By the end of this practical you will be able to:
In the previous practical we bred for salt resistance, a trait controlled by essentially one marker. We could find that single marker with a GWAS and then select plants simply by checking whether they carried it.
Many important traits do not work like that. Yield is a good example: it is a polygenic trait, influenced by many different genes that each contribute only a tiny bit. There is no single marker you can check to know whether a plant will be high yielding.
So how do we select for a trait that is spread across many markers, each with a small effect? This is what genomic selection solves. Instead of looking at one marker, we build a model that looks at all the markers at once and combines them into a single prediction of a plant's performance. In this practical we will:
We start with a genetically diverse population. For every plant we know its markers (its DNA) and its measured yield. This combination of known markers plus known phenotype is exactly what we will use to train the model.
Scroll sideways to see all the markers.
Just like in the previous practical, we can run a GWAS and make a Manhattan plot, this time for yield. If one marker controlled yield, we would expect one tall bar rising above the significance line, the way Marker 12 did for salt resistance.
Manhattan plot for yield. Drag the slider to grow the population.
Instead of picking one marker, we train a model that uses all markers together. The model learns a weight for each marker: how much, and in which direction, that marker tends to push the yield. Adding up the contributions of all markers for a given plant gives a single predicted value for that plant.
To learn these weights, the model needs examples. We give it a population where we know both the markers and the real yield, and we tell it which phenotype to predict. "Training" simply means letting the model adjust its 50 weights until its predictions fit the yields it was shown as closely as it can. This actually runs live in your browser: drag the slider and the model is retrained on the spot.
After changing this, look at the weights in step 4 and the predictions in step 5: notice how the model's picture sharpens as it learns from more plants.
Now that the model is trained, we can look at the weight it assigned to each marker. A large positive weight means that marker tends to increase yield, a large negative weight means it tends to decrease yield, and a weight near zero means the model thinks that marker barely matters for yield.
Each bar is one marker's learned weight. Above zero raises yield, below zero lowers it.
Before we trust the model to guide our breeding, we should check how well it actually predicts yield. The fair test is to use 10 new plants that the model was never trained on: we ask the model to predict their yield from their markers alone, then compare its predictions against their real, measured yields. If the model were perfect, every point would lie exactly on the red diagonal line.
Now we put the model to work. We ask it to predict the yield of every plant. Breeders call this prediction a GEBV (Genomically Estimated Breeding Value): the model's best guess of how good a plant is, based purely on its markers. The big advantage is that you can compute a GEBV for a tiny seedling from its DNA alone, without growing it to maturity and measuring its yield.
We begin from a breeding population made by crossing today's elite variety with diverse material, so it already yields fairly well but still has genetic variation to work with. Each breeding cycle then does three things: predict the GEBV of every plant, keep the best ones as parents, and cross them to make the next generation. We run two programmes side by side from that same starting population: one that selects on GEBV, and a control that just crosses plants at random. The dashed line marks today's elite variety, the best crop farmers currently grow.
Imagine a plant makes a useful compound in two steps, each carried out by its own enzyme, and that there is a marker for an improved version of each of the two enzymes. The improved second enzyme can only work on what the first step hands it, so on its own it barely helps. Only a plant carrying both improved enzymes gets the full benefit, which is more than the two separate effects added together. Now ask yourself what weight the model would learn for each of those two markers.