Identification of the Risk Genes Associated With Vulnerability to Addiction: Major Findings From Transgenic Animals

Understanding risk factors for substance use disorders (SUD) can facilitate medication development for SUD treatment. While a rich literature exists discussing environmental factors that influence SUD, fewer articles have focused on genetic factors that convey vulnerability to drug use. Methods to identify SUD risk genes include Genome-Wide Association Studies (GWAS) and transgenic approaches. GWAS have identified hundreds of gene variants or single nucleotide polymorphisms (SNPs). However, few genes identified by GWAS have been verified by clinical or preclinical studies. In contrast, significant progress has been made in transgenic approaches to identify risk genes for SUD. In this article, we review recent progress in identifying candidate genes contributing to drug use and addiction using transgenic approaches. A central hypothesis is if a particular gene variant (e.g., resulting in reduction or deletion of a protein) is associated with increases in drug self-administration or relapse to drug seeking, this gene variant may be considered a risk factor for drug use and addiction. Accordingly, we identified several candidate genes such as those that encode dopamine D2 and D3 receptors, mGluR2, M4 muscarinic acetylcholine receptors, and α5 nicotinic acetylcholine receptors, which appear to meet the risk-gene criteria when their expression is decreased. Here, we describe the role of these receptors in drug reward and addiction, and then summarize major findings from the gene-knockout mice or rats in animal models of addiction. Lastly, we briefly discuss future research directions in identifying addiction-related risk genes and in risk gene-based medication development for the treatment of addiction.

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Integrated multi-omics data analysis identifies a novel genetics-risk gene of IRF4 associated with prognosis of oral cavity cancer

10.1101/2021.11.17.21266500 ◽

2021 ◽

Author(s):

Yan Lv ◽

Yukuan Huang ◽

Xuejun Xu ◽

Zhiwei Wang ◽

Yunlong Ma ◽

...

Keyword(s):

Gene Expression ◽

Oral Cavity ◽

Genetic Variants ◽

Oral Cavity Cancer ◽

Association Studies ◽

Drug Repurposing ◽

Genome Wide Association Studies ◽

Omics Data ◽

Risk Genes ◽

Risk Gene

Oral cavity cancer (OCC) is one of the most common carcinoma diseases. Recent genome-wide association studies (GWAS) have reported numerous genetic variants associated with OCC susceptibility. However, the regulatory mechanisms of these genetic variants underlying OCC remain largely unclear. By combining GWAS summary statistics (N = 4,151) with expression quantitative trait loci (eQTL) across 49 different tissues from the GTEx database, we performed an integrative genomics analysis to uncover novel risk genes associated with OCC. By leveraging various computational methods based on multi-omics data, risk genes were prioritized as promising candidate genes for drug repurposing in OCC.Using two independent computational algorithms, we found that 14 risk genes whose genetics-modulated expressions showed a notable association with OCC. Among them, nine genes were newly identified, such as IRF4 (P = 2.5x10-9 and P = 1.06x10-4), TNS3 (P = 1.44x10-6 and P = 4.45x10-3), ZFP90 (P = 2.37x10-6 and P = 2.93x10-4), and DRD2 (P = 2.0x10-5 and P = 6.12x10-3). These 14 genes were significantly overrepresented in several cancer-related terms, and 10 of 14 genes were enriched in 10 potential druggable gene categories. Based on differential gene expression analysis, the majority of these genes (71.43%) showed remarkable differential expressions between OCC patients and paracancerous controls. Integration of multi-omics-based evidence from genetics, eQTL, and gene expression, we identified that the novel risk gene of IRF4 exhibited the highest ranked risk score for OCC. Survival analysis showed that dysregulation of IRF4 expression was significantly associated with cancer patients outcomes (P = 8.1x10-5). In summary, we prioritized 14 OCC-associated genes with nine novel risk genes, especially the IRF4 gene, which provides a drug repurposing resource to develop therapeutic drugs for oral cancer.

