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.

scholarly journals Diverse environmental perturbations reveal the evolution and context-dependency of genetic effects on gene expression levels

2021 ◽  
Author(s):  
Amanda J Lea ◽  
Julie Peng ◽  
Julien J Ayroles
Keyword(s):  
Gene Expression ◽  
Complex Traits ◽  
Large Scale ◽  
Context Dependency ◽  
Human Populations ◽  
Evolutionary Mechanisms ◽  
Expression Levels ◽  
Gxe Interactions ◽  
Context Dependent ◽  
Gene Expression Levels

There is increasing appreciation that human complex traits are determined by poorly understood interactions between our genomes and daily environments. These "genotype x environment" (GxE) interactions remain difficult to map at the organismal level, but can be uncovered using molecular phenotypes. To do so at large-scale, we profiled transcriptomes across 12 cellular environments using 544 immortalized B cell lines from the 1000 Genomes Project. We mapped the genetic basis of gene expression across environments and revealed a context-dependent genetic architecture: the average heritability of gene expression levels increased in treatment relative to control conditions and, on average, each treatment revealed expression quantitative trait loci (eQTL) at 11% of genes. In total, 22% of all eQTL were context-dependent, and this group was enriched for trait- and disease-associated loci. Further, evolutionary analyses revealed that positive selection has shaped GxE loci involved in responding to immune challenges and hormones, but not man-made chemicals, suggesting there is reduced opportunity for selection to act on responses to molecules recently introduced into human environments. Together, our work highlights the importance of considering an exposure's evolutionary history when studying and interpreting GxE interactions, and provides new insight into the evolutionary mechanisms that maintain GxE loci in human populations.

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.

scholarly journals Variation in the Large-Scale Organization of Gene Expression Levels in the Hippocampus Relates to Stable Epigenetic Variability in Behavior

PLoS ONE ◽  
2008 ◽  
Vol 3 (10) ◽  
pp. e3344 ◽  
Author(s):  
Mark D. Alter ◽  
Daniel B. Rubin ◽  
Keri Ramsey ◽  
Rebecca Halpern ◽  
Dietrich A. Stephan ◽  
...  
Keyword(s):  
Gene Expression ◽  
Large Scale ◽  
Epigenetic Variability ◽  
Expression Levels ◽  
Gene Expression Levels

scholarly journals Estimating the proportion of disease heritability mediated by gene expression levels

2017 ◽  
Author(s):  
Luke J. O’Connor ◽  
Alexander Gusev ◽  
Xuanyao Liu ◽  
Po-Ru Loh ◽  
Hilary K. Finucane ◽  
...  
Keyword(s):  
Gene Expression ◽  
Complex Traits ◽  
Disease Risk ◽  
Genetic Component ◽  
Effect Sizes ◽  
Expression Levels ◽  
Risk Variants ◽  
Regulatory Effects ◽  
Disease Effect ◽  
Gene Expression Levels

AbstractDisease risk variants identified by GWAS are predominantly noncoding, suggesting that gene regulation plays an important role. eQTL studies in unaffected individuals are often used to link disease-associated variants with the genes they regulate, relying on the hypothesis that noncoding regulatory effects are mediated by steady-state expression levels. To test this hypothesis, we developed a method to estimate the proportion of disease heritability mediated by the cis-genetic component of assayed gene expression levels. The method, gene expression co-score regression (GECS regression), relies on the idea that, for a gene whose expression level affects a phenotype, SNPs with similar effects on the expression of that gene will have similar phenotypic effects. In order to distinguish directional effects mediated by gene expression from non-directional pleiotropic or tagging effects, GECS regression operates on pairs of cis SNPs in linkage equilibrium, regressing pairwise products of disease effect sizes on products of cis-eQTL effect sizes. We verified that GECS regression produces robust estimates of mediated effects in simulations. We applied the method to eQTL data in 44 tissues from the GTEx consortium (average NeQTL = 158 samples) in conjunction with GWAS summary statistics for 30 diseases and complex traits (average NGWAS = 88K) with low pairwise genetic correlation, estimating the proportion of SNP-heritability mediated by the cis-genetic component of assayed gene expression in the union of the 44 tissues. The mean estimate was 0.21 (s.e. = 0.01) across 30 traits, with a significantly positive estimate (p < 0.001) for every trait. Thus, assayed gene expression in bulk tissues mediates a statistically significant but modest proportion of disease heritability, motivating the development of additional assays to capture regulatory effects and the use of our method to estimate how much disease heritability they mediate.

