MicroRNAs play an important role in regulating gene expression, but their target identification is a difficult task due to their short length and imperfect complementarity. Burge and coworkers developed a program called TargetScan that allowed imperfect complementarity and established a procedure favoring targets with multiple binding sites conserved in multiple organisms. We improved their algorithm in two major aspects - (i) using well-defined UTR (untranslated region) database, (ii) examining the extent of conservation inside the 3' UTR specifically. Average length in our UTR database, based on the ECgene annotation, is more than twice longer than the Ensembl. Then, TargetScan was used to identify putative binding sites. The extent of conservation varies significantly inside the 3' UTR. We used the "tight" tracks in the UCSC genome browser to select the conserved binding sites in multiple species. By combining the longer 3' UTR data, TargetScan, and tightly conserved blocks of genomic DNA, we identified 107 putative target genes with multiple binding sites conserved in multiple species, of which 85 putative targets are novel.
3' Untranslated Regions* ; Binding Sites ; DNA ; Gene Expression ; Genome ; Genomics* ; Methods ; MicroRNAs*

To learn the differences between the structure-activity relationship and molecular vibration-activity relationship in the ligand-receptor interaction of the histamine receptor, 47 ligands of the histamine receptor were analyzed by structural similarity and molecular vibrational frequency patterns. The radial tree that was produced by clustering analysis of molecular vibrational frequency patterns shows its potential for the functional classification of histamine receptor ligands.
Histamine ; Ligands ; Receptors, Histamine ; Structure-Activity Relationship

Gene set analysis (GSA) is useful in interpreting a genome-wide association study (GWAS) result in terms of biological mechanism. We compared the performance of two different GSA implementations that accept GWAS p-values of single nucleotide polymorphisms (SNPs) or gene-by-gene summaries thereof, GSA-SNP and i-GSEA4GWAS, under the same settings of inputs and parameters. GSA runs were made with two sets of p-values from a Korean type 2 diabetes mellitus GWAS study: 259,188 and 1,152,947 SNPs of the original and imputed genotype datasets, respectively. When Gene Ontology terms were used as gene sets, i-GSEA4GWAS produced 283 and 1,070 hits for the unimputed and imputed datasets, respectively. On the other hand, GSA-SNP reported 94 and 38 hits, respectively, for both datasets. Similar, but to a lesser degree, trends were observed with Kyoto Encyclopedia of Genes and Genomes (KEGG) gene sets as well. The huge number of hits by i-GSEA4GWAS for the imputed dataset was probably an artifact due to the scaling step in the algorithm. The decrease in hits by GSA-SNP for the imputed dataset may be due to the fact that it relies on Z-statistics, which is sensitive to variations in the background level of associations. Judicious evaluation of the GSA outcomes, perhaps based on multiple programs, is recommended.
Artifacts ; Diabetes Mellitus, Type 2 ; Genome ; Genome-Wide Association Study ; Genotype ; Hand ; Polymorphism, Single Nucleotide

A sample size with sufficient statistical power is critical to the success of genetic association studies to detect causal genes of human complex diseases. Genome-wide association studies require much larger sample sizes to achieve an adequate statistical power. We estimated the statistical power with increasing numbers of markers analyzed and compared the sample sizes that were required in case-control studies and case-parent studies. We computed the effective sample size and statistical power using Genetic Power Calculator. An analysis using a larger number of markers requires a larger sample size. Testing a single-nucleotide polymorphism (SNP) marker requires 248 cases, while testing 500,000 SNPs and 1 million markers requires 1,206 cases and 1,255 cases, respectively, under the assumption of an odds ratio of 2, 5% disease prevalence, 5% minor allele frequency, complete linkage disequilibrium (LD), 1:1 case/control ratio, and a 5% error rate in an allelic test. Under a dominant model, a smaller sample size is required to achieve 80% power than other genetic models. We found that a much lower sample size was required with a strong effect size, common SNP, and increased LD. In addition, studying a common disease in a case-control study of a 1:4 case-control ratio is one way to achieve higher statistical power. We also found that case-parent studies require more samples than case-control studies. Although we have not covered all plausible cases in study design, the estimates of sample size and statistical power computed under various assumptions in this study may be useful to determine the sample size in designing a population-based genetic association study.
Case-Control Studies ; Gene Frequency ; Genetic Association Studies ; Genome-Wide Association Study ; Humans ; Linkage Disequilibrium ; Models, Genetic ; Odds Ratio ; Polymorphism, Single Nucleotide ; Prevalence ; Sample Size

This erratum is being published to correct the author's affiliation.

Structural information has been a major concern for biological and pharmaceutical studies for its intimate relationship to the function of a protein. Three-dimensional representation of the positions of protein atoms is utilized among many structural information repositories that have been published. The reliability of the torsional system, which represents the native processes of structural change in the structural analysis, was partially proven with previous structural alignment studies. Here, a web server providing structural information and analysis based on the backbone torsional representation of a protein structure is newly introduced. The web server offers functions of secondary structure database search, secondary structure calculation, and pair-wise protein structure comparison, based on a backbone torsion angle representation system. Application of the implementation in pair-wise structural alignment showed highly accurate results. The information derived from this web server might be further utilized in the field of ab initio protein structure modeling or protein homology-related analyses.
Databases, Protein ; Protein Structure, Secondary

