XRCC5 as a Risk Gene for Alcohol Dependence: Evidence from a Genome-Wide Gene-Set-Based Analysis and Follow-up Studies in Drosophila and Humans

Abstract It is believed that multiple genetic variants with small individual effects contribute to the risk of alcohol dependence. Such polygenic effects are difficult to detect in genome-wide association studies that test for association of the phenotype with each single nucleotide polymorphism (SNP) individually. To overcome this challenge, gene-set analysis (GSA) methods that jointly test for the effects of pre-defined groups of genes have been proposed. Rather than testing for association between the phenotype and individual SNPs, these analyses evaluate the global evidence of association with a set of related genes enabling the identification of cellular or molecular pathways or biological processes that play a role in development of the disease. It is hoped that by aggregating the evidence of association for all available SNPs in a group of related genes, these approaches will have enhanced power to detect genetic associations with complex traits. We performed GSA using data from a genome-wide study of 1165 alcohol-dependent cases and 1379 controls from the Study of Addiction: Genetics and Environment (SAGE), for all 200 pathways listed in the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Results demonstrated a potential role of the ‘synthesis and degradation of ketone bodies’ pathway. Our results also support the potential involvement of the ‘neuroactive ligand–receptor interaction’ pathway, which has previously been implicated in addictive disorders. These findings demonstrate the utility of GSA in the study of complex disease, and suggest specific directions for further research into the genetic architecture of alcohol dependence.

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DNA Methylation at ATP11A cg11702988 Is a Biomarker of Lung Disease Severity in Cystic Fibrosis: A Longitudinal Study

Genes ◽

10.3390/genes12030441 ◽

2021 ◽

Vol 12 (3) ◽

pp. 441

Author(s):

Fanny Pineau ◽

Davide Caimmi ◽

Sylvie Taviaux ◽

Maurane Reveil ◽

Laura Brosseau ◽

...

Keyword(s):

Cystic Fibrosis ◽

Dna Methylation ◽

Clinical Trials ◽

Lung Disease ◽

Disease Severity ◽

Genome Wide ◽

A Genome ◽

Lung Disease Severity ◽

Epigenetic Biomarker

Cystic fibrosis (CF) is a chronic genetic disease that mainly affects the respiratory and gastrointestinal systems. No curative treatments are available, but the follow-up in specialized centers has greatly improved the patient life expectancy. Robust biomarkers are required to monitor the disease, guide treatments, stratify patients, and provide outcome measures in clinical trials. In the present study, we outline a strategy to select putative DNA methylation biomarkers of lung disease severity in cystic fibrosis patients. In the discovery step, we selected seven potential biomarkers using a genome-wide DNA methylation dataset that we generated in nasal epithelial samples from the MethylCF cohort. In the replication step, we assessed the same biomarkers using sputum cell samples from the MethylBiomark cohort. Of interest, DNA methylation at the cg11702988 site (ATP11A gene) positively correlated with lung function and BMI, and negatively correlated with lung disease severity, P. aeruginosa chronic infection, and the number of exacerbations. These results were replicated in prospective sputum samples collected at four time points within an 18-month period and longitudinally. To conclude, (i) we identified a DNA methylation biomarker that correlates with CF severity, (ii) we provided a method to easily assess this biomarker, and (iii) we carried out the first longitudinal analysis of DNA methylation in CF patients. This new epigenetic biomarker could be used to stratify CF patients in clinical trials.

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Co-Overexpression of TWIST1-CSF1 Is a Common Event in Metastatic Oral Cancer and Drives Biologically Aggressive Phenotype

Cancers ◽

10.3390/cancers13010153 ◽

2021 ◽

Vol 13 (1) ◽

pp. 153

Author(s):

Sabrina Daniela da Silva ◽

Fabio Albuquerque Marchi ◽

Jie Su ◽

Long Yang ◽

Ludmila Valverde ◽

...

