Genomic prediction using a reference population of multiple pure breeds and admixed individuals

Karaman, Emre; Su, Guosheng; Croué, Iola; Lund, Mogens Sandø

doi:10.1186/s12711-021-00637-y

Cited by 31 publications

(50 citation statements)

References 63 publications

(133 reference statements)

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“…The heritabilities of traits varied around 0.4 for the individual breeds at G0, due to the use of a single value of for all breeds. The simulation was similar to that described in more detail in Karaman et al [ 22 ].…”

Section: Methodsmentioning

confidence: 78%

“…The correlation between GEBV for crossbred animals, which was calculated with the estimated marker effects for HOL and JER respectively, was 0.07 on average across the replicates, 0.14 between HOL and RDC, and 0.07 between JER and RDC. These differences reflect the relatedness between the breeds since the base generations of the purebreds (G0) in the simulations were real genotypes from the breeds [ 22 ]. In CRS_G2, the marker alleles at approximately half of the loci were from HOL and RDC, and at the other half from JER and RDC.…”

Section: Discussionmentioning

confidence: 99%

“…For assessing the BOA assignment using the AllOr method and the prediction reliability of the prediction models for crossbred cows, we simulated genotypes, breeding values, and phenotypes of three dairy cattle breeds, Holstein (HOL), Jersey (JER) and Red Dairy Cattle (RDC), and their crosses in four generations of a rotational crossbreeding system as explained below. The simulations were performed with self-written Julia [ 21 ] scripts, based on the simulation tools described in Karaman et al [ 22 ]. Subsets of 2100 animals genotyped with the 50K SNP chip from each of the three dairy cattle populations, Danish Holstein, Danish Jersey and Swedish Red, were used as base populations to simulate three pure breeds, HOL, JER and RDC, respectively, and their crosses (CRS) for four non-overlapping generations (G1 to G4).…”

Section: Methodsmentioning

confidence: 99%

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Breed of origin of alleles and genomic predictions for crossbred dairy cows

Eiríksson

Karaman

et al. 2021

Genet Sel Evol

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Background In dairy cattle, genomic selection has been implemented successfully for purebred populations, but, to date, genomic estimated breeding values (GEBV) for crossbred cows are rarely available, although they are valuable for rotational crossbreeding schemes that are promoted as efficient strategies. An attractive approach to provide GEBV for crossbreds is to use estimated marker effects from the genetic evaluation of purebreds. The effects of each marker allele in crossbreds can depend on the breed of origin of the allele (BOA), thus applying marker effects based on BOA could result in more accurate GEBV than applying only proportional contribution of the purebreds. Application of BOA models in rotational crossbreeding requires methods for detecting BOA, but the existing methods have not been developed for rotational crossbreeding. Therefore, the aims of this study were to develop and test methods for detecting BOA in a rotational crossbreeding system, and to investigate methods for calculating GEBV for crossbred cows using estimated marker effects from purebreds. Results For detecting BOA in crossbred cows from rotational crossbreeding for which pedigree is recorded, we developed the AllOr method based on the comparison of haplotypes in overlapping windows. To calculate the GEBV of crossbred cows, two models were compared: a BOA model where marker effects estimated from purebreds are combined based on the detected BOA; and a breed proportion model where marker effects are combined based on estimated breed proportions. The methods were tested on simulated data that mimic the first four generations of rotational crossbreeding between Holstein, Jersey and Red Dairy Cattle. The AllOr method detected BOA correctly for 99.6% of the marker alleles across the four crossbred generations. The reliability of GEBV was higher with the BOA model than with the breed proportion model for the four generations of crossbreeding, with the largest difference observed in the first generation. Conclusions In rotational crossbreeding for which pedigree is recorded, BOA can be accurately detected using the AllOr method. Combining marker effects estimated from purebreds to predict the breeding value of crossbreds based on BOA is a promising approach to provide GEBV for crossbred dairy cows.

