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Leaf shape in Populus tremula is a complex, omnigenic trait
Umeå University, Faculty of Science and Technology, Umeå Plant Science Centre (UPSC). Umeå University, Faculty of Science and Technology, Department of Plant Physiology.ORCID iD: 0000-0003-2673-9113
Umeå University, Faculty of Science and Technology, Umeå Plant Science Centre (UPSC). Umeå University, Faculty of Science and Technology, Department of Plant Physiology.ORCID iD: 0000-0002-9771-467x
Umeå University, Faculty of Science and Technology, Department of Plant Physiology. Umeå University, Faculty of Science and Technology, Umeå Plant Science Centre (UPSC).ORCID iD: 0000-0002-5249-604x
Umeå University, Faculty of Science and Technology, Umeå Plant Science Centre (UPSC). Umeå University, Faculty of Science and Technology, Department of Plant Physiology.
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2020 (English)In: Ecology and Evolution, E-ISSN 2045-7758, Vol. 10, no 21, p. 11922-11940Article in journal (Refereed) Published
Abstract [en]

Leaf shape is a defining feature of how we recognize and classify plant species. Although there is extensive variation in leaf shape within many species, few studies have disentangled the underlying genetic architecture. We characterized the genetic architecture of leaf shape variation in Eurasian aspen (Populus tremula L.) by performing genome‐wide association study (GWAS) for physiognomy traits. To ascertain the roles of identified GWAS candidate genes within the leaf development transcriptional program, we generated RNA‐Seq data that we used to perform gene co‐expression network analyses from a developmental series, which is publicly available within the PlantGenIE resource. We additionally used existing gene expression measurements across the population to analyze GWAS candidate genes in the context of a population‐wide co‐expression network and to identify genes that were differentially expressed between groups of individuals with contrasting leaf shapes. These data were integrated with expression GWAS (eQTL) results to define a set of candidate genes associated with leaf shape variation. Our results identified no clear adaptive link to leaf shape variation and indicate that leaf shape traits are genetically complex, likely determined by numerous small‐effect variations in gene expression. Genes associated with shape variation were peripheral within the population‐wide co‐expression network, were not highly connected within the leaf development co‐expression network, and exhibited signatures of relaxed selection. As such, our results are consistent with the omnigenic model.

Place, publisher, year, edition, pages
John Wiley & Sons, 2020. Vol. 10, no 21, p. 11922-11940
Keywords [en]
complex trait, GWAS, leaf shape, natural variation, omnigenic, Populus tremula
National Category
Bioinformatics and Systems Biology
Identifiers
URN: urn:nbn:se:umu:diva-170641DOI: 10.1002/ece3.6691ISI: 000578291300001Scopus ID: 2-s2.0-85092478395OAI: oai:DiVA.org:umu-170641DiVA, id: diva2:1429851
Note

Originally included in thesis in manuscript form.

Available from: 2020-05-12 Created: 2020-05-12 Last updated: 2024-01-17Bibliographically approved
In thesis
1. Embracing the data flood: integrating diverse data to improve phenotype association discovery in forest trees
Open this publication in new window or tab >>Embracing the data flood: integrating diverse data to improve phenotype association discovery in forest trees
2020 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Complex traits represent valuable research targets as many highly desirable properties of plants and animals (such as growth rate and height) fall into this group. However, associating biological markers with these traits is incredibly challenging, in part due to their small effect sizes. For the two species at the core of our research, European aspen (Populus tremula) and Norway spruce (Picea abies), association studies are even more challenging, primarily due to the fragmented state of their genome assemblies. These assemblies represent the gene space well, but poorly represented inter-genic regions hinder variant discovery and large scale association studies.

In this thesis, I present my work to improve association discovery of complex traits in forest trees. Firstly, to overcome the issues with assembly fragmentation, I have created an updated version of the P. tremula genome, which is highly contiguous and anchored in full chromosomes. To calculate the dense linkage map required to order and orient the aspen assembly, I developed "BatchMap", a parallel implementation of linkage mapping software. BatchMap has been successfully applied to several dense linkage maps, including aspen and Norway spruce, and was essential to the progress in improving the aspen genome assembly. Further, I developed seidr, which represents a starting point in multi-layer, network-based systems biology, an analysis technique with promising prospects for complex trait association analysis. As a case study, I applied some of the methods developed to the analysis of leaf shape in natural populations of European aspen, a complex, omnigenic trait.

The multi-layer model of systems biology and related analysis techniques offer promise in the analysis of complex traits, and this thesis represents a starting point toward an intricate, holistic model of systems biology that may help to unravel the overwhelmingly complicated nature of complex traits.

Place, publisher, year, edition, pages
Umeå: Umeå University, 2020. p. 83
Keywords
Systems Biology, Association Discovery, Genomics, Transcriptomics, Genome Assembly, Gene Networks, Forest Tree, Aspen, Spruce
National Category
Biological Sciences
Research subject
biology
Identifiers
urn:nbn:se:umu:diva-170643 (URN)978-91-7855-273-3 (ISBN)978-91-7855-274-0 (ISBN)
Public defence
2020-06-12, KBE303 - Stora hörsalen, Umeå, 10:00 (English)
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Supervisors
Note

2020-06-10: Errata spikblad - Ny tid för disputation. 

Available from: 2020-05-20 Created: 2020-05-13 Last updated: 2020-06-10Bibliographically approved

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Mähler, NiklasSchiffthaler, BastianRobinson, Kathryn M.Terebieniec, Barbara K.Mannapperuma, ChanakaJansson, StefanStreet, Nathaniel R.
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