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Knowledge remains limited on genetic variation and genetic correlations for traits in sows and piglets that are reared in an organic or outdoor setting. Here, we estimated genetic variance components for individual piglet weight, litter weight, litter size traits, and number of functional teats in a pig population raised under outdoor organic conditions. Data were collected from the largest organic multiplier farm in Denmark. Individual piglet weight was recorded at birth and on day 10. Number of live and dead piglets were recorded at birth, day 4, and day 11. Mean and total litter weight were calculated based on the individual weight of living piglets at birth and on day 10. The estimated heritability was highest for the number of functional teats (0.49), mean weight of a litter at birth (0.33) and on day 10 (0.25). In contrast, heritability was lowest for litter size traits (0.04-0.08) and piglet weight (0.06-0.07). Maternal heritability was much higher for individual piglet weight than direct heritability. The results showed that selection for higher mean weight results in smaller litters. Also, selection for individual birth weight of piglets results in heavier piglets at 10 days. In conclusion, this study confirmed that there is genetic variation in individual piglet weight, litter traits, and number of functional teats in organically and outdoor-reared pigs., (Copyright © 2023 The Author(s). Published by Elsevier B.V. All rights reserved.)

Selection for the number of living pigs on day 11 (L11) aims to reduce piglet mortality and increase litter size simultaneously. This approach could be sub-optimal, especially for organic pig breeding. This study evaluated the effect of selecting for a trait by separating it into two traits. Genetic parameters for L11, the total number born (TNB), and the number of dead piglets at day 11 (D11) were estimated using data obtained from an organic pig population in Denmark. Based on these estimates, two alternative breeding schemes were simulated. Specifically, selection was made using: (1) a breeding goal with L11 only versus (2) a breeding goal with TNB and D11. Different weightings for TNB and D11 were tested. The simulations showed that selection using the first breeding scheme (L11) produced lower annual genetic gain (0.201) compared to the second (TNB and D11; 0.207). A sensitivity analysis showed that the second scheme performed better because it exploited differences in heritability, and accounted for genetic correlations between the two traits. When the second breeding scheme placed more emphasis on D11, D11 declined, whereas genetic gain for L11 remained high (0.190). In conclusion, selection for L11 could be optimized by separating it into two correlated traits with different heritability, reducing piglet mortality and enhancing L11.

Piglet mortality from farrowing to weaning is a major concern, especially in outdoor organic production systems. This issue might impair animal welfare and generate economic losses for the farmer. In particular, it is difficult to apply management tools that are commonly used for indoor pig production systems to organic or outdoor production systems. Genetics and breeding approaches might be used to improve piglet survival. However, knowledge remains limited on the genetic background underlying survival traits in organic pigs that are born and reared outdoors. Here, we investigated the mortality of piglets from farrowing to weaning in an outdoor organic pig population and suggested genetic strategies to reduce piglet mortality in this production system. The experiment included mortality records of piglets from farrowing to weaning (around 69 days of age). Pedigree-based threshold models were used to analyse the mortality traits of piglets at 0-3 days of age, 4-11 days, and 12 days to weaning. Stillborn piglets were included in the group of piglets that died at 0-3 days of age. We found that the mortality rate from farrowing to weaning was, on average, 19.2%. However, most piglet deaths (79.1%) occurred at 0-11 days of age. As the age of piglets increased, the direct heritability of piglet mortality rose from 0 to 0.04, whereas maternal heritability decreased from 0.03 to a non-significant value. Piglets with higher BW had a lower mortality rate. However, the genetic correlations between maternal effects on piglet mortality and piglet BW were not significant; thus, selection for piglets with higher BW at around 10 days of age, through improving maternal genetics, would not reduce piglet mortality. Piglet mortality increased from sows with increasing number of parities. Crossbreeding also reduced piglet mortality. In conclusion, selection focusing on sow genotype, the use of younger sows, and crossbreeding could contribute to maintain piglet mortality at lower levels in outdoor organic pig production systems., Competing Interests: Declaration of interest At the time of study, Henrik Bovbjerg was the owner of Bovbjerg Økologi (Denmark). The remaining authors declare no conflicts of interest., (Copyright © 2022 The Author(s). Published by Elsevier B.V. All rights reserved.)

