Genome-wide association (GWA) studies have found many important genetic variants that affect various traits. Since these studies are useful to investigate untyped but causal variants using linkage disequilibrium (LD), it would be useful to explore the haplotypes of single-nucleotide polymorphisms (SNPs) within the same LD block of significant associations based on high-density variants from population references. Here, we tried to make a haplotype catalog affecting body mass index (BMI) through an integrative analysis of previously published whole-genome next-generation sequencing (NGS) data of 7 representative Korean individuals and previously known Korean GWA signals. We selected 435 SNPs that were significantly associated with BMI from the GWA analysis and searched 53 LD ranges nearby those SNPs. With the NGS data, the haplotypes were phased within the LDs. A total of 44 possible haplotype blocks for Korean BMI were cataloged. Although the current result constitutes little data, this study provides new insights that may help to identify important haplotypes for traits and low variants nearby significant SNPs. Furthermore, we can build a more comprehensive catalog as a larger dataset becomes available.
Body Mass Index* ; Dataset ; Genome-Wide Association Study ; Haplotypes* ; Korea ; Linkage Disequilibrium ; Polymorphism, Single Nucleotide

The alteration of alternative splicing patterns has an effect on the quantification of functional proteins, leading to phenotype variation. The splicing quantitative trait locus (sQTL) is one of the main genetic elements affecting splicing patterns. Here, we report the results of genome-wide sQTLs across 141 strains of Arabidopsis thaliana with publicly available next generation sequencing datasets. As a result, we found 1,694 candidate sQTLs in Arabidopsis thaliana at a false discovery rate of 0.01. Furthermore, among the candidate sQTLs, we found 25 sQTLs that overlapped with the list of previously examined trait-associated single nucleotide polymorphisms (SNPs). In summary, this sQTL analysis provides new insight into genetic elements affecting alternative splicing patterns in Arabidopsis thaliana and the mechanism of previously reported trait-associated SNPs.
Alternative Splicing ; Arabidopsis* ; Dataset ; Phenotype ; Polymorphism, Single Nucleotide ; Quantitative Trait Loci*

Type 2 diabetes mellitus is a complex metabolic disorder associated with multiple genetic, developmental and environmental factors. The recent advances in gene expression microarray technologies as well as network-based analysis methodologies provide groundbreaking opportunities to study type 2 diabetes mellitus. In the present study, we used previously published gene expression microarray datasets of human skeletal muscle samples collected from 20 insulin sensitive individuals before and after insulin treatment in order to construct insulin-mediated regulatory network. Based on a motif discovery method implemented by iRegulon, a Cytoscape app, we identified 25 candidate regulons, motifs of which were enriched among the promoters of 478 up-regulated genes and 82 down-regulated genes. We then looked for a hierarchical network of the candidate regulators, in such a way that the conditional combination of their expression changes may explain those of their target genes. Using Genomica, a software tool for regulatory network construction, we obtained a hierarchical network of eight regulons that were used to map insulin downstream signaling network. Taken together, the results illustrate the benefits of combining completely different methods such as motif-based regulatory factor discovery and expression level-based construction of regulatory network of their target genes in understanding insulin induced biological processes and signaling pathways.
Biological Processes ; Dataset ; Diabetes Mellitus, Type 2 ; Gene Expression* ; Humans ; Insulin ; Methods* ; Muscle, Skeletal ; Regulon ; Transcription Factors*

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.
Cell Line ; Disease Susceptibility ; DNA ; Gene Expression Regulation ; Metagenomics ; Molecular Epidemiology ; Phenotype ; Quantitative Trait Loci ; Transcriptome

Purpose: Alzheimer disease (AD) is one of the most complex diseases and is characterized by AD-related neuropathological features, including accumulation of amyloid-β plaques and tau neurofibrillary tangles. Dysregulation of alternative splicing (AS) contributes to these features, and there is heterogeneity in features across brain regions between AD patients, leading to different severity and progression rates; however, brain region-specific AS mechanisms still remain unclear. Therefore, we aimed to systemically investigate AS in multiple brain regions of AD patients and how they affect clinical features. Methods: We analyzed RNA sequencing (RNA-Seq) data obtained from brain regions (frontal and temporal) of AD patients. Reads were mapped to the hg19 reference genome using the STAR aligner, and exon skipping (ES) rates were estimated as percent spliced in (PSI) by rMATs. We focused on AD-risk genes discovered by genome-wide association studies, and accordingly evaluated associations between PSI of skipped exons in AD-risk genes and Braak stage and plaque density mean (PM) for each brain region. We also integrated whole-genome sequencing data of the ascertained samples with RNA-Seq data to identify genetic regulators of feature-associated ES. Results: We identified 26 and 41 ES associated with Braak stage in frontal and temporal regions, respectively, and 10 and 50 ES associated with PM. Among those, 10 were frontal-specific (CLU and NTRK2), 65 temporal-specific (HIF1A and TRPC4AP), and 26 shared ES (APP) that accompanied functional Gene Ontology terms, including axonogenesis in shared-ES genes. We further identified genetic regulators that account for 44 ES (44% of the total). Finally, we present as a case study the systematic regulation of an ES in APP, which is important in AD pathogenesis. Conclusions This study provides new insights into brain region-dependent AS regulation of the architecture of AD-risk genes that contributes to AD pathologies, ultimately allowing identification of a treatment target and region-specific biomarkers for AD.