Keyword(s):

Epithelial Mesenchymal Transition ◽

Inflammatory Cells ◽

Rank Test ◽

Mesenchymal Transition ◽

Genome Wide ◽

A Genome ◽

Enhanced Expression ◽

Advanced Stages

Invasive oral squamous cell carcinoma (OSCC) is often ulcerated and heavily infiltrated by pro-inflammatory cells. We conducted a genome-wide profiling of tissues from OSCC patients (early versus advanced stages) with 10 years follow-up. Co-amplification and co-overexpression of TWIST1, a transcriptional activator of epithelial-mesenchymal-transition (EMT), and colony-stimulating factor-1 (CSF1), a major chemotactic agent for tumor-associated macrophages (TAMs), were observed in metastatic OSCC cases. The overexpression of these markers strongly predicted poor patient survival (log-rank test, p = 0.0035 and p = 0.0219). Protein analysis confirmed the enhanced expression of TWIST1 and CSF1 in metastatic tissues. In preclinical models using OSCC cell lines, macrophages, and an in vivo matrigel plug assay, we demonstrated that TWIST1 gene overexpression induces the activation of CSF1 while TWIST1 gene silencing down-regulates CSF1 preventing OSCC invasion. Furthermore, excessive macrophage activation and polarization was observed in co-culture system involving OSCC cells overexpressing TWIST1. In summary, this study provides insight into the cooperation between TWIST1 transcription factor and CSF1 to promote OSCC invasiveness and opens up the potential therapeutic utility of currently developed antibodies and small molecules targeting cancer-associated macrophages.

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Genome-wide imaging screen uncovers molecular determinants of arsenite-induced protein aggregation and toxicity

Journal of Cell Science ◽

10.1242/jcs.258338 ◽

2021 ◽

Author(s):

Stefanie Andersson ◽

Antonia Romero ◽

Joana Isabel Rodrigues ◽

Sansan Hua ◽

Xinxin Hao ◽

...

Keyword(s):

Protein Aggregation ◽

Translational Control ◽

Protein Biosynthesis ◽

Cellular Systems ◽

Genome Wide ◽

A Genome ◽

Toxic Metalloid ◽

Arsenic Stress

The toxic metalloid arsenic causes widespread misfolding and aggregation of cellular proteins. How these protein aggregates are formed in vivo, the mechanisms by which they affect cells, and how cells prevent their accumulation is not fully understood. To find components involved in these processes, we performed a genome-wide imaging screen and identified yeast deletion mutants with either enhanced or reduced protein aggregation levels during arsenite exposure. We show that many of the identified factors are crucial to safeguard protein homeostasis (proteostasis) and to protect cells against arsenite toxicity. The hits were enriched for various functions including protein biosynthesis and transcription, and dedicated follow-up experiments highlight the importance of accurate transcriptional and translational control for mitigating protein aggregation and toxicity during arsenite stress. Some of the hits are associated with pathological conditions, suggesting that arsenite-induced protein aggregation may affect disease processes. The broad network of cellular systems that impinge on proteostasis during arsenic stress identified in this current study provides a valuable resource and a framework for further elucidation of the mechanistic details of metalloid toxicity and pathogenesis.

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A Genome-Wide Association Study Reveals New Genes in a Molecular Network Associated with Alcohol Dependence and Related Clinical Measures

SSRN Electronic Journal ◽

10.2139/ssrn.3940144 ◽

2021 ◽

Author(s):

Anastasia Levchenko ◽

Sergey Malov ◽

Alexey Antonik ◽

Kseniya Rybakova ◽

Alexey Yakovlev ◽

...

Keyword(s):

Alcohol Dependence ◽

Association Study ◽

Genome Wide Association Study ◽

Molecular Network ◽

Genome Wide Association ◽

New Genes ◽

Genome Wide ◽

A Genome ◽

Clinical Measures

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TEGS-CN: A Statistical Method for Pathway Analysis of Genome-wide Copy Number Profile

Cancer Informatics ◽

10.4137/cin.s13978 ◽

2014 ◽

Vol 13s4 ◽

pp. CIN.S13978

Author(s):

Yen-Tsung Huang ◽

Thomas Hsu ◽

David C. Christiani

Keyword(s):

