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 statistica...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.展开更多
Somatosensory evoked potentials(SEPs)have been widely used to assess neurological function in clinical practice.A good understanding of the association between SEP signals and neurological function is helpful for prec...Somatosensory evoked potentials(SEPs)have been widely used to assess neurological function in clinical practice.A good understanding of the association between SEP signals and neurological function is helpful for precise diagnosis of impairment location.Previous studies on SEPs have been reported in animal models.However,few studies have reported the relationships between SEP waveforms in animals and those in humans.In this study,we collected normal SEP waveforms and decomposed them into specific time–frequency components(TFCs).Our results showed three stable TFC distribution regions in intact goats and rats and in humans.After we induced spinal cord injury in the animal models,a greater number of small TFC distribution regions were observed in the injured goat and rat groups than in the normal group.Moreover,there were significant correlations(P<0.05)and linear relationships between the main SEP TFCs of the human group and those of the goat and rat groups.A stable TFC distribution of SEP components was observed in the human,goat and rat groups,and the TFC distribution modes were similar between the three groups.Results in various animal models in this study could be translated to future clinical studies based on SEP TFC analysis.Human studies were approved by the Institutional Review Board of the University of Hong Kong/Hospital Authority Hong Kong West Cluster(approval No.UM 05-312 T/975)on December 5,2005.Rat experiments were approved by the Committee on the Use of Live Animals in Teaching and Research of Li Ka Shing Faculty of Medicine of the University of Hong Kong(approval No.CULART 2912-12)on January 28,2013.Goat experiments were approved by the Animal Ethics Committee of Affiliated Hospital of Guangdong Medical University(approval No.GDY2002132)on March 5,2018.展开更多
基金founded by the National Natural Science Foundation of China(81202283,81473070,81373102 and81202267)Key Grant of Natural Science Foundation of the Jiangsu Higher Education Institutions of China(10KJA330034 and11KJA330001)+1 种基金the Research Fund for the Doctoral Program of Higher Education of China(20113234110002)the Priority Academic Program for the Development of Jiangsu Higher Education Institutions(Public Health and Preventive Medicine)
文摘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.
基金supported by the National Natural Science Foundation of China,No.81871768(to YH)the Natural Science Foundation of Tianjin of China,No.18JCYBJC29600(to HYC)High Level-Hospital Program,Health Commission of Guangdong Province of China,No.HKUSZH201902011(to YH).
文摘Somatosensory evoked potentials(SEPs)have been widely used to assess neurological function in clinical practice.A good understanding of the association between SEP signals and neurological function is helpful for precise diagnosis of impairment location.Previous studies on SEPs have been reported in animal models.However,few studies have reported the relationships between SEP waveforms in animals and those in humans.In this study,we collected normal SEP waveforms and decomposed them into specific time–frequency components(TFCs).Our results showed three stable TFC distribution regions in intact goats and rats and in humans.After we induced spinal cord injury in the animal models,a greater number of small TFC distribution regions were observed in the injured goat and rat groups than in the normal group.Moreover,there were significant correlations(P<0.05)and linear relationships between the main SEP TFCs of the human group and those of the goat and rat groups.A stable TFC distribution of SEP components was observed in the human,goat and rat groups,and the TFC distribution modes were similar between the three groups.Results in various animal models in this study could be translated to future clinical studies based on SEP TFC analysis.Human studies were approved by the Institutional Review Board of the University of Hong Kong/Hospital Authority Hong Kong West Cluster(approval No.UM 05-312 T/975)on December 5,2005.Rat experiments were approved by the Committee on the Use of Live Animals in Teaching and Research of Li Ka Shing Faculty of Medicine of the University of Hong Kong(approval No.CULART 2912-12)on January 28,2013.Goat experiments were approved by the Animal Ethics Committee of Affiliated Hospital of Guangdong Medical University(approval No.GDY2002132)on March 5,2018.
