Despite the great success of genome-wide association studies (GWAS) in identification of the common genetic variants associated with complex diseases, the current GWAS have focused on single-SNP analysis. However, single-SNP analysis often identifies only a few of the most significant SNPs that account for a small proportion of the genetic variants and offers only a limited understanding of complex diseases. To overcome these limitations, we propose gene and pathway-based association analysis as a new paradigm for GWAS. As a proof of concept, we performed a comprehensive gene and pathway-based association analysis of 13 published GWAS. Our results showed that the proposed new paradigm for GWAS not only identified the genes that include significant SNPs found by single-SNP analysis, but also detected new genes in which each single SNP conferred a small disease risk; however, their joint actions were implicated in the development of diseases. The results also showed that the new paradigm for GWAS was able to identify biologically meaningful pathways associated with the diseases, which were confirmed by a gene-set-rich analysis using gene expression data.

Dysregulation of several metabolite pathways, including branched-chain amino acids (BCAAs), are associated with Non-Alcoholic Fatty Liver Disease (NAFLD) and insulin resistance in adults, while studies in youth reported conflicting results. We explored whether, independently of obesity and insulin resistance, obese adolescents with NAFLD display a metabolomic signature consistent with disturbances in amino acid and lipid metabolism. A total of 180 plasma metabolites were measured by a targeted metabolomic approach in 78 obese adolescents with (n = 30) or without (n = 48) NAFLD assessed by magnetic resonance imaging (MRI). All subjects underwent an oral glucose tolerance test and subsets of patients underwent a two-step hyperinsulinemic-euglycemic clamp and/or a second MRI after a 2.2 ± 0.8-year follow-up. Adolescents with NAFLD had higher plasma levels of valine (p = 0.02), isoleucine (p = 0.03), tryptophan (p = 0.02), and lysine (p = 0.02) after adjustment for confounding factors. Circulating BCAAs were negatively correlated with peripheral and hepatic insulin sensitivity. Furthermore, higher baseline valine levels predicted an increase in hepatic fat content (HFF) at follow-up (p = 0.01). These results indicate that a dysregulation of BCAA metabolism characterizes obese adolescents with NAFLD independently of obesity and insulin resistance and predict an increase in hepatic fat content over time.

Current GWAS have primarily focused on testing association of single SNPs. To only test for association of single SNPs has limited utility and is insufficient to dissect the complex genetic structure of many common diseases. To meet conceptual and technical challenges raised by GWAS, we suggest gene and pathway-based GWAS as complementary to the current single SNP-based GWAS. This publication develops three statistics for testing association of genes and pathways with disease: linear combination test, quadratic test and decorrelation test, which take correlations among SNPs within a gene or genes within a pathway into account. The null distribution of the suggested statistics is examined and the statistics are applied to GWAS of rheumatoid arthritis in the Wellcome Trust Case–Control Consortium and the North American Rheumatoid Arthritis Consortium studies. The preliminary results show that the suggested gene and pathway-based GWAS offer several remarkable features. First, not only can they identify the genes that have large genetic effects, but also they can detect new genes in which each single SNP conferred a small amount of disease risk, and their joint actions can be implicated in the development of diseases. Second, gene and pathway-based analysis can allow the formation of the core of pathway definition of complex diseases and unravel the functional bases of an association finding. Third, replication of association findings at the gene or pathway level is much easier than replication at the individual SNP level.

