title: Comparison of dimension reduction-based logistic regression models for case-control genome-wide association study: principal components analysis vs. partial least squares.
abstract: With recent advances in biotechnology, genome-wide association study (GWAS) has been widely used to identify genetic variants that underlie human complex diseases and traits. In case-control GWAS, typical statistical strategy is traditional logistical regression (LR) based on single-locus analysis. However, such a single-locus analysis leads to the well-known multiplicity problem, with a risk of inflating type I error and reducing power. Dimension reduction-based techniques, such as principal component-based logistic regression (PC-LR), partial least squares-based logistic regression (PLS-LR), have recently gained much attention in the analysis of high dimensional genomic data. However, the performance of these methods is still not clear, especially in GWAS. We conducted simulations and real data application to compare the type I error and power of PC-LR, PLS-LR and LR applicable to GWAS within a defined single nucleotide polymorphism (SNP) set region. We found that PC-LR and PLS can reasonably control type I error under null hypothesis. On contrast, LR, which is corrected by Bonferroni method, was more conserved in all simulation settings. In particular, we found that PC-LR and PLS-LR had comparable power and they both outperformed LR, especially when the causal SNP was in high linkage disequilibrium with genotyped ones and with a small effective size in simulation. Based on SNP set analysis, we applied all three methods to analyze non-small cell lung cancer GWAS data.
authors.affiliation: Department of Epidemiology and Biostatistics, School of Public Health.
authors.forename: Honggang
authors.initials: H
authors.lastname: Yi
authors.affiliation: Department of Public Service Management, School of KangDa.
authors.forename: Hongmei
authors.initials: H
authors.lastname: Wo
authors.affiliation: Department of Epidemiology and Biostatistics, School of Public Health.
authors.forename: Yang
authors.initials: Y
authors.lastname: Zhao
authors.affiliation: Department of Epidemiology and Biostatistics, School of Public Health.
authors.forename: Ruyang
authors.initials: R
authors.lastname: Zhang
authors.affiliation: Department of Epidemiology and Biostatistics, School of Public Health.
authors.forename: Junchen
authors.initials: J
authors.lastname: Dai
authors.affiliation: Department of Epidemiology and Biostatistics, School of Public Health.|Section of Clinical Epidemiology, Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Cancer Center.
authors.forename: Guangfu
authors.initials: G
authors.lastname: Jin
authors.affiliation: Department of Epidemiology and Biostatistics, School of Public Health.|Section of Clinical Epidemiology, Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Cancer Center.
authors.forename: Hongxia
authors.initials: H
authors.lastname: Ma
authors.affiliation: Institute of Occupational Medicine and Ministry of Education, Key Laboratory for Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China.
authors.forename: Tangchun
authors.initials: T
authors.lastname: Wu
authors.affiliation: Department of Epidemiology and Biostatistics, School of Public Health.|Section of Clinical Epidemiology, Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Cancer Center.|State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, Jiangsu 211166, China.
authors.forename: Zhibin
authors.initials: Z
authors.lastname: Hu
authors.affiliation: State Key Laboratory of Molecular Oncology and Department of Etiology and Carcinogenesis, Cancer Institute and Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China.
authors.forename: Dongxin
authors.initials: D
authors.lastname: Lin
authors.affiliation: Department of Epidemiology and Biostatistics, School of Public Health.|Section of Clinical Epidemiology, Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Cancer Center.|State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, Jiangsu 211166, China.
authors.forename: Hongbing
authors.initials: H
authors.lastname: Shen
authors.affiliation: Department of Epidemiology and Biostatistics, School of Public Health. fengchen@njmu.edu.cn.
authors.forename: Feng
authors.initials: F
authors.lastname: Chen
keywords: genome-wide association study; partial least squares-based logistic regression; power; principal components analysis; type I error