scholarly journals (Non)Parallel developmental pathways to vertebrate appenda

2021 ◽  
Author(s):  
Samantha Swank ◽  
Thomas Sanger ◽  
Yoel Stuart
Keyword(s):  
Gene Expression ◽  
Complex Traits ◽  
Developmental Pathways ◽  
Vertebrate Evolution ◽  
Use Of Self ◽  
Genetic Pathways ◽  
Developmental Mechanisms

This is the pre-peer reviewed version of the following article: (Non)Parallel developmental mechanisms in vertebrate appendage reduction and loss , which has been published in final form at https://doi.org/10.1002/ece3.8226. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. Appendages have been reduced or lost hundreds of times independently during vertebrate evolution. This suggests that selection routinely favors appendage reduction. How often are the same developmental and genetic pathways used during loss by independent lineages? We reviewed the developmental and evolutionary literatures of appendage reduction in 12 genera spanning fish, reptiles, birds, and mammals. We found that appendage reduction and loss resulted from modified gene expression in each case but one. However, the genes for which expression was modified were rarely shared. Our findings suggest that adaptive loss of complex traits might proceed relatively easily through changes in gene expression along multiple developmental pathways.

scholarly journals (Non)Parallel developmental pathways to vertebrate appendage reduction and loss

2021 ◽  
Author(s):  
Samantha Swank ◽  
Thomas Sanger ◽  
Yoel Stuart
Keyword(s):  
Gene Expression ◽  
Complex Traits ◽  
Developmental Pathways ◽  
Vertebrate Evolution ◽  
Use Of Self ◽  
Genetic Pathways ◽  
Developmental Mechanisms

This is the pre-peer reviewed version of the following article: (Non)Parallel developmental mechanisms in vertebrate appendage reduction and loss , which has been published in final form at https://doi.org/10.1002/ece3.8226. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. Appendages have been reduced or lost hundreds of times independently during vertebrate evolution. This suggests that selection routinely favors appendage reduction. How often are the same developmental and genetic pathways used during loss by independent lineages? We reviewed the developmental and evolutionary literatures of appendage reduction in 12 genera spanning fish, reptiles, birds, and mammals. We found that appendage reduction and loss resulted from modified gene expression in each case but one. However, the genes for which expression was modified were rarely shared. Our findings suggest that adaptive loss of complex traits might proceed relatively easily through changes in gene expression along multiple developmental pathways.

scholarly journals (Non)Parallel developmental pathways to vertebrate appendage reduction

2021 ◽  
Author(s):  
Samantha Swank ◽  
Thomas Sanger ◽  
Yoel Stuart
Keyword(s):  
Gene Expression ◽  
Complex Traits ◽  
Developmental Pathways ◽  
Vertebrate Evolution ◽  
Genetic Pathways

Appendages have been reduced or lost hundreds of times independently during vertebrate evolution. This suggests that selection routinely favors appendage reduction. How often are the same developmental and genetic pathways used during loss by independent lineages? We reviewed the developmental and evolutionary literatures of appendage reduction in 12 genera spanning fish, reptiles, birds, and mammals. We found that appendage reduction and loss resulted from modified gene expression in each case but one. However, the genes for which expression was modified were rarely shared. Our findings suggest that adaptive loss of complex traits might proceed relatively easily through changes in gene expression along multiple developmental pathways.

scholarly journals Transcriptome analysis identified aberrant gene expression in pollen developmental pathways leading to CGMS in cotton (Gossypium hirsutum L.)

PLoS ONE ◽  
2019 ◽  
Vol 14 (6) ◽  
pp. e0218381 ◽  
Author(s):  
Rasmieh Hamid ◽  
Hassan Marashi ◽  
Rukam S. Tomar ◽  
Saeid Malekzadeh Shafaroudi ◽  
Pritesh H. Sabara
Keyword(s):  
Gene Expression ◽  
Gossypium Hirsutum ◽  
Transcriptome Analysis ◽  
Developmental Pathways ◽  
Gossypium Hirsutum L ◽  
Aberrant Gene Expression ◽  
Aberrant Gene

scholarly journals Transcriptional analysis of the response of C. elegans to ethanol exposure

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Mark G. Sterken ◽  
Marijke H. van Wijk ◽  
Elizabeth C. Quamme ◽  
Joost A. G. Riksen ◽  
Lucinda Carnell ◽  
...  
Keyword(s):  
Gene Expression ◽  
Physiological Response ◽  
Physiological Responses ◽  
Ethanol Exposure ◽  
Transcriptional Analysis ◽  
Transcriptional Responses ◽  
Genetic Pathways ◽  
C Elegans ◽  
Chronic Ethanol Consumption ◽  
Transcriptional Changes

