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Background: Colorectal cancer (CRC) is a common, fatal cancer. Identifying subgroups who may benefit more from intervention is of critical public health importance. Previous studies have assessed multiplicative interaction between genetic risk scores and environmental factors, but few have assessed additive interaction, the relevant public health measure., Methods: Using resources from colorectal cancer consortia including 45,247 CRC cases and 52,671 controls, we assessed multiplicative and additive interaction (relative excess risk due to interaction, RERI) using logistic regression between 13 harmonized environmental factors and genetic risk score including 141 variants associated with CRC risk., Results: There was no evidence of multiplicative interaction between environmental factors and genetic risk score. There was additive interaction where, for individuals with high genetic susceptibility, either heavy drinking [RERI = 0.24, 95% confidence interval, CI, (0.13, 0.36)], ever smoking [0.11 (0.05, 0.16)], high BMI [female 0.09 (0.05, 0.13), male 0.10 (0.05, 0.14)], or high red meat intake [highest versus lowest quartile 0.18 (0.09, 0.27)] was associated with excess CRC risk greater than that for individuals with average genetic susceptibility. Conversely, we estimate those with high genetic susceptibility may benefit more from reducing CRC risk with aspirin/NSAID use [-0.16 (-0.20, -0.11)] or higher intake of fruit, fiber, or calcium [highest quartile versus lowest quartile -0.12 (-0.18, -0.050); -0.16 (-0.23, -0.09); -0.11 (-0.18, -0.05), respectively] than those with average genetic susceptibility., Conclusions: Additive interaction is important to assess for identifying subgroups who may benefit from intervention. The subgroups identified in this study may help inform precision CRC prevention., Competing Interests: Conflict of Interest: SB Gruber: Brogent International LLC, co-founder, not related to submitted work. All other authors do not have any conflicts of interest to disclose., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

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., (© 2024. The Author(s).)

Regular, long-term aspirin use may act synergistically with genetic variants, particularly those in mechanistically relevant pathways, to confer a protective effect on colorectal cancer (CRC) risk. We leveraged pooled data from 52 clinical trial, cohort, and case-control studies that included 30,806 CRC cases and 41,861 controls of European ancestry to conduct a genome-wide interaction scan between regular aspirin/nonsteroidal anti-inflammatory drug (NSAID) use and imputed genetic variants. After adjusting for multiple comparisons, we identified statistically significant interactions between regular aspirin/NSAID use and variants in 6q24.1 (top hit rs72833769 ), which has evidence of influencing expression of TBC1D7 (a subunit of the TSC1-TSC2 complex, a key regulator of MTOR activity), and variants in 5p13.1 (top hit rs350047 ), which is associated with expression of PTGER4 (codes a cell surface receptor directly involved in the mode of action of aspirin). Genetic variants with functional impact may modulate the chemopreventive effect of regular aspirin use, and our study identifies putative previously unidentified targets for additional mechanistic interrogation.

Background: Alcohol use disorder (AUD) has been described as a chronic disease given the high rates that affected individuals have in returning to drinking after a change attempt. Many studies have characterized predictors of aggregated alcohol use (e.g., percent heavy drinking days) following treatment for AUD. However, to inform future research on predicting drinking as an AUD outcome measure, a better understanding is needed of the patterns of drinking that surround a treatment episode and which clinical measures predict patterns of drinking., Methods: We analyzed data from the Project MATCH and COMBINE studies (MATCH: n = 1726; 24.3% female, 20.0% non-White; COMBINE: n = 1383; 30.9% female, 23.2% non-White). Daily drinking was measured in the 90 days prior to treatment, 90 days (MATCH) and 120 days (COMBINE) during treatment, and 365 days following treatment. Gradient boosting machine learning methods were used to explore baseline predictors of drinking patterns., Results: Drinking patterns during a prior time period were the most consistent predictors of future drinking patterns. Social network drinking, AUD severity, mental health symptoms, and constructs based on the addiction cycle (incentive salience, negative emotionality, and executive function) were associated with patterns of drinking prior to treatment. Addiction cycle constructs, AUD severity, purpose in life, social network, legal history, craving, and motivation were associated with drinking during the treatment period and following treatment., Conclusions: There is heterogeneity in drinking patterns around an AUD treatment episode. This study provides novel information about variables that may be important to measure to improve the prediction of drinking patterns during and following treatment. Future research should consider which patterns of drinking they aim to predict and which period of drinking is most important to predict. The current findings could guide the selection of predictor variables and generate hypotheses for those predictors., (© 2023 Research Society on Alcohol.)

In this work, we develop a novel Bayesian regression framework that can be used to complete variable selection in high dimensional settings. Unlike existing techniques, the proposed approach can leverage side information to inform about the sparsity structure of the regression coefficients. This is accomplished by replacing the usual inclusion probability in the spike and slab prior with a binary regression model which assimilates this extra source of information. To facilitate model fitting, a computationally efficient and easy to implement Markov chain Monte Carlo posterior sampling algorithm is developed via carefully chosen priors and data augmentation steps. The finite sample performance of our methodology is assessed through numerical simulations, and we further illustrate our approach by using it to identify genetic markers associated with the nicotine metabolite ratio; a key biological marker associated with nicotine dependence and smoking cessation treatment., Competing Interests: Declaration of conflicting interestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

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 association of folate supplement with CRC risk. Experimental studies and studies incorporating other relevant omics data are warranted to validate this finding., (Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.)

