Pioneer Hi-bred International, Inc.

E.i. Dupont De Nemours and Company

James A Janni

J Antoni Rafalski

Jonathan E Lightner

Mary Beatty

Steven Lorenz Wright

Jan P Hazebroek

Jon Lightner

Jan Hazebroek

Antoni J Rafalski

Steven L Wright

A novel method for prediction of the degree of heterotic phenotypes in plants is disclosed. Structural variation analyses of the genome are used to predict the degree of a heterotic phenotype in plants. In some examples, copy number variation is used to predict the degree of heterotic phenotype. In some methods copy number variation is detected using competitive genomic hybridization arrays. Further, methods for optimizing the arrays are disclosed, together with kits for producing such arrays, as well as hybrid plants selected for development based on the predicted results.

Using structural variation to analyze genomic differences for the prediction of heterosis

The invention provides methods for characterizing metabolic profiles, phenotypic profiles and trait profiles in plants or groups of plants. Additionally, methods for establishing an unbiased model between a phenotypic profile and a metabolic profile, or between a trait profile and metabolic profile, are also provided by the invention. Further, methods for using such unbiased models to accurately predict the development of a phenotype of interest or a trait of interest in an independent, immature plant are also provided. In one embodiment, immature plants are selected for use based on their predicted development of a phenotype or trait of interest.

Prediction of phenotypes and traits based on the metabolome

A novel method for prediction of the degree of heterotic phenotypes in plants is disclosed. Structural variation analyzes of the genome are used to predict the degree of a heterotic phenotype in plants. In some examples, copy number variation is used to predict the degree of heterotic phenotype. In some methods copy number variation is detected using competitive genomic hybridization arrays. Further, methods for optimizing the arrays are disclosed, together with kits for producing such arrays, as well as hybrid plants selected for development based on the predicted results.

Using oligonucleotide microarrays to analyze genomic differences for the prediction of heterosis

A method is provided for automatically discerning between object and non-object pixels in a hyperspectral image data cube. In particular embodiments, the object of the method is a plant, plant part, plant trait, plant phenotype, plant pot or a plant medium. The method comprises a first step of providing a partial least squares discriminant analysis (PLSDA) algorithm and a second step of applying the PLSDA algorithm to a hyperspectral image data cube to automatically determine which pixels contain the spectral properties of the object. The PLSDA algorithm of the method can be generated by establishing a training matrix, performing an eigenvector decomposition of the training matrix, experimentally determining a weighted linear combination of object signal-containing eigenvectors, calculating a regression vector using the weighted linear combination of signal-containing eigenvectors, generating a mask matrix and multiplying the mask matrix by the hyperspectral image data cube along two spatial dimensions.

Automatic detection of object pixels for hyperspectral analysis

A method and apparatus for optically interrogating a particle comprising obtaining a plurality of optical interrogations from a plurality of orientations relative the particle. In one aspect, the particle is tumbled relative to optical interrogation direction and reflected or transmitted energy is collected and weight of one or several seeds is derived.

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Johnston, IA, United States of America

James A Janni