AG2PI Field Day #30 - Wednesday March 20, 2024

Low frequency markers tend not to be very useful because their effects can't be measured accurately due to the low sample size in any experiment. This means that they don't tend to help at all. But they don't tend to hurt much either. In my experience, it doesn't make a lot of difference if you leave them in, except it will slow down the analysis.

Basically for the reason discussed above - you have to wait for the plants to grow. If you can collect your phenomic data on seeds or seedlings, then yes, speed can go up a lot. But in all the successful phenomic prediction models we've seen for interesting traits like quality or yield, the phenomics traits have been measured close in time to the target trait, which means waiting as long as you'd have to wait for phenotypic selection. So in these applications, the cycle length will remain slow.

(From Audience): Most of the reports are based on NIRS. Very few have used temporal data, but these seem promising.

(Daniel Runcie): Yes, I agree these data are likely useful, but again collecting temporal data requires waiting for most (or all) of the season before you can plug values into your prediction model, so you won't gain in speed.

First question: The 2x was supposed to refer to speed (1/2 cycle length) as an example of a realistic shortening of cycles relative to phenotyping selection. But you're right this won't necessarily translate to gain for various reasons including potentially reduced accuracy or reduced variance. My point was that it's much easier to construct Genomic Selection (GS) schemes with fold-changes in speed than Phenomic Selection (PS) schemes with fold-changes in speed. As discussed above, most successful phenomic prediction models have required phenomics data from late in the field seasons. One use of genomic selection even without seed chipping is speed breeding, where you just grow plants long enough to make crosses without worrying about trying to phenotype them.

(Follow-Up Question): I agree with Dr. Dekkers that genomic selection with 2x gains has been seen in livestock and is transformative when you have to wait for years in sexual maturity to evaluate progeny, same with trees. But in terms of something like strawberries or annual crops, I have not seen an actual breeding program show 2x gain in elite material, have you found reports of this?

(Daniel Runcie): I have not! But I'm not sure the gains that large commercial corn companies are achieving using genomic selection. But even in annual crops, varieties are often screened over multiple field seasons, and sometimes require early-year growouts to increase seed. This means it still takes several years to complete a cycle.

Second question: There is some concern that GS will erode genetic variation. But I think this is not necessarily the case. Some discussion is here: https://doi.org/10.1093/bfgp/elq001, showing that GS is expected to maintain greater genetic variation than pedigree selection. One possible advantage of genomic selection schemes is that you can directly measure genetic relatedness and make selection decisions that explicitly try to maintain diversity which is harder to do with phenotypic/phenomic selection schemes.

(Response from Audience): Re: Second question: It has been awhile since I read this article but I think you are referring to the discussion at the end (e.g. Goddard) I am not aware of anyone implementing these considerations, as it would be more difficult and likely would not choose the top individuals, are you? In PS, I feel we will likely be selecting more on favorable genomic epistatic combinations (additive and others, which we know are pervasive but hard to measure - see Trudy Mackays review) over pure additive effects that don't show epistasis (which are rare). If this is the case PS would likely maintain genetic diversity better than GS. But this is a hypothesis, and one that would be difficult to test.

(Daniel Runcie): Good points! But that applies mostly to variety release, I think, because these epistatic combinations aren't going to be very helpful for improving the breeding population across generations because they don't contribute to breeding values.

Yes, but that equally applies to genomic data as well. Markers that are not in high LD with causal markers are effectively noise, and genotyping errors are possible too.

The ecophysiological model would get better measures of GxExM, but I think would not necessarily help estimate breeding values directly. Breeding values average over ExM, so predicting GxExM itself isn't really the right goal for population advancement. You'll still need a genetic inheritance model to get estimated breeding values.

I'm referring to narrow-sense heritability, the correlation between observed traits and true breeding values. Part of the reason that traits differ from breeding values is environmental variability (say microenvironmental variation among plots with different genotypes across a single field). But non-additive genetic variation among the lines, and GxE across different environments in the TPE also reduces narrow-sense heritability. If we knew true breeding values, we'd use them to train our genomic prediction models. We don't so we have to use trait values instead. But the more correlated those trait values are to the actual breeding values, the better the genomic prediction models will be. This means the higher the narrow-sense heritability, the better the genomic prediction model.

Yes, that's something that I've wondered about when trying to turn phenomics data into breeding decisions. The idea of Phenomic Prediction is to let a machine learning model figure out what traits are useful. As long as you do the model training/testing carefully, it should work. This means that your training data should be a fair sample of the whole population you're trying to predict, which in practice means that the correlations between the phenomic traits and with the target traits should be the same between the training and target populations.

Yes, this should be possible. I think this should be a reasonable way to increase the heritability of traits you can't measure in every environment. Having predictions in every environment would allow you to do better genomic (or pedigree-based) selection on the average of these traits (i.e. their genetic values).

I agree that Phenomic Prediction should continue to improve and there is an opportunity for innovation there. I expect that eventually it will be possible to predict the final performance of a plant very early in its development, and probably to predict its performance in other environments as well. More generally, I think phenomic models should be possible that greatly reduce the amount of difficult phenotyping that's needed. But I also think that at the end, this is still just phenotyping. We need good phenotypes to do breeding. But you don't want to select on phenotypes themselves, regardless of how good they are. If you have a pedigree or better genetic marker data, you should use them to turn your good phenotypes into better-estimated breeding values.

Certainly, the qualtrics survey is locatred at: https://bit.ly/3Tl7IFO