Next-generation sequencing has revolutionized family-based association tests for rare variants. As the lower power of genome wide association study for detecting casual rare variants, methods aggregating effects of multiple variants have been proposed, such as burden tests and variance component tests. This paper summarizes the methods of rare variants association test that can be applied for family data, introduces their principles, characteristics and applicable conditions and discusses the shortcomings and the improvement of the present methods.
Computer Simulation ; Family Relations ; Genetic Association Studies ; Genetic Variation ; Genome-Wide Association Study/methods* ; Humans

OBJECTIVES: Previous genome-wide association studies have indicated the association between ankyrin 3 (ANK3) and the vulnerability of schizophrenia. We investigated the association between single nucleotide polymorphisms (SNPs) covering the whole ANK3 locus and schizophrenia in the Korean population. METHODS: The study subjects were 582 patients with schizophrenia and 502 healthy controls. Thirty-eight tag SNPs on ANK3 and five additional SNPs showing significant association with schizophrenia in previous studies were genotyped. RESULTS: Three (rs10994181, rs16914791, rs1938526) of 43 SNPs showed a nominally significant association (p < 0.05) with at least one genotype model. But none of these associations remained significant after adjusting for multiple testing errors with Bonferroni's correction. CONCLUSIONS: We could not identify a significant association between ANK3 and schizophrenia in the Korean population. However, three SNPs showing an association signal with nominal significance need to be investigated in future studies with higher statistical power and more specific phenotype crossing the current diagnostic categories.
Ankyrins ; Genetic Association Studies ; Genome-Wide Association Study ; Genotype ; Humans ; Phenotype ; Polymorphism, Single Nucleotide ; Schizophrenia*

Family-based designs are commonly used in genetic association studies to identify and to locate genes that underlie complex diseases. In this paper, we review two examples of genome-wide association studies using family-based cohort studies, including the Framingham Heart Study and International Multi-Center ADHD Genetics Project. We also review statistical methods of family-based designs, including the transmission disequilibrium test (TDT), linkage analysis, and imprinting effect analysis. In addition, we evaluate the strengths and limitations of the family-based cohort design. Despite the costs and difficulties in carrying out this type of study, a family-based cohort study can play avery important role in genome wide studies. First, the design will be free from biases due to population heterogeneity or stratification. Moreover, family-based designs provide the opportunity to conduct joint tests of linkage and association. Finally, family-based designs also allow access to epigenetic phenomena like imprinting. The family-based cohort design should be given careful consideration in planning new studies for genome-wide strategies.
Bias (Epidemiology) ; Cohort Studies ; Epigenomics ; Genetic Association Studies ; Genome ; Genome-Wide Association Study ; Heart ; Joints ; Lifting ; Population Characteristics

For the past two decades, a huge number of genetic studies have been conducted to identify the genetic variants responsible for asthma risk. Several types of genetic and genomic approaches, including linkage analysis, candidate gene single nucleotide polymorphism studies, and whole genome-wide association studies have been applied. In this review article, the results of these approaches are summarized, and their limitations are discussed. Additionally, perspectives for applying upcoming new epigenetic or genomic technologies, such as copy number variation, are introduced to increase our understanding of new omic approaches to asthma genetics.
Asthma ; Coat Protein Complex I ; Epigenomics ; Genetic Association Studies ; Genome ; Genome-Wide Association Study ; Polymorphism, Single Nucleotide

Type 2 diabetes (T2DM) is a common complex metabolic disorder that has a strong genetic predisposition. During the past decade, progress in genetic association studies has enabled the identification of at least 75 independent genetic loci for T2DM, thus allowing a better understanding of the genetic architecture of T2DM. International collaborations and large-scale meta-analyses of genome-wide association studies have made these achievements possible. However, whether the identified common variants are causal is largely unknown. In addition, the detailed mechanism of how these genetic variants exert their effect on the pathogenesis of T2DM requires further investigation. Currently, there are ongoing large-scale sequencing studies to identify rare, functional variants for T2DM. Environmental factors also have a crucial role in the development of T2DM. These could modulate gene expression via epigenetic mechanisms, including DNA methylation, histone modification and microRNA regulation. There is evidence that epigenetic changes are important in the development of T2DM. Recent studies have identified several DNA methylation markers of T2DM from peripheral blood and pancreatic islets. In this review, we will briefly summarize the recent progress in the genetic and epigenetic research on T2DM and discuss how environmental factors, genetics and epigenetics can interact in the pathogenesis of T2DM.
Cooperative Behavior ; DNA Methylation ; Epigenomics* ; Gene Expression ; Genetic Association Studies ; Genetic Loci ; Genetic Predisposition to Disease ; Genetics ; Genome-Wide Association Study ; Histones ; Islets of Langerhans ; MicroRNAs

