Most psychiatric disorders are some kinds of complex genetic traits. Identifying the causal genes of psychiatric disorders has been challenging. Through recent revolutionary advances, such as the HapMap Project and the development of high-throughput genotyping chips, the genome-wide association study (GWAS) has recently become possible and is now in the spotlight in psychiatric genetics. In this article, we reviewed the concepts, rationale, designs and general steps of GWAS, and also introduced a few previous GWAS of several psychiatric disorders.
Genome-Wide Association Study ; HapMap Project

Most psychiatric disorders are some kinds of complex genetic traits. Identifying the causal genes of psychiatric disorders has been challenging. Through recent revolutionary advances, such as the HapMap Project and the development of high-throughput genotyping chips, the genome-wide association study (GWAS) has recently become possible and is now in the spotlight in psychiatric genetics. In this article, we reviewed the concepts, rationale, designs and general steps of GWAS, and also introduced a few previous GWAS of several psychiatric disorders.
Genome-Wide Association Study ; HapMap Project

The human genomic project and the international HapMap project were designed to create a genome-wide database of patterns of human genetic variation, with the expectation that these patterns would be useful for genetic association studies of common diseases, thus lead to molecular diagnosis and personnel therapy. The article briefly reviewed the creation, target and achievement of those two projects. Furthermore, the authors have given four suggestions in facing to the opportunities and challenges brought by the two projects, including cultivation improvement of elites, cross binding of multi-subjects, strengthening construction of research base and initiation of natural key scientific project.
Genome, Human ; Genomics ; HapMap Project ; Humans ; Oral Medicine

Precise and reliable identification of CNV is still important to fully understand the effect of CNV on genetic diversity and background of complex diseases. SNP marker has been used frequently to detect CNVs, but the analysis of SNP chip data for identifying CNV has not been well established. We compared various normalization methods for CNV analysis and suggest optimal normalization procedure for reliable CNV call. Four normal Koreans and NA10851 HapMap male samples were genotyped using Affymetrix Genome-Wide Human SNP array 5.0. We evaluated the effect of median and quantile normalization to find the optimal normalization for CNV detection based on SNP array data. We also explored the effect of Robust Multichip Average (RMA) background correction for each normalization process. In total, the following 4 combinations of normalization were tried: 1) Median normalization without RMA background correction, 2) Quantile normalization without RMA background correction, 3) Median normalization with RMA background correction, and 4) Quantile ormalization with RMA background correction. CNV was called using SW-ARRAY algorithm. We applied 4 different combinations of normalization and compared the effect using intensity ratio profile, box plot, and MA plot. When we applied median and quantile normalizations without RMA background correction, both methods showed similar normalization effect and the final CNV calls were also similar in terms of number and size. In both median and quantile normalizations, RMA background correction resulted in widening the range of intensity ratio distribution, which may suggest that RMA background correction may help to detect more CNVs compared to no correction.
Coat Protein Complex I ; Genetic Variation ; HapMap Project ; Humans ; Male

The Functional Element SNPs Database (FESD) categorizes functional elements in human genic regions and provides a set of single nucleotide polymorphisms (SNPs) located within each area. Users may select a set of SNPs in specific functional elements with haplotype information and obtain flanking sequences for genotyping. Our previous version of FESD has been improved in several ways. We regenerated all the data in FESD II from recently updated source data such as HapMap, UCSC GoldenPath, dbSNP, OMIM, and TRANSFAC(R). Users can obtain information about tagSNPs and simulate LD blocks for each gene from four ethnicities in the HapMap project on the fly. FESD II employs a Java/JSP web interface for better platform portability and higher speed than PHP in the previous version. As a result, FESD II provides its users with more powerful information about functional element SNPs of human ethnicities.
Databases, Genetic ; Diptera ; Haplotypes ; HapMap Project ; Humans* ; Polymorphism, Single Nucleotide

Recent rapid advances in genetic research are ushering us into the genome sequence era, where an individual's genome information is utilized for clinical practice. The most spectacular results of the human genome study have been provided by genome-wide association studies (GWASs). This is a review of the history of GWASs as related to my work. Further efforts are necessary to make full use of its potential power to medicine.
Genetic Research ; Genome ; Genome, Human ; Genome-Wide Association Study ; HapMap Project ; Human Genome Project ; Humans

OBJECTIVE: Recent molecular and genetic investigations have suggested that the current nosology for major psychiatric disorders, based on the "two-entities-principal" is not accurate with respect to clinical observations; patient groups that do not fit to the current operative diagnostic boundaries are readily identified. We aimed to perform an investigation of the signal transducer and activator of transcription 6 (STAT6) gene (located on 12q13), which has an important role in the apoptotic cascade, with patients suffering from periodic psychosis. METHODS: Genetic association study has been employed for the current work. Investigated six tag-SNPs were chosen from Hapmap database. RESULTS: Among six tag-SNPs, one marker (rs10783813), located in the STAT6 gene, showed modest association (p<0.05), although no marker or haplotype block showed association after Bonferroni's correction. CONCLUSION: Future studies will reveal the etiological role of STAT6, and of other genes of the apoptotic cascade, in major psychiatric disorders.
Genetic Association Studies ; Haplotypes ; HapMap Project ; Humans ; Psychotic Disorders* ; STAT6 Transcription Factor*

OBJECTIVETo explore the feasibility of applying autosomal single nucleotide polymorphisms (SNPs) on parentage testing.