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 - OR AA : 1.62, 95% CI: 1.34-1.96; OR AG : 1.41, 95% CI: 1.30-1.54; OR GG : 1.22, 95% CI: 1.13-1.31; p-value 3-d.f. : 5.46 × 10 -11 ) and 13q14.13 (rs9526201, LRCH1 - OR GG : 2.11, 95% CI: 1.56-2.83; OR GA : 1.52, 95% CI: 1.38-1.68; OR AA : 1.13, 95% CI: 1.06-1.21; p-value 2-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., (© 2023. The Author(s).)

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., (©2023 The Authors; Published by the American Association for Cancer Research.)

Once an infrequent disease in parts of Asia, the rate of colorectal cancer in recent decades appears to be steadily increasing. Colorectal cancer represents one of the most important causes of cancer mortality worldwide, including in many regions in Asia. Rapid changes in socioeconomic and lifestyle habits have been attributed to the notable increase in the incidence of colorectal cancers in many Asian countries. Through published data from the International Agency for Cancer Research (IARC), we utilized available continuous data to determine which Asian nations had a rise in colorectal cancer rates. We found that East and South East Asian countries had a significant rise in colorectal cancer rates. Subsequently, we summarized here the known genetics and environmental risk factors for colorectal cancer among populations in this region as well as approaches to screening and early detection that have been considered across various countries in the region., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Pardamean, Sudigyo, Budiarto, Mahesworo, Hidayat, Baurley and Pardamean.)

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., (©2022 American Association for Cancer Research.)

Genome-wide association (GWA) genetic epidemiology research has identified several variants modestly associated with brief self-report smoking measures, predominately in European Americans. GWA research has not applied intensive laboratory-based measures of smoking endophenotypes in African Americans-a population with disproportionately low quit smoking rates and high tobacco-related disease risk. This genetic epidemiology study of non-Hispanic African Americans tested associations of 89 genetic variants identified in previous GWA research and exploratory GWAs with 24 laboratory-derived tobacco withdrawal endophenotypes. African American cigarette smokers (N = 528; ≥10 cig/day; 36.2% female) completed two counterbalanced visits following either 16-hr of tobacco deprivation or ad libitum smoking. At both visits, self-report and behavioral measures across six unique "sub-phenotype" domains within the tobacco withdrawal syndrome were assessed (Urge/Craving, Negative Affect, Low Positive Affect, Cognition, Hunger, and Motivation to Resume Smoking). Results of the candidate variant analysis found two significant small-magnitude associations. The rs11915747 alternate allele in the CAD2M gene region was associated with .09 larger deprivation-induced changes in reported impulsivity (0-4 scale). The rs2471711alternate allele in the AC097480.1/AC097480.2 gene region was associated with 0.26 lower deprivation-induced changes in confusion (0-4 scale). For both variants, associations were opposite in direction to previous research. Individual genetic variants may exert only weak influences on tobacco withdrawal in African Americans. Larger sample sizes of non-European ancestry individuals might be needed to investigate both known and novel loci that may be ancestry-specific. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Background: There is a need to match characteristics of tobacco users with cessation treatments and risks of tobacco attributable diseases such as lung cancer. The rate in which the body metabolizes nicotine has proven an important predictor of these outcomes. Nicotine metabolism is primarily catalyzed by the enzyme cytochrone P450 (CYP2A6) and CYP2A6 activity can be measured as the ratio of two nicotine metabolites: trans-3'-hydroxycotinine to cotinine (NMR). Measurements of these metabolites are only possible in current tobacco users and vary by biofluid source, timing of collection, and protocols; unfortunately, this has limited their use in clinical practice. The NMR depends highly on genetic variation near CYP2A6 on chromosome 19 as well as ancestry, environmental, and other genetic factors. Thus, we aimed to develop prediction models of nicotine metabolism using genotypes and basic individual characteristics (age, gender, height, and weight)., Results: We identified four multiethnic studies with nicotine metabolites and DNA samples. We constructed a 263 marker panel from filtering genome-wide association scans of the NMR in each study. We then applied seven machine learning techniques to train models of nicotine metabolism on the largest and most ancestrally diverse dataset (N=2239). The models were then validated using the other three studies (total N=1415). Using cross-validation, we found the correlations between the observed and predicted NMR ranged from 0.69 to 0.97 depending on the model. When predictions were averaged in an ensemble model, the correlation was 0.81. The ensemble model generalizes well in the validation studies across ancestries, despite differences in the measurements of NMR between studies, with correlations of: 0.52 for African ancestry, 0.61 for Asian ancestry, and 0.46 for European ancestry. The most influential predictors of NMR identified in more than two models were rs56113850, rs11878604, and 21 other genetic variants near CYP2A6 as well as age and ancestry., Conclusions: We have developed an ensemble of seven models for predicting the NMR across ancestries from genotypes and age, gender and BMI. These models were validated using three datasets and associate with nicotine dosages. The knowledge of how an individual metabolizes nicotine could be used to help select the optimal path to reducing or quitting tobacco use, as well as, evaluating risks of tobacco use., (© 2022. The Author(s).)

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., (© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Background: Substance use disorder (SUD) is a heterogeneous disorder. Adapting machine learning algorithms to allow for the parsing of intrapersonal and interpersonal heterogeneity in meaningful ways may accelerate the discovery and implementation of clinically actionable interventions in SUD research. Objectives: Inspired by a study of heavy drinkers that collected daily drinking and substance use (ABQ DrinQ), we develop tools to estimate subject-specific risk trajectories of heavy drinking; estimate and perform inference on patient characteristics and time-varying covariates; and present results in easy-to-use Jupyter notebooks. Methods: We recast support vector machines (SVMs) into a Bayesian model extended to handle mixed effects. We then apply these methods to ABQ DrinQ to model alcohol use patterns. ABQ DrinQ consists of 190 heavy drinkers (44% female) with 109,580 daily observations. Results: We identified male gender (point estimate; 95% credible interval: -0.25;-0.29,-0.21), older age (-0.03;-0.03,-0.03), and time varying usage of nicotine (1.68;1.62,1.73), cannabis (0.05;0.03,0.07), and other drugs (1.16;1.01,1.35) as statistically significant factors of heavy drinking behavior. By adopting random effects to capture the subject-specific longitudinal trajectories, the algorithm outperforms traditional SVM (classifies 84% of heavy drinking days correctly versus 73%). Conclusions: We developed a mixed effects variant of SVM and compare it to the traditional formulation, with an eye toward elucidating the importance of incorporating random effects to account for underlying heterogeneity in SUD data. These tools and examples are packaged into a repository for researchers to explore. Understanding patterns and risk of substance use could be used for developing individualized interventions.

