Jin Ok Yang

Mapping Human Genetic Diversity in Asia

Patterns of Early Migration In order to gain insight into various migrations that must have happened during movement of early humans into Asia and the subsequent populating of the largest continent on Earth, the HUGO Pan-Asian SNP Consortium (p. 1541) analyzed genetic variation in almost 2000 individuals representing 73 Asian and two non-Asian populations. The results suggest that there may have been a single major migration of people into Asia and a subsequent south-to-north migration across the continent. While most populations from the same linguistic group tend to cluster together in terms of relatedness, several do not, clustering instead with their geographic neighbors, suggesting either substantial recent mixing among the populations or language replacement. Furthermore, data from indigenous Taiwanese populations appear to be inconsistent with the idea of a Taiwan homeland for Austronesian populations. Genetic analyses of Asian peoples suggest that the continent was populated through a single migration event. Asia harbors substantial cultural and linguistic diversity, but the geographic structure of genetic variation across the continent remains enigmatic. Here we report a large-scale survey of autosomal variation from a broad geographic sample of Asian human populations. Our results show that genetic ancestry is strongly correlated with linguistic affiliations as well as geography. Most populations show relatedness within ethnic/linguistic groups, despite prevalent gene flow among populations. More than 90% of East Asian (EA) haplotypes could be found in either Southeast Asian (SEA) or Central-South Asian (CSA) populations and show clinal structure with haplotype diversity decreasing from south to north. Furthermore, 50% of EA haplotypes were found in SEA only and 5% were found in CSA only, indicating that SEA was a major geographic source of EA populations.

A High-Dimensional, Deep-Sequencing Study of Lung Adenocarcinoma in Female Never-Smokers

Background Deep sequencing techniques provide a remarkable opportunity for comprehensive understanding of tumorigenesis at the molecular level. As omics studies become popular, integrative approaches need to be developed to move from a simple cataloguing of mutations and changes in gene expression to dissecting the molecular nature of carcinogenesis at the systemic level and understanding the complex networks that lead to cancer development. Results Here, we describe a high-throughput, multi-dimensional sequencing study of primary lung adenocarcinoma tumors and adjacent normal tissues of six Korean female never-smoker patients. Our data encompass results from exome-seq, RNA-seq, small RNA-seq, and MeDIP-seq. We identified and validated novel genetic aberrations, including 47 somatic mutations and 19 fusion transcripts. One of the fusions involves the c-RET gene, which was recently reported to form fusion genes that may function as drivers of carcinogenesis in lung cancer patients. We also characterized gene expression profiles, which we integrated with genomic aberrations and gene regulations into functional networks. The most prominent gene network module that emerged indicates that disturbances in G2/M transition and mitotic progression are causally linked to tumorigenesis in these patients. Also, results from the analysis strongly suggest that several novel microRNA-target interactions represent key regulatory elements of the gene network. Conclusions Our study not only provides an overview of the alterations occurring in lung adenocarcinoma at multiple levels from genome to transcriptome and epigenome, but also offers a model for integrative genomics analysis and proposes potential target pathways for the control of lung adenocarcinoma.

PanSNPdb: The Pan-Asian SNP Genotyping Database

The HUGO Pan-Asian SNP consortium conducted the largest survey to date of human genetic diversity among Asians by sampling 1,719 unrelated individuals among 71 populations from China, India, Indonesia, Japan, Malaysia, the Philippines, Singapore, South Korea, Taiwan, and Thailand. We have constructed a database (PanSNPdb), which contains these data and various new analyses of them. PanSNPdb is a research resource in the analysis of the population structure of Asian peoples, including linkage disequilibrium patterns, haplotype distributions, and copy number variations. Furthermore, PanSNPdb provides an interactive comparison with other SNP and CNV databases, including HapMap3, JSNP, dbSNP and DGV and thus provides a comprehensive resource of human genetic diversity. The information is accessible via a widely accepted graphical interface used in many genetic variation databases. Unrestricted access to PanSNPdb and any associated files is available at: http://www4a.biotec.or.th/PASNP.

Programming of Plant Leaf Senescence with Temporal and Inter-Organellar Coordination of Transcriptome in Arabidopsis.

