Potentiality of Risk SNPs identification Based on GSP Theory

2020 ◽  
Vol 15 ◽  
Author(s):  
Hengyi Zhang ◽  
Qinli Zhang
Keyword(s):  
Association Studies ◽  
Experimental Results ◽  
Age Related Macular Degeneration ◽  
Joint Analysis ◽  
Genome Wide Association Studies ◽  
Lasso Regression ◽  
Chi Square ◽  
Age Related ◽  
Chi Square Test ◽  
Risk Snps

Background: A large number of studies have shown that susceptibility to diseases may be related to some Single Nucleotide Polymorphisms (SNPs). Therefore, the location of SNPs associated with diseases in genes can help us understand the genetic mechanism of disease, intervene in risk SNPs and prevent some genetic diseases/ Method: Based on Graph Signal Processing (GSP) theory, a novel method is proposed to locate the risk SNPs in this paper. The proposed method first builds the graph signal model of all SNP loci, and then realizes the location of abnormal SNPs (risk SNPs) based on the joint analysis of vertex domain and frequency domain of graph Results: The experimental results on synthetic datasets show that our method outperforms many existing methods, including BOOST, SNPHarvester, SNPRule, Random Forest (RF), Chi-square Test and LASSO regression in terms of power. The experimental results on two real Genome-Wide Association Studies (GWAS) datasets, Age-related Macular Degeneration (AMD) and Genetic Disease A (GDA), show that our method not only finds the risk SNPs found by several state-of-the-art methods, including RF, Chi-square Test and LASSO regression, but also discovers three potential risk SNPs. Conclusion: Our method is suitable and effective for the identification of risk SNPs in GWAS.

scholarly journals Causal associations between risk factors and common diseases inferred from GWAS summary data

2017 ◽  
Author(s):  
Zhihong Zhu ◽  
Zhili Zheng ◽  
Futao Zhang ◽  
Yang Wu ◽  
Maciej Trzaskowski ◽  
...  
Keyword(s):  
Risk Factors ◽  
Protective Effect ◽  
Association Studies ◽  
Significant Risk ◽  
Hdl Cholesterol ◽  
Age Related Macular Degeneration ◽  
Genome Wide Association Studies ◽  
Common Diseases ◽  
Age Related ◽  
Summary Data

AbstractHealth risk factors such as body mass index (BMI), serum cholesterol and blood pressure are associated with many common diseases. It often remains unclear whether the risk factors are cause or consequence of disease, or whether the associations are the result of confounding. Genetic methods are useful to infer causality because genetic variants are present from birth and therefore unlikely to be confounded with environmental factors. We develop and apply a method (GSMR) that performs a multi-SNP Mendelian Randomization analysis using summary-level data from large genome-wide association studies (sample sizes of up to 405,072) to test the causal associations of BMI, waist-to-hip ratio, serum cholesterols, blood pressures, height and years of schooling (EduYears) with a range of common diseases. We identify a number of causal associations including a protective effect of LDL-cholesterol against type-2 diabetes (T2D) that might explain the side effects of statins on T2D, a protective effect of EduYears against Alzheimer’s disease, and bidirectional associations with opposite effects (e.g. higher BMI increases the risk of T2D but the effect T2D of BMI is negative). HDL-cholesterol has a significant risk effect on age-related macular degeneration, and the effect size remains significant accounting for the other risk factors. Our study develops powerful tools to integrate summary data from large studies to infer causality, and provides important candidates to be prioritized for further studies in medical research and for drug discovery.

scholarly journals Protective coding variants in CFH and PELI3 and a variant near CTRB1 are associated with age-related macular degeneration

2015 ◽  
Author(s):  
Erin K. Wagner ◽  
Yi Yu ◽  
Eric H. Souied ◽  
Sanna Seitsonen ◽  
Ilkka J. Immonen ◽  
...  
Keyword(s):  
Macular Degeneration ◽  
Rare Variants ◽  
Association Studies ◽  
Low Frequency ◽  
Density Lipoprotein ◽  
Age Related Macular Degeneration ◽  
European Ancestry ◽  
Genome Wide Association Studies ◽  
Age Related ◽  
Meta Analyses