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., (©2022 American Association for Cancer Research.)

The opioid crisis in the United States poses a major threat to public health due to psychiatric and infectious disease comorbidities and death due to opioid use disorder (OUD). OUD is characterized by patterns of opioid misuse leading to persistent heavy use and overdose. The standard of care for treatment of OUD is medication-assisted treatment, in combination with behavioral therapy. Medications for opioid use disorder have been shown to improve OUD outcomes, including reduction and prevention of overdose. However, understanding the effectiveness of such medications has been limited due to non-adherence to assigned dose levels by study patients. To overcome this challenge, herein we develop a model that views dose history as a time-varying covariate. Proceeding in this fashion allows the model to estimate dose effect while accounting for lapses in adherence. The proposed model is used to conduct a secondary analysis of data collected from six efficacy and safety trials of buprenorphine maintenance treatment. This analysis provides further insight into the time-dependent treatment effects of buprenorphine and how different dose adherence patterns relate to risk of opioid use.

The impact of agonist dose and of physician, staff and patient engagement on treatment have not been evaluated together in an analysis of treatment for opioid use disorder. Our hypotheses were that greater agonist dose and therapeutic engagement would be associated with reduced illicit opiate use in a time-dependent manner. Publicly-available treatment data from six buprenorphine efficacy and safety trials from the Federally-supported Clinical Trials Network were used to derive treatment variables. Three novel predictors were constructed to capture the time weighted effects of buprenorphine dosage (mg buprenorphine per day), dosing protocol (whether physician could adjust dose), and clinic visits (whether patient attended clinic). We used time-in-trial as a predictor to account for the therapeutic benefits of treatment persistence. The outcome was illicit opiate use defined by self-report or urinalysis. Trial participants (N = 3022 patients with opioid dependence, mean age 36 years, 33% female, 14% Black, 16% Hispanic) were analyzed using a generalized linear mixed model. Treatment variables dose, Odds Ratio (OR) = 0.63 (95% Confidence Interval (95%CI) 0.59−0.67), dosing protocol, OR = 0.70 (95%CI 0.65−0.76), time-in-trial, OR = 0.75 (95%CI 0.71−0.80) and clinic visits, OR = 0.81 (95%CI 0.76−0.87) were significant (p-values < 0.001) protective factors. Treatment implications support higher doses of buprenorphine and greater engagement of patients with providers and clinic staff.

Introduction: The nicotine metabolite ratio and nicotine equivalents are measures of metabolism rate and intake. Genome-wide prediction of these nicotine biomarkers in multiethnic samples will enable tobacco-related biomarker, behavioral, and exposure research in studies without measured biomarkers., Aims and Methods: We screened genetic variants genome-wide using marginal scans and applied statistical learning algorithms on top-ranked genetic variants, age, ethnicity and sex, and, in additional modeling, cigarettes per day (CPD), (in additional modeling) to build prediction models for the urinary nicotine metabolite ratio (uNMR) and creatinine-standardized total nicotine equivalents (TNE) in 2239 current cigarette smokers in five ethnic groups. We predicted these nicotine biomarkers using model ensembles and evaluated external validity using dependence measures in 1864 treatment-seeking smokers in two ethnic groups., Results: The genomic regions with the most selected and included variants for measured biomarkers were chr19q13.2 (uNMR, without and with CPD) and chr15q25.1 and chr10q25.3 (TNE, without and with CPD). We observed ensemble correlations between measured and predicted biomarker values for the uNMR and TNE without (with CPD) of 0.67 (0.68) and 0.65 (0.72) in the training sample. We observed inconsistency in penalized regression models of TNE (with CPD) with fewer variants at chr15q25.1 selected and included. In treatment-seeking smokers, predicted uNMR (without CPD) was significantly associated with CPD and predicted TNE (without CPD) with CPD, time-to-first-cigarette, and Fagerström total score., Conclusions: Nicotine metabolites, genome-wide data, and statistical learning approaches developed novel robust predictive models for urinary nicotine biomarkers in multiple ethnic groups. Predicted biomarker associations helped define genetically influenced components of nicotine dependence., Implications: We demonstrate development of robust models and multiethnic prediction of the uNMR and TNE using statistical and machine learning approaches. Variants included in trained models for nicotine biomarkers include top-ranked variants in multiethnic genome-wide studies of smoking behavior, nicotine metabolites, and related disease. Association of the two predicted nicotine biomarkers with Fagerström Test for Nicotine Dependence items supports models of nicotine biomarkers as predictors of physical dependence and nicotine exposure. Predicted nicotine biomarkers may facilitate tobacco-related disease and treatment research in samples with genomic data and limited nicotine metabolite or tobacco exposure data., (© The Author(s) 2021. Published by Oxford University Press on behalf of the Society for Research on Nicotine and Tobacco.)