Liver enzyme elevations, as an indicator of liver function, are widely associated with metabolic diseases. Genome-wide population-based association studies have identified a genetic susceptibility to liver enzyme elevations and their related traits; however, the genetic architecture in childhood remains largely unknown. We performed a genome-wide association study to identify new genetic loci for liver enzyme levels in a Korean childhood cohort (n = 484). We observed three novel loci (rs4949718, rs80311637, and rs596406) that were multiply associated with elevated levels of alanine transaminase and aspartate transaminase. Although there are some limitations, including genetic power, additional replication and functional characterization will support the clarity on the genetic contribution that the ST6GALNAC3, ADAMTS9, and CELF2 genes have in childhood liver function.
Alanine Transaminase ; Aspartate Aminotransferases ; Child ; Cohort Studies ; Genetic Loci ; Genetic Predisposition to Disease ; Genome-Wide Association Study ; Humans ; Liver ; Metabolic Diseases

SINE-VNTR-Alu (SVA) elements are present in hominoid primates and are divided into 6 subfamilies (SVA-A to SVA-F) and active in the human population. Using a bioinformatic tool, 22 SVA element-associated genes are identified in the human genome. In an analysis of genomic structure, SVA elements are detected in the 5' untranslated region (UTR) of HGSNAT (SVA-B), MRGPRX3 (SVA-D), HYAL1 (SVA-F), TCHH (SVA-F), and ATXN2L (SVA-F) genes, while some elements are observed in the 3'UTR of SPICE1 (SVA-B), TDRKH (SVA-C), GOSR1 (SVA-D), BBS5 (SVA-D), NEK5 (SVA-D), ABHD2 (SVA-F), C1QTNF7 (SVA-F), ORC6L (SVA-F), TMEM69 (SVA-F), and CCDC137 (SVA-F) genes. They could contribute to exon extension or supplying poly A signals. LEPR (SVA-C), ALOX5 (SVA-D), PDS5B (SVA-D), and ABCA10 (SVA-F) genes also showed alternative transcripts by SVA exonization events. Dominant expression of HYAL1_SVA appeared in lung tissues, while HYAL1_noSVA showed ubiquitous expression in various human tissues. Expression of both transcripts (TDRKH_SVA and TDRKH_noSVA) of the TDRKH gene appeared to be ubiquitous. Taken together, these data suggest that SVA elements cause transcript isoforms that contribute to modulation of gene regulation in various human tissues.
3' Untranslated Regions ; 5' Untranslated Regions ; Exons ; Gene Expression Profiling ; Genome, Human ; Genomics ; Humans ; Lung ; Organ Specificity ; Poly A ; Primates ; Protein Isoforms

Gene set analysis is a powerful tool for interpreting a genome-wide association study result and is gaining popularity these days. Comparison of the gene sets obtained for a variety of traits measured from a single genetic epidemiology dataset may give insights into the biological mechanisms underlying these traits. Based on the previously published single nucleotide polymorphism (SNP) genotype data on 8,842 individuals enrolled in the Korea Association Resource project, we performed a series of systematic genome-wide association analyses for 49 quantitative traits of basic epidemiological, anthropometric, or blood chemistry parameters. Each analysis result was subjected to subsequent gene set analyses based on Gene Ontology (GO) terms using gene set analysis software, GSA-SNP, identifying a set of GO terms significantly associated to each trait (pcorr < 0.05). Pairwise comparison of the traits in terms of the semantic similarity in their GO sets revealed surprising cases where phenotypically uncorrelated traits showed high similarity in terms of biological pathways. For example, the pH level was related to 7 other traits that showed low phenotypic correlations with it. A literature survey implies that these traits may be regulated partly by common pathways that involve neuronal or nerve systems.
Genome-Wide Association Study ; Genotype ; Hydrogen-Ion Concentration ; Korea ; Molecular Epidemiology ; Neurons ; Polymorphism, Single Nucleotide ; Semantics

Orthostatic hypotension (OH) is defined by a 20-mm Hg difference of systolic blood pressure (dtSBP) and/or a 10-mm Hg difference of diastolic blood pressure (dtDBP) between supine and standing, and OH is associated with a failure of the cardiovascular reflex to maintain blood pressure on standing from a supine position. To understand the underlying genetic factors for OH traits (OH, dtSBP, and dtDBP), genome-wide association studies (GWASs) using 333,651 single nucleotide polymorphisms (SNPs) were conducted separately for two population-based cohorts, Ansung (n = 3,173) and Ansan (n = 3,255). We identified 8 SNPs (5 SNPs for dtSBP and 3 SNPs for dtDBP) that were repeatedly associated in both the Ansung and Ansan cohorts and had p-values of <1 x 10(-5) in the meta-analysis. Unfortunately, the SNPs of the OH case control GWAS did not pass our p-value criteria. Four of 8 SNPs were located in the intergenic region of chromosome 2, and the nearest gene (CTNNA2) was located at 1 Mb of distance. CTNNA2 is a linker between cadherin adhesion receptors and the actin cytoskeleton and is essential for stabilizing dendritic spines in rodent hippocampal neurons. Although there is no report about the function in blood pressure regulation, hippocampal neurons interact primarily with the autonomic nervous system and might be related to OH. The remaining SNPs, rs7098785 of dtSBP trait and rs6892553, rs16887217, and rs4959677 of dtDBP trait were located in the PIK3AP1 intron, ACTBL2-3' flanking, STAR intron, and intergenic region, respectively, but there was no clear functional link to blood pressure regulation.
Actin Cytoskeleton ; Autonomic Nervous System ; Blood Pressure ; Case-Control Studies ; Chromosomes, Human, Pair 2 ; Cohort Studies ; Dendritic Spines ; DNA, Intergenic ; Genome-Wide Association Study ; Hypotension, Orthostatic ; Introns ; Neurons ; Polymorphism, Single Nucleotide ; Reflex ; Rodentia ; Supine Position