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A meta-analysis of genome-wide association studies for average daily gain and lean meat percentage in two Duroc pig populations

BMC Genomics ◽

10.1186/s12864-020-07288-1 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Shenping Zhou ◽

Rongrong Ding ◽

Fanming Meng ◽

Xingwang Wang ◽

Zhanwei Zhuang ◽

...

Keyword(s):

Candidate Genes ◽

Growth And Development ◽

Genetic Architecture ◽

Association Studies ◽

Meta Analysis ◽

Average Daily Gain ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Genome Wide ◽

Daily Gain

Abstract Background Average daily gain (ADG) and lean meat percentage (LMP) are the main production performance indicators of pigs. Nevertheless, the genetic architecture of ADG and LMP is still elusive. Here, we conducted genome-wide association studies (GWAS) and meta-analysis for ADG and LMP in 3770 American and 2090 Canadian Duroc pigs. Results In the American Duroc pigs, one novel pleiotropic quantitative trait locus (QTL) on Sus scrofa chromosome 1 (SSC1) was identified to be associated with ADG and LMP, which spans 2.53 Mb (from 159.66 to 162.19 Mb). In the Canadian Duroc pigs, two novel QTLs on SSC1 were detected for LMP, which were situated in 3.86 Mb (from 157.99 to 161.85 Mb) and 555 kb (from 37.63 to 38.19 Mb) regions. The meta-analysis identified ten and 20 additional SNPs for ADG and LMP, respectively. Finally, four genes (PHLPP1, STC1, DYRK1B, and PIK3C2A) were detected to be associated with ADG and/or LMP. Further bioinformatics analysis showed that the candidate genes for ADG are mainly involved in bone growth and development, whereas the candidate genes for LMP mainly participated in adipose tissue and muscle tissue growth and development. Conclusions We performed GWAS and meta-analysis for ADG and LMP based on a large sample size consisting of two Duroc pig populations. One pleiotropic QTL that shared a 2.19 Mb haplotype block from 159.66 to 161.85 Mb on SSC1 was found to affect ADG and LMP in the two Duroc pig populations. Furthermore, the combination of single-population and meta-analysis of GWAS improved the efficiency of detecting additional SNPs for the analyzed traits. Our results provide new insights into the genetic architecture of ADG and LMP traits in pigs. Moreover, some significant SNPs associated with ADG and/or LMP in this study may be useful for marker-assisted selection in pig breeding.

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A gene co-association network regulating gut microbial communities in a Duroc pig population

Microbiome ◽

10.1186/s40168-020-00994-8 ◽

2021 ◽

Vol 9 (1) ◽

Author(s):

Antonio Reverter ◽

Maria Ballester ◽

Pamela A. Alexandre ◽

Emilio Mármol-Sánchez ◽

Antoni Dalmau ◽

...

Keyword(s):

