scholarly journals Parallel Genomic Changes Drive Repeated Evolution of Placentas in Live-Bearing Fish

2021 ◽  
Author(s):  
Henri van Kruistum ◽  
Reindert Nijland ◽  
David N Reznick ◽  
Martien A M Groenen ◽  
Hendrik-Jan Megens ◽  
...  
Keyword(s):  
Complex Traits ◽  
Evolutionary Rate ◽  
Complex Trait ◽  
Regulatory Elements ◽  
Evolutionary Origin ◽  
Regulatory Change ◽  
Protein Coding ◽  
Genomic Changes ◽  
Rate Analysis ◽  
Repeated Evolution

Abstract The evolutionary origin of complex organs challenges empirical study because most organs evolved hundreds of millions of years ago. The placenta of live-bearing fish in the family Poeciliidae represents a unique opportunity to study the evolutionary origin of complex organs, because in this family a placenta evolved at least nine times independently. It is currently unknown whether this repeated evolution is accompanied by similar, repeated, genomic changes in placental species. Here, we compare whole genomes of 26 poeciliid species representing six out of nine independent origins of placentation. Evolutionary rate analysis revealed that the evolution of the placenta coincides with convergent shifts in the evolutionary rate of 78 protein-coding genes, mainly observed in transporter- and vesicle-located genes. Furthermore, differences in sequence conservation showed that placental evolution coincided with similar changes in 76 noncoding regulatory elements, occurring primarily around genes that regulate development. The unexpected high occurrence of GATA simple repeats in the regulatory elements suggests an important function for GATA repeats in developmental gene regulation. The distinction in molecular evolution observed, with protein-coding parallel changes more often found in metabolic and structural pathways, compared with regulatory change more frequently found in developmental pathways, offers a compelling model for complex trait evolution in general: changing the regulation of otherwise highly conserved developmental genes may allow for the evolution of complex traits.

scholarly journals Return of a lost structure in the evolution of felid dentition revisited: A DevoEvo perspective on the irreversibility of evolution

2021 ◽  
Author(s):  
Vincent J. Lynch
Keyword(s):  
Protein Interactions ◽  
Complex Traits ◽  
Ad Hoc ◽  
Complex Trait ◽  
Regulatory Elements ◽  
Developmental Model ◽  
Lynx Lynx ◽  
Eurasian Lynx ◽  
Protein Coding ◽  
Dollo’S Law

AbstractThere is a longstanding interest in whether the loss of complex characters is reversible (so-called “Dollo’s law”). Reevolution has been suggested for numerous traits but among the first was Kurtén (1963), who proposed that the presence of the second lower molar (M2) of the Eurasian lynx (Lynx lynx) was a violation of Dollo’s law because all other Felids lack M2. While an early and often cited example for the reevolution of a complex trait, Kurtén (1963) and Werdelin (1987) used an ad hoc parsimony argument to support their proposition that M2 reevolved in Eurasian lynx. Here I revisit the evidence that M2 reevolved in Eurasian lynx using explicit parsimony and maximum likelihood models of character evolution and find strong evidence that Kurtén (1963) and Werdelin (1987) were correct – M2 reevolved in Eurasian lynx. Next, I explore the developmental mechanisms which may explain this violation of Dollo’s law and suggest that the reevolution of lost complex traits may arise from the reevolution of cis-regulatory elements and protein-protein interactions, which have a longer half-life after silencing that protein coding genes. Finally, I present a model developmental model to explain the reevolution M2 in Eurasian lynx.

scholarly journals The missing link between genetic association and regulatory function

2021 ◽  
Author(s):  
Noah James Connally ◽  
Sumaiya Nazeen ◽  
Daniel Lee ◽  
Huwenbo Shi ◽  
John Stamatoyannopoulos ◽  
...  
Keyword(s):  
Gene Expression ◽  
Complex Traits ◽  
Complex Trait ◽  
Regulatory Elements ◽  
Regulatory Function ◽  
Data Sets ◽  
Protein Coding ◽  
Regulatory Effects ◽  
Complex Forms ◽  
Coding Variants

