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

2015 ◽  
Author(s):  
Jenny Tung ◽  
Xiang Zhou ◽  
Susan C Alberts ◽  
Matthew Stephens ◽  
Yoav Gilad
Keyword(s):  
Gene Expression ◽  
Genetic Architecture ◽  
Author Response ◽  
Expression Levels ◽  
Wild Baboons ◽  
Gene Expression Levels

scholarly journals The Genetic Architecture of Gene Expression Levels in Wild Baboons

2014 ◽  
Author(s):  
Jenny Tung ◽  
Xiang Zhou ◽  
Susan C Alberts ◽  
Matthew Stephens ◽  
Yoav Gilad
Keyword(s):  
Gene Expression ◽  
Genetic Architecture ◽  
Nonhuman Primates ◽  
Rna Seq ◽  
Gene Expression Variation ◽  
Data Set ◽  
Expression Levels ◽  
Expression Variation ◽  
Wild Baboons ◽  
Gene Expression Levels

Gene expression variation is well documented in human populations and its genetic architecture has been extensively explored. However, we still know little about the genetic architecture of gene expression variation in other species, particularly our closest living relatives, the nonhuman primates. To address this gap, we performed an RNA sequencing (RNA-seq)-based study of 63 wild baboons, members of the intensively studied Amboseli baboon population in Kenya. Our study design allowed us to measure gene expression levels and identify genetic variants using the same data set, enabling us to perform complementary mapping of putative cis-acting expression quantitative trait loci (eQTL) and measurements of allele-specific expression (ASE) levels. We discovered substantial evidence for genetic effects on gene expression levels in this population. Surprisingly, we found more power to detect individual eQTL in the baboons relative to a HapMap human data set of comparable size, probably as a result of greater genetic variation, enrichment of SNPs with high minor allele frequencies, and longer-range linkage disequilibrium in the baboons. eQTL were most likely to be identified for lineage-specific, rapidly evolving genes. Interestingly, genes with eQTL significantly overlapped between the baboon and human data sets, suggesting that some genes may tolerate more genetic perturbation than others, and that this property may be conserved across species. Finally, we used a Bayesian sparse linear mixed model to partition genetic, demographic, and early environmental contributions to variation in gene expression levels. We found a strong genetic contribution to gene expression levels for almost all genes, while individual demographic and environmental effects tended to be more modest. Together, our results establish the feasibility of eQTL mapping using RNA-seq data alone, and act as an important first step towards understanding the genetic architecture of gene expression variation in nonhuman primates.

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 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 High heterogeneity undermines generalization of differential expression results in RNA-Seq analysis

2021 ◽  
Vol 15 (1) ◽  
Author(s):  
Weitong Cui ◽  
Huaru Xue ◽  
Lei Wei ◽  
Jinghua Jin ◽  
Xuewen Tian ◽  
...  
Keyword(s):  
Gene Expression ◽  
Differential Expression ◽  
Small Sample ◽  
Differentially Expressed ◽  
Cancer Type ◽  
Rna Seq ◽  
Sample Sizes ◽  
Large Sample ◽  
Expression Levels ◽  
Gene Expression Levels

Abstract Background RNA sequencing (RNA-Seq) has been widely applied in oncology for monitoring transcriptome changes. However, the emerging problem that high variation of gene expression levels caused by tumor heterogeneity may affect the reproducibility of differential expression (DE) results has rarely been studied. Here, we investigated the reproducibility of DE results for any given number of biological replicates between 3 and 24 and explored why a great many differentially expressed genes (DEGs) were not reproducible. Results Our findings demonstrate that poor reproducibility of DE results exists not only for small sample sizes, but also for relatively large sample sizes. Quite a few of the DEGs detected are specific to the samples in use, rather than genuinely differentially expressed under different conditions. Poor reproducibility of DE results is mainly caused by high variation of gene expression levels for the same gene in different samples. Even though biological variation may account for much of the high variation of gene expression levels, the effect of outlier count data also needs to be treated seriously, as outlier data severely interfere with DE analysis. Conclusions High heterogeneity exists not only in tumor tissue samples of each cancer type studied, but also in normal samples. High heterogeneity leads to poor reproducibility of DEGs, undermining generalization of differential expression results. Therefore, it is necessary to use large sample sizes (at least 10 if possible) in RNA-Seq experimental designs to reduce the impact of biological variability and DE results should be interpreted cautiously unless soundly validated.

scholarly journals Genome-Wide Expression and Alternative Splicing in Domesticated Sunflowers (Helianthus annuus L.) under Flooding Stress

Agronomy ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 92
Author(s):  
Joon Seon Lee ◽  
Lexuan Gao ◽  
Laura Melissa Guzman ◽  
Loren H. Rieseberg
Keyword(s):  
Gene Expression ◽  
Alternative Splicing ◽  
Mixed Model ◽  
Agricultural Land ◽  
Alcohol Fermentation ◽  
Expression Levels ◽  
Flooding Stress ◽  
Genome Wide ◽  
And Control ◽  
Gene Expression Levels

Approximately 10% of agricultural land is subject to periodic flooding, which reduces the growth, survivorship, and yield of most crops, reinforcing the need to understand and enhance flooding resistance in our crops. Here, we generated RNA-Seq data from leaf and root tissue of domesticated sunflower to explore differences in gene expression and alternative splicing (AS) between a resistant and susceptible cultivar under both flooding and control conditions and at three time points. Using a combination of mixed model and gene co-expression analyses, we were able to separate general responses of sunflower to flooding stress from those that contribute to the greater tolerance of the resistant line. Both cultivars responded to flooding stress by upregulating expression levels of known submergence responsive genes, such as alcohol dehydrogenases, and slowing metabolism-related activities. Differential AS reinforced expression differences, with reduced AS frequencies typically observed for genes with upregulated expression. Significant differences were found between the genotypes, including earlier and stronger upregulation of the alcohol fermentation pathway and a more rapid return to pre-flooding gene expression levels in the resistant genotype. Our results show how changes in the timing of gene expression following both the induction of flooding and release from flooding stress contribute to increased flooding tolerance.

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