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High-Throughput Computer Vision Introduces the Time Axis to a Quantitative Trait Map of a Plant Growth Response

Automated image acquisition, a custom analysis algorithm, and a distributed computing resource were used to add time as a third dimension to a quantitative trait locus (QTL) map for plant root gravitropism, a model growth response to an environmental cue. Digital images of Arabidopsis thaliana seedl...

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Autores principales: Moore, Candace R., Johnson, Logan S., Kwak, Il-Youp, Livny, Miron, Broman, Karl W., Spalding, Edgar P.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Genetics Society of America 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3813838/
https://www.ncbi.nlm.nih.gov/pubmed/23979570
http://dx.doi.org/10.1534/genetics.113.153346
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author Moore, Candace R.
Johnson, Logan S.
Kwak, Il-Youp
Livny, Miron
Broman, Karl W.
Spalding, Edgar P.
author_facet Moore, Candace R.
Johnson, Logan S.
Kwak, Il-Youp
Livny, Miron
Broman, Karl W.
Spalding, Edgar P.
author_sort Moore, Candace R.
collection PubMed
description Automated image acquisition, a custom analysis algorithm, and a distributed computing resource were used to add time as a third dimension to a quantitative trait locus (QTL) map for plant root gravitropism, a model growth response to an environmental cue. Digital images of Arabidopsis thaliana seedling roots from two independently reared sets of 162 recombinant inbred lines (RILs) and one set of 92 near isogenic lines (NILs) derived from a Cape Verde Islands (Cvi) × Landsberg erecta (Ler) cross were collected automatically every 2 min for 8 hr following induction of gravitropism by 90° reorientation of the sample. High-throughput computing (HTC) was used to measure root tip angle in each of the 1.1 million images acquired and perform statistical regression of tip angle against the genotype at each of the 234 RIL or 102 NIL DNA markers independently at each time point using a standard stepwise procedure. Time-dependent QTL were detected on chromosomes 1, 3, and 4 by this mapping method and by an approach developed to treat the phenotype time course as a function-valued trait. The QTL on chromosome 4 was earliest, appearing at 0.5 hr and remaining significant for 5 hr, while the QTL on chromosome 1 appeared at 3 hr and thereafter remained significant. The Cvi allele generally had a negative effect of 2.6–4.0%. Heritability due to the QTL approached 25%. This study shows how computer vision and statistical genetic analysis by HTC can characterize the developmental timing of genetic architectures.
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spelling pubmed-38138382013-11-01 High-Throughput Computer Vision Introduces the Time Axis to a Quantitative Trait Map of a Plant Growth Response Moore, Candace R. Johnson, Logan S. Kwak, Il-Youp Livny, Miron Broman, Karl W. Spalding, Edgar P. Genetics Investigations Automated image acquisition, a custom analysis algorithm, and a distributed computing resource were used to add time as a third dimension to a quantitative trait locus (QTL) map for plant root gravitropism, a model growth response to an environmental cue. Digital images of Arabidopsis thaliana seedling roots from two independently reared sets of 162 recombinant inbred lines (RILs) and one set of 92 near isogenic lines (NILs) derived from a Cape Verde Islands (Cvi) × Landsberg erecta (Ler) cross were collected automatically every 2 min for 8 hr following induction of gravitropism by 90° reorientation of the sample. High-throughput computing (HTC) was used to measure root tip angle in each of the 1.1 million images acquired and perform statistical regression of tip angle against the genotype at each of the 234 RIL or 102 NIL DNA markers independently at each time point using a standard stepwise procedure. Time-dependent QTL were detected on chromosomes 1, 3, and 4 by this mapping method and by an approach developed to treat the phenotype time course as a function-valued trait. The QTL on chromosome 4 was earliest, appearing at 0.5 hr and remaining significant for 5 hr, while the QTL on chromosome 1 appeared at 3 hr and thereafter remained significant. The Cvi allele generally had a negative effect of 2.6–4.0%. Heritability due to the QTL approached 25%. This study shows how computer vision and statistical genetic analysis by HTC can characterize the developmental timing of genetic architectures. Genetics Society of America 2013-11 /pmc/articles/PMC3813838/ /pubmed/23979570 http://dx.doi.org/10.1534/genetics.113.153346 Text en Copyright © 2013 by the Genetics Society of America Available freely online through the author-supported open access option.
spellingShingle Investigations
Moore, Candace R.
Johnson, Logan S.
Kwak, Il-Youp
Livny, Miron
Broman, Karl W.
Spalding, Edgar P.
High-Throughput Computer Vision Introduces the Time Axis to a Quantitative Trait Map of a Plant Growth Response
title High-Throughput Computer Vision Introduces the Time Axis to a Quantitative Trait Map of a Plant Growth Response
title_full High-Throughput Computer Vision Introduces the Time Axis to a Quantitative Trait Map of a Plant Growth Response
title_fullStr High-Throughput Computer Vision Introduces the Time Axis to a Quantitative Trait Map of a Plant Growth Response
title_full_unstemmed High-Throughput Computer Vision Introduces the Time Axis to a Quantitative Trait Map of a Plant Growth Response
title_short High-Throughput Computer Vision Introduces the Time Axis to a Quantitative Trait Map of a Plant Growth Response
title_sort high-throughput computer vision introduces the time axis to a quantitative trait map of a plant growth response
topic Investigations
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3813838/
https://www.ncbi.nlm.nih.gov/pubmed/23979570
http://dx.doi.org/10.1534/genetics.113.153346
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