Genetic associations of common deletion polymorphisms in families with Avellino corneal dystrophy.

Although the locations of many common deletion variants in the human genome are unknown, such deletions may be causative in rare disorders. Deletions can be mapped through the identification of Mendelian inconsistencies in pedigrees. Data for a total of 341,577 SNPs from an ACD family cohort (n=551) and 341,039 SNPs from a Korean-Vietnamese family cohort (n=554) were collected for a genome-wide association study using Illumina 370K-Duo Beadchips((R)). In the present study, a Mendelian inconsistency analysis of genotype data identified 1029 deletion variants in Korean and Korean-Vietnam family cohorts of 404 trios comprising 1105 individuals. Small-deletion copy number variations adjacent to 10 deletion variants were then validated by the real-time quantitative polymerase chain reaction. The expected copy numbers of each deletion variant were directly matched to its genotype cluster image. Deletion variants were also in strong linkage disequilibrium with nearby SNPs. To determine the overall contribution of the 1029 deletion variants, we analyzed case-control trio associations with the risk for Avellino corneal dystrophy. One SNP marker (rs885945) neighboring the gene encoding major histocompatibility complex class I F (HLA-F) was significantly associated with the risk of Avellino corneal dystrophy (P=0.0003). rs885945 showed high LD with SNPs within the HLA-F gene. Therefore, HLA-F may be a potential candidate gene for Avellino corneal dystrophy.

KAREBrowser: SNP database of Korea Association REsource Project.

The International HapMap Project and the Human Genome Diversity Project (HGDP) provide plentiful resources on human genome information to the public. However, this kind of information is limited because of the small sample size in both databases. A Genome-Wide Association Study has been conducted with 8,842 Korean subjects as a part of the Korea Association Resource (KARE) project. In an effort to build a publicly available browsing system for genome data resulted from large scale KARE GWAS, we developed the KARE browser. This browser provides users with a large amount of single nucleotide polymorphisms (SNPs) information comprising 1.5 million SNPs from population-based cohorts of 8,842 samples. KAREBrowser was based on the generic genome browser (GBrowse), a webbased application tool developed for users to navigate and visualize the genomic features and annotations in an interactive manner. All SNP information and related functions are available at the web site http://ksnp.cdc. go.kr/karebrowser/.

BioSMACK: a linux live CD for genome-wide association analyses.

Recent advances in high-throughput genotyping technologies have enabled us to conduct a genome-wide association study (GWAS) on a large cohort. However, analyzing millions of single nucleotide polymorphisms (SNPs) is still a difficult task for researchers conducting a GWAS. Several difficulties such as compatibilities and dependencies are often encountered by researchers using analytical tools, during the installation of software. This is a huge obstacle to any research institute without computing facilities and specialists. Therefore, a proper research environment is an urgent need for researchers working on GWAS. We developed BioSMACK to provide a research environment for GWAS that requires no configuration and is easy to use. BioSMACK is based on the Ubuntu Live CD that offers a complete Linux-based operating system environment without installation. Moreover, we provide users with a GWAS manual consisting of a series of guidelines for GWAS and useful examples. BioSMACK is freely available at http://ksnp.cdc. go.kr/biosmack.

Data-driven approach to detect common copy-number variations and frequency profiles in a population-based Korean cohort.

To date, hundreds of thousands of copy-number variation (CNV) data have been reported using various platforms. The proportion of Asians in these data is, however, relatively small as compared with that of other ethnic groups, such as Caucasians and Yorubas. Because of limitations in platform resolution and the high noise level in signal intensity, in most CNV studies (particularly those using single nucleotide polymorphism arrays), the average number of CNVs in an individual is less than the number of known CNVs. In this study, we ascertained reliable, common CNV regions (CNVRs) and identified actual frequency rates in the Korean population to provide more CNV information. We performed two-stage analyses for detecting structural variations with two platforms. We discovered 576 common CNVRs (88 CNV segments on average in an individual), and 87% (501 of 576) of these CNVRs overlapped by ≥1 bp with previously validated CNV events. Interestingly, from the frequency analysis of CNV profiles, 52 of 576 CNVRs had a frequency rate of <1% in the 8842 individuals. Compared with other common CNV studies, this study found six common CNVRs that were not reported in previous CNV studies. In conclusion, we propose the data-driven detection approach to discover common CNVRs including those of unreported in the previous Korean CNV study while minimizing false positives. Through our approach, we successfully discovered more common CNVRs than previous Korean CNV study and conducted frequency analysis. These results will be a valuable resource for the effective level of CNVs in the Korean population.

Large-scale genome-wide association studies in East Asians identify new genetic loci influencing metabolic traits.

To identify the genetic bases for nine metabolic traits, we conducted a meta-analysis combining Korean genome-wide association results from the KARE project (n = 8,842) and the HEXA shared control study (n = 3,703). We verified the associations of the loci selected from the discovery meta-analysis in the replication stage (30,395 individuals from the BioBank Japan genome-wide association study and individuals comprising the Health2 and Shanghai Jiao Tong University Diabetes cohorts). We identified ten genome-wide significant signals newly associated with traits from an overall meta-analysis. The most compelling associations involved 12q24.11 (near MYL2) and 12q24.13 (in C12orf51) for high-density lipoprotein cholesterol, 2p21 (near SIX2-SIX3) for fasting plasma glucose, 19q13.33 (in RPS11) and 6q22.33 (in RSPO3) for renal traits, and 12q24.11 (near MYL2), 12q24.13 (in C12orf51 and near OAS1), 4q31.22 (in ZNF827) and 7q11.23 (near TBL2-BCL7B) for hepatic traits. These findings highlight previously unknown biological pathways for metabolic traits investigated in this study.