Estimation of variance components and prediction of breeding values based on group records from varying group sizes

Su, Guosheng; Madsen, Per; Nielsen, Bjarne; Ostersen, Tage; Shirali, Mahmoud; Jensen, Just; Christensen, Ole

doi:10.1186/s12711-018-0413-y

Cited by 11 publications

(33 citation statements)

References 21 publications

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“…The estimates of variance components (mean (SD) over 50 replicates) in scenario S 12 L 2 × 3 (group size = 12, individuals from four litters per pen) for trait 1 and trait 2 using multivariate model based on group or individual records with different proportion of individuals having genotypes. Many studies have concluded that closer relationships between individuals in each group results in more accurate EBVs (Olson et al 2006;Peeters et al 2013;Su et al 2018). Consistent results were obtained in our study.…”

Section: Tablesupporting

confidence: 92%

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Prediction of breeding values for group-recorded traits including genomic information and an individually recorded correlated trait

Christensen

Gao

et al. 2020

Heredity

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Records on groups of individuals could be valuable for predicting breeding values when a trait is difficult or costly to measure on single individuals, such as feed intake and egg production. Adding genomic information has shown improvement in the accuracy of genetic evaluation of quantitative traits with individual records. Here, we investigated the value of genomic information for traits with group records. Besides, we investigated the improvement in accuracy of genetic evaluation for group-recorded traits when including information on a correlated trait with individual records. The study was based on a simulated pig population, including three scenarios of group structure and size. The results showed that both the genomic information and a correlated trait increased the accuracy of estimated breeding values (EBVs) for traits with group records. The accuracies of EBV obtained from group records with a size 24 were much lower than those with a size 12. Random assignment of animals to pens led to lower accuracy due to the weaker relationship between individuals within each group. It suggests that group records are valuable for genetic evaluation of a trait that is difficult to record on individuals, and the accuracy of genetic evaluation can be considerably increased using genomic information. Moreover, the genetic evaluation for a trait with group records can be greatly improved using a bivariate model, including correlated traits that are recorded individually. For efficient use of group records in genetic evaluation, relatively small group size and close relationships between individuals within one group are recommended.

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Section: Tablesupporting

confidence: 92%

“…When trait 1 was measured on groups (pens), the model for the group records can be written as shown by Su et al (2018)…”

Section: Univariate Analysismentioning

confidence: 99%

Prediction of breeding values for group-recorded traits including genomic information and an individually recorded correlated trait

Christensen

Gao

et al. 2020

Heredity

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“…In this study, we have reported variance components and genetic parameters of several measurements of feed efficiency obtained from a model that combines group/cage records of FI and individual records of G and M, under two different feeding regimens commonly applied in rabbit meat production farms. This procedure overcomes difficulties for identification of genetic and environmental random effects of FI when group records are used, as was discussed by Su et al (). In addition, it takes advantage of the definition of FE traits as selection indexes that can be obtained from multiple‐trait genetic evaluations (Kennedy et al, ).…”

Section: Discussionmentioning

confidence: 99%

“…Even in the situation in which electronic feeders are available, it is interesting to explore other sources of information which are less expensive than FI records obtained with them, as it could be FI recorded at the group level (Su et al, ). Several studies have reported models for the estimation of genetic parameters and variance components of FI using group data (Biscarini, Bovenhuis, & Arendonk, ; Cooper, Ferrell, Cundiff, & Vleck, ; Olson, Garrick, & Enns, ; Shirali et al, ; Su et al, ) but only Shirali et al () combine individual records of production traits and group records of FI in a single‐trait model defining phenotypic RFI from a phenotypic regression model of cage FI on body weight of each of the two cage mates. This situation is similar to ours but in our case, given that groups are larger (8 cage mates), the number of available cage records is limited (321).…”

Section: Discussionmentioning

confidence: 99%

“…Only Shirali, Nielsen, Moller, and Jensen () used group records of FI to estimate genetic parameters of the classical definition of RFI using a single‐trait model with different (but correlated) genetic and permanent effects for each cage mate, which could be considered a different approach. The opportunity of using group records is important because measurement of FI at the group level is feasible and cheaper than individual recording due to the expensive equipment required (Su et al, ).…”

Section: Introductionmentioning

confidence: 99%

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Use of group records of feed intake to select for feed efficiency in rabbit

