Reposted by @druncie.bsky.socialbioRxiv Genetics @biorxiv-genetic.bsky.social · 22/01/2026The MexMAGIC population reveals the genetic architecture of clinal trait variation in Mexican native maize www.biorxiv.org/content/10.64898/20… 002
Reposted by @druncie.bsky.socialChenxin Li, PhD @chenxinli2.bsky.social · 13/01/2026My "Friends don't let friends make bad graphs" repo is approaching 7k stars on GitHub. As of this moment, 6962, to be exact. 38 more to go to 7000. github.com/cxli233/Frie...github.comGitHub - cxli233/FriendsDontLetFriends: Friends don't let friends make certain types of data visualization - What are they and why are they bad.Friends don't let friends make certain types of data visualization - What are they and why are they bad. - GitHub - cxli233/FriendsDontLetFriends: Friends don't let friends make certain ty... 0369
druncie.bsky.social @druncie.bsky.social · 09/01/2026Thanks for reading, and I'm happy to get any feedback: www.biorxiv.org/content/10.6... 13/13biorxiv.orgThe use of cross-validation has overestimated the value of genomic selection in plant breedingGenomic Selection (GS) is widely considered to be a transformative approach for plant breeding, and has been a subject of well over a thousand papers since its proposal 25 years ago. The reduced costs... 000
druncie.bsky.social @druncie.bsky.social · 09/01/2026What do you think? - Are there other ways the Genomic Prediction models can be used? - Has the field just "given up" on Recurrent Genomic Selection and decided that focusing on intensity is good enough? - Am I missing Genomic Selection success stories in plant breeding? 12/n 100
druncie.bsky.social @druncie.bsky.social · 09/01/2026In the remainder of the paper, I study why accuracy falls apart more in some GS schemes than others and provide mathematical and graphical diagnostic tools to predict in advance which types of breeding programs are more likely to be able to implement effective GS. 11/n 100
druncie.bsky.social @druncie.bsky.social · 09/01/2026Based on these results, I conclude that cross-validation is not a useful tool for evaluating Genomic Prediction models. We should be evaluating models based on how much they can improve genetic gain, not whether they are "accurate" in contexts that matter little to breeders. 10/n 100
druncie.bsky.social @druncie.bsky.social · 09/01/2026More importantly, in the latter case, the cross-validation estimates are basically uncorrelated with the actual accuracy in most cases. Cross-validation cannot distinguish cases where Genomic Selection will work from cases where it will not. 9/n 100
druncie.bsky.social @druncie.bsky.social · 09/01/2026Here are results for a "typical" case similar to examples from the literature: a model tested in a diverse breeding population of ~400 lines: Cross-validation underestimates accuracy for schemes targeting intensity or accuracy, but usually overestimates accuracy for schemes targeting speed. 8/n 110
druncie.bsky.social @druncie.bsky.social · 09/01/2026So how "bad" are cross-validation estimates? I ran simulations to answer this question: Say you test your GP model by cross-validation and estimate that it has an accuracy of r=0.50. How well is that model likely to work if you use it to improve a) intensity, b) accuracy, or c) speed? 7/n 100
druncie.bsky.social @druncie.bsky.social · 09/01/2026The problem is, most papers (including most of mine!) test their Genomic Prediction models using cross-validation in a way that simulates using it to increase selection intensity. The graphs above show that this is probably the least impactful use of Genomic Prediction models in plant breeding. 6/n 100
druncie.bsky.social @druncie.bsky.social · 09/01/2026However, only targeting speed can have a big effect on genetic gain. In moderate-sized breeding populations, the potential gains for targeting intensity or accuracy < 20-40% unless h2 is very low. But by targeting speed, the rate of gain could increase many-fold! 5/n 100
druncie.bsky.social @druncie.bsky.social · 09/01/2026Based on the breeder's equation, Genomic Selection can improve genetic gain by increasing selection intensity (i), selection accuracy (ρ), or reduce cycle lengths (L). But each of these requires applying Genomic Predictions to entirely different sets of breeding candidates: 4/n 100
druncie.bsky.social @druncie.bsky.social · 09/01/2026But is showing that a Genomic Prediction model is accurate the same as showing that it is useful? Does this prove that it will make Genomic Selection work? It really depends on how Genomic Selection is used. 3/n 100
druncie.bsky.social @druncie.bsky.social · 09/01/2026The literature is full of examples of highly accurate Genomic Prediction models developed for a wide range of crops. Here are accuracy reports from a random sample of papers. These numbers look really good! 2/n 100
druncie.bsky.social @druncie.bsky.social · 09/01/2026How useful is Genomic Prediction really for plant breeders? In this new preprint, I argue that the literature is overselling the value of Genomic Prediction models. www.biorxiv.org/content/10.6... 1/nbiorxiv.orgThe use of cross-validation has overestimated the value of genomic selection in plant breedingGenomic Selection (GS) is widely considered to be a transformative approach for plant breeding, and has been a subject of well over a thousand papers since its proposal 25 years ago. The reduced costs... 100