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Section: Methodsmentioning

confidence: 78%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Breed of origin of alleles and genomic predictions for crossbred dairy cows

Eiríksson

Karaman

et al. 2021

Genet Sel Evol

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“…where I is an identity matrix of size equal to k s if k s > 1 or 1 when k s = 1, and B S is as follows (Karaman et al, 2021):…”

Section: Statistical Modelsmentioning

confidence: 99%

“…However, when breeds are distantly related or unrelated, it is more appropriate to use a BOA model. A model that assumes that the marker effects are different between breeds but correlated (Varona et al, 2010;Karaman et al, 2021) is expected to perform well in both situations. This approach allows differentiating between the marker effects according to their breed origin, maximizing the use of the information of each breed.…”

Section: Introductionmentioning

confidence: 99%

Genomic prediction in Nordic Red dairy cattle considering breed origin of alleles

Guillenea

Lund

et al. 2022

Journal of Dairy Science

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This study investigated the reliability of genomic prediction (GP) using breed origin of alleles (BOA) approach in the Nordic Red (RDC) population, which has an admixed population structure. The RDC population consists of animals with varying degrees of genetic materials from the Danish Red (RDM), Swedish Red (SRB), Finnish Ayrshire (FAY), and Holstein (HOL) because bulls have been used across the breeds. The BOA approach was tested using 39,550 RDC animals in the reference population and 11,786 in the validation population. Deregressed proofs (DRP) of milk, fat and protein were used as response variable for GP. Direct genomic breeding values (DGV) for animals in the validation population were calculated with (BOA model) or without (joint model) considering breed origin of alleles. The joint model assumed homogeneous marker effects and a single set of marker effects were estimated, whereas BOA model assumed heterogeneous marker effects, and different sets of marker effects were estimated across the breeds. For the BOA approach, we tested scenarios assuming both correlated (BOA_cor) and uncorrelated (BOA_uncor) marker effects between the breeds. Additionally, we investigated GP using a standard Illumina 50K chip and including SNP selected from imputed whole-genome sequencing (50K+WGS). We also studied the effect of estimating (co)variances for genome regions of different sizes to exploit the information of the genome regions contributing to the (co) variance between the breeds. Region sizes were set as 1 SNP, a group of 30 or 100 adjacent SNP, or the whole genome. Reliability of DGV was measured as squared correlations between DGV and DRP divided by the reliability of DRP. Across the 3 traits, in general, RS30 and RS100 SNP yielded the highest reliabilities. Including WGS SNP improved reliabilities in almost all scenarios (0.297 on average for 50K and 0.307 on average for 50K+WGS). The BOA_uncor (0.233 on average) was inferior to the joint model (0.339 on average), but the reliabilities obtained using BOA_cor (0.334 on average) in most cases were not significantly different from those obtained using the joint model. The results indicate that both including additional whole-genome sequencing SNP and dividing the genome into fixed regions improve GP in the RDC. The BOA models have the potential to increase the reliability of GP, but the benefit is limited in populations with a high exchange of genetic material for a long time, as is the case for RDC.

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Genome‐wide marker effect heterogeneity is associated with a large effect dormancy locus in winter malting barley

2022

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Prediction of trait values in plant breeding populations typically relies on assumptions about marker effect homogeneity across populations. Evidence is presented for winter malting barley (Hordeum vulgare L.) germination traits that a single, causative, largeeffect gene in the Seed dormancy 1 region on Chromosome 5H, HvAlaAT1 (Qsd1), leads to heterogeneous estimated marker effects genome wide between groups of otherwise related individuals carrying different Qsd1 alleles. This led to reduced prediction accuracy across alleles when a model was trained either on individuals carrying both alleles or one allele. Several genomic prediction models were tested to increase prediction accuracy within the Qsd1 allele groups. Small gains (5-12%) in prediction accuracy were realized using structured genomic best linear unbiased predictor models when information about the Qsd1 allele was used to stratify the population. We concluded that a single large-effect locus can lead to heterogeneous marker effects in the same breeding family. Variance partitioning based on largeeffect loci can be used to inform best practices in designing genomic prediction models; however, there are likely few cases for which it may be practical to do this.For malting barley, if germination traits are highly associated with malting quality traits, then similar steps should be considered for malting quality trait prediction.

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Genomic prediction using a reference population of multiple pure breeds and admixed individuals

Cited by 31 publications

References 63 publications

Breed of origin of alleles and genomic predictions for crossbred dairy cows

Breed of origin of alleles and genomic predictions for crossbred dairy cows

Genomic prediction in Nordic Red dairy cattle considering breed origin of alleles

Genome‐wide marker effect heterogeneity is associated with a large effect dormancy locus in winter malting barley

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