Piles

Sánchez

2019

J Animal Breeding Genetics

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Models for genetic evaluation of feed efficiency (FE) for animals housed in groups when they are either fed ad libitum (F) or on restricted (R) feeding were implemented. Definitions of FE on F included group records of feed intake (boldFI¯F) and individual records of growth rate (GF) and metabolic weight (MF). Growth rate (GR) as FE measurement on R was used. Data corresponded to 5,336 kits from a rabbit sire line, from 1,255 litters in 14 batches and 667 cages. A five‐trait mixed model (also with metabolic weight on R, MR) was implemented including, for each trait, the systematic effects of batch, body weight at weaning, parity order and litter size; and the random effects of litter, additive genetic and individual. A Bayesian analysis was performed. Conditional traits such as FI¯normalFfalse|normalMnormalF,normalGnormalFand normalGnormalFfalse|normalMnormalF,FI¯normalF were obtained from elements of additive genetics (normalFI¯F|MF,GFg and GF|MF,normalFI¯Fg) or phenotypic (normalFI¯F|MF,GFp and GF|MF,normalFI¯Fp) (co)variance matrices. In the first case, heritabilities were low (0.07 and 0.06 for normalFI¯F|MF,GFgand GF|MF,normalFI¯Fg, respectively) but null genetic correlation between the conditional and conditioning traits is guaranteed. In the second case, heritabilities were higher (0.22 and 0.16 for normalFI¯F|MF,GFpand GF|MF,normalFI¯Fp, respectively) but the genetic correlation between normalFI¯F|MF,GFpand GFwas moderate (0.58). Heritability of GR was low (0.08). This trait was negatively correlated with GF|MF,normalFI¯Fpand GF|MF,normalFI¯Fgof animals on F, which indicate a different genetic background. The correlation between GR and GF was also low to moderate (0.48) and the additive variance of GF was almost four times that of GR, suggesting the presence of a substantial genotype by feeding regimen interaction.

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Combined analysis of group recorded feed intake and individually recorded body weight and litter size in mink

Madsen

Villumsen

Hansen

et al. 2020

Animal

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In the mink industry, feed costs are the largest variable expense and breeding for feed efficient animals is warranted. Implementation of selection for feed efficiency must consider the relationships between feed efficiency and the current selection traits BW and litter size. Often, feed intake (FI) is recorded on a cage with a male and a female and there is sexual dimorphism that needs to be accounted for. Study aims were to (1) model group recorded FI accounting for sexual dimorphism, (2) derive genetic residual feed intake (RFI) as a measure of feed efficiency, (3) examine the relationship between feed efficiency and BW in males (BWM) and females (BWF) and litter size at day 21 after whelping (LS21) in Danish brown mink and (4) investigate direct and correlated response to selection on each trait of interest. Feed intake records from 9574 cages, BW records on 16 782 males and 16 875 females and LS21 records on 6446 yearling females were used for analysis. Genetic parameters for FI, BWM, BWF and LS21 were obtained using a multivariate animal model, yielding sex-specific additive genetic variances for FI and BW to account for sexual dimorphism. The analysis was performed in a Bayesian setting using Gibbs sampling, and genetic RFI was obtained from the conditional distribution of FI given BW using genetic regression coefficients. Responses to single trait selection were defined as the posterior distribution of genetic superiority of the top 10% of animals after conditioning on the genetic trends. The heritabilities ranged from 0.13 for RFI in females and LS21 to 0.59 for BWF. Genetic correlations between BW in both sexes and LS21 and FI in both sexes were unfavorable, and single trait selection on BW in either sex showed increased FI in both sexes and reduced litter size. Due to the definition of RFI and high genetic correlation between BWM and BWF, selection on RFI did not significantly alter BW. In addition, selection on RFI in either sex did not affect LS21. Genetic correlation between sexes for FI and BW was high but significantly lower than unity. The high correlations across sex allowed for selection on standardized averages of animals’ breeding values (BVs) for RFI, FI and BW, which yielded selection responses approximately equal to the responses obtained using the sex-specific BVs. The results illustrate the possibility of selecting against RFI in mink with no negative effects on BW and litter size.

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Estimation of variance components and prediction of breeding values based on group records from varying group sizes

Cited by 11 publications

References 21 publications

Prediction of breeding values for group-recorded traits including genomic information and an individually recorded correlated trait

Prediction of breeding values for group-recorded traits including genomic information and an individually recorded correlated trait

Use of group records of feed intake to select for feed efficiency in rabbit

Combined analysis of group recorded feed intake and individually recorded body weight and litter size in mink

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