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Functional Genomics Study

A GWAS is a method for identifying genetic variants associated with traits or diseases by screening the whole-genome data genotyped from large-scale populations. Genome-wide association studies have enhanced the finding of novel common genetic variants that can influence common diseases or traits. We have strived to systematically discover new genetic variants and to elucidate functional annotations. In the discovery stage, quality controlled genomic data were analyzed by statistical methods to detect candidate signals associated with target phenotypes. In the replication stage, follow-up studies were conducted to validate the genetic associations in independent populations [Figure2].

Genome-wide Association Study (GWAS) and its Application
Figure2. Genome-wide Assocoation Study(GWAS) and its Application

So far, population-based GWAS and meta-analyses have reported a number of genetic loci susceptible to complex diseases such as T2DM, hypertension and obesity. These genomic association studies have provided valuable insights into the genetic architecture in common diseases. However, the majority of these studies was conducted in populations of European ancestries and included only a limited number of East Asians. Therefore, identification of novel associations, validation of previous European signals, and comprehensive genetic risk assessments have not been fully evaluatedin the Korean population.
In addition to GWAS approaches, we performed meta-analyses in East Asians and trans-ethnic populations through international collaborations (e.g. AGEN consortium) to address the missing heritability by increasing the statistical power of GWAS. We have identified many Korean-specific novel loci associated with T2DM, hypertension, obesity, lipid levels, and fasting plasma glucose levels. Recently, genetic risk assessment studies have been reported by evaluating the predictive value of cumulative genetic risk scores. Therefore, we developed prediction models of missing heritability using GWAS-derived risk variants. These predictive models could provide additional insights into the understanding of the genetic role in particular diseases.

1) Association Analysis

We published several research articles on GWAS results associated with complex diseases or traits. Interestingly, we identified a novel SNP located in the Epidermal growth factor (EGF) gene gene associated with platelet count in the Korean population. Epidermal growth factors play a role in the proliferation and differentiation of hematopoietic progenitor cells. Sixteen SNPs were previously reported to be associated with 5 hematological traits (hematocrit, hemoglobin concentration, white blood cell count, red blood cell count, and platelet count) in other ethnic groups that were replicated in this study. Meta-analyses of GWASs in East Asians have suggested a number of genetic associations with traits. We have replicated 11 previous European signals associated with fasting plasma glucose(FPG) levels in East Asian samples including Korean, Chinese, Japanese, and Filipino. We have also found 3 novel association signals with FPG in the genomic regions nearby the genes PDK1-RAPGEF4, KANK1, and IGF1R. In addition, we identified 98 height loci including 17 novel and 81 previously reported loci in 93,000 East Asians populations including Koreans, Chinese, Japanese, Singaporeans, and Filipinos.
We conducted a meta-analysis of trans-ancestry GWAS and replication study of blood pressure phenotypes in up to 320,000 individuals composed of East Asian, European, and South Asian ancestries. In this study, we observed associations with blood pressure in 12 novel loci pointing to genes related to vascular smooth muscle (IGFBP3, KCNK3, PDE3A, and PRDM6) and renal functions (ARHGAP24, OSR1, SLC22A7, and TBX2). Moreover, we performed a 1000 Genomesbased GWAS meta-analysis of coronary artery disease (CAD) in up to 185,000 CAD cases and controls. We found ten novel loci containing causal genes implicating biological process in vessel walls. Meanwhile, we have also published a research article on the genotype calling and quality control of exome array data. This study contains an improved method for genotype calling and quality control to overcome the problems of automated calling method (e.g. difficulties in accurate genotype calling of low frequency or rare variants in the Korean population) by providing a practical calling method and quality control guideline for exome array data.

2) Functional Studies of Genetic Variants

To find functional roles of genetic variants in complex diseases, we performed cell-based assays and integrated genomics data with metabolomics data. Several SNPs and CNVs located in/near the genes KCNIP1, ADCK1, FITM2, and microRNA650 were selected and analyzed for their biological roles. The role of KCNIP1 variants in regulating insulin secretion and microRNA650 in inflammation signaling were validated using cell-based assays. Metabolome data was generated and analyzed from human serum to find metabolites related to T2DM. Levels of four metabolites that were significantly altered in individuals with T2DM compared to normal controls were identified by targeted metabolomics and replicated in the Kooperative Gesundheitsforschung in der Region Augsburg (KORA) study. Through an analysis to find genetic variants associated with metabolites, 18 loci were identified as having an association with four T2D-related metabolites.
Most of these loci have been previously reported in terms of their biological relevance in metabolic disease, including T2DM and obesity. Though another study aimed to elucidate metabolic changes with FTO genotype showed that seven metabolites (hexose, valine, and five glycerophospholipids) were significantly altered in obesity and T2DM based on the FTO risk allele. These identified metabolites are relevant to the phosphatidylcholine metabolic pathway, which was previously reported to be metabolic markers of obesity and T2DM. These functional studies using genomics, metabolomics, and cell-based assays will be helpful in determining the associations of genetic variants with mechanisms of disease initiation and progression.
Using GWAS is the first step in identifying genetic risk factors of diseases or traits. Validating the genetic effect of variants found through GWAS, such as through development of prediction model for diseases and functional evaluation of variants,must be conducted to obtain further insights into clinical applications of data from GWAS (e.g. pharmacogenomics). Our efforts for further investigation of GWAS will be continued in 2016.

  - GWAS(Genome-Wide Association Study) of Complex Traits and Disease