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1. Air pollution, weight loss and metabolic benefits of bariatric surgery: a potential model for study of metabolic effects of environmental exposures

Author

Ghosh, R, Gauderman, WJ, Minor, H, Youn, HA, Lurmann, F, Cromar, KR, Chatzi, L, Belcher, B, Fielding, CR, and McConnell, R

Subjects
Epidemiology, Health Sciences, Obesity, Nutrition, Prevention, Climate-Related Exposures and Conditions, Aetiology, 2.2 Factors relating to the physical environment, Metabolic and endocrine, Adolescent, Air Pollution, Alkaline Phosphatase, Bariatric Surgery, Child, Environmental Exposure, Female, Glycated Hemoglobin, Humans, Lipids, Male, Nitrogen Dioxide, Ozone, Pediatric Obesity, Prospective Studies, Treatment Outcome, Weight Loss, Air pollution, bariatric surgery, lipids, metabolic syndrome, obesity, Biomedical and clinical sciences, Health sciences
Abstract

BackgroundEmerging experimental evidence suggests that air pollution may contribute to development of obesity and diabetes, but studies of children are limited.ObjectivesWe hypothesized that pollution effects would be magnified after bariatric surgery for treatment of obesity, reducing benefits of surgery.MethodsIn 75 obese adolescents, excess weight loss (EWL), high-density lipoprotein (HDL) cholesterol, triglycerides, alkaline phosphatase (ALP) and hemoglobin A1c (HbA1c ) were measured prospectively at baseline and following laparoscopic adjustable gastric banding (LAGB). Residential distances to major roads and the average two-year follow-up exposure to particulate matter

Published
2018

2. Genetic risk impacts the association of menopausal hormone therapy with colorectal cancer risk

Author

Tian, Y, Lin, Y, Qu, C, Arndt, V, Baurley, JW, Berndt, SI, Bien, SA, Bishop, DT, Brenner, H, Buchanan, DD, Budiarto, A, Campbell, PT, Carreras-Torres, R, Casey, G, Chan, AT, Chen, R, Chen, X, Conti, DV, Diez-Obrero, V, Dimou, N, Drew, DA, Figueiredo, JC, Gallinger, S, Giles, GG, Gruber, SB, Gunter, MJ, Harlid, S, Harrison, TA, Hidaka, A, Hoffmeister, M, Huyghe, JR, Jenkins, MA, Jordahl, KM, Joshi, AD, Keku, TO, Kawaguchi, E, Kim, AE, Kundaje, A, Larsson, SC, Marchand, LL, Lewinger, JP, Li, L, Moreno, V, Morrison, J, Murphy, N, Nan, H, Nassir, R, Newcomb, PA, Obon-Santacana, M, Ogino, S, Ose, J, Pardamean, B, Pellatt, AJ, Peoples, AR, Platz, EA, Potter, JD, Prentice, RL, Rennert, G, Ruiz-Narvaez, EA, Sakoda, LC, Schoen, RE, Shcherbina, A, Stern, MC, Su, Y-R, Thibodeau, SN, Thomas, DC, Tsilidis, KK, van Duijnhoven, FJB, Van Guelpen, B, Visvanathan, K, White, E, Wolk, A, Woods, MO, Wu, AH, Peters, U, Gauderman, WJ, Hsu, L, Chang-Claude, J, Tian, Y, Lin, Y, Qu, C, Arndt, V, Baurley, JW, Berndt, SI, Bien, SA, Bishop, DT, Brenner, H, Buchanan, DD, Budiarto, A, Campbell, PT, Carreras-Torres, R, Casey, G, Chan, AT, Chen, R, Chen, X, Conti, DV, Diez-Obrero, V, Dimou, N, Drew, DA, Figueiredo, JC, Gallinger, S, Giles, GG, Gruber, SB, Gunter, MJ, Harlid, S, Harrison, TA, Hidaka, A, Hoffmeister, M, Huyghe, JR, Jenkins, MA, Jordahl, KM, Joshi, AD, Keku, TO, Kawaguchi, E, Kim, AE, Kundaje, A, Larsson, SC, Marchand, LL, Lewinger, JP, Li, L, Moreno, V, Morrison, J, Murphy, N, Nan, H, Nassir, R, Newcomb, PA, Obon-Santacana, M, Ogino, S, Ose, J, Pardamean, B, Pellatt, AJ, Peoples, AR, Platz, EA, Potter, JD, Prentice, RL, Rennert, G, Ruiz-Narvaez, EA, Sakoda, LC, Schoen, RE, Shcherbina, A, Stern, MC, Su, Y-R, Thibodeau, SN, Thomas, DC, Tsilidis, KK, van Duijnhoven, FJB, Van Guelpen, B, Visvanathan, K, White, E, Wolk, A, Woods, MO, Wu, AH, Peters, U, Gauderman, WJ, Hsu, L, and Chang-Claude, J

Abstract

BACKGROUND: Menopausal hormone therapy (MHT), a common treatment to relieve symptoms of menopause, is associated with a lower risk of colorectal cancer (CRC). To inform CRC risk prediction and MHT risk-benefit assessment, we aimed to evaluate the joint association of a polygenic risk score (PRS) for CRC and MHT on CRC risk. METHODS: We used data from 28,486 postmenopausal women (11,519 cases and 16,967 controls) of European descent. A PRS based on 141 CRC-associated genetic variants was modeled as a categorical variable in quartiles. Multiplicative interaction between PRS and MHT use was evaluated using logistic regression. Additive interaction was measured using the relative excess risk due to interaction (RERI). 30-year cumulative risks of CRC for 50-year-old women according to MHT use and PRS were calculated. RESULTS: The reduction in odds ratios by MHT use was larger in women within the highest quartile of PRS compared to that in women within the lowest quartile of PRS (p-value = 2.7 × 10-8). At the highest quartile of PRS, the 30-year CRC risk was statistically significantly lower for women taking any MHT than for women not taking any MHT, 3.7% (3.3%-4.0%) vs 6.1% (5.7%-6.5%) (difference 2.4%, P-value = 1.83 × 10-14); these differences were also statistically significant but smaller in magnitude in the lowest PRS quartile, 1.6% (1.4%-1.8%) vs 2.2% (1.9%-2.4%) (difference 0.6%, P-value = 1.01 × 10-3), indicating 4 times greater reduction in absolute risk associated with any MHT use in the highest compared to the lowest quartile of genetic CRC risk. CONCLUSIONS: MHT use has a greater impact on the reduction of CRC risk for women at higher genetic risk. These findings have implications for the development of risk prediction models for CRC and potentially for the consideration of genetic information in the risk-benefit assessment of MHT use.

Published
2024

3. Integration of Mouse and Human Genome-Wide Association Data Identifies KCNIP4 as an Asthma Gene

Author

Burchard, Esteban, Boushey, Homer, Himes, BE, Sheppard, K, Berndt, A, Leme, AS, Myers, RA, Gignoux, CR, Levin, AM, Gauderman, WJ, Yang, JJ, and Mathias, RA

Abstract

Asthma is a common chronic respiratory disease characterized by airway hyperresponsiveness (AHR). The genetics of asthma have been widely studied in mouse and human, and homologous genomic regions have been associated with mouse AHR and human asthma-relate

Published
2013

4. Dietary nutrients associated with preservation of lung function in Hispanic and non-Hispanic white smokers from New Mexico

Author

Leng S, Picchi MA, Tesfaigzi Y, Wu G, Gauderman WJ, Xu F, Gilliland FD, and Belinsky SA

Subjects
Nutrientomics, spirometry, ethnic disparity, Diseases of the respiratory system, RC705-779
Abstract

Shuguang Leng,1,2 Maria A Picchi,1 Yohannes Tesfaigzi,3 Guodong Wu,1 W James Gauderman,4 Fadi Xu,5 Frank D Gilliland,4 Steven A Belinsky1,2,6 1The Lung Cancer Program, Lovelace Respiratory Research Institute, 2Cancer Control Research Program, University of New Mexico Comprehensive Cancer Center, 3COPD Program, Lovelace Respiratory Research Institute, Albuquerque, NM, 4Keck School of Medicine, University of Southern California, Los Angeles, CA, 5Pathophysiology Program, Lovelace Respiratory Research Institute, 6Cancer Genetics and Epigenetics Program, University of New Mexico Comprehensive Cancer Center, Albuquerque, NM, USA Background: COPD is the third leading cause of death in the United States. Cigarette smoking accelerates the age-related forced expiratory volume in 1 s (FEV1) decline, an important determinant for the genesis of COPD. Hispanic smokers have lower COPD prevalence and FEV1 decline than non-Hispanic whites (NHWs). Patients and methods: A nutritional epidemiological study was conducted in the Lovelace Smokers cohort (LSC; n=1,829) and the Veterans Smokers cohort (n=508) to identify dietary nutrients (n=139) associated with average FEV1 and its decline and to assess whether nutrient intakes could explain ethnic disparity in FEV1 decline between Hispanics and NHW smokers. Results: Nutrients discovered and replicated to be significantly associated with better average FEV1 included magnesium, folate, niacin, vitamins A and D, eicosenoic fatty acid (20:1n9), eicosapentaenoic acid (20:5n3), docosapentaenoic acid (DPA; 22:5n3), docosahexaenoic acid (22:6n3), and fiber. In addition, greater intakes of eicosenoic fatty acid and DPA were associated with slower FEV1 decline in the LSC. Among omega 3 polyunsaturated fatty acids, DPA is the most potent nutrient associated with better average FEV1 and slower FEV1 decline. Adverse effect of continuous current smoking on FEV1 decline was completely negated in LSC members with high DPA intake (>20 mg/day). Slower FEV1 decline in Hispanics compared to NHWs may be due to the greater protection of eicosenoic fatty acid and DPA for FEV1 decline rather than greater intake of protective nutrients in this ethnic group. Conclusion: The protective nutrients for the preservation of FEV1 in ever smokers could lay foundation for designing individualized nutritional intervention targeting “optimal physiological levels” in human to improve lung function in ever smokers. Ethnic disparity in FEV1 decline may be explained by difference in magnitude of protection of dietary intakes of eicosenoic fatty acid and DPA between Hispanics and NHWs. Keywords: nutrientomics, spirometry, ethnic disparity

Published
2017

5. A Genetic Locus within the FMN1/GREM1 Gene Region Interacts with Body Mass Index in Colorectal Cancer Risk

Author

Aglago, EK, Kim, A, Lin, Y, Qu, C, Evangelou, M, Ren, Y, Morrison, J, Albanes, D, Arndt, V, Barry, EL, Baurley, JW, Berndt, S, Bien, SA, Bishop, DT, Bouras, E, Brenner, H, Buchanan, DD, Budiarto, A, Carreras-Torres, R, Casey, G, Cenggoro, TW, Chen, AT, Chang-Claude, J, Chen, X, Conti, D, Devall, M, Diez-Obrero, V, Dimou, N, Drew, D, Figueiredo, JC, Gallinger, S, Giles, GG, Gruber, SB, Gsur, A, Gunter, MJ, Hampel, H, Harlid, S, Hidaka, A, Harrison, TA, Hoffmeister, M, Huyghe, JR, Jenkins, MA, Jordahl, K, Joshi, AD, Kawaguchi, ES, Keku, TO, Kundaje, A, Larsson, SC, Le Marchand, L, Lewinger, JP, Li, L, Lynch, BM, Mahesworo, B, Mandic, M, Obon-Santacana, M, Morento, V, Murphy, N, Men, H, Nassir, R, Newcomb, PA, Ogino, S, Ose, J, Pai, RK, Palmer, JR, Papadimitriou, N, Pardamean, B, Peoples, AR, Platz, EA, Potter, JD, Prentice, RL, Rennert, G, Ruiz-Narvaez, E, Sakoda, LC, Scacheri, PC, Schmit, SL, Schoen, RE, Shcherbina, A, Slattery, ML, Stern, MC, Su, Y-R, Tangen, CM, Thibodeau, SN, Thomas, DC, Tian, Y, Ulrich, CM, van Duijnhoven, FJB, Van Guelpen, B, Visvanathan, K, Vodicka, P, Wang, J, White, E, Wolk, A, Woods, MO, Wu, AH, Zemlianskaia, N, Hsu, L, Gauderman, WJ, Peters, U, Tsilidis, KK, Campbell, PT, Aglago, EK, Kim, A, Lin, Y, Qu, C, Evangelou, M, Ren, Y, Morrison, J, Albanes, D, Arndt, V, Barry, EL, Baurley, JW, Berndt, S, Bien, SA, Bishop, DT, Bouras, E, Brenner, H, Buchanan, DD, Budiarto, A, Carreras-Torres, R, Casey, G, Cenggoro, TW, Chen, AT, Chang-Claude, J, Chen, X, Conti, D, Devall, M, Diez-Obrero, V, Dimou, N, Drew, D, Figueiredo, JC, Gallinger, S, Giles, GG, Gruber, SB, Gsur, A, Gunter, MJ, Hampel, H, Harlid, S, Hidaka, A, Harrison, TA, Hoffmeister, M, Huyghe, JR, Jenkins, MA, Jordahl, K, Joshi, AD, Kawaguchi, ES, Keku, TO, Kundaje, A, Larsson, SC, Le Marchand, L, Lewinger, JP, Li, L, Lynch, BM, Mahesworo, B, Mandic, M, Obon-Santacana, M, Morento, V, Murphy, N, Men, H, Nassir, R, Newcomb, PA, Ogino, S, Ose, J, Pai, RK, Palmer, JR, Papadimitriou, N, Pardamean, B, Peoples, AR, Platz, EA, Potter, JD, Prentice, RL, Rennert, G, Ruiz-Narvaez, E, Sakoda, LC, Scacheri, PC, Schmit, SL, Schoen, RE, Shcherbina, A, Slattery, ML, Stern, MC, Su, Y-R, Tangen, CM, Thibodeau, SN, Thomas, DC, Tian, Y, Ulrich, CM, van Duijnhoven, FJB, Van Guelpen, B, Visvanathan, K, Vodicka, P, Wang, J, White, E, Wolk, A, Woods, MO, Wu, AH, Zemlianskaia, N, Hsu, L, Gauderman, WJ, Peters, U, Tsilidis, KK, and Campbell, PT

Abstract

UNLABELLED: Colorectal cancer risk can be impacted by genetic, environmental, and lifestyle factors, including diet and obesity. Gene-environment interactions (G × E) can provide biological insights into the effects of obesity on colorectal cancer risk. Here, we assessed potential genome-wide G × E interactions between body mass index (BMI) and common SNPs for colorectal cancer risk using data from 36,415 colorectal cancer cases and 48,451 controls from three international colorectal cancer consortia (CCFR, CORECT, and GECCO). The G × E tests included the conventional logistic regression using multiplicative terms (one degree of freedom, 1DF test), the two-step EDGE method, and the joint 3DF test, each of which is powerful for detecting G × E interactions under specific conditions. BMI was associated with higher colorectal cancer risk. The two-step approach revealed a statistically significant G×BMI interaction located within the Formin 1/Gremlin 1 (FMN1/GREM1) gene region (rs58349661). This SNP was also identified by the 3DF test, with a suggestive statistical significance in the 1DF test. Among participants with the CC genotype of rs58349661, overweight and obesity categories were associated with higher colorectal cancer risk, whereas null associations were observed across BMI categories in those with the TT genotype. Using data from three large international consortia, this study discovered a locus in the FMN1/GREM1 gene region that interacts with BMI on the association with colorectal cancer risk. Further studies should examine the potential mechanisms through which this locus modifies the etiologic link between obesity and colorectal cancer. SIGNIFICANCE: This gene-environment interaction analysis revealed a genetic locus in FMN1/GREM1 that interacts with body mass index in colorectal cancer risk, suggesting potential implications for precision prevention strategies.

Published
2023

6. Genome-wide Interaction Study with Smoking for Colorectal Cancer Risk Identifies Novel Genetic Loci Related to Tumor Suppression, Inflammation, and Immune Response

Author

Carreras-Torres, R, Kim, AE, Lin, Y, Diez-Obrero, V, Bien, SA, Qu, C, Wang, J, Dimou, N, Aglago, EK, Albanes, D, Arndt, V, Baurley, JW, Berndt, SI, Bezieau, S, Bishop, DT, Bouras, E, Brenner, H, Budiarto, A, Campbell, PT, Casey, G, Chan, AT, Chang-Claude, J, Chen, X, Conti, D, Dampier, CH, Devall, MAM, Drew, DA, Figueiredo, JC, Gallinger, S, Giles, GG, Gruber, SB, Gsur, A, Gunter, MJ, Harrison, TA, Hidaka, A, Hoffmeister, M, Huyghe, JR, Jenkins, MA, Jordahl, KM, Kawaguchi, E, Keku, TO, Kundaje, A, Le Marchand, L, Lewinger, JP, Li, L, Mahesworo, B, Morrison, JL, Murphy, N, Nan, H, Nassir, R, Newcomb, PA, Obon-Santacana, M, Ogino, S, Ose, J, Pai, RK, Palmer, JR, Papadimitriou, N, Pardamean, B, Peoples, AR, Pharoah, PDP, Platz, EA, Rennert, G, Ruiz-Narvaez, E, Sakoda, LC, Scacheri, PC, Schmit, SL, Schoen, RE, Shcherbina, A, Slattery, ML, Stern, MC, Su, Y-R, Tangen, CM, Thomas, DC, Tian, Y, Tsilidis, KK, Ulrich, CM, van Duijnhoven, FJB, Van Guelpen, B, Visvanathan, K, Vodicka, P, Cenggoro, TW, Weinstein, SJ, White, E, Wolk, A, Woods, MO, Hsu, L, Peters, U, Moreno, V, Gauderman, WJ, Carreras-Torres, R, Kim, AE, Lin, Y, Diez-Obrero, V, Bien, SA, Qu, C, Wang, J, Dimou, N, Aglago, EK, Albanes, D, Arndt, V, Baurley, JW, Berndt, SI, Bezieau, S, Bishop, DT, Bouras, E, Brenner, H, Budiarto, A, Campbell, PT, Casey, G, Chan, AT, Chang-Claude, J, Chen, X, Conti, D, Dampier, CH, Devall, MAM, Drew, DA, Figueiredo, JC, Gallinger, S, Giles, GG, Gruber, SB, Gsur, A, Gunter, MJ, Harrison, TA, Hidaka, A, Hoffmeister, M, Huyghe, JR, Jenkins, MA, Jordahl, KM, Kawaguchi, E, Keku, TO, Kundaje, A, Le Marchand, L, Lewinger, JP, Li, L, Mahesworo, B, Morrison, JL, Murphy, N, Nan, H, Nassir, R, Newcomb, PA, Obon-Santacana, M, Ogino, S, Ose, J, Pai, RK, Palmer, JR, Papadimitriou, N, Pardamean, B, Peoples, AR, Pharoah, PDP, Platz, EA, Rennert, G, Ruiz-Narvaez, E, Sakoda, LC, Scacheri, PC, Schmit, SL, Schoen, RE, Shcherbina, A, Slattery, ML, Stern, MC, Su, Y-R, Tangen, CM, Thomas, DC, Tian, Y, Tsilidis, KK, Ulrich, CM, van Duijnhoven, FJB, Van Guelpen, B, Visvanathan, K, Vodicka, P, Cenggoro, TW, Weinstein, SJ, White, E, Wolk, A, Woods, MO, Hsu, L, Peters, U, Moreno, V, and Gauderman, WJ

Abstract

BACKGROUND: Tobacco smoking is an established risk factor for colorectal cancer. However, genetically defined population subgroups may have increased susceptibility to smoking-related effects on colorectal cancer. METHODS: A genome-wide interaction scan was performed including 33,756 colorectal cancer cases and 44,346 controls from three genetic consortia. RESULTS: Evidence of an interaction was observed between smoking status (ever vs. never smokers) and a locus on 3p12.1 (rs9880919, P = 4.58 × 10-8), with higher associated risk in subjects carrying the GG genotype [OR, 1.25; 95% confidence interval (CI), 1.20-1.30] compared with the other genotypes (OR <1.17 for GA and AA). Among ever smokers, we observed interactions between smoking intensity (increase in 10 cigarettes smoked per day) and two loci on 6p21.33 (rs4151657, P = 1.72 × 10-8) and 8q24.23 (rs7005722, P = 2.88 × 10-8). Subjects carrying the rs4151657 TT genotype showed higher risk (OR, 1.12; 95% CI, 1.09-1.16) compared with the other genotypes (OR <1.06 for TC and CC). Similarly, higher risk was observed among subjects carrying the rs7005722 AA genotype (OR, 1.17; 95% CI, 1.07-1.28) compared with the other genotypes (OR <1.13 for AC and CC). Functional annotation revealed that SNPs in 3p12.1 and 6p21.33 loci were located in regulatory regions, and were associated with expression levels of nearby genes. Genetic models predicting gene expression revealed that smoking parameters were associated with lower colorectal cancer risk with higher expression levels of CADM2 (3p12.1) and ATF6B (6p21.33). CONCLUSIONS: Our study identified novel genetic loci that may modulate the risk for colorectal cancer of smoking status and intensity, linked to tumor suppression and immune response. IMPACT: These findings can guide potential prevention treatments.

