40 research outputs found
Whole-exome Sequencing and an iPSC-Derived Cardiomyocyte Model Provides a Powerful Platform for Gene Discovery in Left Ventricular Hypertrophy
Rationale: Left ventricular hypertrophy (LVH) is a heritable predictor of cardiovascular disease, particularly in blacks. Objective: Determine the feasibility of combining evidence from two distinct but complementary experimental approaches to identify novel genetic predictors of increased LV mass. Methods: Whole-exome sequencing (WES) was conducted in seven African-American sibling trios ascertained on high average familial LV mass indexed to height (LVMHT) using Illumina HiSeq technology. Identified missense or nonsense (MS/NS) mutations were examined for association with LVMHT using linear mixed models adjusted for age, sex, body weight, and familial relationship. To functionally assess WES findings, human induced pluripotent stem cell-derived cardiomyocytes (induced pluripotent stem cell-CM) were stimulated to induce hypertrophy; mRNA sequencing (RNA-seq) was used to determine gene expression differences associated with hypertrophy onset. Statistically significant findings under both experimental approaches identified LVH candidate genes. Candidate genes were further prioritized by seven supportive criteria that included additional association tests (two criteria), regional linkage evidence in the larger HyperGEN cohort (one criterion), and publically available gene and variant based annotations (four criteria). Results: WES reads covered 91% of the target capture region (of size 37.2 MB) with an average coverage of 65×. WES identified 31,426 MS/NS mutations among the 21 individuals. A total of 295 MS/NS variants in 265 genes were associated with LVMHT with q-value <0.25. Of the 265 WES genes, 44 were differentially expressed (P < 0.05) in hypertrophied cells. Among the 44 candidate genes identified, 5, including HLA-B, HTT, MTSS1, SLC5A12, and THBS1, met 3 of 7 supporting criteria. THBS1 encodes an adhesive glycoprotein that promotes matrix preservation in pressure-overload LVH. THBS1 gene expression was 34% higher in hypertrophied cells (P = 0.0003) and a predicted conserved and damaging NS variant in exon 13 (A2099G) was significantly associated with LVHMT (P = 4 × 10−6). Conclusion: Combining evidence from cutting-edge genetic and cellular experiments can enable identification of novel LVH risk loci
Identification of Gene Modules Associated with Drought Response in Rice by Network-Based Analysis
Understanding the molecular mechanisms that underlie plant responses to drought stress is challenging due to the complex interplay of numerous different genes. Here, we used network-based gene clustering to uncover the relationships between drought-responsive genes from large microarray datasets. We identified 2,607 rice genes that showed significant changes in gene expression under drought stress; 1,392 genes were highly intercorrelated to form 15 gene modules. These drought-responsive gene modules are biologically plausible, with enrichments for genes in common functional categories, stress response changes, tissue-specific expression and transcription factor binding sites. We observed that a gene module (referred to as module 4) consisting of 134 genes was significantly associated with drought response in both drought-tolerant and drought-sensitive rice varieties. This module is enriched for genes involved in controlling the response of the plant to water and embryonic development, including a heat shock transcription factor as the key regulator in the expression of ABRE-containing genes. These results suggest that module 4 is highly conserved in the ABA-mediated drought response pathway in different rice varieties. Moreover, our study showed that many hub genes clustered in rice chromosomes had significant associations with QTLs for drought stress tolerance. The relationship between hub gene clusters and drought tolerance QTLs may provide a key to understand the genetic basis of drought tolerance in rice
A general co-expression network-based approach to gene expression analysis: comparison and applications
<p>Abstract</p> <p>Background</p> <p>Co-expression network-based approaches have become popular in analyzing microarray data, such as for detecting functional gene modules. However, co-expression networks are often constructed by ad hoc methods, and network-based analyses have not been shown to outperform the conventional cluster analyses, partially due to the lack of an unbiased evaluation metric.</p> <p>Results</p> <p>Here, we develop a general co-expression network-based approach for analyzing both genes and samples in microarray data. Our approach consists of a simple but robust rank-based network construction method, a parameter-free module discovery algorithm and a novel reference network-based metric for module evaluation. We report some interesting topological properties of rank-based co-expression networks that are very different from that of value-based networks in the literature. Using a large set of synthetic and real microarray data, we demonstrate the superior performance of our approach over several popular existing algorithms. Applications of our approach to yeast, Arabidopsis and human cancer microarray data reveal many interesting modules, including a fatal subtype of lymphoma and a gene module regulating yeast telomere integrity, which were missed by the existing methods.</p> <p>Conclusions</p> <p>We demonstrated that our novel approach is very effective in discovering the modular structures in microarray data, both for genes and for samples. As the method is essentially parameter-free, it may be applied to large data sets where the number of clusters is difficult to estimate. The method is also very general and can be applied to other types of data. A MATLAB implementation of our algorithm can be downloaded from <url>http://cs.utsa.edu/~jruan/Software.html</url>.</p
Whole Genome Sequence analysis of apparent Treatment Resistant Hypertension Status in Participants From the Trans-Omics For Precision Medicine Program
Identification of gene modules associated with low temperatures response in Bambara groundnut by network-based analysis