AbstractEthanol-induced transcriptional changes underlie important physiological responses to ethanol that are likely to contribute to the addictive properties of the drug. We examined the transcriptional responses of Caenorhabditis elegans across a timecourse of ethanol exposure, between 30 min and 8 h, to determine what genes and genetic pathways are regulated in response to ethanol in this model. We found that short exposures to ethanol (up to 2 h) induced expression of metabolic enzymes involved in metabolizing ethanol and retinol, while longer exposure (8 h) had much more profound effects on the transcriptome. Several genes that are known to be involved in the physiological response to ethanol, including direct ethanol targets, were regulated at 8 h of exposure. This longer exposure to ethanol also resulted in the regulation of genes involved in cilia function, which is consistent with an important role for the effects of ethanol on cilia in the deleterious effects of chronic ethanol consumption in humans. Finally, we found that food deprivation for an 8-h period induced gene expression changes that were somewhat ameliorated by the presence of ethanol, supporting previous observations that worms can use ethanol as a calorie source.

scholarly journals On Using Local Ancestry to Characterize the Genetic Architecture of Human Phenotypes: Genetic Regulation of Gene Expression in Multiethnic or Admixed Populations as a Model

2018 ◽  
Author(s):  
Yizhen Zhong ◽  
Minoli Perera ◽  
Eric R. Gamazon
Keyword(s):  
Gene Expression ◽  
Complex Traits ◽  
Genetic Architecture ◽  
Genetic Regulation ◽  
Regulation Of Gene Expression ◽  
Type I ◽  
Eqtl Mapping ◽  
Entire Genome ◽  
Local Ancestry ◽  
Heritability Estimation

AbstractBackgroundUnderstanding the nature of the genetic regulation of gene expression promises to advance our understanding of the genetic basis of disease. However, the methodological impact of use of local ancestry on high-dimensional omics analyses, including most prominently expression quantitative trait loci (eQTL) mapping and trait heritability estimation, in admixed populations remains critically underexplored.ResultsHere we develop a statistical framework that characterizes the relationships among the determinants of the genetic architecture of an important class of molecular traits. We estimate the trait variance explained by ancestry using local admixture relatedness between individuals. Using National Institute of General Medical Sciences (NIGMS) and Genotype-Tissue Expression (GTEx) datasets, we show that use of local ancestry can substantially improve eQTL mapping and heritability estimation and characterize the sparse versus polygenic component of gene expression in admixed and multiethnic populations respectively. Using simulations of diverse genetic architectures to estimate trait heritability and the level of confounding, we show improved accuracy given individual-level data and evaluate a summary statistics based approach. Furthermore, we provide a computationally efficient approach to local ancestry analysis in eQTL mapping while increasing control of type I and type II error over traditional approaches.ConclusionOur study has important methodological implications on genetic analysis of omics traits across a range of genomic contexts, from a single variant to a prioritized region to the entire genome. Our findings highlight the importance of using local ancestry to better characterize the heritability of complex traits and to more accurately map genetic associations.

scholarly journals TIGAR: An Improved Bayesian Tool for Transcriptomic Data Imputation Enhances Gene Mapping of Complex Traits

2018 ◽  
Author(s):  
Sini Nagpal ◽  
Xiaoran Meng ◽  
Michael P. Epstein ◽  
Lam C. Tsoi ◽  
Matthew Patrick ◽  
...  
Keyword(s):  
Gene Expression ◽  
Complex Traits ◽  
Bayesian Model ◽  
Genetic Architecture ◽  
Bayesian Method ◽  
Association Studies ◽  
Gwas Data ◽  
Nonparametric Bayesian ◽  
Transcriptomic Data ◽  
Special Cases

AbstractThe transcriptome-wide association studies (TWAS) that test for association between the study trait and the imputed gene expression levels from cis-acting expression quantitative trait loci (cis-eQTL) genotypes have successfully enhanced the discovery of genetic risk loci for complex traits. By using the gene expression imputation models fitted from reference datasets that have both genetic and transcriptomic data, TWAS facilitates gene-based tests with GWAS data while accounting for the reference transcriptomic data. The existing TWAS tools like PrediXcan and FUSION use parametric imputation models that have limitations for modeling the complex genetic architecture of transcriptomic data. Therefore, we propose an improved Bayesian method that assumes a data-driven nonparametric prior to impute gene expression. Our method is general and flexible and includes both the parametric imputation models used by PrediXcan and FUSION as special cases. Our simulation studies showed that the nonparametric Bayesian model improved both imputation R2 for transcriptomic data and the TWAS power over PrediXcan. In real applications, our nonparametric Bayesian method fitted transcriptomic imputation models for 2X number of genes with 1.7X average regression R2 over PrediXcan, thus improving the power of follow-up TWAS. Hence, the nonparametric Bayesian model is preferred for modeling the complex genetic architecture of transcriptomes and is expected to enhance transcriptome-integrated genetic association studies. We implement our Bayesian approach in a convenient software tool “TIGAR” (Transcriptome-Integrated Genetic Association Resource), which imputes transcriptomic data and performs subsequent TWAS using individual-level or summary-level GWAS data.