Microbial Communities ◽

Candidate Genes ◽

Relative Abundance ◽

Association Studies ◽

Single Gene ◽

Host Genome ◽

Genome Wide Association Studies ◽

Vaccine Response ◽

Microbiome Composition ◽

Host Genetic

Abstract Background Analyses of gut microbiome composition in livestock species have shown its potential to contribute to the regulation of complex phenotypes. However, little is known about the host genetic control over the gut microbial communities. In pigs, previous studies are based on classical “single-gene-single-trait” approaches and have evaluated the role of host genome controlling gut prokaryote and eukaryote communities separately. Results In order to determine the ability of the host genome to control the diversity and composition of microbial communities in healthy pigs, we undertook genome-wide association studies (GWAS) for 39 microbial phenotypes that included 2 diversity indexes, and the relative abundance of 31 bacterial and six commensal protist genera in 390 pigs genotyped for 70 K SNPs. The GWAS results were processed through a 3-step analytical pipeline comprised of (1) association weight matrix; (2) regulatory impact factor; and (3) partial correlation and information theory. The inferred gene regulatory network comprised 3561 genes (within a 5 kb distance from a relevant SNP–P < 0.05) and 738,913 connections (SNP-to-SNP co-associations). Our findings highlight the complexity and polygenic nature of the pig gut microbial ecosystem. Prominent within the network were 5 regulators, PRDM15, STAT1, ssc-mir-371, SOX9 and RUNX2 which gathered 942, 607, 588, 284 and 273 connections, respectively. PRDM15 modulates the transcription of upstream regulators of WNT and MAPK-ERK signaling to safeguard naive pluripotency and regulates the production of Th1- and Th2-type immune response. The signal transducer STAT1 has long been associated with immune processes and was recently identified as a potential regulator of vaccine response to porcine reproductive and respiratory syndrome. The list of regulators was enriched for immune-related pathways, and the list of predicted targets includes candidate genes previously reported as associated with microbiota profile in pigs, mice and human, such as SLIT3, SLC39A8, NOS1, IL1R2, DAB1, TOX3, SPP1, THSD7B, ELF2, PIANP, A2ML1, and IFNAR1. Moreover, we show the existence of host-genetic variants jointly associated with the relative abundance of butyrate producer bacteria and host performance. Conclusions Taken together, our results identified regulators, candidate genes, and mechanisms linked with microbiome modulation by the host. They further highlight the value of the proposed analytical pipeline to exploit pleiotropy and the crosstalk between bacteria and protists as significant contributors to host-microbiome interactions and identify genetic markers and candidate genes that can be incorporated in breeding program to improve host-performance and microbial traits.

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Microglia and its genetics in Alzheimer's Disease

Current Alzheimer Research ◽

10.2174/1567205018666211105140732 ◽

2021 ◽

Vol 18 ◽

Author(s):

Xinyan Liang ◽

Haijian Wu ◽

Mark Colt ◽

Xinying Guo ◽

Brock Pluimer ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Immune Cell ◽

Morphological Changes ◽

Association Studies ◽

Unmet Need ◽

Genome Wide Association Studies ◽

Protective Effects ◽

Risk Genes ◽

Main Component

: Alzheimer’s Disease (AD) is the most prevalent form of dementia across the world. While its discovery and pathological manifestations are centered on protein aggregations of amyloid-beta (Aβ) and hyperphosphorylated tau protein, neuroinflammation has emerged in the last decade as a main component of the disease in both pathogenesis and progression. As the main innate immune cell type in central nervous system (CNS), microglia play a very important role in regulating neuroinflammation, which occurs commonly in neurodegenerative conditions including AD. Under inflammatory response, microglia undergo morphological changes and status transition from homeostatic to activated forms. Different microglia subtypes displaying distinct genetic profiles have been identified in AD, and these signatures often link to AD risk genes identified from the genome-wide association studies (GWAS), such as APOE and TREM2. Furthermore, many of AD risk genes are highly enriched in microglia and specifically influence the functions of microglia in pathogenesis, e.g. releasing inflammatory cytokines and clearing Aβ. Therefore, building up a landscape of these risk genes in microglia, based on current preclinical studies and in the context of their pathogenic or protective effects, would largely help us to understand the complexed etiology of AD and provide new insight for the unmet need of effective treatment.

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A Network-based Deep Learning Framework Catalyzes GWAS and Multi-Omics Findings to Biology and Drug Repurposing for Alzheimer's Disease

10.1101/2021.10.20.465087 ◽

2021 ◽

Author(s):