Bambara groundnut (Vigna subterranea (L.) Verdc.) is an African legume and is a promising underutilized crop with good seed nutritional values. Low temperature stress in a number of African countries at night, such as Botswana, can effect the growth and development of bambara groundnut, leading to losses in potential crop yield. Therefore, in this study we developed a computational pipeline to identify and analyze the genes and gene modules associated with low temperature stress responses in bambara groundnut using the cross-species microarray technique (as bambara groundnut has no microarray chip) coupled with network-based analysis. Analyses of the bambara groundnut transcriptome using cross-species gene expression data resulted in the identification of 375 and 659 differentially expressed genes (p<0.01) under the sub-optimal (23°C) and very sub-optimal (18°C) temperatures, respectively, of which 110 genes are commonly shared between the two stress conditions. The construction of a Highest Reciprocal Rank-based gene co-expression network, followed by its partition using a Heuristic Cluster Chiseling Algorithm resulted in 6 and 7 gene modules in sub-optimal and very sub-optimal temperature stresses being identified, respectively. Modules of sub-optimal temperature stress are principally enriched with carbohydrate and lipid metabolic processes, while most of the modules of very sub-optimal temperature stress are significantly enriched with responses to stimuli and various metabolic processes. Several transcription factors (from MYB, NAC, WRKY, WHIRLY & GATA classes) that may regulate the downstream genes involved in response to stimulus in order for the plant to withstand very sub-optimal temperature stress were highlighted. The identified gene modules could be useful in breeding for low-temperature stress tolerant bambara groundnut varieties
Genome-wide meta-analysis of SNP-by9-ACEI/ARB and SNP-by-thiazide diuretic and effect on serum potassium in cohorts of European and African ancestry
We evaluated interactions of SNP-by-ACE-I/ARB and SNP-by-TD on serum potassium (K+) among users of antihypertensive treatments (anti-HTN). Our study included seven European-ancestry (EA) (N = 4835) and four African-ancestry (AA) cohorts (N = 2016). We performed race-stratified, fixed-effect, inverse-variance-weighted meta-analyses of 2.5 million SNP-by-drug interaction estimates; race-combined meta-analysis; and trans-ethnic fine-mapping. Among EAs, we identified 11 significant SNPs (P < 5 × 10 −8 ) for SNP-ACE-I/ARB interactions on serum K+ that were located between NR2F1-AS1 and ARRDC3-AS1 on chromosome 5 (top SNP rs6878413 P = 1.7 × 10 −8 ; ratio of serum K+ in ACE-I/ARB exposed compared to unexposed is 1.0476, 1.0280, 1.0088 for the TT, AT, and AA genotypes, respectively). Trans-ethnic fine mapping identified the same group of SNPs on chromosome 5 as genome-wide significant for the ACE-I/ARB analysis. In conclusion, SNP-by-ACE-I /ARB interaction analyses uncovered loci that, if replicated, could have future implications for the prevention of arrhythmias due to anti-HTN treatment-related hyperkalemia. Before these loci can be identified as clinically relevant, future validation studies of equal or greater size in comparison to our discovery effort are needed
Publisher Correction: Stroke genetics informs drug discovery and risk prediction across ancestries (Nature, (2022), 611, 7934, (115-123), 10.1038/s41586-022-05165-3)
In the version of this article initially published, the name of the PRECISE4Q Consortium was misspelled as “PRECISEQ” and has now been amended in the HTML and PDF versions of the article. Further, data in the first column of Supplementary Table 55 were mistakenly shifted and have been corrected in the file accompanying the HTML version of the article
Novel functional insights into ischemic stroke biology provided by the first genome-wide association study of stroke in indigenous Africans
\ua9 The Author(s) 2024. Background: African ancestry populations have the highest burden of stroke worldwide, yet the genetic basis of stroke in these populations is obscure. The Stroke Investigative Research and Educational Network (SIREN) is a multicenter study involving 16 sites in West Africa. We conducted the first-ever genome-wide association study (GWAS) of stroke in indigenous Africans. Methods: Cases were consecutively recruited consenting adults (aged > 18 years) with neuroimaging-confirmed ischemic stroke. Stroke-free controls were ascertained using a locally validated Questionnaire for Verifying Stroke-Free Status. DNA genotyping with the H3Africa array was performed, and following initial quality control, GWAS datasets were imputed into the NIH Trans-Omics for Precision Medicine (TOPMed) release2 from BioData Catalyst. Furthermore, we performed fine-mapping, trans-ethnic meta-analysis, and in silico functional characterization to identify likely causal variants with a functional interpretation. Results: We observed genome-wide significant (P-value < 5.0E−8) SNPs associations near AADACL2 and miRNA (MIR5186) genes in chromosome 3 after adjusting for hypertension, diabetes, dyslipidemia, and cardiac status in the base model as covariates. SNPs near the miRNA (MIR4458) gene in chromosome 5 were also associated with stroke (P-value < 1.0E−6). The putative genes near AADACL2, MIR5186, and MIR4458 genes were protective and novel. SNPs associations with stroke in chromosome 2 were more than 77 kb from the closest gene LINC01854 and SNPs in chromosome 7 were more than 116 kb to the closest gene LINC01446 (P-value < 1.0E−6). In addition, we observed SNPs in genes STXBP5-AS1 (chromosome 6), GALTN9 (chromosome 12), FANCA (chromosome 16), and DLGAP1 (chromosome 18) (P-value < 1.0E−6). Both genomic regions near genes AADACL2 and MIR4458 remained significant following fine mapping. Conclusions: Our findings identify potential roles of regulatory miRNA, intergenic non-coding DNA, and intronic non-coding RNA in the biology of ischemic stroke. These findings reveal new molecular targets that promise to help close the current gaps in accurate African ancestry-based genetic stroke’s risk prediction and development of new targeted interventions to prevent or treat stroke