Advances in genome-wide association studies of sporadic Alzheimer’s disease / 国际药学研究杂志

Alzheimer’s disease AD is a neurodegenerative disorder resulted from complicate interactions between genes and environment. There is no effective therapy so far. The genome-wide association study GWAS provides the opportunity to discover the risk genes of sporadic AD, which is informative for revealing the pathogenesis of AD and guiding new drug development. In this review, we summarize the current findings of genetic studies of AD, the risk genes and their biological relevance with AD, and new drug development strategy supported by genetic studies.

Advances in genome-wide association studies of sporadic Alzheimer′s disease / 国际药学研究杂志

Alzheimer′s disease(AD)is a neurodegenerative disorder resulted from complicate interactions between genes and environment. There is no effective therapy so far. The genome-wide association study(GWAS)provides the opportunity to discover the risk genes of sporadic AD,which is informative for revealing the pathogenesis of AD and guiding new drug development. In this re?view,we summarize the current findings of genetic studies of AD,the risk genes and their biological relevance with AD,and new drug development strategy supported by genetic studies.

Lack of Replication of Genetic Association with Body Mass Index Detected by Genome-wide Association Study

Obesity provokes many serious human diseases, including various cardiovascular diseases and diabetes. Body mass index (BMI) is a highly heritable trait that is broadly used to diagnose obesity. To identify genetic loci associated with obesity in Asians, we conducted a genome-wide association study (GWAS) of a population of Korean adults (n=6,742, age 40~60 years) and detected six BMI risk loci (TNR, FAM124B, RGS12, NFE2L3, MC4R and FTO) having p<1x10(-5). However, in the replication study, only melanocortin 4 receptor gene (MC4R) (rs9946888, p=4.58x10(-7)) was replicated with marginal significance (p<0.05) in the second cohort (n=5,102, age 40~60 years). This study indicates that each locus associated with BMI has very weak genetic effect.

Application of Structural Equation Models to Genome-wide Association Analysis

Genome-wise association studies (GWASs) have become popular approaches to identify genetic variants associated with human biological traits. In this study, we applied Structural Equation Models (SEMs) in order to model complex relationships between genetic networks and traits as risk factors. SEMs allow us to achieve a better understanding of biological mechanisms through identifying greater numbers of genes and pathways that are associated with a set of traits and the relationship among them. For efficient SEM analysis for GWASs, we developed a procedure, comprised of four stages. In the first stage, we conducted single-SNP analysis using regression models, where age, sex, and recruited area were included as adjusting covariates. In the second stage, Fisher's combination test was conducted for each gene to detect significant genes using p-values obtained from the single-SNP analysis. In the third stage, Fisher's exact test was adopted to determine which biological pathways were enriched with significant SNPs. Finally, based on a pathway that was associated with the four traits in common, a SEM was fit to model a causal relationship among the genetic factors and traits. We applied our SEM model to GWAS data with four central obesity related traits: suprailiac and subscapular measures for upper body fat, BMI, and hypertension. Study subjects were collected from two Korean cohort regions. After quality control, 327,872 SNPs for 8842 individuals were included in the analysis. After comparing two SEMs, we concluded that suprailiac and subscapular measures may indirectly affect hypertension susceptibility by influencing BMI. In conclusion, our analysis demonstrates that SEMs provide a better understanding of biological mechanisms by identifying greater numbers of genes and pathways.

Copy Number Variations in the Human Genome: Potential Source for Individual Diversity and Disease Association Studies

The widespread presence of large-scale genomic variations, termed copy number variation (CNVs), has been recently recognized in phenotypically normal individuals. Judging by the growing number of reports on CNVs, it is now evident that these variants contribute significantly to genetic diversity in the human genome. Like single nucleotide polymorphisms (SNPs), CNVs are expected to serve as potential biomarkers for disease susceptibility or drug responses. However, the technical and practical concerns still remain to be tackled. In this review, we examine the current status of CNV DBs and research, including the ongoing efforts of CNV screening in the human genome. We also discuss the characteristics of platforms that are available at the moment and suggest the potential of CNVs in clinical research and application.

Copy Number Variations in the Human Genome: Potential Source for Individual Diversity and Disease Association Studies

The widespread presence of large-scale genomic variations, termed copy number variation (CNVs), has been recently recognized in phenotypically normal individuals. Judging by the growing number of reports on CNVs, it is now evident that these variants contribute significantly to genetic diversity in the human genome. Like single nucleotide polymorphisms (SNPs), CNVs are expected to serve as potential biomarkers for disease susceptibility or drug responses. However, the technical and practical concerns still remain to be tackled. In this review, we examine the current status of CNV DBs and research, including the ongoing efforts of CNV screening in the human genome. We also discuss the characteristics of platforms that are available at the moment and suggest the potential of CNVs in clinical research and application.