RNA-seq analysis of total and small RNAs throughout the lifespan of Arabidopsis leaves revealed that leaf senescence proceeds with tight temporal and distinctive inter-organellar coordination of transcriptomes. Plant leaves, harvesting light energy and fixing CO2, are a major source of foods on the earth. Leaves undergo developmental and physiological shifts during their lifespan, ending with senescence and death. We characterized the key regulatory features of the leaf transcriptome during aging by analyzing total- and small-RNA transcriptomes throughout the lifespan of Arabidopsis (Arabidopsis thaliana) leaves at multidimensions, including age, RNA-type, and organelle. Intriguingly, senescing leaves showed more coordinated temporal changes in transcriptomes than growing leaves, with sophisticated regulatory networks comprising transcription factors and diverse small regulatory RNAs. The chloroplast transcriptome, but not the mitochondrial transcriptome, showed major changes during leaf aging, with a strongly shared expression pattern of nuclear transcripts encoding chloroplast-targeted proteins. Thus, unlike animal aging, leaf senescence proceeds with tight temporal and distinct interorganellar coordination of various transcriptomes that would be critical for the highly regulated degeneration and nutrient recycling contributing to plant fitness and productivity.

ssSNPTarget: genome-wide splice-site Single Nucleotide Polymorphism database.

Deep sequencing has shown that over 90% of human genes undergo alternative splicing. The splicing process requires exon‐intron boundary recognition. SNPs located in the boundaries (splice sites) influence exon configuration. Also, splice site SNPs (ssSNPs) alter translation efficiency of the mRNA and lead to important changes in disease susceptibility. We developed the ssSNPTarget database to provide ssSNPs on human and mouse genes. It includes: 1) ssSNP distribution information in human and mouse genes; 2) effects of SNPs in splice sites: junction strength change, protein domain change, and alternative splicing events (exon skipping, 5′‐ or 3′‐exon extension); 3) splice site conservation in eukaryotes; and 4) associated disease information derived from OMIM, GAD, and HGMD. ssSNPTarget contains 1,576 human ssSNPs associated with 1,193 genes and 538 mouse ssSNPs associated with 281 genes. Users can query ssSNPTarget with several types of search terms (gene symbol, SNP rs number, transcript ID, or genomic position), and the information can be accessed at http://variome.kobic.re.kr/ssSNPTarget/ or http://ssSNPTarget.org.

PDbase: a database of Parkinson's Disease-related genes and genetic variation using substantia nigra ESTs

BackgroundParkinsons disease (PD) is one of the most common neurodegenerative disorders, clinically characterized by impaired motor function. Since the etiology of PD is diverse and complex, many researchers have created PD-related research resources. However, resources for brain and PD studies are still lacking. Therefore, we have constructed a database of PD-related gene and genetic variations using the substantia nigra (SN) in PD and normal tissues. In addition, we integrated PD-related information from several resources.ResultsWe collected the 6,130 SN expressed sequenced tags (ESTs) from brain SN normal tissues and PD patients SN tissues using full-cDNA library and normalized cDNA library construction methods from our previous study. The SN ESTs were clustered in 2,951 unigene clusters and assigned in 2,678 genes. We then found up-regulated 57 genes and down-regulated 48 genes by comparing normal and PD SN ESTs frequencies with over 0.9 cut-off probability of differential expression based on the Audic and Claverie method. In addition, we integrated disease-related information from public resources. To examine the characteristics of these PD-related genes, we analyzed alternative splicing events, single nucleotide polymorphism (SNP) markers located in the gene regions, repeat elements, gene regulation elements, and pathways and protein-protein interaction networks.ConclusionWe constructed the PDbase database to capture the PD-related gene, genetic variation, and functional elements. This database contains 2,698 PD-related genes through ESTs discovered from human normal and PD patients SN tissues, and through integrating several public resources. PDbase provides the mitochondrion proteins, microRNA gene regulation elements, single nucleotide polymorphisms (SNPs) markers within PD-related gene structures, repeat elements, and pathways and networks with protein-protein interaction information. The PDbase information can aid in understanding the causation of PD. It is available at http://bioportal.kobic.re.kr/PDbase/. Supplementary data is available at http://bioportal.kobic.re.kr/PDbase/suppl.jsp

An integrated database-pipeline system for studying single nucleotide polymorphisms and diseases

