TY - JOUR
T1 - Bayesian logistic regression in detection of gene–steroid interaction for cancer at PDLIM5 locus
AU - Wang, Ke Sheng
AU - Owusu, Daniel
AU - Pan, Yue
AU - Xie, Changchun
N1 - Funding Information:
Funding support for the Personalized Medicine Research Project (PMRP) was provided through a cooperative agreement (U01HG004608) with the National Human Genome Research Institute (NHGRI), with additional funding from the National Institute for General Medical Sciences (NIGMS). The samples used for PMRP analyses were obtained with funding from Marshfield Clinic, Health Resources Service Administration Office of Rural Health Policy grant number D1A RH00025, and Wisconsin Department of Commerce Technology Development Fund contract number TDF FYO10718. Funding support for genotyping, which was performed at Johns Hopkins University, was provided by the NIH (U01HG004438). Assistance with phenotype harmonization and genotype cleaning was provided by the eMERGE Administrative Coordinating Center (U01HG004603) and the National Center for Biotechnology Information (NCBI). The datasets used for the analyses described in this manuscript were obtained from dbGaP at http://www.ncbi.nlm.nih.gov/gap through dbGaP accession number phs000170.v1.p1. This study was approved by the Internal Review Board (IRB), East Tennessee State University.
Publisher Copyright:
© 2016, Indian Academy of Sciences.
PY - 2016/6/1
Y1 - 2016/6/1
N2 - 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.
AB - 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.
KW - Bayesian
KW - cancer
KW - gene–steroid interaction
KW - logistic regression
KW - PDLIM5 locus
KW - single-nucleotide polymorphism
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U2 - 10.1007/s12041-016-0642-1
DO - 10.1007/s12041-016-0642-1
M3 - Article
C2 - 27350677
AN - SCOPUS:84977504614
VL - 95
SP - 331
EP - 340
JO - Journal of Genetics
JF - Journal of Genetics
SN - 0022-1333
IS - 2
ER -