Colorectal cancer is a common cancer in Indonesia, yet it has been understudied in this resource-constrained setting. We conducted a genome-wide association study focused on evaluation and preliminary discovery of colorectal cancer risk factors in Indonesians. We administered detailed questionnaires and collecting blood samples from 162 colorectal cancer cases throughout Makassar, Indonesia. We also established a control set of 193 healthy individuals frequency matched by age, sex, and ethnicity. A genome-wide association analysis was performed on 84 cases and 89 controls passing quality control. We evaluated known colorectal cancer genetic variants using logistic regression and established a genome-wide polygenic risk model using a Bayesian variable selection technique. We replicate associations for rs9497673, rs6936461 and rs7758229 on chromosome 6; rs11255841 on chromosome 10; and rs4779584, rs11632715, and rs73376930 on chromosome 15. Polygenic modeling identified 10 SNP associated with colorectal cancer risk. This work helps characterize the relationship between variants in the SCL22A3, SCG5, GREM1, and STXBP5-AS1 genes and colorectal cancer in a diverse Indonesian population. With further biobanking and international research collaborations, variants specific to colorectal cancer risk in Indonesians will be identified.

Modern epidemiologic studies permit investigation of the complex pathways that mediate effects of social, behavioral, and molecular factors on health outcomes. Conventional analytical approaches struggle with high-dimensional data, leading to high likelihoods of both false-positive and false-negative inferences. Herein, we describe a novel Bayesian pathway analysis approach, the algorithm for learning pathway structure (ALPS), which addresses key limitations in existing approaches to complex data analysis. ALPS uses prior information about pathways in concert with empirical data to identify and quantify complex interactions within networks of factors that mediate an association between an exposure and an outcome. We illustrate ALPS through application to a complex gene-drug interaction analysis in the Predictors of Breast Cancer Recurrence (ProBe CaRe) Study, a Danish cohort study of premenopausal breast cancer patients (2002-2011), for which conventional analyses severely limit the quality of inference., (© The Author(s) 2020. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Background: Tamoxifen and its metabolites compete with estrogen to occupy the estrogen receptor. The conventional dose of adjuvant tamoxifen overwhelms estrogen in this competition, reducing breast cancer recurrence risk by nearly half. Phase I metabolism generates active tamoxifen metabolites, and phase II metabolism deactivates them. No earlier pharmacogenetic study has comprehensively evaluated the metabolism and transport pathways, and no earlier study has included a large population of premenopausal women., Methods: We completed a cohort study of 5,959 Danish nonmetastatic premenopausal breast cancer patients, in whom 938 recurrences occurred, and a case-control study of 541 recurrent cases in a cohort of Danish predominantly postmenopausal breast cancer patients, all followed for 10 years. We collected formalin-fixed paraffin-embedded tumor blocks and genotyped 32 variants in 15 genes involved in tamoxifen metabolism or transport. We estimated conventional associations for each variant and used prior information about the tamoxifen metabolic path to evaluate the importance of metabolic and transporter pathways., Results: No individual variant was notably associated with risk of recurrence in either study population. Both studies showed weak evidence of the importance of phase I metabolism in the clinical response to adjuvant tamoxifen therapy., Conclusions: Consistent with prior knowledge, our results support the role of phase I metabolic capacity in clinical response to tamoxifen. Nonetheless, no individual variant substantially explained the modest phase I effect on tamoxifen response., Impact: These results are consistent with guidelines recommending against genotype-guided prescribing of tamoxifen, and for the first time provide evidence supporting these guidelines in premenopausal women., (©2020 American Association for Cancer Research.)

Background: Our institution developed a 1-hour mandatory narcotics-prescribing education program as well as postoperative opioid-prescribing guidelines in response to the opioid epidemic. The purpose of this study was to evaluate the effect of this hospital-wide opioid education and subsequent institution of postoperative opioid guidelines on opioid-prescribing practices after ambulatory surgery., Methods: This retrospective study was performed at 1 academic orthopaedic hospital. In November 2016, a 1-hour mandatory opioid education program was completed by all hospital prescribers. Postoperative opioid guidelines were then developed and were disseminated in February 2017. All postoperative narcotic prescriptions after ambulatory procedures performed by 3 separate services (hand, sports, and foot and ankle services) were evaluated over 4 months prior to and after the mandatory opioid education and subsequent release of service-specific guidelines., Results: Overall, there was a significant decrease in pills and total oral morphine equivalents prescribed after dissemination of guidelines compared with the pre-intervention cohort procedures (p < 0.001) performed by the sports and hand services. With regard to the sports medicine service, the mean difference in pills prescribed was 6.47 pills (95% confidence interval [CI], 5.4 to 7.5 pills) for knee arthroscopy, 5.6 pills (95% CI, 2.5 to 8.7 pills) for shoulder arthroscopy, and 16.3 pills (95% CI, 13.6 to 19.1 pills) for hip arthroscopy. With regard to the hand service, the mean difference in pills prescribed was 13.0 pills (95% CI, 10.2 to 15.8 pills) for level-1 procedures, 12.4 pills (95% CI, 9.9 to 15.0 pills) for carpal tunnel release, and 21.7 pills (95% CI, 18.0 to 25.3 pills) for distal radial fractures. The decrease in pills prescribed in the post-intervention cohort amounts to almost 30,000 fewer opioid pills prescribed per year after these 6 procedures alone. There was no significant change (p > 0.05) in either the number of pills or the oral morphine equivalents prescribed after any of the 3 procedures performed by the foot and ankle service (ankle arthroscopy, bunion surgery, and Achilles tendon repair)., Conclusions: We developed a prescriber education program and followed up with consensus-based guidelines for postoperative opioid prescriptions. These interventions caused a significant decrease in excessive opioid-prescribing practices after ambulatory orthopaedic surgery at our hospital. We urge initiatives by national orthopaedic organizations to develop and promote education programs and procedure and disease-specific opioid-prescribing guidelines.