Published
2023

7. Probing the diabetes and colorectal cancer relationship using gene - environment interaction analyses

Author

Dimou, N, Kim, AE, Flanagan, O, Murphy, N, Diez-Obrero, V, Shcherbina, A, Aglago, EK, Bouras, E, Campbell, PT, Casey, G, Gallinger, S, Gruber, SB, Jenkins, MA, Lin, Y, Moreno, V, Ruiz-Narvaez, E, Stern, MC, Tian, Y, Tsilidis, KK, Arndt, V, Barry, EL, Baurley, JW, Berndt, SI, Bezieau, S, Bien, SA, Bishop, DT, Brenner, H, Budiarto, A, Carreras-Torres, R, Cenggoro, TW, Chan, AT, Chang-Claude, J, Chanock, SJ, Chen, X, Conti, DV, Dampier, CH, Devall, M, Drew, DA, Figueiredo, JC, Giles, GG, Gsur, A, Harrison, TA, Hidaka, A, Hoffmeister, M, Huyghe, JR, Jordahl, K, Kawaguchi, E, Keku, TO, Larsson, SC, Le Marchand, L, Lewinger, JP, Li, L, Mahesworo, B, Morrison, J, Newcomb, PA, Newton, CC, Obon-Santacana, M, Ose, J, Pai, RK, Palmer, JR, Papadimitriou, N, Pardamean, B, Peoples, AR, Pharoah, PDP, Platz, EA, Potter, JD, Rennert, G, Scacheri, PC, Schoen, RE, Su, Y-R, Tangen, CM, Thibodeau, SN, Thomas, DC, Ulrich, CM, Um, CY, van Duijnhoven, FJB, Visvanathan, K, Vodicka, P, Vodickova, L, White, E, Wolk, A, Woods, MO, Qu, C, Kundaje, A, Hsu, L, Gauderman, WJ, Gunter, MJ, Peters, U, Dimou, N, Kim, AE, Flanagan, O, Murphy, N, Diez-Obrero, V, Shcherbina, A, Aglago, EK, Bouras, E, Campbell, PT, Casey, G, Gallinger, S, Gruber, SB, Jenkins, MA, Lin, Y, Moreno, V, Ruiz-Narvaez, E, Stern, MC, Tian, Y, Tsilidis, KK, Arndt, V, Barry, EL, Baurley, JW, Berndt, SI, Bezieau, S, Bien, SA, Bishop, DT, Brenner, H, Budiarto, A, Carreras-Torres, R, Cenggoro, TW, Chan, AT, Chang-Claude, J, Chanock, SJ, Chen, X, Conti, DV, Dampier, CH, Devall, M, Drew, DA, Figueiredo, JC, Giles, GG, Gsur, A, Harrison, TA, Hidaka, A, Hoffmeister, M, Huyghe, JR, Jordahl, K, Kawaguchi, E, Keku, TO, Larsson, SC, Le Marchand, L, Lewinger, JP, Li, L, Mahesworo, B, Morrison, J, Newcomb, PA, Newton, CC, Obon-Santacana, M, Ose, J, Pai, RK, Palmer, JR, Papadimitriou, N, Pardamean, B, Peoples, AR, Pharoah, PDP, Platz, EA, Potter, JD, Rennert, G, Scacheri, PC, Schoen, RE, Su, Y-R, Tangen, CM, Thibodeau, SN, Thomas, DC, Ulrich, CM, Um, CY, van Duijnhoven, FJB, Visvanathan, K, Vodicka, P, Vodickova, L, White, E, Wolk, A, Woods, MO, Qu, C, Kundaje, A, Hsu, L, Gauderman, WJ, Gunter, MJ, and Peters, U

Abstract

BACKGROUND: Diabetes is an established risk factor for colorectal cancer. However, the mechanisms underlying this relationship still require investigation and it is not known if the association is modified by genetic variants. To address these questions, we undertook a genome-wide gene-environment interaction analysis. METHODS: We used data from 3 genetic consortia (CCFR, CORECT, GECCO; 31,318 colorectal cancer cases/41,499 controls) and undertook genome-wide gene-environment interaction analyses with colorectal cancer risk, including interaction tests of genetics(G)xdiabetes (1-degree of freedom; d.f.) and joint testing of Gxdiabetes, G-colorectal cancer association (2-d.f. joint test) and G-diabetes correlation (3-d.f. joint test). RESULTS: Based on the joint tests, we found that the association of diabetes with colorectal cancer risk is modified by loci on chromosomes 8q24.11 (rs3802177, SLC30A8 - ORAA: 1.62, 95% CI: 1.34-1.96; ORAG: 1.41, 95% CI: 1.30-1.54; ORGG: 1.22, 95% CI: 1.13-1.31; p-value3-d.f.: 5.46 × 10-11) and 13q14.13 (rs9526201, LRCH1 - ORGG: 2.11, 95% CI: 1.56-2.83; ORGA: 1.52, 95% CI: 1.38-1.68; ORAA: 1.13, 95% CI: 1.06-1.21; p-value2-d.f.: 7.84 × 10-09). DISCUSSION: These results suggest that variation in genes related to insulin signaling (SLC30A8) and immune function (LRCH1) may modify the association of diabetes with colorectal cancer risk and provide novel insights into the biology underlying the diabetes and colorectal cancer relationship.

Published
2023

8. Genome-wide interaction analysis of folate for colorectal cancer risk

Author

Bouras, E, Kim, AE, Lin, Y, Morrison, J, Du, M, Albanes, D, Barry, EL, Baurley, JW, Berndt, SI, Bien, SA, Bishop, TD, Brenner, H, Budiarto, A, Burnett-Hartman, A, Campbell, PT, Carreras-Torres, R, Casey, G, Cenggoro, TW, Chan, AT, Chang-Claude, J, Conti, DV, Cotterchio, M, Devall, M, Diez-Obrero, V, Dimou, N, Drew, DA, Figueiredo, JC, Giles, GG, Gruber, SB, Gunter, MJ, Harrison, TA, Hidaka, A, Hoffmeister, M, Huyghe, JR, Joshi, AD, Kawaguchi, ES, Keku, TO, Kundaje, A, Le Marchand, L, Lewinger, JP, Li, L, Lynch, BM, Mahesworo, B, Mannisto, S, Moreno, V, Murphy, N, Newcomb, PA, Obon-Santacana, M, Ose, J, Palmer, JR, Papadimitriou, N, Pardamean, B, Pellatt, AJ, Peoples, AR, Platz, EA, Potter, JD, Qi, L, Qu, C, Rennert, G, Ruiz-Narvaez, E, Sakoda, LC, Schmit, SL, Shcherbina, A, Stern, MC, Su, Y-R, Tangen, CM, Thomas, DC, Tian, Y, Um, CY, van Duijnhoven, FJB, Van Guelpen, B, Visvanathan, K, Wang, J, White, E, Wolk, A, Woods, MO, Ulrich, CM, Hsu, L, Gauderman, WJ, Peters, U, Tsilidis, KK, Bouras, E, Kim, AE, Lin, Y, Morrison, J, Du, M, Albanes, D, Barry, EL, Baurley, JW, Berndt, SI, Bien, SA, Bishop, TD, Brenner, H, Budiarto, A, Burnett-Hartman, A, Campbell, PT, Carreras-Torres, R, Casey, G, Cenggoro, TW, Chan, AT, Chang-Claude, J, Conti, DV, Cotterchio, M, Devall, M, Diez-Obrero, V, Dimou, N, Drew, DA, Figueiredo, JC, Giles, GG, Gruber, SB, Gunter, MJ, Harrison, TA, Hidaka, A, Hoffmeister, M, Huyghe, JR, Joshi, AD, Kawaguchi, ES, Keku, TO, Kundaje, A, Le Marchand, L, Lewinger, JP, Li, L, Lynch, BM, Mahesworo, B, Mannisto, S, Moreno, V, Murphy, N, Newcomb, PA, Obon-Santacana, M, Ose, J, Palmer, JR, Papadimitriou, N, Pardamean, B, Pellatt, AJ, Peoples, AR, Platz, EA, Potter, JD, Qi, L, Qu, C, Rennert, G, Ruiz-Narvaez, E, Sakoda, LC, Schmit, SL, Shcherbina, A, Stern, MC, Su, Y-R, Tangen, CM, Thomas, DC, Tian, Y, Um, CY, van Duijnhoven, FJB, Van Guelpen, B, Visvanathan, K, Wang, J, White, E, Wolk, A, Woods, MO, Ulrich, CM, Hsu, L, Gauderman, WJ, Peters, U, and Tsilidis, KK

Abstract

BACKGROUND: Epidemiological and experimental evidence suggests that higher folate intake is associated with decreased colorectal cancer (CRC) risk; however, the mechanisms underlying this relationship are not fully understood. Genetic variation that may have a direct or indirect impact on folate metabolism can provide insights into folate's role in CRC. OBJECTIVES: Our aim was to perform a genome-wide interaction analysis to identify genetic variants that may modify the association of folate on CRC risk. METHODS: We applied traditional case-control logistic regression, joint 3-degree of freedom, and a 2-step weighted hypothesis approach to test the interactions of common variants (allele frequency >1%) across the genome and dietary folate, folic acid supplement use, and total folate in relation to risk of CRC in 30,550 cases and 42,336 controls from 51 studies from 3 genetic consortia (CCFR, CORECT, GECCO). RESULTS: Inverse associations of dietary, total folate, and folic acid supplement with CRC were found (odds ratio [OR]: 0.93; 95% confidence interval [CI]: 0.90, 0.96; and 0.91; 95% CI: 0.89, 0.94 per quartile higher intake, and 0.82 (95% CI: 0.78, 0.88) for users compared with nonusers, respectively). Interactions (P-interaction < 5×10-8) of folic acid supplement and variants in the 3p25.2 locus (in the region of Synapsin II [SYN2]/tissue inhibitor of metalloproteinase 4 [TIMP4]) were found using traditional interaction analysis, with variant rs150924902 (located upstream to SYN2) showing the strongest interaction. In stratified analyses by rs150924902 genotypes, folate supplementation was associated with decreased CRC risk among those carrying the TT genotype (OR: 0.82; 95% CI: 0.79, 0.86) but increased CRC risk among those carrying the TA genotype (OR: 1.63; 95% CI: 1.29, 2.05), suggesting a qualitative interaction (P-interaction = 1.4×10-8). No interactions were observed for dietary and total folate. CONCLUSIONS: Variation in 3p25.2 locus may modify the assoc

Published
2023

9. Interactions between folate intake and genetic predictors of gene expression levels associated with colorectal cancer risk

Author

Haas, CB, Su, Y-R, Petersen, P, Wang, X, Bien, SA, Lin, Y, Albanes, D, Weinstein, SJ, Jenkins, MA, Figueiredo, JC, Newcomb, PA, Casey, G, Le Marchand, L, Campbell, PT, Moreno, V, Potter, JD, Sakoda, LC, Slattery, ML, Chan, AT, Li, L, Giles, GG, Milne, RL, Gruber, SB, Rennert, G, Woods, MO, Gallinger, SJ, Berndt, S, Hayes, RB, Huang, W-Y, Wolk, A, White, E, Nan, H, Nassir, R, Lindor, NM, Lewinger, JP, Kim, AE, Conti, D, Gauderman, WJ, Buchanan, DD, Peters, U, Hsu, L, Haas, CB, Su, Y-R, Petersen, P, Wang, X, Bien, SA, Lin, Y, Albanes, D, Weinstein, SJ, Jenkins, MA, Figueiredo, JC, Newcomb, PA, Casey, G, Le Marchand, L, Campbell, PT, Moreno, V, Potter, JD, Sakoda, LC, Slattery, ML, Chan, AT, Li, L, Giles, GG, Milne, RL, Gruber, SB, Rennert, G, Woods, MO, Gallinger, SJ, Berndt, S, Hayes, RB, Huang, W-Y, Wolk, A, White, E, Nan, H, Nassir, R, Lindor, NM, Lewinger, JP, Kim, AE, Conti, D, Gauderman, WJ, Buchanan, DD, Peters, U, and Hsu, L

Abstract

Observational studies have shown higher folate consumption to be associated with lower risk of colorectal cancer (CRC). Understanding whether and how genetic risk factors interact with folate could further elucidate the underlying mechanism. Aggregating functionally relevant genetic variants in set-based variant testing has higher power to detect gene-environment (G × E) interactions and may provide information on the underlying biological pathway. We investigated interactions between folate consumption and predicted gene expression on colorectal cancer risk across the genome. We used variant weights from the PrediXcan models of colon tissue-specific gene expression as a priori variant information for a set-based G × E approach. We harmonized total folate intake (mcg/day) based on dietary intake and supplemental use across cohort and case-control studies and calculated sex and study specific quantiles. Analyses were performed using a mixed effects score tests for interactions between folate and genetically predicted expression of 4839 genes with available genetically predicted expression. We pooled results across 23 studies for a total of 13,498 cases with colorectal tumors and 13,918 controls of European ancestry. We used a false discovery rate of 0.2 to identify genes with suggestive evidence of an interaction. We found suggestive evidence of interaction with folate intake on CRC risk for genes including glutathione S-Transferase Alpha 1 (GSTA1; p = 4.3E-4), Tonsuko Like, DNA Repair Protein (TONSL; p = 4.3E-4), and Aspartylglucosaminidase (AGA: p = 4.5E-4). We identified three genes involved in preventing or repairing DNA damage that may interact with folate consumption to alter CRC risk. Glutathione is an antioxidant, preventing cellular damage and is a downstream metabolite of homocysteine and metabolized by GSTA1. TONSL is part of a complex that functions in the recovery of double strand breaks and AGA plays a role in lysosomal breakdown of glycoprotein.

Published
2022

10. Genome-Wide Interaction Analysis of Genetic Variants With Menopausal Hormone Therapy for Colorectal Cancer Risk

Author

Tian, Y, Kim, AE, Bien, SA, Lin, Y, Qu, C, Harrison, TA, Carreras-Torres, R, Diez-Obrero, V, Dimou, N, Drew, DA, Hidaka, A, Huyghe, JR, Jordahl, KM, Morrison, J, Murphy, N, Obon-Santacana, M, Ulrich, CM, Ose, J, Peoples, AR, Ruiz-Narvaez, EA, Shcherbina, A, Stern, MC, Su, Y-R, van Duijnhoven, FJB, Arndt, V, Baurley, JW, Berndt, S, Bishop, DT, Brenner, H, Buchanan, DD, Chan, AT, Figueiredo, JC, Gallinger, S, Gruber, SB, Harlid, S, Hoffmeister, M, Jenkins, MA, Joshi, AD, Keku, TO, Larsson, SC, Le Marchand, L, Li, L, Giles, GG, Milne, RL, Nan, H, Nassir, R, Ogino, S, Budiarto, A, Platz, EA, Potter, JD, Prentice, RL, Rennert, G, Sakoda, LC, Schoen, RE, Slattery, ML, Thibodeau, SN, Van Guelpen, B, Visvanathan, K, White, E, Wolk, A, Woods, MO, Wu, AH, Campbell, PT, Casey, G, Conti, D, Gunter, MJ, Kundaje, A, Lewinger, JP, Moreno, V, Newcomb, PA, Pardamean, B, Thomas, DC, Tsilidis, KK, Peters, U, Gauderman, WJ, Hsu, L, Chang-Claude, J, Tian, Y, Kim, AE, Bien, SA, Lin, Y, Qu, C, Harrison, TA, Carreras-Torres, R, Diez-Obrero, V, Dimou, N, Drew, DA, Hidaka, A, Huyghe, JR, Jordahl, KM, Morrison, J, Murphy, N, Obon-Santacana, M, Ulrich, CM, Ose, J, Peoples, AR, Ruiz-Narvaez, EA, Shcherbina, A, Stern, MC, Su, Y-R, van Duijnhoven, FJB, Arndt, V, Baurley, JW, Berndt, S, Bishop, DT, Brenner, H, Buchanan, DD, Chan, AT, Figueiredo, JC, Gallinger, S, Gruber, SB, Harlid, S, Hoffmeister, M, Jenkins, MA, Joshi, AD, Keku, TO, Larsson, SC, Le Marchand, L, Li, L, Giles, GG, Milne, RL, Nan, H, Nassir, R, Ogino, S, Budiarto, A, Platz, EA, Potter, JD, Prentice, RL, Rennert, G, Sakoda, LC, Schoen, RE, Slattery, ML, Thibodeau, SN, Van Guelpen, B, Visvanathan, K, White, E, Wolk, A, Woods, MO, Wu, AH, Campbell, PT, Casey, G, Conti, D, Gunter, MJ, Kundaje, A, Lewinger, JP, Moreno, V, Newcomb, PA, Pardamean, B, Thomas, DC, Tsilidis, KK, Peters, U, Gauderman, WJ, Hsu, L, and Chang-Claude, J

Abstract

BACKGROUND: The use of menopausal hormone therapy (MHT) may interact with genetic variants to influence colorectal cancer (CRC) risk. METHODS: We conducted a genome-wide, gene-environment interaction between single nucleotide polymorphisms and the use of any MHT, estrogen only, and combined estrogen-progestogen therapy with CRC risk, among 28 486 postmenopausal women (11 519 CRC patients and 16 967 participants without CRC) from 38 studies, using logistic regression, 2-step method, and 2- or 3-degree-of-freedom joint test. A set-based score test was applied for rare genetic variants. RESULTS: The use of any MHT, estrogen only and estrogen-progestogen were associated with a reduced CRC risk (odds ratio [OR] = 0.71, 95% confidence interval [CI] = 0.64 to 0.78; OR = 0.65, 95% CI = 0.53 to 0.79; and OR = 0.73, 95% CI = 0.59 to 0.90, respectively). The 2-step method identified a statistically significant interaction between a GRIN2B variant rs117868593 and MHT use, whereby MHT-associated CRC risk was statistically significantly reduced in women with the GG genotype (OR = 0.68, 95% CI = 0.64 to 0.72) but not within strata of GC or CC genotypes. A statistically significant interaction between a DCBLD1 intronic variant at 6q22.1 (rs10782186) and MHT use was identified by the 2-degree-of-freedom joint test. The MHT-associated CRC risk was reduced with increasing number of rs10782186-C alleles, showing odds ratios of 0.78 (95% CI = 0.70 to 0.87) for TT, 0.68 (95% CI = 0.63 to 0.73) for TC, and 0.66 (95% CI = 0.60 to 0.74) for CC genotypes. In addition, 5 genes in rare variant analysis showed suggestive interactions with MHT (2-sided P < 1.2 × 10-4). CONCLUSION: Genetic variants that modify the association between MHT and CRC risk were identified, offering new insights into pathways of CRC carcinogenesis and potential mechanisms involved.

