scholarly journals Can the microbiome influence host evolutionary trajectories?

2019 ◽  
Author(s):  
Lucas P. Henry ◽  
Marjolein Bruijning ◽  
Simon K.G. Forsberg ◽  
Julien F. Ayroles
Keyword(s):  
Host Selection ◽  
Meta Analysis ◽  
Complex Interplay ◽  
Multifaceted Approach ◽  
Quantitative Genetic ◽  
Evolutionary Trajectories ◽  
Complex Inheritance ◽  
Host Evolution ◽  
Analytical Approaches ◽  
Stressful Environments

AbstractThe microbiome shapes many traits in hosts, but we still do not understand how it influences host evolution. To impact host evolution, the microbiome must be heritable and have phenotypic effects on the host. However, the complex inheritance and context-dependence of the microbiome challenges traditional models of organismal evolution. Here, we take a multifaceted approach to identify conditions in which the microbiome influences host evolutionary trajectories. We explore quantitative genetic models to highlight how microbial inheritance and phenotypic effects can modulate host evolutionary responses to selection. We synthesize the literature across diverse taxa to find common scenarios of microbiome driven host evolution. First, hosts may leverage locally adapted microbes, increasing survivorship in stressful environments. Second, microbial variation may increase host phenotypic variation, enabling exploration of novel fitness landscapes. We further illustrate these effects by performing a meta-analysis of artificial selection in Drosophila, finding that bacterial diversity also frequently responds to host selection. We conclude by outlining key avenues of research and experimental procedures to improve our understanding of the complex interplay between hosts and microbiomes. By synthesizing perspectives through multiple conceptual and analytical approaches, we show how microbiomes can influence the evolutionary trajectories of hosts.

scholarly journals Meta-analysis suggests the microbiome responds to Evolve and Resequence experiments in Drosophila melanogaster

2020 ◽  
Author(s):  
Lucas P Henry ◽  
Julien F Ayroles
Keyword(s):  
Drosophila Melanogaster ◽  
Bacterial Diversity ◽  
Experimental Evolution ◽  
Host Selection ◽  
Host Response ◽  
Meta Analysis ◽  
Response To Selection ◽  
Evolutionary Processes ◽  
History Of ◽  
Host Evolution

Experimental evolution has a long history of uncovering fundamental insights into evolutionary processes but has largely neglected one underappreciated component--the microbiome. As eukaryotic hosts evolve, the microbiome may also evolve in response. However, the microbial contribution to host evolution remains poorly understood. Here, we analyzed the metagenomes from 10 E&R experiments in Drosophila melanogaster to determine how the microbiome changes in response to host selection. Bacterial diversity was significantly different in 5/10 studies in traits associated with metabolism or immunity. Additionally, we find that excluding reads from a facultative symbiont, Wolbachia, in the analysis of bacterial diversity changes the inference, raising important questions for future E&R experiments in D. melanogaster. Our results suggest the microbiome often responds to host selection but highlights the need for more work to understand how the microbiome changes the host response to selection.

scholarly journals Meta-analysis suggests the microbiome responds to Evolve and Resequence experiments in Drosophila melanogaster

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Lucas P. Henry ◽  
Julien F. Ayroles
Keyword(s):  
Drosophila Melanogaster ◽  
Bacterial Diversity ◽  
Experimental Evolution ◽  
Host Selection ◽  
Meta Analysis ◽  
History Of ◽  
Fundamental Insight ◽  
Host Evolution ◽  
Genomic Basis ◽  
Insight Into