scholarly journals Leveraging DNA methylation quantitative trait loci to characterize the relationship between methylomic variation, gene expression and complex traits

2018 ◽  
Author(s):  
Eilis Hannon ◽  
Tyler J Gorrie-Stone ◽  
Melissa C Smart ◽  
Joe Burrage ◽  
Amanda Hughes ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Genetic Variation ◽  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Complex Traits ◽  
Common Genetic Variation ◽  
Online Data ◽  
The Relationship ◽  
Methylation Quantitative Trait Loci

ABSTRACTCharacterizing the complex relationship between genetic, epigenetic and transcriptomic variation has the potential to increase understanding about the mechanisms underpinning health and disease phenotypes. In this study, we describe the most comprehensive analysis of common genetic variation on DNA methylation (DNAm) to date, using the Illumina EPIC array to profile samples from the UK Household Longitudinal study. We identified 12,689,548 significant DNA methylation quantitative trait loci (mQTL) associations (P < 6.52x10-14) occurring between 2,907,234 genetic variants and 93,268 DNAm sites, including a large number not identified using previous DNAm-profiling methods. We demonstrate the utility of these data for interpreting the functional consequences of common genetic variation associated with > 60 human traits, using Summary data–based Mendelian Randomization (SMR) to identify 1,662 pleiotropic associations between 36 complex traits and 1,246 DNAm sites. We also use SMR to characterize the relationship between DNAm and gene expression, identifying 6,798 pleiotropic associations between 5,420 DNAm sites and the transcription of 1,702 genes. Our mQTL database and SMR results are available via a searchable online database (http://www.epigenomicslab.com/online-data-resources/) as a resource to the research community.

scholarly journals A transcriptome-wide Mendelian randomization study to uncover tissue-dependent regulatory mechanisms across the human phenome

2019 ◽  
Author(s):  
Tom G Richardson ◽  
Gibran Hemani ◽  
Tom R Gaunt ◽  
Caroline L Relton ◽  
George Davey Smith
Keyword(s):  
Gene Expression ◽  
Genetic Variants ◽  
Complex Traits ◽  
Mendelian Randomization ◽  
Drug Repositioning ◽  
Association Studies ◽  
Thyroid Tissue ◽  
Genome Wide Association Studies ◽  
Tissue Specific ◽  
Genome Wide

AbstractBackgroundDeveloping insight into tissue-specific transcriptional mechanisms can help improve our understanding of how genetic variants exert their effects on complex traits and disease. By applying the principles of Mendelian randomization, we have undertaken a systematic analysis to evaluate transcriptome-wide associations between gene expression across 48 different tissue types and 395 complex traits.ResultsOverall, we identified 100,025 gene-trait associations based on conventional genome-wide corrections (P < 5 × 10−08) that also provided evidence of genetic colocalization. These results indicated that genetic variants which influence gene expression levels in multiple tissues are more likely to influence multiple complex traits. We identified many examples of tissue-specific effects, such as genetically-predicted TPO, NR3C2 and SPATA13 expression only associating with thyroid disease in thyroid tissue. Additionally, FBN2 expression was associated with both cardiovascular and lung function traits, but only when analysed in heart and lung tissue respectively.We also demonstrate that conducting phenome-wide evaluations of our results can help flag adverse on-target side effects for therapeutic intervention, as well as propose drug repositioning opportunities. Moreover, we find that exploring the tissue-dependency of associations identified by genome-wide association studies (GWAS) can help elucidate the causal genes and tissues responsible for effects, as well as uncover putative novel associations.ConclusionsThe atlas of tissue-dependent associations we have constructed should prove extremely valuable to future studies investigating the genetic determinants of complex disease. The follow-up analyses we have performed in this study are merely a guide for future research. Conducting similar evaluations can be undertaken systematically at http://mrcieu.mrsoftware.org/Tissue_MR_atlas/.

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