Abstract
Obesity is a leading contributor to colorectal cancer risk. We investigated whether the risk variants identified in genome-wide association studies of body mass index (BMI) and waist size are associated with colorectal cancer risk, independently of the effect of obesity phenotype due to a shared etiology. Twenty four SNPs in 15 loci (BDNF, FAIM2, FTO, GNPDA2, KCTD15, LYPLAL1, MC4R, MSRA, MTCH2, NEGR1, NRXN3, SEC16B, SH2B1, TFAP2B, and TMEM18) were genotyped in a case-control study of 2,033 colorectal cancer cases and 9,640 controls nested within the Multiethnic Cohort Study, as part of the Population Architecture using Genomics and Epidemiology (PAGE) consortium. Risk alleles for two obesity SNPs were associated with colorectal cancer risk – KCTD15 rs29941 [odds ratio (OR) for C allele = 0.90, 95% confidence interval (CI) 0.83–0.98; p = 0.01] and MC4R rs17782313 (OR for C allele = 1.12, 95% CI 1.02–1.22; p = 0.02). These associations were independent of the effect of BMI. However, none of the results remained significant after adjustment for multiple comparisons. No heterogeneity was observed across race/ethnic groups. Our findings suggest that the obesity risk variants are not likely to affect the risk of colorectal cancer substantially.
Keywords: genotype-phenotype interactions, obesity, pleiotropy, prospective nested case-control studies, race/ethnicity
INTRODUCTION
Obesity is a leading modifiable risk factor for colorectal cancer. About 35% of new colorectal cancer cases among men and 21% of cases among women have been attributed to obesity in the U.S.1 Furthermore, larger waist size has been associated with risk of colon cancer independently of body mass index (BMI; kg/m2).2 Just as there are common behavioral factors (e.g., diet and physical inactivity) that independently increase the risk of obesity or colorectal cancer, inherited susceptibility may also contribute to the development of both conditions. Accordingly, risk variants identified for obesity in genome-wide association studies (GWAS) have been considered for potential pleiotropic effects in carcinogenesis. In particular, the best-replicated obesity risk locus in FTO has been associated inversely with lung 3 and low-grade prostate 4 cancers but positively with high-grade prostate cancer 4 and endometrial cancer,5 although the associations were weak and of borderline significance or attenuated with BMI adjustment. To date, there has been no epidemiologic study on colorectal cancer in relation to FTO or other GWAS-replicated risk variants of obesity.
GWAS to date have identified over 30 common single nucleotide polymorphisms (SNPs) associated with overall adiposity, assessed by BMI, 6 and additional SNPs associated with abdominal obesity, assessed by waist size.7 Some of these variants appear to be involved in the hypothalamic regulation of energy balance, such as BDNF, MC4R, POMC, and SH2B1,6 but other GWAS variants for obesity, including those in the FTO locus, 8 are still being investigated for their functional effects, which may involve potentially carcinogenic disturbances.
We examined the effects of 15 risk loci for obesity identified in GWAS of BMI 9, 10 and waist size11, 12 as of September 2009 for their effects on the risk of colorectal cancer in a nested case-control study of the Multiethnic Cohort, as part of the Population Architecture using Genomics and Epidemiology (PAGE) consortium.13
MATERIALS AND METHODS
Study Population and Baseline Data
The Multiethnic Cohort (MEC) Study, established between 1993 and 1996 in Hawaii and Los Angeles, is a prospective investigation of the roles of lifestyle and genetic risk factors in common cancers among five ethnic groups (African Americans, Japanese Americans, Latinos, Native Hawaiians and whites), as described in detail previously.14 A questionnaire was mailed to men and women of ages 45–75 and of the five ethnicities who were identified primarily through the drivers’ license files for the state of Hawaii and the county of Los Angeles, California. The over 215,000 MEC Study participants are broadly representative of the study area populations, as reflected in the distribution of various census demographics.14
The baseline questionnaire queried information on demographics and risk factors for cancer: ethnicity, medical and reproductive history, smoking history, dietary intake and physical activity. Medical history questions included family history of colon or rectal cancer among first-degree relatives, history of intestinal polyps and aspirin use.14 Participants were asked to write in their current weight and height, from which BMI was calculated. Usual dietary intake in the past year was assessed using a quantitative food frequency questionnaire (QFFQ) with over 180 items, developed and calibrated specifically for this multiethnic population.15 Physical activity was assessed as numbers of hours spent in various sedentary, sports and work-related activities, expressed as metabolic equivalents (METs). History of diabetes in the current analysis also incorporated self-reports in the follow-up questionnaires (approximately 5 and 10 years after baseline), medication use reported at the time of specimen collection, and records of hospital discharge and insurance.