Implications: This article outlines a framework for the consistent integration of biological data/samples into smoking cessation pharmacotherapy trials, aligned with the objectives of the recently unveiled Precision Medicine Initiative. Our goal is to encourage and provide support for treatment researchers to consider biosample collection and genotyping their existing samples as well as integrating genetic analyses into their study design in order to realize precision medicine in treatment of nicotine dependence.

Introduction: Human genetic research has succeeded in definitively identifying multiple genetic variants associated with risk for nicotine dependence and heavy smoking. To build on these advances, and to aid in reducing the prevalence of smoking and its consequent health harms, the next frontier is to identify genetic predictors of successful smoking cessation and also of the efficacy of smoking cessation treatments ("pharmacogenomics"). More broadly, additional biomarkers that can be quantified from biosamples also promise to aid "Precision Medicine" and the personalization of treatment, both pharmacological and behavioral., Aims and Methods: To motivate ongoing and future efforts, here we review several compelling genetic and biomarker findings related to smoking cessation and treatment., Results: These Key results involve genetic variants in the nicotinic receptor subunit gene CHRNA5, variants in the nicotine metabolism gene CYP2A6, and the nicotine metabolite ratio. We also summarize reports of epigenetic changes related to smoking behavior., Conclusions: The results to date demonstrate the value and utility of data generated from biosamples in clinical treatment trial settings. This article cross-references a companion paper in this issue that provides practical guidance on how to incorporate biosample collection into a planned clinical trial and discusses avenues for harmonizing data and fostering consortium-based, collaborative research on the pharmacogenomics of smoking cessation., Implications: Evidence is emerging that certain genotypes and biomarkers are associated with smoking cessation success and efficacy of smoking cessation treatments. We review key findings that open potential avenues for personalizing smoking cessation treatment according to an individual's genetic or metabolic profile. These results provide important incentive for smoking cessation researchers to collect biosamples and perform genotyping in research studies and clinical trials.

There are limited biomarkers for substance use disorders (SUDs). Traditional statistical approaches are identifying simple biomarkers in large samples, but clinical use cases are still being established. High-throughput clinical, imaging, and 'omic' technologies are generating data from SUD studies and may lead to more sophisticated and clinically useful models. However, analytic strategies suited for high-dimensional data are not regularly used. We review strategies for identifying biomarkers and biosignatures from high-dimensional data types. Focusing on penalized regression and Bayesian approaches, we address how to leverage evidence from existing studies and knowledge bases, using nicotine metabolism as an example. We posit that big data and machine learning approaches will considerably advance SUD biomarker discovery. However, translation to clinical practice, will require integrated scientific efforts., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

Introduction: Metabolic enzyme variation and other patient and environmental characteristics influence smoking behaviors, treatment success, and risk of related disease. Population-specific variation in metabolic genes contributes to challenges in developing and optimizing pharmacogenetic interventions. We applied a custom genome-wide genotyping array for addiction research (Smokescreen), to three laboratory-based studies of nicotine metabolism with oral or venous administration of labeled nicotine and cotinine, to model nicotine metabolism in multiple populations. The trans-3'-hydroxycotinine/cotinine ratio, the nicotine metabolite ratio (NMR), was the nicotine metabolism measure analyzed., Methods: Three hundred twelve individuals of self-identified European, African, and Asian American ancestry were genotyped and included in ancestry-specific genome-wide association scans (GWAS) and a meta-GWAS analysis of the NMR. We modeled natural-log transformed NMR with covariates: principal components of genetic ancestry, age, sex, body mass index, and smoking status., Results: African and Asian American NMRs were statistically significantly (P values ≤ 5E-5) lower than European American NMRs. Meta-GWAS analysis identified 36 genome-wide significant variants over a 43 kilobase pair region at CYP2A6 with minimum P = 2.46E-18 at rs12459249, proximal to CYP2A6. Additional minima were located in intron 4 (rs56113850, P = 6.61E-18) and in the CYP2A6-CYP2A7 intergenic region (rs34226463, P = 1.45E-12). Most (34/36) genome-wide significant variants suggested reduced CYP2A6 activity; functional mechanisms were identified and tested in knowledge-bases. Conditional analysis resulted in intergenic variants of possible interest (P values < 5E-5)., Conclusions: This meta-GWAS of the NMR identifies CYP2A6 variants, replicates the top-ranked single nucleotide polymorphism from a recent Finnish meta-GWAS of the NMR, identifies functional mechanisms, and provides pan-continental population biomarkers for nicotine metabolism., Implications: This multiple ancestry meta-GWAS of the laboratory study-based NMR provides novel evidence and replication for genome-wide association of CYP2A6 single nucleotide and insertion-deletion polymorphisms. We identify three regions of genome-wide significance: proximal, intronic, and distal to CYP2A6. We replicate the top-ranking single nucleotide polymorphism from a recent GWAS of the NMR in Finnish smokers, identify a functional mechanism for this intronic variant from in silico analyses of RNA-seq data that is consistent with CYP2A6 expression measured in postmortem lung and liver, and provide additional support for the intergenic region between CYP2A6 and CYP2A7., (© The Author 2016. Published by Oxford University Press on behalf of the Society for Research on Nicotine and Tobacco.)

