• Genomics & Informatics
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• v.4 no.2
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• pp.51-56
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• 2006

In planning a genetic association study, it is necessary to determine the number of samples to be collected for the study in order to achieve sufficient power to detect the hypothesized effect. The case-control design is increasingly used for genetic association studies due to the simplicity of its design. We review the methods for the sample size and power calculations in case-control genetic association studies between a marker locus and a disease phenotype.

• Genomics & Informatics
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• v.14 no.4
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• pp.187-195
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• 2016

In genetic association studies with high-dimensional genomic data, multiple group testing procedures are often required in order to identify disease/trait-related genes or genetic regions, where multiple genetic sites or variants are located within the same gene or genetic region. However, statistical testing procedures based on an individual test suffer from multiple testing issues such as the control of family-wise error rate and dependent tests. Moreover, detecting only a few of genes associated with a phenotype outcome among tens of thousands of genes is of main interest in genetic association studies. In this reason regularization procedures, where a phenotype outcome regresses on all genomic markers and then regression coefficients are estimated based on a penalized likelihood, have been considered as a good alternative approach to analysis of high-dimensional genomic data. But, selection performance of regularization procedures has been rarely compared with that of statistical group testing procedures. In this article, we performed extensive simulation studies where commonly used group testing procedures such as principal component analysis, Hotelling's $T^2$ test, and permutation test are compared with group lasso (least absolute selection and shrinkage operator) in terms of true positive selection. Also, we applied all methods considered in simulation studies to identify genes associated with ovarian cancer from over 20,000 genetic sites generated from Illumina Infinium HumanMethylation27K Beadchip. We found a big discrepancy of selected genes between multiple group testing procedures and group lasso.

• Animal cells and systems
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• v.8 no.1
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• pp.41-47
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• 2004

The kallikrein-kinin system affects regulation of blood pressure, and genes encoding for the components of this system have been considered as good candidates for hypertension. To evaluate the relationship between genetic polymorphisms of candidate genes involved in this system and hypertension, we performed case-control studies using genetic markers in Korean normotensives and hypertensives, respectively. By association study, there was a marginal association with hypertension in AA genotype distribution of A1789G polymorphism in the hKLK1 gene (P=0.0754). Thus, this genetic polymorphism may weakly contribute to the susceptibility to hypertension in Koreans. We also observed that significant linkage disequilibrium exists among three polymorphic sites in the hKLK1 gene studied, suggesting that the three genetic polymorph isms can be useful as genetic markers in clinical association studies. Further studies using larger sample sizes and more genetic markers will be needed to clarify genetic influence of kallikrein-kinin system for hypertension.

• Genomics & Informatics
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• v.10 no.2
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• pp.117-122
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• 2012

A sample size with sufficient statistical power is critical to the success of genetic association studies to detect causal genes of human complex diseases. Genome-wide association studies require much larger sample sizes to achieve an adequate statistical power. We estimated the statistical power with increasing numbers of markers analyzed and compared the sample sizes that were required in case-control studies and case-parent studies. We computed the effective sample size and statistical power using Genetic Power Calculator. An analysis using a larger number of markers requires a larger sample size. Testing a single-nucleotide polymorphism (SNP) marker requires 248 cases, while testing 500,000 SNPs and 1 million markers requires 1,206 cases and 1,255 cases, respectively, under the assumption of an odds ratio of 2, 5% disease prevalence, 5% minor allele frequency, complete linkage disequilibrium (LD), 1:1 case/control ratio, and a 5% error rate in an allelic test. Under a dominant model, a smaller sample size is required to achieve 80% power than other genetic models. We found that a much lower sample size was required with a strong effect size, common SNP, and increased LD. In addition, studying a common disease in a case-control study of a 1:4 case-control ratio is one way to achieve higher statistical power. We also found that case-parent studies require more samples than case-control studies. Although we have not covered all plausible cases in study design, the estimates of sample size and statistical power computed under various assumptions in this study may be useful to determine the sample size in designing a population-based genetic association study.

• Journal of Genetic Medicine
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• v.12 no.1
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• pp.19-24
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• 2015

Speech and language are uniquely human-specific traits, which contributed to humans becoming the predominant species on earth. Disruptions in the human speech and language function may result in diverse disorders. These include stuttering, aphasia, articulation disorder, spasmodic dysphonia, verbal dyspraxia, dyslexia and specific language impairment. Among these disorders, stuttering is the most common speech disorder characterized by disruptions in the normal flow of speech. Twin, adoption, and family studies have suggested that genetic factors are involved in susceptibility to stuttering. For several decades, multiple genetic studies including linkage analysis were performed to connect causative gene to stuttering, and several genetic studies have revealed the association of specific gene mutation with stuttering. One notable genetic discovery came from the genetic studies in the consanguineous Pakistani families. These studies suggested that mutations in the lysosomal enzyme-targeting pathway genes (GNPTAB, GNPTG and NAPGA) are associated with non-syndromic persistent stuttering. Although these studies have revealed some clues in understanding the genetic causes of stuttering, only a small fraction of patients are affected by these genes. In this study, we summarize recent advances and future challenges in an effort to understand genetic causes underlying stuttering.

