<scp>BIBI</scp>: Bayesian inference of breed composition

Martínez, Carlos Alberto; Khare, Kshitij; Elzo, Mauricio A.

doi:10.1111/jbg.12305

Cited by 3 publications

(4 citation statements)

References 23 publications

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“…The adjustments for BBR force genomic estimates for each animal to sum to 100%, with no estimates lower than 0% or higher than 100%. The adjustments limit estimates to be within the parameter space as in Martínez et al (2018), simplify interpretation, and provide better weights to combine marker effects for traits with phenotypes; however, all ancestors are assumed to be from breeds that have a GBC reference population. The mathematical steps for adjusting GBC to obtain BBR are as follows: (1) sum GBC across breeds; (2) adjust GBC mean by subtracting from each GBC value the sum of GBC divided by number of breeds (N b ); (3) obtain the range of adjusted GBC from maximum adjusted breed GBC and minimum adjusted breed GBC; (4) compute an adjustment for standard deviation (SD) if any adjusted GBC are higher than 100 or lower than 0: maximum of…”

Section: Genomic Breed Compositionmentioning

confidence: 99%

Genomic predictions for crossbred dairy cattle

VanRaden

Tooker

Chud

et al. 2020

Journal of Dairy Science

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to be predicted. Estimates of breed composition were adjusted so that no percentages were negative or exceeded 100%, and breed percentages summed to 100%. Another adjustment set percentages above 93.5% equal to 100%, and the resulting value was termed breed base representation (BBR). Larger percentages of missing alleles were imputed by using a crossbred reference population rather than only the closest purebred reference population. Crossbred predictions were averages of genomic predictions computed using marker effects for each pure breed, which were weighted by the animal's BBR. Marker and polygenic effects were estimated separately for each breed on the all-breed scale instead of within-breed scales. For crossbreds, genomic predictions weighted by BBR were more accurate than the average of parents' breeding values and slightly more accurate than predictions using only the predominant breed. For purebreds, single-trait predictions using only within-breed data were as accurate as multi-trait predictions with allele effects in different breeds treated as correlated effects. Crossbred genomic predicted transmitting abilities were implemented by the Council on Dairy Cattle Breeding in April 2019 and will aid producers in managing their breeding programs and selecting replacement heifers.

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Section: Genomic Breed Compositionmentioning

confidence: 99%

Genomic predictions for crossbred dairy cattle

VanRaden

Tooker

Chud

et al. 2020

Journal of Dairy Science

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“…GBC reflects the genomic contribution of each ancestral breed to the genome of the test animal. Multiple methods can be used to estimate the GBC of crossbreds, such as linear models, supervised admixture models [ 44 ], and Bayesian inference of breed composition (BIBI) methods [ 45 ]. He et al [ 41 ] reported high correlations between GBC that were calculated from a linear model and those from an admixture model.…”

Section: Discussionmentioning

confidence: 99%

A web tool for the global identification of pig breeds

et al. 2023

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Background Natural and artificial selection for more than 9000 years have led to a variety of domestic pig breeds. Accurate identification of pig breeds is important for breed conservation, sustainable breeding, pork traceability, and local resource registration. Results We evaluated the performance of four selectors and six classifiers for breed identification using a wide range of pig breeds (N = 91). The internal cross-validation and external independent testing showed that partial least squares regression (PLSR) was the most effective selector and partial least squares-discriminant analysis (PLS-DA) was the most powerful classifier for breed identification among many breeds. Five-fold cross-validation indicated that using PLSR as the selector and PLS-DA as the classifier to discriminate 91 pig breeds yielded 98.4% accuracy with only 3K single nucleotide polymorphisms (SNPs). We also constructed a reference dataset with 124 pig breeds and used it to develop the web tool iDIGs (http://alphaindex.zju.edu.cn/iDIGs_en/) as a comprehensive application for global pig breed identification. iDIGs allows users to (1) identify pig breeds without a reference population and (2) design small panels to discriminate several specific pig breeds. Conclusions In this study, we proved that breed identification among a wide range of pig breeds is feasible and we developed a web tool for such pig breed identification.

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“…When considering only one founder breed in the model and an average of allele frequencies for the remaining breeds, the constrained linear regression and ADMIXTURE yielded very similar results [ 8 ], in line with our results. Similarly, a Bayesian method has been proposed that guarantees estimates to be within the parameter space, which showed higher accuracy in a multibreed Angus-Brahman population compared to linear regression, while estimates between both methods had a high correlation of ~ 0.92 [ 10 ].…”

Section: Discussionmentioning

confidence: 99%

“…Empirical validation of methods to estimate line proportions in real data is challenging in the sense that a gold standard is needed to evaluate the estimated line proportions. In unstructured crosses, expected line proportions that can be computed from pedigree data vary across animals, and as such can be used to evaluate the line proportions that are estimated from genomic data [ 6 , 9 , 10 ]. This allows to verify whether the estimated line proportions are unbiased, both in terms of average level and dispersion of the estimates, but not whether they estimate the actual line proportions accurately.…”

Section: Introductionmentioning

confidence: 99%

Estimation of dam line composition of 3-way crossbred animals using genomic information

Calus

Henshall²,

Hawken³

et al. 2022

Genet Sel Evol

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Background In genomic prediction including data of 3- or 4-way crossbred animals, line composition is usually fitted as a regression on expected line proportions, which are 0.5, 0.25 and 0.25, respectively, for 3-way crossbred animals. However, actual line proportions for the dam lines can vary between ~ 0.1 and 0.4, and ignoring this variation may affect the genomic estimated breeding values of purebred selection candidates. Our aim was to validate a proposed gold standard to evaluate different approaches for estimating line proportions using simulated data, and to subsequently use this in actual 3-way crossbred broiler data to evaluate several other methods. Results Analysis of simulated data confirmed that line proportions computed from assigned breed-origin-of-alleles (BOA) provide a very accurate gold standard, even if the parental lines are closely related. Alternative investigated methods were linear regression of genotypes on line-specific allele frequencies, maximum likelihood estimation using the program ADMIXTURE, and the genomic relationship of crossbred animals with their maternal grandparents. The results from the simulated data showed that the genomic relationship with the maternal grandparent was most accurate, and least affected by closer relationships between the dam lines. Linear regression and ADMIXTURE performed similarly for unrelated lines, but their accuracy dropped considerably when the dam lines were more closely related. In almost all cases, estimates improved after adjusting them to ensure that the sum of dam line contributions within animals was equal to 0.5, and within dam line and across animals the average was equal to 0.25. Results from the broiler data were much more similar between methods. In both cases, stringent linkage disequilibrium pruning of genotype data led to a relatively low accuracy of predicted line proportions, due to the loss of too many single nucleotide polymorphisms. Conclusions With relatively unrelated parental lines as typical in crosses in pigs and poultry, linear regression of crossbred genotypes on line-specific allele frequencies and ADMIXTURE are very competitive methods. Thus, linear regression may be the method of choice, as it does not require genotypes of grandparents, is computationally very efficient, and easily implemented and adapted for considering the specific nature of the crossbred animals analysed.

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BIBI: Bayesian inference of breed composition

Cited by 3 publications

References 23 publications

Genomic predictions for crossbred dairy cattle

Genomic predictions for crossbred dairy cattle

A web tool for the global identification of pig breeds

Estimation of dam line composition of 3-way crossbred animals using genomic information

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