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Browsing by Author "Joshi, Amit D."
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Item Beyond GWAS of Colorectal Cancer: Evidence of Interaction with Alcohol Consumption and Putative Causal Variant for the 10q24.2 Region(American Association for Cancer Research, 2022) Jordahl, Kristina M.; Shcherbina, Anna; Kim, Andre E.; Su, Yu-Ru; Lin, Yi; Wang, Jun; Qu, Conghui; Albanes, Demetrius; Arndt, Volker; Baurley, James W.; Berndt, Sonja I.; Bien, Stephanie A.; Bishop, D. Timothy; Bouras, Emmanouil; Brenner, Hermann; Buchanan, Daniel D.; Budiarto, Arif; Campbell, Peter T.; Carreras-Torres, Robert; Casey, Graham; Cenggoro, Tjeng Wawan; Chan, Andrew T.; Conti, David V.; Dampier, Christopher H.; Devall, Matthew A.; Díez-Obrero, Virginia; Dimou, Niki; Drew, David A.; Figueiredo, Jane C.; Gallinger, Steven; Giles, Graham G.; Gruber, Stephen B.; Gsur, Andrea; Gunter, Marc J.; Hampel, Heather; Harlid, Sophia; Harrison, Tabitha A.; Hidaka, Akihisa; Hoffmeister, Michael; Huyghe, Jeroen R.; Jenkins, Mark A.; Joshi, Amit D.; Keku, Temitope O.; Larsson, Susanna C.; Le Marchand, Loic; Lewinger, Juan Pablo; Li, Li; Mahesworo, Bharuno; Moreno, Victor; Morrison, John L.; Murphy, Neil; Nan, Hongmei; Nassir, Rami; Newcomb, Polly A.; Obón-Santacana, Mireia; Ogino, Shuji; Ose, Jennifer; Pai, Rish K.; Palmer, Julie R.; Papadimitriou, Nikos; Pardamean, Bens; Peoples, Anita R.; Pharoah, Paul D. P.; Platz, Elizabeth A.; Potter, John D.; Prentice, Ross L.; Rennert, Gad; Ruiz-Narvaez, Edward; Sakoda, Lori C.; Scacheri, Peter C.; Schmit, Stephanie L.; Schoen, Robert E.; Slattery, Martha L.; Stern, Mariana C.; Tangen, Catherine M.; Thibodeau, Stephen N.; Thomas, Duncan C.; Tian, Yu; Tsilidis, Konstantinos K.; Ulrich, Cornelia M.; van Duijnhoven, Franzel J. B.; Van Guelpen, Bethany; Visvanathan, Kala; Vodicka, Pavel; White, Emily; Wolk, Alicja; Woods, Michael O.; Wu, Anna H.; Zemlianskaia, Natalia; Chang-Claude, Jenny; Gauderman, W. James; Hsu, Li; Kundaje, Anshul; Peters, Ulrike; Epidemiology, School of Public HealthBackground: Currently known associations between common genetic variants and colorectal cancer explain less than half of its heritability of 25%. As alcohol consumption has a J-shape association with colorectal cancer risk, nondrinking and heavy drinking are both risk factors for colorectal cancer. Methods: Individual-level data was pooled from the Colon Cancer Family Registry, Colorectal Transdisciplinary Study, and Genetics and Epidemiology of Colorectal Cancer Consortium to compare nondrinkers (≤1 g/day) and heavy drinkers (>28 g/day) with light-to-moderate drinkers (1-28 g/day) in GxE analyses. To improve power, we implemented joint 2df and 3df tests and a novel two-step method that modifies the weighted hypothesis testing framework. We prioritized putative causal variants by predicting allelic effects using support vector machine models. Results: For nondrinking as compared with light-to-moderate drinking, the hybrid two-step approach identified 13 significant SNPs with pairwise r2 > 0.9 in the 10q24.2/COX15 region. When stratified by alcohol intake, the A allele of lead SNP rs2300985 has a dose-response increase in risk of colorectal cancer as compared with the G allele in light-to-moderate drinkers [OR for GA genotype = 1.11; 95% confidence interval (CI), 1.06-1.17; OR for AA genotype = 1.22; 95% CI, 1.14-1.31], but not in nondrinkers or heavy drinkers. Among the correlated candidate SNPs in the 10q24.2/COX15 region, rs1318920 was predicted to disrupt an HNF4 transcription factor binding motif. Conclusions: Our study suggests that the association with colorectal cancer in 10q24.2/COX15 observed in genome-wide association study is strongest in nondrinkers. We also identified rs1318920 as the putative causal regulatory variant for the region. Impact: The study identifies multifaceted evidence of a possible functional effect for rs1318920.Item Exploratory genome-wide interaction analysis of non-steroidal anti-inflammatory drugs and predicted gene expression on colorectal cancer risk(American Association for Cancer Research, 2020-09) Wang, Xiaoliang; Su, Yu-Ru; Petersen, Paneen S.; Bien, Stephanie; Schmit, Stephanie L.; Drew, David A.; Albanes, Demetrius; Berndt, Sonja I.; Brenner, Hermann; Campbell, Peter T.; Casey, Graham; Chang-Claude, Jenny; Gallinger, Steven J.; Gruber, Stephen B.; Haile, Robert W.; Harrison, Tabitha A.; Hoffmeister, Michael; Jacobs, Eric J.; Jenkins, Mark A.; Joshi, Amit D.; Li, Li; Lin, Yi; Lindor, Noralane M.; Le Marchand, Loïc; Martin, Vicente; Milne, Roger; Maclnnis, Robert; Moreno, Victor; Nan, Hongmei; Newcomb, Polly A.; Potter, John D.; Rennert, Gad; Rennert, Hedy; Slattery, Martha L.; Thibodeau, Steve N.; Weinstein, Stephanie J.; Woods, Michael O.; Chan, Andrew T.; White, Emily; Hsu, Li; Peters, Ulrike; Global Health, School of Public HealthBackground: Regular use of nonsteroidal anti-inflammatory drugs (NSAID) is associated with lower risk of colorectal cancer. Genome-wide interaction analysis on single