Compositions and Methods of Plant Breeding Using High Density Marker Information

a marker information and high density technology, applied in knowledge representation, instruments, computing models, etc., can solve the problems of limiting inferences, reducing the efficiency of breeding programs, and reducing the efficiency of plant breeding, so as to improve the germplasm of plants

Inactive Publication Date: 2010-11-18
MONSANTO TECH LLC
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Benefits of technology

[0016]The present invention includes a method for breeding of a crop plant, such as maize (Zea mays), soybean (Glycine max), cotton (Gossypium hirsutum), peanut (Arachis hypogaea), barley (Hordeum vulgare); oats (Avena sativa); orchard grass (Dactylis glomerata); rice (Oryza sativa, including indica and japonica varieties); sorghum (Sorghum bicolor); sugar cane (Saccharum sp); tall fescue (Festuca arundinacea); turfgrass species (e.g. species: Agrostis stolonifera, Poa pratensis, Stenotaphrum secundatum); wheat (Triticum aestivum), and alfalfa (Medicago sativa), members of the genus Brassica, broccoli, cabbage, carrot, cauliflower, Chinese cabbage, cucumber, dry bean, eggplant, fennel, garden beans, gourd, leek, lettuce, melon, okra, onion, pea, pepper, pumpkin, radish, spinach, squash, sweet corn, tomato, watermelon, ornamental plants, and other fruit, vegetable, tuber, oilseed, and root crops, wherein oilseed crops include soybean, canola, oil seed rape, oil palm, sunflower, olive, corn, cottonseed, peanut, flaxseed, safflower, and coconut, with enhanced traits comprising at least one sequence of interest, further defined as conferring a preferred property selected from the group consisting of herbicide tolerance, disease resistance, insect or pest resistance, altered fatty acid, protein or carbohydrate metabolism, increased grain yield, increased oil, increased nutritional content, increased growth rates, enhanced stress tolerance, preferred maturity, enhanced organoleptic properties, altered morphological characteristics, other phenotypic traits, traits for industrial uses, or traits for improved consumer appeal, wherein the traits may be nontransgenic or transgenic.
[0017]Non-limiting examples of silage quality traits include brown midrib (BMR) traits, in vitro digestability of dry matter, leafiness, horny endosperm, crude protein, neutral detergent fiber, neutral detergent fiber digestability, starch content, starch availability, kernel texture, milk / ton, fat content of milk, readily available energy, soluble carbohydrate digestability, nonsoluble carbohydrate digestability, reduced phytate production, reduced waste production, and silage yield.

Problems solved by technology

Using markers to infer phenotype in these cases results in the economization of a breeding program by substitution of costly, time-intensive phenotyping with genotyping.
Further, other limitations of traditional QTL mapping research include the fact that inferences are restricted to the particular parents of the mapping population and the genes or gene combinations of these parental varieties.
2006 Crop Sci. 46:1323-1330), with the major limitation being the lack of knowledge of identity by descent at a specific genomic region (Bunter et al.
Historically, genetic markers were not appropriate for distinguishing identical in state or by descent.

Method used

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  • Compositions and Methods of Plant Breeding Using High Density Marker Information
  • Compositions and Methods of Plant Breeding Using High Density Marker Information
  • Compositions and Methods of Plant Breeding Using High Density Marker Information

Examples

Experimental program
Comparison scheme
Effect test

example 1

An Example of Haplotype-Trait Association Analysis: Grain Yield QTL on Chromosome 4 in Corn

[0118]A key benefit of associating traits at the haplotype, rather than marker, level is the degree of resolution achieved. An initial QTL analysis from two different breeding crosses projects (herein denoted 1 and 2) were yield tested at 8 locations. A QTL was identified for grain yield on Chromosome 4 located approximately between 48 and 78 cM. The estimated QTL effect was similar in magnitude (4.2 Bu / Acre) for both projects. In the project 1, the genomic region from the inbred 5750 increased grain yield relative to the genomic region from the inbred 3140 when tested on the inbred 7051. In the project 2, the genomic region from the inbred 3323 increased grain yield relative to the genomic region from the inbred 90LDC2 when tested on the inbred WQDS7. The current breeding methodology uses this type of information (marker-QTL associations) to do recurrent selection within each population (proj...

example 2

Use of Breeding Values for Informing Decisions in a Breeding Program

[0121]A primary innovation of the present invention is the ability to simultaneously select for multiple traits and target regions throughout the genome. Furthermore, this invention leverages historical marker-phenotype information, enabling pre-selection.

[0122]A key aspect of predictive haplotype-assisted selection is the ability to rank haplotypes. This example includes a subset of 10 preferred haplotypes, across 10 haplotype windows, for yield from elite temperate female corn inbreds that have been ranked using haplotype breeding value calculations. The haplotype effect estimates for each of the haplotypes for six different phenotypic traits is shown in Table 10. This example illustrates the application of breeding values in decisions relating to germplasm improvement.

TABLE 10Haplotype effect estimates for six traits in 10 haplotypes from 10 differenthaplotype windows based on historical haplotype-phenotype assoc...

example 3

Implementation of Pre-Selection in a Breeding Program Via Automatic Model Picking

[0126]With haplotype estimation, pre-selection can be applied to a breeding program. This enables breeders, through marker-assisted selection on pre-determined significant haplotypes, to make genetic gain before new lines and breeding crosses are tested in the field. Breeders start pre-selection projects by selecting a list of crosses and building models based on the haplotypes carried by each parental line in the cross. One approach is to manually select haplotypes, but this hampers the breeders' ability to sort through a large number of possible crosses. There may also be inconsistencies in the way haplotypes are selected from cross to cross and there may be a need to restrain the choice of too many genomic regions in the model. For instance, if the model is too complex, predictive ability, and potential genetic gain, will likely be compromised. To control for model complexity and also meet high-throu...

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Abstract

The present invention relates to breeding methods to enhance the germplasm of a plant. The methods describe the identification and accumulation of preferred haplotype genomic regions in the germplasm of breeding populations of maize (Zea mays) and soybean (Glycine max). The invention also relates to maize and soybean plants comprising preferred haplotypes.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS[0001]This application claims priority to U.S. Provisional Application Ser. No. 60 / 837,864 (filed Aug. 15, 2006), which is incorporated herein by reference in its entirety.FIELD OF THE INVENTION[0002]This invention relates to the field of plant breeding, in particular to methods to facilitating informed germplasm improvement activities within a breeding program by defining haplotypes within pre-determined chromosomal windows within a genome and associating the haplotypes with haplotype effect estimates for one or more traits, wherein the associations can be made de novo or by leveraging historical marker-trait association data. Accordingly, the methods of the present invention enable decisions related to germplasm improvement activities to be made by ranking haplotypes based on numerical values, wherein the values represent the haplotype effect estimates, haplotype frequency, and / or breeding values. Herein, breeding values are calculated based ...

Claims

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Application Information

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Patent Type & Authority Applications(United States)
IPC IPC(8): A01H5/00G06N5/02C12Q1/68
CPCC12Q1/6895C12Q2600/13C12Q2600/156C12Q2600/172A01H6/542A01H5/10
Inventor BULL, JASON
Owner MONSANTO TECH LLC
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