1. Software to Simulate Genomic Selection
Takao Shibamoto
Dr. Lizhi Wang
Hello. Everyone. I am going to talk about my undergraduate research, software to simulate genomic selection.

2. Plant Breeding
Science of changing the traits of plants in order to produce desired characteristics.
Plant breeding is the purposeful manipulation of plant species in order to create desired characteristics for specific purposes. Genomic selection is a new approach for improving quantitative traits in large plant breeding populations that use genome information.

3. Significance of research
Corn size evolved significantly in the past.
This is thanks to plant breeding.
Genomic selection is a new way of plant breeding.
Corn size has become significantly bigger in the past. This is thanks to plant breeding. Our research is to improve and optimize plant breeding, so that we can breed better plant varieties with higher yield, better nutritional content, that are more resistant to drought and diseases, and more adaptive to changing climate. I am going to show two ways of plant breeding, genomic selection and phenotypic selection, and why genomic selection is better.

4. Phenotype vs Genotype Definition Genome Characteristics
Observable traits,
Genotype + Environment
Genome
Characteristics
Very different under different environmental conditions
Environment doesn’t matter. Next generation inherits genotype but not phenotype
First of all, there are two ways of describing plants for breeding purposes, phenotype and genotype. Phenotypes are observable traits. Genotypes are based on the genome, DNA information. Phenotypes are based on genotypes and environments, which means phenotypes vary under different environments. The next generation inherits genotypes, but not phenotypes.

5. Phenotypic Selection vs Genomic Selection
Focus
Observable characteristics (Phenotype)
Genome (Genotype)
Advantage
Very easy, has been used for thousands of years
Very reliable and efficient
Disadvantage
Slow and sometimes destructive
Needs high spec computer
There are mainly two ways to do plant breeding.
The traditional one is phenotypic selection. Phenotypic selection only looks at the observable characteristics such as yield, color, and height and then mates plants based on those traits. This has been used successfully for thousands of years because it is easy to do.
However, Phenotypic selection is often very slow and even destructive. You can never get back the lost trait. So breeders always wanted a faster and cheaper approach.
Recently, Genomic selection has been used successfully as computers have become more powerful.
Genomic selection involves estimated breeding values, such as GEBV and OHV. I am going to explain about breeding values later.
Genomic Selection makes breeding programs more efficient and more reliable.

6. Example of Genotype and Phenotype
The color of flower is based on genotype.
The color of flower is phenotype.
Here are the examples of genotype and phenotype.
Uppercase B is a dominant trait, lowercase b is a recessive trait in this case.
Both mother and father are pink flowers, however, if you breed them, there is a 25% chance that their child is a white flower because dominant trait is likely to appear as phenotype.
So in phenotypic selection, uppercase B uppercase B and uppercase B lowercase b are considered the same flower, but in genomic selection those are different flowers.
"Phenotype." Wikipedia. Wikimedia Foundation, 24 Mar Web. 30 Mar

7. Genome information Genome = Information stored in DNA
Chromosome = piece of genome
Locus (or gene) = position on a chromosome
Allele = each bit of information in genome (A/G/C/T)
Chromesome
DNA stores genome as information. Creatures that have male and female have two chromosomes, one from mother and the other from father. Creatures that do asexual reproduction have only one chromosome

8. Example
The figure is the comparisons of distributions of progeny phenotypes from two pairs of parents
A genius can be born from average parents.
Here is the intuitive explanation of phenotypic selection versus genomic selection.
The selection step makes the assumption that two taller parents will produce taller progeny. While it is intuitive and generally true, this
assumption does have exceptions, which present an opportunity to improve breeding efficiency.
In the figure, The x coordinate represents the height of the plant as a phenotype and the y coordinate represents the probability density of the generation.
Two shorter parents may carry complementary desirable alleles, and those of
their progeny that inherit the complementary desirable alleles from both parents could be
taller than either parent. This example is illustrated in the Figure by the wider blue
distribution of progeny between two short parents that carry complementary desirable
alleles.
On the other hand, if two taller parents are genetically similar with respect to
favorable alleles, then their progeny are expected to be tall, but similar to the parents as
illustrated in the Figure by the narrower red distribution. Under this situation, it may be
desirable to cross the shorter parents because some of the resulting blue progeny may be
even taller than the tallest individuals from the red distribution.
We want to develop an efficient way to get the right most blue part.