• Genomics & Informatics
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• v.8 no.3
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• pp.142-149
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• 2010

In recent years, genome-wide association (GWA) studies have successfully led to many discoveries of genetic variants affecting common complex traits, including height, blood pressure, and diabetes. Although GWA studies have made much progress in finding single nucleotide polymorphisms (SNPs) associated with many complex traits, such SNPs have been shown to explain only a very small proportion of the underlying genetic variance of complex traits. This is partly due to that fact that most current GWA studies have relied on single-marker approaches that identify single genetic factors individually and have limitations in considering the joint effects of multiple genetic factors on complex traits. Joint identification of multiple genetic factors would be more powerful and provide a better prediction of complex traits, since it utilizes combined information across variants. Recently, a new statistical method for joint identification of genetic variants for common complex traits via the elastic-net regularization method was proposed. In this study, we applied this joint identification approach to a large-scale GWA dataset (i.e., 8842 samples and 327,872 SNPs) in order to identify genetic variants of obesity for the Korean population. In addition, in order to test for the biological significance of the jointly identified SNPs, gene ontology and pathway enrichment analyses were further conducted.

• The Korean Journal of Applied Statistics
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• v.28 no.2
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• pp.295-308
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• 2015

We have experienced a substantial improvement in and cost-drop for genotyping that enables genetic epidemiological studies with large-scale genetic data. Genome-wide association studies have identified more than ten thousand causal variants. Many statistical methods based on linear mixed models have been developed for various goals such as estimating heritability and identifying disease susceptibility locus. Empirical results also repeatedly stress the importance of linear mixed models. Therefore, we review the statistical methods related with to linear mixed models and illustrate the meaning of their estimates.

• Journal of Genetic Medicine
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• v.15 no.2
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• pp.55-63
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• 2018

Clinical and genetic heterogeneity in association with overlapping spectrum is characteristic in pediatric neuromuscular disorders, which makes confirmative diagnosis difficult and time consuming. Considering evolution of molecular genetic diagnosis and resultant upcoming genetically modifiable therapeutic options, rapid and cost-effective genetic testing should be applied in conjunction with existing diagnostic methods of clinical examinations, laboratory tests, electrophysiologic studies and pathologic studies. Earlier correct diagnosis would enable better clinical management for these patients in addition to new genetic drug options and genetic counseling.

• Journal of Korean Neurosurgical Society
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• v.57 no.6
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• pp.422-427
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• 2015

Moyamoya disease (MMD) is a chronic, progressive, cerebrovascular occlusive disorder that displays various clinical features and results in cerebral infarct or hemorrhagic stroke. Specific genes associated with the disease have not yet been identified, making identification of at-risk patients difficult before clinical manifestation. Familial MMD is not uncommon, with as many as 15% of MMD patients having a family history of the disease, suggesting a genetic etiology. Studies of single nucleotide polymorphisms (SNPs) in MMD have mostly focused on mechanical stress on vessels, endothelium, and the relationship to atherosclerosis. In this review, we discuss SNPs studies targeting the genetic etiology of MMD. Genetic analyses in familial MMD and genome-wide association studies represent promising strategies for elucidating the pathophysiology of this condition. This review also discusses future research directions, not only to offer new insights into the origin of MMD, but also to enhance our understanding of the genetic aspects of MMD. There have been several SNP studies of MMD. Current SNP studies suggest a genetic contribution to MMD, but further reliable and replicable data are needed. A large cohort or family-based design would be important. Modern SNP studies of MMD depend on novel genetic, experimental, and database methods that will hopefully hasten the arrival of a consensus conclusion.

• Genomics & Informatics
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• v.21 no.2
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• pp.26.1-26.9
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• 2023

Stevens-Johnson syndrome (SJS) produces a severe hypersensitivity reaction caused by Herpes simplex virus or mycoplasma infection, vaccination, systemic disease, or other agents. Several studies have investigated the genetic susceptibility involved in SJS. To provide further genetic insights into the pathogenesis of SJS, this study prioritized high-impact, SJS-associated pathogenic variants through integrating bioinformatic and population genetic data. First, we identified SJS-associated single nucleotide polymorphisms from the genome-wide association studies catalog, followed by genome annotation with HaploReg and variant validation with Ensembl. Subsequently, expression quantitative trait locus (eQTL) from GTEx identified human genetic variants with differential gene expression across human tissues. Our results indicate that two variants, namely rs2074494 and rs5010528, which are encoded by the HLA-C (human leukocyte antigen C) gene, were found to be differentially expressed in skin. The allele frequencies for rs2074494 and rs5010528 also appear to significantly differ across continents. We highlight the utility of these population-specific HLA-C genetic variants for genetic association studies, and aid in early prognosis and disease treatment of SJS.