Bone mineral density (BMD) is one of the quantitative traits that are genetically inherited and affected by various factors. Over the past years, genome-wide association studies (GWASs) have searched for many genetic loci that influence BMD. A recent meta-analysis of 17 GWASs for BMD of the femoral neck and lumbar spine is the largest GWAS for BMD to date and offers 64 single-nucleotide polymorphisms (SNPs) in 56 associated loci. We investigated these BMD loci in a Korean population called Korea Association REsource (KARE) to identify their validity in an independent study. The KARE population contains genotypes from 8,842 individuals, and their BMD levels were measured at the distal radius (BMD-RT) and midshaft tibia (BMD-TT). Thirteen genomic loci among 56 loci were significantly associated with BMD variations, and 3 loci were involved in known biological pathways related to BMD. In order to find putative functional variants, nearby SNPs in relation to linkage equilibrium were annotated, and their possible functional effects were predicted. These findings reveal that tens of variants, not a single factor, may contribute to the genetic architecture of BMD; have an important role regardless of ethnic group; and may highlight the importance of a replication study in GWASs to validate genuine loci for BMD variation.
Bone Density* ; Ethnic Groups ; Femur Neck ; Follow-Up Studies* ; Genetic Loci ; Genome-Wide Association Study ; Genotype ; Humans ; Korea ; Polymorphism, Single Nucleotide ; Radius ; Spine ; Tibia ; Transcutaneous Electric Nerve Stimulation

Chronic obstructive pulmonary disease (COPD) is a type of progressive lung disease, featured by airflow obstruction. Recently, a comprehensive analysis of the transcriptome in lung tissue of COPD patients was performed, but the heterogeneity of the sample was not seriously considered in characterizing the mechanistic dysregulation of COPD. Here, we established a new transcriptome analysis pipeline using a deconvolution process to reduce the heterogeneity and clearly identified that these transcriptome data originated from the mild or moderate stage of COPD patients. Differentially expressed or co-expressed genes in the protein interaction subnetworks were linked with mitochondrial dysfunction and the immune response, as expected. Computational protein localization prediction revealed that 19 proteins showing changes in subcellular localization were mostly related to mitochondria, suggesting that mislocalization of mitochondria-targeting proteins plays an important role in COPD pathology. Our extensive evaluation of COPD transcriptome data could provide guidelines for analyzing heterogeneous gene expression profiles and classifying potential candidate genes that are responsible for the pathogenesis of COPD.
Gene Expression Profiling ; Humans ; Lung ; Lung Diseases ; Mitochondria ; Pathology ; Population Characteristics ; Pulmonary Disease, Chronic Obstructive ; Transcriptome

The explosive growth of next-generation sequencing data has resulted in ultra-large-scale datasets and ensuing computational problems. In Korea, the amount of genomic data has been increasing rapidly in the recent years. Leveraging these big data requires researchers to use large-scale computational resources and analysis pipelines. A promising solution for addressing this computational challenge is cloud computing, where CPUs, memory, storage, and programs are accessible in the form of virtual machines. Here, we present a cloud computing-based system, Bio-Express, that provides user-friendly, cost-effective analysis of massive genomic datasets. Bio-Express is loaded with predefined multi-omics data analysis pipelines, which are divided into genome, transcriptome, epigenome, and metagenome pipelines. Users can employ predefined pipelines or create a new pipeline for analyzing their own omics data. We also developed several web-based services for facilitating downstream analysis of genome data. Bio-Express web service is freely available at https://www.bioexpress.re.kr/.

The explosive growth of next-generation sequencing data has resulted in ultra-large-scale datasets and ensuing computational problems. In Korea, the amount of genomic data has been increasing rapidly in the recent years. Leveraging these big data requires researchers to use large-scale computational resources and analysis pipelines. A promising solution for addressing this computational challenge is cloud computing, where CPUs, memory, storage, and programs are accessible in the form of virtual machines. Here, we present a cloud computing-based system, Bio-Express, that provides user-friendly, cost-effective analysis of massive genomic datasets. Bio-Express is loaded with predefined multi-omics data analysis pipelines, which are divided into genome, transcriptome, epigenome, and metagenome pipelines. Users can employ predefined pipelines or create a new pipeline for analyzing their own omics data. We also developed several web-based services for facilitating downstream analysis of genome data. Bio-Express web service is freely available at https://www.bioexpress.re.kr/.