Estimation of litter size variability phenotypes in Large White sows

Dobrzański, Jan .; Mulder, Henk; Knol, E.F.; Szwaczkowski, T.; Sell‐Kubiak, Ewa

doi:10.1111/jbg.12465

Cited by 12 publications

(12 citation statements)

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“…The LnVar is a simpler method than DHGLM to compute variability phenotypes and requires less computation time, making it more suitable for application in breeding programs. The DHGLM method was described in detail by Berghof et al [ 15 ] and Dobrzański et al [ 28 ]. Dobrzański et al [ 28 ] also tested whether accounting for the “parity curve” in litter size, i.e.…”

Section: Discussionmentioning

confidence: 99%

“…The DHGLM method was described in detail by Berghof et al [ 15 ] and Dobrzański et al [ 28 ]. Dobrzański et al [ 28 ] also tested whether accounting for the “parity curve” in litter size, i.e. a change in average litter size in subsequent parities [ 40 ], affected the variability phenotype.…”

Section: Discussionmentioning

confidence: 99%

“…Variability phenotype was obtained as the log-transformed variance of residuals (LnVar) of TNB in two steps. First, to obtain estimates of variance components, the TNB data were analyzed with the following traditional animal model, as previously described and tested by Dobrzański et al [ 28 ], using the ASReml 3.0 package [ 29 ]: where is a vector of phenotypes for TNB; , , and are incidence matrices relating phenotypes to effects; is a vector of the fixed effects of herd-year-season and parity; is a vector of additive genetic effects, with , where is the augmented numerator relationship matrix and is the additive genetic variance; is a vector of permanent environmental sow effects, which accounts for repeated observations per sow, with where is the appropriate identity matrix and is the permanent environmental sow variance; and is a vector of residuals, with , where is the appropriate identity matrix and is the residual variance.…”

Section: Methodsmentioning

confidence: 99%

“…The variability phenotypes from LnVar were analyzed with the following model, as previously used by Berghof et al [ 15 ] and Dobrzański et al [ 28 ]: where is a vector of LnVarTNB phenotypes; and are incidence matrices relating phenotypes to model effects; is a vector of the fixed effects of farm-year-season of the first farrowing; is a vector of additive genetic effects, with , where is the additive genetic variance; and is a vector of residuals, with , where is the appropriate incidence matrix and is the residual variance. To account for differences in residual variance due to the varying numbers of TNB records available per sow, we estimated a unique residual variance for each of the nine groups of sows based on numbers of records (Group 1: 19,670 sows with two litters; Group 2: 18,173 sows with three litters; Group 3: 17,601 sows with four litters; Group 4: 16,868 sows with five litters; Group 5: 15,661 sows with six litters; Group 6: 13,365 sows with seven litters; Group 7: 9516 sows with eight litters; Group 8: 5581 sows with nine litters; and Group 9: 4188 sows with ten or more litters).…”

Section: Methodsmentioning

confidence: 99%

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Evaluation of the phenotypic and genomic background of variability based on litter size of Large White pigs

2022

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Background The genetic background of trait variability has captured the interest of ecologists and animal breeders because the genes that control it could be involved in buffering various environmental effects. Phenotypic variability of a given trait can be assessed by studying the heterogeneity of the residual variance, and the quantitative trait loci (QTL) that are involved in the control of this variability are described as variance QTL (vQTL). This study focuses on litter size (total number born, TNB) and its variability in a Large White pig population. The variability of TNB was evaluated either using a simple method, i.e. analysis of the log-transformed variance of residuals (LnVar), or the more complex double hierarchical generalized linear model (DHGLM). We also performed a single-SNP (single nucleotide polymorphism) genome-wide association study (GWAS). To our knowledge, this is only the second study that reports vQTL for litter size in pigs and the first one that shows GWAS results when using two methods to evaluate variability of TNB: LnVar and DHGLM. Results Based on LnVar, three candidate vQTL regions were detected, on Sus scrofa chromosomes (SSC) 1, 7, and 18, which comprised 18 SNPs. Based on the DHGLM, three candidate vQTL regions were detected, i.e. two on SSC7 and one on SSC11, which comprised 32 SNPs. Only one candidate vQTL region overlapped between the two methods, on SSC7, which also contained the most significant SNP. Within this vQTL region, two candidate genes were identified, ADGRF1, which is involved in neurodevelopment of the brain, and ADGRF5, which is involved in the function of the respiratory system and in vascularization. The correlation between estimated breeding values based on the two methods was 0.86. Three-fold cross-validation indicated that DHGLM yielded EBV that were much more accurate and had better prediction of missing observations than LnVar. Conclusions The results indicated that the LnVar and DHGLM methods resulted in genetically different traits. Based on their validation, we recommend the use of DHGLM over the simpler method of log-transformed variance of residuals. These conclusions can be useful for future studies on the evaluation of the variability of any trait in any species.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Evaluation of the phenotypic and genomic background of variability based on litter size of Large White pigs

