Browsing by Subject "Gene-environment interaction"

Now showing 1 - 5 of 5
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    Citrus-Gene Interaction and Melanoma Risk in the UK Biobank
    (Wiley, 2022) Marley, Andrew R.; Li, Ming; Champion, Victoria L.; Song, Yiqing; Han, Jiali; Li, Xin; Epidemiology, School of Public Health
    High citrus consumption may increase melanoma risk; however, little is known about the biological mechanisms of this association, or whether it is modified by genetic variants. We conducted a genome-wide analysis of gene-citrus consumption interactions on melanoma risk among 1563 melanoma cases and 193 296 controls from the UK Biobank. Both the 2-degrees-of-freedom (df) joint test of genetic main effect and gene-environment (G-E) interaction and the standard 1-df G-E interaction test were performed. Three index SNPs (lowest P-value SNP among highly correlated variants [r2 > .6]) were identified from among the 365 genome-wide significant 2-df test results (rs183783391 on chromosome 3 [MITF], rs869329 on chromosome 9 [MTAP] and rs11446223 on chromosome 16 [DEF8]). Although all three were statistically significant for the 2-df test (4.25e-08, 1.98e-10 and 4.93e-13, respectively), none showed evidence of interaction according to the 1-df test (P = .73, .24 and .12, respectively). Eight nonindex, 2-df test significant SNPs on chromosome 16 were significant (P < .05) according to the 1-df test, providing evidence of citrus-gene interaction. Seven of these SNPs were mapped to AFG3L1P (rs199600347, rs111822773, rs113178244, rs3803683, rs73283867, rs78800020, rs73283871), and one SNP was mapped to GAS8 (rs74583214). We identified several genetic loci that may elucidate the association between citrus consumption and melanoma risk. Further studies are needed to confirm these findings.
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    Gene-by-Diet Interactions Affect Serum 1,25-Dihydroxyvitamin D Levels in Male BXD Recombinant Inbred Mice
    (Oxford University Press, 2016-02) Fleet, James C.; Replogle, Rebecca A.; Reyes-Fernandez, Perla; Wang, Libo; Zhang, Min; Clinkenbeard, Erica L.; White, Kenneth E.; Department of Medical & Molecular Genetics, IU School of Medicine
    1,25-Dihydroxyvitamin D (1,25[OH]2D) regulates calcium (Ca), phosphate, and bone metabolism. Serum 1,25(OH)2D levels are reduced by low vitamin D status and high fibroblast growth factor 23 (FGF23) levels and increased by low Ca intake and high PTH levels. Natural genetic variation controls serum 25-hydroxyvitamin D (25[OH]D) levels, but it is unclear how it controls serum 1,25(OH)2D or the response of serum 1,25(OH)2D levels to dietary Ca restriction (RCR). Male mice from 11 inbred lines and from 51 BXD recombinant inbred lines were fed diets with either 0.5% (basal) or 0.25% Ca from 4 to 12 weeks of age (n = 8 per line per diet). Significant variation among the lines was found in basal serum 1,25(OH)2D and in the RCR as well as basal serum 25(OH)D and FGF23 levels. 1,25(OH)2D was not correlated to 25(OH)D but was negatively correlated to FGF23 (r = -0.5). Narrow sense heritability of 1,25(OH)2D was 0.67 on the 0.5% Ca diet, 0.66 on the 0.25% Ca diet, and 0.59 for the RCR, indicating a strong genetic control of serum 1,25(OH)2D. Genetic mapping revealed many loci controlling 1,25(OH)2D (seven loci) and the RCR (three loci) as well as 25(OH)D (four loci) and FGF23 (two loci); a locus on chromosome 18 controlled both 1,25(OH)2D and FGF23. Candidate genes underlying loci include the following: Ets1 (1,25[OH]2D), Elac1 (FGF23 and 1,25[OH]2D), Tbc1d15 (RCR), Plekha8 and Lyplal1 (25[OH]D), and Trim35 (FGF23). This report is the first to reveal that serum 1,25(OH)2D levels are controlled by multiple genetic factors and that some of these genetic loci interact with the dietary environment.
