2024-03-29T05:14:01Zhttps://research-repository.st-andrews.ac.uk/oai/requestoai:research-repository.st-andrews.ac.uk:10023/33632023-04-18T09:45:14Zcom_10023_45com_10023_17com_10023_2323com_10023_39com_10023_879com_10023_878col_10023_46col_10023_2324col_10023_880
Genetic analysis of life-history constraint and evolution in a wild ungulate population
Morrissey, Michael Blair
Walling, Craig
Wilson, Alastair
Pemberton, Josephine
Clutton-Brock, Tim
Kruuk, Loeske
University of St Andrews. School of Biology
University of St Andrews. Centre for Biological Diversity
Life history
Quantitative genetics
Natural selection
Constraint
Projection model
Sensitivity
Red deer
Cervus elaphus
QH Natural history
QH
Trade-offs among life-history traits are central to evolutionary theory. In quantitative genetic terms, trade-offs may be manifested as negative genetic covariances relative to the direction of selection on phenotypic traits. Although the expression and selection of ecologically important phenotypic variation are fundamentally multivariate phenomena, the in situ quantification of genetic covariances is challenging. Even for life-history traits, where well-developed theory exists with which to relate phenotypic variation to fitness variation, little evidence exists from in situ studies that negative genetic covariances are an important aspect of the genetic architecture of life-history traits. In fact, the majority of reported estimates of genetic covariances among life-history traits are positive. Here we apply theory of the genetics and selection of life histories in organisms with complex life cycles to provide a framework for quantifying the contribution of multivariate genetically based relationships among traits to evolutionary constraint. We use a Bayesian framework to link pedigree-based inference of the genetic basis of variation in life-history traits to evolutionary demography theory regarding how life histories are selected. Our results suggest that genetic covariances may be acting to constrain the evolution of female life-history traits in a wild population of red deer Cervus elaphus: genetic covariances are estimated to reduce the rate of adaptation by about 40%, relative to predicted evolutionary change in the absence of genetic covariances. Furthermore, multivariate phenotypic (rather than genetic) relationships among female life-history traits do not reveal this constraint.
Publisher PDF
Peer reviewed
2012-04
2013-02-24T00:21:59Z
2013-02-24T00:21:59Z
2013-02-24
Journal article
Morrissey , M B , Walling , C , Wilson , A , Pemberton , J , Clutton-Brock , T & Kruuk , L 2012 , ' Genetic analysis of life-history constraint and evolution in a wild ungulate population ' , American Naturalist , vol. 179 , no. 4 , pp. E97-E114 . https://doi.org/10.1086/664686
0003-0147
PURE: 20353286
PURE UUID: d3f96f7d-5fff-4cbb-b141-7155aa13f9b3
Scopus: 84858785923
http://hdl.handle.net/10023/3363
https://doi.org/10.1086/664686
eng
American Naturalist
© 2012 by The University of Chicago.
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oai:research-repository.st-andrews.ac.uk:10023/129942023-04-18T23:45:54Zcom_10023_45com_10023_17com_10023_2323com_10023_39com_10023_879com_10023_878col_10023_46col_10023_2324col_10023_880
Professional medical writing support and the reporting quality of randomized controlled trial abstracts among high-impact general medical journals
Mills, Ira
Sheard, Catherine
Hays, Meredith
Douglas, Kevin
Winchester, Christopher C.
Gattrell, William T.
University of St Andrews. School of Biology
University of St Andrews. Centre for Biological Diversity
R Medicine
DAS
R
Background : In articles reporting randomized controlled trials, professional medical writing support is associated with increased adherence to Consolidated Standards of Reporting Trials (CONSORT). We set out to determine whether professional medical writing support was also associated with improved adherence to CONSORT for Abstracts. Methods : Using data from a previously published cross-sectional study of 463 articles reporting randomized controlled trials published between 2011 and 2014 in five top medical journals, we determined the association between professional medical writing support and CONSORT for Abstracts items using a Wilcoxon rank-sum test. Results : The mean proportion of adherence to CONSORT for Abstracts items reported was similar with and without professional medical writing support (64.3% vs 66.5%, respectively; p=0.30). Professional medical writing support was associated with lower adherence to reporting study setting (relative risk [RR]; 0.40; 95% confidence interval [CI], 0.23–0.70), and higher adherence to disclosing harms/side effects (RR 2.04; 95% CI, 1.37–3.03) and funding source (RR 1.75; 95% CI, 1.18–2.60). Conclusions : Although professional medical writing support was not associated with increased overall adherence to CONSORT for Abstracts, important aspects were improved with professional medical writing support, including reporting of adverse events and funding source. This study identifies areas to consider for improvement.