Background: Addictive disorders are a class of chronic, relapsing mental disorders that are responsible for increased risk of mental and medical disorders and represent the largest, potentially modifiable cause of death. Tobacco dependence is associated with increased risk of disease and premature death. While tobacco control efforts and therapeutic interventions have made good progress in reducing smoking prevalence, challenges remain in optimizing their effectiveness based on patient characteristics, including genetic variation. In order to maximize collaborative efforts to advance addiction research, we have developed a genotyping array called Smokescreen. This custom array builds upon previous work in the analyses of human genetic variation, the genetics of addiction, drug metabolism, and response to therapy, with an emphasis on smoking and nicotine addiction., Results: The Smokescreen genotyping array includes 646,247 markers in 23 categories. The array design covers genome-wide common variation (65.67, 82.37, and 90.72% in African (YRI), East Asian (ASN), and European (EUR) respectively); most of the variation with a minor allele frequency ≥ 0.01 in 1014 addiction genes (85.16, 89.51, and 90.49% for YRI, ASN, and EUR respectively); and nearly all variation from the 1000 Genomes Project Phase 1, NHLBI GO Exome Sequencing Project and HapMap databases in the regions related to smoking behavior and nicotine metabolism: CHRNA5-CHRNA3-CHRNB4 and CYP2A6-CYP2B6. Of the 636 pilot DNA samples derived from blood or cell line biospecimens that were genotyped on the array, 622 (97.80%) passed quality control. In passing samples, 90.08% of markers passed quality control. The genotype reproducibility in 25 replicate pairs was 99.94%. For 137 samples that overlapped with HapMap2 release 24, the genotype concordance was 99.76%. In a genome-wide association analysis of the nicotine metabolite ratio in 315 individuals participating in nicotine metabolism laboratory studies, we identified genome-wide significant variants in the CYP2A6 region (min p = 9.10E-15)., Conclusions: We developed a comprehensive genotyping array for addiction research and demonstrated its analytic validity and utility through pilot genotyping of HapMap and study samples. This array allows researchers to perform genome-wide, candidate gene, and pathway-based association analyses of addiction, tobacco-use, treatment response, comorbidities, and associated diseases in a standardized, high-throughput platform.

Background: Several lifestyle and environmental exposures have been suspected as risk factors for oral clefts, although few have been convincingly demonstrated. Studies across global diverse populations could offer additional insight given varying types and levels of exposures., Methods: We performed an international case-control study in the Democratic Republic of the Congo (133 cases, 301 controls), Vietnam (75 cases, 158 controls), the Philippines (102 cases, 152 controls), and Honduras (120 cases, 143 controls). Mothers were recruited from hospitals and their exposures were collected from interviewer-administered questionnaires. We used logistic regression modeling to estimate odds ratios (OR) and 95% confidence intervals (CI)., Results: Family history of clefts was strongly associated with increased risk (maternal: OR = 4.7; 95% CI, 3.0-7.2; paternal: OR = 10.5; 95% CI, 5.9-18.8; siblings: OR = 5.3; 95% CI, 1.4-19.9). Advanced maternal age (5 year OR = 1.2; 95% CI, 1.0-1.3), pregestational hypertension (OR = 2.6; 95% CI, 1.3-5.1), and gestational seizures (OR = 2.9; 95% CI, 1.1-7.4) were statistically significant risk factors. Lower maternal (secondary school OR = 1.6; 95% CI, 1.2-2.2; primary school OR = 2.4, 95% CI, 1.6-2.8) and paternal education (OR = 1.9; 95% CI, 1.4-2.5; and OR = 1.8; 95% CI, 1.1-2.9, respectively) and paternal tobacco smoking (OR = 1.5, 95% CI, 1.1-1.9) were associated with an increased risk. No other significant associations between maternal and paternal factors were found; some environmental factors including rural residency, indoor cooking with wood, chemicals and water source appeared to be associated with an increased risk in adjusted models., Conclusion: Our study represents one of the first international studies investigating risk factors for clefts among multiethnic underserved populations. Our findings suggest a multifactorial etiology including both maternal and paternal factors., (© 2015 The Authors Birth Defects Research Part A: Clinical and Molecular Teratology Published by Wiley Periodicals, Inc.)

Background: The Total Exposure Study was a stratified, multi-center, cross-sectional study designed to estimate levels of biomarkers of tobacco-specific and non-specific exposure and of potential harm in U.S. adult current cigarette smokers (≥one manufactured cigarette per day over the last year) and tobacco product non-users (no smoking or use of any nicotine containing products over the last 5 years). The study was designed and sponsored by a tobacco company and implemented by contract research organizations in 2002-2003. Multiple analyses of smoking behavior, demographics, and biomarkers were performed. Study data and banked biospecimens were transferred from the sponsor to the Virginia Tobacco and Health Research Repository in 2010, and then to SRI International in 2012, for independent analysis and dissemination., Methods: We analyzed biomarker distributions overall, and by biospecimen availability, for comparison with existing studies, and to evaluate generalizability to the entire sample. We calculated genome-wide statistical power for a priori hypotheses. We performed clinical chemistries, nucleic acid extractions and genotyping, and report correlation and quality control metrics., Results: Vital signs, clinical chemistries, and laboratory measures of tobacco specific and non-specific toxicants are available from 3585 current cigarette smokers, and 1077 non-users. Peripheral blood mononuclear cells, red blood cells, plasma and 24-h urine biospecimens are available from 3073 participants (2355 smokers and 719 non-users). In multivariate analysis, participants with banked biospecimens were significantly more likely to self-identify as White, to be older, to have increased total nicotine equivalents per cigarette, decreased serum cotinine, and increased forced vital capacity, compared to participants without. Effect sizes were small (Cohen's d-values ≤ 0.11). Power for a priori hypotheses was 57 % in non-Hispanic Black (N = 340), and 96 % in non-Hispanic White (N = 1840), smokers. All DNA samples had genotype completion rates ≥97.5 %; 68 % of RNA samples yielded RIN scores ≥6.0., Conclusions: Total Exposure Study clinical and laboratory assessments and biospecimens comprise a unique resource for cigarette smoke health effects research. The Total Exposure Study Analysis Consortium seeks to perform molecular studies in multiple domains and will share data and analytic results in public repositories and the peer-reviewed literature. Data and banked biospecimens are available for independent or collaborative research.