Abstract Background Experimental evolution has a long history of uncovering fundamental insights into evolutionary processes, but has largely neglected one underappreciated component--the microbiome. As eukaryotic hosts evolve, the microbiome may also respond to selection. However, the microbial contribution to host evolution remains poorly understood. Here, we re-analyzed genomic data to characterize the metagenomes from ten Evolve and Resequence (E&R) experiments in Drosophila melanogaster to determine how the microbiome changed in response to host selection. Results Bacterial diversity was significantly different in 5/10 studies, primarily in traits associated with metabolism or immunity. Duration of selection did not significantly influence bacterial diversity, highlighting the importance of associations with specific host traits. Conclusions Our genomic re-analysis suggests the microbiome often responds to host selection; thus, the microbiome may contribute to the response of Drosophila in E&R experiments. We outline important considerations for incorporating the microbiome into E&R experiments. The E&R approach may provide critical insights into host-microbiome interactions and fundamental insight into the genomic basis of adaptation.

scholarly journals Mapping oligogenes for atopy and asthma by meta-analysis

2000 ◽  
Vol 23 (1) ◽  
pp. 1-10 ◽  
Author(s):  
A. Collins ◽  
S. Ennis ◽  
W. Tapper ◽  
N.E. Morton
Keyword(s):  
Alternative Hypothesis ◽  
Rank Order ◽  
Meta Analysis ◽  
Genetic Effect ◽  
Allelic Association ◽  
Type I ◽  
A Genome ◽  
Significance Levels ◽  
Complex Inheritance ◽  
Candidate Regions

Meta-analysis is presented for published studies on linkage or allelic association that have in common only reported significance levels. Reporting is biassed, and nonsignificance is seldom quantified. Therefore meta-analysis cannot identify oligogenes within a candidate region nor establish their significance, but it defines candidate regions well. Applied to a database on atopy and asthma, candidate regions are identified on chromosomes 6, 5, 16, 11, 12, 13, 14, 7, 20, and 10, in rank order from strongest to weakest evidence. On the other hand, there is little support for chromosomes 9, 8, 18, 1, and 15 in the same rank order. The evidence from 156 publications is reviewed for each region. With reasonable type I and II errors several thousand affected sib pairs would be required to detect a locus accounting for 1/10 of the genetic effect on asthma. Identification of regions by a genome scan for linkage and allelic association requires international collaborative studies to reach the necessary sample size, using lod-based methods that specify a weakly parametric alternative hypothesis and can be combined over studies that differ in ascertainment, phenotypes, and markers. This has become the central problem in complex inheritance.

Determinants of Technology Acceptance: Two Model-Based Meta-Analytic Reviews

2020 ◽  
pp. 107769902095240 ◽  
Author(s):  
Guangchao Charles Feng ◽  
Xianglin Su ◽  
Zhiliang Lin ◽  
Yiru He ◽  
Nan Luo ◽  
...  
Keyword(s):  
Technology Acceptance ◽  
Technology Acceptance Model ◽  
Meta Analysis ◽  
Model Fit ◽  
Theoretical Frameworks ◽  
Model Based ◽  
Fit Testing ◽  
Central Interest ◽  
Acceptance Model ◽  
Analytical Approaches

Examining the determinants of technology acceptance has been a central interest across disciplines. The technology acceptance model (TAM) and its variants and extensions are the most popular theoretical frameworks in this line of research. Two model-based meta-analytical approaches, that is, meta-meta-analysis and conventional meta-analysis, are used to pool the correlations and to test the path relationships among the variables of the TAM. We find that the extended TAM, which we term the TAM Plus, prevails in the model fit testing and that the results of the pooled correlations and path coefficients estimated using the meta-meta-analysis and meta-analysis are generally consistent.

scholarly journals Risk of malignancies in patients with spondyloarthritis treated with biologics compared with those treated with non-biologics: a systematic review and meta-analysis

2020 ◽  
Vol 12 ◽  
pp. 1759720X2092569
Author(s):  
Yu Heng Kwan ◽  
Ka Keat Lim ◽  
Warren Fong ◽  
Hendra Goh ◽  
Linkai Ng ◽  
...  
Keyword(s):  
Meta Analysis ◽  
Risk Of Bias ◽  
Cochrane Library ◽  
Interleukin 17 ◽  
Randomized Controlled ◽  
Risk Of Malignancy ◽  
Meta Analyses ◽  
Analytical Approaches