Current organic pig-breeding programs use pigs from conventional breeding populations. However, there are considerable differences between conventional and organic production systems. This simulation study aims to evaluate how the organic pig sector could benefit from having an independent breeding program. Two organic pig-breeding programs were simulated: one used sires from a conventional breeding population (conventional sires), and the other used sires from an organic breeding population (organic sires). For maintaining the breeding population, the conventional population used a conventional breeding goal, whereas the organic population used an organic breeding goal. Four breeding goals were simulated: one conventional breeding goal, and three organic breeding goals. When conventional sires were used, genetic gain in the organic population followed the conventional breeding goal, even when an organic breeding goal was used to select conventional sires. When organic sires were used, genetic gain followed the organic breeding goal. From an economic point of view, using conventional sires for breeding organic pigs is best, but only if there are no genotype-by-environment interactions. However, these results show that from a biological standpoint, using conventional sires biologically adapts organic pigs for a conventional production system.

Genomic selection relies on single-nucleotide polymorphisms (SNPs), which are often collected using medium-density SNP arrays. In mink, no such array is available; instead, genotyping by sequencing (GBS) can be used to generate marker information. Here, we evaluated the effect of genomic selection for mink using GBS. We compared the estimated breeding values (EBVs) from single-step genomic best linear unbiased prediction (SSGBLUP) models to the EBV from ordinary pedigree-based BLUP models. We analyzed seven size and quality traits from the live grading of brown mink. The phenotype data consisted of ~20,600 records for the seven traits from the mink born between 2013 and 2016. Genotype data included 2,103 mink born between 2010 and 2014, mostly breeding animals. In total, 28,336 SNP markers from 391 scaffolds were available for genomic prediction. The pedigree file included 29,212 mink. The predictive ability was assessed by the correlation (r) between progeny trait deviation (PTD) and EBV, and the regression of PTD on EBV, using 5-fold cross-validation. For each fold, one-fifth of animals born in 2014 formed the validation set. For all traits, the SSGBLUP model resulted in higher accuracies than the BLUP model. The average increase in accuracy was 15% (between 3% for fur clarity and 28% for body weight). For three traits (body weight, silky appearance of the under wool, and guard hair thickness), the difference in r between the two models was significant (P < 0.05). For all traits, the regression slopes of PTD on EBV from SSGBLUP models were closer to 1 than regression slopes from BLUP models, indicating SSGBLUP models resulted in less bias of EBV for selection candidates than the BLUP models. However, the regression coefficients did not differ significantly. In conclusion, the SSGBLUP model is superior to conventional BLUP model in the accurate selection of superior animals, and, thus, it would increase genetic gain in a selective breeding program. In addition, this study shows that GBS data work well in genomic prediction in mink, demonstrating the potential of GBS for genomic selection in livestock species., (© The Author(s) 2021. Published by Oxford University Press on behalf of the American Society of Animal Science.)

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.

