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Field-based high throughput phenotyping rapidly identifies genomic regions controlling yield components in rice.


ABSTRACT: To ensure food security in the face of population growth, decreasing water and land for agriculture, and increasing climate variability, crop yields must increase faster than the current rates. Increased yields will require implementing novel approaches in genetic discovery and breeding. Here we demonstrate the potential of field-based high throughput phenotyping (HTP) on a large recombinant population of rice to identify genetic variation underlying important traits. We find that detecting quantitative trait loci (QTL) with HTP phenotyping is as accurate and effective as traditional labor-intensive measures of flowering time, height, biomass, grain yield, and harvest index. Genetic mapping in this population, derived from a cross of an modern cultivar (IR64) with a landrace (Aswina), identified four alleles with negative effect on grain yield that are fixed in IR64, demonstrating the potential for HTP of large populations as a strategy for the second green revolution.

SUBMITTER: Tanger P 

PROVIDER: S-EPMC5318881 | biostudies-literature | 2017 Feb

REPOSITORIES: biostudies-literature

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Field-based high throughput phenotyping rapidly identifies genomic regions controlling yield components in rice.

Tanger Paul P   Klassen Stephen S   Mojica Julius P JP   Lovell John T JT   Moyers Brook T BT   Baraoidan Marietta M   Naredo Maria Elizabeth B ME   McNally Kenneth L KL   Poland Jesse J   Bush Daniel R DR   Leung Hei H   Leach Jan E JE   McKay John K JK  

Scientific reports 20170221


To ensure food security in the face of population growth, decreasing water and land for agriculture, and increasing climate variability, crop yields must increase faster than the current rates. Increased yields will require implementing novel approaches in genetic discovery and breeding. Here we demonstrate the potential of field-based high throughput phenotyping (HTP) on a large recombinant population of rice to identify genetic variation underlying important traits. We find that detecting quan  ...[more]

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