Case/Control Ascertainment and Biospecimen Collection
All primary cancer cases within the MEC occurring during follow-up since the baseline have been identified by regular linkage with NCI Surveillance, Epidemiology, and End Results (SEER) registries: the Hawaii Tumor Registry, the Los Angeles County Surveillance Program and the State of California Cancer Registry. Case ascertainment in the MEC has been observed to be close to complete with a low out-migration rate.14 Blood samples for genetic studies were collected in two phases: first, in 1996–2001, specimens were obtained retrospectively from incident colorectal, breast and prostate cancer cases, together with a random sample of the cohort to serve as controls; secondly, in 2001–2006, specimens were obtained from over 67,000 consenting surviving cancer-free participants who have been followed for cancer incidence. The distribution of established risk factors for colorectal cancer was similar in the entire cohort and in the biospecimen subgroup. Among the MEC participants with biospecimens, we identified 1,125 male and 908 female incident colorectal cancer cases (ICD-O-3: C180–187, C199, C209) by October, 2010 (median of 8 years since entry). Controls for the current analysis consisted of men and women without colorectal cancer diagnosis identified by October, 2010 and with blood samples available (n = 9,640). The Institutional Review Boards at the University of Hawaii and at the University of Southern California (USC) approved the study, and all study participants provided informed consent.
SNP Selection and Genotyping
DNA was purified from buffy coat samples stored in vapor phase of liquid nitrogen. Twenty four SNPs in 15 loci (BDNF, FAIM2, FTO, GNPDA2, KCTD15, LYPLAL1, MC4R, MSRA, MTCH2, NEGR1, NRXN3, SEC16B, SH2B1, TFAP2B, and TMEM18) were considered in the current analysis, comprising all the published risk variants for BMI 9, 10 or body weight 10 and waist size 11, 12 as of September 2009. Genotyping was conducted using the standard TaqMan and the OpenArray systems (Applied Biosystems, Carlsbad, CA) at the University of Hawaii Cancer Center (UHCC) and USC. Laboratory technicians were blinded to case-control status. Quality assurance data indicated high genotype call rates (>97%), high concordance (≥ 99.9%) between the UHCC and USC labs for all 24 variants on the 375 HapMap samples, and high concordance (>99.5%) among the 8.8% blinded duplicate samples. All SNPs were consistent with the Hardy-Weinberg distributions (p>0.01 in 5 ethnic groups).
Statistical Analysis
Each biallelic SNP was examined in relation to BMI in general linear models that adjusted for age, sex, and ethnicity. The association between each SNP and colorectal cancer risk was estimated by the odds ratios (ORs) and 95% confidence intervals (CIs) from unconditional logistic regression models. The number of obesity risk allele in each SNP was coded as 2 dichotomous indicator variables for nominal associations (1 or 2 vs. 0) and as a continuous variable for trend tests. The base model was adjusted for age, sex, ethnicity, and the interaction between sex and ethnicity. BMI was added in the model to examine whether it mediated the associations of the SNPs with colorectal cancer. Effect modification by sex, ethnicity, and levels of BMI and physical activity was assessed by a Wald test of the cross-product terms of the SNP trend variable and each covariate in separate models. For the FTO and KCTD15 loci, for which two or more SNPs were genotyped, haplotypes were reconstructed using PHASE v2.1.116, 17 and tested for an association with CRC. Polytomous logistic regression was used to examine the SNP-cancer associations by tumor site of colon vs. rectum in reference to common controls. Analyses were conducted using SAS v9.2, and significance was considered at p<0.05 (two-sided). To control the potentially inflated Type 1 error due to multiple comparisons, we performed false discovery rate (FDR)-adjustments, 18 permutation testing,19 and false positive report probability (FPRP) estimation.20
RESULTS
The obesity risk variants and their allele frequencies among controls for each ethnic group are described in Supplemental Table 1. Allele frequencies in whites in the MEC were similar to those observed in populations of European descent.6, 9, 10
Among the MEC controls, we confirmed the positive associations between BMI and 13 variants in 6 loci (BDNF, FTO, KCTD15, NEGR1, NRXN3 and SH2B1) and also between BMI-adjusted waist circumference and 19 SNPs in 10 loci (the 6 loci above plus LYPLAL1, MSRA, SEC16B and TMEM18) (reported separately by the PAGE consortium).
