scholarly journals Overlapping Genetic Architecture Between Schizophrenia and Neurodegenerative Disorders

2021 ◽  
Vol 9 ◽  
Author(s):  
Chunyu Li ◽  
Tianmi Yang ◽  
Ruwei Ou ◽  
Huifang Shang
Keyword(s):  
Gene Expression ◽  
Genetic Correlation ◽  
Neurodegenerative Disorders ◽  
Genetic Architecture ◽  
Large Scale ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Genome Wide ◽  
Lateral Sclerosis ◽  
Shared Genetic

Epidemiological and clinical studies have suggested comorbidity between schizophrenia and several neurodegenerative disorders. However, little is known whether there exists shared genetic architecture. To explore their relationship from a genetic and transcriptomic perspective, we applied polygenic and linkage disequilibrium-informed methods to examine the genetic correlation between schizophrenia and amyotrophic lateral sclerosis (ALS), Parkinson’s disease, Alzheimer’s disease and frontotemporal dementia. We further combined genome-wide association summary statistics with large-scale transcriptomic datasets, to identify putative shared genes and explore related pathological tissues. We identified positive and significant correlation between schizophrenia and ALS at genetic (correlation 0.22; 95% CI: 0.16–0.28; p = 4.00E-04) and transcriptomic (correlation 0.08; 95% CI: 0.04–0.11; p = 0.034) levels. We further demonstrated that schizophrenia- and ALS-inferred gene expression overlap significantly in four tissues including skin, small intestine, brain cortex and lung, and highlighted three genes, namely GLB1L3, ZNHIT3 and TMEM194A as potential mediators of the correlation between schizophrenia and ALS. Our findings revealed overlapped gene expression profiles in specific tissues between schizophrenia and ALS, and identified novel potential shared genes. These results provided a better understanding for the pleiotropy of schizophrenia, and paved way for future studies to further elucidate the molecular drivers of schizophrenia.

scholarly journals Shared genetic architecture and casual relationship between leptin levels and type 2 diabetes: large-scale cross-trait meta-analysis and Mendelian randomization analysis

2020 ◽  
Vol 8 (1) ◽  
pp. e001140
Author(s):  
Xinpei Wang ◽  
Jinzhu Jia ◽  
Tao Huang
Keyword(s):  
Type 2 Diabetes ◽  
Genetic Correlation ◽  
Genetic Architecture ◽  
Large Scale ◽  
Meta Analysis ◽  
Genetic Correlations ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Shared Genetic

ObjectiveWe aimed to estimate genetic correlation, identify shared loci and test causality between leptin levels and type 2 diabetes (T2D).Research design and methodsOur study consists of three parts. First, we calculated the genetic correlation of leptin levels and T2D or glycemic traits by using linkage disequilibrium score regression analysis. Second, we conducted a large-scale genome-wide cross-trait meta-analysis using cross-phenotype association to identify shared loci between trait pairs that showed significant genetic correlations in the first part. In the end, we carried out a bidirectional MR analysis to find out whether there is a causal relationship between leptin levels and T2D or glycemic traits.ResultsWe found positive genetic correlations between leptin levels and T2D (Rg=0.3165, p=0.0227), fasting insulin (FI) (Rg=0.517, p=0.0076), homeostasis model assessment-insulin resistance (HOMA-IR) (Rg=0.4785, p=0.0196), as well as surrogate estimates of β-cell function (HOMA-β) (Rg=0.4456, p=0.0214). We identified 12 shared loci between leptin levels and T2D, 1 locus between leptin levels and FI, 1 locus between leptin levels and HOMA-IR, and 1 locus between leptin levels and HOMA-β. We newly identified eight loci that did not achieve genome-wide significance in trait-specific genome-wide association studies. These shared genes were enriched in pancreas, thyroid gland, skeletal muscle, placenta, liver and cerebral cortex. In addition, we found that 1-SD increase in HOMA-IR was causally associated with a 0.329 ng/mL increase in leptin levels (β=0.329, p=0.001).ConclusionsOur results have shown the shared genetic architecture between leptin levels and T2D and found causality of HOMA-IR on leptin levels, shedding light on the molecular mechanisms underlying the association between leptin levels and T2D.

scholarly journals Overlapping Genetic Architecture between Parkinson Disease and Melanoma