The genetic basis of most complex traits is highly polygenic and dominated by non-coding alleles, and it is widely assumed that such alleles exert small regulatory effects on the expression of cis-linked genes. However, despite availability of expansive gene expression and epigenomic data sets, few variant-to-gene links have emerged. We identified 139 genes in which protein-coding variants cause severe or familial forms of nine human traits. We then computed the association between common complex forms of the same traits and non-coding variation, revealing that most such traits are also associated with non-coding variation in the vicinity of the same genes. However, we found colocalization evidence--the same variant influencing both the physiological trait and gene expression--for only 7% of genes, and transcriptome-wide association evidence with correct direction of effect for only 4% of genes, despite an abundance of eQTLs in most loci. Fine mapping variants to regulatory elements and assigning these to genes by linear distance similarly failed to implicate most genes in complex traits. These results contradict the hypothesis that most complex trait-associated variants coincide with currently ascertained expression quantitative trait loci. The field must confront this deficit, and pursue the "missing regulation."

scholarly journals Disease heritability enrichment of regulatory elements is concentrated in elements with ancient sequence age and conserved function across species

2018 ◽  
Author(s):  
Margaux L.A. Hujoel ◽  
Steven Gazal ◽  
Farhad Hormozdiari ◽  
Bryce van de Geijn ◽  
Alkes L. Price
Keyword(s):  
Complex Traits ◽  
Negative Selection ◽  
Target Gene ◽  
Regulatory Element ◽  
Complex Trait ◽  
Regulatory Elements ◽  
Mean Value ◽  
Regulatory Function ◽  
Loss Of Function ◽  
The Mean

AbstractRegulatory elements, e.g. enhancers and promoters, have been widely reported to be enriched for disease and complex trait heritability. We investigated how this enrichment varies with the age of the underlying genome sequence, the conservation of regulatory function across species, and the target gene of the regulatory element. We estimated heritability enrichment by applying stratified LD score regression to summary statistics from 41 independent diseases and complex traits (average N =320K) and meta-analyzing results across traits. Enrichment of human enhancers and promoters was larger in elements with older sequence age, assessed via alignment with other species irrespective of conserved functionality: enhancer elements with ancient sequence age (older than the split between marsupial and placental mammals) were 8.8x enriched (vs. 2.5x for all enhancers; p = 3e-14), and promoter elements with ancient sequence age were 13.5x enriched (vs. 5.1x for all promoters; p = 5e-16). Enrichment of human enhancers and promoters was also larger in elements whose regulatory function was conserved across species, e.g. human enhancers that were enhancers in ≥5 of 9 other mammals were 4.6x enriched (p = 5e-12 vs. all enhancers). Enrichment of human promoters was larger in promoters of loss-of-function intolerant genes: 12.0x enrichment (p = 8e-15 vs. all promoters). The mean value of several measures of negative selection within these genomic annotations mirrored all of these findings. Notably, the annotations with these excess heritability enrichments were jointly significant conditional on each other and on our baseline-LD model, which includes a broad set of coding, conserved, regulatory and LD-related annotations.

scholarly journals An evolutionary genomic approach reveals both conserved and species-specific genetic elements related to human disease in closely related Aspergillus fungi

Genetics ◽  
2021 ◽  
Author(s):  
Matthew E Mead ◽  
Jacob L Steenwyk ◽  
Lilian P Silva ◽  
Patrícia A de Castro ◽  
Nauman Saeed ◽  
...  
Keyword(s):  
Human Disease ◽  
Evolutionary Rate ◽  
Fungal Disease ◽  
Comparative Genomic ◽  
Genetic Elements ◽  
Genomic Approach ◽  
Evolutionary Genomic ◽  
Repeated Evolution ◽  
Species Specific ◽  
Encoding Genes

