Work

Accuracy of local ancestry inference and its impact on genomic prediction in admixed dairy cattle populations

Year: 2025

Type: article

Abstract: Accurate local ancestry (LA) inference is important for genomic evaluations in admixed dairy cattle.This study had 2 main objectives: to compare the performance of 3 LA inference software: AllOr, BreedOrigin, and Chro-moPainter, and to evaluate the impact of LA inference errors on genomic prediction accuracy.Two simulated admixed populations were used: a structured DairyCross population created through three-way rotational crossbreeding and a Red Dairy cattle (RDC) population representing historical admixture among multiple breeds.For LA software evaluation, true breed-of-origin labels were available to benchmark accuracy in LA inference.To assess robustness of genomic prediction, we varied heritability (h 2 = 0.1 or 0.4), QTL effect correlation (ρ = 1.0 or 0.3), and the proportion of LA assignment errors at 3 levels (1%, 5%, and 10%) across the genome, referring to the fraction of alleles misassigned.Two prediction methods that incorporate LA information were tested: (1) the breed-of-origin method (BOM), which uses purebred phenotypes, and (2) the breed-of-origin of alleles method (BOA), which uses both purebred and crossbred phenotypes, to estimate SNP effects.We also employed a joint SNP-BLUP method (Joint) as a baseline prediction method assuming the same SNP effects across breeds.Results based on simulated data showed that ChromoPainter achieved the highest LA inference accuracy (RDC 97.98%, DairyCross 99.93%) among the software but required substantially more computing time than acceptable in practice.BreedOrigin was the fastest.AllOr and BreedOrigin performed comparably well in LA inference accuracy (RDC up to 96.71% and 97.04%; DairyCross up to 99.45% and 99.81%, respectively).In genomic prediction, both BOM and BOA methods were robust to 1% to 10% LA assignment errors, with prediction accuracy declining by ~0.5% to 5.7% (BOM) and ~0.9% to 7.3% (BOA).The BOA consistently outperformed BOM (at h 2 = 0.1, +15.1%-18.0%;at h 2 = 0.4, ~+8.8%), especially when ρ = 0.3, reflecting BOA's use of crossbred phenotypes in the training set.However, BOA did not always exceed Joint: under ρ = 0.1, Joint was similar to or slightly better than BOA in prediction accuracy (BOA 0.754-0.804vs. Joint 0.799-0.805across 0-10% LA assignment error), whereas under ρ = 0.3, BOA was generally higher.Overall, the results provide software guidelines for LA inference and support LA-informed prediction, showing that the performance depends on genetic architecture and is only slightly affected by moderate LA inference errors.

Source: Journal of Dairy Science

DOI: 10.3168/jds.2025-27019