2019 ◽  
Author(s):  
Umber Dube ◽  
Laura Ibanez ◽  
John P Budde ◽  
Bruno A Benitez ◽  
Albert A Davis ◽  
...  
Keyword(s):  
Gene Expression ◽  
Parkinson Disease ◽  
Genetic Correlation ◽  
Genetic Architecture ◽  
Genome Wide Association Study ◽  
Case Control ◽  
Epidemiologic Studies ◽  
Risk Genes ◽  
Genome Wide ◽  
Shared Genetic

AbstractEpidemiologic studies have reported inconsistent results regarding an association between Parkinson disease (PD) and cutaneous melanoma (melanoma). Identifying shared genetic architecture between these diseases can support epidemiologic findings and identify common risk genes and biological pathways. Here we apply polygenic, linkage disequilibrium-informed methods to the largest available case-control, genome-wide association study summary statistic data for melanoma and PD. We identify positive and significant genetic correlation (correlation: 0.17, 95% CI 0.10 to 0.24; P = 4.09 × 10-06) between melanoma and PD. We further demonstrate melanoma and PD-inferred gene expression to overlap across tissues (correlation: 0.14, 95% CI 0.06 to 0.22; P = 7.87 × 10-04), and highlight seven genes including PIEZO1, TRAPPC2L, and SOX6 as potential mediators of the genetic correlation between melanoma and PD. These findings demonstrate specific, shared genetic architecture between PD and melanoma that manifests at the level of gene expression.

scholarly journals Genome-Wide Identification of Soybean ABC Transporters Relate to Aluminum Toxicity

2021 ◽  
Vol 22 (12) ◽  
pp. 6556
Author(s):  
Junjun Huang ◽  
Xiaoyu Li ◽  
Xin Chen ◽  
Yaru Guo ◽  
Weihong Liang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Abc Transporters ◽  
Developmental Stages ◽  
Aluminum Toxicity ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Purifying Selection ◽  
Biotic Stresses ◽  
Genome Wide ◽  
New Germplasm

ATP-binding cassette (ABC) transporter proteins are a gene super-family in plants and play vital roles in growth, development, and response to abiotic and biotic stresses. The ABC transporters have been identified in crop plants such as rice and buckwheat, but little is known about them in soybean. Soybean is an important oil crop and is one of the five major crops in the world. In this study, 255 ABC genes that putatively encode ABC transporters were identified from soybean through bioinformatics and then categorized into eight subfamilies, including 7 ABCAs, 52 ABCBs, 48 ABCCs, 5 ABCDs, 1 ABCEs, 10 ABCFs, 111 ABCGs, and 21 ABCIs. Their phylogenetic relationships, gene structure, and gene expression profiles were characterized. Segmental duplication was the main reason for the expansion of the GmABC genes. Ka/Ks analysis suggested that intense purifying selection was accompanied by the evolution of GmABC genes. The genome-wide collinearity of soybean with other species showed that GmABCs were relatively conserved and that collinear ABCs between species may have originated from the same ancestor. Gene expression analysis of GmABCs revealed the distinct expression pattern in different tissues and diverse developmental stages. The candidate genes GmABCB23, GmABCB25, GmABCB48, GmABCB52, GmABCI1, GmABCI5, and GmABCI13 were responsive to Al toxicity. This work on the GmABC gene family provides useful information for future studies on ABC transporters in soybean and potential targets for the cultivation of new germplasm resources of aluminum-tolerant soybean.

scholarly journals LncGSEA: a versatile tool to infer lncRNA associated pathways from large-scale cancer transcriptome sequencing data

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Yanan Ren ◽  
Ting-You Wang ◽  
Leah C. Anderton ◽  
Qi Cao ◽  
Rendong Yang
Keyword(s):  
Gene Expression ◽  
Large Scale ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Clinical Samples ◽  
Sequencing Data ◽  
Multiple Cancer ◽  
Regulatory Pathways ◽  
Cancer Transcriptome ◽  
Versatile Tool

