Genome-wide association study (GWAS) is a promising approach for identifying common genetic variants of the diseases on the basis of millions of single nucleotide polymorphisms (SNPs). In order to avoid low power caused by overmuch correction for multiple comparisons in single locus association study, some methods have been proposed by grouping SNPs together into a SNP set based on genomic features, then testing the joint effect of the SNP set. We compare the performances of principal component analysis (PCA), supervised principal component analysis (SPCA), kernel principal component analysis (KPCA), and sliced inverse regression (SIR). Simulated SNP sets are generated under scenarios of 0, 1 and ≥2 causal SNPs model. Our simulation results show that all of these methods can control the type I error at the nominal significance level. SPCA is always more powerful than the other methods at different settings of linkage disequilibrium structures and minor allele frequency of the simulated datasets. We also apply these four methods to a real GWAS of non-small cell lung cancer (NSCLC) in Han Chinese population
To compare the expression patterns of steroid receptor coactivators (SRCs) and steroid-induced stromal cell-derived factor 1 (CXCL12 (SDF1)) in normal and ectopic endometrium and to explore the roles of NCOA1 (SRC1) and NCOA2 (SRC2) in the steroid-induced CXCL12 expression in normal and ectopic endometrial stromal cells (ESCs). The NCOA1, NCOA2, NCOA3 (SRC3), and CXCL12 (SDF1)a mRNA levels in normal and ectopic endometrium were analyzed by quantitative real-time PCR. Steroid-induced CXCL12 expression was detected by the ELISA method and the chemotactic activity of conditioned supernatant to monocyte was assessed by the Boyden chamber method before and after the silencing of NCOA1 or NCOA2 with siRNA in normal and ectopic ESCs. The expression of NCOA1 and CXCL12 in ectopic endometrium was significantly greater than that in normal endometrium in the secretory phase. Progesterone (P 4 ) was able to significantly inhibit estradiol (E 2 )-stimulated CXCL12 expression in normal and ectopic ESCs. The inhibitory rate of P 4 in ectopic ESCs at 72 and 96 h was significantly lower than that in normal ESCs. Silencing of NCOA1 but not NCOA2 significantly reduced the E 2 -induced CXCL12 expression in normal and ectopic ESCs. The ability of P 4 to inhibit E 2 -induced CXCL12 expression and monocyte chemotaxis in normal and ectopic ESCs was significantly attenuated when NCOA2 was silenced. NCOA1 plays a necessary role in E 2 -induced CXCL12 expression and NCOA2 is required for P 4 to inhibit the E 2 -induced CXCL12 production in normal and ectopic endometrium.
ObjectivesRecent dramatic increases in cardiovascular disease mortality in China can be mostly explained by adverse changes in hypertension, dyslipidaemia, diabetes and obesity, known as cardiometabolic risk factors. Our study aimed to assess the trend of these four signatures by a 10-year lag in Nanjing, China.Methods8017 subjects attended the routine health examination in 2008, and 9379 subjects in 2017, from multiple work units of Nanjing, were included in the present study. The prevalence and trend of four cardiometabolic risk factors: hypertension, dyslipidaemia, diabetes and obesity were analysed.ResultsFrom 2008 to 2017, the prevalence of hypertension declined, while the prevalence of dyslipidaemia, diabetes and obesity increased. Besides, the population in 2008 and 2017 had an average of 0.66 and 0.78 risk factors, respectively.ConclusionCardiometabolic risk factors are common for the staff in administrative agencies and institutions of Nanjing, China. Effective screening and interventions against these risk factors should be adopted in high-risk populations such as the office-working population in China.
Genome-wide association studies (GWAS) are popular for identifying genetic variants which are associated with disease risk. Many approaches have been proposed to test multiple single nucleotide polymorphisms (SNPs) in a region simultaneously which considering disadvantages of methods in single locus association analysis. Kernel machine based SNP set analysis is more powerful than single locus analysis, which borrows information from SNPs correlated with causal or tag SNPs. Four types of kernel machine functions and principal component based approach (PCA) were also compared. However, given the loss of power caused by low minor allele frequencies (MAF), we conducted an extension work on PCA and used a new method called weighted PCA (wPCA). Comparative analysis was performed for weighted principal component analysis (wPCA), logistic kernel machine based test (LKM) and principal component analysis (PCA) based on SNP set in the case of different minor allele frequencies (MAF) and linkage disequilibrium (LD) structures. We also applied the three methods to analyze two SNP sets extracted from a real GWAS dataset of non-small cell lung cancer in Han Chinese population. Simulation results show that when the MAF of the causal SNP is low, weighted principal component and weighted IBS are more powerful than PCA and other kernel machine functions at different LD structures and different numbers of causal SNPs. Application of the three methods to a real GWAS dataset indicates that wPCA and wIBS have better performance than the linear kernel, IBS kernel and PCA.
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