Background: The aim of our study was to synthesize evidence on the occurrence of malignancy in spondyloarthritis (SpA), from randomized controlled trials (RCTs) comparing biologics with non-biologics and biologics to each other. Methods: We systematically searched Medline, Cochrane Library, EMBASE, Scopus and ClinicalTrials.gov from inception until 31 October 2018. RCTs with ⩾24-week follow-up were included. We extracted data using standardized forms and assessed the risk of bias using the Cochrane Risk of Bias Tool. We performed pair-wise meta-analyses and network meta-analyses to compare the risk of malignancy for each biologics class and SpA type. We reported the Peto odds ratio (OR) of any malignancy along with 95% confidence intervals (95% CI). Bayesian posterior probabilities comparing risk of malignancy of each biologic class with non-biologics were computed as supplementary measures. Results: Fifty-four trials were included; most (44/54) had follow-up <1 year. Among 14,245 patients, 63 developed a malignancy. While most Peto ORs were >1, they had wide 95% CI and p >0.05. The overall Peto OR comparing biologics with non-biologics was 1.42 (95% CI 0.80–2.53). Only interleukin-17 inhibitors in peripheral SpA had p <0.05 (Peto OR 2.77, 95% CI 1.07–7.13); the posterior probability that the risk was higher than non-biologics was 98%. Stratified analyses revealed no consistent trend by prior exposure to biologics, duration of follow-up, study quality, study-arm crossover, analytical approaches and type of malignancy. Conclusions: Our findings indicate no overall elevated risk of malignancy with biologics in SpA. As our meta-analyses are unable to conclude on the long-term risk, long-term pharmacovigilance of biologics in SpA may still be warranted.

scholarly journals Updating insights into rosiglitazone and cardiovascular risk through shared data: individual patient and summary level meta-analyses

BMJ ◽  
2020 ◽  
pp. l7078 ◽  
Author(s):  
Joshua D Wallach ◽  
Kun Wang ◽  
Audrey D Zhang ◽  
Deanna Cheng ◽  
Holly K Grossetta Nardini ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Heart Failure ◽  
Systematic Review ◽  
Cardiovascular Risk ◽  
Meta Analysis ◽  
Odds Ratios ◽  
Level Data ◽  
Increased Risk ◽  
Meta Analyses ◽  
Analytical Approaches

