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Introduction: Genome wide association studies (GWAS) have been widely used in recent years to identify new information on genetic variants which are associated with complex trait in many diseases. Advances in identifying the Single Nucleotide Polymorphisms (SNPs) facilitate the study of etiologies of common disorders including cancers, inflammatory bowel diseases (IBD) and colorectal cancer. Variations in gene expression demonstrate that transcript levels of many RNAs behave as heritable quantitative traits. Studying the genetics of gene expression can provide additional power to the roles of GWAS variants. Expression quantitative trait loci (eQTL) mapping links the genome-wide SNPs with RNA expression.Methods: In this study, we performed expression quantitative trait loci (eQTL) analysis using the Matrix eQTL R package. This technique implements matrix covariance calculation and efficiently runs ANOVA and linear regression analysis for eQTL studies. The statistical test determines the association between SNP and gene expression, where the null hypothesis is no association between genotype and phenotypes. False Discovery Rate (FDR) is used to identify significant cis and trans eQTL and adjust for multiple hypothesis testing. Results:We applied matrix eQTL to a real data set consisting of 730,256 SNP and 33,298 RNA for 173 samples. SNPs with minor allele frequency (MAF) less than 0.05 and those violating the Hardy_Weinberg equilibrium (HWE), were excluded from the study. In this study, 15,408 cis eQTL and 27,562 trans eQTL are identified at a FDR less than 0.05, corresponding to p value thresholds of 8e-5 and 1e-8, respectively. Conclusion:We found out that matrix eQTL is a computationally efficient and user friendly method for analysis of eQTL studies. Our application provides insight into the genomic architecture of gene regulation in inflammatory bowel disease (IBD).

Background Studying the effect of primary health care development when simultaneously implemented with health insurance schemes assesses effectiveness and use of health care services and gives us insight on how to develop such interventions in different countries. Aim To analyze the impact of health insurance and the family physician program on total hospitalizations, and the relation between avoidable hospitalizations and access to family physicians among the rural population in Iran. Methods We conducted an interrupted time series (ITS) analysis of monthly hospitalization rates between the years of 2003 and 2014 to assess the immediate and gradual effects of these reforms on total hospitalization rates in the rural areas of Tehran province. In addition, we used a sample of 22 570 hospitalizations between 2006 and 2013 to develop a logistic regression model to measure the association between access to a family physician and avoidable hospitalizations. Findings ITS analysis showed that there was an immediate increase of about 1.96 hospitalizations per 1000 inhabitants (P<0.0001, CI=1.58, 2.34) hospitalization rates after the reforms. This was followed by a significant increase of about 0.089 per 1000 inhabitants (P<0.0001, CI=0.07, 0.1). Hospitalization increase continued up to four years after the policy implementation. Following that, hospitalization rates decreased among the rural population (a decrease of 0.066 per 1000, P<0.0001, CI=−0.084, −0.048). Studying the hospitalizations that occurred between 2006 and 2013 showed that there were 4106 avoidable hospitalizations from among a sample of 22 570 hospitalizations. Results of logistic regression models including gender, age and access to family physician variables showed that there was no statistical relation between access to a family physician and avoidable hospitalizations. Conclusion Reforms had access effect and caused increased hospital services uses in people with unmet needs. Also the reforms did not decrease avoidable hospitalizations, and therefore had no efficiency effect.