scholarly journals The architecture of phenotypic flexibility within a complex trait: an empirical case study using avian thermogenic performance

2021 ◽  
Author(s):  
Maria Stager ◽  
Zachary A. Cheviron
Keyword(s):  
Complex Traits ◽  
Complex Trait ◽  
Temperature Acclimation ◽  
Component Part ◽  
Phenotypic Flexibility ◽  
Synthetic Temperature ◽  
Mechanistic Basis ◽  
Response To Temperature ◽  
Generally True

ABSTRACTReversible modifications to trait values can allow individuals to match their phenotypes to changing environmental conditions, a phenomenon known as phenotypic flexibility. A system’s capacity for flexibility may be determined by its underlying architecture, and these relationships can have important implications for both organismal adaptation and the evolvability of acclimatization responses. Theory provides two possible alternatives to explain the ways in which lower-level traits respond to environmental challenges and contribute to phenotypic flexibility in complex, whole-organism traits: symmorphosis predicts correspondence between structure and demand across all levels of a physiological system, while the alternative predicts that influence is concentrated in select elements of a physiological network. Here we provide a rich dataset — composed of 20 sub-organismal, physiological traits paired with whole-organism metabolic rates for 106 adult Dark-eyed Juncos (Junco hyemalis) — to explore the mechanistic basis of phenotypic flexibility in complex traits. When exposed to synthetic temperature cues, these individuals have previously been shown to increase their thermogenic capacity (Msum) and enhance their ability to maintain their body temperature in the cold. We show that the relationships among a number of the traits that contribute to Msum varied as the environmental context changed. Moreover, variation in Msum in response to temperature acclimation was correlated with only a handful of subordinate phenotypes. As a result, avian thermogenic flexibility does not appear to be a symmorphotic response. If this is generally true of complex traits, it suggests that simple and reversible modifications can significantly impact whole-organism performance, and thus that the evolution of phenotypic flexibility in a single component part could impart flexibility for the entire system.

scholarly journals SUMMIT: An integrative approach for better transcriptomic data imputation improves causal gene identification

2021 ◽  
Author(s):  
Zichen Zhang ◽  
Ye Eun Bae ◽  
Jonathan R. Bradley ◽  
Lang Wu ◽  
Chong Wu
Keyword(s):  
Complex Traits ◽  
Statistical Power ◽  
Prediction Models ◽  
Association Studies ◽  
Complex Trait ◽  
Integrative Approach ◽  
Level Data ◽  
Causal Genes ◽  
Low Expression

AbstractGenes with moderate to low expression heritability may explain a large proportion of complex trait heritability, but these genes are insufficiently captured in transcriptome-wide association studies (TWAS) partly due to the relatively small available reference datasets for developing expression genetic prediction models to capture the moderate to low genetically regulated components of gene expression. Here, we introduce a new method, Summary-level Unified Method for Modeling Integrated Transcriptome (SUMMIT), to improve the expression prediction model accuracy and the power of TWAS by using a large expression quantitative trait loci (eQTL) summary-level dataset. We applied SUMMIT to the eQTL summary-level data provided by the eQTLGen consortium, which involve 31,684 blood samples from 37 cohorts. Through simulation studies and analyses of GWAS summary statistics for 24 complex traits, we show that SUMMIT substantially improves the accuracy of expression prediction in blood, successfully builds expression prediction models for genes with low expression heritability, and achieves higher statistical power than several benchmark methods. In the end, we conducted a case study of COVID-19 severity with SUMMIT and identified 11 likely causal genes associated with COVID-19 severity.

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.

Do different parasite species interact in their effects on host fitness? A case study on parasites of the amphipod Paracalliope fluviatilis

Parasitology ◽  
2011 ◽  
Vol 138 (9) ◽  
pp. 1176-1182 ◽  
Author(s):  
C. A. RAUQUE ◽  
R. A. PATERSON ◽  
R. POULIN ◽  
D. M. TOMPKINS
Keyword(s):  
Parasite Species ◽  
Low Frequency ◽  
Host Population ◽  
Interactive Effects ◽  
Population Means ◽  
Significant Relationships ◽  
Coitocaecum Parvum ◽  
Paracalliope Fluviatilis ◽  
Generally True

SUMMARYThere is a gap in our understanding of the relative and interactive effects of different parasite species on the same host population. Here we examine the effects of the acanthocephalan Acanthocephalus galaxii, an unidentified cyclophyllidean cestode, and the trematodes Coitocaecum parvum and Microphallus sp. on several fitness components of the amphipod Paracalliope fluviatilis, using a combination of infection surveys and both survival and behavioural trials. In addition to significant relationships between specific parasites and measures of amphipod survival, maturity, mating success and behaviour, interactions between parasite species with respect to amphipod photophilia were also significant. While infection by either A. galaxii or C. parvum was associated with increased photophilia, such increases were negated by co-infection with Microphallus sp. We hypothesize that this is due to the more subtle manipulative effect of A. galaxii and C. parvum being impaired by Microphallus sp. We conclude that the low frequency at which such double infections occur in our sampled population means that such interactions are unlikely to be important beyond the scale of the host individual. Whether or not this is generally true, implying that parasitological models and theory based on single parasite species studies do generally hold, requires cross-species meta-analytical studies.

scholarly journals Evolution of halophytes: multiple origins of salt tolerance in land plants