AbstractObjectivesTo conduct a systematic review and meta-analysis of the effects of rosiglitazone treatment on cardiovascular risk and mortality using multiple data sources and varying analytical approaches with three aims in mind: to clarify uncertainties about the cardiovascular risk of rosiglitazone; to determine whether different analytical approaches are likely to alter the conclusions of adverse event meta-analyses; and to inform efforts to promote clinical trial transparency and data sharing.DesignSystematic review and meta-analysis of randomized controlled trials.Data sourcesGlaxoSmithKline’s (GSK’s) ClinicalStudyDataRequest.com for individual patient level data (IPD) and GSK’s Study Register platforms, MEDLINE, PubMed, Embase, Web of Science, Cochrane Central Registry of Controlled Trials, Scopus, and ClinicalTrials.gov from inception to January 2019 for summary level data.Eligibility criteria for selecting studiesRandomized, controlled, phase II-IV clinical trials that compared rosiglitazone with any control for at least 24 weeks in adults.Data extraction and synthesisFor analyses of trials for which IPD were available, a composite outcome of acute myocardial infarction, heart failure, cardiovascular related death, and non-cardiovascular related death was examined. These four events were examined independently as secondary analyses. For analyses including trials for which IPD were not available, myocardial infarction and cardiovascular related death were examined, which were determined from summary level data. Multiple meta-analyses were conducted that accounted for trials with zero events in one or both arms with two different continuity corrections (0.5 constant and treatment arm) to calculate odds ratios and risk ratios with 95% confidence intervals.Results33 eligible trials were identified from ClinicalStudyDataRequest.com for which IPD were available (21 156 patients). Additionally, 103 trials for which IPD were not available were included in the meta-analyses for myocardial infarction (23 683 patients), and 103 trials for which IPD were not available contributed to the meta-analyses for cardiovascular related death (22 772 patients). Among 29 trials for which IPD were available and that were included in previous meta-analyses using GSK’s summary level data, more myocardial infarction events were identified by using IPD instead of summary level data for 26 trials, and fewer cardiovascular related deaths for five trials. When analyses were limited to trials for which IPD were available, and a constant continuity correction of 0.5 and a random effects model were used to account for trials with zero events in only one arm, patients treated with rosiglitazone had a 33% increased risk of a composite event compared with controls (odds ratio 1.33, 95% confidence interval 1.09 to 1.61; rosiglitazone population: 274 events among 11 837 patients; control population: 219 events among 9319 patients). The odds ratios for myocardial infarction, heart failure, cardiovascular related death, and non-cardiovascular related death were 1.17 (0.92 to 1.51), 1.54 (1.14 to 2.09), 1.15 (0.55 to 2.41), and 1.18 (0.60 to 2.30), respectively. For analyses including trials for which IPD were not available, odds ratios for myocardial infarction and cardiovascular related death were attenuated (1.09, 0.88 to 1.35, and 1.12, 0.72 to 1.74, respectively). Results were broadly consistent when analyses were repeated using trials with zero events across both arms and either of the two continuity corrections was used.ConclusionsThe results suggest that rosiglitazone is associated with an increased cardiovascular risk, especially for heart failure events. Although increased risk of myocardial infarction was observed across analyses, the strength of the evidence varied and effect estimates were attenuated when summary level data were used in addition to IPD. Because more myocardial infarctions and fewer cardiovascular related deaths were reported in the IPD than in the summary level data, sharing IPD might be necessary when performing meta-analyses focused on safety.Systematic review registrationOSF Home https://osf.io/4yvp2/.

New and Future Directions in Integrative Medicine Research Methods with a Focus on Aging Populations: A Review

Gerontology ◽  
2015 ◽  
Vol 62 (4) ◽  
pp. 467-476 ◽  
Author(s):  
Lindsey M. Knowles ◽  
Perry Skeath ◽  
Min Jia ◽  
Bijan Najafi ◽  
Julian Thayer ◽  
...  
Keyword(s):  
Integrative Medicine ◽  
Healthy Aging ◽  
Real Life ◽  
Noninvasive Methods ◽  
Multifaceted Approach ◽  
Medicine Research ◽  
Biomarker Analysis ◽  
Aging Populations ◽  
Treatment Research ◽  
Analytical Approaches

This review discusses existing and developing state-of-the-art noninvasive methods for quantifying the effects of integrative medicine (IM) in aging populations. The medical conditions of elderly patients are often more complex than those of younger adults, making the multifaceted approach of IM particularly suitable for aging populations. However, because IM interventions are multidimensional, it has been difficult to examine their effectiveness and mechanisms of action. Optimal assessment of IM intervention effects in the elderly should include a multifaceted approach, utilizing advanced analytic methods to integrate psychological, behavioral, physiological, and biomolecular measures of a patient's response to IM treatment. Research is presented describing methods for collecting and analyzing psychological data; wearable unobtrusive devices for monitoring heart rate variability, activity and other behavioral responses in real time; immunochemical methods for noninvasive molecular biomarker analysis, and considerations and analytical approaches for the integration of these measures. The combination of methods and devices presented in this review will provide new approaches for evaluating the effects of IM interventions in real-life ambulatory settings of older adults, and will extend the concept of mobile health to the domains of IM and healthy aging.

scholarly journals Genome variants associated with RNA splicing variation in bovine are extensively shared between tissues