ABSTRACTAlthough >20 common frequency age-related macular degeneration (AMD) alleles have been discovered with genome-wide association studies, substantial disease heritability remains unexplained. In this study we sought to identify additional variants, both common and rare, that have an association with advanced AMD. We genotyped 4,332 cases and 25,268 controls of European ancestry from three different populations using the Illumina Infinium HumanExome BeadChip. We performed meta-analyses to identify associations with common variants and performed single variant and gene-based burden tests to identify associations with rare variants. Two protective, low frequency, non-synonymous variants A307V in PELI3 (odds ratio [OR]=0.14, P=4.3×10−10) and N1050Y in CFH (OR=0.76, Pconditional=1.6×10−11) were significantly associated with a decrease in risk of AMD. Additionally, we identified an enrichment of protective alleles in PELI3 using a burden test (OR=0.14). The new variants have a large effect size, similar to rare mutations we reported previously in a targeted sequencing study, which remain significant in this analysis: CFH R1210C (OR=18.82, P=3.5×10−07), C3 K155Q (OR=3.27, P=1.5×10−10), and C9 P167S (OR=2.04, P=2.8×10−07). We also identified a strong protective signal for a common variant (rs8056814) near CTRB1 associated with a decrease in AMD risk (logistic regression: OR = 0.71, P = 1.8x10−07; Firth corrected OR = 0.64, P = 9.6x10−11). This study supports the involvement of both common and low frequency protective variants in AMD. It also may expand the role of the high-density lipoprotein pathway and branches of the innate immune pathway, outside that of the complement system, in the etiology of AMD.

scholarly journals Single-cell transcriptomic atlas of the human retina identifies cell types associated with age-related macular degeneration

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Madhvi Menon ◽  
Shahin Mohammadi ◽  
Jose Davila-Velderrain ◽  
Brittany A. Goods ◽  
Tanina D. Cadwell ◽  
...  
Keyword(s):  
Macular Degeneration ◽  
Single Cell ◽  
Association Studies ◽  
Enrichment Analysis ◽  
Cell Types ◽  
Age Related Macular Degeneration ◽  
Retinal Cell ◽  
Genome Wide Association Studies ◽  
Human Retina ◽  
Age Related

Abstract Genome-wide association studies (GWAS) have identified genetic variants associated with age-related macular degeneration (AMD), one of the leading causes of blindness in the elderly. However, it has been challenging to identify the cell types associated with AMD given the genetic complexity of the disease. Here we perform massively parallel single-cell RNA sequencing (scRNA-seq) of human retinas using two independent platforms, and report the first single-cell transcriptomic atlas of the human retina. Using a multi-resolution network-based analysis, we identify all major retinal cell types, and their corresponding gene expression signatures. Heterogeneity is observed within macroglia, suggesting that human retinal glia are more diverse than previously thought. Finally, GWAS-based enrichment analysis identifies glia, vascular cells, and cone photoreceptors to be associated with the risk of AMD. These data provide a detailed analysis of the human retina, and show how scRNA-seq can provide insight into cell types involved in complex, inflammatory genetic diseases.

scholarly journals Prioritization of Cancer-Related Genomic Variants by SNP Association Network

2015 ◽  
Vol 14s2 ◽  
pp. CIN.S17288
Author(s):  
Changning Liu ◽  
Zhenyu Xuan
Keyword(s):  
Association Studies ◽  
Age Related Macular Degeneration ◽  
Chromatin Interaction ◽  
Reference Database ◽  
Genome Wide Association Studies ◽  
Association Network ◽  
Nucleotide Polymorphisms ◽  
Gwas Study ◽  
Snp Association ◽  
Age Related

