title: Bayesian logistic regression in detection of gene-steroid interaction for cancer at PDLIM5 locus.
abstract: The PDZ and LIM domain 5 (PDLIM5) gene may play a role in cancer, bipolar disorder, major depression, alcohol dependence and schizophrenia; however, little is known about the interaction effect of steroid and PDLIM5 gene on cancer. This study examined 47 single-nucleotide polymorphisms (SNPs) within the PDLIM5 gene in the Marshfield sample with 716 cancer patients (any diagnosed cancer, excluding minor skin cancer) and 2848 noncancer controls. Multiple logistic regression model in PLINK software was used to examine the association of each SNP with cancer. Bayesian logistic regression in PROC GENMOD in SAS statistical software, ver. 9.4 was used to detect gene- steroid interactions influencing cancer. Single marker analysis using PLINK identified 12 SNPs associated with cancer (P< 0.05); especially, SNP rs6532496 revealed the strongest association with cancer (P = 6.84 × 10⁻³); while the next best signal was rs951613 (P = 7.46 × 10⁻³). Classic logistic regression in PROC GENMOD showed that both rs6532496 and rs951613 revealed strong gene-steroid interaction effects (OR=2.18, 95% CI=1.31-3.63 with P = 2.9 × 10⁻³ for rs6532496 and OR=2.07, 95% CI=1.24-3.45 with P = 5.43 × 10⁻³ for rs951613, respectively). Results from Bayesian logistic regression showed stronger interaction effects (OR=2.26, 95% CI=1.2-3.38 for rs6532496 and OR=2.14, 95% CI=1.14-3.2 for rs951613, respectively). All the 12 SNPs associated with cancer revealed significant gene-steroid interaction effects (P < 0.05); whereas 13 SNPs showed gene-steroid interaction effects without main effect on cancer. SNP rs4634230 revealed the strongest gene-steroid interaction effect (OR=2.49, 95% CI=1.5-4.13 with P = 4.0 × 10⁻⁴ based on the classic logistic regression and OR=2.59, 95% CI=1.4-3.97 from Bayesian logistic regression; respectively). This study provides evidence of common genetic variants within the PDLIM5 gene and interactions between PLDIM5 gene polymorphisms and steroid use influencing cancer.
authors.affiliation: Department of Biostatistics and Epidemiology, College of Public Health, East Tennessee State University, Johnson City, TN 37614, USA.wangk@etsu.edu.
authors.forename: Ke-Sheng
authors.initials: KS
authors.lastname: Wang
authors.forename: Daniel
authors.initials: D
authors.lastname: Owusu
authors.forename: Yue
authors.initials: Y
authors.lastname: Pan
authors.forename: Changchun
authors.initials: C
authors.lastname: Xie
mesh_terms: D048868:Adaptor Proteins, Signal Transducing / Q000235:genetics*; D000368:Aged; D000369:Aged, 80 and over; D000483:Alleles; D001499:Bayes Theorem; D005260:Female; D015870:Gene Expression; D005787:Gene Frequency; D056426:Genetic Loci; D020022:Genetic Predisposition to Disease; D055106:Genome-Wide Association Study; D006239:Haplotypes; D006801:Humans; D007249:Inflammation / Q000188:drug therapy; D060588:LIM Domain Proteins / Q000235:genetics*; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D009369:Neoplasms / Q000139:chemically induced / Q000235:genetics* / Q000473:pathology; D010597:Pharmacogenetics; D020641:Polymorphism, Single Nucleotide; D013256:Steroids / Q000008:administration & dosage / Q000009:adverse effects*
chemical_list: D048868:Adaptor Proteins, Signal Transducing; D060588:LIM Domain Proteins; C512485:PDLIM5 protein, human; D013256:Steroids