2022

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“…The total number of piglets born and the number of born alive are affected by genetic factors of both sows and piglets [32]. However, these traits' heritability is low (ranging from 0.16 to 0.19 and 0.04 to 0.29, respectively) [33,34]. Therefore, they are strongly affected by the environment.…”

Section: Discussionmentioning

confidence: 99%

Effects of Low ω6:ω3 Ratio in Sow Diet and Seaweed Supplement in Piglet Diet on Performance, Colostrum and Milk Fatty Acid Profiles, and Oxidative Status

Nguyen

Agazzi

Comi³

et al. 2020

Animals

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The ratio of omega-6 (ω6) to omega-3 (ω3) polyunsaturated fatty acids (PUFAs) in the diet contributes to animal health and performance modulations because they have mostly opposite physiological functions. Increasing ω3 PUFAs content in the maternal diet can stimulate antioxidative capacity in sow and piglets; however, the optimal ratio of ω6 and ω3 PUFAs in the sow diet is still under discussion. Rich sources of bioactive constituents such as brown seaweed are an excellent supplementation to promote animal health and antioxidant status. However, the knowledge of the effects of this compound, specifically in post-weaning piglets, is still limited. Moreover, the combined effect of a low ω6:ω3 PUFAs ratio in sow diet and seaweed supplementation in post-weaning piglets’ diet has never been studied. This research aims to assess the combined effect of a low ω6:ω3 ratio in sow diets and seaweed supplementation in piglet diets on their growth and oxidative status. We also assessed the impact of a low ω6:ω3 ratio in the maternal diet on reproduction, milk fatty acid (FA) profile, and plasma leptin concentration. Two sow diets (n = 8 each) contained either a control ratio (CR, 13:1 during gestation, starting from day 28 (G28) and 10:1 during lactation) or a low ratio (LR, 4:1 from G28 until the end of lactation (L-End)) of ω6:ω3 FA by adding soybean oil or linseed oil, respectively. Reproductive performance was evaluated. Colostrum and milk at lactation day 7 (L7) and L-End were collected to analyze FA profile. Plasma was collected at G28, G79, G108, L7, L14, and L-End for determination of leptin and oxidative status. At weaning, 20 male piglets were selected per sow group to form 4 diet treatments (n = 10 each), which were supplemented with or without 4 g/kg seaweed. Recording of growth performance and collection of blood were performed at days 0, 7, 15, and 21 of post-weaning for oxidative status. LR diet increased (p < 0.05) the survival rate of piglets at weaning, and individual and litter weight gains. Colostrum and milk at L7 and L-End had lower (p < 0.05) ω6:ω3 ratio in LR sows. Interaction between dietary treatments on sows and piglets was revealed for all examined growth parameters at most time points (p < 0.05). LR diet did not affect plasma leptin levels and oxidative status. These findings suggest that the seaweed supplement during post-weaning could not improve growth rate and oxidative status of piglets born from mothers receiving a low dietary ω6:ω3 ratio (4:1) during gestation and lactation. However, this low ratio was beneficial for weaning survival rate, sucking piglets’ weight gain, and ω3 enrichment in colostrum and milk.

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Selection for litter size and litter birthweight in Large White pigs: Maximum, mean and variability of reproduction traits

Sell‐Kubiak

2021

Animal

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Estimation of litter size variability phenotypes in Large White sows

Abstract: During the last decades, selection for litter size in pigs provided significant improvement of prolificacy (Nielsen,

Cited by 12 publications

References 39 publications

Evaluation of the phenotypic and genomic background of variability based on litter size of Large White pigs

Evaluation of the phenotypic and genomic background of variability based on litter size of Large White pigs

Effects of Low ω6:ω3 Ratio in Sow Diet and Seaweed Supplement in Piglet Diet on Performance, Colostrum and Milk Fatty Acid Profiles, and Oxidative Status

Selection for litter size and litter birthweight in Large White pigs: Maximum, mean and variability of reproduction traits

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