variants (G × E) has identified several SNPs that may interact with NSAIDs to confer colorectal cancer risk, but variations in gene expression levels may also modify the effect of NSAID use. Therefore, we tested interactions between NSAID use and predicted gene expression levels in relation to colorectal cancer risk. Methods: Genetically predicted gene expressions were tested for interaction with NSAID use on colorectal cancer risk among 19,258 colorectal cancer cases and 18,597 controls from 21 observational studies. A Mixed Score Test for Interactions (MiSTi) approach was used to jointly assess G × E effects which are modeled via fixed interaction effects of the weighted burden within each gene set (burden) and residual G × E effects (variance). A false discovery rate (FDR) at 0.2 was applied to correct for multiple testing. Results: Among the 4,840 genes tested, genetically predicted expression levels of four genes modified the effect of any NSAID use on colorectal cancer risk, including DPP10 (PG×E = 1.96 × 10-4), KRT16 (PG×E = 2.3 × 10-4), CD14 (PG×E = 9.38 × 10-4), and CYP27A1 (PG×E = 1.44 × 10-3). There was a significant interaction between expression level of RP11-89N17 and regular use of aspirin only on colorectal cancer risk (PG×E = 3.23 × 10-5). No interactions were observed between predicted gene expression and nonaspirin NSAID use at FDR < 0.2. Conclusions: By incorporating functional information, we discovered several novel genes that interacted with NSAID use.Item Genome-Wide Interaction Analysis of Genetic Variants With Menopausal Hormone Therapy for Colorectal Cancer Risk(Oxford, 2022) Tian, Yu; Kim, Andre E.; Bien, Stephanie A.; Lin, Yi; Qu, Conghui; Harrison, Tabitha A.; Carreras-Torres, Robert; Díez-Obrero, Virginia; Dimou, Niki; Drew , David A.; Hidaka, Akihisa; Huyghe, Jeroen R.; Jordahl, Kristina M.; Morrison , John; Murphy, Neil; Obón-Santacana, Mireia; Ulrich, Cornelia M.; Ose, Jennifer; Peoples, Anita R.; Ruiz-Narvaez, Edward A.; Shcherbina, Anna; Stern , Mariana C.; Su, Yu-Ru; van Duijnhoven, Franzel J. B.; Arndt, Volker; Baurley, James W.; Berndt, Sonja I.; Bishop, D. Timothy; Brenner, Hermann; Buchanan, Daniel D.; Chan, Andrew T.; Figueiredo, Jane C.; Gallinger, Steven; Gruber, Stephen B.; Harlid, Sophia; Hoffmeister, Michael; Jenkins, Mark A.; Joshi, Amit D.; Keku, Temitope O.; Larsson, Susanna C.; Marchand, Loic Le; Li, Li; Giles, Graham G.; Milne, Roger L.; Nan, Hongmei; Nassir, Rami; Ogino, Shuji; Budiarto, Arif; Platz, Elizabeth A.; Potter, John D.; Prentice, Ross L.; Rennert, Gad; Sakoda, Lori C.; Schoen, Robert E.; Slattery, Martha L.; Thibodeau, Stephen N.; Van Guelpen, Bethany; Visvanathan, Kala; White, Emily; Wolk, Alicja; Woods, Michael O.; Wu, Anna H.; Campbell, Peter T.; Casey, Graham; Conti, David V.; Gunter, Marc J.; Kundaje, Anshul; Lewinger, Juan Pablo; Moreno, Victor; Newcomb, Polly A.; Pardamean, Bens; Thomas, Duncan C.; Tsilidis, Konstantinos K.; Peters, Ulrike; Gauderman, W. James; Hsu, Li; Chang-Claude, Jenny; Global Health, School of Public HealthBackground: 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.Item Hierarchical modeling of melanocortin 1 receptor variants with skin cancer risk(Wiley, 2018-09) Joshi, Amit D.; Li, Xin; Kraft, Peter; Han, Jiali; Epidemiology, School of Public HealthThe human MC1R gene is highly polymorphic among lightly pigmented populations, and several variants in the MC1R gene have been associated with increased risk of both melanoma and nonmelanoma skin cancers. The functional consequences of MC1R gene variants have been studied in vitro and in vivo in postulated causal pathways, such as G-protein-coupled signaling transduction, pigmentation, immune response, inflammatory response, cell proliferation, and extracellular matrix adhesion. In a case-control study nested within the Nurses' Health Study, we utilized hierarchical modeling approaches, incorporating quantitative information from these functional studies, to examine the association between particular MC1R alleles and the risk of skin cancers. Different prior matrices were constructed according to the phenotypic associations in controls, cell surface expression, and enzymatic kinetics. Our results showed the parameter variance estimates of each single nucleotide polymorphism (SNP) were smaller when using a hierarchical modeling approach compared to standard multivariable regression. Estimates of second-level parameters gave information about the relative importance of MC1R effects on different pathways, and odds ratio estimates changed depending on prior models (e.g., the change ranged from -21% to 7% for melanoma risk assessment). In addition, the estimates of prior model hyperparameters in the hierarchical modeling approach allow us to determine the relevance of individual pathways on the risk of each of the skin cancer types. In conclusion, hierarchical modeling provides a useful analytic approach in addition to the widely used conventional models in genetic association studies that can incorporate measures of allelic function.