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dc.contributor.authorHighfill, Chad Allen
dc.contributor.authorReeves, G. Adam
dc.contributor.authorMacdonald, Stuart J.
dc.date.accessioned2017-11-10T20:25:46Z
dc.date.available2017-11-10T20:25:46Z
dc.date.issued2016-08-02
dc.identifier.citationHighfill, C. A., Reeves, G. A., & Macdonald, S. J. (2016). Genetic analysis of variation in lifespan using a multiparental advanced intercross Drosophila mapping population. BMC Genetics, 17(1). doi:10.1186/s12863-016-0419-9en_US
dc.identifier.urihttp://hdl.handle.net/1808/25329
dc.description.abstractBackground: Considerable natural variation for lifespan exists within human and animal populations. Genetically dissecting this variation can elucidate the pathways and genes involved in aging, and help uncover the genetic mechanisms underlying risk for age-related diseases. Studying aging in model systems is attractive due to their relatively short lifespan, and the ability to carry out programmed crosses under environmentally-controlled conditions. Here we investigate the genetic architecture of lifespan using the Drosophila Synthetic Population Resource (DSPR), a multiparental advanced intercross mapping population.

Results: We measured lifespan in females from 805 DSPR lines, mapping five QTL (Quantitative Trait Loci) that each contribute 4–5 % to among-line lifespan variation in the DSPR. Each of these QTL co-localizes with the position of at least one QTL mapped in 13 previous studies of lifespan variation in flies. However, given that these studies implicate >90 % of the genome in the control of lifespan, this level of overlap is unsurprising. DSPR QTL intervals harbor 11–155 protein-coding genes, and we used RNAseq on samples of young and old flies to help resolve pathways affecting lifespan, and identify potentially causative loci present within mapped QTL intervals. Broad agerelated patterns of expression revealed by these data recapitulate results from previous work. For example, we see an increase in antimicrobial defense gene expression with age, and a decrease in expression of genes involved in the electron transport chain. Several genes within QTL intervals are highlighted by our RNAseq data, such as Relish, a critical immune response gene, that shows increased expression with age, and UQCR-14, a gene involved in mitochondrial electron transport, that has reduced expression in older flies.

Conclusions: The five QTL we isolate collectively explain a considerable fraction of the genetic variation for female lifespan in the DSPR, and implicate modest numbers of genes. In several cases the candidate loci we highlight reside in biological pathways already implicated in the control of lifespan variation. Thus, our results provide further evidence that functional genetics tests targeting these genes will be fruitful, lead to the identification of natural sequence variants contributing to lifespan variation, and help uncover the mechanisms of aging.
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dc.publisherBioMed Centralen_US
dc.rights© 2016 The Author(s). Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.en_US
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en_US
dc.subjectAgingen_US
dc.subjectLifespanen_US
dc.subjectQTL mappingen_US
dc.subjectRNAseqen_US
dc.subjectComplex traitsen_US
dc.subjectMultiparental populationsen_US
dc.titleGenetic analysis of variation in lifespan using a multiparental advanced intercross Drosophila mapping populationen_US
dc.typeArticleen_US
kusw.kuauthorHighfill, Chad Allen
kusw.kuauthorReeves, G. Adam
kusw.kuauthorMacdonald, Stuart J.
kusw.kudepartmentMolecular Biosciencesen_US
kusw.kudepartmentCenter for Computational Biologyen_US
dc.identifier.doi10.1186/s12863-016-0419-9en_US
kusw.oaversionScholarly/refereed, publisher versionen_US
kusw.oapolicyThis item meets KU Open Access policy criteria.en_US
dc.rights.accessrightsopenAccess


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© 2016 The Author(s). Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver
(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Except where otherwise noted, this item's license is described as: © 2016 The Author(s). Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.