scholarly journals Alternative splicing in normal and pathological human placentas is correlated to genetic variants

2021 ◽  
Author(s):  
Camino S. M. Ruano ◽  
Clara Apicella ◽  
Sébastien Jacques ◽  
Géraldine Gascoin ◽  
Cassandra Gaspar ◽  
...  
Keyword(s):  
Gene Expression ◽  
Alternative Splicing ◽  
Genetic Variants ◽  
Gene Expression Regulation ◽  
Essential Feature ◽  
Normal Growth ◽  
Brain Diseases ◽  
Individual Level ◽  
The Individual ◽  
Placental Diseases

AbstractTwo major obstetric diseases, preeclampsia (PE), a pregnancy-induced endothelial dysfunction leading to hypertension and proteinuria, and intra-uterine growth-restriction (IUGR), a failure of the fetus to acquire its normal growth, are generally triggered by placental dysfunction. Many studies have evaluated gene expression deregulations in these diseases, but none has tackled systematically the role of alternative splicing. In the present study, we show that alternative splicing is an essential feature of placental diseases, affecting 1060 and 1409 genes in PE vs controls and IUGR vs controls, respectively, many of those involved in placental function. While in IUGR placentas, alternative splicing affects genes specifically related to pregnancy, in preeclamptic placentas, it impacts a mix of genes related to pregnancy and brain diseases. Also, alternative splicing variations can be detected at the individual level as sharp splicing differences between different placentas. We correlate these variations with genetic variants to define splicing Quantitative Trait Loci (sQTL) in the subset of the 48 genes the most strongly alternatively spliced in placental diseases. We show that alternative splicing is at least partly piloted by genetic variants located either in cis (52 QTL identified) or in trans (52 QTL identified). In particular, we found four chromosomal regions that impact the splicing of genes in the placenta. The present work provides a new vision of placental gene expression regulation that warrants further studies.

scholarly journals CoMM-S2: a collaborative mixed model using summary statistics in transcriptome-wide association studies

2019 ◽  
Author(s):  
Yi Yang ◽  
Xingjie Shi ◽  
Yuling Jiao ◽  
Jian Huang ◽  
Min Chen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Genetic Variants ◽  
Complex Traits ◽  
Mixed Model ◽  
Association Studies ◽  
Gwas Data ◽  
Supplementary Information ◽  
Summary Statistics ◽  
Individual Level ◽  
The Relationship

AbstractMotivationAlthough genome-wide association studies (GWAS) have deepened our understanding of the genetic architecture of complex traits, the mechanistic links that underlie how genetic variants cause complex traits remains elusive. To advance our understanding of the underlying mechanistic links, various consortia have collected a vast volume of genomic data that enable us to investigate the role that genetic variants play in gene expression regulation. Recently, a collaborative mixed model (CoMM) [42] was proposed to jointly interrogate genome on complex traits by integrating both the GWAS dataset and the expression quantitative trait loci (eQTL) dataset. Although CoMM is a powerful approach that leverages regulatory information while accounting for the uncertainty in using an eQTL dataset, it requires individual-level GWAS data and cannot fully make use of widely available GWAS summary statistics. Therefore, statistically efficient methods that leverages transcriptome information using only summary statistics information from GWAS data are required.ResultsIn this study, we propose a novel probabilistic model, CoMM-S2, to examine the mechanistic role that genetic variants play, by using only GWAS summary statistics instead of individual-level GWAS data. Similar to CoMM which uses individual-level GWAS data, CoMM-S2 combines two models: the first model examines the relationship between gene expression and genotype, while the second model examines the relationship between the phenotype and the predicted gene expression from the first model. Distinct from CoMM, CoMM-S2 requires only GWAS summary statistics. Using both simulation studies and real data analysis, we demonstrate that even though CoMM-S2 utilizes GWAS summary statistics, it has comparable performance as CoMM, which uses individual-level GWAS [email protected] and implementationThe implement of CoMM-S2 is included in the CoMM package that can be downloaded from https://github.com/gordonliu810822/CoMM.Supplementary informationSupplementary data are available at Bioinformatics online.