Cases were slightly older than controls (Table 1). After controlling for age, cases were more likely than controls to have a higher BMI and personal history of diabetes, to be a former smoker, with higher mean pack-years among smokers, and to have consumed more alcohol. Cases had slightly fewer years of education and were likely to consume less multivitamins, dietary fiber and total calcium. Among women, cases were less likely to use postmenopausal hormone treatment than controls.
Table 1.
Characteristics* of colorectal cancer cases and controls
| Cases n = 2,033 | Controls n = 9,640 | P * | ||
|---|---|---|---|---|
| Age at case diagnosis or control blood draw, years | 70.0 (8.6) | 68.0 (8.6) | <.0001 | |
| Sex, n (%) | .29 | |||
| Male | 1125 (55%) | 5260 (55%) | ||
| Female | 908 (45%) | 4380 (45%) | ||
| Ethnicity, n (%) | .002 | |||
| White | 381 (19%) | 1915 (20%) | ||
| African American | 406 (20%) | 2474 (26%) | ||
| Japanese American | 694 (34%) | 2623 (27%) | ||
| Latino | 439 (22%) | 1984 (21%) | ||
| Native Hawaiian | 113 (6%) | 644 (7%) | ||
| Education, years of school | 13.2 (3.0) | 13.6 (3.1) | .0002 | |
| Body mass index (BMI), kg/m2 | 27.2 (4.9) | 26.8 (4.8) | <.0001 | |
| Family history of colorectal cancer, % yes | 219 (11%) | 876 (9%) | .05 | |
| History of intestinal polyps, % yes | 131 (6.4%) | 659 (6.8%) | .10 | |
| Diabetes, % | 500 (24%) | 1928 (20%) | <.0001 | |
| Cigarette-smoking history, n (%) | .001 | |||
| Never | 762 (38%) | 4006 (42%) | ||
| Former | 954 (47%) | 4191 (44%) | ||
| Current | 300 (15%) | 1342 (14%) | ||
| Pack-years among ever smokers | 19.6 (16.7) | 17.9 (15.4) | .004 | |
| Physical activity, metabolic equivalents (METs) | 1.62 (0.29) | 1.62 (0.29) | .28 | |
| Aspirin, % current use | 418 (21%) | 2062 (21%) | .11 | |
| Multivitamin, % current use of ≥1/week | 926 (46%) | 4793 (50%) | .0005 | |
| Dietary intake** | ||||
| Alcohol, servings/day | 0.83 (2.15) | 0.66 (1.76) | .0003 | |
| Fiber, g/1000kcal/day | 11.6 (4.4) | 11.9 (4.3) | <.0001 | |
| Total calcium, mg/day | 958 (578) | 1010 (639) | .0002 | |
| Hormone treatment, % current use among women | 225 (25%) | 1520 (35%) | <.0001 | |
Mean (standard deviation) for continuous traits and number of subjects (percent) for categorical traits. P-values for case-control comparisons are from general linear models. All comparisons, other than for age, were adjusted for age.
Dietary intake of alcohol was compared in servings (14g ethanol per serving). Intake of dietary fiber was adjusted for total energy intake by nutrient density (per 1,000 kcal). Total intake of calcium from foods and supplements was not energy-adjusted.
