Comparison of dimension reduction-based logistic regression models for case-control genome-wide association study： principal components analysis vs. partial least squares

Honggang Yi; Hongmei Wo; Yang Zhao; Ruyang Zhang; Junchen Dai; Guangfu Jin; Hongxia Ma; Tangchun Wu; Zhibin Hu; Dongxin Lin; Hongbing Shen; Feng Chen

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Comparison of dimension reduction-based logistic regression models for case-control genome-wide association study： principal components analysis vs. partial least squares

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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 perfor?mance 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.

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《生物医学研究杂志：英文版》 |2015年第004期|P.298-307|共10页
作者
Honggang Yi; Hongmei Wo; Yang Zhao; Ruyang Zhang; Junchen Dai; Guangfu Jin; Hongxia Ma; Tangchun Wu; Zhibin Hu; Dongxin Lin; Hongbing Shen; Feng Chen;
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作者单位

[1]Department of Epidemiology and Biostatistics, School of Public Health;

[2]Department of Public Service Management, School of KangDa;

[3]Section of Clinical Epidemiology, Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Cancer Center;

[4]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;

[5]State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanfing, Jiangsu 211166, China;

[6]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;

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原文格式 PDF
正文语种 CHI
中图分类基因工程（遗传工程）;
关键词
偏最小二乘回归全基因组主成分分析降维技术 Logistic回归回归模型关联单核苷酸多态性;

机译：偏最小二乘回归全基因组主成分分析降维技术 Logistic回归回归模型关联单核苷酸多态性;

Comparison of dimension reduction-based logistic regression models for case-control genome-wide association study： principal components analysis vs. partial least squares

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