MRPL53, a New Candidate Gene for Orofacial Clefting, Identified Using an eQTL Approach

2017 ◽  
Vol 97 (1) ◽  
pp. 33-40 ◽  
Author(s):  
C. Masotti ◽  
L.A. Brito ◽  
A.C. Nica ◽  
K.U. Ludwig ◽  
K. Nunes ◽  
...  
Keyword(s):  
Gene Expression ◽  
Native American ◽  
Genetic Variants ◽  
Cleft Lip ◽  
Cultured Cells ◽  
Protein Subunit ◽  
Brazilian Sample ◽  
Mitochondrial Ribosomes ◽  
The Individual ◽  
The Impact

A valuable approach to understand how individual and population genetic differences can predispose to disease is to assess the impact of genetic variants on cellular functions (e.g., gene expression) of cell and tissue types related to pathological states. To understand the genetic basis of nonsyndromic cleft lip with or without cleft palate (NSCL/P) susceptibility, a complex and highly prevalent congenital malformation, we searched for genetic variants with a regulatory role in a disease-related tissue, the lip muscle (orbicularis oris muscle [OOM]), of affected individuals. From 46 OOM samples, which are frequently discarded during routine corrective surgeries on patients with orofacial clefts, we derived mesenchymal stem cells and correlated the individual genetic variants with gene expression from these cultured cells. Through this strategy, we detected significant cis-eQTLs (i.e., DNA variants affecting gene expression) and selected a few candidates to conduct an association study in a large Brazilian cohort (624 patients and 668 controls). This resulted in the discovery of a novel susceptibility locus for NSCL/P, rs1063588, the best eQTL for the MRPL53 gene, where evidence for association was mostly driven by the Native American ancestry component of our Brazilian sample. MRPL53 (2p13.1) encodes a 39S protein subunit of mitochondrial ribosomes and interacts with MYC, a transcription factor required for normal facial morphogenesis. Our study illustrates not only the importance of sampling admixed populations but also the relevance of measuring the functional effects of genetic variants over gene expression to dissect the complexity of disease phenotypes.

scholarly journals Gene expression is encoded in all parts of a co-evolving interacting gene regulatory structure

2019 ◽  
Author(s):  
Jan Zrimec ◽  
Filip Buric ◽  
Azam Sheikh Muhammad ◽  
Rhongzen Chen ◽  
Vilhelm Verendel ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Regulation ◽  
Model Organisms ◽  
Regulatory Structure ◽  
Expression Levels ◽  
Coding Regions ◽  
Dna Regulatory Motifs ◽  
Gene Regulatory ◽  
The Individual ◽  
Gene Expression Levels

AbstractUnderstanding the genetic regulatory code that governs gene expression is a primary, yet challenging aspiration in molecular biology that opens up possibilities to cure human diseases and solve biotechnology problems. However, the fundamental question of how each of the individual coding and non-coding regions of the gene regulatory structure interact and contribute to the mRNA expression levels remains unanswered. Considering that all the information for gene expression regulation is already present in living cells, here we applied deep learning on over 20,000 mRNA datasets in 7 model organisms ranging from bacteria to Human. We show that in all organisms, mRNA abundance can be predicted directly from the DNA sequence with high accuracy, demonstrating that up to 82% of the variation of gene expression levels is encoded in the gene regulatory structure. Coding and non-coding regions carry both overlapping and orthogonal information and additively contribute to gene expression levels. By searching for DNA regulatory motifs present across the whole gene regulatory structure, we discover that motif interactions can regulate gene expression levels in a range of over three orders of magnitude. The uncovered co-evolution of coding and non-coding regions challenges the current paradigm that single motifs or regions are solely responsible for gene expression levels. Instead, we show that the correct combination of all regulatory regions must be established in order to accurately control gene expression levels. Therefore, the holistic system that spans the entire gene regulatory structure is required to analyse, understand, and design any future gene expression systems.

scholarly journals Genetic factors contributing to extensive variability of sex-specific hepatic gene expression in Diversity Outbred mice

2020 ◽  
Author(s):  
Tisha Melia ◽  
David J. Waxman
Keyword(s):  
Gene Expression ◽  
Growth Hormone ◽  
Sex Differences ◽  
Genetic Variants ◽  
Genetic Factors ◽  
Disease Susceptibility ◽  
Gene Clusters ◽  
Individual Variability ◽  
Diversity Outbred ◽  
The Individual

