Browsing by Author "Baurley, James W."

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    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, Richard M. Fairbanks School of Public Health
    Background: 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.
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    Candidate Genes from an FDA-Approved Algorithm Fail to Predict Opioid Use Disorder Risk in Over 450,000 Veterans
    (medRxiv, 2024-05-16) Davis, Christal N.; Jinwala, Zeal; Hatoum, Alexander S.; Toikumo, Sylvanus; Agrawal, Arpana; Rentsch, Christopher T.; Edenberg, Howard J.; Baurley, James W.; Hartwell, Emily E.; Crist, Richard C.; Gray, Joshua C.; Justice, Amy C.; Gelernter, Joel; Kember, Rachel L.; Kranzler, Henry R.; Biochemistry and Molecular Biology, School of Medicine
    Importance: Recently, the Food and Drug Administration gave pre-marketing approval to algorithm based on its purported ability to identify genetic risk for opioid use disorder. However, the clinical utility of the candidate genes comprising the algorithm has not been independently demonstrated. Objective: To assess the utility of 15 variants in candidate genes from an algorithm intended to predict opioid use disorder risk. Design: This case-control study examined the association of 15 candidate genetic variants with risk of opioid use disorder using available electronic health record data from December 20, 1992 to September 30, 2022. Setting: Electronic health record data, including pharmacy records, from Million Veteran Program participants across the United States. Participants: Participants were opioid-exposed individuals enrolled in the Million Veteran Program (n = 452,664). Opioid use disorder cases were identified using International Classification of Disease diagnostic codes, and controls were individuals with no opioid use disorder diagnosis. Exposures: Number of risk alleles present across 15 candidate genetic variants. Main outcome and measures: Predictive performance of 15 genetic variants for opioid use disorder risk assessed via logistic regression and machine learning models. Results: Opioid exposed individuals (n=33,669 cases) were on average 61.15 (SD = 13.37) years old, 90.46% male, and had varied genetic similarity to global reference panels. Collectively, the 15 candidate genetic variants accounted for 0.4% of variation in opioid use disorder risk. The accuracy of the ensemble machine learning model using the 15 genes as predictors was 52.8% (95% CI = 52.1 - 53.6%) in an independent testing sample. Conclusions and relevance: Candidate genes that comprise the approved algorithm do not meet reasonable standards of efficacy in predicting opioid use disorder risk. Given the algorithm's limited predictive accuracy, its use in clinical care would lead to high rates of false positive and negative findings. More clinically useful models are needed to identify individuals at risk of developing opioid use disorder.
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    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; Community and Global Health, Richard M. Fairbanks School of Public Health
    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.
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    Genome-wide Interaction Study with Smoking for Colorectal Cancer Risk Identifies Novel Genetic Loci Related to Tumor Suppression, Inflammation, and Immune Response
    (American Association for Cancer Research, 2023) Carreras-Torres, Robert; Kim, Andre E.; Lin, Yi; Díez-Obrero, Virginia; Bien, Stephanie A.; Qu, Conghui; Wang, Jun; Dimou, Niki; Aglago, Elom K.; Albanes, Demetrius; Arndt, Volker; Baurley, James W.; Berndt, Sonja I.; Bézieau, Stéphane; Bishop, D. Timothy; Bouras, Emmanouil; Brenner, Hermann; Budiarto, Arif; Campbell, Peter T.; Casey, Graham; Chan, Andrew T.; Chang-Claude, Jenny; Chen, Xuechen; Conti, David V.; Dampier, Christopher H.; Devall, Matthew A. M.; Drew, David A.; Figueiredo, Jane C.; Gallinger, Steven; Giles, Graham G.; Gruber, Stephen B.; Gsur, Andrea; Gunter, Marc J.; Harrison, Tabitha A.; Hidaka, Akihisa; Hoffmeister, Michael; Huyghe, Jeroen R.; Jenkins, Mark A.; Jordahl, Kristina M.; Kawaguchi, Eric; Keku, Temitope O.; Kundaje, Anshul; Le Marchand, Loic; Lewinger, Juan Pablo; Li, Li; Mahesworo, Bharuno; 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.; Rennert, Gad; Ruiz-Narvaez, Edward; Sakoda, Lori C.; Scacheri, Peter C.; Schmit, Stephanie L.; Schoen, Robert E.; Shcherbina, Anna; Slattery, Martha L.; Stern, Mariana C.; Su, Yu-Ru; Tangen, Catherine M.; Thomas, Duncan C.; Tian, Yu; Tsilidis, Konstantinos K.; Ulrich, Cornelia M.; van Duijnhoven, Fränzel J. B.; Van Guelpen, Bethany; Visvanathan, Kala; Vodicka, Pavel; Wawan Cenggoro, Tjeng; Weinstein, Stephanie J.; White, Emily; Wolk, Alicja; Woods, Michael O.; Hsu, Li; Peters, Ulrike; Moreno, Victor; Gauderman, W. James; Epidemiology, Richard M. Fairbanks School of Public Health
    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.
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    Utility of Candidate Genes From an Algorithm Designed to Predict Genetic Risk for Opioid Use Disorder
    (American Medical Association, 2025-01-02) Davis, Christal N.; Jinwala, Zeal; Hatoum, Alexander S.; Toikumo, Sylvanus; Agrawal, Arpana; Rentsch, Christopher T.; Edenberg, Howard J.; Baurley, James W.; Hartwell, Emily E.; Crist, Richard C.; Gray, Joshua C.; Justice, Amy C.; Gelernter, Joel; Kember, Rachel L.; Kranzler, Henry R.; VA Million Veteran Program; Biochemistry and Molecular Biology, School of Medicine
    Importance: Recently, the US Food and Drug Administration gave premarketing approval to an algorithm based on its purported ability to identify individuals at genetic risk for opioid use disorder (OUD). However, the clinical utility of the candidate genetic variants included in the algorithm has not been independently demonstrated. Objective: To assess the utility of 15 genetic variants from an algorithm intended to predict OUD risk. Design, setting, and participants: This case-control study examined the association of 15 candidate genetic variants with risk of OUD using electronic health record data from December 20, 1992, to September 30, 2022. Electronic health record data, including pharmacy records, were accrued from participants in the Million Veteran Program across the US with opioid exposure (n = 452 664). Cases with OUD were identified using International Classification of Diseases, Ninth Revision, or International Classification of Diseases, Tenth Revision, diagnostic codes, and controls were individuals with no OUD diagnosis. Exposures: Number of risk alleles present across 15 candidate genetic variants. Main outcome and measures: Performance of 15 genetic variants for identifying OUD risk assessed via logistic regression and machine learning models. Results: A total of 452 664 individuals with opioid exposure (including 33 669 with OUD) had a mean (SD) age of 61.15 (13.37) years, and 90.46% were male; the sample was ancestrally diverse (with individuals of genetically inferred European, African, and admixed American ancestries). Using Nagelkerke R2, collectively, the 15 candidate genes accounted for 0.40% of variation in OUD risk. In comparison, age and sex alone accounted for 3.27% of the variation. The ensemble machine learning. The ensemble machine learning model using the 15 variants as predictive factors correctly classified 52.83% (95% CI, 52.07%-53.59%) of individuals in an independent testing sample. Conclusions and relevance: Results of this study suggest that the candidate genetic variants included in the approved algorithm do not meet reasonable standards of efficacy in identifying OUD risk. Given the algorithm's limited predictive accuracy, its use in clinical care would lead to high rates of both false-positive and false-negative findings. More clinically useful models are needed to identify individuals at risk of developing OUD.