Selection of core animals in the Algorithm for Proven and Young using a simulation model

Bradford, H. L.; Pocrnić, Ivan; Fragomeni, B. O.; Lourenço, Daniela; Misztal, I.

doi:10.1111/jbg.12276

Cited by 30 publications

(33 citation statements)

References 20 publications

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“…Regression coefficients (b1) of adjusted phenotypes on genomic EBV, for different groups of validation animals (purebred animals L1 and L2, and their crosses C) with a different source of phenotypes available, shown for traits 1 (T1) and 2 (T2), under the first model (M1; 2-trait animal model without the distinction between the lines) and second model (M2; when that trait was separated into 3 traits based on the line of the animals) populations in different species (e.g., Pocrnic et al, 2016b); however, the application of this concept for crossbred/multibreed contexts was unclear. Bradford et al (2017) found, by simulating a purebred population, that any core definition is robust in populations with complete pedigree; otherwise, selecting core animals randomly across multiple generations gives desirable accuracies. This is attributed to a random sample that increases the likelihood of all generations of genotyped animals being represented in the core group.…”

Section: Resultsmentioning

confidence: 99%

208 Crossbred evaluations using single-step genomic BLUP and algorithm for proven and young with different sources of data

Pocrnić¹,

Lourenco²,

Chen

et al. 2019

Journal of Animal Science

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Genomic selection is routinely applied to many purebred farm species but can be extended to predictions across purebreds as well as for crossbreds. This is useful for swine and poultry, for which selection in nucleus herds is typically performed on purebreds, whereas the commercial products are crossbreds. Single-step genomic BLUP (ssGBLUP) is a widely applied method that can use algorithm for proven and young (APY), that allows for greater computing efficiency by exploiting the theory of limited dimensionality of genomic information and chromosome segments (Me). This study investigates the predictivity as a proxy for accuracy across and within two purebred pig lines and their crosses, under the application of APY in ssGBLUP setup, and different levels of Me overlapping across populations. The data consisted of approximately 210k phenotypic records for two traits and more than 720k animals in pedigree. Genotypes for 43k SNP were available for 46k animals, from which 26k and 16k belong to purebreds, and 4k to crossbreds. The models included bivariate animal model with three lines evaluated as one joint line, and for each trait individually a three-trait animal model with each line treated as a separate trait. Both models provided the same predictivity across and within the lines. Using either of the pure lines data as a training set resulted in a similar predictivity for the crossbreeds. Across-line predictive ability was limited to less than half of the maximum predictivity for each line. For crossbreds, APY performed equivalently to direct inverse when the number of core animals was equal to the number of eigenvalues explaining 98–99% of the variance of G including all lines. Predictivity across the lines is achievable because of the shared Me between them. The number of those shared segments can be obtained via eigenvalue decomposition of genomic information available for each line.

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Section: Resultsmentioning

confidence: 99%

208 Crossbred evaluations using single-step genomic BLUP and algorithm for proven and young with different sources of data

Pocrnić¹,

Lourenco²,

Chen

et al. 2019

Journal of Animal Science

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show abstract

“…This was successfully demonstrated for several purebred populations in different species (e.g., Pocrnic et al, 2016b); however, the application of this concept for crossbred/multibreed contexts was unclear. Bradford et al (2017) found, by simulating a purebred population, that any core definition is robust in populations with complete pedigree; otherwise, selecting core animals randomly across multiple generations gives desirable accuracies. This is attributed to a random sample that increases the likelihood of all generations of genotyped animals being represented in the core group.…”

Section: Resultsmentioning

confidence: 99%

Crossbred evaluations using single-step genomic BLUP and algorithm for proven and young with different sources of data1