Abstract Aspergillosis is an important opportunistic human disease caused by filamentous fungi in the genus Aspergillus. Roughly 70% of infections are caused by Aspergillus fumigatus, with the rest stemming from approximately a dozen other Aspergillus species. Several of these pathogens are closely related to A. fumigatus and belong in the same taxonomic section, section Fumigati. Pathogenic species are frequently most closely related to non-pathogenic ones, suggesting Aspergillus pathogenicity evolved multiple times independently. To understand the repeated evolution of Aspergillus pathogenicity, we performed comparative genomic analyses on 18 strains from 13 species, including 8 species in section Fumigati, which aimed to identify genes, both ones previously connected to virulence as well as ones never before implicated, whose evolution differs between pathogens and non-pathogens. We found that most genes were present in all species, including approximately half of those previously connected to virulence, but a few genes were section- or species-specific. Evolutionary rate analyses identified over 1,700 genes whose evolutionary rate differed between pathogens and non-pathogens and dozens of genes whose rates differed between specific pathogens and the rest of the taxa. Functional testing of deletion mutants of 17 transcription factor-encoding genes whose evolution differed between pathogens and non-pathogens identified eight genes that affect either fungal survival in a model of phagocytic killing, host survival in an animal model of fungal disease, or both. These results suggest that the evolution of pathogenicity in Aspergillus involved both conserved and species-specific genetic elements, illustrating how an evolutionary genomic approach informs the study of fungal disease.

scholarly journals Leveraging Multiple Layers of Data To Predict Drosophila Complex Traits

2020 ◽  
Vol 10 (12) ◽  
pp. 4599-4613
Author(s):  
Fabio Morgante ◽  
Wen Huang ◽  
Peter Sørensen ◽  
Christian Maltecca ◽  
Trudy F. C. Mackay
Keyword(s):  
Precision Agriculture ◽  
Complex Traits ◽  
Prediction Accuracy ◽  
Cellular Response ◽  
Complex Trait ◽  
Sources Of Information ◽  
Starvation Resistance ◽  
Expression Levels ◽  
Chill Coma ◽  
Additional Layer

The ability to accurately predict complex trait phenotypes from genetic and genomic data are critical for the implementation of personalized medicine and precision agriculture; however, prediction accuracy for most complex traits is currently low. Here, we used data on whole genome sequences, deep RNA sequencing, and high quality phenotypes for three quantitative traits in the ∼200 inbred lines of the Drosophila melanogaster Genetic Reference Panel (DGRP) to compare the prediction accuracies of gene expression and genotypes for three complex traits. We found that expression levels (r = 0.28 and 0.38, for females and males, respectively) provided higher prediction accuracy than genotypes (r = 0.07 and 0.15, for females and males, respectively) for starvation resistance, similar prediction accuracy for chill coma recovery (null for both models and sexes), and lower prediction accuracy for startle response (r = 0.15 and 0.14 for female and male genotypes, respectively; and r = 0.12 and 0.11, for females and male transcripts, respectively). Models including both genotype and expression levels did not outperform the best single component model. However, accuracy increased considerably for all the three traits when we included gene ontology (GO) category as an additional layer of information for both genomic variants and transcripts. We found strongly predictive GO terms for each of the three traits, some of which had a clear plausible biological interpretation. For example, for starvation resistance in females, GO:0033500 (r = 0.39 for transcripts) and GO:0032870 (r = 0.40 for transcripts), have been implicated in carbohydrate homeostasis and cellular response to hormone stimulus (including the insulin receptor signaling pathway), respectively. In summary, this study shows that integrating different sources of information improved prediction accuracy and helped elucidate the genetic architecture of three Drosophila complex phenotypes.