Abstract Background Long non-coding RNAs (lncRNAs) are a growing focus in cancer research. Deciphering pathways influenced by lncRNAs is important to understand their role in cancer. Although knock-down or overexpression of lncRNAs followed by gene expression profiling in cancer cell lines are established approaches to address this problem, these experimental data are not available for a majority of the annotated lncRNAs. Results As a surrogate, we present lncGSEA, a convenient tool to predict the lncRNA associated pathways through Gene Set Enrichment Analysis of gene expression profiles from large-scale cancer patient samples. We demonstrate that lncGSEA is able to recapitulate lncRNA associated pathways supported by literature and experimental validations in multiple cancer types. Conclusions LncGSEA allows researchers to infer lncRNA regulatory pathways directly from clinical samples in oncology. LncGSEA is written in R, and is freely accessible at https://github.com/ylab-hi/lncGSEA.

scholarly journals Genome-wide genetic links between amyotrophic lateral sclerosis and autoimmune diseases

BMC Medicine ◽  
2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Chun Yu Li ◽  
Tian Mi Yang ◽  
Ru Wei Ou ◽  
Qian Qian Wei ◽  
Hui Fang Shang
Keyword(s):  
Amyotrophic Lateral Sclerosis ◽  
Autoimmune Diseases ◽  
Genetic Correlation ◽  
Functional Enrichment ◽  
Genome Wide Association Studies ◽  
Risk Genes ◽  
Genome Wide ◽  
Shared Risk ◽  
Lateral Sclerosis ◽  
Shared Genetic

Abstract Background Epidemiological and clinical studies have suggested comorbidity between amyotrophic lateral sclerosis (ALS) and autoimmune disorders. However, little is known about their shared genetic architecture. Methods To examine the relation between ALS and 10 autoimmune diseases, including asthma, celiac disease (CeD), Crohn’s disease (CD), inflammatory bowel disease (IBD), multiple sclerosis (MS), psoriasis, rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), type 1 diabetes (T1D), and ulcerative colitis (UC), and identify shared risk loci, we first estimated the genetic correlation using summary statistics from genome-wide association studies, and then analyzed the genetic enrichment leveraging the conditional false discovery rate statistical method. Results We identified a significant positive genetic correlation between ALS and CeD, MS, RA, and SLE, as well as a significant negative genetic correlation between ALS and IBD, UC, and CD. Robust genetic enrichment was observed between ALS and CeD and MS, and moderate enrichment was found between ALS and UC and T1D. Thirteen shared genetic loci were identified, among which five were suggestively significant in another ALS GWAS, namely rs3828599 (GPX3), rs3849943 (C9orf72), rs7154847 (G2E3), rs6571361 (SCFD1), and rs9903355 (GGNBP2). By integrating cis-expression quantitative trait loci analyses in Braineac and GTEx, we further identified GGNBP2, ATXN3, and SLC9A8 as novel ALS risk genes. Functional enrichment analysis indicated that the shared risk genes were involved in four pathways including membrane trafficking, vesicle-mediated transport, ER to Golgi anterograde transport, and transport to the Golgi and subsequent modification. Conclusions Our findings demonstrate a specific genetic correlation between ALS and autoimmune diseases and identify shared risk loci, including three novel ALS risk genes. These results provide a better understanding for the pleiotropy of ALS and have implications for future therapeutic trials.

scholarly journals Analysis of blood-based gene expression in idiopathic Parkinson disease

Neurology ◽  
2017 ◽  
Vol 89 (16) ◽  
pp. 1676-1683 ◽  
Author(s):  
Ron Shamir ◽  
Christine Klein ◽  
David Amar ◽  
Eva-Juliane Vollstedt ◽  
Michael Bonin ◽  
...  
Keyword(s):  
Gene Expression ◽  
Parkinson Disease ◽  
Gene Networks ◽  
Large Scale ◽  
Expression Profiles ◽  
Area Under The Curve ◽  
Gene Expression Profiles ◽  
Gene Signature ◽  
Gene Profiles ◽  
Independent Test

