New preprint: "Genomic models for accurate estimation of breeding values in Sitka spruce (Picea sitchensis Bong. Carr)" from the Sitka Spruced project researchsquare.com/article/r…

AlphaSimR course edin.ac/3wfGSEj Week 3 opens with a discussion on genetic variation between relatives, with a focus on siblings. While mutation is the ultimate source of genetic variation (by introducing new alleles), recombination shuffles it through meiosis.

Please share: Registration is now open for EUCARPIA Biometrics in Plant Breeding 2025 highlanderlab.github.io/EUCA…

* Imprinting / gametic models on simulated and real beef data David Lopez Carbonell * Selection for stability in plant breeding Dominic Waters * Tracking inheritance of alleles within a dog pedigree Rosalind Craddock * EUCARPIA Conference organisation highlanderlab.github.io/EUCA…

I feel blessed & hyped after another fantastic HighlanderLab meeting covering: * Selective breeding of artemia Bruna Santana * Selection index (Smith-Hazel, desired gains, economic weights, …) @dantolly19

Lastly I gave a short course on Stochastic simulations of breeding programmes with AlphaSimR - a teaser for our free on-line course edx.org/learn/animal-breedin…

@janaobsteter presented the work of Laura Strachan on Optimizing pedigree reconstruction and patriline determination in honeybees (with Jernej Bubnič and Janez Presern)

@Ireneee_dc presented her work on Modelling the Impact of Non-Native Honey Bee Importation on Native Apis mellifera mellifera Populations (with Laura Strachan, Grace McCormack, Jana Obšteter)

I presented the work of Letícia Lara on Evaluation of selective breeding programme designs for black soldier fly larvae body weight (with María Martínez Castillero, Thiago Oliveira, Ivan Pocrnić, Jana Obšteter)

Last week some of us attended the 1st @EAAPofficial Insect Genetics Workshop, which was organised with COST InsectIMP @CA22140 Action action by @beegirl_nz and @janaobsteter . We presented

In cross-validation for yield prediction, the ARG-based branch relationship matrix (BRM) demonstrated higher predictive ability than the standard site-based relationship matrix (SRM) when combining both subspecies.

GWAS hits with SRM (A) and BRM (B) were similar

(A) The standard site-based relationship matrix (SRM, VanRaden’s) and (B) the ARG-based branch relationship matrix (BRM) revealed similar population structure, with highly correlated (C) diagonal and (D) off-diagonal elements, though on different scales.

The age distributions for (A) nodes (ancestors), (B) mutations, and (C) SNP sites (i.e., first mutation at each site) were heavily right-skewed towards the present (as expected).

The ARG encoded genomic data more efficiently than the standard VCF: the tree sequence file for all chromosomes was 62 MB, compared to 228 MB for the VCF—nearly four times smaller!

Local trees from two genomic regions showed distinct patterns: (A) revealed deep separation between indica and japonica, linked to the DST gene associated with panicle length in japonica. The (B) region segregated in both subspecies and was linked to panicle traits in both.

After building the ARG, we demonstrated it captures biological signals using genealogical nearest neighbors (GNN) - it clearly distinguished indica and japonica rice subspecies and effectively represented population structure.