The Long Road to Understanding RNAPII Transcription Initiation and Related Syndromes

2021 ◽  
Vol 90 (1) ◽  
pp. 193-219
Author(s):  
Emmanuel Compe ◽  
Jean-Marc Egly
Keyword(s):  
Transcription Factors ◽  
Rna Polymerase Ii ◽  
Chromatin Remodeling ◽  
Transcription Initiation ◽  
Rna Synthesis ◽  
Regulatory Elements ◽  
Initiation Mechanism ◽  
Protein Coding ◽  
Core Promoters ◽  
General Transcription Factors

In eukaryotes, transcription of protein-coding genes requires the assembly at core promoters of a large preinitiation machinery containing RNA polymerase II (RNAPII) and general transcription factors (GTFs). Transcription is potentiated by regulatory elements called enhancers, which are recognized by specific DNA-binding transcription factors that recruit cofactors and convey, following chromatin remodeling, the activating cues to the preinitiation complex. This review summarizes nearly five decades of work on transcription initiation by describing the sequential recruitment of diverse molecular players including the GTFs, the Mediator complex, and DNA repair factors that support RNAPII to enable RNA synthesis. The elucidation of the transcription initiation mechanism has greatly benefited from the study of altered transcription components associated with human diseases that could be considered transcription syndromes.

scholarly journals Identification and characterization of cis-regulatory elements for photoreceptor type-specific transcription in zebrafish

2019 ◽  
Author(s):  
Wei Fang ◽  
Yi Wen ◽  
Xiangyun Wei
Keyword(s):  
Core Promoter ◽  
Regulatory Elements ◽  
Specific Gene ◽  
Protein Coding ◽  
Core Promoters ◽  
Protein Coding Genes ◽  
The Core ◽  
Cell Type Specific ◽  
Identification And Characterization

AbstractTissue-specific or cell type-specific transcription of protein-coding genes is controlled by both trans-regulatory elements (TREs) and cis-regulatory elements (CREs). However, it is challenging to identify TREs and CREs, which are unknown for most genes. Here, we describe a protocol for identifying two types of transcription-activating CREs—core promoters and enhancers—of zebrafish photoreceptor type-specific genes. This protocol is composed of three phases: bioinformatic prediction, experimental validation, and characterization of the CREs. To better illustrate the principles and logic of this protocol, we exemplify it with the discovery of the core promoter and enhancer of the mpp5b apical polarity gene (also known as ponli), whose red, green, and blue (RGB) cone-specific transcription requires its enhancer, a member of the rainbow enhancer family. While exemplified with an RGB cone-specific gene, this protocol is general and can be used to identify the core promoters and enhancers of other protein-coding genes.

scholarly journals Chromatin accessibility is dynamically regulated across C. elegans development and ageing

2018 ◽  
Author(s):  
Jürgen Jänes ◽  
Yan Dong ◽  
Michael Schoof ◽  
Jacques Serizay ◽  
Alex Appert ◽  
...  
Keyword(s):  
Regulatory Mechanism ◽  
Regulatory Elements ◽  
Chromatin Accessibility ◽  
Protein Coding ◽  
C Elegans ◽  
Transcription Profiles ◽  
Physiological Processes ◽  
Global Identification ◽  
Identification And Characterization

AbstractAn essential step for understanding the transcriptional circuits that control development and physiology is the global identification and characterization of regulatory elements. Here we present the first map of regulatory elements across the development and ageing of an animal, identifying 42,245 elements accessible in at least one C. elegans stage. Based on nuclear transcription profiles, we define 15,714 protein-coding promoters and 19,231 putative enhancers, and find that both types of element can drive orientation-independent transcription. Additionally, hundreds of promoters produce transcripts antisense to protein coding genes, suggesting involvement in a widespread regulatory mechanism. We find that the accessibility of most elements is regulated during development and/or ageing and that patterns of accessibility change are linked to specific developmental or physiological processes. The map and characterization of regulatory elements across C. elegans life provides a platform for understanding how transcription controls development and ageing.

scholarly journals Opportunities and limits of combining microbiome and genome data for complex trait prediction