Pocrnić

Lourenço

Chen

et al. 2019

Journal of Animal Science

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Genomic selection ( GS ) is routinely applied to many purebreds and lines of farm species. However, this method can be extended to predictions across purebreds as well as for crossbreds. This is useful for swine and poultry, for which selection in nucleus herds is typically performed on purebred animals, whereas the commercial product is the crossbred animal. Single-step genomic BLUP ( ssGBLUP ) is a widely applied method that can explore the recently developed algorithm for proven and young ( APY ). The APY allows for greater efficiency in computing BLUP solutions by exploiting the theory of limited dimensionality of genomic information and chromosome segments ( Me ). This study investigates the predictivity as a proxy for accuracy across and within 2 purebred pig lines and their crosses, under the application of APY in ssGBLUP setup, and different levels of Me overlapping across populations. The data consisted of approximately 210k phenotypic records for 2 traits (T1 and T2) with moderate heritability. Genotypes for 43k SNP markers were available for approximately 46k animals, from which 26k and 16k belong to 2 pure lines (L1 and L2), and approximately 4k are crossbreds. The complete pedigree had more than 720k animals. Different multivariate ssGBLUP models were applied, either with the regular or APY inverse of the genomic relationship matrix ( G ). The models included a standard bivariate animal model with 3 lines evaluated as 1 joint line, and for each trait individually, a 3-trait animal model with each line treated as a separate trait. Both models provided the same predictivity across and within the lines. Using either of the pure lines data as a training set resulted in a similar predictivity for the crossbreed animals (0.18 to 0.21). Across-line predictive ability was limited to less than half of the maximum predictivity for each line (L1T1 0.33, L1T2 0.25, L2T1 0.35, L2T2 0.36). For crossbred predictions, APY performed equivalently to regular G inverse when the number of core animals was equal to the number of eigenvalues explaining between 98% and 99% of the variance of G (4k to 8k) including all lines. Predictivity across the lines is achievable because of the shared Me between them. The number of those shared segments can be obtained via eigenvalue decomposition of genomic information available for each line.

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“…To form G APY −1 , we randomly chose a core group of genotyped animals. A randomly selected set of core animals has worked well in earlier analyses (Fragomeni et al, 2015;Bradford et al, 2017). Pocrnic et al (2016) found that the choice of core animals did not affect the accuracy of genomic evaluation as long as their number was equivalent to or larger than the number of largest eigenvalues that explained 98% of variation in G. In this study, we set the number of core animals to 18,359, which was determined with the eigenvalues calculated as the squared singular values of the centered marker matrix (say, Z) that includes all genotyped animals formed as formed by VanRaden (2008).…”

Section: Single-mentioning

confidence: 99%

Differing genetic trend estimates from traditional and genomic evaluations of genotyped animals as evidence of preselection bias in US Holsteins

Masuda

VanRaden

Misztal

et al. 2018

Journal of Dairy Science

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The objective of this study was to compare genetic trends from single-step genomic BLUP (ssGBLUP) and traditional BLUP models for milk production traits of US Holsteins. Phenotypes were 305-d milk, fat, and protein yields from 21,527,040 cows recorded between January 1990 and August 2015. The pedigree file included 29,651,623 animals and was limited to 3 generations back from recorded or genotyped animals. Genotypes for 764,029 animals were used, and analyses were by a 3-trait repeatability model as used in the US official genetic evaluation. Unknown-parent groups were incorporated into the inverse of a relationship matrix (H in ssGBLUP and A in BLUP) with the QP transformation. For ssGBLUP, 18,359 genotyped animals were randomly chosen as core animals to calculate the inverse of the genomic relationship matrix with the APY algorithm. Computations took 6.5 h and 1.4 GB of memory for BLUP, and 13 h and 115 GB of memory for ssGBLUP. For genotyped sires with at least 10 daughters, the average genetic levels for predicted transmitting ability (PTA) and genomic PTA were similar up to 2008, with a higher level for ssGBLUP later (approximately by 36 kg for milk, 2.1 kg for fat, and 1.1 kg for protein for bulls born in 2010). For genotyped cows, the average genetic levels were similar up to 2006, with a higher level for ssGBLUP (approximately by 91 kg for milk, 3.6 kg for fat, and 2.7 kg for protein for cows born in 2012). For all cows, the average levels were slightly higher for ssGBLUP, with much smaller differences than for genotyped cows. Trends for BLUP indicate bias due to genomic preselection for genotyped sires and cows. For official evaluations released in December 2016, traditional PTA had the same trend as multiple-step genomic PTA for both genotyped bulls and cows except for the youngest bulls, who had traditional PTA slightly lower than genomic PTA. For genotyped bulls born in recent years, genetic gain for official traditional and genomic evaluations was similar in contrast to ssGBLUP and BLUP differences. Official PTA for cows were adjusted so that the Mendelian sampling variance was comparable with that for bulls, and those adjustments likely removed bias due to genomic preselection from traditional PTA, especially for genotyped cows. The ssGBLUP method seems to account partially for that bias and is computationally suitable for national evaluations.

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Selection of core animals in the Algorithm for Proven and Young using a simulation model

Cited by 30 publications

References 20 publications

208 Crossbred evaluations using single-step genomic BLUP and algorithm for proven and young with different sources of data

208 Crossbred evaluations using single-step genomic BLUP and algorithm for proven and young with different sources of data

Crossbred evaluations using single-step genomic BLUP and algorithm for proven and young with different sources of data1

Differing genetic trend estimates from traditional and genomic evaluations of genotyped animals as evidence of preselection bias in US Holsteins

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