3.8

CiteScore

2.4

Impact Factor
  • ISSN 1674-8301
  • CN 32-1810/R
Honggang Yi, Hongmei Wo, Yang Zhao, Ruyang Zhang, Junchen Dai, Guangfu Jin, Hongxia Ma, Tangchun Wu, Zhibin Hu, Dongxin Lin, Hongbing Shen, Feng Chen. Comparison of dimension reduction-based logistic regression models forcase-control genome-wide association study: principal components analysis vs. partial least squares[J]. The Journal of Biomedical Research, 2015, 29(4): 298-307. DOI: 10.7555/JBR.29.20140043
Citation: Honggang Yi, Hongmei Wo, Yang Zhao, Ruyang Zhang, Junchen Dai, Guangfu Jin, Hongxia Ma, Tangchun Wu, Zhibin Hu, Dongxin Lin, Hongbing Shen, Feng Chen. Comparison of dimension reduction-based logistic regression models forcase-control genome-wide association study: principal components analysis vs. partial least squares[J]. The Journal of Biomedical Research, 2015, 29(4): 298-307. DOI: 10.7555/JBR.29.20140043

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

Funds: 

the National Natural Science Foundation of China (81202283, 81473070, 81373102 and 81202267), Key Grant of Natural Science Foundation of the Jiangsu Higher Education Institutions of China (10KJA330034 and 11KJA330001)

More Information
  • Received Date: March 10, 2015
  • 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 logisticregression (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)setregion.WefoundthatPC-LRandPLScanreasonablycontroltypeIerrorundernullhypothesis. Oncontrast,LR,whichiscorrectedbyBonferronimethod,wasmoreconservedinallsimulationsettings.Inparticular,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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