Jielin Xu ◽

Yuan Hou ◽

Yadi Zhou ◽

Ming Hu ◽

Feixiong Cheng

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Deep Learning ◽

Disease Risk ◽

Association Studies ◽

Open Chromatin ◽

Genome Wide Association Studies ◽

Risk Genes ◽

Learning Framework ◽

Protein Interactome

Human genome sequencing studies have identified numerous loci associated with complex diseases, including Alzheimer's disease (AD). Translating human genetic findings (i.e., genome-wide association studies [GWAS]) to pathobiology and therapeutic discovery, however, remains a major challenge. To address this critical problem, we present a network topology-based deep learning framework to identify disease-associated genes (NETTAG). NETTAG is capable of integrating multi-genomics data along with the protein-protein interactome to infer putative risk genes and drug targets impacted by GWAS loci. Specifically, we leverage non-coding GWAS loci effects on expression quantitative trait loci (eQTLs), histone-QTLs, and transcription factor binding-QTLs, enhancers and CpG islands, promoter regions, open chromatin, and promoter flanking regions. The key premises of NETTAG are that the disease risk genes exhibit distinct functional characteristics compared to non-risk genes and therefore can be distinguished by their aggregated genomic features under the human protein interactome. Applying NETTAG to the latest AD GWAS data, we identified 156 putative AD-risk genes (i.e., APOE, BIN1, GSK3B, MARK4, and PICALM). We showed that predicted risk genes are: 1) significantly enriched in AD-related pathobiological pathways, 2) more likely to be differentially expressed regarding transcriptome and proteome of AD brains, and 3) enriched in druggable targets with approved medicines (i.e., choline and ibudilast). In summary, our findings suggest that understanding of human pathobiology and therapeutic development could benefit from a network-based deep learning methodology that utilizes GWAS findings under the multimodal genomic analyses.

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Identification of Novel Genome-Wide Associations for Oral Inflammatory Traits

10.21203/rs.3.rs-104530/v1 ◽

2020 ◽

Author(s):

Yanjiao Jin ◽

Jie Yang ◽

Shuyue Zhang ◽

Jin Li ◽

Songlin Wang

Keyword(s):

Candidate Genes ◽

Immune Regulation ◽

Functional Annotation ◽

Inflammatory Diseases ◽

Association Studies ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Protein Protein Interaction ◽

Genome Wide ◽

Causal Variants

Abstract Background: Oral diseases impact the majority of the world’s population. The following traits are common in oral inflammatory diseases: mouth ulcers, painful gums, bleeding gums, loose teeth, and toothache. Despite the prevalence of genome-wide association studies, the associations between these traits and common genomic variants, and whether pleiotropic loci are shared by some of these traits remain poorly understood. Methods: In this work, we conducted multi-trait joint analyses based on the summary statistics of genome-wide association studies of these five oral inflammatory traits from the UK Biobank, each of which is comprised of over 10,000 cases and over 300,000 controls. We estimated the genetic correlations between the five traits. We conducted fine-mapping and functional annotation based on multi-omics data to better understand the biological functions of the potential causal variants at each locus. To identify the pathways in which the candidate genes were mainly involved, we applied gene-set enrichment analysis, and further performed protein-protein interaction (PPI) analyses.Results: We identified 39 association signals that surpassed genome-wide significance, including three that were shared between two or more oral inflammatory traits, consistent with a strong correlation. Among these genome-wide significant loci, two were novel for both painful gums and toothache. We performed fine-mapping and identified causal variants at each novel locus. Further functional annotation based on multi-omics data suggested IL10 and IL12A/TRIM59 as potential candidate genes at the novel pleiotropic loci, respectively. Subsequent analyses of pathway enrichment and protein-protein interaction networks suggested the involvement of candidate genes at genome-wide significant loci in immune regulation.Conclusions: Our results highlighted the importance of immune regulation in the pathogenesis of oral inflammatory diseases. Some common immune-related pleiotropic loci or genetic variants are shared by multiple oral inflammatory traits. These findings will be beneficial for risk prediction, prevention, and therapy of oral inflammatory diseases.