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    Identifying Gene–Environment Interactions With Robust Marginal Bayesian Variable Selection
    (Frontiers Media, 2021-12-08) Lu, Xi; Fan, Kun; Ren, Jie; Wu, Cen; Biostatistics & Health Data Science, School of Medicine
    In high-throughput genetics studies, an important aim is to identify gene-environment interactions associated with the clinical outcomes. Recently, multiple marginal penalization methods have been developed and shown to be effective in G×E studies. However, within the Bayesian framework, marginal variable selection has not received much attention. In this study, we propose a novel marginal Bayesian variable selection method for G×E studies. In particular, our marginal Bayesian method is robust to data contamination and outliers in the outcome variables. With the incorporation of spike-and-slab priors, we have implemented the Gibbs sampler based on Markov Chain Monte Carlo (MCMC). The proposed method outperforms a number of alternatives in extensive simulation studies. The utility of the marginal robust Bayesian variable selection method has been further demonstrated in the case studies using data from the Nurse Health Study (NHS). Some of the identified main and interaction effects from the real data analysis have important biological implications.
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    The Impact of Peer Substance Use and Polygenic Risk on Trajectories of Heavy Episodic Drinking Across Adolescence and Emerging Adulthood
    (Wiley, 2017-01) Li, James J.; Cho, Seung Bin; Salvatore, Jessica E.; Edenberg, Howard J.; Agrawal, Arpana; Chorlian, David B.; Porjesz, Bernice; Hesselbrock, Victor; COGA Investigators; Dick, Danielle M.; Biochemistry and Molecular Biology, School of Medicine
    BACKGROUND: Heavy episodic drinking is developmentally normative among adolescents and young adults, but is linked to adverse consequences in later life, such as drug and alcohol dependence. Genetic and peer influences are robust predictors of heavy episodic drinking in youth, but little is known about the interplay between polygenic risk and peer influences as they impact developmental patterns of heavy episodic drinking. METHODS: Data were from a multisite prospective study of alcohol use among adolescents and young adults with genome-wide association data (n = 412). Generalized linear mixed models were used to characterize the initial status and slopes of heavy episodic drinking between age 15 and 28. Polygenic risk scores (PRS) were derived from a separate genome-wide association study for alcohol dependence and examined for their interaction with substance use among the adolescents' closest friends in predicting the initial status and slopes of heavy episodic drinking. RESULTS: Close friend substance use was a robust predictor of adolescent heavy episodic drinking, even after controlling for parental knowledge and peer substance use in the school. PRS were predictive of the initial status and early patterns of heavy episodic drinking in males, but not in females. No interaction was detected between PRS and close friend substance use for heavy episodic drinking trajectories in either males or females. CONCLUSIONS: Although substance use among close friends and genetic influences play an important role in predicting heavy episodic drinking trajectories, particularly during the late adolescent to early adult years, we found no evidence of interaction between these influences after controlling for other social processes, such as parental knowledge and broader substance use among other peers outside of close friends. The use of longitudinal models and accounting for multiple social influences may be crucial for future studies focused on uncovering gene-environment interplay. Clinical implications are also discussed.
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    Sparse group variable selection for gene-environment interactions in the longitudinal study
    (Wiley, 2022) Zhou, Fei; Lu, Xi; Ren, Jie; Fan, Kun; Ma, Shuangge; Wu, Cen; Biostatistics and Health Data Science, School of Medicine
    Penalized variable selection for high dimensional longitudinal data has received much attention as it can account for the correlation among repeated measurements while providing additional and essential information for improved identification and prediction performance. Despite the success, in longitudinal studies, the potential of penalization methods is far from fully understood for accommodating structured sparsity. In this article, we develop a sparse group penalization method to conduct the bi-level gene-environment (G×E) interaction study under the repeatedly measured phenotype. Within the quadratic inference function (QIF) framework, the proposed method can achieve simultaneous identification of main and interaction effects on both the group and individual level. Simulation studies have shown that the proposed method outperforms major competitors. In the case study of asthma data from the Childhood Asthma Management Program (CAMP), we conduct G×E study by using high dimensional SNP data as genetic factors and the longitudinal trait, forced expiratory volume in one second (FEV1), as the phenotype. Our method leads to improved prediction and identification of main and interaction effects with important implications.