Objective:To examine whether gene expression analysis of a large-scale Parkinson disease (PD) patient cohort produces a robust blood-based PD gene signature compared to previous studies that have used relatively small cohorts (≤220 samples).Methods:Whole-blood gene expression profiles were collected from a total of 523 individuals. After preprocessing, the data contained 486 gene profiles (n = 205 PD, n = 233 controls, n = 48 other neurodegenerative diseases) that were partitioned into training, validation, and independent test cohorts to identify and validate a gene signature. Batch-effect reduction and cross-validation were performed to ensure signature reliability. Finally, functional and pathway enrichment analyses were applied to the signature to identify PD-associated gene networks.Results:A gene signature of 100 probes that mapped to 87 genes, corresponding to 64 upregulated and 23 downregulated genes differentiating between patients with idiopathic PD and controls, was identified with the training cohort and successfully replicated in both an independent validation cohort (area under the curve [AUC] = 0.79, p = 7.13E–6) and a subsequent independent test cohort (AUC = 0.74, p = 4.2E–4). Network analysis of the signature revealed gene enrichment in pathways, including metabolism, oxidation, and ubiquitination/proteasomal activity, and misregulation of mitochondria-localized genes, including downregulation of COX4I1, ATP5A1, and VDAC3.Conclusions:We present a large-scale study of PD gene expression profiling. This work identifies a reliable blood-based PD signature and highlights the importance of large-scale patient cohorts in developing potential PD biomarkers.

scholarly journals Discovering Distinct Patterns in Gene Expression Profiles

2008 ◽  
Vol 5 (2) ◽  
Author(s):  
Li Teng ◽  
Laiwan Chan
Keyword(s):  
Gene Expression ◽  
Large Scale ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Gene Expression Profiles ◽  
Clustering Methods ◽  
Gene Expressions ◽  
Real Gene ◽  
Large Scale Dataset ◽  
Coexpressed Genes

SummaryTraditional analysis of gene expression profiles use clustering to find groups of coexpressed genes which have similar expression patterns. However clustering is time consuming and could be diffcult for very large scale dataset. We proposed the idea of Discovering Distinct Patterns (DDP) in gene expression profiles. Since patterns showing by the gene expressions reveal their regulate mechanisms. It is significant to find all different patterns existing in the dataset when there is little prior knowledge. It is also a helpful start before taking on further analysis. We propose an algorithm for DDP by iteratively picking out pairs of gene expression patterns which have the largest dissimilarities. This method can also be used as preprocessing to initialize centers for clustering methods, like K-means. Experiments on both synthetic dataset and real gene expression datasets show our method is very effective in finding distinct patterns which have gene functional significance and is also effcient.

FRI0309 The genome-wide expression of human osteoarthritic cartilage shows different GENE-expression profiles OA-related

2013 ◽  
Vol 71 (Suppl 3) ◽  
pp. 418.3-418
Author(s):  
J. Fernandez-Tajes ◽  
A. Soto-Hermida ◽  
M. Fernandez-Moreno ◽  
M.E. Vazquez-Mosquera ◽  
N. Oreiro ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Osteoarthritic Cartilage ◽  
Genome Wide ◽  
Genome Wide Expression

CFTR ΔF508 mutation has minimal effect on the gene expression profile of differentiated human airway epithelia

2005 ◽  
Vol 289 (4) ◽  
pp. L545-L553 ◽  
Author(s):  
Joseph Zabner ◽  
Todd E. Scheetz ◽  
Hakeem G. Almabrazi ◽  
Thomas L. Casavant ◽  
Jian Huang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cystic Fibrosis ◽  
Large Scale ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Primary Cultures ◽  
Gene Expression Profiles ◽  
Filter Method ◽  
Tissue Destruction ◽  
Airway Epithelia

Cystic fibrosis (CF) is caused by mutations in the cystic fibrosis transmembrane conductance regulator (CFTR), an epithelial chloride channel regulated by phosphorylation. Most of the disease-associated morbidity is the consequence of chronic lung infection with progressive tissue destruction. As an approach to investigate the cellular effects of CFTR mutations, we used large-scale microarray hybridization to contrast the gene expression profiles of well-differentiated primary cultures of human CF and non-CF airway epithelia grown under resting culture conditions. We surveyed the expression profiles for 10 non-CF and 10 ΔF508 homozygote samples. Of the 22,283 genes represented on the Affymetrix U133A GeneChip, we found evidence of significant changes in expression in 24 genes by two-sample t-test ( P < 0.00001). A second, three-filter method of comparative analysis found no significant differences between the groups. The levels of CFTR mRNA were comparable in both groups. There were no significant differences in the gene expression patterns between male and female CF specimens. There were 18 genes with significant increases and 6 genes with decreases in CF relative to non-CF samples. Although the function of many of the differentially expressed genes is unknown, one transcript that was elevated in CF, the KCl cotransporter (KCC4), is a candidate for further study. Overall, the results indicate that CFTR dysfunction has little direct impact on airway epithelial gene expression in samples grown under these conditions.

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