Published
2022

11. Genetic architectures of proximal and distal colorectal cancer are partly distinct

Author

Huyghe, JR, Harrison, TA, Bien, SA, Hampel, H, Figueiredo, JC, Schmit, SL, Conti, D, Chen, S, Qu, C, Lin, Y, Barfield, R, Baron, JA, Cross, AJ, Diergaarde, B, Duggan, D, Harlid, S, Imaz, L, Kang, HM, Levine, DM, Perduca, V, Perez-Cornago, A, Sakoda, LC, Schumacher, FR, Slattery, ML, Toland, AE, van Duijnhoven, FJB, Van Guelpen, B, Agudo, A, Albanes, D, Alonso, MH, Anderson, K, Arnau-Collell, C, Arndt, V, Banbury, BL, Bassik, MC, Berndt, S, Bezieau, S, Bishop, DT, Boehm, J, Boeing, H, Boutron-Ruault, M-C, Brenner, H, Brezina, S, Buch, S, Buchanan, DD, Burnett-Hartman, A, Caan, BJ, Campbell, PT, Carr, PR, Castells, A, Castellvi-Bel, S, Chan, AT, Chang-Claude, J, Chanock, SJ, Curtis, KR, de la Chapelle, A, Easton, DF, English, DR, Feskens, EJM, Gala, M, Gallinger, SJ, Gauderman, WJ, Giles, GG, Goodman, PJ, Grady, WM, Grove, JS, Gsur, A, Gunter, MJ, Haile, RW, Hampe, J, Hoffmeister, M, Hopper, JL, Hsu, W-L, Huang, W-Y, Hudson, TJ, Jenab, M, Jenkins, MA, Joshi, AD, Keku, TO, Kooperberg, C, Kuhn, T, Kury, S, Le Marchand, L, Lejbkowicz, F, Li, C, Li, L, Lieb, W, Lindblom, A, Lindor, NM, Mannisto, S, Markowitz, SD, Milne, RL, Moreno, L, Murphy, N, Nassir, R, Offit, K, Ogino, S, Panico, S, Parfrey, PS, Pearlman, R, Pharoah, PDP, Phipps, A, Platz, EA, Potter, JD, Prentice, RL, Qi, L, Raskin, L, Rennert, G, Rennert, HS, Riboli, E, Schafmayer, C, Schoen, RE, Seminara, D, Song, M, Su, Y-R, Tangen, CM, Thibodeau, SN, Thomas, DC, Trichopoulou, A, Ulrich, CM, Visvanathan, K, Vodicka, P, Vodickova, L, Vymetalkova, V, Weigl, K, Weinstein, SJ, White, E, Wolk, A, Woods, MO, Wu, AH, Abecasis, GR, Nickerson, DA, Scacheri, PC, Kundaje, A, Casey, G, Gruber, SB, Hsu, L, Moreno, V, Hayes, RB, Newcomb, PA, Peters, U, Huyghe, JR, Harrison, TA, Bien, SA, Hampel, H, Figueiredo, JC, Schmit, SL, Conti, D, Chen, S, Qu, C, Lin, Y, Barfield, R, Baron, JA, Cross, AJ, Diergaarde, B, Duggan, D, Harlid, S, Imaz, L, Kang, HM, Levine, DM, Perduca, V, Perez-Cornago, A, Sakoda, LC, Schumacher, FR, Slattery, ML, Toland, AE, van Duijnhoven, FJB, Van Guelpen, B, Agudo, A, Albanes, D, Alonso, MH, Anderson, K, Arnau-Collell, C, Arndt, V, Banbury, BL, Bassik, MC, Berndt, S, Bezieau, S, Bishop, DT, Boehm, J, Boeing, H, Boutron-Ruault, M-C, Brenner, H, Brezina, S, Buch, S, Buchanan, DD, Burnett-Hartman, A, Caan, BJ, Campbell, PT, Carr, PR, Castells, A, Castellvi-Bel, S, Chan, AT, Chang-Claude, J, Chanock, SJ, Curtis, KR, de la Chapelle, A, Easton, DF, English, DR, Feskens, EJM, Gala, M, Gallinger, SJ, Gauderman, WJ, Giles, GG, Goodman, PJ, Grady, WM, Grove, JS, Gsur, A, Gunter, MJ, Haile, RW, Hampe, J, Hoffmeister, M, Hopper, JL, Hsu, W-L, Huang, W-Y, Hudson, TJ, Jenab, M, Jenkins, MA, Joshi, AD, Keku, TO, Kooperberg, C, Kuhn, T, Kury, S, Le Marchand, L, Lejbkowicz, F, Li, C, Li, L, Lieb, W, Lindblom, A, Lindor, NM, Mannisto, S, Markowitz, SD, Milne, RL, Moreno, L, Murphy, N, Nassir, R, Offit, K, Ogino, S, Panico, S, Parfrey, PS, Pearlman, R, Pharoah, PDP, Phipps, A, Platz, EA, Potter, JD, Prentice, RL, Qi, L, Raskin, L, Rennert, G, Rennert, HS, Riboli, E, Schafmayer, C, Schoen, RE, Seminara, D, Song, M, Su, Y-R, Tangen, CM, Thibodeau, SN, Thomas, DC, Trichopoulou, A, Ulrich, CM, Visvanathan, K, Vodicka, P, Vodickova, L, Vymetalkova, V, Weigl, K, Weinstein, SJ, White, E, Wolk, A, Woods, MO, Wu, AH, Abecasis, GR, Nickerson, DA, Scacheri, PC, Kundaje, A, Casey, G, Gruber, SB, Hsu, L, Moreno, V, Hayes, RB, Newcomb, PA, and Peters, U

Abstract

OBJECTIVE: An understanding of the etiologic heterogeneity of colorectal cancer (CRC) is critical for improving precision prevention, including individualized screening recommendations and the discovery of novel drug targets and repurposable drug candidates for chemoprevention. Known differences in molecular characteristics and environmental risk factors among tumors arising in different locations of the colorectum suggest partly distinct mechanisms of carcinogenesis. The extent to which the contribution of inherited genetic risk factors for CRC differs by anatomical subsite of the primary tumor has not been examined. DESIGN: To identify new anatomical subsite-specific risk loci, we performed genome-wide association study (GWAS) meta-analyses including data of 48 214 CRC cases and 64 159 controls of European ancestry. We characterised effect heterogeneity at CRC risk loci using multinomial modelling. RESULTS: We identified 13 loci that reached genome-wide significance (p<5×10-8) and that were not reported by previous GWASs for overall CRC risk. Multiple lines of evidence support candidate genes at several of these loci. We detected substantial heterogeneity between anatomical subsites. Just over half (61) of 109 known and new risk variants showed no evidence for heterogeneity. In contrast, 22 variants showed association with distal CRC (including rectal cancer), but no evidence for association or an attenuated association with proximal CRC. For two loci, there was strong evidence for effects confined to proximal colon cancer. CONCLUSION: Genetic architectures of proximal and distal CRC are partly distinct. Studies of risk factors and mechanisms of carcinogenesis, and precision prevention strategies should take into consideration the anatomical subsite of the tumour.

Published
2021

12. Functional informed genome-wide interaction analysis of body mass index, diabetes and colorectal cancer risk

Author

Xia, Z, Su, Y-R, Petersen, P, Qi, L, Kim, AE, Figueiredo, JC, Lin, Y, Nan, H, Sakoda, LC, Albanes, D, Berndt, SI, Bezieau, S, Bien, S, Buchanan, DD, Casey, G, Chan, AT, Conti, DV, Drew, DA, Gallinger, SJ, Gauderman, WJ, Giles, GG, Gruber, SB, Gunter, MJ, Hoffmeister, M, Jenkins, MA, Joshi, AD, Le Marchand, L, Lewinger, JP, Li, L, Lindor, NM, Moreno, V, Murphy, N, Nassir, R, Newcomb, PA, Ogino, S, Rennert, G, Song, M, Wang, X, Wolk, A, Woods, MO, Brenner, H, White, E, Slattery, ML, Giovannucci, EL, Chang-Claude, J, Pharoah, PDP, Hsu, L, Campbell, PT, Peters, U, Xia, Z, Su, Y-R, Petersen, P, Qi, L, Kim, AE, Figueiredo, JC, Lin, Y, Nan, H, Sakoda, LC, Albanes, D, Berndt, SI, Bezieau, S, Bien, S, Buchanan, DD, Casey, G, Chan, AT, Conti, DV, Drew, DA, Gallinger, SJ, Gauderman, WJ, Giles, GG, Gruber, SB, Gunter, MJ, Hoffmeister, M, Jenkins, MA, Joshi, AD, Le Marchand, L, Lewinger, JP, Li, L, Lindor, NM, Moreno, V, Murphy, N, Nassir, R, Newcomb, PA, Ogino, S, Rennert, G, Song, M, Wang, X, Wolk, A, Woods, MO, Brenner, H, White, E, Slattery, ML, Giovannucci, EL, Chang-Claude, J, Pharoah, PDP, Hsu, L, Campbell, PT, and Peters, U

Abstract

BACKGROUND: Body mass index (BMI) and diabetes are established risk factors for colorectal cancer (CRC), likely through perturbations in metabolic traits (e.g. insulin resistance and glucose homeostasis). Identification of interactions between variation in genes and these metabolic risk factors may identify novel biologic insights into CRC etiology. METHODS: To improve statistical power and interpretation for gene-environment interaction (G × E) testing, we tested genetic variants that regulate expression of a gene together for interaction with BMI (kg/m2 ) and diabetes on CRC risk among 26 017 cases and 20 692 controls. Each variant was weighted based on PrediXcan analysis of gene expression data from colon tissue generated in the Genotype-Tissue Expression Project for all genes with heritability ≥1%. We used a mixed-effects model to jointly measure the G × E interaction in a gene by partitioning the interactions into the predicted gene expression levels (fixed effects), and residual G × E effects (random effects). G × BMI analyses were stratified by sex as BMI-CRC associations differ by sex. We used false discovery rates to account for multiple comparisons and reported all results with FDR <0.2. RESULTS: Among 4839 genes tested, genetically predicted expressions of FOXA1 (P = 3.15 × 10-5 ), PSMC5 (P = 4.51 × 10-4 ) and CD33 (P = 2.71 × 10-4 ) modified the association of BMI on CRC risk for men; KIAA0753 (P = 2.29 × 10-5 ) and SCN1B (P = 2.76 × 10-4 ) modified the association of BMI on CRC risk for women; and PTPN2 modified the association between diabetes and CRC risk in both sexes (P = 2.31 × 10-5 ). CONCLUSIONS: Aggregating G × E interactions and incorporating functional information, we discovered novel genes that may interact with BMI and diabetes on CRC risk.

Published
2020

13. Author Correction: Genome-wide association and HLA fine-mapping studies identify risk loci and genetic pathways underlying allergic rhinitis

Author

Waage, J, Standl, M, Curtin, JA, Jessen, LE, Thorsen, J, Tian, C, Schoettler, N, 23andMe Research Team, AAGC collaborators, Flores, C, Abdellaoui, A, Ahluwalia, TS, Alves, AC, Amaral, AFS, Antó, JM, Arnold, A, Barreto-Luis, A, Baurecht, H, van Beijsterveldt, CEM, Bleecker, ER, Bonàs-Guarch, S, Boomsma, DI, Brix, S, Bunyavanich, S, Burchard, EG, Chen, Z, Curjuric, I, Custovic, A, den Dekker, HT, Dharmage, SC, Dmitrieva, J, Duijts, L, Ege, MJ, Gauderman, WJ, Georges, M, Gieger, C, Gilliland, F, Granell, R, Gui, H, Hansen, T, Heinrich, J, Henderson, J, Hernandez-Pacheco, N, Holt, P, Imboden, M, Jaddoe, VWV, Jarvelin, M-R, Jarvis, DL, Jensen, KK, Jónsdóttir, I, Kabesch, M, Kaprio, J, Kumar, A, Lee, Y-A, Levin, AM, Li, X, Lorenzo-Diaz, F, Melén, E, Mercader, JM, Meyers, DA, Myers, R, Nicolae, DL, Nohr, EA, Palviainen, T, Paternoster, L, Pennell, CE, Pershagen, G, Pino-Yanes, M, Probst-Hensch, NM, Rüschendorf, F, Simpson, A, Stefansson, K, Sunyer, J, Sveinbjornsson, G, Thiering, E, Thompson, PJ, Torrent, M, Torrents, D, Tung, JY, Wang, CA, Weidinger, S, Weiss, S, Willemsen, G, Williams, LK, Ober, C, Hinds, DA, Ferreira, MA, Bisgaard, H, Strachan, DP, and Bønnelykke, K

Subjects
ComputingMethodologies_DOCUMENTANDTEXTPROCESSING, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, ComputingMethodologies_COMPUTERGRAPHICS
Abstract

In the version of this article initially published, in Fig. 3, the y-axis numbering did not match the log scale indicated in the axis label. The error has been corrected in the HTML and PDF version of the article.

Published
2018

14. Genome-wide association and HLA fine-mapping studies identify risk loci and genetic pathways underlying allergic rhinitis

Author

Waage, J, Standl, M, Curtin, JA, Jessen, L, Thorsen, J, Tian, C, Schoettler, N, The 23andMe Research Team, AAGC Collaborators, Flores, C, Abdellaoui, A, Ahluwalia, TS, Alves, A, Amaral, AFS, Antó, JM, Arnold, A, Barreto-Luis, A, Baurecht, H, van Beijsterveldt, CEM, Bleecker, ER, Bonàs-Guarch, S, Boomsma, D, Brix, S, Bunyavanich, S, Burchard, E, Chen, Z, Curjuric, I, Custovic, A, den Dekker, HT, Dharmage, SC, Dmitrieva, J, Duijts, L, Ege, MJ, Gauderman, WJ, Georges, M, Gieger, C, Gilliland, F, Granell, R, Gui, H, Hansen, T, Heinrich, J, Henderson, J, Hernandez-Pacheco, N, Holt, P, Imboden, M, Jaddoe, VWV, Jarvelin, M-R, Jarvis, DL, Jensen, KK, Jónsdóttir, I, Kabesch, M, Kaprio, J, Kumar, A, Lee, Y-A, Levin, AM, Li, X, Lorenzo-Diaz, F, Melén, E, Mercader, JM, Meyers, DA, Myers, R, Nicolae, DL, Nohr, EA, Palviainen, T, Paternoster, L, Pennell, C, Pershagen, G, Pino-Yanes, M, Probst-Hensch, NM, Rüschendorf, F, Simpson, A, Stefansson, K, Sunyer, J, Sveinbjornsson, G, Thiering, E, Thompson, PJ, Torrent, M, Torrents, D, Tung, JY, Wang, CA, Weidinger, S, Weiss, S, Willemsen, G, Williams, LK, Ober, C, Hinds, DA, Ferreira, MA, Bisgaard, H, Strachan, DP, and Bønnelykke, K

Abstract

Allergic rhinitis is the most common clinical presentation of allergy, affecting 400 million people worldwide, with increasing incidence in westernized countries1,2. To elucidate the genetic architecture and understand the underlying disease mechanisms, we carried out a meta-analysis of allergic rhinitis in 59,762 cases and 152,358 controls of European ancestry and identified a total of 41 risk loci for allergic rhinitis, including 20 loci not previously associated with allergic rhinitis, which were confirmed in a replication phase of 60,720 cases and 618,527 controls. Functional annotation implicated genes involved in various immune pathways, and fine mapping of the HLA region suggested amino acid variants important for antigen binding. We further performed genome-wide association study (GWAS) analyses of allergic sensitization against inhalant allergens and nonallergic rhinitis, which suggested shared genetic mechanisms across rhinitis-related traits. Future studies of the identified loci and genes might identify novel targets for treatment and prevention of allergic rhinitis.

