SmartGrain: High-Throughput Phenotyping Software for Measuring Seed Shape through Image Analysis1[C][W][OA]

Seed shape and size are among the most important agronomic traits because they affect yield and market price. To obtain accurate seed size data, a large number of measurements are needed because there is little difference in size among seeds from one plant. To promote genetic analysis and selection for seed shape in plant breeding, efficient, reliable, high-throughput seed phenotyping methods are required. We developed SmartGrain software for high-throughput measurement of seed shape. This software uses a new image analysis method to reduce the time taken in the preparation of seeds and in image capture. Outlines of seeds are automatically recognized from digital images, and several shape parameters, such as seed length, width, area, and perimeter length, are calculated. To validate the software, we performed a quantitative trait locus (QTL) analysis for rice (Oryza sativa) seed shape using backcrossed inbred lines derived from a cross between japonica cultivars Koshihikari and Nipponbare, which showed small differences in seed shape. SmartGrain removed areas of awns and pedicels automatically, and several QTLs were detected for six shape parameters. The allelic effect of a QTL for seed length detected on chromosome 11 was confirmed in advanced backcross progeny; the cv Nipponbare allele increased seed length and, thus, seed weight. High-throughput measurement with SmartGrain reduced sampling error and made it possible to distinguish between lines with small differences in seed shape. SmartGrain could accurately recognize seed not only of rice but also of several other species, including Arabidopsis (Arabidopsis thaliana). The software is free to researchers.

[1]  M. Yano,et al.  Variation in heading date conceals quantitative trait loci for other traits of importance in breeding selection of rice , 2012, Breeding science.

[2]  Qian Qian,et al.  Control of grain size, shape and quality by OsSPL16 in rice , 2012, Nature Genetics.

[3]  Lijun Luo,et al.  Natural variation in GS5 plays an important role in regulating grain size and yield in rice , 2011, Nature Genetics.

[4]  R. Macknight,et al.  Rapid analysis of seed size in Arabidopsis for mutant and QTL discovery , 2011, Plant Methods.

[5]  M. Yano,et al.  Core single-nucleotide polymorphisms—a tool for genetic analysis of the Japanese rice population , 2010 .

[6]  M. Yano,et al.  Fine definition of the pedigree haplotypes of closely related rice cultivars by means of genome-wide discovery of single-nucleotide polymorphisms , 2010, BMC Genomics.

[7]  M. Yano,et al.  Detection of quantitative trait loci controlling pre-harvest sprouting resistance by using backcrossed populations of japonica rice cultivars , 2010, Theoretical and Applied Genetics.

[8]  Takeshi Hayashi,et al.  Genome-wide association study of grain shape variation among Oryza sativa L. germplasms based on elliptic Fourier analysis , 2010, Molecular Breeding.

[9]  Tony Pridmore,et al.  High-Throughput Quantification of Root Growth Using a Novel Image-Analysis Tool1[C][W] , 2009, Plant Physiology.

[10]  Ioan Vlad Uilecan,et al.  HYPOTrace: Image Analysis Software for Measuring Hypocotyl Growth and Shape Demonstrated on Arabidopsis Seedlings Undergoing Photomorphogenesis1[OA] , 2009, Plant Physiology.

[11]  Zhijun Cheng,et al.  Isolation and initial characterization of GW5, a major QTL associated with rice grain width and weight , 2008, Cell Research.

[12]  T. Nishio,et al.  Novel QTLs for photoperiodic flowering revealed by using reciprocal backcross inbred lines from crosses between japonica rice cultivars , 2008, Theoretical and Applied Genetics.

[13]  J. Trygg,et al.  LAMINA: a tool for rapid quantification of leaf size and shape parameters , 2008, BMC Plant Biology.

[14]  Kaworu Ebana,et al.  Deletion in a gene associated with grain size increased yields during rice domestication , 2008, Nature Genetics.

[15]  Daniel Parnham,et al.  LeafAnalyser: a computational method for rapid and large-scale analyses of leaf shape variation. , 2008, The Plant journal : for cell and molecular biology.

[16]  Wei Huang,et al.  A QTL for rice grain width and weight encodes a previously unknown RING-type E3 ubiquitin ligase , 2007, Nature Genetics.

[17]  Kikuo Fujimura,et al.  Development of a Controlled Vocabulary and Software Application to Analyze Fruit Shape Variation in Tomato and Other Plant Species1[W] , 2006, Plant Physiology.

[18]  Bin Han,et al.  GS3, a major QTL for grain length and weight and minor QTL for grain width and thickness in rice, encodes a putative transmembrane protein , 2006, Theoretical and Applied Genetics.

[19]  H. Iwata,et al.  SHAPE: a computer program package for quantitative evaluation of biological shapes based on elliptic Fourier descriptors. , 2002, The Journal of heredity.

[20]  R. Doerge,et al.  Empirical threshold values for quantitative trait mapping. , 1994, Genetics.

[21]  Z. Zeng.  Precision mapping of quantitative trait loci. , 1994, Genetics.

[22]  Z B Zeng,et al.  Theoretical basis for separation of multiple linked gene effects in mapping quantitative trait loci. , 1993, Proceedings of the National Academy of Sciences of the United States of America.

[23]  M. Daly,et al.  MAPMAKER: an interactive computer package for constructing primary genetic linkage maps of experimental and natural populations. , 1987, Genomics.

[24]  Keiichi Abe,et al.  Topological structural analysis of digitized binary images by border following , 1985, Comput. Vis. Graph. Image Process..

[25]  T. Tanabata,et al.  Development of automatic segmentation software for efficient measurement of area on the digital images of plant organs. , 2010 .

[26]  T. Chang,et al.  The morphology and varietal characteristics of the rice plant. , 1965 .