In the main effect analysis of the obesity risk variants on colorectal cancer, adjusted for age, sex and ethnicity, two out of the 24 SNPs showed a significant association (Table 2; results for all SNPs are shown in Supplemental Table 2). The KCTD15 rs29941 obesity risk allele (C) was associated with a lower colorectal cancer risk, whereas the MC4R rs17782313 obesity risk allele (C) showed a positive association with colorectal cancer. Further adjustments for BMI or for the risk factors that differed between cases and controls in Table 1 did not materially change the risk estimates (i.e., difference <10%). However, neither association was statistically significant when corrected for multiple comparisons, either by FDR-adjustment (adjusted p-value for both rs29941 and rs17782313 = 0.24), by case-control status permutation in sex/race/SNP-strata (permutation p-value = 0.056 for rs29941, p = 0.060 for rs17782313) or by the FPRP approach (FPRP = 0.650 for rs17782313 and 0.752 for rs29941, at a prior probability level of 0.01 and power to detect an OR of 1.5). Similarly, haplotypes estimated from the 8 FTO variants and 2 KCTD15 variants did not show a significant association with CRC (Supplemental Table 3).
Table 2.
Association* of selected genetic risk variants for obesity with colorectal cancer, overall and by BMI level
| Gene | SNP | Genotype | Combined | By Obesity Level
|
|||||
|---|---|---|---|---|---|---|---|---|---|
| BMI < 30 | P | BMI ≥ 30 | P | P-het | |||||
| OR (95% CI) | P | OR (95% CI) | OR (95% CI) | ||||||
| FTO | rs1121980 | GG | 1.0 (reference) | 1.0 (reference) | 1.0 (reference) | ||||
| GA | 1.09 (0.96, 1.23) | 0.18 | 1.14 (0.99, 1.32) | 0.06 | 0.86 (0.66, 1.13) | 0.28 | |||
| AA | 1.08 (0.90, 1.29) | 0.40 | 1.21 (0.98, 1.49) | 0.08 | 0.68 (0.47, 0.98) | 0.04 | |||
| trend | 1.05 (0.97, 1.14) | 0.24 | 1.11 (1.01, 1.22) | 0.04 | 0.84 (0.70, 0.99) | 0.04 | 0.005 | ||
|
| |||||||||
| FTO | rs1421085 | TT | 1.0 (reference) | 1.0 (reference) | 1.0 (reference) | ||||
| TC | 0.98 (0.86, 1.10) | 0.69 | 1.01 (0.88, 1.16) | 0.88 | 0.82 (0.63, 1.06) | 0.13 | |||
| CC | 0.99 (0.79, 1.24) | 0.93 | 1.04 (0.80, 1.34) | 0.80 | 0.78 (0.49, 1.23) | 0.28 | |||
| trend | 0.99 (0.90, 1.08) | 0.77 | 1.02 (0.92, 1.13) | 0.71 | 0.84 (0.70, 1.01) | 0.07 | 0.07 | ||
|
| |||||||||
| FTO | rs1558902 | TT | 1.0 (reference) | 1.0 (reference) | 1.0 (reference) | ||||
| TA | 0.94 (0.83, 1.06) | 0.30 | 0.97 (0.85, 1.12) | 0.72 | 0.81 (0.61, 1.06) | 0.13 | |||
| AA | 1.02 (0.81, 1.28) | 0.85 | 1.07 (0.82, 1.39) | 0.63 | 0.82 (0.51, 1.32) | 0.42 | |||
| trend | 0.98 (0.89, 1.07) | 0.63 | 1.01 (0.91, 1.13) | 0.79 | 0.84 (0.69, 1.01) | 0.07 | 0.08 | ||
|
| |||||||||
| FTO | rs3751812 | GG | 1.0 (reference) | 1.0 (reference) | 1.0 (reference) | ||||
| GT | 0.96 (0.87, 1.07) | 0.50 | 0.99 (0.88, 1.12) | 0.89 | 0.84 (0.67, 1.06) | 0.14 | |||