Published
2018

15. Novel Common Genetic Susceptibility Loci for Colorectal Cancer

Author

Schmit, SL, Edlund, CK, Schumacher, FR, Gong, J, Harrison, TA, Huyghe, JR, Qu, C, Melas, M, Van den Berg, DJ, Wang, H, Tring, S, Plummer, SJ, Albanes, D, Alonso, MH, Amos, CI, Anton, K, Aragaki, AK, Arndt, V, Barry, EL, Berndt, SI, Bezieau, S, Bien, S, Bloomer, A, Boehm, J, Boutron-Ruault, M-C, Brenner, H, Brezina, S, Buchanan, DD, Butterbach, K, Caan, BJ, Campbell, PT, Carlson, CS, Castelao, JE, Chan, AT, Chang-Claude, J, Chanock, SJ, Cheng, I, Cheng, Y-W, Chin, LS, Church, JM, Church, T, Coetzee, GA, Cotterchio, M, Correa, MC, Curtis, KR, Duggan, D, Easton, DF, English, D, Feskens, EJM, Fischer, R, FitzGerald, LM, Fortini, BK, Fritsche, LG, Fuchs, CS, Gago-Dominguez, M, Gala, M, Gallinger, SJ, Gauderman, WJ, Giles, GG, Giovannucci, EL, Gogarten, SM, Gonzalez-Villalpando, C, Gonzalez-Villalpando, EM, Grady, WM, Greenson, JK, Gsur, A, Gunter, M, Haiman, CA, Hampe, J, Harlid, S, Harju, JF, Hayes, RB, Hofer, P, Hoffmeister, M, Hopper, JL, Huang, S-C, Huerta, JM, Hudson, TJ, Hunter, DJ, Idos, GE, Iwasaki, M, Jackson, RD, Jacobs, EJ, Jee, SH, Jenkins, MA, Jia, W-H, Jiao, S, Joshi, AD, Kolonel, LN, Kono, S, Kooperberg, C, Krogh, V, Kuehn, T, Kury, S, LaCroix, A, Laurie, CA, Lejbkowicz, F, Lemire, M, Lenz, H-J, Levine, D, Li, CI, Li, L, Lieb, W, Lin, Y, Lindor, NM, Liu, Y-R, Loupakis, F, Lu, Y, Luh, F, Ma, J, Mancao, C, Manion, FJ, Markowitz, SD, Martin, V, Matsuda, K, Matsuo, K, McDonnell, KJ, McNeil, CE, Milne, R, Molina, AJ, Mukherjee, B, Murphy, N, Newcomb, PA, Offit, K, Omichessan, H, Palli, D, Cotore, JPP, Perez-Mayoral, J, Pharoah, PD, Potter, JD, Raskin, L, Rennert, G, Rennert, HS, Riggs, BM, Schafmayer, C, Schoen, RE, Sellers, TA, Seminara, D, Severi, G, Shi, W, Shibata, D, Shu, X-O, Siegel, EM, Slattery, ML, Southey, M, Stadler, ZK, Stern, MC, Stintzing, S, Taverna, D, Thibodeau, SN, Thomas, DC, Trichopoulou, A, Tsugane, S, Ulrich, CM, van Duijnhoven, FJB, van Guelpan, B, Vijai, J, Virtamo, J, Weinstein, SJ, White, E, Win, AK, Wolk, A, Woods, M, Wu, AH, Wu, K, Xiang, Y-B, Yen, Y, Zanke, BW, Zeng, Y-X, Zhang, B, Zubair, N, Kweon, S-S, Figueiredo, JC, Zheng, W, Le Marchand, L, Lindblom, A, Moreno, V, Peters, U, Casey, G, Hsu, L, Conti, DV, Gruber, SB, Schmit, SL, Edlund, CK, Schumacher, FR, Gong, J, Harrison, TA, Huyghe, JR, Qu, C, Melas, M, Van den Berg, DJ, Wang, H, Tring, S, Plummer, SJ, Albanes, D, Alonso, MH, Amos, CI, Anton, K, Aragaki, AK, Arndt, V, Barry, EL, Berndt, SI, Bezieau, S, Bien, S, Bloomer, A, Boehm, J, Boutron-Ruault, M-C, Brenner, H, Brezina, S, Buchanan, DD, Butterbach, K, Caan, BJ, Campbell, PT, Carlson, CS, Castelao, JE, Chan, AT, Chang-Claude, J, Chanock, SJ, Cheng, I, Cheng, Y-W, Chin, LS, Church, JM, Church, T, Coetzee, GA, Cotterchio, M, Correa, MC, Curtis, KR, Duggan, D, Easton, DF, English, D, Feskens, EJM, Fischer, R, FitzGerald, LM, Fortini, BK, Fritsche, LG, Fuchs, CS, Gago-Dominguez, M, Gala, M, Gallinger, SJ, Gauderman, WJ, Giles, GG, Giovannucci, EL, Gogarten, SM, Gonzalez-Villalpando, C, Gonzalez-Villalpando, EM, Grady, WM, Greenson, JK, Gsur, A, Gunter, M, Haiman, CA, Hampe, J, Harlid, S, Harju, JF, Hayes, RB, Hofer, P, Hoffmeister, M, Hopper, JL, Huang, S-C, Huerta, JM, Hudson, TJ, Hunter, DJ, Idos, GE, Iwasaki, M, Jackson, RD, Jacobs, EJ, Jee, SH, Jenkins, MA, Jia, W-H, Jiao, S, Joshi, AD, Kolonel, LN, Kono, S, Kooperberg, C, Krogh, V, Kuehn, T, Kury, S, LaCroix, A, Laurie, CA, Lejbkowicz, F, Lemire, M, Lenz, H-J, Levine, D, Li, CI, Li, L, Lieb, W, Lin, Y, Lindor, NM, Liu, Y-R, Loupakis, F, Lu, Y, Luh, F, Ma, J, Mancao, C, Manion, FJ, Markowitz, SD, Martin, V, Matsuda, K, Matsuo, K, McDonnell, KJ, McNeil, CE, Milne, R, Molina, AJ, Mukherjee, B, Murphy, N, Newcomb, PA, Offit, K, Omichessan, H, Palli, D, Cotore, JPP, Perez-Mayoral, J, Pharoah, PD, Potter, JD, Raskin, L, Rennert, G, Rennert, HS, Riggs, BM, Schafmayer, C, Schoen, RE, Sellers, TA, Seminara, D, Severi, G, Shi, W, Shibata, D, Shu, X-O, Siegel, EM, Slattery, ML, Southey, M, Stadler, ZK, Stern, MC, Stintzing, S, Taverna, D, Thibodeau, SN, Thomas, DC, Trichopoulou, A, Tsugane, S, Ulrich, CM, van Duijnhoven, FJB, van Guelpan, B, Vijai, J, Virtamo, J, Weinstein, SJ, White, E, Win, AK, Wolk, A, Woods, M, Wu, AH, Wu, K, Xiang, Y-B, Yen, Y, Zanke, BW, Zeng, Y-X, Zhang, B, Zubair, N, Kweon, S-S, Figueiredo, JC, Zheng, W, Le Marchand, L, Lindblom, A, Moreno, V, Peters, U, Casey, G, Hsu, L, Conti, DV, and Gruber, SB

Abstract

BACKGROUND: Previous genome-wide association studies (GWAS) have identified 42 loci (P < 5 × 10-8) associated with risk of colorectal cancer (CRC). Expanded consortium efforts facilitating the discovery of additional susceptibility loci may capture unexplained familial risk. METHODS: We conducted a GWAS in European descent CRC cases and control subjects using a discovery-replication design, followed by examination of novel findings in a multiethnic sample (cumulative n = 163 315). In the discovery stage (36 948 case subjects/30 864 control subjects), we identified genetic variants with a minor allele frequency of 1% or greater associated with risk of CRC using logistic regression followed by a fixed-effects inverse variance weighted meta-analysis. All novel independent variants reaching genome-wide statistical significance (two-sided P < 5 × 10-8) were tested for replication in separate European ancestry samples (12 952 case subjects/48 383 control subjects). Next, we examined the generalizability of discovered variants in East Asians, African Americans, and Hispanics (12 085 case subjects/22 083 control subjects). Finally, we examined the contributions of novel risk variants to familial relative risk and examined the prediction capabilities of a polygenic risk score. All statistical tests were two-sided. RESULTS: The discovery GWAS identified 11 variants associated with CRC at P < 5 × 10-8, of which nine (at 4q22.2/5p15.33/5p13.1/6p21.31/6p12.1/10q11.23/12q24.21/16q24.1/20q13.13) independently replicated at a P value of less than .05. Multiethnic follow-up supported the generalizability of discovery findings. These results demonstrated a 14.7% increase in familial relative risk explained by common risk alleles from 10.3% (95% confidence interval [CI] = 7.9% to 13.7%; known variants) to 11.9% (95% CI = 9.2% to 15.5%; known and novel variants). A polygenic risk score identified 4.3% of the population at an odds ratio for developing CRC of at least 2.0. CONCLUSIONS: T

Published
2019

16. The Early Growth Genetics (EGG) and EArly Genetics and Lifecourse Epidemiology (EAGLE) consortia: design, results and future prospects

Author

Middeldorp, CM, Mahajan, A, Horikoshi, M, Robertson, NR, Beaumont, RN, Bradfield, JP, Bustamante, M, Cousminer, DL, Day, FR, De Silva, NM, Guxens, M, Mook-Kanamori, DO, St Pourcain, B, Warrington, NM, Adair, LS, Ahlqvist, E, Ahluwalia, TS, Almgren, P, Ang, W, Atalay, M, Auvinen, J, Bartels, M, Beckmann, JS, Bilbao, JR, Bond, T, Borja, JB, Cavadino, A, Charoen, P, Chen, Z, Coin, L, Cooper, C, Curtin, JA, Custovic, A, Das, S, Davies, GE, Dedoussis, GV, Duijts, L, Eastwood, PR, Eliasen, AU, Elliott, P, Eriksson, JG, Estivill, X, Fadista, J, Fedko, IO, Frayling, TM, Gaillard, R, Gauderman, WJ, Geller, F, Gilliland, F, Gilsanz, V, Granell, R, Grarup, N, Groop, L, Hadley, D, Hakonarson, H, Hansen, T, Hartman, CA, Hattersley, AT, Hayes, MG, Hebebrand, J, Heinrich, J, Helgeland, O, Henders, AK, Henderson, J, Henriksen, TB, Hirschhorn, JN, Hivert, M-F, Hocher, B, Holloway, JW, Holt, P, Hottenga, J-J, Hypponen, E, Iniguez, C, Johansson, S, Jugessur, A, Kahonen, M, Kalkwarf, HJ, Kaprio, J, Karhunen, V, Kemp, JP, Kerkhof, M, Koppelman, GH, Korner, A, Kotecha, S, Kreiner-Moller, E, Kulohoma, B, Kumar, A, Kutalik, Z, Lahti, J, Lappe, JM, Larsson, H, Lehtimaki, T, Lewin, AM, Li, J, Lichtenstein, P, Lindgren, CM, Lindi, V, Linneberg, A, Liu, X, Liu, J, Lowe, WL, Lundstrom, S, Lyytikainen, L-P, Ma, RCW, Mace, A, Magi, R, Magnus, P, Mamun, AA, Mannikko, M, Martin, NG, Mbarek, H, McCarthy, NS, Medland, SE, Melbye, M, Melen, E, Mohlke, KL, Monnereau, C, Morgen, CS, Morris, AP, Murray, JC, Myhre, R, Najman, JM, Nivard, MG, Nohr, EA, Nolte, IM, Ntalla, I, O'Reilly, P, Oberfield, SE, Oken, E, Oldehinkel, AJ, Pahkala, K, Palviainen, T, Panoutsopoulou, K, Pedersen, O, Pennell, CE, Pershagen, G, Pitkanen, N, Plomin, R, Power, C, Prasad, RB, Prokopenko, I, Pulkkinen, L, Raikkonen, K, Raitakari, OT, Reynolds, RM, Richmond, RC, Rivadeneira, F, Rodriguez, A, Rose, RJ, Salem, R, Santa-Marina, L, Saw, S-M, Schnurr, TM, Scott, JG, Selzam, S, Shepherd, JA, Simpson, A, Skotte, L, Sleiman, PMA, Snieder, H, Sorensen, TIA, Standl, M, Steegers, EAP, Strachan, DP, Straker, L, Strandberg, T, Taylor, M, Teo, Y-Y, Thiering, E, Torrent, M, Tyrrell, J, Uitterlinden, AG, van Beijsterveldt, T, van der Most, PJ, van Duijn, CM, Viikari, J, Vilor-Tejedor, N, Vogelezang, S, Vonk, JM, Vrijkotte, TGM, Vuoksimaa, E, Wang, CA, Watkins, WJ, Wichmann, H-E, Willemsen, G, Williams, GM, Wilson, JF, Wray, NR, Xu, S, Xu, C-J, Yaghootkar, H, Yi, L, Zafarmand, MH, Zeggini, E, Zemel, BS, Hinney, A, Lakka, TA, Whitehouse, AJO, Sunyer, J, Widen, EE, Feenstra, B, Sebert, S, Jacobsson, B, Njolstad, PR, Stoltenberg, C, Smith, GD, Lawlor, DA, Paternoster, L, Timpson, NJ, Ong, KK, Bisgaard, H, Bonnelykke, K, Jaddoe, VWV, Tiemeier, H, Jarvelin, M-R, Evans, DM, Perry, JRB, Grant, SFA, Boomsma, DI, Freathy, RM, McCarthy, MI, Felix, JF, Middeldorp, CM, Mahajan, A, Horikoshi, M, Robertson, NR, Beaumont, RN, Bradfield, JP, Bustamante, M, Cousminer, DL, Day, FR, De Silva, NM, Guxens, M, Mook-Kanamori, DO, St Pourcain, B, Warrington, NM, Adair, LS, Ahlqvist, E, Ahluwalia, TS, Almgren, P, Ang, W, Atalay, M, Auvinen, J, Bartels, M, Beckmann, JS, Bilbao, JR, Bond, T, Borja, JB, Cavadino, A, Charoen, P, Chen, Z, Coin, L, Cooper, C, Curtin, JA, Custovic, A, Das, S, Davies, GE, Dedoussis, GV, Duijts, L, Eastwood, PR, Eliasen, AU, Elliott, P, Eriksson, JG, Estivill, X, Fadista, J, Fedko, IO, Frayling, TM, Gaillard, R, Gauderman, WJ, Geller, F, Gilliland, F, Gilsanz, V, Granell, R, Grarup, N, Groop, L, Hadley, D, Hakonarson, H, Hansen, T, Hartman, CA, Hattersley, AT, Hayes, MG, Hebebrand, J, Heinrich, J, Helgeland, O, Henders, AK, Henderson, J, Henriksen, TB, Hirschhorn, JN, Hivert, M-F, Hocher, B, Holloway, JW, Holt, P, Hottenga, J-J, Hypponen, E, Iniguez, C, Johansson, S, Jugessur, A, Kahonen, M, Kalkwarf, HJ, Kaprio, J, Karhunen, V, Kemp, JP, Kerkhof, M, Koppelman, GH, Korner, A, Kotecha, S, Kreiner-Moller, E, Kulohoma, B, Kumar, A, Kutalik, Z, Lahti, J, Lappe, JM, Larsson, H, Lehtimaki, T, Lewin, AM, Li, J, Lichtenstein, P, Lindgren, CM, Lindi, V, Linneberg, A, Liu, X, Liu, J, Lowe, WL, Lundstrom, S, Lyytikainen, L-P, Ma, RCW, Mace, A, Magi, R, Magnus, P, Mamun, AA, Mannikko, M, Martin, NG, Mbarek, H, McCarthy, NS, Medland, SE, Melbye, M, Melen, E, Mohlke, KL, Monnereau, C, Morgen, CS, Morris, AP, Murray, JC, Myhre, R, Najman, JM, Nivard, MG, Nohr, EA, Nolte, IM, Ntalla, I, O'Reilly, P, Oberfield, SE, Oken, E, Oldehinkel, AJ, Pahkala, K, Palviainen, T, Panoutsopoulou, K, Pedersen, O, Pennell, CE, Pershagen, G, Pitkanen, N, Plomin, R, Power, C, Prasad, RB, Prokopenko, I, Pulkkinen, L, Raikkonen, K, Raitakari, OT, Reynolds, RM, Richmond, RC, Rivadeneira, F, Rodriguez, A, Rose, RJ, Salem, R, Santa-Marina, L, Saw, S-M, Schnurr, TM, Scott, JG, Selzam, S, Shepherd, JA, Simpson, A, Skotte, L, Sleiman, PMA, Snieder, H, Sorensen, TIA, Standl, M, Steegers, EAP, Strachan, DP, Straker, L, Strandberg, T, Taylor, M, Teo, Y-Y, Thiering, E, Torrent, M, Tyrrell, J, Uitterlinden, AG, van Beijsterveldt, T, van der Most, PJ, van Duijn, CM, Viikari, J, Vilor-Tejedor, N, Vogelezang, S, Vonk, JM, Vrijkotte, TGM, Vuoksimaa, E, Wang, CA, Watkins, WJ, Wichmann, H-E, Willemsen, G, Williams, GM, Wilson, JF, Wray, NR, Xu, S, Xu, C-J, Yaghootkar, H, Yi, L, Zafarmand, MH, Zeggini, E, Zemel, BS, Hinney, A, Lakka, TA, Whitehouse, AJO, Sunyer, J, Widen, EE, Feenstra, B, Sebert, S, Jacobsson, B, Njolstad, PR, Stoltenberg, C, Smith, GD, Lawlor, DA, Paternoster, L, Timpson, NJ, Ong, KK, Bisgaard, H, Bonnelykke, K, Jaddoe, VWV, Tiemeier, H, Jarvelin, M-R, Evans, DM, Perry, JRB, Grant, SFA, Boomsma, DI, Freathy, RM, McCarthy, MI, and Felix, JF

Abstract

The impact of many unfavorable childhood traits or diseases, such as low birth weight and mental disorders, is not limited to childhood and adolescence, as they are also associated with poor outcomes in adulthood, such as cardiovascular disease. Insight into the genetic etiology of childhood and adolescent traits and disorders may therefore provide new perspectives, not only on how to improve wellbeing during childhood, but also how to prevent later adverse outcomes. To achieve the sample sizes required for genetic research, the Early Growth Genetics (EGG) and EArly Genetics and Lifecourse Epidemiology (EAGLE) consortia were established. The majority of the participating cohorts are longitudinal population-based samples, but other cohorts with data on early childhood phenotypes are also involved. Cohorts often have a broad focus and collect(ed) data on various somatic and psychiatric traits as well as environmental factors. Genetic variants have been successfully identified for multiple traits, for example, birth weight, atopic dermatitis, childhood BMI, allergic sensitization, and pubertal growth. Furthermore, the results have shown that genetic factors also partly underlie the association with adult traits. As sample sizes are still increasing, it is expected that future analyses will identify additional variants. This, in combination with the development of innovative statistical methods, will provide detailed insight on the mechanisms underlying the transition from childhood to adult disorders. Both consortia welcome new collaborations. Policies and contact details are available from the corresponding authors of this manuscript and/or the consortium websites.