METHODSAll SNP genotyping results of HapMap (r27) were downloaded from the website. With self-made computer programs, SNPs were extracted when their minor allele frequency (MAF) were ≥ 0.30 among all of the 11 HapMap populations. Ninety-six SNPs were chosen and integrated into the Illumina Goldengate bead arrays on the condition that no linkage disequilibrium was found between them. Three father-child-mother trios (9 samples in total) were tested with the arrays. Cumulative paternity index (CPI) was then calculated and compared with genotyping results using 15 short tandem repeats (STRs)(Identifiler(TM)).

RESULTSFamily 1 was found to have nine SNPs or seven STRs that did not conform to the Mendelian laws, Family 2 had 13 such SNPs or seven STRs, and Family 3 only had one such SNP but no STR. For Family 3, when all of the 96 SNPs were used in combine, the CPI was 1207, which had contrasted with the CPI by the 15 STRs, i.e., 355 869.

CONCLUSIONWhen applied to paternity testing, the paternity exclusion (PE) value for a SNP is usually less than 1/3 of that of a STR. The proportion of SNPs not comforming to the Mendelian laws for the tested SNPs may not be as high as that of inconsistent STRs over all tested STRs. Because of the low mutation rate of a SNP, the CPI will be greatly reduced even if one SNP did not conform to the Mendelian laws. Therefore, highly accurate testing methods are required to reduce artificial errors when applying SNPs for paternity testing.

Fathers ; Female ; Genetic Testing ; methods ; Genotype ; HapMap Project ; Humans ; Male ; Mothers ; Paternity ; Polymorphism, Single Nucleotide ; genetics

Genome-wide association (GWA) studies may benefit from the inclusion of imputed SNPs into their dataset. Due to its predictive nature, the imputation process is typically not perfect. Thus, it would be desirable to develop a scheme for filtering out the imputed SNPs by maximizing the concordance with the observed genotypes. We report such a scheme, which is based on the combination of several parameters that are calculated by PLINK, a popular GWA analysis software program. We imputed the genotypes of 8,842 Korean individuals, based on approximately 2 million SNP genotypes of the CHB+JPT panel in the International HapMap Project Phase II data, complementing the 352k SNPs in the original Affymetrix 5.0 dataset. A total of 333,418 SNPs were found in both datasets, with a median concordance rate of 98.7%. The concordance rates were calculated at different ranges of parameters, such as the number of proxy SNPs (NPRX), the fraction of successfully imputed individuals (IMPUTED), and the information content (INFO). The poor concordance that was observed at the lower values of the parameters allowed us to develop an optimal combination of the cutoffs (IMPUTED> or =0.9 and INFO> or =0.9). A total of 1,026,596 SNPs passed the cutoff, of which 94,364 were found in both datasets and had 99.4% median concordance. This study illustrates a conservative scheme for filtering imputed SNPs that would be useful in GWA studies
Complement System Proteins ; Genome-Wide Association Study ; Genotype ; HapMap Project ; Humans ; Polymorphism, Single Nucleotide ; Proxy

Most common diseases are caused by multiple genetic and environmental factors. Among the genetic factors, single nucleotide polymorphisms (SNPs) are common DNA sequence variations in individuals and can serve as important genetic markers. Recently, investigations of gene-based and whole genome-based SNPs have been applied to association studies for marker discovery. However, SNPs are so population-specific that the association needs to be verified. Fifty-five genes and 384 SNPs were selected based on association with disease. Genotypes of 337 SNPs in candidate genes were determined using Illumina Sentrix Array Matrix (SAM) chips by an allelespecific extension method in 364 unrelated Korean individuals. Allelic frequencies of SNPs were compared with those of other populations obtained from the International HapMap database. Minor allele frequencies, linkage disequilibrium blocks, tagSNPs, and haplotypes of functional candidate SNPs in 55 genetic disease-associated genes were provided. Our data may provide useful information for the selection of genetic markers for genebased genetic disease-association studies of the Korean population.
Base Sequence ; Gene Frequency ; Genetic Markers ; Genotype ; Haplotypes ; HapMap Project ; Linkage Disequilibrium ; Polymorphism, Single Nucleotide*