2017 ◽  
Author(s):  
Ruidong Xiang ◽  
Ben J. Hayes ◽  
Christy J. Vander Jagt ◽  
Iona M. MacLeod ◽  
Majid Khansefid ◽  
...  
Keyword(s):  
Complex Traits ◽  
Rna Splicing ◽  
Sequence Data ◽  
Meta Analysis ◽  
Additive Genetic Variance ◽  
Genetic Correlations ◽  
Production Traits ◽  
Analytical Approaches ◽  
Genome Variants ◽  
Different Tissues

AbstractBackgroundMammalian phenotypes are shaped by numerous genome variants, many of which may regulate gene transcription or RNA splicing. To identify variants with regulatory functions in cattle, an important economic and model species, we used sequence variants to map a type of expression quantitative trait loci (expression QTLs) that are associated with variations in the RNA splicing, i.e., sQTLs. To further the understanding of regulatory variants, sQTLs were compare with other two types of expression QTLs, 1) variants associated with variations in gene expression, i.e., geQTLs and 2) variants associated with variations in exon expression, i.e., eeQTLs, in different tissues.ResultsUsing whole genome and RNA sequence data from four tissues of over 200 cattle, sQTLs identified using exon inclusion ratios were verified by matching their effects on adjacent intron excision ratios. sQTLs contained the highest percentage of variants that are within the intronic region of genes and contained the lowest percentage of variants that are within intergenic regions, compared to eeQTLs and geQTLs. Many geQTLs and sQTLs are also detected as eeQTLs. Many expression QTLs, including sQTLs, were significant in all four tissues and had a similar effect in each tissue. To verify such expression QTL sharing between tissues, variants surrounding (±1Mb) the exon or gene were used to build local genomic relationship matrices (LGRM) and estimated genetic correlations between tissues. For many exons, the splicing and expression level was determined by the same cis additive genetic variance in different tissues. Thus, an effective but simple-to-implement meta-analysis combining information from three tissues is introduced to increase power to detect and validate sQTLs. sQTLs and eeQTLs together were more enriched for variants associated with cattle complex traits, compared to geQTLs. Several putative causal mutations were identified, including an sQTL at Chr6:87392580 within the 5th exon of kappa casein (CSN3) associated with milk production traits.ConclusionsUsing novel analytical approaches, we report the first identification of numerous bovine sQTLs which are extensively shared between multiple tissue types. The significant overlaps between bovine sQTLs and complex traits QTL highlight the contribution of regulatory mutations to phenotypic variations.

Factor Structure and Criterion-Related Validity of the Air Force Officer Qualifying Test Form T for Pilot Applicants

Assessment ◽  
2021 ◽  
pp. 107319112110586
Author(s):  
Khalid ALMamari
Keyword(s):  
Factor Analysis ◽  
Factor Structure ◽  
Air Force ◽  
Structural Equation ◽  
Meta Analysis ◽  
Performance Criteria ◽  
Equation Modeling ◽  
Test Form ◽  
General Ability ◽  
Analytical Approaches

The Air Force Officer Qualifying Test (AFOQT) is the primary selection tool for officer applicants in the U.S. Air Force (USAF) for nearly seven decades. The AFOQT is revised and modified periodically, with rigorous equating and linking effort to ensure comparability and connectivity across forms. The most recent version of AFOQT is Form T that includes 10 cognitive ability and knowledge subtests. Despite the continuing validation effort of the AFOQT across forms, it was mostly directed to the general population of officer applicants, but not to any specific subpopulation. The current investigation reported three studies in an attempt to provide evidence for factor structure and criterion-related validity of AFOQT Form T for pilot applicants via four analytical approaches: meta-analysis, exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and structural equation modeling (SEM). The results suggested that AFOQT Form T data are best represented by a bifactor model with a general ability and four specific abilities, and that each latent construct has a distinct predictive utility for pilot performance criteria.

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