Published
2019

17. Discovery of common and rare genetic risk variants for colorectal cancer

Author

Huyghe, JR, Bien, SA, Harrison, TA, Kang, HM, Chen, S, Schmit, SL, Conti, DV, Qu, C, Jeon, J, Edlund, CK, Greenside, P, Wainberg, M, Schumacher, FR, Smith, JD, Levine, DM, Nelson, SC, Sinnott-Armstrong, NA, Albanes, D, Alonso, MH, Anderson, K, Arnau-Collell, C, Arndt, V, Bamia, C, Banbury, BL, Baron, JA, Berndt, SI, Bezieau, S, Bishop, DT, Boehm, J, Boeing, H, Brenner, H, Brezina, S, Buch, S, Buchanan, DD, Burnett-Hartman, A, Butterbach, K, Caan, BJ, Campbell, PT, Carlson, CS, Castellvi-Bel, S, Chan, AT, Chang-Claude, J, Chanock, SJ, Chirlaque, M-D, Cho, SH, Connolly, CM, Cross, AJ, Cuk, K, Curtis, KR, de la Chapelle, A, Doheny, KF, Duggan, D, Easton, DF, Elias, SG, Elliott, F, English, DR, Feskens, EJM, Figueiredo, JC, Fischer, R, FitzGerald, LM, Forman, D, Gala, M, Gallinger, S, Gauderman, WJ, Giles, GG, Gillanders, E, Gong, J, Goodman, PJ, Grady, WM, Grove, JS, Gsur, A, Gunter, MJ, Haile, RW, Hampe, J, Hampel, H, Harlid, S, Hayes, RB, Hofer, P, Hoffmeister, M, Hopper, JL, Hsu, W-L, Huang, W-Y, Hudson, TJ, Hunter, DJ, Ibanez-Sanz, G, Idos, GE, Ingersoll, R, Jackson, RD, Jacobs, EJ, Jenkins, MA, Joshi, AD, Joshu, CE, Keku, TO, Key, TJ, Kim, HR, Kobayashi, E, Kolonel, LN, Kooperberg, C, Kuehn, T, Kury, S, Kweon, S-S, Larsson, SC, Laurie, CA, Le Marchand, L, Leal, SM, Lee, SC, Lejbkowicz, F, Lemire, M, Li, CI, Li, L, Lieb, W, Lin, Y, Lindblom, A, Lindor, NM, Ling, H, Louie, TL, Mannisto, S, Markowitz, SD, Martin, V, Masala, G, McNeil, CE, Melas, M, Milne, RL, Moreno, L, Murphy, N, Myte, R, Naccarati, A, Newcomb, PA, Offit, K, Ogino, S, Onland-Moret, NC, Pardini, B, Parfrey, PS, Pearlman, R, Perduca, V, Pharoah, PDP, Pinchev, M, Platz, EA, Prentice, RL, Pugh, E, Raskin, L, Rennert, G, Rennert, HS, Riboli, E, Rodriguez-Barranco, M, Romm, J, Sakoda, LC, Schafmayer, C, Schoen, RE, Seminara, D, Shah, M, Shelford, T, Shin, M-H, Shulman, K, Sieri, S, Slattery, ML, Southey, MC, Stadler, ZK, Stegmaier, C, Su, Y-R, Tangen, CM, Thibodeau, SN, Thomas, DC, Thomas, SS, Toland, AE, Trichopoulou, A, Ulrich, CM, Van den Berg, DJ, van Duijnhoven, FJB, Van Guelpen, B, van Kranen, H, Vijai, J, Visvanathan, K, Vodicka, P, Vodickova, L, Vymetalkova, V, Weigl, K, Weinstein, SJ, White, E, Win, AK, Wolf, CR, Wolk, A, Woods, MO, Wu, AH, Zaidi, SH, Zanke, BW, Zhang, Q, Zheng, W, Scacheri, PC, Potter, JD, Bassik, MC, Kundaje, A, Casey, G, Moreno, V, Abecasis, GR, Nickerson, DA, Gruber, SB, Hsu, L, Peters, U, Huyghe, JR, Bien, SA, Harrison, TA, Kang, HM, Chen, S, Schmit, SL, Conti, DV, Qu, C, Jeon, J, Edlund, CK, Greenside, P, Wainberg, M, Schumacher, FR, Smith, JD, Levine, DM, Nelson, SC, Sinnott-Armstrong, NA, Albanes, D, Alonso, MH, Anderson, K, Arnau-Collell, C, Arndt, V, Bamia, C, Banbury, BL, Baron, JA, Berndt, SI, Bezieau, S, Bishop, DT, Boehm, J, Boeing, H, Brenner, H, Brezina, S, Buch, S, Buchanan, DD, Burnett-Hartman, A, Butterbach, K, Caan, BJ, Campbell, PT, Carlson, CS, Castellvi-Bel, S, Chan, AT, Chang-Claude, J, Chanock, SJ, Chirlaque, M-D, Cho, SH, Connolly, CM, Cross, AJ, Cuk, K, Curtis, KR, de la Chapelle, A, Doheny, KF, Duggan, D, Easton, DF, Elias, SG, Elliott, F, English, DR, Feskens, EJM, Figueiredo, JC, Fischer, R, FitzGerald, LM, Forman, D, Gala, M, Gallinger, S, Gauderman, WJ, Giles, GG, Gillanders, E, Gong, J, Goodman, PJ, Grady, WM, Grove, JS, Gsur, A, Gunter, MJ, Haile, RW, Hampe, J, Hampel, H, Harlid, S, Hayes, RB, Hofer, P, Hoffmeister, M, Hopper, JL, Hsu, W-L, Huang, W-Y, Hudson, TJ, Hunter, DJ, Ibanez-Sanz, G, Idos, GE, Ingersoll, R, Jackson, RD, Jacobs, EJ, Jenkins, MA, Joshi, AD, Joshu, CE, Keku, TO, Key, TJ, Kim, HR, Kobayashi, E, Kolonel, LN, Kooperberg, C, Kuehn, T, Kury, S, Kweon, S-S, Larsson, SC, Laurie, CA, Le Marchand, L, Leal, SM, Lee, SC, Lejbkowicz, F, Lemire, M, Li, CI, Li, L, Lieb, W, Lin, Y, Lindblom, A, Lindor, NM, Ling, H, Louie, TL, Mannisto, S, Markowitz, SD, Martin, V, Masala, G, McNeil, CE, Melas, M, Milne, RL, Moreno, L, Murphy, N, Myte, R, Naccarati, A, Newcomb, PA, Offit, K, Ogino, S, Onland-Moret, NC, Pardini, B, Parfrey, PS, Pearlman, R, Perduca, V, Pharoah, PDP, Pinchev, M, Platz, EA, Prentice, RL, Pugh, E, Raskin, L, Rennert, G, Rennert, HS, Riboli, E, Rodriguez-Barranco, M, Romm, J, Sakoda, LC, Schafmayer, C, Schoen, RE, Seminara, D, Shah, M, Shelford, T, Shin, M-H, Shulman, K, Sieri, S, Slattery, ML, Southey, MC, Stadler, ZK, Stegmaier, C, Su, Y-R, Tangen, CM, Thibodeau, SN, Thomas, DC, Thomas, SS, Toland, AE, Trichopoulou, A, Ulrich, CM, Van den Berg, DJ, van Duijnhoven, FJB, Van Guelpen, B, van Kranen, H, Vijai, J, Visvanathan, K, Vodicka, P, Vodickova, L, Vymetalkova, V, Weigl, K, Weinstein, SJ, White, E, Win, AK, Wolf, CR, Wolk, A, Woods, MO, Wu, AH, Zaidi, SH, Zanke, BW, Zhang, Q, Zheng, W, Scacheri, PC, Potter, JD, Bassik, MC, Kundaje, A, Casey, G, Moreno, V, Abecasis, GR, Nickerson, DA, Gruber, SB, Hsu, L, and Peters, U

Abstract

To further dissect the genetic architecture of colorectal cancer (CRC), we performed whole-genome sequencing of 1,439 cases and 720 controls, imputed discovered sequence variants and Haplotype Reference Consortium panel variants into genome-wide association study data, and tested for association in 34,869 cases and 29,051 controls. Findings were followed up in an additional 23,262 cases and 38,296 controls. We discovered a strongly protective 0.3% frequency variant signal at CHD1. In a combined meta-analysis of 125,478 individuals, we identified 40 new independent signals at P < 5 × 10-8, bringing the number of known independent signals for CRC to ~100. New signals implicate lower-frequency variants, Krüppel-like factors, Hedgehog signaling, Hippo-YAP signaling, long noncoding RNAs and somatic drivers, and support a role for immune function. Heritability analyses suggest that CRC risk is highly polygenic, and larger, more comprehensive studies enabling rare variant analysis will improve understanding of biology underlying this risk and influence personalized screening strategies and drug development.

Published
2019

18. Multi-ancestry study of blood lipid levels identifies four loci interacting with physical activity

Author

Kilpelainen, TO, Bentley, AR, Noordam, R, Sung, YJ, Schwander, K, Winkler, TW, Jakupovic, H, Chasman, DI, Manning, A, Ntalla, I, Aschard, H, Brown, MR, de Las Fuentes, L, Franceschini, N, Guo, XQ, Vojinovic, Dina, Aslibekyan, S, Feitosa, MF, Kho, M, Musani, SK, Richard, M, Wang, HM, Wang, Z, Bartz, TM, Bielak, LF, Campbell, A (Archie), Dorajoo, R, Fisher, V, Hartwig, FP, Horimoto, A, Li, CW, Lohman, KK, Marten, J, Sim, XL, Smith, AV, Tajuddin, S M, Alver, M, Amini, M, Boissel, M, Chai, JF, Chen, X, Divers, J, Evangelou, E, Gao, C, Graff, M, Harris, SE, He, MA, Hsu, FC, Jackson, AU, Zhao, JH, Kraja, AT, Kuhnel, B, Laguzzi, F, Lyytikainen, LP, Nolte, IM, Rauramaa, R, Riaz, M, Robino, A, Rueedi, R, Stringham, HM, Takeuchi, F, van der Most, PJ, Varga, TV, Verweij, N, Ware, EB, Wen, WQ, Li, X Y, Yanek, LR, Amin, Najaf, Arnett, DK, Boerwinkle, E, Brumat, M, Cade, B, Canouil, M, Chen, YDI, Concas, MP, Connell, J, de Mutsert, R, de Silva, HJ, de Vries, PS, Demirkan, Ayse, Ding, JZ (Jing Zhong), Eaton, CB, Faul, JD, Friedlander, Y, Gabriel, KP, Ghanbari, Mohsen, Giulianini, F, Gu, CC, Gu, DF, Harris, TB, He, J, Heikkinen, S, Heng, CK, Hunt, SC, Ikram, Arfan, Jonas, JB, Koh, WP, Komulainen, P, Krieger, JE, Kritchevsky, SB, Kutalik, Z, Kuusisto, J, Langefeld, CD, Langenberg, C, Launer, LJ, Leander, K, Lemaitre, RN, Lewis, CE, Liang, JJ, Alizadeh, BZ, Boezen, HM, Franke, L, Navis, G, Rots, M, Swertz, M, Wolffenbuttel, BHR, Wijmenga, C, Liu, JJ, Maagi, R, Manichaikul, A, Meitinger, T, Metspalu, A, Milaneschi, Y, Mohlke, KL, Mosley, TH, Murray, AD, Nalls, MA, Nang, EEK, Nelson, CP, Nona, S, Norris, JM, Nwuba, CV, O'Connell, J, Palmer, ND, Papanicolau, GJ, Pazoki, R, Pedersen, NL, Peters, A, Peyser, PA, Polasek, O, Porteous, DJ, Poveda, A, Raitakari, OT, Rich, SS, Risch, N, Robinson, JG, Rose, LM, Rudan, I, Schreiner, PJ, Scott, RA, Sidney, SS, Sims, M, Smith, JA, Snieder, H, Sofer, T, Starr, JM, Sternfeld, B, Strauch, K, Tang, H, Taylor, KD, Tsai, MY, Tuomilehto, J, Uitterlinden, André, van der Ende, MY, van Heemst, D, Voortman, Trudy, Waldenberger, M, Wennberg, P, Wilson, G, Xiang, YB, Yao, J, Yu, CZ, Yuan, JM, Zhao, W, Zonderman, AB, Becker, DM, Boehnke, M, Bowden, DW, de Faire, U, Deary, IJ, Elliott, P, Esko, T, Freedman, BI, Froguel, P, Gasparini, P, Gieger, C, Kato, N, Laakso, M, Lakka, TA, Lehtimaki, T, Magnusson, PKE, Oldehinkel, AJ, Penninx, B, Samani, NJ, Shu, XO, van der Harst, P, van Vliet-Ostaptchouk, JV, Vollenweider, P, Wagenknecht, LE, Wang, YX, Wareham, NJ, Weir, DR, Wu, TC, Zheng, W, Zhu, XF, Evans, MK, Franks, PW, Gudnason, V, Hayward, C, Horta, BL, Kelly, TN, Liu, YM, North, KE, Pereira, AC, Ridker, PM, Tai, ES, van Dam, RM, Fox, ER, Kardia, SLR, Liu, CT, Mook, Dennis, Province, MA, Redline, S, Duijn, Cornelia, Rotter, JI, Kooperberg, CB, Gauderman, WJ, Psaty, BM, Rice, K, Munroe, PB, Fornage, M, Cupples, LA, Rotimi, CN, Morrison, AC, Rao, DC, Loos, RJF, Kilpelainen, TO, Bentley, AR, Noordam, R, Sung, YJ, Schwander, K, Winkler, TW, Jakupovic, H, Chasman, DI, Manning, A, Ntalla, I, Aschard, H, Brown, MR, de Las Fuentes, L, Franceschini, N, Guo, XQ, Vojinovic, Dina, Aslibekyan, S, Feitosa, MF, Kho, M, Musani, SK, Richard, M, Wang, HM, Wang, Z, Bartz, TM, Bielak, LF, Campbell, A (Archie), Dorajoo, R, Fisher, V, Hartwig, FP, Horimoto, A, Li, CW, Lohman, KK, Marten, J, Sim, XL, Smith, AV, Tajuddin, S M, Alver, M, Amini, M, Boissel, M, Chai, JF, Chen, X, Divers, J, Evangelou, E, Gao, C, Graff, M, Harris, SE, He, MA, Hsu, FC, Jackson, AU, Zhao, JH, Kraja, AT, Kuhnel, B, Laguzzi, F, Lyytikainen, LP, Nolte, IM, Rauramaa, R, Riaz, M, Robino, A, Rueedi, R, Stringham, HM, Takeuchi, F, van der Most, PJ, Varga, TV, Verweij, N, Ware, EB, Wen, WQ, Li, X Y, Yanek, LR, Amin, Najaf, Arnett, DK, Boerwinkle, E, Brumat, M, Cade, B, Canouil, M, Chen, YDI, Concas, MP, Connell, J, de Mutsert, R, de Silva, HJ, de Vries, PS, Demirkan, Ayse, Ding, JZ (Jing Zhong), Eaton, CB, Faul, JD, Friedlander, Y, Gabriel, KP, Ghanbari, Mohsen, Giulianini, F, Gu, CC, Gu, DF, Harris, TB, He, J, Heikkinen, S, Heng, CK, Hunt, SC, Ikram, Arfan, Jonas, JB, Koh, WP, Komulainen, P, Krieger, JE, Kritchevsky, SB, Kutalik, Z, Kuusisto, J, Langefeld, CD, Langenberg, C, Launer, LJ, Leander, K, Lemaitre, RN, Lewis, CE, Liang, JJ, Alizadeh, BZ, Boezen, HM, Franke, L, Navis, G, Rots, M, Swertz, M, Wolffenbuttel, BHR, Wijmenga, C, Liu, JJ, Maagi, R, Manichaikul, A, Meitinger, T, Metspalu, A, Milaneschi, Y, Mohlke, KL, Mosley, TH, Murray, AD, Nalls, MA, Nang, EEK, Nelson, CP, Nona, S, Norris, JM, Nwuba, CV, O'Connell, J, Palmer, ND, Papanicolau, GJ, Pazoki, R, Pedersen, NL, Peters, A, Peyser, PA, Polasek, O, Porteous, DJ, Poveda, A, Raitakari, OT, Rich, SS, Risch, N, Robinson, JG, Rose, LM, Rudan, I, Schreiner, PJ, Scott, RA, Sidney, SS, Sims, M, Smith, JA, Snieder, H, Sofer, T, Starr, JM, Sternfeld, B, Strauch, K, Tang, H, Taylor, KD, Tsai, MY, Tuomilehto, J, Uitterlinden, André, van der Ende, MY, van Heemst, D, Voortman, Trudy, Waldenberger, M, Wennberg, P, Wilson, G, Xiang, YB, Yao, J, Yu, CZ, Yuan, JM, Zhao, W, Zonderman, AB, Becker, DM, Boehnke, M, Bowden, DW, de Faire, U, Deary, IJ, Elliott, P, Esko, T, Freedman, BI, Froguel, P, Gasparini, P, Gieger, C, Kato, N, Laakso, M, Lakka, TA, Lehtimaki, T, Magnusson, PKE, Oldehinkel, AJ, Penninx, B, Samani, NJ, Shu, XO, van der Harst, P, van Vliet-Ostaptchouk, JV, Vollenweider, P, Wagenknecht, LE, Wang, YX, Wareham, NJ, Weir, DR, Wu, TC, Zheng, W, Zhu, XF, Evans, MK, Franks, PW, Gudnason, V, Hayward, C, Horta, BL, Kelly, TN, Liu, YM, North, KE, Pereira, AC, Ridker, PM, Tai, ES, van Dam, RM, Fox, ER, Kardia, SLR, Liu, CT, Mook, Dennis, Province, MA, Redline, S, Duijn, Cornelia, Rotter, JI, Kooperberg, CB, Gauderman, WJ, Psaty, BM, Rice, K, Munroe, PB, Fornage, M, Cupples, LA, Rotimi, CN, Morrison, AC, Rao, DC, and Loos, RJF

Published
2019

19. Current Challenges and New Opportunities for Gene-Environment Interaction Studies of Complex Diseases

Author

McAllister, K, Mechanic, LE, Amos, C, Aschard, H, Blair, IA, Chatterjee, N, Conti, D, Gauderman, WJ, Hsu, L, Hutter, CM, Jankowska, MM, Kerr, J, Kraft, P, Montgomery, SB, Mukherjee, B, Papanicolaou, GJ, Patel, CJ, Ritchie, MD, Ritz, BR, Thomas, DC, Wei, P, Witte, JS, and Participants, W

Subjects
genome-wide association study, environmental exposure, gene-environment interaction
Published
2017
Full Text
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20. Novel genetic associations for blood pressure identified via gene-alcohol interaction in up to 570K individuals across multiple ancestries