scholarly journals Large Scale Comparison of Gene Expression Levels by Microarrays and RNAseq Using TCGA Data

PLoS ONE ◽  
2013 ◽  
Vol 8 (8) ◽  
pp. e71462 ◽  
Author(s):  
Yan Guo ◽  
Quanhu Sheng ◽  
Jiang Li ◽  
Fei Ye ◽  
David C. Samuels ◽  
...  
Keyword(s):  
Gene Expression ◽  
Large Scale ◽  
Expression Levels ◽  
Gene Expression Levels

scholarly journals The genetic architecture of gene expression levels in wild baboons

eLife ◽  
2015 ◽  
Vol 4 ◽  
Author(s):  
Jenny Tung ◽  
Xiang Zhou ◽  
Susan C Alberts ◽  
Matthew Stephens ◽  
Yoav Gilad
Keyword(s):  
Gene Expression ◽  
Genetic Architecture ◽  
Eqtl Mapping ◽  
Rna Seq ◽  
Specific Expression ◽  
Data Set ◽  
Expression Levels ◽  
Trait Locus ◽  
Eqtl Data ◽  
Gene Expression Levels

Primate evolution has been argued to result, in part, from changes in how genes are regulated. However, we still know little about gene regulation in natural primate populations. We conducted an RNA sequencing (RNA-seq)-based study of baboons from an intensively studied wild population. We performed complementary expression quantitative trait locus (eQTL) mapping and allele-specific expression analyses, discovering substantial evidence for, and surprising power to detect, genetic effects on gene expression levels in the baboons. eQTL were most likely to be identified for lineage-specific, rapidly evolving genes; interestingly, genes with eQTL significantly overlapped between baboons and a comparable human eQTL data set. Our results suggest that genes vary in their tolerance of genetic perturbation, and that this property may be conserved across species. Further, they establish the feasibility of eQTL mapping using RNA-seq data alone, and represent an important step towards understanding the genetic architecture of gene expression in primates.

scholarly journals Sex-Specific Associations of Genetically-Predicted Circulating Lipoprotein(a) and Hepatic LPA Gene Expression Levels with Cardiovascular Outcomes: Mendelian Randomization and Observational Analyses

2021 ◽  
Author(s):  
Jakie Guertin ◽  
Yannick Kaiser ◽  
Hasanga Manikpurage ◽  
Nicolas Perrot ◽  
Raphaëlle Bourgeois ◽  
...  
Keyword(s):  
Gene Expression ◽  
Large Scale ◽  
Disease Risk ◽  
Cardiovascular Disease Risk ◽  
Cardiovascular Outcomes ◽  
European Ancestry ◽  
Nucleotide Polymorphisms ◽  
Men And Women ◽  
Expression Levels ◽  
Lipoprotein A

Background - Elevated Lipoprotein(a) (Lp[a]) levels are associated with coronary artery disease (CAD), ischemic stroke (IS) and calcific aortic valve stenosis (CAVS). Studies investigating the association between Lp(a) levels and these diseases in women have yielded inconsistent results. Methods - To investigate the association of Lp(a) with sex-specific cardiovascular outcomes, we determined the association between genetically-predicted Lp(a) levels (using 27 single nucleotide polymorphisms (SNPs) at the LPA locus) and hepatic LPA expression (using 80 SNPs at the LPA locus associated with LPA mRNA expression in liver samples from the Genotype-Tissue Expression dataset) on CAD, IS and CAVS using individual participant data from the UK Biobank: 408,403 participants of European ancestry (37,102, 4283 and 2574 with prevalent CAD, IS and CAVS, respectively). The long-term association between Lp(a) levels and incident CAD, IS and CAVS in was also investigated in EPIC-Norfolk: 18,721 participants (3964, 846 and 424 with incident CAD, IS and CAVS, respectively). Results - Genetically-predicted and plasma Lp(a) levels were positively and similarly associated with prevalent and incident CAD and CAVS in men and women. Genetically-predicted and plasma Lp(a) levels was associated with prevalent and incident IS when we studied men and women pooled together, and in men only. Genetically-predicted LPA expression levels was associated with prevalent CAD and CAVS in men and women, but not with IS. Conclusions - Genetically-predicted blood Lp(a) and hepatic LPA gene expression as well as serum Lp(a) levels predict the risk of CAD and CAVS in men and in women. Whether RNA interference therapies aiming at lowering Lp(a) levels could be useful in reducing cardiovascular disease risk in both men and women with high Lp(a) levels needs to be determined in large-scale cardiovascular outcomes trials.

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.

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