scholarly journals Integrating Genome-Wide Association and eQTLs Studies Identifies the Genes and Gene Sets Associated with Diabetes

2017 ◽  
Vol 2017 ◽  
pp. 1-4 ◽  
Author(s):  
Xiao Liang ◽  
Awen He ◽  
Wenyu Wang ◽  
Li Liu ◽  
Yanan Du ◽  
...  
Keyword(s):  
Fasting Glucose ◽  
Association Studies ◽  
Integrative Analysis ◽  
Gene Set Enrichment Analysis ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Gene Set ◽  
Gene Sets ◽  
Genome Wide ◽  
Summary Data

Aim. To identify novel candidate genes and gene sets for diabetes. Methods. We performed an integrative analysis of genome-wide association studies (GWAS) and expression quantitative trait loci (eQTLs) data for diabetes. Summary data was driven from a large-scale GWAS of diabetes, totally involving 58,070 individuals. eQTLs dataset included 923,021 cis-eQTL for 14,329 genes and 4,732 trans-eQTL for 2,612 genes. Integrative analysis of GWAS and eQTLs data was conducted by summary data-based Mendelian randomization (SMR). To identify the gene sets associated with diabetes, the SMR single gene analysis results were further subjected to gene set enrichment analysis (GSEA). A total of 13,311 annotated gene sets were analyzed in this study. Results. SMR analysis identified 6 genes significantly associated with fasting glucose, such as C11ORF10 (p value = 6.04 × 10−8), MRPL33 (p value = 1.24 × 10−7), and FADS1 (p value = 2.39 × 10−7). Gene set analysis identified HUANG_FOXA2_TARGETS_UP (false discovery rate = 0.047) associated with fasting glucose. Conclusion. Our study provides novel clues for clarifying the genetic mechanism of diabetes. This study also illustrated the good performance of SMR approach and extended it to gene set association analysis for complex diseases.

scholarly journals Importance of SNP Dependency Correction and Association Integration for Gene Set Analysis in Genome-Wide Association Studies

2021 ◽  
Vol 12 ◽  
Author(s):  
Michal Marczyk ◽  
Agnieszka Macioszek ◽  
Joanna Tobiasz ◽  
Joanna Polanska ◽  
Joanna Zyla
Keyword(s):  
Association Studies ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Genome Wide Association ◽  
Gene Set Analysis ◽  
Genome Wide Association Studies ◽  
Gene Set Enrichment ◽  
Gene Set ◽  
Genome Wide ◽  
The Impact

A typical genome-wide association study (GWAS) analyzes millions of single-nucleotide polymorphisms (SNPs), several of which are in a region of the same gene. To conduct gene set analysis (GSA), information from SNPs needs to be unified at the gene level. A widely used practice is to use only the most relevant SNP per gene; however, there are other methods of integration that could be applied here. Also, the problem of nonrandom association of alleles at two or more loci is often neglected. Here, we tested the impact of incorporation of different integrations and linkage disequilibrium (LD) correction on the performance of several GSA methods. Matched normal and breast cancer samples from The Cancer Genome Atlas database were used to evaluate the performance of six GSA algorithms: Coincident Extreme Ranks in Numerical Observations (CERNO), Gene Set Enrichment Analysis (GSEA), GSEA-SNP, improved GSEA for GWAS (i-GSEA4GWAS), Meta-Analysis Gene-set Enrichment of variaNT Associations (MAGENTA), and Over-Representation Analysis (ORA). Association of SNPs to phenotype was calculated using modified McNemar’s test. Results for SNPs mapped to the same gene were integrated using Fisher and Stouffer methods and compared with the minimum p-value method. Four common measures were used to quantify the performance of all combinations of methods. Results of GSA analysis on GWAS were compared to the one performed on gene expression data. Comparing all evaluation metrics across different GSA algorithms, integrations, and LD correction, we highlighted CERNO, and MAGENTA with Stouffer as the most efficient. Applying LD correction increased prioritization and specificity of enrichment outcomes for all tested algorithms. When Fisher or Stouffer were used with LD, sensitivity and reproducibility were also better. Using any integration method was beneficial in comparison with a minimum p-value method in specific combinations. The correlation between GSA results from genomic and transcriptomic level was the highest when Stouffer integration was combined with LD correction. We thoroughly evaluated different approaches to GSA in GWAS in terms of performance to guide others to select the most effective combinations. We showed that LD correction and Stouffer integration could increase the performance of enrichment analysis and encourage the usage of these techniques.

scholarly journals Impact of repeated measures and sample selection on genome-wide association studies of fasting glucose