AbstractSex-specific transcription characterizes hundreds of genes in mouse liver, many implicated in sex-differential drug and lipid metabolism and disease susceptibility. While the regulation of liver sex differences by growth hormone-activated STAT5 is well established, little is known about autosomal genetic factors regulating the sex-specific liver transcriptome. Here we show, using genotyping and expression data from a large population of Diversity Outbred mice, that genetic factors work in tandem with growth hormone to control the individual variability of hundreds of sex-biased genes, including many lncRNA genes. Significant associations between single nucleotide polymorphisms and sex-specific gene expression were identified as expression quantitative trait loci (eQTLs), many of which showed strong sex-dependent associations. Remarkably, autosomal genetic modifiers of sex-specific genes were found to account for more than 200 instances of gain or loss of sex-specificity across eight Diversity Outbred mouse founder strains. Sex-biased STAT5 binding sites and open chromatin regions with strain-specific variants were significantly enriched at eQTL regions regulating correspondingly sex-specific genes, supporting the proposed functional regulatory nature of the eQTL regions identified. Binding of the male-biased, growth hormone-regulated repressor BCL6 was most highly enriched at trans-eQTL regions controlling female-specific genes. Co-regulated gene clusters defined by overlapping eQTLs included sets of highly correlated genes from different chromosomes, further supporting trans-eQTL action. These findings elucidate how an unexpectedly large number of autosomal factors work in tandem with growth hormone signaling pathways to regulate the individual variability associated with sex differences in liver metabolism and disease.Author summaryMale-female differences in liver gene expression confer sex differences in many biological processes relevant to health and disease, including lipid and drug metabolism and liver disease susceptibility. While the role of hormonal factors, most notably growth hormone, in regulating hepatic sex differences is well established, little is known about how autosomal genetic factors impact sex differences on an individual basis. Here, we harness the power of mouse genetics provided by the Diversity Outbred mouse model to discover significant genome-wide associations between genetic variants and sex-specific liver gene expression. Remarkably, we found that autosomal expression quantitative trait loci with a strong sex-bias account for the loss or gain of sex-specific expression of more than 200 autosomal genes seen across eight founder mice strains. Genetic associations with sex-specific genes were enriched for sex-biased and growth hormone-dependent regulatory regions harboring strain-specific genetic variants. Co-regulated gene clusters identified by overlapping regulatory regions included highly correlated genes from different chromosomes. These findings reveal the extensive regulatory role played by autosomal genetic variants, working in tandem with growth hormone signaling pathways, in the transcriptional control of sex-biased genes, many of which have been implicated in sex differential outcomes in liver metabolism and disease susceptibility.

scholarly journals CoMM-S2: a collaborative mixed model using summary statistics in transcriptome-wide association studies

2019 ◽  
Vol 36 (7) ◽  
pp. 2009-2016 ◽  
Author(s):  
Yi Yang ◽  
Xingjie Shi ◽  
Yuling Jiao ◽  
Jian Huang ◽  
Min Chen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Genetic Variants ◽  
Complex Traits ◽  
Mixed Model ◽  
Association Studies ◽  
Gwas Data ◽  
Supplementary Information ◽  
Summary Statistics ◽  
Individual Level ◽  
The Relationship

Abstract Motivation Although genome-wide association studies (GWAS) have deepened our understanding of the genetic architecture of complex traits, the mechanistic links that underlie how genetic variants cause complex traits remains elusive. To advance our understanding of the underlying mechanistic links, various consortia have collected a vast volume of genomic data that enable us to investigate the role that genetic variants play in gene expression regulation. Recently, a collaborative mixed model (CoMM) was proposed to jointly interrogate genome on complex traits by integrating both the GWAS dataset and the expression quantitative trait loci (eQTL) dataset. Although CoMM is a powerful approach that leverages regulatory information while accounting for the uncertainty in using an eQTL dataset, it requires individual-level GWAS data and cannot fully make use of widely available GWAS summary statistics. Therefore, statistically efficient methods that leverages transcriptome information using only summary statistics information from GWAS data are required. Results In this study, we propose a novel probabilistic model, CoMM-S2, to examine the mechanistic role that genetic variants play, by using only GWAS summary statistics instead of individual-level GWAS data. Similar to CoMM which uses individual-level GWAS data, CoMM-S2 combines two models: the first model examines the relationship between gene expression and genotype, while the second model examines the relationship between the phenotype and the predicted gene expression from the first model. Distinct from CoMM, CoMM-S2 requires only GWAS summary statistics. Using both simulation studies and real data analysis, we demonstrate that even though CoMM-S2 utilizes GWAS summary statistics, it has comparable performance as CoMM, which uses individual-level GWAS data. Availability and implementation The implement of CoMM-S2 is included in the CoMM package that can be downloaded from https://github.com/gordonliu810822/CoMM. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Alternative Splicing Events Is Not a Key Event for Gene Expression Regulation in Uremia

PLoS ONE ◽  
2013 ◽  
Vol 8 (12) ◽  
pp. e82702
Author(s):  
Marion Sallée ◽  
Michel Fontès ◽  
Laurence Louis ◽  
Claire Cérini ◽  
Philippe Brunet ◽  
...  
Keyword(s):  
Gene Expression ◽  
Alternative Splicing ◽  
Gene Expression Regulation ◽  
Expression Regulation ◽  
Alternative Splicing Events

scholarly journals The molecular basis of socially-mediated phenotypic plasticity in a eusocial paper wasp

2020 ◽  
Author(s):  
Benjamin A. Taylor ◽  
Alessandro Cini ◽  
Christopher D. R. Wyatt ◽  
Max Reuter ◽  
Seirian Sumner
Keyword(s):  
Gene Expression ◽  
Phenotypic Plasticity ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Paper Wasp ◽  
Strong Response ◽  
Individual Level ◽  
Multiple Phenotypes ◽  
The Individual ◽  
The Relationship