| TT | 0.99 (0.80, 1.21) | 0.90 | 1.04 (0.82, 1.32) | 0.77 | 0.77 (0.50, 1.18) | 0.23 | |||
| trend | 0.98 (0.90, 1.06) | 0.61 | 1.01 (0.92, 1.11) | 0.82 | 0.84 (0.71, 1.00) | 0.05 | 0.06 | ||
|
| |||||||||
| FTO | rs8050136 | CC | 1.0 (reference) | 1.0 (reference) | 1.0 (reference) | ||||
| CA | 1.04 (0.93, 1.16) | 0.47 | 1.08 (0.95, 1.22) | 0.24 | 0.89 (0.70, 1.12) | 0.31 | |||
| AA | 1.04 (0.87, 1.24) | 0.64 | 1.17 (0.95, 1.44) | 0.14 | 0.70 (0.49, 0.99) | 0.04 | |||
| trend | 1.03 (0.95, 1.11) | 0.49 | 1.08 (0.99, 1.19) | 0.08 | 0.84 (0.72, 0.99) | 0.03 | 0.006 | ||
|
| |||||||||
| FTO | rs9930506 | AA | 1.0 (reference) | 1.0 (reference) | 1.0 (reference) | ||||
| AG | 1.07 (0.95, 1.21) | 0.28 | 1.14 (0.99, 1.31) | 0.06 | 0.89 (0.68, 1.16) | 0.38 | |||
| GG | 1.03 (0.85, 1.26) | 0.75 | 1.08 (0.86, 1.37) | 0.51 | 0.83 (0.55, 1.26) | 0.38 | |||
| trend | 1.04 (0.95, 1.13) | 0.43 | 1.08 (0.99, 1.20) | 0.12 | 0.88 (0.73, 1.05) | 0.16 | 0.04 | ||
|
| |||||||||
| FTO | rs9939609 | TT | 1.0 (reference) | 1.0 (reference) | 1.0 (reference) | ||||
| TA | 1.03 (0.91, 1.16) | 0.68 | 1.05 (0.91, 1.21) | 0.51 | 0.89 (0.68, 1.17) | 0.40 | |||
| AA | 1.03 (0.85, 1.24) | 0.78 | 1.17 (0.94, 1.46) | 0.16 | 0.64 (0.44, 0.95) | 0.02 | |||
| trend | 1.02 (0.93, 1.11) | 0.70 | 1.06 (0.96, 1.17) | 0.23 | 0.84 (0.71, 1.01) | 0.06 | 0.02 | ||
|
| |||||||||
| FTO | rs9941349 | CC | 1.0 (reference) | 1.0 (reference) | 1.0 (reference) | ||||
| CT | 1.02 (0.92, 1.13) | 0.75 | 1.07 (0.95, 1.20) | 0.29 | 0.87 (0.68, 1.07) | 0.17 | |||
| TT | 1.02 (0.84, 1.23) | 0.86 | 1.07 (0.87, 1.32) | 0.57 | 0.81 (0.57, 1.18) | 0.27 | |||
| trend | 1.01 (0.94, 1.09) | 0.76 | 1.05 (0.96, 1.15) | 0.25 | 0.86 (0.73, 1.01) | 0.06 | 0.03 | ||
|
| |||||||||
| KCTD15 | rs11084753 | AA | 1.0 (reference) | 1.0 (reference) | 1.0 (reference) | ||||
| AG | 0.92 (0.80, 1.05) | 0.21 | 0.95 (0.81, 1.11) | 0.49 | 0.80 (0.59, 1.08) | 0.14 | |||
| GG | 0.88 (0.75, 1.02) | 0.10 | 0.94 (0.78, 1.12) | 0.49 | 0.68 (0.49, 0.94) | 0.02 | |||
| trend | 0.94 (0.87, 1.01) | 0.10 | 0.99 (0.91, 1.09) | 0.89 | 0.77 (0.66, 0.90) | 0.0008 | 0.004 | ||
|
| |||||||||
| KCTD15 | rs29941 | TT | 1.0 (reference) | 1.0 (reference) | 1.0 (reference) | ||||
| TC | 0.86 (0.75, 1.00) | 0.045 | 0.89 (0.76, 1.05) | 0.17 | 0.68 (0.49, 0.95) | 0.02 | |||
| CC | 0.81 (0.68, 0.96) | 0.01 | 0.90 (0.74, 1.09) | 0.27 | 0.54 (0.38, 0.78) | 0.001 | |||
| trend | 0.90 (0.83, 0.98) | 0.01 | 0.97 (0.88, 1.06) | 0.49 | 0.70 (0.60, 0.82) | <0.0001 | 0.0003 | ||
|
| |||||||||
| MC4R | rs17782313 | TT | 1.0 (reference) | 1.0 (reference) | 1.0 (reference) | ||||
| TC | 1.10 (0.98, 1.23) | 0.10 | 1.07 (0.94, 1.23) | 0.29 | 1.18 (0.92, 1.50) | 0.19 | |||
| CC | 1.28 (1.01, 1.61) | 0.04 | 1.31 (1.00, 1.70) | 0.05 | 1.15 (0.69, 1.92) | 0.60 | |||
| trend | 1.12 (1.02, 1.22) | 0.02 | 1.10 (1.00, 1.22) | 0.06 | 1.16 (0.97, 1.41) | 0.11 | 0.61 | ||