Although great progress in genome-wide association studies (GWAS) has been made, the significant SNP associations identified by GWAS account for only a few percent of the genetic variance, leading many to question where and how we can find the missing heritability. There is increasing interest in genome-wide interaction analysis as a possible source of finding heritability unexplained by current GWAS. However, the existing statistics for testing interaction have low power for genome-wide interaction analysis. To meet challenges raised by genome-wide interactional analysis, we have developed a novel statistic for testing interaction between two loci (either linked or unlinked). The null distribution and the type I error rates of the new statistic for testing interaction are validated using simulations. Extensive power studies show that the developed statistic has much higher power to detect interaction than classical logistic regression. The results identified 44 and 211 pairs of SNPs showing significant evidence of interactions with FDR<0.001 and 0.001<FDR<0.003, respectively, which were seen in two independent studies of psoriasis. These included five interacting pairs of SNPs in genes LST1/NCR3, CXCR5/BCL9L, and GLS2, some of which were located in the target sites of miR-324-3p, miR-433, and miR-382, as well as 15 pairs of interacting SNPs that had nonsynonymous substitutions. Our results demonstrated that genome-wide interaction analysis is a valuable tool for finding remaining missing heritability unexplained by the current GWAS, and the developed novel statistic is able to search significant interaction between SNPs across the genome. Real data analysis showed that the results of genome-wide interaction analysis can be replicated in two independent studies. It is expected that genome-wide interaction analysis can be a possible source of finding heritability unexplained by current GWAS. However, the existing statistics for testing interaction have low power for genome-wide interaction analysis. To meet challenges raised by genome-wide interactional analysis, we develop a novel statistic for testing interaction between two loci (either linked or unlinked) and validate the null distribution and the type I error rates of the new statistic through simulations. By extensive power studies we show that the developed novel statistic has much higher power to detect interaction than the classical logistic regression. To provide evidence of gene–gene interactions as a possible source of the missing heritability unexplained by the current GWAS, we performed the genome-wide interaction analysis of psoriasis in two independent studies. The preliminary results identified 44 and 211 pairs of SNPs showing significant evidence of interactions with FDR<0.001 and 0.001<FDR<0.003, respectively, which were common in two independent studies. These included five interacting pairs of SNPs, some of which were located in the target sites: LST1/NCR3, CXCR5/BCL9L and GLS2 of miR-324-3p, miR-433, and miR-382, and 15 pairs of interacting SNPs that had nonsynonymous substitutions.

Background: Vascular smooth muscle cells (SMCs) synthesize extracellular matrix (ECM) that contributes to tissue remodeling after revascularization interventions. The cytokine transforming growth factor β (TGF-β) is induced on tissue injury and regulates tissue remodeling and wound healing, but dysregulated signaling results in excess ECM deposition and fibrosis. The LIM (Lin11, Isl-1 & Mec-3) domain protein LIM domain only 7 (LMO7) is a TGF-β1 target gene in hepatoma cells, but its role in vascular physiology and fibrosis is unknown. Methods: We use carotid ligation and femoral artery denudation models in mice with global or inducible smooth muscle–specific deletion of LMO7, and knockout, knockdown, overexpression, and mutagenesis approaches in mouse and human SMC, and human arteriovenous fistula and cardiac allograft vasculopathy samples to assess the role of LMO7 in neointima and fibrosis. Results: We demonstrate that LMO7 is induced postinjury and by TGF-β in SMC in vitro. Global or SMC-specific LMO7 deletion enhanced neointimal formation, TGF-β signaling, ECM deposition, and proliferation in vascular injury models. LMO7 loss of function in human and mouse SMC enhanced ECM protein expression at baseline and after TGF-β treatment. TGF-β neutralization or receptor antagonism prevented the exacerbated neointimal formation and ECM synthesis conferred by loss of LMO7. Notably, loss of LMO7 coordinately amplified TGF-β signaling by inducing expression of Tgfb1 mRNA, TGF-β protein, αv and β3 integrins that promote activation of latent TGF-β, and downstream effectors SMAD3 phosphorylation and connective tissue growth factor. Mechanistically, the LMO7 LIM domain interacts with activator protein 1 transcription factor subunits c-FOS and c-JUN and promotes their ubiquitination and degradation, disrupting activator protein 1–dependent TGF-β autoinduction. Importantly, preliminary studies suggest that LMO7 is upregulated in human intimal hyperplastic arteriovenous fistula and cardiac allograft vasculopathy samples, and inversely correlates with SMAD3 phosphorylation in cardiac allograft vasculopathy. Conclusions: LMO7 is induced by TGF-β and serves to limit vascular fibrotic responses through negative feedback regulation of the TGF-β pathway. This mechanism has important implications for intimal hyperplasia, wound healing, and fibrotic diseases.