We have developed a general framework to construct an association network of single nucleotide polymorphisms (SNPs) (SNP association network, SAN) based on the functional interactions of genes located in the flanking regions of SNPs. SAN, which was constructed based on protein-protein interactions in the Human Protein Reference Database (HPRD), showed significantly enriched signals in both linkage disequilibrium (LD) and long-range chromatin interaction (Hi-C). We used this network to further develop two methods for predicting and prioritizing disease-associated genes from genome-wide association studies (GWASs). We found that random walk with restart (RWR) using SAN (RWR-SAN) can greatly improve the prediction of lung-cancer-associated genes by comparing RWR with the use of network in HPRD (AUC 0.81 vs 0.66). In a reanalysis of the GWAS dataset of age-related macular degeneration (AMD), SAN could identify more potential AMD-associated genes that were previously ranked lower in the GWAS study. The interactions in SAN could facilitate the study of complex diseases.

scholarly journals Genetic Mechanism Revealed of Age-Related Macular Degeneration Based on Fusion of Statistics and Machine Learning Method

2021 ◽  
Vol 12 ◽  
Author(s):  
Yongyi Du ◽  
Ning Kong ◽  
Jibin Zhang
Keyword(s):  
Macular Degeneration ◽  
Vision Loss ◽  
Association Studies ◽  
Area Under The Curve ◽  
Gene Interaction ◽  
Age Related Macular Degeneration ◽  
Support Vector ◽  
Genome Wide Association Studies ◽  
Age Related ◽  
Causal Genes

Age-related macular degeneration (AMD) is the most common cause of irreversible vision loss in the developed world which affects the quality of life for millions of elderly individuals worldwide. Genome-wide association studies (GWAS) have identified genetic variants at 34 loci contributing to AMD. To better understand the disease pathogenesis and identify causal genes for AMD, we applied random walk (RW) and support vector machine (SVM) to identify AMD-related genes based on gene interaction relationship and significance of genes. Our model achieved 0.927 of area under the curve (AUC), and 65 novel genes have been identified as AMD-related genes. To verify our results, a statistics method called summary data-based Mendelian randomization (SMR) has been implemented to integrate GWAS data and transcriptome data to verify AMD susceptibility-related genes. We found 45 genes are related to AMD by SMR. Among these genes, 37 genes overlap with those found by SVM-RW. Finally, we revealed the biological process of genetic mutations leading to changes in gene expression leading to AMD. Our results reveal the genetic pathogenic factors and related mechanisms of AMD.

scholarly journals Improved Score Statistics for Meta-analysis in Single-variant and Gene-level Association Studies

2017 ◽  
Author(s):  
Jingjing Yang ◽  
Sai Chen ◽  
Gonçalo Abecasis ◽  
Keyword(s):  
Power Loss ◽  
Association Studies ◽  
Meta Analysis ◽  
Genetic Association Studies ◽  
Age Related Macular Degeneration ◽  
Joint Analysis ◽  
Analysis Methods ◽  
Age Related ◽  
Score Statistics ◽  
Gene Level

AbstractMeta-analysis is now an essential tool for genetic association studies, allowing these to combine large studies and greatly accelerating the pace of genetic discovery. Although the standard meta-analysis methods perform equivalently as the more cumbersome joint analysis under ideal settings, they result in substantial power loss under unbalanced settings with various case-control ratios. Here, we investigate why the standard meta-analysis methods lose power under unbalanced settings, and further propose a novel meta-analysis method that performs as efficiently as joint analysis under general settings. Our proposed method can accurately approximate the score statistics obtainable by joint analysis, for both linear and logistic regression models, with and without covariates. In addition, we propose a novel approach to adjust for population stratification by correcting for known population structures through minor allele frequencies (MAFs). In the simulated gene-level association studies under unbalanced settings, our method recovered up to 85% power loss caused by the standard method. We further showed the power gain of our method in gene-level association studies with 26 unbalanced real studies of Age-related Macular Degeneration (AMD). In addition, we took the meta-analysis of three studies of type 2 diabetes (T2D) as an example to discuss the challenges of meta-analyzing multi-ethnic samples. In summary, we propose improved single-variant score statistics in meta-analysis, requiring “accurate” population-specific MAFs for multi-ethnic studies. These improved score statistics can be used to construct both single-variant and gene-level association studies, providing a useful framework for ensuring well-powered, convenient, cross-study analyses.

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