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The ARG: * Captured key biological signals * Encoded genomic data more efficiently than conventional formats * Resulted in the highest predictive ability when combining both subspecies (though not by much)

Taken together, the results demonstrated the potential of ARGs for the quantitative genetic analysis of diverse populations.

This builds upon the work of tskit community - there is an advanced manuscript in the pipeline on how to build relationship/relatedness matrix from tree sequence encoding of an ARG!

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.

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.

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!

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).

(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.

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

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.

Last week some of us attended the 1st EAAPofficial Insect Genetics Workshop, which was organised with COST InsectIMP Insect - IMP Action action by Gertje Petersen 🏳️‍🌈🇩🇪 in 🇳🇿 and Jana Obsteter . We presented

Irene De Carlos 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)

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

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)

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-b…

We demonstrate the new software using a pedigree of UK Labradors consisting of roughly 1.5 million observations. (2/n)

It can be tricky to visualise large #pedigrees. We have develped 'randPedPCA' for running #PCA on large pedigrees (can be >1M individuals). Check out our preprint doi.org/10.1101/2025.0… and the R package github.com/HighlanderLab/… Gregor Gorjanc Hanbin Lee Hannes Becher

Please share: Registration is now open for EUCARPIA Biometrics in Plant Breeding 2025 highlanderlab.github.io/EUCARPIA2025Bi…

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

* 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/EUCARPIA2025Bi…