2010 ◽  
Vol 37 (7) ◽  
pp. 604 ◽  
Author(s):  
Timothy J. Flowers ◽  
Hanaa K. Galal ◽  
Lindell Bromham
Keyword(s):  
Salt Tolerance ◽  
Novel Species ◽  
Complex Trait ◽  
Land Plants ◽  
Salt Tolerant ◽  
Physiological Mechanisms ◽  
Multiple Origins ◽  
Naturally Occurring ◽  
Tolerant Plants

The evolution of salt tolerance is interesting for several reasons. First, since salt-tolerant plants (halophytes) employ several different mechanisms to deal with salt, the evolution of salt tolerance represents a fascinating case study in the evolution of a complex trait. Second, the diversity of mechanisms employed by halophytes, based on processes common to all plants, sheds light on the way that a plant’s physiology can become adapted to deal with extreme conditions. Third, as the amount of salt-affected land increases around the globe, understanding the origins of the diversity of halophytes should provide a basis for the use of novel species in bioremediation and conservation. In this review we pose the question, how many times has salt tolerance evolved since the emergence of the land plants some 450–470 million years ago? We summarise the physiological mechanisms underlying salt-tolerance and provide an overview of the number and diversity of salt-tolerant terrestrial angiosperms (defined as plants that survive to complete their life cycle in at least 200 mM salt). We consider the evolution of halophytes using information from fossils and phylogenies. Finally, we discuss the potential for halophytes to contribute to agriculture and land management and ask why, when there are naturally occurring halophytes, it is proving to be difficult to breed salt-tolerant crops.

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.

A Framework for Semantic Grid in E-Science

2009 ◽  
pp. 235-240
Author(s):  
Diego Liberati
Keyword(s):  
Web Services ◽  
Open Source ◽  
Organizational Dynamics ◽  
Semantic Grid ◽  
Collaborative Environment ◽  
Cooperative Enterprise ◽  
Open Source Systems ◽  
The Web ◽  
Generally True

A framework is proposed that creates, uses, and communicates information, whose organizational dynamics allows performing a distributed cooperative enterprise in public environments, even over open source systems. The approach assumes the web services as the enacting paradigm possibly over a grid, to formalize interactions as cooperative services on various computational nodes of a network. The illustrated case study shows that some portions, both of processes and of data or knowledge, can be shared in a collaborative environment, which is also more generally true for any kind of either complex or resource demanding (or both) interaction that will benefit any of the approaches.

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.

LDH-B enzyme expression: the mechanisms of altered gene expression in acclimation and evolutionary adaptation

1994 ◽  
Vol 267 (4) ◽  
pp. R1150-R1153 ◽  
Author(s):  
J. A. Segal ◽  
D. L. Crawford
Keyword(s):  
Fundulus Heteroclitus ◽  
Temperature Acclimation ◽  
Acclimation Temperature ◽  
Enzyme Expression ◽  
Temperature Dependent ◽  
Northern Population ◽  
Protein Levels ◽  
Altered Gene ◽  
Highly Correlated ◽  
Response To Temperature

The temperature-dependent expression of lactate dehydrogenase-B (LDH-B) was compared between two environmentally distinct populations of Fundulus heteroclitus acclimated to 10 degrees C and 20 degrees C. The variability in LDH-B protein expression both within and between populations is consistent with a model of thermal compensation. The northern population from the colder environment expresses a twofold greater amount of LDH-B protein than the warmer southern population at both acclimation temperatures. Correspondingly, both populations have 1.3-fold greater levels of the enzyme at an acclimation temperature of 10 degrees C in comparison to 20 degrees C. In 20 degrees C-acclimated individuals there is a similar twofold difference between populations for LDH-B mRNA concentrations, and LDH-B protein and mRNA are highly correlated (r = 0.81). After acclimation to 10 degrees C, this difference between populations is not seen and in the northern population there is no relationship between LDH-B mRNA and protein levels. Thus the molecular mechanism regulating LDH-B enzyme expression changes in response to temperature acclimation and is different between populations.

scholarly journals Dr. Jekyll or Mr. Hyde? President Donald Trump’s Personality Profile as Perceived from Different Political Viewpoints

2018 ◽  
Vol 4 (1) ◽  
Author(s):  
Courtland Hyatt ◽  
W. Keith Campbell ◽  
Donald R. Lynam ◽  
Joshua D. Miller
Keyword(s):  
Complex Traits ◽  
Factor Model ◽  
Five Factor Model ◽  
Complex Trait ◽  
Personality Profile ◽  
Hillary Clinton ◽  
Donald Trump ◽  
Political Views ◽  
Before And After ◽  
Perceived Helpfulness

The present research used an empirical, crowdsourced trait profiling approach to describe the personality of President Donald Trump (hereafter Trump) that accounts for political views. We recruited participants who voted for Hillary Clinton (N = 120; hereafter Clinton) and Trump (N = 118), and asked them to rate Trump’s personality on the 30 facets of the Five Factor Model. Participants also provided perceived helpfulness and harmfulness ratings of the facets before and after the election. We treated these facet level ratings as trait profiles, which were transformed into estimates of personality disorders (PDs) and complex trait-based constructs based on expert profiles. Results suggest only modest agreement between Clinton and Trump voters on Trump’s personality. Clinton voters perceived much greater antagonism, lower conscientiousness, and higher levels of impairment in Trump’s personality than did Trump voters who primarily perceived high levels of extraversion and emotional stability (i.e., low neuroticism). At the level of PDs and complex traits, there was some convergence with both groups seeing Trump as high in narcissism and psychopathy.

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