BackgroundStudies on the relationship between disease and genetic variations such as single nucleotide polymorphisms (SNPs) are important. Genetic variations can cause disease by influencing important biological regulation processes. Despite the needs for analyzing SNP and disease correlation, most existing databases provide information only on functional variants at specific locations on the genome, or deal with only a few genes associated with disease. There is no combined resource to widely support gene-, SNP-, and disease-related information, and to capture relationships among such data. Therefore, we developed an integrated database-pipeline system for studying SNPs and diseases.ResultsTo implement the pipeline system for the integrated database, we first unified complicated and redundant disease terms and gene names using the Unified Medical Language System (UMLS) for classification and noun modification, and the HUGO Gene Nomenclature Committee (HGNC) and NCBI gene databases. Next, we collected and integrated representative databases for three categories of information. For genes and proteins, we examined the NCBI mRNA, UniProt, UCSC Table Track and MitoDat databases. For genetic variants we used the dbSNP, JSNP, ALFRED, and HGVbase databases. For disease, we employed OMIM, GAD, and HGMD databases. The database-pipeline system provides a disease thesaurus, including genes and SNPs associated with disease. The search results for these categories are available on the web page http://diseasome.kobic.re.kr/, and a genome browser is also available to highlight findings, as well as to permit the convenient review of potentially deleterious SNPs among genes strongly associated with specific diseases and clinical phenotypes.ConclusionOur system is designed to capture the relationships between SNPs associated with disease and disease-causing genes. The integrated database-pipeline provides a list of candidate genes and SNP markers for evaluation in both epidemiological and molecular biological approaches to diseases-gene association studies. Furthermore, researchers then can decide semi-automatically the data set for association studies while considering the relationships between genetic variation and diseases. The database can also be economical for disease-association studies, as well as to facilitate an understanding of the processes which cause disease. Currently, the database contains 14,674 SNP records and 109,715 gene records associated with human diseases and it is updated at regular intervals.

VnD: a structure-centric database of disease-related SNPs and drugs

Numerous genetic variations have been found to be related to human diseases. Significant portion of those affect the drug response as well by changing the protein structure and function. Therefore, it is crucial to understand the trilateral relationship among genomic variations, diseases and drugs. We present the variations and drugs (VnD), a consolidated database containing information on diseases, related genes and genetic variations, protein structures and drug information. VnD was built in three steps. First, we integrated various resources systematically to deduce catalogs of disease-related genes, single nucleotide polymorphisms (SNPs), protein mutations and relevant drugs. VnD contains 137 195 disease-related gene records (13 940 distinct genes) and 16 586 genetic variation records (1790 distinct variations). Next, we carried out structure modeling and docking simulation for wild-type and mutant proteins to examine the structural and functional consequences of non-synonymous SNPs in the drug-related genes. Conformational changes in 590 wild-type and 4437 mutant proteins from drug-related genes were included in our database. Finally, we investigated the structural and biochemical properties relevant to drug binding such as the distribution of SNPs in proximal protein pockets, thermo-chemical stability, interactions with drugs and physico-chemical properties. The VnD database, available at http://vnd.kobic.re.kr:8080/VnD/ or vandd.org, would be a useful platform for researchers studying the underlying mechanism for association among genetic variations, diseases and drugs.

Global transcription network incorporating distal regulator binding reveals selective cooperation of cancer drivers and risk genes.

Global network modeling of distal regulatory interactions is essential in understanding the overall architecture of gene expression programs. Here, we developed a Bayesian probabilistic model and computational method for global causal network construction with breast cancer as a model. Whereas physical regulator binding was well supported by gene expression causality in general, distal elements in intragenic regions or loci distant from the target gene exhibited particularly strong functional effects. Modeling the action of long-range enhancers was critical in recovering true biological interactions with increased coverage and specificity overall and unraveling regulatory complexity underlying tumor subclasses and drug responses in particular. Transcriptional cancer drivers and risk genes were discovered based on the network analysis of somatic and genetic cancer-related DNA variants. Notably, we observed that the risk genes were functionally downstream of the cancer drivers and were selectively susceptible to network perturbation by tumorigenic changes in their upstream drivers. Furthermore, cancer risk alleles tended to increase the susceptibility of the transcription of their associated genes. These findings suggest that transcriptional cancer drivers selectively induce a combinatorial misregulation of downstream risk genes, and that genetic risk factors, mostly residing in distal regulatory regions, increase transcriptional susceptibility to upstream cancer-driving somatic changes.

Inference of kinship coefficients from Korean SNP genotyping data.

The determination of relatedness between individuals in a family is crucial in analysis of common complex diseases. We present a method to infer close inter-familial relationships based on SNP genotyping data and provide the relationship coefficient of kinship in Korean families. We obtained blood samples from 43 Korean individuals in two families. SNP data was obtained using the Affymetrix Genome-wide Human SNP array 6.0 and the Illumina Human 1M-Duo chip. To measure the kinship coefficient with the SNP genotyping data, we considered all possible pairs of individuals in each family. The genetic distance between two individuals in a pair was determined using the allele sharing distance method. The results show that genetic distance is proportional to the kinship coefficient and that a close degree of kinship can be confirmed with SNP genotyping data. This study represents the first attempt to identify the genetic distance between very closely related individuals. [BMB Reports 2013; 46(6): 305-309]