9. How does genomic selection work?
Selection and mating processes are not well optimized yet.
So, how does genomic selection work? First, you have to identify the genome of your target plant. This is called genotyping. Next, you predict which genome is good for the next generation. This is genomic prediction. The next step is selection, where breeders choose better genome based on genomic prediction. The last step is that breeders actually mate the selected plants. Genomic selection simulation allows to do this for many generations on a computer. Our research objective is to improve selection and mating steps. There are many proposed selection methods and it is very good if there is a handy tool to simulate genomic selection using various selection approaches.
Genomic selection is a new field, so selection and mating steps are not so good yet. Even a small improvement in these steps can significantly reduce the cost of genomic selection.
The overall goal of our research is to increase the rate of genetic gain in crop breeding programs by developing and deploying improved genomic selection strategies that rely on improvements in the selection and mating steps
Now, I am going to briefly explain how selection and mating steps work on a computer

10. Matrix representation of genome
Locus
Allele
Chromosome
So how is genome represented on a computer?
This whole matrix with a bunch of 0s and 1s is genome.
Each row is a locus.
Each number is an allele. In my simulation program, 0 means a useless allele, 1 means a useful allele, it has been determined by the past research.
Each column is one of a pair of chromosomes, one from mother, one from father.

11. Various selection approaches based on estimated breeding values
GEBV - Genomic Estimated Breeding Value
CGS - Conventional Genomic Selection Value
OHV - Optimal Haploid Value
PCV - Predicted Cross Value
etc...
There are various selection approaches. One way is to take a look at the estimated breeding values. These values can be obtained from the matrix representation of genome and effect values.
PCV is proposed by my mentor, Dr. Lizhi Wang’s Group

12. Mating function The mating function is very well optimized
The simulation tool’s mating function is well optimized. It takes about 3 seconds to mate 1 pair of genome with one hundred and 40 million loci. It is very fast and well optimized so that users don’t need to worry about this step.

13. How to obtain GEBV It's very simple G = Genome E = Effect GEBV Genome
Now, how do you get breeding values? This is how to get GEBV, one of the breeding values. You can get GEBV by summing up all the values of multiplication of the genome and effect values.
The effect values are gained from the past research

14. Genomic Selection Simulation Tool
Informative
Interactive
Efficient
Cross platform
Now, this is the software I made. This software can simulate breeding of pretty much any plants with various selection approaches. This tool works on Windows, Mac, and Linux. You can save the paused simulation and restart from there later. The simulation is quite fast. This slide shows the simulation using GEBV.
Dark blue sticks represent histogram of GEBVs. Light blue filled part is maximum and minimum GEBVs. Red line is the average GEBV. The grey part represents the upper bound and lower bound. They show how close the genome are to each other. As you can see in the graph, the genomes get closer together and after generation 6, most genomes have GEBV of 8 million. You can also see that the breeding value is always going up. In phenotypic selection, this value can often go down. This is what I meant by slow and destructive.

15. Future work
Improving selection approaches is very important in genomic selection.
This tool will be publicly available.
We can create better and stronger plants more efficiently using this cool technology, which will lead to cheaper and more delicious food for consumers.
Improving selection approaches is the key to Genomic Selection. Even a small improvement in selection approaches can significantly reduce the cost of plant breeding. I am going to make this tool publicly available so that other plant breeders can use it in their breeding process. We can use this tool to design more efficient genomic selection approaches. And I hope this tool contributes to the field.

16. Acknowledgements Dr. Lizhi Wang Iowa State University
I would like to thank Dr Lizhi Wang for helping me and giving me this awesome opportunity.

17. Thank you for listening!
Any questions?
Thank you for listening. Do you have any questions?