Literature DB >> 30883803

Convergent evolution of root system architecture in two independently evolved lineages of weedy rice.

Marshall J Wedger1, Christopher N Topp2, Kenneth M Olsen1.   

Abstract

Root system architecture (RSA) is a critical aspect of plant growth and competitive ability. Here we used two independently evolved strains of weedy rice, a de-domesticated form of rice, to study the evolution of weed-associated RSA traits and the extent to which they evolve through shared or different genetic mechanisms. We characterised 98 two-dimensional and three-dimensional RSA traits in 671 plants representing parents and descendants of two recombinant inbred line populations derived from two weed × crop crosses. A random forest machine learning model was used to assess the degree to which root traits can predict genotype and the most diagnostic traits for doing so. We used quantitative trait locus (QTL) mapping to compare genetic architecture between the weed strains. The two weeds were distinguishable from the crop in similar and predictable ways, suggesting independent evolution of a 'weedy' RSA phenotype. Notably, comparative QTL mapping revealed little evidence for shared underlying genetic mechanisms. Our findings suggest that despite the double bottlenecks of domestication and de-domestication, weedy rice nonetheless shows genetic flexibility in the repeated evolution of weedy RSA traits. Whereas the root growth of cultivated rice may facilitate interactions among neighbouring plants, the weedy rice phenotype may minimise below-ground contact as a competitive strategy.
© 2019 The Authors. New Phytologist © 2019 New Phytologist Trust.

Entities:  

Keywords:  zzm321990GiaRootszzm321990; comparative QTL mapping; convergent evolution; parallel evolution; random forest; rice (Oryza sativa); root system architecture (RSA); weedy rice

Mesh:

Year:  2019        PMID: 30883803     DOI: 10.1111/nph.15791

Source DB:  PubMed          Journal:  New Phytol        ISSN: 0028-646X            Impact factor:   10.151


  3 in total

1.  Genomic prediction and QTL mapping of root system architecture and above-ground agronomic traits in rice (Oryza sativa L.) with a multitrait index and Bayesian networks.

Authors:  Santosh Sharma; Shannon R M Pinson; David R Gealy; Jeremy D Edwards
Journal:  G3 (Bethesda)       Date:  2021-09-27       Impact factor: 3.154

2.  Machine-learning-based detection of adaptive divergence of the stream mayfly Ephemera strigata populations.

Authors:  Bin Li; Sakiko Yaegashi; Thaddeus M Carvajal; Maribet Gamboa; Ming-Chih Chiu; Zongming Ren; Kozo Watanabe
Journal:  Ecol Evol       Date:  2020-06-15       Impact factor: 2.912

3.  Diverse genetic mechanisms underlie worldwide convergent rice feralization.

Authors:  Jie Qiu; Lei Jia; Dongya Wu; Xifang Weng; Lijuan Chen; Jian Sun; Meihong Chen; Lingfeng Mao; Bowen Jiang; Chuyu Ye; Guilherme Menegol Turra; Longbiao Guo; Guoyou Ye; Qian-Hao Zhu; Toshiyuki Imaizumi; Beng-Kah Song; Laura Scarabel; Aldo Merotto; Kenneth M Olsen; Longjiang Fan
Journal:  Genome Biol       Date:  2020-03-26       Impact factor: 13.583

  3 in total

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