Publisher PDF
Peer reviewed
2017-09-23
2018-03-22T10:30:08Z
2018-03-22T10:30:08Z
Journal article
Mills , I , Sheard , C , Hays , M , Douglas , K , Winchester , C C & Gattrell , W T 2017 , ' Professional medical writing support and the reporting quality of randomized controlled trial abstracts among high-impact general medical journals ' , F1000Research , vol. 6 , 1489 . https://doi.org/10.12688/f1000research.12268.2
2046-1402
PURE: 252359840
PURE UUID: e904e441-25ab-4d5c-863d-88029419a629
crossref: 10.12688/f1000research.12268.2
Scopus: 85030674147
http://hdl.handle.net/10023/12994
https://doi.org/10.12688/f1000research.12268.2
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5615774/
eng
F1000Research
© 2017 Mills I et al. This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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oai:research-repository.st-andrews.ac.uk:10023/166362023-04-18T23:47:32Zcom_10023_45com_10023_17com_10023_2323com_10023_39com_10023_301com_10023_792com_10023_879com_10023_878col_10023_46col_10023_2324col_10023_303col_10023_795col_10023_880
BayesPiles : visualisation support for Bayesian network structure learning
Vogogias, Athanasios
Kennedy, Jessie
Archambault, Daniel
Bach, Benjamin
Smith, V Anne
Currant, Hannah
University of St Andrews. School of Biology
University of St Andrews. Scottish Oceans Institute
University of St Andrews. Institute of Behavioural and Neural Sciences
University of St Andrews. Centre for Biological Diversity
Visualisation
Graphs
Bioinformatics
QA75 Electronic computers. Computer science
QA76 Computer software
QH301 Biology
NDAS
QA75
QA76
QH301
We address the problem of exploring, combining, and comparing large collections of scored, directed networks for understanding inferred Bayesian networks used in biology. In this field, heuristic algorithms explore the space of possible network solutions, sampling this space based on algorithm parameters and a network score that encodes the statistical fit to the data. The goal of the analyst is to guide the heuristic search and decide how to determine a final consensus network structure, usually by selecting the top-scoring network or constructing the consensus network from a collection of high-scoring networks. BayesPiles, our visualisation tool, helps with understanding the structure of the solution space and supporting the construction of a final consensus network that is representative of the underlying dataset. BayesPiles builds upon and extends MultiPiles to meet our domain requirements. We developed BayesPiles in conjunction with computational biologists who have used this tool on datasets used in their research. The biologists found our solution provides them with new insights and helps them achieve results that are representative of the underlying data.
Postprint
Peer reviewed
2018-11
2018-12-06T16:30:08Z
2018-12-06T16:30:08Z
2018-11-28
Journal article
Vogogias , A , Kennedy , J , Archambault , D , Bach , B , Smith , V A & Currant , H 2018 , ' BayesPiles : visualisation support for Bayesian network structure learning ' , ACM Transactions on Intelligent Systems and Technology , vol. 10 , no. 1 , 5 . https://doi.org/10.1145/3230623
2157-6904
PURE: 252830854
PURE UUID: e1b4c0c9-92ce-409a-9494-3bf1f97f11a6
Scopus: 85057586933
ORCID: /0000-0002-0487-2469/work/51470191
WOS: 000458017400005
http://hdl.handle.net/10023/16636
https://doi.org/10.1145/3230623
eng
ACM Transactions on Intelligent Systems and Technology
© 2018, Association for Computing Machinery. This work has been made available online in accordance with the publisher’s policies. This is the author created, accepted version manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at https://doi.org/10.1145/3230623
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