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Genome-wide association study reveals candidate genes for flowering time in cowpea (Vigna unguiculata [L.] Walp)

10.1101/2021.04.01.438123 ◽

2021 ◽

Author(s):

Dev Paudel ◽

Rocheteau Dareus ◽

Julia Rosenwald ◽

Maria Munoz-Amatriain ◽

Esteban Rios

Keyword(s):

Flowering Time ◽

Candidate Genes ◽

Vigna Unguiculata ◽

Association Studies ◽

Snp Markers ◽

Genome Wide Association ◽

Human Consumption ◽

Phenotypic Variance ◽

Genome Wide Association Studies ◽

Genome Wide

Cowpea (Vigna unguiculata [L.] Walp., diploid, 2n = 22) is a major crop used as a protein source for human consumption as well as a quality feed for livestock. It is drought and heat tolerant and has been bred to develop varieties that are resilient to changing climates. Plant adaptation to new climates and their yield are strongly affected by flowering time. Therefore, understanding the genetic basis of flowering time is critical to advance cowpea breeding. The aim of this study was to perform genome-wide association studies (GWAS) to identify marker trait associations for flowering time in cowpea using single nucleotide polymorphism (SNP) markers. A total of 367 accessions from a cowpea mini-core collection were evaluated in Ft. Collins, CO in 2019 and 2020, and 292 accessions were evaluated in Citra, FL in 2018. These accessions were genotyped using the Cowpea iSelect Consortium Array that contained 51,128 SNPs. GWAS revealed seven reliable SNPs for flowering time that explained 8-12% of the phenotypic variance. Candidate genes including FT, GI, CRY2, LSH3, UGT87A2, LIF2, and HTA9 that are associated with flowering time were identified for the significant SNP markers. Further efforts to validate these loci will help to understand their role in flowering time in cowpea, and it could facilitate the transfer of some of this knowledge to other closely related legume species.

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Current knowledge in hypertension genetics: mosaic theory, candidate genes and genome-wide association studies

Arterial’naya Gipertenziya (Arterial Hypertension) ◽

10.18705/1607-419x-2020-26-5-490-500 ◽

2020 ◽

Vol 26 (5) ◽

pp. 490-500

Author(s):

A. O. Konradi

Keyword(s):

Candidate Genes ◽

High Performance ◽

New Technologies ◽

Current Knowledge ◽

Association Studies ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Modern Approach ◽

Genome Wide

The article reviews monogenic forms of hypertension, data on the role of heredity of essential hypertension and candidate genes, as well as genome-wide association studies. Modern approach for the role of genetics is driven by implementation of new technologies and their productivity. High performance speed of new technologies like genome-wide association studies provide data for better knowledge of genetic markers of hypertension. The major goal nowadays for research is to reveal molecular pathways of blood pressure regulation, which can help to move from populational to individual level of understanding of pathogenesis and treatment targets.

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Chromatin architecture in addiction circuitry elucidates biological mechanisms underlying cigarette smoking and alcohol use traits.

10.1101/2021.03.18.436046 ◽

2021 ◽

Author(s):

Nancy Y.A Sey ◽

Benxia Hu ◽

Marina Iskhakova ◽

Huaigu Sun ◽

Neda Shokrian ◽

...

Keyword(s):