Danish and European legislation recommend mink breeding programs that include selection for "confidence," defined as exploratory activity in a standardized behavioral test. Although this recommendation may improve mink welfare, farmers may consider this criterion risky due to possible negative consequences on other traits. The overall objectives of this study were to estimate the heritability of exploratory/fearful behavior and to identify genetic correlations with other traits of major economic importance in mink fur production. Various aspects of social influence on exploratory/fearful behavior, such as effects of the mother and litter siblings before weaning, the mother's age, and cage mates after weaning, were analyzed. In total, 26,371 1-yr-old Brown mink (Neovison vison) individuals born during the period of 2013 to2016 were included in the study. Exploratory/fearful behavior was the main trait analyzed. The production traits analyzed were live pelt quality and body weight. Both of these traits were assessed during live grading in November. Pelt length and quality were determined using the dried pelts of nonbreeders. Fertility data were obtained from the Fur Farm database. Linear mixed models were run using the restricted maximum-likelihood method. The genetic correlation between female and male behavior was 0.95 (SE = 0.06), indicating similar genetic backgrounds for both sexes (P = 0.40). For both sexes, the estimated heritability of behavior was 0.19 (SE = 0.03). We found no significant genetic correlation between behavior and production/fertility traits (P > 0.05). Common litter variance indicated a preweaning effect of litter mates and/or dam on postweaning temperament. There was a tendency for offspring from older mothers to explore more than offspring from 1-yr-old mothers. This trend was especially pronounced for males of 2-yr-old mothers (P = 0.05) and females of 4-yr-old mothers (P = 0.06). We conclude that confidence may be selected for among farm mink without detrimental effects on economically important production traits, such as pelt quality and fertility., (© The Author(s) 2019. Published by Oxford University Press on behalf of the American Society of Animal Science. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Background: Identification of genes underlying production traits is a key aim of the mink research community. Recent availability of genomic tools have opened the possibility for faster genetic progress in mink breeding. Availability of mink genome assembly allows genome-wide association studies in mink., Results: In this study, we used genotyping-by-sequencing to obtain single nucleotide polymorphism (SNP) genotypes of 2496 mink. After multiple rounds of filtering, we retained 28,336 high quality SNPs and 2352 individuals for a genome-wide association study (GWAS). We performed the first GWAS for body weight, behavior, along with 10 traits related to fur quality in mink., Conclusions: Combining association results with existing functional information of genes and mammalian phenotype databases, we proposed WWC3, MAP2K4, SLC7A1 and USP22 as candidate genes for body weight and pelt length in mink.

Breeding values for animals with marker data are estimated using a genomic selection approach where data is analyzed using Bayesian multi-marker association models. Fourteen model scenarios with varying haplotype lengths, hyper parameter and prior distributions were compared to find the scenario expected to give the most correct genomic estimated breeding values for animals with marker information only. Five-fold cross validation was performed to assess the ability of models to estimate breeding values for animals in generation 3. In each of the five subsets, 20% of phenotypic records in generation 3 were left out. Correlations between breeding values estimated on full data and on subsets for the "leave-out" animals varied between 0.77-0.99. Regression coefficients of breeding values from full data on breeding values from subsets ranged from 0.78-1.01. Single-SNP marker models didn't perform well. Correlations were 0.77-0.89 and predicted breeding values were biased. In addition the models seemed to over fit the genomic part of the variation. Highest correlations and most unbiased results were obtained when SNP markers were joined into haplotypes. Especially the scenarios with 5-SNP haplotypes gave promising results (distance between adjacent SNPs is 0.1 cM evenly over the genome). All correlations were 0.99 and regression coefficients were 0.99-1.01. Models with 5-SNP markers seemed robust to hyper parameter and prior changes. Haplotypes up to 40 SNPs also gave good results. However, longer haplotypes are expected to have less predictive ability over several generations and therefore the 5-SNP haplotypes are expected to give the best predictions for generations 4-6.

Reliabilities for genomic estimated breeding values (GEBV) were investigated by simulation for a typical dairy cattle breeding setting. Scenarios were simulated with different heritabilites (h2) and for different haplotype sizes, and seven generations with only genotypes were generated to investigate reliability of GEBV over time. A genome with 5000 single nucleotide polymorphisms (SNP) at distances of 0.1 cM and 50 quantitative trait loci (QTL) was simulated, and a Bayesian variable selection model was implemented to predict GEBV. Highest reliabilities were obtained for 10 SNP haplotypes. At optimal haplotype size, reliabilities in generation 1 without phenotypes ranged from 0.80 for h2 = 0.02 to 0.93 for h2 = 0.30, and in the seventh generation without phenotypes ranged from 0.69 for h2 = 0.02 to 0.86 for h2 = 0.30. Reliabilities of GEBV were found sufficiently high to implement dairy selection schemes without progeny testing in which case a data time-lag of two to three generations may be present. Reliabilities were also relatively high for low heritable traits, implying that genomic selection could be especially beneficial to improve the selection on, e.g. health and fertility.