AbstractPhenotypic plasticity, the ability to produce multiple phenotypes from a single genotype, represents an excellent model with which to examine the relationship between gene expression and phenotypes. Despite this, analyses of the molecular bases of plasticity have been limited by the challenges of linking individual phenotypes with individual-level gene expression profiles, especially in the case of complex social phenotypes. Here, we tackle this challenge by analysing the individual-level gene expression profiles of Polistes dominula paper wasps following the loss of a queen, a perturbation that induces some individuals to undergo a significant phenotypic shift and become replacement reproductives. Using a machine learning approach, we find a strong response of caste-associated gene expression to queen loss, wherein individuals’ expression profiles become intermediate between queen and worker states. Importantly, this change occurs even in individuals that appear phenotypically unaffected. Part of this response is explained by individual attributes, most prominently age. These results demonstrate that large changes in gene expression may occur in the absence of detectable phenotypic changes, resulting here in a socially mediated de-differentiation of individuals at the transcriptomic but not the phenotypic level. Our findings also highlight the complexity of the relationship between gene expression and phenotype, where transcriptomes are neither a direct reflection of the genotype nor a proxy for the molecular underpinnings of the external phenotype.

scholarly journals Characterizing Xenopus tropicalis endurance capacities with multilevel transcriptomics

2016 ◽  
Author(s):  
Adam J. Richards ◽  
Anthony Herrel ◽  
Mathieu Videlier ◽  
Konrad Paszkiewicz ◽  
Nicolas Pollet ◽  
...  
Keyword(s):  
Gene Expression ◽  
Xenopus Tropicalis ◽  
Endurance Capacity ◽  
Functional Modules ◽  
Gene Set ◽  
Individual Level ◽  
Gene Level ◽  
Genetic Mechanisms ◽  
Ideal Tool ◽  
The Individual

AbstractVertebrate endurance capacity is a phenotype with considerable genetic heterogeneity. RNA-Seq technologies are an ideal tool to investigate the involved genes and processes, but several challenges exist when the phenotype of interest has a complex genetic background. Difficulties manifest at the level of results interpretation because commonly used statistical methods are designed to identify strongly associated genes. If an observed phenotype can be achieved though multiple distinct genetic mechanisms then typical gene-centric methods come with the attached risk that signal may be lost or misconstrued.Gene set analysis (GSA) methods are now widely accepted as a means to address some of the shortcomings of gene-by-gene analysis methods. We carry out both gene level and gene set level analyses on Xenopus tropicalis to identify the genetic factors that contribute to endurance heterogeneity. A typical workflow might consider gene level and pathway level analyses, but in this work we propose an additional focus at the intermediate level of functional modules. We generate functional modules for GSA testing in order to be explicit in how ontology information is used with respect to the functional genomics of Xenopus. Additionally, we make use of multiple assemblies to corroborate implicated genes and processes.We identified 42 core genes, 10 functional modules, and 14 pathways based on gene expression differences between endurant and non-endurant frogs. The majority of the genes and processes are readily associated with muscle contraction or catabolism. A substantial number of these genes are involved in lipid metabolic processes, suggesting an important role in frog endurance heterogeneity. Unsurprisingly, many of the gene expression differences between endurant and non-endurant frogs can be distilled down to the capacity to utilize substrate for energy, but at the individual level frogs appear to make use of diverse machinery to achieve these differences.

scholarly journals A joint analysis of longevity and age-related disease variants for gene expression association

2021 ◽  
Author(s):  
Lu Zeng ◽  
Shouneng Peng ◽  
Seungsoo Kim ◽  
Jun Zhu ◽  
Bin Zhang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Genetic Variants ◽  
Gene Expression Regulation ◽  
Human Longevity ◽  
Joint Analysis ◽  
Putative Gene ◽  
Related Disease ◽  
Age Related ◽  
Genome Wide

AbstractA large number of genetic variants associated with human longevity have been reported but how they play their functions remains elusive. We performed an integrative analysis on 113 genome-wide significant longevity and 14,529 age-related disease variants in the context of putative gene expression regulation. We found that most of the longevity allele types were different from the genotype of disease alleles when they were localized at the same chromosomal positions. Longevity variants were about eight times more likely to be associated with gene expression than randomly selected variants. The directions of the gene expression association were more likely to be opposite between longevity and disease variants when the association occurred to the same gene. Many longevity variants likely function through down-regulating inflammatory response and up-regulating healthy lipid metabolisms. In conclusion, this work helps to elucidate the potential mechanisms of longevity variants for follow-up studies to discover methods to extend human healthspan.

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