Faculty Opinions recommendation of MIPoD: a hypothesis-testing framework for microevolutionary inference from patterns of divergence.

Author(s):  
David Houle
Author(s):  
Nikolay Atanasov ◽  
Bharath Sankaran ◽  
Jerome Le Ny ◽  
Thomas Koletschka ◽  
George J. Pappas ◽  
...  

2019 ◽  
Author(s):  
Jennafer A. P. Hamlin ◽  
Leonie C. Moyle

ABSTRACTAn increasing number of phylogenomic studies have documented a clear ‘footprint’ of post-speciation introgression among closely-related species. Nonetheless, systematic genome-wide studies of factors influencing the likelihood of introgression remain rare. Here, we use an a priori hypothesis-testing framework, and introgression statistics, to evaluate the prevalence and frequency of introgression. Specifically, with whole genome sequences from 32 lineages of wild tomato species, we assess the effect of three factors on introgression: genetic relatedness, geographical proximity, and mating system differences. Using multiple trios within the ‘ABBA-BABA’ test, we find that one of our factors, geographic proximity, is consistently associated with evidence for recent introgression between species. Of 14 species pairs with ‘proximate’ versus ‘distant’ population comparisons, 12 showed evidence of introgression; in ten of these cases, this was more prevalent between geographically-closer populations. We found no evidence that introgression varies systematically with increasing genetic divergence between lineages or with mating system differences, although we have limited power to address the latter effect. While our analysis indicates that recent post-speciation introgression is frequent in this group, estimated levels of genetic exchange are modest (0.05-1.5% of the genome), so the relative importance of hybridization in shaping the evolutionary trajectories of these species could be limited. Regardless, similar clade-wide analyses of genomic introgression would be valuable for disentangling the major ecological, reproductive, and historical determinants of post-speciation gene flow, and for assessing the relative importance of introgression as a source of evolutionary change.IMPACT STATEMENTThe formation of new species is traditionally viewed as a tree-like branching process, in which species are discrete branches that no longer share an ongoing genealogical connection with other, equally discrete, species. Recently this view has been challenged by numerous studies examining genealogical patterns across entire genomes (all the DNA of an organism); these studies suggest that the exchange of genes between different species (known as ‘introgression’) is much more common than previously appreciated. This unexpected observation raises questions about which conditions are most important in determining whether species continue to exchange genes after they diverge. Factors such as physical proximity, differences in reproductive mechanisms, and time since species shared a common ancestor, might all contribute to determining the prevalence of introgression. But to evaluate the general importance of these factors requires more than individual cases; many species comparisons, that differ systematically in one or more of these conditions, are needed. Here we use whole-genome information from 32 lineages to evaluate patterns of introgression among multiple species in a single, closely related group—the wild tomatoes of south America. We contrast these patterns among pairs of lineages that differ in their geographical proximity, reproductive system, and time since common ancestry, to assess the individual influence of each condition on the prevalence of introgression. We find that only one of our factors—geographical proximity—is consistently associated with greater evidence for recent introgression, indicating that this is largely shaped by the geographical opportunity for hybridization, rather than other plausible biological processes. Our study is one of the first to systematically assess the influence of general ecological and evolutionary conditions on the frequency of post-speciation introgression. It also provides a straightforward, generalizable, hypothesis-testing framework for similar systematic analyses of introgression in groups of other organisms in the future.


2012 ◽  
Vol 9 (1) ◽  
pp. 1345-1365 ◽  
Author(s):  
B. Merz ◽  
S. Vorogushyn ◽  
S. Uhlemann ◽  
J. Delgado ◽  
Y. Hundecha

Abstract. The question whether the magnitude and frequency of floods have changed due to climate change or other drivers of change is of high interest. The number of flood trend studies is rapidly rising. When changes are detected, many studies link the identified change to the underlying causes, i.e. they attribute the changes in flood behaviour to certain drivers of change. We propose a hypothesis testing framework for trend attribution which consists of essential ingredients for a sound attribution: proof of consistency, proof of inconsistency and provision of confidence statement. Further, we evaluate the current state-of-the-art of flood trend attribution. To this end, we assess how selected recent studies approach the attribution problem, and to which extent their attribution statements seem defendable. In our opinion, the current state of flood trend attribution is poor. Attribution statements are mostly based on qualitative reasoning or even speculation. Typically, the focus of flood trend studies is the detection of change, i.e. the statistical analysis of time series, and attribution is regarded as an appendix: (1) flood time series are analysed by means of trend tests, (2) if a significant change is detected, a hypothesis on the cause of change is given, and (3) explanations or published studies are sought which support the hypothesis. We believe that we need a change in perspective and more scientific rigour: detection should be seen as an integral part of the more challenging attribution problem, and detection and attribution should be placed in a sound hypothesis testing framework.