Copy Number ◽

Copy Number Data ◽

Copy Number Profile ◽

Test Statistic ◽

Gene Set ◽

Bonferroni Adjustment ◽

Gene Sets ◽

Genome Wide ◽

A Genome ◽

Pathway Analyses

The effects of copy number alterations make up a significant part of the tumor genome profile, but pathway analyses of these alterations are still not well established. We proposed a novel method to analyze multiple copy numbers of genes within a pathway, termed Test for the Effect of a Gene Set with Copy Number data (TEGS-CN). TEGS-CN was adapted from TEGS, a method that we previously developed for gene expression data using a variance component score test. With additional development, we extend the method to analyze DNA copy number data, accounting for different sizes and thus various numbers of copy number probes in genes. The test statistic follows a mixture of X 2 distributions that can be obtained using permutation with scaled X 2 approximation. We conducted simulation studies to evaluate the size and the power of TEGS-CN and to compare its performance with TEGS. We analyzed a genome-wide copy number data from 264 patients of non-small-cell lung cancer. With the Molecular Signatures Database (MSigDB) pathway database, the genome-wide copy number data can be classified into 1814 biological pathways or gene sets. We investigated associations of the copy number profile of the 1814 gene sets with pack-years of cigarette smoking. Our analysis revealed five pathways with significant P values after Bonferroni adjustment (<2.8 x 10-5), including the PTEN pathway (7.8 x 10-7), the gene set up-regulated under heat shock (3.6 x 10-6), the gene sets involved in the immune profile for rejection of kidney transplantation (9.2 x 10-6) and for transcriptional control of leukocytes (2.2 x 10-5), and the ganglioside biosynthesis pathway (2.7 x 10-5). In conclusion, we present a new method for pathway analyses of copy number data, and causal mechanisms of the five pathways require further study.

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A genome-wide linkage analysis of alcoholism on microsatellite and single-nucleotide polymorphism data, using alcohol dependence phenotypes and electroencephalogram measures

BMC Genetics ◽

10.1186/1471-2156-6-s1-s17 ◽

2005 ◽

Vol 6 (Suppl 1) ◽

pp. S17 ◽

Cited By ~ 5

Author(s):

Chun Zhang ◽

Simon Cawley ◽

Guoying Liu ◽

Manqiu Cao ◽

Harley Gorrell ◽

...

Keyword(s):

Single Nucleotide Polymorphism ◽

Linkage Analysis ◽

Alcohol Dependence ◽

Single Nucleotide Polymorphism Data ◽

Nucleotide Polymorphism ◽

Single Nucleotide ◽

Genome Wide ◽

A Genome ◽

Polymorphism Data

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Efficient estimation of disease odds ratios for follow-up genetic association studies

Statistical Methods in Medical Research ◽

10.1177/0962280217741771 ◽

2017 ◽

Vol 28 (7) ◽

pp. 1927-1941

Author(s):

Jiyuan Hu ◽

Wei Zhang ◽

Xinmin Li ◽

Dongdong Pan ◽

Qizhai Li

Keyword(s):

Genetic Association ◽

Association Studies ◽

Genetic Association Studies ◽

Efficient Estimation ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Odds Ratios ◽

Genome Wide ◽

Follow Up Studies

In the past decade, genome-wide association studies have identified thousands of susceptible variants associated with complex human diseases and traits. Conducting follow-up genetic association studies has become a standard approach to validate the findings of genome-wide association studies. One problem of high interest in genetic association studies is to accurately estimate the strength of the association, which is often quantified by odds ratios in case-control studies. However, estimating the association directly by follow-up studies is inefficient since this approach ignores information from the genome-wide association studies. In this article, an estimator called GFcom, which integrates information from genome-wide association studies and follow-up studies, is proposed. The estimator includes both the point estimate and corresponding confidence interval. GFcom is more efficient than competing estimators regarding MSE and the length of confidence intervals. The superiority of GFcom is particularly evident when the genome-wide association study suffers from severe selection bias. Comprehensive simulation studies and applications to three real follow-up studies demonstrate the performance of the proposed estimator. An R package, “GFcom”, implementing our method is publicly available at https://github.com/JiyuanHu/GFcom .

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