Odds ratios (ORs) and 95% confidence intervals (CIs) estimated in logistic regression models that adjusted for age and ethnicity, and also sex for the obesity-stratification. P-het.: P-values for heterogeneity by sex or BMI (<30 or ≥ 30).
We also examined individuals’ risk score by summing the number of risk alleles for the 15 loci (including two SNPs in low linkage disequilibrium (LD) for BDNF, rs8050136 for FTO, and rs29941 for KCTD15). The risk score, ranging between 4 and 22, showed a significant positive association with BMI (p <0.0001) but not with colorectal cancer (p = 0.47). The MC4R rs17782313 variant, but not KCTD15 rs29941, showed a slightly stronger association with rectal cancer (n = 444 cases; OR = 1.29, 95% CI 1.10–1.51) than with colon cancer (n = 1,369 cases; OR = 1.07, 0.96–1.18; p-heterogeneity = 0.03).
In a subgroup (1,640 cases, 8,878 controls) that had 109 ancestry informative markers (AIMs) data available, the SNP-colorectal cancer associations were examined with and without adjustment for the four principal components that represented the differential AIMs patterns for the 5 race/ethnic groups in the cohort.21 The results were similar, indicating no evidence of population stratification (data not shown).
BMI was positively associated with colorectal cancer risk (OR for a 5 kg/m2 increment = 1.19, 95% CI 1.12–1.26), with a slightly stronger association for colon cancer (OR = 1.21, 95% CI 1.13–1.29) than for rectal cancer (OR = 1.10, 95% CI 0.99–1.23), as reported previously.2 These risk estimates were not changed after adjustment for the rs29941 and rs17782313 variants (data not shown).
Because some of the obesity variants have shown different associations with obesity phenotypes by sex,7 race,22 or physical activity,23 we tested for evidence of heterogeneity. Results for FTO and KCTD15 that showed significant heterogeneity by weight status in some or all variants examined are presented in Table 2 (Phet <0.05). Three of the 8 variants in FTO (r2 of 80–95% in whites, 54–98% in the other ethnic groups) and both KCTD15 variants (rs11084753 and rs29941; r2 ranging from 31% in African Americans to 60% in Japanese Americans and Native Hawaiians) had a significant inverse association with colorectal cancer among obese individuals, but not among normal-weight or overweight individuals. As in the main effects analysis, adjusting for BMI did not yield a notable change in the risk estimates (data not shown).
The SNP-cancer associations did not vary across ethnicity (see Supplemental Table 4), except for the NRXN3 rs10146997 variant, which was inversely associated with colorectal cancer risk in whites only (p-heterogeneity = 0.02). Similarly, there was no evidence of heterogeneity by sex, age (by median age of 70) or physical activity (by median 1.60 METs; data not shown).