2016 ◽  
Author(s):  
Shreya S Gollamudi ◽  
Eric J Topol ◽  
Nathan E Wineinger

Background: Digital medicine and smartphone-enabled health technologies provide a novel source of human health and human biology data. However, in part due to its intricacies, few methods have been established to analyze and interpret data in this domain. We previously conducted a six-month interventional trial examining the efficacy of a comprehensive smartphone-based health monitoring program for individuals with chronic disease. This included 38 individuals with hypertension who recorded 6,290 blood pressure readings over the trial. Methods: In the present study we provide a hypothesis testing framework for unstructured time series data, typical of patient-generated mobile device data. We used a mixed model approach for unequally spaced repeated measures using autoregressive and generalized autoregressive models, and applied this to the blood pressure data generated in this trial. Results: We were able to detect, roughly, a 2 mmHg decrease in both systolic and diastolic blood pressure over the course of the trial despite considerable intra- and inter-individual variation. Furthermore, by supplementing this finding by using a sequential analysis approach, we observed this result over three months prior to the official study end – highlighting the effectiveness of leveraging the digital nature of this data source to form timely conclusions. Conclusions: Health data generated through the use of smartphones and other mobile devices allow individuals the opportunity to make informed health decisions, and provide researchers the opportunity to address innovative health and biology questions. The hypothesis testing framework we present can be applied in future studies utilizing digital medicine technology or implemented in the technology itself to support the quantified self. The study was registered at clinicaltrials.gov (NCT01975428).


2015 ◽  
Vol 4 (1) ◽  
Author(s):  
João M. C. Santos Silva ◽  
Silvana Tenreyro ◽  
Frank Windmeijer

AbstractIn economic applications it is often the case that the variate of interest is non-negative and its distribution has a mass-point at zero. Many regression strategies have been proposed to deal with data of this type but, although there has been a long debate in the literature on the appropriateness of different models, formal statistical tests to choose between the competing specifications are not often used in practice. We use the non-nested hypothesis testing framework of Davidson and MacKinnon (Davidson and MacKinnon 1981. “Several Tests for Model Specification in the Presence of Alternative Hypotheses.”


PeerJ ◽  
2016 ◽  
Vol 4 ◽  
pp. e2284 ◽  
Author(s):  
Shreya S. Gollamudi ◽  
Eric J. Topol ◽  
Nathan E. Wineinger

Background:Digital medicine and smartphone-enabled health technologies provide a novel source of human health and human biology data. However, in part due to its intricacies, few methods have been established to analyze and interpret data in this domain. We previously conducted a six-month interventional trial examining the efficacy of a comprehensive smartphone-based health monitoring program for individuals with chronic disease. This included 38 individuals with hypertension who recorded 6,290 blood pressure readings over the trial.Methods:In the present study, we provide a hypothesis testing framework for unstructured time series data, typical of patient-generated mobile device data. We used a mixed model approach for unequally spaced repeated measures using autoregressive and generalized autoregressive models, and applied this to the blood pressure data generated in this trial.Results:We were able to detect, roughly, a 2 mmHg decrease in both systolic and diastolic blood pressure over the course of the trial despite considerable intra- and inter-individual variation. Furthermore, by supplementing this finding by using a sequential analysis approach, we observed this result over three months prior to the official study end—highlighting the effectiveness of leveraging the digital nature of this data source to form timely conclusions.Conclusions:Health data generated through the use of smartphones and other mobile devices allow individuals the opportunity to make informed health decisions, and provide researchers the opportunity to address innovative health and biology questions. The hypothesis testing framework we present can be applied in future studies utilizing digital medicine technology or implemented in the technology itself to support the quantified self.


2009 ◽  
Vol 22 (7) ◽  
pp. 716-729 ◽  
Author(s):  
Raisa Z. Freidlin ◽  
Evren Özarslan ◽  
Yaniv Assaf ◽  
Michal E. Komlosh ◽  
Peter J. Basser

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