Author

Feitosa, MF, Kraja, AT, Chasman, DI, Sung, YJ, Winkler, TW, Ntalla, I, Guo, XQ, Franceschini, N, Cheng, CY (Ching-Yu), Sim, XL, Vojinovic, Dina, Marten, J, Musani, SK, Li, CW, Bentley, AR, Brown, MR, Schwander, K, Richard, MA, Noordam, R, Aschard, H, Bartz, TM, Bielak, LF, Dorajoo, R, Fisher, V, Hartwig, FP, Horimoto, A, Lohman, KK, Manning, AK, Rankinen, T, Smith, AV, Tajuddin, S M, Wojczynski, MK, Alver, M, Boissel, M, Cai, QY, Campbell, A (Archie), Chai, JF, Chen, X, Divers, J, Gao, C, Goel, A, Hagemeijer, Y, Harris, SE, He, MI, Hsu, FC, Jackson, AU, Kahonen, M, Kasturiratne, A, Komulainen, P, Kuhnel, B, Laguzzi, F, Luan, J, Matoba, N, Nolte, IM, Padmanabhan, S, Riaz, M, Rueedi, R, Robino, A, Said, MA, Scott, RA, Sofer, T, Stancakova, A, Takeuchi, F, Tayo, BO, van der Most, PJ, Varga, TV, Vitart, V, Wang, YJ, Ware, EB, Warren, HR, Weiss, S, Wen, WQ, Yanek, LR, Zhang, WH, Zhao, JH, Afaq, S, Amin, Najaf, Amini, M, Arking, DE, Aung, T, Boerwinkle, E, Borecki, I, Broeckel, U, Brown, M, Brumat, M, Burke, GL, Canouil, M, Chakravarti, A, Charumathi, S, Chen, YDI, Connell, JM, Correa, A, Fuentes, LDL, de Mutsert, R, de Silva, HJ, Deng, X, Ding, J, Duan, Q, Eaton, CB, Ehret, G, Eppinga, RN, Evangelou, E, Fau, JD, Felix, SB, Forouhi, NG, Forrester, T, Franco Duran, OH, Friedlander, Y, Gandin, I, Gao, H, Ghanbari, Mohsen, Gigante, B, Gu, CC, Gu, DF, Hagenaars, SP, Hallmans, G, Harris, TB, He, J, Heikkinen, S, Heng, CK, Hirata, M, Howard, BV, Ikram, Arfan, John, U, Katsuya, T, Khor, CC, Kilpelainen, TO, Koh, WP, Krieger, JE, Kritchevsky, SB, Kubo, M, Kuusisto, J, Lakka, TA, Langefeld, CD, Langenberg, C, Launer, LJ, Lehne, B, Lewis, CE, Li, YZ, Lin, S, Liu, JJ, Liu, JM, Loh, M, Louie, T, Magi, R, McKenzie, CA, Meitinger, T, Metspalu, A, Milaneschi, Y, Milani, L, Mohlke, KL, Momozawa, Y, Nalls, MA, Nelson, CP, Sotoodehnia, N, Norris, JM, O'Connell, JR, Palmer, ND, Perls, T, Pedersen, NL, Peters, A, Peyser, PA, Poulter, N, Raffel, LJ, Raitakari, OT, Roll, K, Rose, LM, Rosendaal, FR, Rotter, JI, Schmidt, CO, Schreiner, PJ, Schupf, N, Scott, WR, Sever, PS, Shi, Y, Sidney, S, Sims, M, Sitlani, CM, Smith, JA, Snieder, H, Starr, JM, Strauch, K, Stringham, HM, Tan, NYQ, Tang, H, Taylor, KD, Teo, YY, Tham, YC, Turner, ST, Uitterlinden, André, Vollenweider, P, Waldenberger, M, Wang, LH, Wang, YX, Wei, W, Williams, C, Yao, J, Yu, CZ, Yuan, JM, Zhao, W, Zonderman, AB, Becker, DM, Boehnke, M, Bowden, DW, Chambers, JC, Deary, IJ, Esko, T, Farrall, M, Franks, PW, Freedman, BI, Froguel, P, Gasparini, P, Gieger, C, Jonas, JB, Kamatani, Y, Kato, N, Kooner, JS, Kutalik, Z, Laakso, M, Laurie, CC, Leander, K, Lehtimaki, T, Study, LC, Magnusson, PKE, Oldehinkel, AJ, Penninx, B, Poiasek, O, Porteous, DJ, Rauramaa, R, Samani, NJ, Scott, J, Shu, XO, van der Harst, P, Wagenknecht, LE, Wareham, NJ, Watkins, H, Weir, DR, Wickremasinghe, AR, Wu, TC, Zheng, W, Bouchard, C, Christensen, K, Evans, MK, Gudnason, V, Horta, BL, Kardia, SLR, Liu, YM, Pereira, AC, Psaty, BM, Ridker, PM, van Dam, RM, Gauderman, WJ, Zhu, XF, Mook, Dennis, Fornage, M, Rotimi, CN, Cupples, LA, Kelly, TN, Fox, ER, Hayward, C, Duijn, Cornelia, Tai, ES, Wong, TY, Kooperberg, C, Palmas, W, Rice, K, Morrison, AC, Elliott, P, Caulfield, MJ, Munroe, PB, Rao, DC, Province, MA, Levy, D, Feitosa, MF, Kraja, AT, Chasman, DI, Sung, YJ, Winkler, TW, Ntalla, I, Guo, XQ, Franceschini, N, Cheng, CY (Ching-Yu), Sim, XL, Vojinovic, Dina, Marten, J, Musani, SK, Li, CW, Bentley, AR, Brown, MR, Schwander, K, Richard, MA, Noordam, R, Aschard, H, Bartz, TM, Bielak, LF, Dorajoo, R, Fisher, V, Hartwig, FP, Horimoto, A, Lohman, KK, Manning, AK, Rankinen, T, Smith, AV, Tajuddin, S M, Wojczynski, MK, Alver, M, Boissel, M, Cai, QY, Campbell, A (Archie), Chai, JF, Chen, X, Divers, J, Gao, C, Goel, A, Hagemeijer, Y, Harris, SE, He, MI, Hsu, FC, Jackson, AU, Kahonen, M, Kasturiratne, A, Komulainen, P, Kuhnel, B, Laguzzi, F, Luan, J, Matoba, N, Nolte, IM, Padmanabhan, S, Riaz, M, Rueedi, R, Robino, A, Said, MA, Scott, RA, Sofer, T, Stancakova, A, Takeuchi, F, Tayo, BO, van der Most, PJ, Varga, TV, Vitart, V, Wang, YJ, Ware, EB, Warren, HR, Weiss, S, Wen, WQ, Yanek, LR, Zhang, WH, Zhao, JH, Afaq, S, Amin, Najaf, Amini, M, Arking, DE, Aung, T, Boerwinkle, E, Borecki, I, Broeckel, U, Brown, M, Brumat, M, Burke, GL, Canouil, M, Chakravarti, A, Charumathi, S, Chen, YDI, Connell, JM, Correa, A, Fuentes, LDL, de Mutsert, R, de Silva, HJ, Deng, X, Ding, J, Duan, Q, Eaton, CB, Ehret, G, Eppinga, RN, Evangelou, E, Fau, JD, Felix, SB, Forouhi, NG, Forrester, T, Franco Duran, OH, Friedlander, Y, Gandin, I, Gao, H, Ghanbari, Mohsen, Gigante, B, Gu, CC, Gu, DF, Hagenaars, SP, Hallmans, G, Harris, TB, He, J, Heikkinen, S, Heng, CK, Hirata, M, Howard, BV, Ikram, Arfan, John, U, Katsuya, T, Khor, CC, Kilpelainen, TO, Koh, WP, Krieger, JE, Kritchevsky, SB, Kubo, M, Kuusisto, J, Lakka, TA, Langefeld, CD, Langenberg, C, Launer, LJ, Lehne, B, Lewis, CE, Li, YZ, Lin, S, Liu, JJ, Liu, JM, Loh, M, Louie, T, Magi, R, McKenzie, CA, Meitinger, T, Metspalu, A, Milaneschi, Y, Milani, L, Mohlke, KL, Momozawa, Y, Nalls, MA, Nelson, CP, Sotoodehnia, N, Norris, JM, O'Connell, JR, Palmer, ND, Perls, T, Pedersen, NL, Peters, A, Peyser, PA, Poulter, N, Raffel, LJ, Raitakari, OT, Roll, K, Rose, LM, Rosendaal, FR, Rotter, JI, Schmidt, CO, Schreiner, PJ, Schupf, N, Scott, WR, Sever, PS, Shi, Y, Sidney, S, Sims, M, Sitlani, CM, Smith, JA, Snieder, H, Starr, JM, Strauch, K, Stringham, HM, Tan, NYQ, Tang, H, Taylor, KD, Teo, YY, Tham, YC, Turner, ST, Uitterlinden, André, Vollenweider, P, Waldenberger, M, Wang, LH, Wang, YX, Wei, W, Williams, C, Yao, J, Yu, CZ, Yuan, JM, Zhao, W, Zonderman, AB, Becker, DM, Boehnke, M, Bowden, DW, Chambers, JC, Deary, IJ, Esko, T, Farrall, M, Franks, PW, Freedman, BI, Froguel, P, Gasparini, P, Gieger, C, Jonas, JB, Kamatani, Y, Kato, N, Kooner, JS, Kutalik, Z, Laakso, M, Laurie, CC, Leander, K, Lehtimaki, T, Study, LC, Magnusson, PKE, Oldehinkel, AJ, Penninx, B, Poiasek, O, Porteous, DJ, Rauramaa, R, Samani, NJ, Scott, J, Shu, XO, van der Harst, P, Wagenknecht, LE, Wareham, NJ, Watkins, H, Weir, DR, Wickremasinghe, AR, Wu, TC, Zheng, W, Bouchard, C, Christensen, K, Evans, MK, Gudnason, V, Horta, BL, Kardia, SLR, Liu, YM, Pereira, AC, Psaty, BM, Ridker, PM, van Dam, RM, Gauderman, WJ, Zhu, XF, Mook, Dennis, Fornage, M, Rotimi, CN, Cupples, LA, Kelly, TN, Fox, ER, Hayward, C, Duijn, Cornelia, Tai, ES, Wong, TY, Kooperberg, C, Palmas, W, Rice, K, Morrison, AC, Elliott, P, Caulfield, MJ, Munroe, PB, Rao, DC, Province, MA, and Levy, D

Published
2018

21. An Empirical Comparison of Joint and Stratified Frameworks for Studying G x E Interactions: Systolic Blood Pressure and Smoking in the CHARGE Gene-Lifestyle Interactions Working Group

Author

Sung, YJ, Winkler, TW, Manning, AK, Aschard, H, Gudnason, V, Harris, TB, Smith, AV, Boerwinkle, E, Brown, MR, Morrison, AC, Fornage, M, Lin, LA, Richard, M, Bartz, TM, Psaty, BM, Hayward, C, Polasek, O, Marten, J, Rudan, I, Feitosa, MF, Kraja, AT, Province, MA, Deng, X, Fisher, V A, Zhou, Y, Bielak, LF, Smith, J, Huffman, JE, Padmanabhan, S, Smith, BH, Ding, J, Liu, Y, Lohman, K, Bouchard, C, Rankinen, T, Rice, TK, Arnett, D, Schwander, K, Guo, X, Palmas, W, Rotter, JI, Alfred, T, Bottinger, EP, Loos, RJF, Amin, Najaf, Franco Duran, OH, Duijn, Cornelia, Vojinovic, Dina, Chasman, DI, Ridker, PM, Rose, LM, Kardia, S, Zhu, X, Rice, K, Borecki, IB, Rao, DC, Gauderman, WJ, Cupples, LA, and Epidemiology

Published
2016

22. Association Tests in Nuclear Families

Author

Kimberly D. Siegmund and Gauderman Wj

Subjects
Family Health, Genetic Markers, Genetics, Score test, Likelihood Functions, Linkage disequilibrium, Models, Genetic, Disequilibrium, Genetic Diseases, Inborn, Locus (genetics), Biology, Linkage Disequilibrium, Trait, medicine, Humans, Computer Simulation, Sibling, Allele, medicine.symptom, Nuclear family, Genetics (clinical)
Abstract

We present a conditional likelihood approach for testing linkage disequilibrium in nuclear families having multiple affected offspring. The likelihood, conditioned on the identity-by-descent (IBD) structure of the sibling genotypes, is unaffected by familial correlation in disease status that arises from linkage between a marker locus and the unobserved trait locus. Two such conditional likelihoods are compared: one that conditions on IBD and phase of the transmitted alleles and a second which conditions only on IBD of the transmitted alleles. Under the log-additive model, the first likelihood is equivalent to the allele-counting methods proposed in the literature. The second likelihood is valid under the added assumption of equal male and female recombination fractions. In a simulation study, we demonstrated that in sibships having two or three affected siblings the score test from each likelihood had the correct test size for testing disequilibrium. They also led to equivalent power to detect linkage disequilibrium at the 5% significance level.

Published
2001
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23. CYP24A1 variant modifies the association between use of oestrogen plus progestogen therapy and colorectal cancer risk

Author

Garcia-Albeniz, X, Rudolph, A, Hutter, C, White, E, Lin, Y, Rosse, SA, Figueiredo, JC, Harrison, TA, Jiao, S, Brenner, H, Casey, G, Hudson, TJ, Thornquist, M, Le Marchand, L, Potter, J, Slattery, ML, Zanke, B, Baron, JA, Caan, BJ, Chanock, SJ, Berndt, SI, Stelling, D, Fuchs, CS, Hoffmeister, M, Butterbach, K, Du, M, Gauderman, WJ, Gunter, MJ, Lemire, M, Ogino, S, Lin, J, Hayes, RB, Haile, RW, Schoen, RE, SWarnick, G, Jenkins, MA, Thibodeau, SN, Schumacher, FR, Lindor, NM, Kolonel, LN, Hopper, JL, Gong, J, Seminara, D, Pflugeisen, BM, Ulrich, CM, Qu, C, Duggan, D, Cotterchio, M, Campbell, PT, Carlson, CS, Newcomb, PA, Giovannucci, E, Hsu, L, Chan, AT, Peters, U, Chang-Claude, J, Garcia-Albeniz, X, Rudolph, A, Hutter, C, White, E, Lin, Y, Rosse, SA, Figueiredo, JC, Harrison, TA, Jiao, S, Brenner, H, Casey, G, Hudson, TJ, Thornquist, M, Le Marchand, L, Potter, J, Slattery, ML, Zanke, B, Baron, JA, Caan, BJ, Chanock, SJ, Berndt, SI, Stelling, D, Fuchs, CS, Hoffmeister, M, Butterbach, K, Du, M, Gauderman, WJ, Gunter, MJ, Lemire, M, Ogino, S, Lin, J, Hayes, RB, Haile, RW, Schoen, RE, SWarnick, G, Jenkins, MA, Thibodeau, SN, Schumacher, FR, Lindor, NM, Kolonel, LN, Hopper, JL, Gong, J, Seminara, D, Pflugeisen, BM, Ulrich, CM, Qu, C, Duggan, D, Cotterchio, M, Campbell, PT, Carlson, CS, Newcomb, PA, Giovannucci, E, Hsu, L, Chan, AT, Peters, U, and Chang-Claude, J

Abstract

BACKGROUND: Menopausal hormone therapy (MHT) use has been consistently associated with a decreased risk of colorectal cancer (CRC) in women. Our aim was to use a genome-wide gene-environment interaction analysis to identify genetic modifiers of CRC risk associated with use of MHT. METHODS: We included 10 835 postmenopausal women (5419 cases and 5416 controls) from 10 studies. We evaluated use of any MHT, oestrogen-only (E-only) and combined oestrogen-progestogen (E+P) hormone preparations. To test for multiplicative interactions, we applied the empirical Bayes (EB) test as well as the Wald test in conventional case-control logistic regression as primary tests. The Cocktail test was used as secondary test. RESULTS: The EB test identified a significant interaction between rs964293 at 20q13.2/CYP24A1 and E+P (interaction OR (95% CIs)=0.61 (0.52-0.72), P=4.8 × 10(-9)). The secondary analysis also identified this interaction (Cocktail test OR=0.64 (0.52-0.78), P=1.2 × 10(-5) (alpha threshold=3.1 × 10(-4)). The ORs for association between E+P and CRC risk by rs964293 genotype were as follows: C/C, 0.96 (0.61-1.50); A/C, 0.61 (0.39-0.95) and A/A, 0.40 (0.22-0.73), respectively. CONCLUSIONS: Our results indicate that rs964293 modifies the association between E+P and CRC risk. The variant is located near CYP24A1, which encodes an enzyme involved in vitamin D metabolism. This novel finding offers additional insight into downstream pathways of CRC etiopathogenesis.

Published
2016

24. Метаанализ геномных ассоциаций выявил новые локусы, ассоциированные с ожирением у детей

Author

Bradfield, Jp, Taal, Hr, Timpson, Nj, Scherag, A., Lecoeur, C., Warrington, Nm, Hypponen, E., Holst, C., Valcarcel, B., Thiering, E., Salem, Rm, Schumacher, Fr, Cousminer, Dl, Sleiman, Pm, Zhao, J., Berkowitz, Ri, Vimaleswaran, Ks, Jarick, I., Pennell, Ce, Evans, Dm, St, Pourcain, Berry, Dj, Mook-kanamori, Do, Hofman, A., Rivadeneira, F., Uitterlinden, Ag, Van, Duijn, VAN DER VALK RJ, De, Jongste, Postma, Ds, Boomsma, Di, Gauderman, Wj, Hassanein, Mt, Lindgren, Cm, Mägi, R., Boreham, Ca, Neville, Ce, Moreno, La, Elliott, P., Pouta, A., Hartikainen, Al, Li, M., Raitakari, O., Lehtimäki, T., Eriksson, Jg, Palotie, A., Dallongeville, J., Das, S., Deloukas, P., Mcmahon, G., Ring, Sm, Kemp, Jp, Buxton, Jl, Blakemore, Ai, Bustamante, M., Guxens, M., Hirschhorn, Jn, Gillman, Mw, Kreiner-møller, E., Bisgaard, H., Gilliland, Fd, Heinrich, J., Wheeler, E., Barroso, I., O'rahilly, S., Meirhaeghe, A., Sørensen, Ti, Power, C., Palmer, Lj, Hinney, A., Widen, E., Farooqi, Is, Mccarthy, Mi, Froguel, P., Meyre, D., Hebebrand, J., Jarvelin, Mr, Jaddoe, Vw, Smith, Gd, Hakonarson, H., and Grant, Sf

Published
2012

25. Genome-wide association study of colorectal cancer identifies six new susceptibility loci

Author

Schumacher, FR, Schmit, SL, Jiao, S, Edlund, CK, Wang, H, Zhang, B, Hsu, L, Huang, S-C, Fischer, CP, Harju, JF, Idos, GE, Lejbkowicz, F, Manion, FJ, McDonnell, K, McNeil, CE, Melas, M, Rennert, HS, Shi, W, Thomas, DC, Van Den Berg, DJ, Hutter, CM, Aragaki, AK, Butterbach, K, Caan, BJ, Carlson, CS, Chanock, SJ, Curtis, KR, Fuchs, CS, Gala, M, Giocannucci, EL, Gogarten, SM, Hayes, RB, Henderson, B, Hunter, DJ, Jackson, RD, Kolonel, LN, Kooperberg, C, Kury, S, LaCroix, A, Laurie, CC, Laurie, CA, Lemire, M, Levine, D, Ma, J, Makar, KW, Qu, C, Taverna, D, Ulrich, CM, Wu, K, Kono, S, West, DW, Berndt, SI, Bezieau, S, Brenner, H, Campbell, PT, Chan, AT, Chang-Claude, J, Coetzee, GA, Conti, DV, Duggan, D, Figueiredo, JC, Fortini, BK, Gallinger, SJ, Gauderman, WJ, Giles, G, Green, R, Haile, R, Harrison, TA, Hoffmeister, M, Hopper, JL, Hudson, TJ, Jacobs, E, Iwasaki, M, Jee, SH, Jenkins, M, Jia, W-H, Joshi, A, Li, L, Lindor, NM, Matsuo, K, Moreno, V, Mukherjee, B, Newcomb, PA, Potter, JD, Raskin, L, Rennert, G, Rosse, S, Severi, G, Schoen, RE, Seminara, D, Shu, X-O, Slattery, ML, Tsugane, S, White, E, Xiang, Y-B, Zanke, BW, Zheng, W, Le Marchand, L, Casey, G, Gruber, SB, Peters, U, Schumacher, FR, Schmit, SL, Jiao, S, Edlund, CK, Wang, H, Zhang, B, Hsu, L, Huang, S-C, Fischer, CP, Harju, JF, Idos, GE, Lejbkowicz, F, Manion, FJ, McDonnell, K, McNeil, CE, Melas, M, Rennert, HS, Shi, W, Thomas, DC, Van Den Berg, DJ, Hutter, CM, Aragaki, AK, Butterbach, K, Caan, BJ, Carlson, CS, Chanock, SJ, Curtis, KR, Fuchs, CS, Gala, M, Giocannucci, EL, Gogarten, SM, Hayes, RB, Henderson, B, Hunter, DJ, Jackson, RD, Kolonel, LN, Kooperberg, C, Kury, S, LaCroix, A, Laurie, CC, Laurie, CA, Lemire, M, Levine, D, Ma, J, Makar, KW, Qu, C, Taverna, D, Ulrich, CM, Wu, K, Kono, S, West, DW, Berndt, SI, Bezieau, S, Brenner, H, Campbell, PT, Chan, AT, Chang-Claude, J, Coetzee, GA, Conti, DV, Duggan, D, Figueiredo, JC, Fortini, BK, Gallinger, SJ, Gauderman, WJ, Giles, G, Green, R, Haile, R, Harrison, TA, Hoffmeister, M, Hopper, JL, Hudson, TJ, Jacobs, E, Iwasaki, M, Jee, SH, Jenkins, M, Jia, W-H, Joshi, A, Li, L, Lindor, NM, Matsuo, K, Moreno, V, Mukherjee, B, Newcomb, PA, Potter, JD, Raskin, L, Rennert, G, Rosse, S, Severi, G, Schoen, RE, Seminara, D, Shu, X-O, Slattery, ML, Tsugane, S, White, E, Xiang, Y-B, Zanke, BW, Zheng, W, Le Marchand, L, Casey, G, Gruber, SB, and Peters, U

Abstract

Genetic susceptibility to colorectal cancer is caused by rare pathogenic mutations and common genetic variants that contribute to familial risk. Here we report the results of a two-stage association study with 18,299 cases of colorectal cancer and 19,656 controls, with follow-up of the most statistically significant genetic loci in 4,725 cases and 9,969 controls from two Asian consortia. We describe six new susceptibility loci reaching a genome-wide threshold of P<5.0E-08. These findings provide additional insight into the underlying biological mechanisms of colorectal cancer and demonstrate the scientific value of large consortia-based genetic epidemiology studies.