DISCUSSION
Considering that total and abdominal fatness is an established risk factor for colorectal cancer, we examined whether risk variants for higher BMI or larger waist size from GWAS may contribute to the risk of this cancer through shared etiology. Our study is one of the first analyses of the FTO and other obesity GWAS variants for pleiotropic effects on colorectal cancer and utilized the uniquely wide range of genetic and pre-diagnostic phenotype data in a multiethnic cohort. Only 2 of the 24 variants examined showed an initial significant association, but after accounting for multiple comparisons, these findings were no longer significant. Carriers of the obesity risk allele for the KCTD15 rs29941 variant, which, as expected, was positively correlated with BMI in our study, showed a reduced risk for colorectal cancer, whereas for MC4R rs17782313, the obesity risk allele carriers had an elevated risk. Furthermore, variants in FTO and KCTD15 had a stronger inverse association among the obese than non-obese. These findings suggest that the potential effects of obesity variants on colorectal cancer risk are likely to be small and, possibly, vary by weight status.
We did not find an overall association between any of the FTO variants considered here and colorectal cancer risk. This is consistent with a previous study of FTO and colorectal adenoma.24 Among whites and African Americans with (n = 321) and without (n = 903) colonoscopy-confirmed adenomas, the obesity risk alleles for rs8050136 and rs9939609 in FTO were associated with higher self-reported adult BMI (in 30s and 40s), and higher BMI in turn was associated with greater risk of colorectal adenoma; however, the FTO variants showed no overall association with adenoma and a significant inverse association among African Americans.24 We observed no associations between 8 FTO obesity risk variants and incident colorectal cancer across ethnicity, including African Americans. However, we found an inverse association of the obesity risk alleles in FTO and KCTD15 with colorectal cancer among obese individuals. Thus, the role of the obesity-associated variants in colorectal carcinogenesis may entail more complex mechanisms.
The KCTD15 gene encodes a protein, “potassium channel tetramerisation domain containing 15”, whose function remains largely undetermined. Its variant (rs29941) explained less than 0.01% of the variance in BMI in previous GWAS, as compared to the 0.34% explained by the FTO risk alleles (rs1558902).6 Several other genes encoding potassium channel-regulating proteins have been identified in GWAS of obesity 25 as well as of Type 2 diabetes and other metabolic diseases. This may implicate genetic alterations in potassium channel regulator proteins as likely candidates for pleiotropic effects in metabolic disorders. Carriers of the risk allele in MC4R showed increased risk of colorectal cancer, despite the lack of clear association with BMI or waist size in our data, possibly due to our relatively limited sample size. Also, imprecision in BMI based on self-reported weight and height in our study might have contributed to a slight misclassification, resulting in some attenuation of any genotype-phenotype associations – however, the association between the FTO variants and BMI in our data was comparable to that observed in studies with measured BMI.
Our study constitutes an initial examination of the potential association between genetic susceptibility to increased adiposity and colorectal cancer risk and suggests no substantial effects.
Supplementary Material
Novelty/Impact.
This is the first report on testing pleiotropic effects of obesity risk variants on the risk of colorectal cancer. The findings suggest that, although obesity is the leading modifiable risk factor for colorectal cancer, the effect of obesity risk variants is likely small.
Acknowledgments
The Population Architecture Using Genomics and Epidemiology (PAGE) program is funded by the National Human Genome Research Institute (NHGRI), supported by U01HG004803 (CALiCo), U01HG004798 (EAGLE), U01HG004802 (MEC), U01HG004790 (WHI), and U01HG004801 (Coordinating Center), and their respective NHGRI ARRA supplements. The contents of this paper are solely the responsibility of the authors and do not necessarily represent the official views of the NIH. The complete list of PAGE members can be found at http://www.pagestudy.org.
The Multiethnic Cohort study (MEC) characterization of epidemiological architecture is funded through the NHGRI PAGE program (U01HG004802 and its NHGRI ARRA supplement). The MEC study is funded through the National Cancer Institute (R37CA54281, R01 CA63, P01CA33619, U01CA136792, and U01CA98758). The UHCC Genomics Shared Resource (GSR) is supported by NCI CCSG 3P30CA071789-12S8. The authors thank study participants and acknowledge the dedicated efforts of the following staff of the GSR and the MEC at the University of Hawaii Cancer Center: Ann Seifried, Annette Lum-Jones, Maj Earle, and Wileen Mau.
Footnotes
CONFLICT OF INTEREST
The authors declared no conflict of interest.
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