For the past three decades, a large number of genetic studies have been performed to examine genetic variants associated with asthma and its subtypes in hopes of gaining better understanding of the mechanisms underlying disease pathology and to identify genetic biomarkers predictive of disease outcomes. Various methods have been used to achieve these objectives, including linkage analysis, candidate gene polymorphism analysis, and genome-wide association studies (GWAS); however, the degree to which genetic variants contribute to asthma pathogenesis has proven to be much less significant than originally expected. Subsequent application of GWAS to well-defined phenotypes, such as occupational asthma and non-steroidal anti-inflammatory drugexacerbated respiratory diseases, has overcome some of these limitations, although with only partial success. Recently, a combinatorial analysis of single nucleotide polymorphisms (SNPs) identified by GWAS has been used to develop sets of genetic markers able to more accurately stratify asthma subtypes. In this review, we discuss the implications of the identified SNPs in diagnosis of asthma and its subtypes and the progress being made in combinatorial analysis of genetic variants.
Anti-Inflammatory Agents, Non-Steroidal ; Aspirin ; Asthma* ; Asthma, Occupational ; Biomarkers ; Diagnosis* ; Genetic Association Studies ; Genetic Markers ; Genetic Techniques ; Genome-Wide Association Study ; Hope ; Pathology ; Phenotype ; Polymorphism, Single Nucleotide*

This review aimed to arrange the process of a systematic review of genome-wide association studies in order to practice and apply a genome-wide meta-analysis (GWMA). The process has a series of five steps: searching and selection, extraction of related information, evaluation of validity, meta-analysis by type of genetic model, and evaluation of heterogeneity. In contrast to intervention meta-analyses, GWMA has to evaluate the Hardy–Weinberg equilibrium (HWE) in the third step and conduct meta-analyses by five potential genetic models, including dominant, recessive, homozygote contrast, heterozygote contrast, and allelic contrast in the fourth step. The ‘genhwcci’ and ‘metan’ commands of STATA software evaluate the HWE and calculate a summary effect size, respectively. A meta-regression using the ‘metareg’ command of STATA should be conducted to evaluate related factors of heterogeneities.
Case-Control Studies* ; Genome-Wide Association Study* ; Heterozygote ; Homozygote ; Models, Genetic ; Population Characteristics

Vitiligo is a depigmentation disease affected by a variety offactors,of which genetic factors play a key role.Single nucleotide polymorphism as a common type of genetic variation can be detected by candidate gene analysis and genome-wide association study.The recent studies have demonstrated that multiple susceptibility genes play a vital role in the occurrence and development of vitiligo.This article introduces the single nucleotide polymorphisms of vitiligo susceptibility genes according to the gene functions.
Humans ; Polymorphism, Single Nucleotide ; Vitiligo/epidemiology* ; Genome-Wide Association Study ; Genetic Predisposition to Disease ; Case-Control Studies

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.
Case-Control Studies ; Gene Frequency ; Genetic Association Studies ; Genome-Wide Association Study ; Humans ; Linkage Disequilibrium ; Models, Genetic ; Odds Ratio ; Polymorphism, Single Nucleotide ; Prevalence ; Sample Size

Rheumatoid arthritis (RA) is a chronic autoimmune disease that mainly involves the joints. Although the full etiology remains unclear, both genetic and environmental factors contribute to disease. Recently, genetic studies of RA, which include whole-genome linkage study, candidate gene association study, and especially genome-wide association study, made much progress through the advances in technology and large collection of case-controls. These various approaches provided new susceptible genes such as PTPN22, PADI4, STAT4, TNFAIP3, TRAF1-C5, CD40 gene in addition to HLA-DRB1. This review will cover an update in recent progress in RA genetics, focusing mainly on the several novel loci.
Arthritis ; Arthritis, Rheumatoid ; Autoimmune Diseases ; Case-Control Studies ; Genetic Association Studies ; Genetic Predisposition to Disease ; Genome-Wide Association Study ; HLA-DRB1 Chains ; Joints