HighlanderLab

Xinger (Evie) Tang presented a poster “A Pedigree-Based Method for Localization of Recombination Events”

@Jaime17Ortiz presented a poster “Accounting for uncertainty in Optimal Contribution Selection” - joint work with Josh Fogg, Julian Hall, and @IvanPocrnic

Yu (Uni) Zhang presented a poster “How well must we characterise empirical populations for breeding simulations?”

@hannes_becher presented a poster “Fast pedigree PCA with the randPedPCA R package” - joint work with @epigenci

Ros Craddock talked about “General Pedigree Tracking of Disease-causing Alleles for Recessive Monogenic Diseases in Dogs” joint work with Joanna ilska, Cathryn Mellersh, Pam Wiener, and Audrey Martin

HighlanderLab at @popgroup in Sheffield! It’s always interesting to attend the highly diverse set of talks and posters on population genetics and associated fields at the PopGroup conference. This year it was hosted by the University of Sheffield. We had 2 talks and 4 posters.

🌱 To obtain more realistic long-term projections, we integrated our framework in a phenotypic and genomic selection line breeding program simulation using AlphaSimR and compared outcomes in TPE and MET under different levels of GxE.

📊 To highlight how our framework can aid decision-making for statistical methods, we sampled 1000 MET datasets from large simulated TPEs with different levels of GxE and compared models.

➡️ Embed GxE effects including spatial effects and other terms in the linear mixed model to generate phenotypes for constructing realistic MET datasets.

The framework has four key steps: 1️⃣ Provide real or simulated between-environment genetic variance matrix 2️⃣ Decompose it to obtain k eigenvectors/values