The Nicotine Metabolite Ratio (NMR, ratio of trans-3'-hydroxycotinine and cotinine), has previously been associated with CYP2A6 activity, response to smoking cessation treatments, and cigarette consumption. We searched for drug metabolizing enzyme and transporter (DMET) gene variation associated with the NMR and prospective abstinence in 2,946 participants of laboratory studies of nicotine metabolism and of clinical trials of smoking cessation therapies. Stage I was a meta-analysis of the association of 507 common single nucleotide polymorphisms (SNPs) at 173 DMET genes with the NMR in 449 participants of two laboratory studies. Nominally significant associations were identified in ten genes after adjustment for intragenic SNPs; CYP2A6 and two CYP2A6 SNPs attained experiment-wide significance adjusted for correlated SNPs (CYP2A6 PACT=4.1E-7, rs4803381 PACT=4.5E-5, rs1137115, PACT=1.2E-3). Stage II was mega-regression analyses of 10 DMET SNPs with pretreatment NMR and prospective abstinence in up to 2,497 participants from eight trials. rs4803381 and rs1137115 SNPs were associated with pretreatment NMR at genome-wide significance. In post-hoc analyses of CYP2A6 SNPs, we observed nominally significant association with: abstinence in one pharmacotherapy arm; cigarette consumption among all trial participants; and lung cancer in four case:control studies. CYP2A6 minor alleles were associated with reduced NMR, CPD, and lung cancer risk. We confirmed the major role that CYP2A6 plays in nicotine metabolism, and made novel findings with respect to genome-wide significance and associations with CPD, abstinence and lung cancer risk. Additional multivariate analyses with patient variables and genetic modeling will improve prediction of nicotine metabolism, disease risk and smoking cessation treatment prognosis.

Genome-wide association studies (GWAS) for orofacial clefts have identified several susceptibility regions, but have largely focused on non-Hispanic White populations in developed countries. We performed a targeted genome-wide study of single nucleotide polymorphisms (SNPs) in exons using the Illumina HumanExome+ array with custom fine mapping of 16 cleft susceptibility regions in three underserved populations: Congolese (87 case-mother, 210 control-mother pairs), Vietnamese (131 case-parent trios), and Filipinos (42 case-mother, 99 control-mother pairs). All cases were children with cleft lip with or without cleft palate. Families were recruited from local hospitals and parental exposures were collected using interviewer-administered questionnaires. We used logistic regression models for case-control analyses, family-based association tests for trios, and fixed-effect meta-analyses to determine individual SNP effects corrected for multiple testing. Of the 16 known susceptibility regions tested, SNPs in four regions reached statistical significance in one or more of these populations: 1q32.2 (IRF6), 10q25.3 (VAX1), and 17q22 (NOG). Due to different linkage disequilibrium patterns, significant SNPs in these regions differed between the Vietnamese and Filipino populations from the index SNP selected from previous GWAS studies. Among Africans, there were no significant associations identified for any of the susceptibility regions. rs10787738 near VAX1 (P = 4.98E-3) and rs7987165 (P = 6.1E-6) were significant in the meta-analysis of all three populations combined. These results confirm several known susceptibility regions and identify novel risk alleles in understudied populations., (© 2014 Wiley Periodicals, Inc.)

Background: Testing for marginal associations between numerous genetic variants and disease may miss complex relationships among variables (e.g., gene-gene interactions). Bayesian approaches can model multiple variables together and offer advantages over conventional model building strategies, including using existing biological evidence as modeling priors and acknowledging that many models may fit the data well. With many candidate variables, Bayesian approaches to variable selection rely on algorithms to approximate the posterior distribution of models, such as Markov-Chain Monte Carlo (MCMC). Unfortunately, MCMC is difficult to parallelize and requires many iterations to adequately sample the posterior. We introduce a scalable algorithm called PEAK that improves the efficiency of MCMC by dividing a large set of variables into related groups using a rooted graph that resembles a mountain peak. Our algorithm takes advantage of parallel computing and existing biological databases when available., Results: By using graphs to manage a model space with more than 500,000 candidate variables, we were able to improve MCMC efficiency and uncover the true simulated causal variables, including a gene-gene interaction. We applied PEAK to a case-control study of childhood asthma with 2,521 genetic variants. We used an informative graph for oxidative stress derived from Gene Ontology and identified several variants in ERBB4, OXR1, and BCL2 with strong evidence for associations with childhood asthma., Conclusions: We introduced an extremely flexible analysis framework capable of efficiently performing Bayesian variable selection on many candidate variables. The PEAK algorithm can be provided with an informative graph, which can be advantageous when considering gene-gene interactions, or a symmetric graph, which simply divides the model space into manageable regions. The PEAK framework is compatible with various model forms, allowing for the algorithm to be configured for different study designs and applications, such as pathway or rare-variant analyses, by simple modifications to the model likelihood and proposal functions.

Asthma is a common disease with a complex risk architecture including both genetic and environmental factors. We performed a meta-analysis of North American genome-wide association studies of asthma in 5,416 individuals with asthma (cases) including individuals of European American, African American or African Caribbean, and Latino ancestry, with replication in an additional 12,649 individuals from the same ethnic groups. We identified five susceptibility loci. Four were at previously reported loci on 17q21, near IL1RL1, TSLP and IL33, but we report for the first time, to our knowledge, that these loci are associated with asthma risk in three ethnic groups. In addition, we identified a new asthma susceptibility locus at PYHIN1, with the association being specific to individuals of African descent (P = 3.9 × 10(-9)). These results suggest that some asthma susceptibility loci are robust to differences in ancestry when sufficiently large samples sizes are investigated, and that ancestry-specific associations also contribute to the complex genetic architecture of asthma.

