs T	33.17	All	10	6,059	8,084	1.01 (0.97-1.07)	0.579	0	0.98
TGFBR1	rs11466445	9 bp del vs ins	9.19	All	10	6,338	6,689	1.04 (0.96-1.13)	0.379	1	0.43
TP53	rs1042522	C vs G	31.28	All	31	10,515	12,909	1.00 (0.92-1.10)	0.922	72	0.00
VDR	rs2228570	A vs G	37.32	All	20	13,631	15,155	1.00 (0.94-1.06)	0.959	53	0.00
VDR	rs7975232	A vs C	43.08	All	9	5,421	5,377	1.08 (0.98-1.19)	0.105	58	0.02
XRCC1	rs25487	G vs A	31.87	All	25	9,541	14,448	1.04 (0.97-1.11)	0.281	49	0.00

OR=odds ratio; CI=confidence interval.

^*Genetic comparison used in meta-analysis: Minor allele vs Major allele.

^†Frequency of minor allele or effect genotype(s) in controls in primary meta-analysis

Discussion

To our knowledge, this study is the largest and most comprehensive assessment of the literature regarding candidate-gene association studies for CRC risk conducted to date. We systematically evaluated data for 3,603 variants in 1,378 independent candidate genes from 950 reports published in the past two decades. Several meta-analyses have been conducted to evaluate candidate-gene association studies of CRC risk for single gene or several genes. These early analyses, however, were limited to 52 variants in 34 genes (Webappendix Table 6). Recently, Theodoratou et al (42) evaluated genetic variants for CRC risk using data from 635 publications and conducted meta-analyses for 92 polymorphisms in 64 genes, including 18 variants identified from GWAS studies. We did not include GWAS-identified risk variants in this study since they have been robustly replicated and should be considered to have strong evidence of association. Our study not only provides an update of the variants meta-analyzed previously using data from more studies and a bigger sample size, but also assessed more than 193 variants that have not been assessed in any previous meta-analyses, including the meta-analysis conducted by Theodoratou, et al (42). Of the 267 variants in 150 genes summarized by our 910 meta-analyses, 62 variants in 50 genes showed nominally significant association with CRC risk. Using Venice criteria plus FPRP results, we graded eight variants strong for cumulative epidemiological evidence of association with CRC risk (APC rs1801155, CHEK2 1100delC and rs17879961, DNMT3B rs1569686, MLH1 rs1800734, MUTYH biallelic mutations, rs34612342, rs36053993), two variants moderate for cumulative evidence of association with CRC risk (GSTM1 Present/Null, TERT rs2736100), and the remaining 52 variants weak. Of the eight strong variants, MUTYH rs36053993 was also rated as having ‘strong’ evidence for association in Theodoratou's study (42). For 40 variants in 33 genes, we showed no evidence of association with CRC risk in meta-analyses with large sample sizes (10,000 individuals minimum). Our study provides a comprehensive research synopsis of candidate-gene association studies of CRC risk. Results from this study will be helpful for future studies to evaluate genetic risk factors for CRC.

The adenomatous polyposis coli (APC) gene, a tumor suppressor gene at chromosome 5q21, encodes a large multidomain protein including 2,843 amino acids that play a central role in the Wnt singling pathway (43). Germline pathogenic mutations in the APC gene result in autosomal dominant inherited familial adenomatous polyposis (FAP) in which more than 100 adenomatous polyps can develop (3;6). Our meta-analysis provides strong evidence of association for CRC risk with a heterozygous variant at codon 1,307 in exon 15 of the gene (rs1801155), with a 1.96-fold increased risk of CRC in Jews (including Ashkenazi and Israeli Jews). This variant is present in 7% of Ashkenazi Jews, while population frequency is very low in Europeans and Asians (based on HapMap data).

The CHEK2 gene maps to chromosome 22q12.1 and encodes a protein kinase that is activated in response to DNA damage and is involved in cell cycle arrest (44). Our meta-analysis revealed strong evidence of association with CRC risk for a truncating mutation at codon 381 in exon 10 (1100delC) and a missense polymorphism in exon 3 (rs17879961, Ile157Thr). The 1100delC mutation leads to kinase-deficient molecules due to protein truncation (45), while Ile157Thr results in a CHEK2 protein with deficient binding and phosphorylation of downstream substrates (46). Interestingly, in a previous meta-analysis, we found strong cumulative evidence of association for these two variants with breast-cancer risk (29), indicating the CHEK2 gene may play a role in both CRC and breast cancer.

Our meta-analyses revealed strong evidence for an association of CRC risk with three rare variants in the MUTYH gene based on data from 17 population-based studies excluding cases with MUTYH-associated polyposis (MAP). Biallelic mutations in the MUTYH gene mainly constitute either homozygotes (two same) or compound heterozygotes (two different) of Gly382Asp and Tyr165Cys. Gly382Asp and Tyr165Cys are located in exon 7 and exon 13 of the MUTYH gene, respectively, and have been predicted to be deleterious by SIFT (47) and confirmed to be pathogenic (41). However, the monoallelic mutation, including a heterozygous genotype of 12 mutations in the MUTYH gene showed only weak evidence for association with CRC risk in our study. Two common variants (MLH1 rs1800734, DNMT3B rs1569686) showed strong cumulative evidence of association with CRC risk. MLH1, which maps to chromosome 3p22.2, is a human homolog of the E. coli DNA mismatch repair gene mutL and is a locus frequently mutated in hereditary nonpolyposis colon cancer (HNPCC) (48). Approximately 85% of genetically defined HNPCC patients have germline mutations in the MLH1 gene (49). Interestingly, meta-analysis of five studies, comprised of 801 microsatellite instability high (MSI-H) cases and 10,890 controls, identified a highly significant association of rs1800734 (-93G>A) with MSI-H CRC (p=1.67×10^-12). This promoter SNP showed a much stronger association with MSI-H CRC (OR=1.51) than overall CRC cases (OR=1.05, p=0.013) based on meta-analysis of six studies: 17,174 cases, 13,166 controls. The DNMT3B gene plays an important role in the generation of aberrant methylation in carcinogenesis (50). Although this gene was not identified as a susceptibility locus for CRC by GWAS, we still rated the SNP (rs1569686) in this gene as having strong evidence for association given the highly consistent results across studies included in our meta-analysis.

