scholarly journals Identification of Novel Susceptible Genes of Gastric Cancer Based on Integrated Omics Data

2021 ◽  
Vol 9 ◽  
Author(s):  
Huang Yaoxing ◽  
Yu Danchun ◽  
Sun Xiaojuan ◽  
Jiang Shuman ◽  
Yan Qingqing ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Association Studies ◽  
Gene Expression Level ◽  
Expression Level ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Coding Region ◽  
Level Data ◽  
Integrated Omics

Gastric cancer (GC) is one of the most common causes of cancer-related deaths in the world. This cancer has been regarded as a biological and genetically heterogeneous disease with a poorly understood carcinogenesis at the molecular level. Thousands of biomarkers and susceptible loci have been explored via experimental and computational methods, but their effects on disease outcome are still unknown. Genome-wide association studies (GWAS) have identified multiple susceptible loci for GC, but due to the linkage disequilibrium (LD), single-nucleotide polymorphisms (SNPs) may fall within the non-coding region and exert their biological function by modulating the gene expression level. In this study, we collected 1,091 cases and 410,350 controls from the GWAS catalog database. Integrating with gene expression level data obtained from stomach tissue, we conducted a machine learning-based method to predict GC-susceptible genes. As a result, we identified 787 novel susceptible genes related to GC, which will provide new insight into the genetic and biological basis for the mechanism and pathology of GC development.

scholarly journals Genome-wide association studies of yield-related traits in high-latitude japonica rice

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Guomin Zhang ◽  
Rongsheng Wang ◽  
Juntao Ma ◽  
Hongru Gao ◽  
Lingwei Deng ◽  
...  
Keyword(s):  
Linear Model ◽  
High Latitude ◽  
Association Studies ◽  
Genome Wide Association ◽  
Japonica Rice ◽  
Genome Wide Association Studies ◽  
Heilongjiang Province ◽  
Nucleotide Polymorphisms ◽  
Coding Region ◽  
Genome Wide

Abstract Background Heilongjiang Province is a high-quality japonica rice cultivation area in China. One in ten bowls of Chinese rice is produced here. Increasing yield is one of the main aims of rice production in this area. However, yield is a complex quantitative trait composed of many factors. The purpose of this study was to determine how many genetic loci are associated with yield-related traits. Genome-wide association studies (GWAS) were performed on 450 accessions collected from northeast Asia, including Russia, Korea, Japan and Heilongjiang Province of China. These accessions consist of elite varieties and landraces introduced into Heilongjiang Province decade ago. Results After resequencing of the 450 accessions, 189,019 single nucleotide polymorphisms (SNPs) were used for association studies by two different models, a general linear model (GLM) and a mixed linear model (MLM), examining four traits: days to heading (DH), plant height (PH), panicle weight (PW) and tiller number (TI). Over 25 SNPs were found to be associated with each trait. Among them, 22 SNPs were selected to identify candidate genes, and 2, 8, 1 and 11 SNPs were found to be located in 3′ UTR region, intron region, coding region and intergenic region, respectively. Conclusions All SNPs detected in this research may become candidates for further fine mapping and may be used in the molecular breeding of high-latitude rice.

scholarly journals A selective inference approach for false discovery rate control using multiomics covariates yields insights into disease risk

2020 ◽  
Vol 117 (26) ◽  
pp. 15028-15035 ◽  
Author(s):  
Ronald Yurko ◽  
Max G’Sell ◽  
Kathryn Roeder ◽  
Bernie Devlin
Keyword(s):  
Gene Expression ◽  
Multiple Testing ◽  
Disease Risk ◽  
Association Studies ◽  
Auxiliary Information ◽  
Primary Data ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Hypothesis Tests ◽  
Selective Inference

To correct for a large number of hypothesis tests, most researchers rely on simple multiple testing corrections. Yet, new methodologies of selective inference could potentially improve power while retaining statistical guarantees, especially those that enable exploration of test statistics using auxiliary information (covariates) to weight hypothesis tests for association. We explore one such method, adaptiveP-value thresholding (AdaPT), in the framework of genome-wide association studies (GWAS) and gene expression/coexpression studies, with particular emphasis on schizophrenia (SCZ). Selected SCZ GWAS associationPvalues play the role of the primary data for AdaPT; single-nucleotide polymorphisms (SNPs) are selected because they are gene expression quantitative trait loci (eQTLs). This natural pairing of SNPs and genes allow us to map the following covariate values to these pairs: GWAS statistics from genetically correlated bipolar disorder, the effect size of SNP genotypes on gene expression, and gene–gene coexpression, captured by subnetwork (module) membership. In all, 24 covariates per SNP/gene pair were included in the AdaPT analysis using flexible gradient boosted trees. We demonstrate a substantial increase in power to detect SCZ associations using gene expression information from the developing human prefrontal cortex. We interpret these results in light of recent theories about the polygenic nature of SCZ. Importantly, our entire process for identifying enrichment and creating features with independent complementary data sources can be implemented in many different high-throughput settings to ultimately improve power.

