Genomics-assisted Breeding

It's Match.com for plants... where the goal of the match is to produce the best possible offspring. 

The goal of plant breeding is to produce 'better' crop plants, e.g., rice or plants that produce more rice, or require less water, or are more resistant to disease. Plant breeders get these better crops by selecting the best plants they have to cross (mate) in order to produce future generations of, hopefully, improved plants. But how do breeders know which individuals to cross? 

Genomics assisted breeding, or Genomic Selection, is a new way of choosing plants to cross based on the plants' DNA sequences. In a genomics-assisted breeding program, a breeding population of plants known as the "training population" is both phenotyped (i.e., data is collected on how the plant looks, e.g. how tall is the plant? how much rice does it produce?), and genotyped, meaning data is collected on the plants' DNA sequences. Computers are then used to generate a statistical model that can predict plant performance based on the plant genotype!   

 Diagram: how genomic Selection works 

 Diagram: how genomic Selection works 

Individuals undergoing selection, i.e., individuals in the "testing population" are then genotyped only. The DNA sequence information is input into the previously trained model to calculate a "genome estimated breeding value", or, in other words, quantitative measure of how good each plant would be as a parent of the next generation. Individuals with the highest GEBVs are selected to produce the next round of crossing. Genomic selection allows breeders to make better decisions in less time, which means breeding progress is faster. This is essential if we are feed everyone in the next thirty years!

Genomic selection was first used in dairy cattle breeding, and is now also used to breed corn and wheat. My research focuses on applying genomic selection to rice, one of the world's most important staple food crops. Early data suggest it will be very effective for this important plant! Check out my publications for more information:

 

Spindel and McCouch, 2016. When more is better: how data sharing would accelerate genomic selection of crop plants. New Phytologist 212 (4): 814-826. 

Spindel et al., 2016. Genome-wide prediction models that incorporate de novo GWAS are a powerful new tool for tropical rice improvement. Heredity 116: 395-408.

Spindel et al., 2015. Genomic Selection and Association Mapping in Rice (Oryza sativa): Effect of Trait Genetic Architecture, Training Population Composition, Marker Number and Statistical Model on Accuracy of Rice Genomic Selection in Elite, Tropical Rice Breeding Lines. PlOS GENETICS 1004982

Spindel and Begum et al., 2015. Genome-Wide Association Mapping for Yield and Other Agronomic Traits in an Elite Breeding Population of Tropical Rice (Oryza sativa). PlOS ONE 0119873.