ABSTRACT
Epigenetic changes are potentially important for the ontogeny and progression of tumors but are not usually studied because of the complexity of analyzing transcript regulation resulting from epigenetic alterations. Prostate cancer (PCa) is characterized by variable clinical manifestations and frequently unpredictable outcomes. We performed an expression quantitative trait loci (eQTL) analysis to identify the genomic regions that regulate gene expression in PCa and identified a relationship between DNA methylation and clinical information. Using multi-level information published in The Cancer Genome Atlas, we performed eQTL-based analyses on DNA methylation and gene expression. To better interpret these data, we correlated loci and clinical indexes to identify the important loci for both PCa development and progression. Our data demonstrated that although only a small proportion of genes are regulated via DNA methylation in PCa, these genes are enriched in important cancer-related groups. In addition, single nucleotide polymorphism analysis identified the locations of CpG sites and genes within at-risk loci, including the 19q13.2-q13.43 and 16q22.2-q23.1 loci. Further, an epigenetic association study of clinical indexes detected risk loci and pyrosequencing for site validation. Although DNA methylation-regulated genes across PCa samples are a small proportion, the associated genes play important roles in PCa carcinogenesis.
ABSTRACT
Objective: To investigate the association between early growth response gene 1 (EGR1) and Alzheimer's disease (AD) in Han Chinese people. Methods: A total of 715 AD patients and 760 health controls were recruited in two independent samples from Eastern China (382 AD patients and 426 normal individuals) and Southwest China (333 AD patients and 334 normal individuals). SNaPshot technique was utilized to analyse the single nucleotide polymorphism (SNP) of rs11743810. A public database was used to explore whether EGR1 gene was differentially expressed in the brain of AD patients and health controls. Then the protein-protein interaction (PPI) assessment was conducted using the STRING database, and the brain eQTL (expression quantitative trait loci) analysis was used to explore the difference in rs11743810 expression between different genotypes in different brain regions. Results: Cross-platform normalized data showed that there was significant difference of EGR1 expression in temporal cortex between AD patients and control subjects (|log2FC|=0.780, P=0.000 before FDR corrected; P=0.001 after FDR corrected). PPI analysis revealed that EGR1 was physically connected with amyloid precursor protein (APP) and clusterin (CLU) protein in the network. However, different genotypes of rs11743810 showed no significant difference in expression in 10 brain regions, and no significant difference in the genotype and allele frequency of rs11743810 between AD patients and controls were found in our two independent samples. Conclusion: The rs11743810 in EGR1 may not be major susceptibility gene site for AD in Han Chinese people.
ABSTRACT
In the era of genome-wide association study (GWAS), a large number of drug response-related loci have been identified in the non-coding sequences. The interpretation of these loci in mechanism is concerned with the effects on the mRNA expression level of these genes. Expression quantitative trait loci (eQTL) studies indicate the relationship of genome variants and the level of mRNA. Its elucidation of the relationship between genetic variation and gene expression, gene interaction and gene regulatory network provides an efficacious mean for pharmacogenomics. The effects of gene polymorphism on drug responses have been unraveled thoroughly in studies which combined pharmacogenomics with eQTL and GWAS.
ABSTRACT
Metabolic syndrome (METS) is a disorder of energy utilization and storage and increases the risk of developing cardiovascular disease and diabetes. To identify the genetic risk factors of METS, we carried out a genome-wide association study (GWAS) for 2,657 cases and 5,917 controls in Korean populations. As a result, we could identify 2 single nucleotide polymorphisms (SNPs) with genome-wide significance level p-values (<5 x 10(-8)), 8 SNPs with genome-wide suggestive p-values (5 x 10(-8) < or = p < 1 x 10(-5)), and 2 SNPs of more functional variants with borderline p-values (5 x 10(-5) < or = p < 1 x 10(-4)). On the other hand, the multiple correction criteria of conventional GWASs exclude false-positive loci, but simultaneously, they discard many true-positive loci. To reconsider the discarded true-positive loci, we attempted to include the functional variants (nonsynonymous SNPs [nsSNPs] and expression quantitative trait loci [eQTL]) among the top 5,000 SNPs based on the proportion of phenotypic variance explained by genotypic variance. In total, 159 eQTLs and 18 nsSNPs were presented in the top 5,000 SNPs. Although they should be replicated in other independent populations, 6 eQTLs and 2 nsSNP loci were located in the molecular pathways of LPL, APOA5, and CHRM2, which were the significant or suggestive loci in the METS GWAS. Conclusively, our approach using the conventional GWAS, reconsidering functional variants and pathway-based interpretation, suggests a useful method to understand the GWAS results of complex traits and can be expanded in other genomewide association studies.
Subject(s)
Cardiovascular Diseases , Genome-Wide Association Study , Hand , Metabolic Networks and Pathways , Polymorphism, Single Nucleotide , Quantitative Trait Loci , Risk FactorsABSTRACT
Genetic epidemiology studies have established that the natural variation of gene expression profiles is heritable and has genetic bases. A number of proximal and remote DNA variations, known as expression quantitative trait loci (eQTLs), that are associated with the expression phenotypes have been identified, first in Epstein-Barr virus-transformed lymphoblastoid cell lines and later expanded to other cell and tissue types. Integration of the eQTL information and the network analysis of transcription modules may lead to a better understanding of gene expression regulation. As these network modules have relevance to biological or disease pathways, these findings may be useful in predicting disease susceptibility.