scholarly journals THE EFFECT OF SINGLE NUCLEOTIDE POLYMORPHISM (SNP) IN GLIOBLASTOMA MULTIFORME

2021 ◽  
Author(s):  
Asmita Ghosh ◽  
Dattatreya Mukherjee ◽  
Parth Patel ◽  
Debraj Mukhopadhyay
Keyword(s):  
Single Nucleotide Polymorphism ◽  
Glioblastoma Multiforme ◽  
Association Studies ◽  
Disease Onset ◽  
Genetic Alterations ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Coding Region ◽  
Nucleotide Polymorphism ◽  
Single Nucleotide

Single nucleotide polymorphism is a genetic substitution of a base pair at a single position of the genome. SNPs are a common phenomenon and influence mRNA expression. Half of the SNPs occur in the non-coding region with 25% being mis-sense mutation and 25% being silent mutations. SNPs belong to the last generation of molecular markers which is identified through SNP mapping. SNPs are extensively studied to distinguish genetic expression and protein synthesis. These genetic differences are a major source of diseases in humans like cancers. One of the most common types of cancer of the brain is the Glioblastoma Multiforme that accounts for more than 80% of the malignant primary brain tumors (PBT). Researchers have found out a potential role of various SNPs in the genome to have a strong relation with Glioma formation and proliferation. Most SNPs are either not discovered, or their biological mechanisms are unknown, making it difficult to link putative associations with disease onset. The given review aims to identify some of the most common SNPs associated with GBM and classify the genetic basis along with future prospects. These SNPs are pioneer in Genome Wide Association studies to help in cancer research and identification of specific genetic alterations liked to GBM. Single Nucleotide Polymorphisms in a gene can be used as genetic biomarkers to aid better understanding of the mechanism of cancer formation, its aetiology, progression and metastatic behaviour.

scholarly journals A novel quantile regression approach for eQTL discovery

2016 ◽  
Author(s):  
Xiaoyu Song ◽  
Gen Li ◽  
Iuliana Ionita-Laza ◽  
Ying Wei
Keyword(s):  
Gene Expression ◽  
Genetic Variation ◽  
Linear Regression ◽  
Genetic Variants ◽  
Molecular Mechanisms ◽  
Association Studies ◽  
Expression Level ◽  
Special Focus ◽  
Genome Wide Association Studies ◽  
Genome Wide

AbstractOver the past decade, there has been a remarkable improvement in our understanding of the role of genetic variation in complex human diseases, especially via genome-wide association studies. However, the underlying molecular mechanisms are still poorly characterized, impending the development of therapeutic interventions. Identifying genetic variants that influence the expression level of a gene, i.e. expression quantitative trait loci (eQTLs), can help us understand how genetic variants influence traits at the molecular level. While most eQTL studies focus on identifying mean effects on gene expression using linear regression, evidence suggests that genetic variation can impact the entire distribution of the expression level. Indeed, several studies have already investigated higher order associations with a special focus on detecting heteroskedasticity. In this paper, we develop a Quantile Rank-score Based Test (QRBT) to identify eQTLs that are associated with the conditional quantile functions of gene expression. We have applied the proposed QRBT to the Genotype-Tissue Expression project, an international tissue bank for studying the relationship between genetic variation and gene expression in human tissues, and found that the proposed QRBT complements the existing methods, and identifies new eQTLs with heterogeneous effects genome-wideacross different quantile levels. Notably, we show that the eQTLs identified by QRBT but missed by linear regression are more likely to be tissue specific, and also associated with greater enrichment in genome-wide significant SNPs from the GWAS catalog. An R package implementing QRBT is available on our website.

scholarly journals VRK2 gene expression in schizophrenia, bipolar disorder and healthy controls

2016 ◽  
Vol 209 (2) ◽  
pp. 114-120 ◽  
Author(s):  
Martin Tesli ◽  
Katrine Verena Wirgenes ◽  
Timothy Hughes ◽  
Francesco Bettella ◽  
Lavinia Athanasiu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Bipolar Disorder ◽  
Association Studies ◽  
Mrna Level ◽  
Genome Wide Association Studies ◽  
Mrna Levels ◽  
Healthy Controls ◽  
Nucleotide Polymorphisms ◽  
Schizophrenia Spectrum Disorders ◽  
Underlying Mechanisms

