Literature DB >> 33822935

Validation of Functional Polymorphisms Affecting Maize Plant Height by Unoccupied Aerial Systems (UAS) Discovers Novel Temporal Phenotypes.

Alper Adak1, Clarissa Conrad1, Yuanyuan Chen1,2, Scott C Wilde1, Seth C Murray1, Steven Anderson1,3, Nithya K Subramanian1.   

Abstract

Plant height (PHT) in maize (Zea mays L.) has been scrutinized genetically and phenotypically due to relationship with other agronomically valuable traits (e.g. yield). Heritable variation of PHT is determined by many discovered quantitative trait loci (QTLs); however, phenotypic effects of such loci often lack validation across environments and genetic backgrounds, especially in the hybrid state grown by farmers rather than the inbred state more often used by geneticists. A previous genome wide association study using a topcrossed hybrid diversity panel identified two novel quantitative trait variants (QTVs) controlling both PHT and grain yield. Here, heterogeneous inbred families demonstrated that these two loci, characterized by two single nucleotide polymorphisms (SNPs), cause phenotypic variation in inbred lines, but that size of these effects were variable across four different genetic backgrounds, ranging from 1 to 10 cm. Weekly unoccupied aerial system flights demonstrated the two SNPs had larger effects, varying from 10 to 25 cm, in early growth while effects decreased towards the end of the season. These results show that allelic effect sizes of economically valuable loci are both dynamic in temporal growth and dynamic across genetic backgrounds, resulting in informative phenotypic variability overlooked following traditional phenotyping methods. Public genotyping data shows recent favorable allele selection in elite temperate germplasm with little change across tropical backgrounds. As these loci remain rarer in tropical germplasm, with effects most visible early in growth, they are useful for breeding and selection to expand the genetic basis of maize.
© The Author(s) (2021). Published by Oxford University Press on the Genetics Society of America.

Entities:  

Keywords:  High Throughput Phenotyping; Temporal Loci Effects; Unoccupied Aerial System

Year:  2021        PMID: 33822935     DOI: 10.1093/g3journal/jkab075

Source DB:  PubMed          Journal:  G3 (Bethesda)        ISSN: 2160-1836            Impact factor:   3.154


  2 in total

Review 1.  Plant Genotype to Phenotype Prediction Using Machine Learning.

Authors:  Monica F Danilevicz; Mitchell Gill; Robyn Anderson; Jacqueline Batley; Mohammed Bennamoun; Philipp E Bayer; David Edwards
Journal:  Front Genet       Date:  2022-05-18       Impact factor: 4.772

2.  Phenomic data-facilitated rust and senescence prediction in maize using machine learning algorithms.

Authors:  Aaron J DeSalvio; Alper Adak; Seth C Murray; Scott C Wilde; Thomas Isakeit
Journal:  Sci Rep       Date:  2022-05-09       Impact factor: 4.996

  2 in total

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