Systems and methods for use in identifying multiple genome edits and predicting the aggregate effects of the identified genome edits

1. A method for predicting a phenotypic impact of multiple genome edits in an organism, the method comprising:

identifying a population of candidate edits to a genomic sequence of said organism based on at least one of genome annotation, genome-wide association study (GWAS) analysis, gene expression data, and a biochemical pathway model;

ranking, by a computing device, each of the candidate edits based on a predicted ability of each candidate edit to affect one or more traits of interest in said organism, the predicted ability based on at least one of a probability of causing an effect, a magnitude of the effect, and a non-parametric classification parameter;

selecting, by the computing device, at least four of the candidate edits based on the ranking;

based on an additive model of predicted effects of the selected at least four of the candidate edits, per type, predicting, by the computing device, an aggregate effect of the selected at least four of the candidate edits for the one or more traits of interest expressed by a specimen of the organism having a genomic sequence and edited according to the selected at least four of the candidate edits, as compared to an unedited specimen of the organism;

determining, by the computing device, a sample size of replicates of the organism to be modified consistent with the selected at least four of the candidate edits for the one or more traits, based on the predicted aggregate effect and a defined probability threshold; and

modifying a number of the replicates of the organism, consistent with the sample size, according to the selected at least four of the candidate edits.

2. The method of claim 1 , further comprising measuring an aggregate effect of the selected at least four of the candidate edits on the one or more traits of interest, based on the modified replicates of the organism and at least one un-modified replicate of the organism, thereby indicating the aggregate effect of the selected at least four of the candidate edits without measuring an effect of each of the selected at least four of the candidate edits individually.

3. The method of claim 1 , further comprising subjecting the modified replicates of the organism to a cultivation space, whereby the aggregate effect is permitted to be verified.

4. The method of claim 3 , wherein the organism includes a plant; and

wherein the one or more traits of interest is associated with economic importance.

5. The method of claim 4 , wherein the organism includes a maize plant.

6. A method for generating a plant organism with an enhanced trait or phenotype, the method comprising:

identifying a population of candidate edits to a genomic sequence based on at least one of genome annotation, genome-wide association study (GWAS) analysis, gene expression data, and a biochemical pathway model;

ranking, by a computing device, each of the candidate edits based on a predicted ability of each candidate edit to affect a trait of interest, the predicted ability based on at least one of a probability of causing an effect, a magnitude of the effect, and a non-parametric classification parameter;

selecting, by the computing device, multiple ones of the candidate edits based on the ranking;

based on an additive model of predicted effects of the selected multiple ones of the candidate edits, per type, predicting, by the computing device, an aggregate effect of the selected multiple ones of the candidate edits for the trait of interest expressed by a specimen of a plant organism having a genomic sequence and edited according to the selected one or more of the candidate edits, as compared to an unedited specimen having the genomic sequence;

determining, by the computing device, a sample size of replicates of the specimen to be modified consistent with the selected multiple ones of the candidate edits for the trait of interest, based on the predicted aggregate effect and a defined probability threshold;

modifying a number of the replicates of the specimen, consistent with the sample size, according to the selected multiple ones of the candidate edits; and

growing and/or cultivating said modified replicates in a cultivation space, along with an unedited plant organism consistent with the genomic sequence, thereby permitting validation of the predicted aggregate effect.

7. The method of claim 6 , wherein the plant organism includes a maize plant.

8. The method of claim 6 , wherein identifying the population of candidate edits includes identifying, by the computing device, the population of candidate edits; and

wherein the method further comprises:

prior to modifying an unedited plant organism, determining a sample size to be used in invalidating the aggregate effect; and

wherein modifying the unedited plant organism includes modifying multiple unedited plant organisms, consistent with the determined sample size, with the selected candidate edits.

9. A method of creating an agronomically advanced plant, the method comprising:

identifying a genomic sequence for a genome or pan-genome,

identifying a population of candidate edits to the genomic sequence according to prior information including genome annotation, GWAS analyses, gene expression data, or biochemical pathway models;

rating, by a computing device, each of the candidate edits or groups of candidate edits in relation to a predicted ability to affect one or more phenotypes of interest, based on probability of causing an effect, magnitude of the predicted ability to cause the effect, and/or non-parametric classification parameter;

selecting, by the computing device, based on the rating, a subset of edits;

based on an additive model of predicted effects of the selected multiple ones of the candidate edits, per type, predicting, by the computing device, the aggregate effects of the preferred edits on the one or more phenotypes expressed by a plant with its genome sequence edited as compared to its unedited version, where a phenotypic impact of each individual edit may be too small to be proven significantly different from zero or to be estimated precisely;

determining, by the computing device, a sample size of replicates of the plant to be modified consistent with the selected subset of edits for the one or more phenotypes, based on the predicted aggregate effects and a defined probability threshold; and

modifying a number of the replicates of the plant, consistent with the sample size, according to the selected multiple ones of the candidate edits.

10. The method of claim 9 , wherein selection the subset of edits is further based on DNA fingerprints and/or pedigree relationships; and

wherein the method further comprises:

measuring the aggregate effects of the edits by comparing the modified replicates of the plant to an unedited plant, the unedited plant having the same origin as the modified replicates of the plant; and

confirming, based on the measured aggregate effect, the modified replicates of the plant, without individually measuring agronomic impact of each of the multiple edits.