Background: Single nucleotide polymorphisms (SNPs) in thymic stromal lymphopoietin (TSLP) have been associated with IgE (in girls) and asthma (in general). We sought to determine whether TSLP SNPs are associated with asthma in a sex-specific fashion., Methods: We conducted regular and sex-stratified analyses of association between SNPs in TSLP and asthma in families of children with asthma in Costa Rica. Significant findings were replicated in whites and African-American participants in the Childhood Asthma Management Program, in African-Americans in the Genomic Research on Asthma in the African Diaspora study, in whites and Hispanics in the Children's Health Study, and in whites in the Framingham Heart Study (FHS)., Main Results: Two SNPs in TSLP (rs1837253 and rs2289276) were significantly associated with a reduced risk of asthma in combined analyses of all cohorts (P values of 2 × 10(-5) and 1 × 10(-5) , respectively). In a sex-stratified analysis, the T allele of rs1837253 was significantly associated with a reduced risk of asthma in males only (P = 3 × 10(-6) ). Alternately, the T allele of rs2289276 was significantly associated with a reduced risk of asthma in females only (P = 2 × 10(-4) ). Findings for rs2289276 were consistent in all cohorts except the FHS., Conclusions: TSLP variants are associated with asthma in a sex-specific fashion., (© 2010 John Wiley & Sons A/S.)

Unraveling complex interactions has been a challenge in epidemiologic research. We introduce a pathway modeling framework that discovers plausible pathways from observational data, and allows estimation of both the net effect of the pathway and the types of interactions occurring among genetic or environmental risk factors. Each discovered pathway structure links combinations of observed variables through intermediate latent nodes to a final node, the outcome. Biologic knowledge can be readily applied in this framework as a prior on pathway structure to give preference to more biologically plausible models, thereby providing more precise estimation of Bayes factors for pathways of greatest interest by Markov Chain Monte Carlo (MCMC) methods.Data were simulated for binary inputs of which only a subset was involved in different pathway topologies. Our algorithm was then used to recover the pathway from the simulated data. The posterior distributions of inputs, pairwise and higher-order interactions, and topologies were obtained by MCMC methods. The evidence in favor of a particular pathway or interaction was summarized using Bayes factors. Our method can correctly identify the risk factors and interactions involved in the simulated pathway. We apply our framework to an asthma case-control data set with polymorphisms in 12 genes., (Copyright (c) 2010 John Wiley & Sons, Ltd.)

Genetic epidemiology is increasingly focused on complex diseases involving multiple genes and environmental factors, often interacting in complex ways. Although standard frequentist methods still have a role in hypothesis generation and testing for discovery of novel main effects and interactions, Bayesian methods are particularly well suited to modeling the relationships in an integrated "systems biology" manner. In this chapter, we provide an overview of the principles of Bayesian analysis and their advantages in this context and describe various approaches to applying them for both model building and discovery in a genome-wide setting. In particular, we highlight the ability of Bayesian methods to construct complex probability models via a hierarchical structure and to account for uncertainty in model specification by averaging over large spaces of alternative models., (Copyright © 2010 Elsevier Inc. All rights reserved.)

Asthma, a chronic airway disease with known heritability, affects more than 300 million people around the world. A genome-wide association (GWA) study of asthma with 359 cases from the Childhood Asthma Management Program (CAMP) and 846 genetically matched controls from the Illumina ICONdb public resource was performed. The strongest region of association seen was on chromosome 5q12 in PDE4D. The phosphodiesterase 4D, cAMP-specific (phosphodiesterase E3 dunce homolog, Drosophila) gene (PDE4D) is a regulator of airway smooth-muscle contractility, and PDE4 inhibitors have been developed as medications for asthma. Allelic p values for top SNPs in this region were 4.3 x 10(-07) for rs1588265 and 9.7 x 10(-07) for rs1544791. Replications were investigated in ten independent populations with different ethnicities, study designs, and definitions of asthma. In seven white and Hispanic replication populations, two PDE4D SNPs had significant results with p values less than 0.05, and five had results in the same direction as the original population but had p values greater than 0.05. Combined p values for 18,891 white and Hispanic individuals (4,342 cases) in our replication populations were 4.1 x 10(-04) for rs1588265 and 9.2 x 10(-04) for rs1544791. In three black replication populations, which had different linkage disequilibrium patterns than the other populations, original findings were not replicated. Further study of PDE4D variants might lead to improved understanding of the role of PDE4D in asthma pathophysiology and the efficacy of PDE4 inhibitor medications.

We describe a hierarchical regression modeling approach to selection of a subset of markers from the first stage of a genomewide association scan to carry forward to subsequent stages for testing on an independent set of subjects. Rather than simply selecting a subset of most significant marker-disease associations at some cutoff chosen to maximize the cost efficiency of a multistage design, we propose a prior model for the true noncentrality parameters of these associations composed of a large mass at zero and a continuous distribution of nonzero values. The prior probability of nonzero values and their prior means can be functions of various covariates characterizing each marker, such as their location relative to genes or evolutionary conserved regions, or prior linkage or association data. We propose to take the top ranked posterior expectations of the noncentrality parameters for confirmation in later stages of a genomewide scan. The statistical performance of this approach is compared with the traditional p-value ranking by simulation studies. We show that the ranking by posterior expectations performs better at selecting the true positive association than a simple ranking of p-values if at least some of the prior covariates have predictive value., ((c) 2007 Wiley-Liss, Inc.)