Published
2015

26. Genome-wide association study of colorectal cancer identifies six new susceptibility loci (vol 6, 7138, 2015)

Author

Schumacher, FR, Schmit, SL, Jiao, S, Edlund, CK, Wang, H, Zhang, B, Hsu, L, Huang, S-C, Fischer, CP, Harju, JF, Idos, GE, Lejbkowicz, F, Manion, FJ, McDonnell, K, McNeil, CE, Melas, M, Rennert, HS, Shi, W, Thomas, DC, Van den Berg, DJ, Hutter, CM, Aragaki, AK, Butterbach, K, Caan, BJ, Carlson, CS, Chanock, SJ, Curtis, KR, Fuchs, CS, Gala, M, Giovannucci, EL, Gogarten, SM, Hayes, RB, Henderson, B, Hunter, DJ, Jackson, RD, Kolonel, LN, Kooperberg, C, Kuery, S, LaCroix, A, Laurie, CC, Laurie, CA, Lemire, M, Levine, D, Ma, J, Makar, KW, Qu, C, Taverna, D, Ulrich, CM, Wu, K, Kono, S, West, DW, Berndt, SI, Bezieau, S, Brenner, H, Campbell, PT, Chan, AT, Chang-Claude, J, Coetzee, GA, Conti, DV, Duggan, D, Figueiredo, JC, Fortini, BK, Gallinger, SJ, Gauderman, WJ, Giles, G, Green, R, Haile, R, Harrison, TA, Hoffmeister, M, Hopper, JL, Hudson, TJ, Jacobs, E, Iwasaki, M, Jee, SH, Jenkins, M, Jia, W-H, Joshi, A, Li, L, Lindor, NM, Matsuo, K, Moreno, V, Mukherjee, B, Newcomb, PA, Potter, JD, Raskin, L, Rennert, G, Rosse, S, Severi, G, Schoen, RE, Seminara, D, Shu, X-O, Slattery, ML, Tsugane, S, White, E, Xiang, Y-B, Zanke, BW, Zheng, W, Le Marchand, L, Casey, G, Gruber, SB, Peters, U, Schumacher, FR, Schmit, SL, Jiao, S, Edlund, CK, Wang, H, Zhang, B, Hsu, L, Huang, S-C, Fischer, CP, Harju, JF, Idos, GE, Lejbkowicz, F, Manion, FJ, McDonnell, K, McNeil, CE, Melas, M, Rennert, HS, Shi, W, Thomas, DC, Van den Berg, DJ, Hutter, CM, Aragaki, AK, Butterbach, K, Caan, BJ, Carlson, CS, Chanock, SJ, Curtis, KR, Fuchs, CS, Gala, M, Giovannucci, EL, Gogarten, SM, Hayes, RB, Henderson, B, Hunter, DJ, Jackson, RD, Kolonel, LN, Kooperberg, C, Kuery, S, LaCroix, A, Laurie, CC, Laurie, CA, Lemire, M, Levine, D, Ma, J, Makar, KW, Qu, C, Taverna, D, Ulrich, CM, Wu, K, Kono, S, West, DW, Berndt, SI, Bezieau, S, Brenner, H, Campbell, PT, Chan, AT, Chang-Claude, J, Coetzee, GA, Conti, DV, Duggan, D, Figueiredo, JC, Fortini, BK, Gallinger, SJ, Gauderman, WJ, Giles, G, Green, R, Haile, R, Harrison, TA, Hoffmeister, M, Hopper, JL, Hudson, TJ, Jacobs, E, Iwasaki, M, Jee, SH, Jenkins, M, Jia, W-H, Joshi, A, Li, L, Lindor, NM, Matsuo, K, Moreno, V, Mukherjee, B, Newcomb, PA, Potter, JD, Raskin, L, Rennert, G, Rosse, S, Severi, G, Schoen, RE, Seminara, D, Shu, X-O, Slattery, ML, Tsugane, S, White, E, Xiang, Y-B, Zanke, BW, Zheng, W, Le Marchand, L, Casey, G, Gruber, SB, and Peters, U

Published
2015

27. Association of Aspirin and NSAID Use With Risk of Colorectal Cancer According to Genetic Variants

Author

Nan, H, Hutter, CM, Lin, Y, Jacobs, EJ, Ulrich, CM, White, E, Baron, JA, Berndt, SI, Brenner, H, Butterbach, K, Caan, BJ, Campbell, PT, Carlson, CS, Casey, G, Chang-Claude, J, Chanock, SJ, Cotterchio, M, Duggan, D, Figueiredo, JC, Fuchs, CS, Giovannucci, EL, Gong, J, Haile, RW, Harrison, TA, Hayes, RB, Hoffmeister, M, Hopper, JL, Hudson, TJ, Jenkins, MA, Jiao, S, Lindor, NM, Lemire, M, Le Marchand, L, Newcomb, PA, Ogino, S, Pflugeisen, BM, Potter, JD, Qu, C, Rosse, SA, Rudolph, A, Schoen, RE, Schumacher, FR, Seminara, D, Slattery, ML, Thibodeau, SN, Thomas, F, Thornquist, M, Warnick, GS, Zanke, BW, Gauderman, WJ, Peters, U, Hsu, L, Chan, AT, Nan, H, Hutter, CM, Lin, Y, Jacobs, EJ, Ulrich, CM, White, E, Baron, JA, Berndt, SI, Brenner, H, Butterbach, K, Caan, BJ, Campbell, PT, Carlson, CS, Casey, G, Chang-Claude, J, Chanock, SJ, Cotterchio, M, Duggan, D, Figueiredo, JC, Fuchs, CS, Giovannucci, EL, Gong, J, Haile, RW, Harrison, TA, Hayes, RB, Hoffmeister, M, Hopper, JL, Hudson, TJ, Jenkins, MA, Jiao, S, Lindor, NM, Lemire, M, Le Marchand, L, Newcomb, PA, Ogino, S, Pflugeisen, BM, Potter, JD, Qu, C, Rosse, SA, Rudolph, A, Schoen, RE, Schumacher, FR, Seminara, D, Slattery, ML, Thibodeau, SN, Thomas, F, Thornquist, M, Warnick, GS, Zanke, BW, Gauderman, WJ, Peters, U, Hsu, L, and Chan, AT

Abstract

IMPORTANCE: Use of aspirin and other nonsteroidal anti-inflammatory drugs (NSAIDs) is associated with lower risk of colorectal cancer. OBJECTIVE: To identify common genetic markers that may confer differential benefit from aspirin or NSAID chemoprevention, we tested gene × environment interactions between regular use of aspirin and/or NSAIDs and single-nucleotide polymorphisms (SNPs) in relation to risk of colorectal cancer. DESIGN, SETTING, AND PARTICIPANTS: Case-control study using data from 5 case-control and 5 cohort studies initiated between 1976 and 2003 across the United States, Canada, Australia, and Germany and including colorectal cancer cases (n=8634) and matched controls (n=8553) ascertained between 1976 and 2011. Participants were all of European descent. EXPOSURES: Genome-wide SNP data and information on regular use of aspirin and/or NSAIDs and other risk factors. MAIN OUTCOMES AND MEASURES: Colorectal cancer. RESULTS: Regular use of aspirin and/or NSAIDs was associated with lower risk of colorectal cancer (prevalence, 28% vs 38%; odds ratio [OR], 0.69 [95% CI, 0.64-0.74]; P = 6.2 × 10(-28)) compared with nonregular use. In the conventional logistic regression analysis, the SNP rs2965667 at chromosome 12p12.3 near the MGST1 gene showed a genome-wide significant interaction with aspirin and/or NSAID use (P = 4.6 × 10(-9) for interaction). Aspirin and/or NSAID use was associated with a lower risk of colorectal cancer among individuals with rs2965667-TT genotype (prevalence, 28% vs 38%; OR, 0.66 [95% CI, 0.61-0.70]; P = 7.7 × 10(-33)) but with a higher risk among those with rare (4%) TA or AA genotypes (prevalence, 35% vs 29%; OR, 1.89 [95% CI, 1.27-2.81]; P = .002). In case-only interaction analysis, the SNP rs16973225 at chromosome 15q25.2 near the IL16 gene showed a genome-wide significant interaction with use of aspirin and/or NSAIDs (P = 8.2 × 10(-9) for interaction). Regular use was associated with a lower risk of colorectal cancer among individuals

Published
2015

28. Clearance rate of macrophages from the vitreous in rabbits

Author

van Meurs Jc, Gauderman Wj, Stephen J. Ryan, and Nino Sorgente

Subjects
Proliferative vitreoretinopathy, Pathology, medicine.medical_specialty, Lagomorpha, genetic structures, biology, Macrophages, Phagocytosis, medicine.disease, biology.organism_classification, Microspheres, eye diseases, Sensory Systems, Microsphere, Vitreous Body, Cellular and Molecular Neuroscience, Ophthalmology, medicine, Animals, Macrophage, Rabbits, sense organs, Clearance rate, Half-Life
Abstract

Macrophages are usually present in epiretinal membranes from eyes with proliferative vitreoretinopathy (PVR). Information on the kinetics of macrophages in the eye may be of help in identifying their role in this disease. To determine the half-life of macrophages in the vitreous, peritoneal macrophages were labeled by allowing them to phagocytose 141Cerium (gamma-emitter) labeled microspheres, and were then injected into the vitreous of the same rabbit from which they were obtained. The animals were sacrificed at various times post-injection and the radioactivity remaining in the vitreous was measured. Using this procedure, the half-life was found to be 4.8 days.

Published
1990
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29. Genome-Wide Diet-Gene Interaction Analyses for Risk of Colorectal Cancer

Author

Amos, CI, Figueiredo, JC, Hsu, L, Hutter, CM, Lin, Y, Campbell, PT, Baron, JA, Berndt, SI, Jiao, S, Casey, G, Fortini, B, Chan, AT, Cotterchio, M, Lemire, M, Gallinger, S, Harrison, TA, Le Marchand, L, Newcomb, PA, Slattery, ML, Caan, BJ, Carlson, CS, Zanke, BW, Rosse, SA, Brenner, H, Giovannucci, EL, Wu, K, Chang-Claude, J, Chanock, SJ, Curtis, KR, Duggan, D, Gong, J, Haile, RW, Hayes, RB, Hoffmeister, M, Hopper, JL, Jenkins, MA, Kolonel, LN, Qu, C, Rudolph, A, Schoen, RE, Schumacher, FR, Seminara, D, Stelling, DL, Thibodeau, SN, Thornquist, M, Warnick, GS, Henderson, BE, Ulrich, C, Gauderman, WJ, Potter, JD, White, E, Peters, U, Amos, CI, Figueiredo, JC, Hsu, L, Hutter, CM, Lin, Y, Campbell, PT, Baron, JA, Berndt, SI, Jiao, S, Casey, G, Fortini, B, Chan, AT, Cotterchio, M, Lemire, M, Gallinger, S, Harrison, TA, Le Marchand, L, Newcomb, PA, Slattery, ML, Caan, BJ, Carlson, CS, Zanke, BW, Rosse, SA, Brenner, H, Giovannucci, EL, Wu, K, Chang-Claude, J, Chanock, SJ, Curtis, KR, Duggan, D, Gong, J, Haile, RW, Hayes, RB, Hoffmeister, M, Hopper, JL, Jenkins, MA, Kolonel, LN, Qu, C, Rudolph, A, Schoen, RE, Schumacher, FR, Seminara, D, Stelling, DL, Thibodeau, SN, Thornquist, M, Warnick, GS, Henderson, BE, Ulrich, C, Gauderman, WJ, Potter, JD, White, E, and Peters, U

Abstract

Dietary factors, including meat, fruits, vegetables and fiber, are associated with colorectal cancer; however, there is limited information as to whether these dietary factors interact with genetic variants to modify risk of colorectal cancer. We tested interactions between these dietary factors and approximately 2.7 million genetic variants for colorectal cancer risk among 9,287 cases and 9,117 controls from ten studies. We used logistic regression to investigate multiplicative gene-diet interactions, as well as our recently developed Cocktail method that involves a screening step based on marginal associations and gene-diet correlations and a testing step for multiplicative interactions, while correcting for multiple testing using weighted hypothesis testing. Per quartile increment in the intake of red and processed meat were associated with statistically significant increased risks of colorectal cancer and vegetable, fruit and fiber intake with lower risks. From the case-control analysis, we detected a significant interaction between rs4143094 (10p14/near GATA3) and processed meat consumption (OR = 1.17; p = 8.7E-09), which was consistently observed across studies (p heterogeneity = 0.78). The risk of colorectal cancer associated with processed meat was increased among individuals with the rs4143094-TG and -TT genotypes (OR = 1.20 and OR = 1.39, respectively) and null among those with the GG genotype (OR = 1.03). Our results identify a novel gene-diet interaction with processed meat for colorectal cancer, highlighting that diet may modify the effect of genetic variants on disease risk, which may have important implications for prevention.

Published
2014

30. Genome-Wide Diet-Gene Interaction Analyses for Risk of Colorectal Cancer

Author

Figueiredo, JC, Hsu, L, Hutter, CM, Lin, Y, Campbell, PT, Baron, JA, Berndt, SI, Jiao, S, Casey, G, Fortini, B, Chan, AT, Cotterchio, M, Lemire, M, Gallinger, S, Harrison, TA, Le Marchand, L, Newcomb, PA, Slattery, ML, Caan, BJ, Carlson, CS, Zanke, BW, Rosse, SA, Brenner, H, Giovannucci, EL, Wu, K, Chang-Claude, J, Chanock, SJ, Curtis, KR, Duggan, D, Gong, J, Haile, RW, Hayes, RB, Hoffmeister, M, Hopper, JL, Jenkins, MA, Kolonel, LN, Qu, C, Rudolph, A, Schoen, RE, Schumacher, FR, Seminara, D, Stelling, DL, Thibodeau, SN, Thornquist, M, Warnick, GS, Henderson, BE, Ulrich, CM, Gauderman, WJ, Potter, JD, White, E, Peters, U, Figueiredo, JC, Hsu, L, Hutter, CM, Lin, Y, Campbell, PT, Baron, JA, Berndt, SI, Jiao, S, Casey, G, Fortini, B, Chan, AT, Cotterchio, M, Lemire, M, Gallinger, S, Harrison, TA, Le Marchand, L, Newcomb, PA, Slattery, ML, Caan, BJ, Carlson, CS, Zanke, BW, Rosse, SA, Brenner, H, Giovannucci, EL, Wu, K, Chang-Claude, J, Chanock, SJ, Curtis, KR, Duggan, D, Gong, J, Haile, RW, Hayes, RB, Hoffmeister, M, Hopper, JL, Jenkins, MA, Kolonel, LN, Qu, C, Rudolph, A, Schoen, RE, Schumacher, FR, Seminara, D, Stelling, DL, Thibodeau, SN, Thornquist, M, Warnick, GS, Henderson, BE, Ulrich, CM, Gauderman, WJ, Potter, JD, White, E, and Peters, U

Abstract

Dietary factors, including meat, fruits, vegetables and fiber, are associated with colorectal cancer; however, there is limited information as to whether these dietary factors interact with genetic variants to modify risk of colorectal cancer. We tested interactions between these dietary factors and approximately 2.7 million genetic variants for colorectal cancer risk among 9,287 cases and 9,117 controls from ten studies. We used logistic regression to investigate multiplicative gene-diet interactions, as well as our recently developed Cocktail method that involves a screening step based on marginal associations and gene-diet correlations and a testing step for multiplicative interactions, while correcting for multiple testing using weighted hypothesis testing. Per quartile increment in the intake of red and processed meat were associated with statistically significant increased risks of colorectal cancer and vegetable, fruit and fiber intake with lower risks. From the case-control analysis, we detected a significant interaction between rs4143094 (10p14/near GATA3) and processed meat consumption (OR = 1.17; p = 8.7E-09), which was consistently observed across studies (p heterogeneity = 0.78). The risk of colorectal cancer associated with processed meat was increased among individuals with the rs4143094-TG and -TT genotypes (OR = 1.20 and OR = 1.39, respectively) and null among those with the GG genotype (OR = 1.03). Our results identify a novel gene-diet interaction with processed meat for colorectal cancer, highlighting that diet may modify the effect of genetic variants on disease risk, which may have important implications for prevention. © 2014.

Published
2014

31. Combined linkage and association analysis in pedigrees

Author

Kimberly D. Siegmund, Gauderman Wj, and Vora H

Subjects
0301 basic medicine, Adult, Male, Linkage disequilibrium, Genotype, Epidemiology, Population, Pedigree chart, 030105 genetics & heredity, Quantitative trait locus, Biology, Population stratification, Linkage Disequilibrium, 03 medical and health sciences, Polymorphism (computer science), Humans, education, Child, Genetics (clinical), Genetic association, Genetics, Linkage (software), education.field_of_study, Polymorphism, Genetic, Models, Genetic, Chromosome Mapping, Genetic Variation, Pedigree, 030104 developmental biology, Genetics, Population, Female, Lod Score
Abstract

We applied a combined linkage and association model for quantitative traits in pedigrees to identify possible functional polymorphisms and to test for association resulting from population stratification and admixture. Functional polymorphisms are identified as variants that are significantly associated with a trait (high chi 2 value) and showing no residual evidence of linkage (low lod score). Applying our model to the simulated data in the population isolate (replicate 1) we correctly identified the polymorphism in gene 6 (MG1) that affects Q1. Without modeling association the lod score for Q1 was 5.4. At the site of the functional variant (5782 bp) the association chi 2 was 88.1 on 1 df (p0.001) and the lod score was 0.003. We estimated a 3.7-unit increase in the average Q1 for each extra copy of the polymorphism (95% CI = 2.95-4.41) and there was no evidence of population stratification or admixture (chi 2 = 0.08 on 2 df). For Q5 and gene 2, modeling the sequence variants at 11 loci simultaneously identified multiple functional variants. Including the main effect of 11 marker genotypes reduced the lod score at gene 2 from 8.7 to 0.9. Again, no evidence of population stratification or admixture was found (all chi 24.9 on 2 df; p0.05).

Published
2002

32. Integration of Mouse and Human Genome-Wide Association Data Identifies KCNIP4 as an Asthma Gene

Author

Himes, BE, Sheppard, K, Berndt, A, Leme, AS, Myers, RA, Gignoux, CR, Levin, AM, Gauderman, WJ, Yang, JJ, Mathias, RA, Romieu, I, Torgerson, DG, Roth, LA, Huntsman, S, Eng, C, Klanderman, B, Ziniti, J, Senter-Sylvia, J, Szefler, SJ, Lemanske, RF, Zeiger, RS, Strunk, RC, Martinez, FD, Boushey, H, Chinchilli, VM, Israel, E, Mauger, D, Koppelman, GH, Postma, DS, Nieuwenhuis, MAE, Vonk, JM, Lima, JJ, Irvin, CG, Peters, SP, Kubo, M, Tamari, M, Nakamura, Y, Litonjua, AA, Tantisira, KG, Raby, BA, Bleecker, ER, Meyers, DA, London, SJ, Barnes, KC, Gilliland, FD, Williams, LK, Burchard, EG, Nicolae, DL, Ober, C, DeMeo, DL, Silverman, EK, Paigen, B, Churchill, G, Shapiro, SD, Weiss, ST, Himes, BE, Sheppard, K, Berndt, A, Leme, AS, Myers, RA, Gignoux, CR, Levin, AM, Gauderman, WJ, Yang, JJ, Mathias, RA, Romieu, I, Torgerson, DG, Roth, LA, Huntsman, S, Eng, C, Klanderman, B, Ziniti, J, Senter-Sylvia, J, Szefler, SJ, Lemanske, RF, Zeiger, RS, Strunk, RC, Martinez, FD, Boushey, H, Chinchilli, VM, Israel, E, Mauger, D, Koppelman, GH, Postma, DS, Nieuwenhuis, MAE, Vonk, JM, Lima, JJ, Irvin, CG, Peters, SP, Kubo, M, Tamari, M, Nakamura, Y, Litonjua, AA, Tantisira, KG, Raby, BA, Bleecker, ER, Meyers, DA, London, SJ, Barnes, KC, Gilliland, FD, Williams, LK, Burchard, EG, Nicolae, DL, Ober, C, DeMeo, DL, Silverman, EK, Paigen, B, Churchill, G, Shapiro, SD, and Weiss, ST

Abstract

Asthma is a common chronic respiratory disease characterized by airway hyperresponsiveness (AHR). The genetics of asthma have been widely studied in mouse and human, and homologous genomic regions have been associated with mouse AHR and human asthma-related phenotypes. Our goal was to identify asthma-related genes by integrating AHR associations in mouse with human genome-wide association study (GWAS) data. We used Efficient Mixed Model Association (EMMA) analysis to conduct a GWAS of baseline AHR measures from males and females of 31 mouse strains. Genes near or containing SNPs with EMMA p-values <0.001 were selected for further study in human GWAS. The results of the previously reported EVE consortium asthma GWAS meta-analysis consisting of 12,958 diverse North American subjects from 9 study centers were used to select a subset of homologous genes with evidence of association with asthma in humans. Following validation attempts in three human asthma GWAS (i.e., Sepracor/LOCCS/LODO/Illumina, GABRIEL, DAG) and two human AHR GWAS (i.e., SHARP, DAG), the Kv channel interacting protein 4 (KCNIP4) gene was identified as nominally associated with both asthma and AHR at a gene- and SNP-level. In EVE, the smallest KCNIP4 association was at rs6833065 (P-value 2.9e-04), while the strongest associations for Sepracor/LOCCS/LODO/Illumina, GABRIEL, DAG were 1.5e-03, 1.0e-03, 3.1e-03 at rs7664617, rs4697177, rs4696975, respectively. At a SNP level, the strongest association across all asthma GWAS was at rs4697177 (P-value 1.1e-04). The smallest P-values for association with AHR were 2.3e-03 at rs11947661 in SHARP and 2.1e-03 at rs402802 in DAG. Functional studies are required to validate the potential involvement of KCNIP4 in modulating asthma susceptibility and/or AHR. Our results suggest that a useful approach to identify genes associated with human asthma is to leverage mouse AHR association data. © 2013 Himes et al.