BackgroundCommon variants in the Vaccinia-related kinase 2 (VRK2) gene have been associated with schizophrenia, but the relevance of its encoded protein VRK2 in the disorder remains unclear.AimsTo identify potential differences in VRK2 gene expression levels between schizophrenia, bipolar disorder, psychosis not otherwise specified (PNOS) and healthy controls.MethodVRK2 mRNA level was measured in whole blood in 652 individuals (schizophrenia, n = 201; bipolar disorder, n = 167; PNOS, n = 61; healthy controls, n = 223), and compared across diagnostic categories and subcategories. Additionally, we analysed for association between 1566 VRK2 single nucleotide polymorphisms and mRNA levels.ResultsWe found lower VRK2 mRNA levels in schizophrenia compared with healthy controls (P<10–12), bipolar disorder (P<10–12) and PNOS (P = 0.0011), and lower levels in PNOS than in healthy controls (P = 0.0042) and bipolar disorder (P = 0.00026). Expression quantitative trait loci in close proximity to the transcription start site of the short isoforms of the VRK2 gene were identified.ConclusionsAltered VRK2 gene expression seems specific for schizophrenia and PNOS, which is in accordance with findings from genome-wide association studies. These results suggest that reduced VRK2 mRNA levels are involved in the underlying mechanisms in schizophrenia spectrum disorders.

scholarly journals Identifying Liver Cancer-Related Enhancer SNPs by Integrating GWAS and Histone Modification ChIP-seq Data

2016 ◽  
Vol 2016 ◽  
pp. 1-6 ◽  
Author(s):  
Tianjiao Zhang ◽  
Yang Hu ◽  
Xiaoliang Wu ◽  
Rui Ma ◽  
Qinghua Jiang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Liver Cancer ◽  
Histone Modification ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Protein Coding ◽  
Noncoding Regions ◽  
Coding Regions ◽  
Genome Wide

Many disease-related single nucleotide polymorphisms (SNPs) have been inferred from genome-wide association studies (GWAS) in recent years. Numerous studies have shown that some SNPs located in protein-coding regions are associated with numerous diseases by affecting gene expression. However, in noncoding regions, the mechanism of how SNPs contribute to disease susceptibility remains unclear. Enhancer elements are functional segments of DNA located in noncoding regions that play an important role in regulating gene expression. The SNPs located in enhancer elements may affect gene expression and lead to disease. We presented a method for identifying liver cancer-related enhancer SNPs through integrating GWAS and histone modification ChIP-seq data. We identified 22 liver cancer-related enhancer SNPs, 9 of which were regulatory SNPs involved in distal transcriptional regulation. The results highlight that these enhancer SNPs may play important roles in liver cancer.

scholarly journals Identification of genes with correlated patterns of variations in DNA copy number and gene expression level in gastric cancer

Genomics ◽  
2007 ◽  
Vol 89 (4) ◽  
pp. 451-459 ◽  
Author(s):  
Sanghwa Yang ◽  
Hei-Cheul Jeung ◽  
Ha Jin Jeong ◽  
Yeon Ho Choi ◽  
Ji Eun Kim ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Copy Number ◽  
Gene Expression Level ◽  
Expression Level ◽  
Dna Copy Number ◽  
Correlated Patterns

Reference SVA insertion polymorphisms are associated with dopaminergic degeneration in Parkinson’s Disease and differential gene expression in the PPMI cohort

2020 ◽  
Author(s):  
Abigail L Pfaff ◽  
Vivien J. Bubb ◽  
John P. Quinn ◽  
Sulev Koks
Keyword(s):  
Gene Expression ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Differential Gene Expression ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Sequencing Data ◽  
Genome Wide ◽  
Differential Gene

Abstract Background: The development of Parkinson’s disease (PD) involves a complex interaction of genetic and environmental factors. The majority of studies investigating the genetic component of complex diseases, including PD, have focused on single nucleotide polymorphisms as this enables genome wide analysis of a large number of samples. Genome wide association studies have been crucial in identifying PD risk variants, however a large proportion of the heritability of PD remains to be identified. To investigate the component of PD that may involve complex genetic variants we characterised SINE-VNTR-Alus (SVAs), a retrotransposon known to affect gene expression, in the Parkinson’s Progression Markers Initiative (PPMI) cohort.Results: Utilising whole genome sequencing from the PPMI cohort that consisted of 179 healthy controls, 371 individuals with PD and 58 individuals classified as SWEDD (scans without evidence of dopaminergic deficit) we genotyped SVAs in the reference genome for their presence or absence identifying 81 such SVAs. Seven of these SVAs were associated with progression of the disease, including four whose specific genotypes were linked to an increase in the gradient of dopaminergic loss when comparing the caudate to putamen from DaTscan imaging analysis. These seven SVAs also demonstrated regulatory properties as they were associated with differential gene expression in whole blood RNA sequencing data.Conclusion: This study highlights the importance of addressing variation of SVAs and potentially other types of retrotransposons in PD genetics, furthermore these SVA elements should be considered as regulatory domains that could play a role in disease progression.

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