scholarly journals Asymmetric Divergence in Transmitted SNPs of DNA Replication/Transcription and Their Impact on Gene Expression in Polyploid Brassica napus

2021 ◽  
Vol 12 ◽  
Author(s):  
Minqiang Tang ◽  
Juanling Li ◽  
Xu Hu ◽  
Lu Sun ◽  
MMU Helal ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dna Replication ◽  
Brassica Napus ◽  
Genome Evolution ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide ◽  
Expression Levels ◽  
Dna And Rna ◽  
Polyploid Genome ◽  
Gene Expression Levels

The marked increase in plant genomic data has provided valuable resources for investigating the dynamic evolution of duplicate genes in polyploidy. Brassica napus is an ideal model species for investigating polyploid genome evolution. The present study comprehensively analyzed DNA and RNA variation of two representative B. napus inbredlines, Zhongshuang11 and Zhongyou821, and we investigated gene expression levels of An and Cn subgenomes in multiple tissues of the two lines. The distribution of transmitted single nucleotide polymorphisms (SNPs) was significantly different in two subgenomes of B. napus. Gene expression levels were significantly negatively correlated with number of variations in replication and transcription of the corresponding genes, but were positively correlated with the ratios of transmitted SNPs from DNA to RNA. We found a higher density of SNP variation in An than that in Cn during DNA replication and more SNPs were transmitted to RNA during transcription, which may contribute to An expression dominance. These activities resulted in asymmetrical gene expression in polyploid B. napus. The SNPs transmitted from DNA to RNA could be an important complement feature in comparative genomics, and they may play important roles in asymmetrical genome evolution in polyploidy.

scholarly journals LORSEN: Fast and Efficient eQTL Mapping With Low Rank Penalized Regression

2021 ◽  
Vol 12 ◽  
Author(s):  
Cheng Gao ◽  
Hairong Wei ◽  
Kui Zhang
Keyword(s):  
Gene Expression ◽  
Complex Traits ◽  
Large Scale ◽  
Penalized Regression ◽  
Low Rank ◽  
Eqtl Mapping ◽  
Nucleotide Polymorphisms ◽  
Cellular Mechanisms ◽  
Expression Levels ◽  
Gene Expression Levels

Characterization of genetic variations that are associated with gene expression levels is essential to understand cellular mechanisms that underline human complex traits. Expression quantitative trait loci (eQTL) mapping attempts to identify genetic variants, such as single nucleotide polymorphisms (SNPs), that affect the expression of one or more genes. With the availability of a large volume of gene expression data, it is necessary and important to develop fast and efficient statistical and computational methods to perform eQTL mapping for such large scale data. In this paper, we proposed a new method, the low rank penalized regression method (LORSEN), for eQTL mapping. We evaluated and compared the performance of LORSEN with two existing methods for eQTL mapping using extensive simulations as well as real data from the HapMap3 project. Simulation studies showed that our method outperformed two commonly used methods for eQTL mapping, LORS and FastLORS, in many scenarios in terms of area under the curve (AUC). We illustrated the usefulness of our method by applying it to SNP variants data and gene expression levels on four chromosomes from the HapMap3 Project.

Effect of the African-specific promoter polymorphisms on the SLC22A2 gene expression levels

2018 ◽  
Vol 33 (2) ◽  
pp. 85-89
Author(s):  
Brendon Pearce ◽  
Zainonesa Abrahams-October ◽  
Lettilia Xhakaza ◽  
Clifford Jacobs ◽  
Mongi Benjeddou
Keyword(s):  
Gene Expression ◽  
Promoter Activity ◽  
Hek293 Cells ◽  
Nucleotide Polymorphisms ◽  
Variable Response ◽  
Promoter Polymorphisms ◽  
Expression Levels ◽  
Significant Difference ◽  
Gene Expression Levels

Abstract Background: Single nucleotide polymorphisms in promoter regions have been shown to alter the transcription of genes. Thus, SNPs in SLC22A2 can result in inter-individual variable response to medication. Methods: The objective of the study was to investigate the effect of the African-specific promoter polymorphisms on the SLC22A2 gene expression levels in vitro. These included rs572296424 and rs150063153, which have been previously identified in the Xhosa population of South Africa. The promoter region (300 bp) for the two haplotypes was cloned into the pGLOW promoterless GFP reporter vector. The GFP expression levels of each haplotype was determined in the HEK293 cells using a GlowMax Multi-Detection E7031 luminometer in the form of light emission. Results: The relative promoter activity suggests that no significant variation exists between the expression levels of the WT and -95 haplotypes and the -95 and -156 haplotypes (p=0.498). However, the relative promoter activity of the WT haplotype in comparison to the -156 haplotype displayed a significant difference in expression level (p=0.016). Conclusions: The data presented here show that the African-specific promoter polymorphisms can cause a decrease in the SLC22A2 gene expression levels in vitro, which in turn, may influence the pharmacokinetic profiles of cationic drugs.

scholarly journals A Sparse and Low-Rank Regression Model for Identifying the Relationships Between DNA Methylation and Gene Expression Levels in Gastric Cancer and the Prediction of Prognosis

Genes ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 854
Author(s):  
Yishu Wang ◽  
Lingyun Xu ◽  
Dongmei Ai
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Dna Methylation ◽  
Regression Model ◽  
The Cancer Genome Atlas ◽  
Low Rank ◽  
Expression Levels ◽  
Rank Regression ◽  
Prediction Of Prognosis ◽  
Gene Expression Levels

DNA methylation is an important regulator of gene expression that can influence tumor heterogeneity and shows weak and varying expression levels among different genes. Gastric cancer (GC) is a highly heterogeneous cancer of the digestive system with a high mortality rate worldwide. The heterogeneous subtypes of GC lead to different prognoses. In this study, we explored the relationships between DNA methylation and gene expression levels by introducing a sparse low-rank regression model based on a GC dataset with 375 tumor samples and 32 normal samples from The Cancer Genome Atlas database. Differences in the DNA methylation levels and sites were found to be associated with differences in the expressed genes related to GC development. Overall, 29 methylation-driven genes were found to be related to the GC subtypes, and in the prognostic model, we explored five prognoses related to the methylation sites. Finally, based on a low-rank matrix, seven subgroups were identified with different methylation statuses. These specific classifications based on DNA methylation levels may help to account for heterogeneity and aid in personalized treatments.

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