2010 ◽  
Vol 34 (7) ◽  
pp. 665-673 ◽  
Author(s):  
Laura J. Rasmussen-Torvik ◽  
Alvaro Alonso ◽  
Man Li ◽  
Wen Kao ◽  
Anna Köttgen ◽  
...  
Keyword(s):  
Repeated Measures ◽  
Fasting Glucose ◽  
Association Studies ◽  
Sample Selection ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide

scholarly journals Meta-analyses of genome wide association studies in lines of laying hens divergently selected for feather pecking using imputed sequence level genotypes

BMC Genetics ◽  
2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Clemens Falker-Gieske ◽  
Hanna Iffland ◽  
Siegfried Preuß ◽  
Werner Bessei ◽  
Cord Drögemüller ◽  
...  
Keyword(s):  
Brain Structure ◽  
Laying Hens ◽  
Association Studies ◽  
Gene Set Enrichment Analysis ◽  
Genome Wide Association ◽  
Economic Losses ◽  
Genome Wide Association Studies ◽  
Feather Pecking ◽  
Genome Wide ◽  
Meta Analyses

Abstract Background Feather pecking (FP) is damaging behavior in laying hens leading to global economic losses in the layer industry and massive impairments of animal welfare. The objective of the study was to discover genetic variants and affected genes that lead to FP behavior. To achieve that we imputed low-density genotypes from two different populations of layers divergently selected for FP to sequence level by performing whole genome sequencing on founder and half-sib individuals. In order to decipher the genetic structure of FP, genome wide association studies and meta-analyses of two resource populations were carried out by focusing on the traits ‘feather pecks delivered’ (FPD) and the ‘posterior probability of a hen to belong to the extreme feather pecking subgroup’ (pEFP). Results In this meta-analysis, we discovered numerous genes that are affected by polymorphisms significantly associated with the trait FPD. Among them SPATS2L, ZEB2, KCHN8, and MRPL13 which have been previously connected to psychiatric disorders with the latter two being responsive to nicotine treatment. Gene set enrichment analysis revealed that phosphatidylinositol signaling is affected by genes identified in the GWAS and that the Golgi apparatus as well as brain structure may be involved in the development of a FP phenotype. Further, we were able to validate a previously discovered QTL for the trait pEFP on GGA1, which contains variants affecting NIPA1, KIAA1211L, AFF3, and TSGA10. Conclusions We provide evidence for the involvement of numerous genes in the propensity to exhibit FP behavior that could aid in the selection against this unwanted trait. Furthermore, we identified variants that are involved in phosphatidylinositol signaling, Golgi metabolism and cell structure and therefore propose changes in brain structure to be an influential factor in FP, as already described in human neuropsychiatric disorders.

Identification of Critical Core Genes of Sarcoma Based on Centrality Analysis of Networks Nodes

2020 ◽  
Vol 10 (7) ◽  
pp. 1776-1784
Author(s):  
Shudong Wang ◽  
Jixiao Wang ◽  
Xinzeng Wang ◽  
Yuanyuan Zhang ◽  
Tao Yi
Keyword(s):  
Association Studies ◽  
Meta Analysis ◽  
Complex Diseases ◽  
Enrichment Analysis ◽  
Gene Interaction ◽  
Core Gene ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Gene Set ◽  
Genome Wide

Genome-wide association studies (GWAS) are powerful tools for identifying pathogenic genes of complex diseases and revealing genetic structure of diseases. However, due to gene-to-gene interactions, only a part of the hereditary factors can be revealed. The meta-analysis based on GWAS can integrate gene expression data at multiple levels and reveal the complex relationship between genes. Therefore, we used meta-analysis to integrate GWAS data of sarcoma to establish complex networks and discuss their significant genes. Firstly, we established gene interaction networks based on the data of different subtypes of sarcoma to analyze the node centralities of genes. Secondly, we calculated the significant score of each gene according to the Staged Significant Gene Network Algorithm (SSGNA). Then, we obtained the critical gene set HYC of sarcoma by ranking the scores, and then combined Gene Ontology enrichment analysis and protein network analysis to further screen it. Finally, the critical core gene set Hcore containing 47 genes was obtained and validated by GEPIA analysis. Our method has certain generalization performance to the study of complex diseases with prior knowledge and it is a useful supplement to genome-wide association studies.

scholarly journals FINEMAP: efficient variable selection using summary data from genome-wide association studies

2016 ◽  
Vol 32 (10) ◽  
pp. 1493-1501 ◽  
Author(s):  
Christian Benner ◽  
Chris C.A. Spencer ◽  
Aki S. Havulinna ◽  
Veikko Salomaa ◽  
Samuli Ripatti ◽  
...  
Keyword(s):  
Variable Selection ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Summary Data
Sign in / Sign up

Export Citation Format

Share Document