Genome-Wide Association Study ; Genotype ; Humans ; Molecular Epidemiology ; methods

Chromosome Mapping ; Genome-Wide Association Study ; methods ; Humans

Bipolar Disorder ; genetics ; Genome-Wide Association Study ; Genomics ; methods ; Humans

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

China ; Genes, Neoplasm ; Genome, Human ; Genome-Wide Association Study ; methods ; Humans ; Neoplasms ; genetics ; Polymorphism, Single Nucleotide

"Active health" has been emphasized in "Healthy China 2030" in dealing with the challenges of population aging, so the anti-aging strategies are requires to be more precise and effective at both individual and population levels. Aging is influenced by both genetic and environmental factors. In the recent 20 years, the research of genetics of human ageing has been greatly facilitated owning to the development of high-throughput sequencing techniques, statistical methodology for multi-omics data, as well as the growing qualified evidence of large-scale population-based genomic research. This paper provides a review of genome-wide association research of aging.
Aging/genetics* ; Genome-Wide Association Study/methods* ; Genomics/methods* ; High-Throughput Nucleotide Sequencing ; Humans ; Phenotype

The meta-analysis has become a widely used tool for many applications in bioinformatics, including genome-wide association studies. A commonly used approach for meta-analysis is the fixed effects model approach, for which there are two popular methods: the inverse variance-weighted average method and weighted sum of z-scores method. Although previous studies have shown that the two methods perform similarly, their characteristics and their relationship have not been thoroughly investigated. In this paper, we investigate the optimal characteristics of the two methods and show the connection between the two methods. We demonstrate that the each method is optimized for a unique goal, which gives us insight into the optimal weights for the weighted sum of z-scores method. We examine the connection between the two methods both analytically and empirically and show that their resulting statistics become equivalent under certain assumptions. Finally, we apply both methods to the Wellcome Trust Case Control Consortium data and demonstrate that the two methods can give distinct results in certain study designs.
Case-Control Studies ; Computational Biology ; Genome-Wide Association Study ; Methods* ; Weights and Measures

Although a large number of genetic variants have been identified to be associated with common diseases through genome-wide association studies, there still exits limitations in explaining the missing heritability. One approach to solving this missing heritability problem is to investigate gene-gene interactions, rather than a single-locus approach. For gene-gene interaction analysis, the multifactor dimensionality reduction (MDR) method has been widely applied, since the constructive induction algorithm of MDR efficiently reduces high-order dimensions into one dimension by classifying multi-level genotypes into high- and low-risk groups. The MDR method has been extended to various phenotypes and has been improved to provide a significance test for gene-gene interactions. In this paper, we propose a simple method, called accelerated failure time (AFT) UM-MDR, in which the idea of a unified model-based MDR is extended to the survival phenotype by incorporating AFT-MDR into the classification step. The proposed AFT UM-MDR method is compared with AFT-MDR through simulation studies, and a short discussion is given.
Classification ; Genome-Wide Association Study ; Genotype ; Methods* ; Multifactor Dimensionality Reduction* ; Phenotype

OBJECTIVE: To examine whether bone mineral density (BMD) is causally associated with osteoarthritis (OA). METHODS: We performed a two-sample Mendelian randomization (MR) analysis using the inverse-variance weighting (IVW), weighted median, and MR-Egger regression methods. We used publicly available summary statistics datasets of a genome-wide association study (GWAS) on femur neck (FN) BMD of individuals of European ancestry as the exposure and a GWAS for non-cancer illness code self-reported: OA from the individuals included in the UK Biobank as the outcome. RESULTS: We selected 21 independent single-nucleotide polymorphisms with genome-wide significance (p<5.00E-08) from GWAS on FN BMD as the instrumental variables. The IVW method (beta=0.010, standard error [SE]=0.003, p=0.002) and the weighted median approach (beta=0.011, SE=0.004, p=0.006) yielded evidence of a causal association between FN BMD and OA. However, the MR-Egger analysis showed no causal association between FN BMD and OA (beta=0.005, SE=0.017, p=0.753). Since MR-Egger regression suffers from a lack of power and a susceptibility to weak instrument bias, the MR analysis results may support a causal association between FN BMD and OA. CONCLUSION: The results of MR analysis by IVW and weighted median, but not MR-Egger regression indicate that FN BMD is likely to be causally associated with an increased risk of OA incidence The current findings may provide an opportunity to elucidate the underlying mechanisms of the effects of BMD on the OA incidence.
Bias (Epidemiology) ; Bone Density ; Dataset ; Femur Neck ; Genome-Wide Association Study ; Incidence ; Methods ; Osteoarthritis ; Random Allocation

Gene-gene interaction (GGI) analysis is known to play an important role in explaining missing heritability. Many previous studies have already proposed software to analyze GGI, but most methods focus on a binary phenotype in a case-control design. In this study, we developed “Hierarchical structural CoMponent analysis of Gene-Gene Interactions” (HisCoM-GGI) software for GGI analysis with a continuous phenotype. The HisCoM-GGI method considers hierarchical structural relationships between genes and single nucleotide polymorphisms (SNPs), enabling both gene-level and SNP-level interaction analysis in a single model. Furthermore, this software accepts various types of genomic data and supports data management and multithreading to improve the efficiency of genome-wide association study data analysis. We expect that HisCoM-GGI software will provide advanced accessibility to researchers in genetic interaction studies and a more effective way to understand biological mechanisms of complex diseases.
Case-Control Studies ; Genome-Wide Association Study ; Methods ; Phenotype ; Polymorphism, Single Nucleotide ; Statistics as Topic