Literature DB >> 26426343

Diversity and population structure of northern switchgrass as revealed through exome capture sequencing.

Joseph Evans1,2, Emily Crisovan1,2, Kerrie Barry3, Chris Daum3, Jerry Jenkins4, Govindarajan Kunde-Ramamoorthy3, Aruna Nandety5, Chew Yee Ngan3, Brieanne Vaillancourt1,2, Chia-Lin Wei3, Jeremy Schmutz3,4, Shawn M Kaeppler6,7, Michael D Casler6,8, Carol Robin Buell1,2.   

Abstract

Panicum virgatum L. (switchgrass) is a polyploid, perennial grass species that is native to North America, and is being developed as a future biofuel feedstock crop. Switchgrass is present primarily in two ecotypes: a northern upland ecotype, composed of tetraploid and octoploid accessions, and a southern lowland ecotype, composed of primarily tetraploid accessions. We employed high-coverage exome capture sequencing (~2.4 Tb) to genotype 537 individuals from 45 upland and 21 lowland populations. From these data, we identified ~27 million single-nucleotide polymorphisms (SNPs), of which 1 590 653 high-confidence SNPs were used in downstream analyses of diversity within and between the populations. From the 66 populations, we identified five primary population groups within the upland and lowland ecotypes, a result that was further supported through genetic distance analysis. We identified conserved, ecotype-restricted, non-synonymous SNPs that are predicted to affect the protein function of CONSTANS (CO) and EARLY HEADING DATE 1 (EHD1), key genes involved in flowering, which may contribute to the phenotypic differences between the two ecotypes. We also identified, relative to the near-reference Kanlow population, 17 228 genes present in more copies than in the reference genome (up-CNVs), 112 630 genes present in fewer copies than in the reference genome (down-CNVs) and 14 430 presence/absence variants (PAVs), affecting a total of 9979 genes, including two upland-specific CNV clusters. In total, 45 719 genes were affected by an SNP, CNV, or PAV across the panel, providing a firm foundation to identify functional variation associated with phenotypic traits of interest for biofuel feedstock production.
© 2015 The Authors The Plant Journal published by Society for Experimental Biology and John Wiley & Sons Ltd.

Entities:  

Keywords:  PRJNA280418; Panicum virgatum; exome capture; genomics; polyploid; switchgrass

Mesh:

Substances:

Year:  2015        PMID: 26426343     DOI: 10.1111/tpj.13041

Source DB:  PubMed          Journal:  Plant J        ISSN: 0960-7412            Impact factor:   6.417


  11 in total

1.  Genomic Prediction of Complex Traits in Forage Plants Species: Perennial Grasses Case.

Authors:  Philippe Barre; Torben Asp; Stephen Byrne; Michael Casler; Marty Faville; Odd Arne Rognli; Isabel Roldan-Ruiz; Leif Skøt; Marc Ghesquière
Journal:  Methods Mol Biol       Date:  2022

2.  Accuracy of Genomic Prediction in Switchgrass (Panicum virgatum L.) Improved by Accounting for Linkage Disequilibrium.

Authors:  Guillaume P Ramstein; Joseph Evans; Shawn M Kaeppler; Robert B Mitchell; Kenneth P Vogel; C Robin Buell; Michael D Casler
Journal:  G3 (Bethesda)       Date:  2016-04-07       Impact factor: 3.154

3.  Genomic prediction accuracy for switchgrass traits related to bioenergy within differentiated populations.

Authors:  Jason D Fiedler; Christina Lanzatella; Serge J Edmé; Nathan A Palmer; Gautam Sarath; Rob Mitchell; Christian M Tobias
Journal:  BMC Plant Biol       Date:  2018-07-09       Impact factor: 4.215

4.  Extensions of BLUP Models for Genomic Prediction in Heterogeneous Populations: Application in a Diverse Switchgrass Sample.

Authors:  Guillaume P Ramstein; Michael D Casler
Journal:  G3 (Bethesda)       Date:  2019-03-07       Impact factor: 3.154

5.  Benchmarking Parametric and Machine Learning Models for Genomic Prediction of Complex Traits.

Authors:  Christina B Azodi; Emily Bolger; Andrew McCarren; Mark Roantree; Gustavo de Los Campos; Shin-Han Shiu
Journal:  G3 (Bethesda)       Date:  2019-11-05       Impact factor: 3.154

Review 6.  Network-based approaches for understanding gene regulation and function in plants.

Authors:  Dae Kwan Ko; Federica Brandizzi
Journal:  Plant J       Date:  2020-08-28       Impact factor: 6.417

7.  Segmental duplications are hot spots of copy number variants affecting barley gene content.

Authors:  Gianluca Bretani; Laura Rossini; Chiara Ferrandi; Joanne Russell; Robbie Waugh; Benjamin Kilian; Paolo Bagnaresi; Luigi Cattivelli; Agostino Fricano
Journal:  Plant J       Date:  2020-05-17       Impact factor: 6.417

8.  Genome-Wide Association Study in Pseudo-F2 Populations of Switchgrass Identifies Genetic Loci Affecting Heading and Anthesis Dates.

Authors:  Megan Taylor; Carl-Erik Tornqvist; Xiongwei Zhao; Paul Grabowski; Rebecca Doerge; Jianxin Ma; Jeffrey Volenec; Joseph Evans; Guillaume P Ramstein; Millicent D Sanciangco; C Robin Buell; Michael D Casler; Yiwei Jiang
Journal:  Front Plant Sci       Date:  2018-09-13       Impact factor: 5.753

9.  Natural variation in genes potentially involved in plant architecture and adaptation in switchgrass (Panicum virgatum L.).

Authors:  Bochra A Bahri; Guillaume Daverdin; Xiangyang Xu; Jan-Fang Cheng; Kerrie W Barry; E Charles Brummer; Katrien M Devos
Journal:  BMC Evol Biol       Date:  2018-06-14       Impact factor: 3.260

10.  Gas exchange characteristics and their influencing factors for halophytic plant communities on west coast of Bohai Sea.

Authors:  Fude Liu; Xue Mo; Sen Zhang; Feijie Chen; Desheng Li
Journal:  PLoS One       Date:  2020-02-12       Impact factor: 3.240

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