2020 ◽  
Author(s):  
Miguel Pérez-Enciso ◽  
Laura M. Zingaretti ◽  
Yuliaxis Ramayo-Caldas ◽  
Gustavo de los Campos
Keyword(s):  
Complex Traits ◽  
Variance Components ◽  
Complex Trait ◽  
Parameter Estimates ◽  
Phenotypic Variance ◽  
Genome Data ◽  
Individual Snps ◽  
Simulation Strategy ◽  
Microbiome Data ◽  
Trait Prediction

AbstractThe analysis and prediction of complex traits using microbiome data combined with host genomic information is a topic of utmost interest. However, numerous questions remain to be answered: How useful can the microbiome be for complex trait prediction? Are microbiability estimates reliable? Can the underlying biological links between the host’s genome, microbiome, and the phenome be recovered? Here, we address these issues by (i) developing a novel simulation strategy that uses real microbiome and genotype data as input, and (ii) proposing a variance-component approach which, in the spirit of mediation analyses, quantifies the proportion of phenotypic variance explained by genome and microbiome, and dissects it into direct and indirect effects. The proposed simulation approach can mimic a genetic link between the microbiome and SNP data via a permutation procedure that retains the distributional properties of the data. Results suggest that microbiome data could significantly improve phenotype prediction accuracy, irrespective of whether some abundances are under direct genetic control by the host or not. Overall, random-effects linear methods appear robust for variance components estimation, despite the highly leptokurtic distribution of microbiota abundances. Nevertheless, we observed that accuracy depends in part on the number of microorganisms’ taxa influencing the trait of interest. While we conclude that overall genome-microbiome-links can be characterized via variance components, we are less optimistic about the possibility of identifying the causative effects, i.e., individual SNPs affecting abundances; power at this level would require much larger sample sizes than the ones typically available for genome-microbiome-phenome data.Author summaryThe microbiome consists of the microorganisms that live in a particular environment, including those in our organism. There is consistent evidence that these communities play an important role in numerous traits of relevance, including disease susceptibility or feed efficiency. Moreover, it has been shown that the microbiome can be relatively stable throughout an individual’s life and that is affected by the host genome. These reasons have prompted numerous studies to determine whether and how the microbiome can be used for prediction of complex phenotypes, either using microbiome alone or in combination with host’s genome data. However, numerous questions remain to be answered such as the reliability of parameter estimates, or which is the underlying relationship between microbiome, genome, and phenotype. The few available empirical studies do not provide a clear answer to these problems. Here we address these issues by developing a novel simulation strategy and we show that, although the microbiome can significantly help in prediction, it will be difficult to retrieve the actual biological basis of interactions between the microbiome and the trait.

Single Nucleotide Polymorphism and its Application in Mapping Loci Involved in Developing Human Diseases and Traits

2011 ◽  
pp. 113-127
Author(s):  
Rui-Ru Ji
Keyword(s):  
Single Nucleotide Polymorphism ◽  
Complex Traits ◽  
Single Gene ◽  
Complex Trait ◽  
Human Diseases ◽  
Nucleotide Polymorphism ◽  
Single Nucleotide ◽  
Systematic Analysis ◽  
Success Stories ◽  
Mapping Process

Common diseases or traits in humans are often influenced by complex interactions among multiple genes as well as environmental and lifestyle factors rather than being attributable to a genetic variation within a single gene. Identification of genes that confer disease susceptibility can be facilitated by studying DNA markers such as single nucleotide polymorphism (SNP) associated with a disease trait. Genome-wide association approaches offers a systematic analysis of the association of hundreds of thousands of SNPs with a quantitative complex trait. This method has been successfully applied to a wide variety of common human diseases and traits, and has generated valuable findings that have improved the understanding of the genetic basis of many complex traits. This chapter outlines the general mapping process and methods, highlights the success stories, and describes some limitations and challenges that lie ahead.

Sign in / Sign up

Export Citation Format

Share Document