Two common variants (GSTM1 null, TERT rs2736100) scored moderate for cumulative evidence of association with CRC risk, and both of them were upgraded from ‘weak’ for having a low false-positive report probability (<0.05). Additional investigations of these variants are needed, particularly since sample sizes of studies for both variants are relatively small. Cumulative epidemiological evidence of association with CRC was weak for the remaining 52 variants, many of which are common and were identified through ethnicity-specific meta-analyses or meta-analyses using dominant or recessive models. Well-designed studies with large samples are warranted to clarify association with CRC for these variants.

Our meta-analysis provides no evidence for association with CRC risk for 205 of the 267 variants evaluated in our study, supporting the notion that the vast majority of genetic variants evaluated in candidate gene association studies may not be truly related to CRC risk. Methodological limitations in previous candidate gene studies, such as small sample size, may explain some of the null associations. However, of the 205 non-significant variants, 40 variants in 33 genes showed no association with CRC risk in meta-analyses including a minimum of 5,000 cases, 5,000 controls, which provides approximately 85% power to detect an OR of 1.15 under the additive model for a variant with MAF 0.10, Type 1 error 0.05. Thus, future epidemiological studies with a similar sample size are unlikely to be helpful in assessing effects of these variants.

There are several limitations of this study. First, although we have systematically searched the literature to identify eligible studies using two stages, it is possible that some studies might have been missed. PubMed was the main database we used for our literature search. To expand our search, we also queried Google Scholar which links multiple databases. Compared with previous meta-analyses which also used multiple databases (Webappendix Table 7), we yielded more studies with a bigger combined sample size for most variants included in our evaluation. Second, we did not assess gene-gene or gene-environment interactions. Additional studies specifically designed to identify these interactions are needed. Third, heterogeneity across studies, including differences in study populations, study designs and genotyping platforms, may have contributed to some of the null associations in this study. More than one-third of the meta-analyses had high heterogeneity, especially for variants with non-significant association. We attempted to address study heterogeneity through stratification analyses by ethnicity. Other sources of heterogeneity also exist and are difficult to address in this meta-analysis because of limited available data. Finally, Venice criteria use p-value<0.05 as significance level to determine association. However, we found most associations with a p-value 0.005-0.05 to have weak evidence for association with CRC in this study. Thus, a more stringent threshold of p-value would be helpful to evaluate evidence for a true-positive association. In addition, Venice criteria offer the advantage of evaluating multiple sources of potential bias, some of which, such as genotyping error, phenotype misclassification, and population stratification, are difficult to assess in meta-analyses.

In our meta-analyses, we identified ten genetic variants showing strong or moderate epidemiological evidence of associations with CRC risk. If all these 10 variants are confirmed to be associated with CRC risk, they could explain approximately 5% of familial CRC risk in European populations. Nevertheless, genetic risk factors identified to date account for less than 30% familial risk of CRC. Some of the missing heritability could be due to methylation markers, copy number variations, structural variants, and rare variants, for which conventional candidate gene association studies and GWAS are inadequate to investigate. Gene-gene and gene-environment interactions may also play a significant role in the etiology of CRC. Additional research, including those with a large sample size, use of higher density SNP arrays and next-generation sequencing technologies, imputation using data from the 1000 Genomes Project and better defined CRC subtypes, are needed to clarify the missing heritability of CRC. Our study, the largest field synopsis conducted to date for CRC candidate gene association studies, not only summarizes the current literature regarding genetic epidemiology of CRC, but also provides comprehensive data and helpful clues for designing future studies to further investigate genetic risk factors for CRC.

Significance of this study.

What is already known about this subject?

• Colorectal cancer (CRC) is one of most commonly diagnosed cancers in the world.

• Approximately 35% of CRC risk could be attributable to inheritable factors.

• Many studies have been conducted to evaluate associations between genetic variants in candidate genes and risk of CRC over the past two decades – with inconsistent results.

What are the new findings?

• This study is the largest, most comprehensive assessment of the literature to date regarding genetic association studies in CRC risk.

• Of the 267 variants evaluated, 62 variants in 50 candidate genes showed a statistically significant association with CRC risk.

• Eight variants in five genes showed strong cumulative evidence of association with CRC risk, and two variants in two genes showed moderate evidence.

• This study provides clues for designing future studies to further investigate genetic risk factors for CRC.

How might it impact on clinical practice in the foreseeable future?

• Genetic risk variants may be used to identify high-risk individuals for CRC screening and prevention.

Abbreviations

CRC

colorectal cancer

GWAS

genome-wide association studies

MAF

minor allele frequency

HWE

Hardy-Weinberg equilibrium

ORs

odds ratios

CIs

confidence intervals

FPRP

false-positive report probability

APC

adenomatous polyposis coli

FAP

familial adenomatous polyposis

MAP

MUTYH-associated polyposis

HNPCC

hereditary nonpolyposis colon cancer

MSI-H

microsatellite instability high