Methods For Identifying Crosses For Use In Plant Breeding

Patent metadata

Field	Value
Publication number	US-2023255155-A1
Application number	US-202318138716-A
Country	US
Kind code	A1
Filing date	Apr 24, 2023
Priority date	Jun 8, 2016
Publication date	Aug 17, 2023
Grant date	—

Abstract

Exemplary methods for use in identifying crosses for use in plant breeding are disclosed. One exemplary method includes generating population prediction scores for each potential cross within a set of potential crosses, where each population prediction score is associated with a prediction of commercial success for the associated potential cross within the set of potential crosses. The method also includes selecting a subgroup of potential crosses, based on thresholds associated with the population prediction scores for the set of potential crosses. The exemplary method further includes selecting multiple target crosses from the subgroup of potential crosses based on a genetic relatedness of the parents in the subgroup of potential crosses, and directing ones of the selected target crosses into a breeding pipeline, thereby providing crosses to the breeding pipeline based, at least in part, on commercial success of parents included in the selected ones of the filtered crosses.

First claim

What is claimed is:

1 . A method for use in identifying crosses for use in plant breeding, the method comprising:
accessing a data structure representative of multiple parents; identifying a set of potential crosses, each potential cross in the set of potential crosses including at least two of the multiple parents included in the data structure; generating, by the at least one computing device, population prediction scores for each potential cross within the set of potential crosses, each population prediction score associated with a prediction of commercial success for the associated potential cross within the set of potential crosses; selecting, by at least one computing device, a subgroup of potential crosses, from the set of potential crosses, based on one or more thresholds associated with the population prediction scores for the set of potential crosses; selecting, by the at least one computing device, multiple target crosses from the subgroup of potential crosses based on a genetic relatedness of the parents in the subgroup of potential crosses and a risk associated with the multiple target crosses; and directing ones of the selected multiple target crosses into a breeding pipeline, thereby providing crosses to the breeding pipeline based, at least in part, on commercial success of parents included in the ones of the selected multiple target crosses.

2 . The method of claim 1 , wherein generating the population prediction scores includes generating, by the at least one computing device, the population prediction scores based on the following algorithm:
p ( s i |x i ,D )=Σ m=1 M p ( s i |x i ,m,D ) p ( m|D ).

3 . The method of claim 1 , wherein selecting the multiple target crosses from the subgroup of potential crosses based on the genetic relatedness includes:
clustering, by the at least one computing device, the parents of the potential crosses included in the subgroup, based on the relatedness of the parents; and wherein selecting the multiple target crosses is based on a relatedness threshold associated with the clustered parents of the target crosses.

4 . The method of claim 3 , wherein clustering the parents includes spectral clustering, by the at least one computing device, of the parents of the potential crosses included in the subgroup; and
wherein selecting the multiple target crosses includes:
combining, by the at least one computing device, a cluster score associated with at least one parent of one of the potential crosses included in the subgroup and a cluster score associated with said one of the potential crosses; and
selecting the multiple target crosses based on a comparison of the combined cluster scores to the relatedness threshold.

5 . The method of claim 1 , wherein selecting the multiple target crosses from the subgroup of potential crosses is further based on at least one rule is associated with at least one of stalk lodging, root lodging, Goss Wilt, parental similarity, and a difference between expected relative maturity (ERM) between the two parents.

6 . The method of claim 1 , wherein selecting the multiple target crosses based on risk includes determining, by the at least one computing device, risks associated with the potential crosses based on a quadratic algorithm dependent on a risk variable, a diversity variable, and a performance variable of the crosses.

7 . The method of claim 6 , wherein determining the risks includes determining the risks, by the at least one computing device, based on the following algorithm(s):

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Assignees

• Monsanto Technology Llc

Inventors

• Chavali Srinivas Phani Kumar
• Dasgupta Sambarta
• Polavarapu Nalini

Classifications

• G16B40/30

  Unsupervised data analysis · CPC title

• G16B20/20

  Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection · CPC title

• A01H1/02 Primary

  Methods or apparatus for hybridisation; Artificial pollination {; Fertility} · CPC title

• G06N20/00

  Machine learning · CPC title

• A01H1/04 Primary

  Processes of selection {involving genotypic or phenotypic markers; Methods of using phenotypic markers for selection} · CPC title