Published
2013

33. Gene-environment interaction and affected sib pair linkage analysis

Author

Gauderman Wj and Kimberly D. Siegmund

Subjects
Linkage (software), Genetics, Family Characteristics, Models, Statistical, Models, Genetic, Genetic Linkage, Statistics as Topic, Complex disease, Chromosome Mapping, Biology, Environment, Genome, Sib pairs, Genetic Techniques, Genetic linkage, Research Design, Sample Size, Chromosomes, Human, Humans, Computer Simulation, Gene–environment interaction, Genetics (clinical), Software
Abstract

Objectives: Gene-environment (G×E) interaction influences risk for many complex disease traits. However, genome screens using affected sib pair linkage techniques are typically conducted without regard for G×E interaction. We propose a simple extension of the commonly used mean test and evaluate its power for several forms of G×E interaction. Methods: We compute expected IBD sharing by sibling exposure profile, that is by whether two sibs are exposed (EE), unexposed (UU), or are discordant for exposure (EU). We describe a simple extension of the mean test, the ‘mean-interaction’ test that utilizes heterogeneity in IBD sharing across EE, EU, and UU sib pairs in a test for linkage. Results: The mean-interaction test provides greater power than the mean test for detecting linkage in the presence of moderate or strong G×E interaction, typically when the interaction relative risk (Rge) exceeds 3 or is less than 1/3. In the presence of strong interaction (Rge = 10), the required number of affected sib pairs to achieve 80% power for detecting linkage is approximately 30% higher when the environmental factor is ignored in the mean test, than when it is utilized in the mean-interaction test. Conclusion: Linkage methods that incorporate environmental data and allow for interaction can lead to increased power for localizing a disease gene involved in a G×E interaction.

Published
2001

35. A genome-wide association meta-analysis identifies new childhood obesity loci

Author

Bradfield JP, -, primary, Taal HR, -, additional, Timpson NJ, -, additional, Scherag A, -, additional, Lecoeur C, -, additional, Warrington NM, -, additional, Hypponen E, -, additional, Holst C, -, additional, Valcarcel B, -, additional, Thiering E, -, additional, Salem RM, -, additional, Schumacher FR, -, additional, Cousminer DL, -, additional, Sleiman PM, -, additional, Zhao J, -, additional, Berkowitz RI, -, additional, Vimaleswaran KS, -, additional, Jarick I, -, additional, Pennell CE, -, additional, Evans DM, -, additional, St Pourcain B, -, additional, Berry DJ, -, additional, Mook-Kanamori DO, -, additional, Hofman A, -, additional, Rivadeneira F, -, additional, Uitterlinden AG, -, additional, Van Duijn CM, -, additional, Van der Valk RJ, -, additional, De Jongste JC, -, additional, Postma DS, -, additional, Boomsma DI, -, additional, Gauderman WJ, -, additional, Hassanein MT, -, additional, Lindgren CM, -, additional, Mägi R, -, additional, Boreham CA, -, additional, Neville CE, -, additional, Moreno LA, -, additional, Elliott P, -, additional, Pouta A, -, additional, Hartikainen AL, -, additional, Li M, -, additional, Raitakari O, -, additional, Lehtimäki T, -, additional, Eriksson JG, -, additional, Palotie A, -, additional, Dallongeville J, -, additional, Das S, -, additional, Deloukas P, -, additional, McMahon G, -, additional, Ring SM, -, additional, Kemp JP, -, additional, Buxton JL, -, additional, Blakemore AI, -, additional, Bustamante M, -, additional, Guxens M, -, additional, Hirschhorn JN, -, additional, Gillman MW, -, additional, Kreiner-Møller E, -, additional, Bisgaard H, -, additional, Gilliland FD, -, additional, Heinrich J, -, additional, Wheeler E, -, additional, Barroso I, -, additional, O'Rahilly S, -, additional, Meirhaeghe A, -, additional, Sørensen TI, -, additional, Power C, -, additional, Palmer LJ, -, additional, Hinney A, -, additional, Widen E, -, additional, Farooqi IS, -, additional, McCarthy MI, -, additional, Froguel P, -, additional, Meyre D, -, additional, Hebebrand J, -, additional, Jarvelin MR, -, additional, Jaddoe VW, -, additional, Smith GD, -, additional, Hakonarson H, -, additional, and Grant, SF, additional

Published
2012
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40. Variation in the GST mu locus and tobacco smoke exposure as determinants of childhood lung function.

Author

Breton CV, Vora H, Salam MT, Islam T, Wenten M, Gauderman WJ, Van den Berg D, Berhane K, Peters JM, Gilliland FD, Breton, Carrie V, Vora, Hita, Salam, Muhammad T, Islam, Talat, Wenten, Made, Gauderman, W James, Van den Berg, David, Berhane, Kiros, Peters, John M, and Gilliland, Frank D

Abstract

Rationale: The glutathione S-transferases (GSTs) are important detoxification enzymes.Objectives: To investigate effects of variants in GST mu genes on lung function and assess their interactions with tobacco smoke exposure.Methods: In this prospective study, 14,836 lung function measurements were collected from 2,108 children who participated in two Southern California cohorts. For each child, tagging single nucleotide polymorphisms in GSTM2, GSTM3, GSTM4, and GSTM5 loci were genotyped. Using principal components and haplotype analyses, the significance of each locus in relation to level and growth of FEV1, maximum midexpiratory flow rate (MMEF), and FVC was evaluated. Interactions between loci and tobacco smoke on lung function were also investigated.Measurements and Main Results: Variation in the GST mu family locus was associated with lower FEV1 (P = 0.01) and MMEF (0.04). Two haplotypes of GSTM2 were associated with FEV1 and MMEF, with effect estimates in opposite directions. One haplotype in GSTM3 showed a decrease in growth for MMEF (-164.9 ml/s) compared with individuals with other haplotypes. One haplotype in GSTM4 showed significantly decreased growth in FEV1 (-51.3 ml), MMEF (-69.1 ml/s), and FVC (-44.4 ml), compared with all other haplotypes. These results were consistent across two independent cohorts. Variation in GSTM2 was particularly important for FVC and FEV(1) among children whose mothers smoked during pregnancy.Conclusions: Genetic variation across the GST mu locus is associated with 8-year lung function growth. Children of mothers who smoked during pregnancy and had variation in GSTM2 had lower lung function growth. [ABSTRACT FROM AUTHOR]

Published
2009
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41. Gene-environment interaction in genome-wide association studies.

Author

Murcray CE, Lewinger JP, and Gauderman WJ

Abstract

It is a commonly held belief that most complex diseases (e.g., diabetes, asthma, cancer) are affected in part by interactions between genes and environmental factors. However, investigators conducting genome-wide association studies typically test for only the marginal effects of each genetic marker on disease. In this paper, the authors propose an efficient and easily implemented 2-step analysis of genome-wide association study data aimed at identifying genes involved in a gene-environment interaction. The procedure complements screening for marginal genetic effects and thus has the potential to uncover new genetic signals that have not been identified previously. [ABSTRACT FROM AUTHOR]

Published
2009

42. Glutathione s-transferase p1, maternal smoking, and asthma in children: a haplotype-based analysis.

Author

Li YF, Gauderman WJ, Conti DV, Lin PC, Avol E, and Gilliland FD

Abstract

BACKGROUND: Glutathione S-transferase P1 (GSTP1) plays a role in a spectrum of respiratory diseases; however, the effects of sequence variation across the entire locus in asthma pathogenesis have yet to be determined. OBJECTIVES: This study was designed to investigate whether sequence variations in the GSTP1 coding and promoter regions are associated with asthma and wheezing outcomes and to determine whether variants affect susceptibility to maternal smoking. METHODS: Four haplotype tagging SNPs were selected that accounted for 83% of the common haplotypic variation in GSTP1. The associations of GSTP1 variants with asthma and wheezing were assessed among white children in the Children's Health Study (CHS). RESULTS: The Ile105Val allele and a SNP in the upstream promoter region (SNP1: rs6591255, putative transcription factor 1 binding site) were associated with asthma and wheezing outcomes, an association observed in two cohorts of the CHS recruited in different years. Haplotypes that included both the promoter SNP (i.e., rs6591255) and the 105 Val variant were associated with an increased risk for asthma in non-Hispanic whites. Using SNP- and haplotype-based approaches, the effect of maternal smoking on wheezing was largest in children with the Ile105Val allele. CONCLUSIONS: Variants in both the promoter and coding regions of the GSTP1 locus may contribute to the occurrence of childhood asthma and wheezing and may increase susceptibility to adverse effects of tobacco-smoke exposure. [ABSTRACT FROM AUTHOR]

Published
2008

43. Transforming growth factor- 1 C-509T polymorphism, oxidant stress, and early-onset childhood asthma.

Author

Salam MT, Gauderman WJ, McConnell R, Lin PC, Gilliland FD, Salam, Muhammad T, Gauderman, W James, McConnell, Rob, Lin, Pi-Chu, and Gilliland, Frank D

Abstract

Rationale: Transforming growth factor (TGF)-beta1 is involved in airway inflammation and remodeling, two key processes in asthma pathogenesis. Tobacco smoke and traffic emissions induce airway inflammation and modulate TGF-beta1 gene expression. We hypothesized that the effects of functional TGF-beta1 variants on asthma occurrence vary by these exposures.Objectives: We tested these hypotheses among 3,023 children who participated in the Children's Health Study.Methods: Tagging single-nucleotide polymorphisms rs4803457 C>T and C-509T (a functional promoter polymorphism) accounted for 94% of the haplotype diversity of the upstream region. Exposure to maternal smoking in utero was based on smoking by biological mother during pregnancy. Residential distance from nearest freeway was calculated based on residential address at study entry.Measurements and Main Results: Children with the -509TT genotype had a 1.8-fold increased risk of early persistent asthma (95% confidence interval [CI], 1.11-2.95). This association varied marginally significantly by in utero exposure to maternal smoking. Compared with children with the -509CC/CT genotype with no in utero exposure to maternal smoking, those with the -509TT genotype with such exposure had a 3.4-fold increased risk of early persistent asthma (95% CI, 1.46-7.80; interaction, P = 0.11). The association between TGF-beta1 C-509T and lifetime asthma varied by residential proximity to freeways (interaction P = 0.02). Children with the -509TT genotype living within 500 m of a freeway had over three-fold increased lifetime asthma risk (95% CI, 1.29-7.44) compared with children with CC/CT genotype living > 1500 m from a freeway.Conclusions: Children with the TGF-beta1 -509TT genotype are at increased risk of asthma when they are exposed to maternal smoking in utero or to traffic-related emissions. [ABSTRACT FROM AUTHOR]

Published
2007
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45. Health effects of the 2003 Southern California wildfires on children.

Author

Künzli N, Avol E, Wu J, Gauderman WJ, Rappaport E, Millstein J, Bennion J, McConnell R, Gilliland FD, Berhane K, Lurmann F, Winer A, Peters JM, Künzli, Nino, Avol, Ed, Wu, Jun, Gauderman, W James, Rappaport, Ed, Millstein, Joshua, and Bennion, Jonathan

Abstract

Rationale: In late October 2003, Southern California wildfires burned more than 3,000 km2. The wildfires produced heavy smoke that affected several communities participating in the University of Southern California Children's Health Study (CHS).Objectives: To study the acute effects of fire smoke on the health of CHS participants.Methods: A questionnaire was used to assess smoke exposure and occurrence of symptoms among CHS high-school students (n = 873; age, 17-18 yr) and elementary-school children (n = 5,551; age, 6-7 yr), in a total of 16 communities. Estimates of particulate matter (PM10) concentrations during the 5 d with the highest fire activity were used to characterize community smoke level.Main Results: All symptoms (nose, eyes, and throat irritations; cough; bronchitis; cold; wheezing; asthma attacks), medication usage, and physician visits were associated with individually reported exposure differences within communities. Risks increased monotonically with the number of reported smoky days. For most outcomes, reporting rates between communities were also associated with the fire-related PM10 levels. Associations tended to be strongest among those without asthma. Individuals with asthma were more likely to take preventive action, such as wearing masks or staying indoors during the fire.Conclusions: Exposure to wildfire smoke was associated with increased eye and respiratory symptoms, medication use, and physician visits. [ABSTRACT FROM AUTHOR]

Published
2006
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46. Regular smoking and asthma incidence in adolescents.

Author

Gilliland FD, Islam T, Berhane K, Gauderman WJ, McConnell R, Avol E, Peters JM, Gilliland, Frank D, Islam, Talat, Berhane, Kiros, Gauderman, W James, McConnell, Rob, Avol, Edward, and Peters, John M

Abstract

Rationale: Although involuntary exposure to maternal smoking during the in utero period and to secondhand smoke are associated with occurrence of childhood asthma, few studies have investigated the role of active cigarette smoking on asthma onset during adolescence.Objectives: To determine whether regular smoking is associated with the new onset of asthma during adolescence.Methods: We conducted a prospective cohort study among 2,609 children with no lifetime history of asthma or wheezing who were recruited from fourth- and seventh-grade classrooms and followed annually in schools in 12 southern California communities. Regular smoking was defined as smoking at least seven cigarettes per day on average over the week before and 300 cigarettes in the year before each annual interview. Incident asthma was defined using new cases of physician-diagnosed asthma.Measurements and Main Results: Regular smoking was associated with increased risk of new-onset asthma. Children who reported smoking 300 or more cigarettes per year had a relative risk (RR) of 3.9 (95% confidence interval [95% CI], 1.7-8.5) for new-onset asthma compared with nonsmokers. The increased risk from regular smoking was greater in nonallergic than in allergic children. Regular smokers who were exposed to maternal smoking during gestation had the largest risk from active smoking (RR, 8.8; 95% CI, 3.2-24.0).Conclusions: Regular smoking increased risk for asthma among adolescents, especially for nonallergic adolescents and those exposed to maternal smoking during the in utero period. [ABSTRACT FROM AUTHOR]

Published
2006
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48. Associations of tumor necrosis factor G-308A with childhood asthma and wheezing.

Author

Li YF, Gauderman WJ, Avol E, Dubeau L, Gilliland FD, Li, Yu-Fen, Gauderman, W James, Avol, Ed, Dubeau, Louis, and Gilliland, Frank D

Abstract

Rationale: Tumor necrosis factor (TNF) mediates a spectrum of airway inflammatory responses, including those to air pollutants, and is an asthma candidate gene. One TNF promoter variant (G-308A) affects expression of TNF and has been associated with inflammatory diseases; however, studies of asthma have been inconsistent. Because ozone produces oxidative stress, increased airway TNF, and inflammation, the associations of the -308 TNF polymorphism with asthma may vary by ozone exposure and variants of oxidant defense genes glutathione-S-transferase (GST) M1 and GSTP1.Objectives: To investigate the association of TNF G-308A with asthma and wheezing and to determine whether these associations vary with ozone exposure and GSTM1 and GSTP1 genotype.Methods: We studied associations of TNF-308 genotype with lifetime and current wheezing and asthma among 3,699 children in the Children's Health Study. We examined differences in associations with community ozone and by GSTM1 null and GSTP1 105 Ile/Val (A105G) genotype.Results: Children with TNF-308 GG had decreased risk of asthma (odds ratio, 0.8; 95% confidence interval, 0.7-0.9) and lifetime wheezing (odds ratio, 0.8; 95% confidence interval, 0.7-0.9). The protective effects of GG genotype on wheezing outcomes were of greater magnitude in lower compared with higher ozone communities. These findings were replicated in the two cohorts of fourth-grade children recruited in 1993 and 1996. The reduction of the protective effect from the -308 GG genotype with higher ozone exposure was most marked in the GSTM1 null and GSTP1 Ile/Ile groups.Conclusions: The TNF-308 GG genotype may have a protective role in asthma pathogenesis, depending on airway oxidative stress levels. [ABSTRACT FROM AUTHOR]

Published
2006

49. TNF-308 modifies the effect of second-hand smoke on respiratory illness-related school absences.

Author

Wenten M, Berhane K, Rappaport EB, Avol E, Tsai W, Gauderman WJ, McConnell R, Dubeau L, Gilliland FD, Wenten, Madé, Berhane, Kiros, Rappaport, Edward B, Avol, Edward, Tsai, Wei-Wei, Gauderman, W James, McConnell, Rob, Dubeau, Louis, and Gilliland, Frank D

Abstract

Rationale: Exposure to second-hand smoke (SHS) has been associated with increased risk of respiratory illness in children including respiratory illness-related school absences. The role of genetic susceptibility in risk for adverse effects from SHS has not been extensively investigated in children.Objective: To determine whether the tumor necrosis factor (TNF) G-308A genotype influences the risk for respiratory illness-related school absences associated with SHS exposure.Methods: Incident school absences were collected, using an active surveillance system, between January and June 1996, as part of the Air Pollution and Absence Study, a prospective cohort study nested in the Children's Health Study. Buccal cells and absence reports were collected on 1,351 students from 27 elementary schools in California.Measurements and Main Results: Illness-related school absences were classified as nonrespiratory and respiratory illness-related, which were further categorized into upper or lower respiratory illness-related absences based on symptoms. The effect of SHS exposure on respiratory illness-related absences differed by TNF genotype (p interaction, 0.02). In children possessing at least one copy of the TNF-308 A variant, exposure to two or more household smokers was associated with a twofold risk of a school absence due to respiratory illness (relative risk, 2.13; 95% confidence interval, 1.34, 3.40) and a fourfold risk of lower respiratory illness-related school absence (relative risk, 4.15; 95% confidence interval, 2.57, 6.71) compared with unexposed children homozygous for the common TNF-308 G allele.Conclusions: These results indicate that a subgroup of genetically susceptible children are at substantially greater risk of respiratory illness if exposed to SHS. [ABSTRACT FROM AUTHOR]

Published
2005
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