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Yuan Z.,Shandong University | Liu H.,Shandong Academy of Sciences | Zhang X.,Shandong University | Li F.,Shandong University | And 3 more authors.
PLoS ONE | Year: 2013

Currently, the genetic variants identified by genome wide association study (GWAS) generally only account for a small proportion of the total heritability for complex disease. One crucial reason is the underutilization of gene-gene joint effects commonly encountered in GWAS, which includes their main effects and co-association. However, gene-gene co-association is often customarily put into the framework of gene-gene interaction vaguely. From the causal graph perspective, we elucidate in detail the concept and rationality of gene-gene co-association as well as its relationship with traditional gene-gene interaction, and propose two Fisher r-to-z transformation-based simple statistics to detect it. Three series of simulations further highlight that gene-gene co-association refers to the extent to which the joint effects of two genes differs from the main effects, not only due to the traditional interaction under the nearly independent condition but the correlation between two genes. The proposed statistics are more powerful than logistic regression under various situations, cannot be affected by linkage disequilibrium and can have acceptable false positive rate as long as strictly following the reasonable GWAS data analysis roadmap. Furthermore, an application to gene pathway analysis associated with leprosy confirms in practice that our proposed gene-gene co-association concepts as well as the correspondingly proposed statistics are strongly in line with reality. © 2013 Yuan et al. Source


Zhang X.,Shandong University | Yang X.,City College of New York | Yang X.,Bayessoft, Inc. | Yuan Z.,Shandong University | And 6 more authors.
PLoS ONE | Year: 2013

For genome-wide association data analysis, two genes in any pathway, two SNPs in the two linked gene regions respectively or in the two linked exons respectively within one gene are often correlated with each other. We therefore proposed the concept of gene-gene co-association, which refers to the effects not only due to the traditional interaction under nearly independent condition but the correlation between two genes. Furthermore, we constructed a novel statistic for detecting gene-gene co-association based on Partial Least Squares Path Modeling (PLSPM). Through simulation, the relationship between traditional interaction and co-association was highlighted under three different types of co-association. Both simulation and real data analysis demonstrated that the proposed PLSPM-based statistic has better performance than single SNP-based logistic model, PCA-based logistic model, and other gene-based methods. © 2013 Zhang et al. Source


Li F.,Shandong University | Zhao J.,MRC Epidemiology Unit and Institute of Metabolic Science | Yuan Z.,Shandong University | Zhang X.,Shandong University | And 2 more authors.
BMC Genetics | Year: 2013

Background: On thinking quantitatively of complex diseases, there are at least three statistical strategies for analyzing the gene-gene interaction: SNP by SNP interaction on single trait, gene-gene (each can involve multiple SNPs) interaction on single trait and gene-gene interaction on multiple traits. The third one is the most general in dissecting the genetic mechanism underlying complex diseases underpinning multiple quantitative traits. In this paper, we developed a novel statistic for this strategy through modifying the Partial Least Squares Path Modeling (PLSPM), called mPLSPM statistic.Results: Simulation studies indicated that mPLSPM statistic was powerful and outperformed the principal component analysis (PCA) based linear regression method. Application to real data in the EPIC-Norfolk GWAS sub-cohort showed suggestive interaction (γ) between TMEM18 gene and BDNF gene on two composite body shape scores (γ = 0.047 and γ = 0.058, with P = 0.021, P = 0.005), and BMI (γ = 0.043, P = 0.034). This suggested these scores (synthetically latent traits) were more suitable to capture the obesity related genetic interaction effect between genes compared to single trait.Conclusions: The proposed novel mPLSPM statistic is a valid and powerful gene-based method for detecting gene-gene interaction on multiple quantitative phenotypes. © 2013 Li et al.; licensee BioMed Central Ltd. Source


Xue F.,Shandong University | Xue F.,MRC Epidemiology Unit and Institute of Metabolic Science | Li S.,Tulane University | Luan J.,MRC Epidemiology Unit and Institute of Metabolic Science | And 6 more authors.
PLoS ONE | Year: 2012

Genetic association studies are now routinely used to identify single nucleotide polymorphisms (SNPs) linked with human diseases or traits through single SNP-single trait tests. Here we introduced partial least squares path modeling (PLSPM) for association between single or multiple SNPs and a latent trait that can involve single or multiple correlated measurement(s). Furthermore, the framework naturally provides estimators of polygenic effect by appropriately weighting trait-attributing alleles. We conducted computer simulations to assess the performance via multiple SNPs and human obesity-related traits as measured by body mass index (BMI), waist and hip circumferences. Our results showed that the associate statistics had type I error rates close to nominal level and were powerful for a range of effect and sample sizes. When applied to 12 candidate regions in data (N = 2,417) from the European Prospective Investigation of Cancer (EPIC)-Norfolk study, a region in FTO was found to have stronger association (rs7204609~rs9939881 at the first intron P = 4.29×10 -7) than single SNP analysis (all with P>10 -4) and a latent quantitative phenotype was obtained using a subset sample of EPIC-Norfolk (N = 12,559). We believe our method is appropriate for assessment of regional association and polygenic effect on a single or multiple traits. © 2012 Xue et al. Source

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