Risk Factors ◽

Alcohol Use ◽

Cigarette Smoking ◽

Genetic Risk ◽

Target Genes ◽

Association Studies ◽

Critical Role ◽

Genetic Risk Factors ◽

Genome Wide Association Studies ◽

Risk Genes

Cigarette smoking and alcohol use are among the most prevalent substances used worldwide and account for a substantial proportion of preventable morbidity and mortality, underscoring the public health significance of understanding their etiology. Genome-wide association studies (GWAS) have successfully identified genetic variants associated with cigarette smoking and alcohol use traits. However, the vast majority of risk variants reside in non-coding regions of the genome, and their target genes and neurobiological mechanisms are unknown. Chromosomal conformation mappings can address this knowledge gap by charting the interaction profiles of risk-associated regulatory variants with target genes. To investigate the functional impact of common variants associated with cigarette smoking and alcohol use traits, we applied Hi-C coupled MAGMA (H-MAGMA) built upon cortical and midbrain dopaminergic neuronal Hi-C datasets to GWAS summary statistics of nicotine dependence, cigarettes per day, problematic alcohol use, and drinks per week. The identified risk genes mapped to key pathways associated with cigarette smoking and alcohol use traits, including drug metabolic processes and neuronal apoptosis. Risk genes were highly expressed in cortical glutamatergic, midbrain dopaminergic, GABAergic, and serotonergic neurons, suggesting them as relevant cell types in understanding the mechanisms by which genetic risk factors influence cigarette smoking and alcohol use. Lastly, we identified pleiotropic genes between cigarette smoking and alcohol use traits under the assumption that they may reveal substance-agnostic, shared neurobiological mechanisms of addiction. The number of pleiotropic genes was ~26-fold higher in dopaminergic neurons than in cortical neurons, emphasizing the critical role of ascending dopaminergic pathways in mediating general addiction phenotypes. Collectively, brain region- and neuronal subtype-specific 3D genome architecture refines neurobiological hypotheses for smoking, alcohol, and general addiction phenotypes by linking genetic risk factors to their target genes.

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Association of GWAS-Based Candidate Genes with HDL-Cholesterol Levels before and after Bariatric Surgery in the Swedish Obese Subjects Study

The Journal of Clinical Endocrinology & Metabolism ◽

10.1210/jc.2010-2227 ◽

2011 ◽

Vol 96 (6) ◽

pp. E953-E957 ◽

Cited By ~ 21

Author(s):

Mark A. Sarzynski ◽

Peter Jacobson ◽

Tuomo Rankinen ◽

Björn Carlsson ◽

Lars Sjöström ◽

...

Keyword(s):

Bariatric Surgery ◽

Weight Loss ◽

Candidate Genes ◽

Association Studies ◽

Statistical Significance ◽

Genome Wide Association Studies ◽

Nucleotide Polymorphisms ◽

Cross Sectional ◽

Time Points ◽

Obese Subjects

Context and Objective: The magnitude of weight loss-induced high-density lipoprotein cholesterol (HDL-C) changes may depend on genetic factors. We examined the associations of eight candidate genes, identified by genome-wide association studies, with HDL-C at baseline and 10 yr after bariatric surgery in the Swedish Obese Subjects study. Methods: Single-nucleotide polymorphisms (SNP) (n = 60) in the following gene loci were genotyped: ABCA1, APOA5, CETP, GALNT2, LIPC, LIPG, LPL, and MMAB/MVK. Cross-sectional associations were tested before (n = 1771) and 2 yr (n = 1583) and 10 yr (n = 1196) after surgery. Changes in HDL-C were tested between baseline and yr 2 (n = 1518) and yr 2 and 10 (n = 1149). A multiple testing corrected threshold of P = 0.00125 was used for statistical significance. Results: In adjusted multivariate models, CETP SNP rs3764261 explained from 3.2–4.2% (P < 10−14) of the variation in HDL-C at all three time points, whereas CETP SNP rs9939224 contributed an additional 0.6 and 0.9% at baseline and yr 2, respectively. LIPC SNP rs1077834 showed consistent associations across all time points (R2 = 0.4–1.1%; 3.8 × 10−6 < P < 3 × 10−3), whereas LPL SNP rs6993414 contributed approximately 0.5% (5 × 10−4 < P < 0.0012) at yr 2 and 10. In aggregate, four SNP in three genes explained 4.2, 6.8, and 5.6% of the HDL-C variance at baseline, yr 2, and yr 10, respectively. None of the SNP was significantly associated with weight loss-related changes in HDL-C. Conclusions: SNP in the CETP, LIPC, and LPL loci contribute significantly to plasma HDL-C levels in obese individuals, and the associations persist even after considerable weight loss due to bariatric surgery. However, they are not associated with surgery-induced changes in HDL-C levels.

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