New breeding value evaluation of fertility traits in Finnish mink

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ORIGINAL ARTICLENew breeding value evaluation of fertility traits in Finnish minkM. KOIVULA, E. A. MANTYSAARI & I. STRANDENMTT Agrifood Research Finland, Biotechnology and Food Research, Biometrical Genetics, FI-31600 Jokioinen, FinlandAbstractLitter size (LS) has been included in the Finnish mink breeding goal for several generations. Still, the phenotypic trend inthe average number of pups per mated female has slightly decreased while animal size (AS) has increased. The aim of thisstudy was to estimate genetic parameters for pregnancy rate (PREG) and felicity (FEL), and their genetic correlations to LSand AS. The estimated heritabilities were low for PREG (0.032) and FEL (0.026). The genetic correlations between LS andPREG (0.34), and LS and FEL (0.53) were clearly positive. Thus, on average females having genetically larger LS havehigher PREG and FEL. The genetic correlation between AS and PREG was low (0.13), and correlation between AS andFEL was moderate (0.27) indicating that larger animals are more likely barren or lose their kits during pregnancy or rightafter birth.Keywords: Fertility, litter size, mink, pregnancy.IntroductionThe main goals in Finnish mink breeding have beenimproved fur quality, and increase in body size andlitter size (LS). Consequently, average pelt size hasincreased considerably. However, at the same timethe average number of kits per mated female hasslightly decreased in Finland as well as in othercountries (Hansen & Berg, 2007, 2008; Hansen,2009). Increase in body size may have lead to smallerLS (Hansen & Berg, 2007, 2008; Hansen, 2009;Koivula et al., 2009b, 2010). This seems to be aproblem also in other species because when selectingfor body size negative genetic trend has often beenobserved in traits measuring reproduction andsurvival despite their importance to profitability(Peura et al., 2007; Goddard, 2009; Koivula et al.,2009a). One reason is a strong negative geneticcorrelation between large animal size (AS) and LS.For example in minks this correlation has variedfrom 0.18 to 0.28, (Lagerkvist et al., 1994;Rozempolska-Rucinsca, 2004; Peura et al., 2007;Koivula et al., 2009b, 2010), and in blue foxesnegative correlation has been even higher (0.36 to0.43) (Peura et al., 2007; Koivula et al., 2009a).Fertility can be measured in many ways. InFinland, farmers record mink LS at two weeks afterwhelping, but also barren females, aborting femalesor females losing their kits are recorded in a routinerecording scheme. However, breeding values arebased on LS only, and barren females or femalesaborting or losing their kits are not included in thebreeding value evaluation. In blue foxes heritabilityof pregnancy rate (PREG) was 0.028 and felicity(FEL) 0.049, and the genetic correlations betweenLS and PREG and LS and FEL were clearly positive(Koivula et al., 2009a). Thus, it was possible toinclude these traits in breeding programmes ofFinnish blue foxes.The aim of this study was to estimate geneticparameters for PREG (representing the proportionof females whelping, i.e. the non-barren females)and FEL (including both aborting females andfemales losing all kits after birth), and their geneticcorrelations to LS and AS. In addition, we examinedgenetic trends in the traits studied.Correspondence: M. Koivula, MTT Agrifood Research Finland, Biotechnology and Food Research, Biometrical Genetics, FI-31600 Jokioinen, Finland.Tel: 358-40-1960986. Fax: 358-3-4188-3244. E-mail: minna.koivula@mtt.fiActa Agriculturae Scand Section A, 2011; 61: 16(Received 14 September 2010; revised 1 November 2010; accepted 2 November 2010)ISSN 0906-4702 print/ISSN 1651-1972 online # 2011 Taylor & FrancisDOI: 10.1080/09064702.2010.538715Material and methodsMink data were obtained from the Finnish FurBreeders Association. The data had informationfrom 3.7 million animals. Data for the variancecomponent estimation were sampled from the fulldata. Sampling was done by farm. The completepedigree contained about 4.1 million animals from136 farms. The pedigree had many disconnectedsubpopulations, so it had to be pruned with Relax2(Stranden & Vuori, 2006) to have only informativeanimals. In the end, the sample had observationsfrom 12 farms having 69,441 animals born in years19982006. The pedigree file contained 93,632animals.The analysed traits were the first parity LS,PREG, FEL and AS. LS was recorded as numbersof kits alive two weeks after whelping. PREG andFEL were binary (1/0) traits, value 0 representingthe event when the female was barren or aborted/lost her kits. Females were scored as pregnant whenshe showed visual signs of pregnancy. If pregnancywas recorded, PREG1, and also if PREG recordwas missing but FEL and LS records exist. Ifabortion or kit loss was observed, females wererecorded as FEL0, similarly if she was recordedas pregnant and LS was missing. Because allpregnant females and all females giving birth orlosing kits are not observed, PREG and FEL arealways approximations. Abortion and kit loss afterbirth was treated as a single FEL trait, because thenumber of observations for aborting females was solow that it would have been difficult to analyse it as aseparate trait. AS was graded subjectively by thefarmer. The grading scale ranged from 1 (smallest)to 5 (largest). The recommendation was that theaverage AS should be close to 3 within farm andyear.Restricted maximum likelihood (REML) esti-mates of (co)variance components were calculatedusing DMU software (Madsen & Jensen, 2000). Themulti-trait animal model was:yXbWcZaewhere y is a vector of observations, b is the vector offixed effects, c is the vector of random effect of thelitter in which the female is born, and a is the vectorof random genetic effects for animal and e is therandom residual, and X, W and Z are knownincidence matrices for the fixed and random effects.Random effects were assumed to be independentand normally distributed. In particular, c N(0;C0I); a N (0;G0A); e N (0;R0I)where C0 is common litter effect (co)variancematrix, G0 is direct additive genetic (co)variancematrix, A is numerator relationship matrix and R0residual (co)variance matrix.The fertility traits were exclusive by nature of theirdefinition: when PREG had value 0, both FEL andLS information were missing; when PREG had value1, and FEL had value 0, then LS was missing. Thus,LS was observed only when both PREG and FELhad value 1. Consequently, the residual covariancebetween LS and PREG, LS and FEL, and PREGand FEL was assigned as non-existing (zero) becausethese trait combinations are not present in the data,and thus, cannot be estimated.Fixed effects for the traits were studied with thegeneral linear model by excluding random effectsother than the residual (SAS, 2004). Fixed effectsfor LS, PREG and FEL were farmyear, time ofbirth for animal (three classes: 99119, 120140 and141160 days from the beginning of the year,reflecting timing of birth and thus also age of animal)and number of matings (three classes: 1, 2 or 2mating/season). Fixed effects for AS were farmyear,time of birth for animal, sex of animal (three classes:male, female and unknown) and age of dam (threeclass: 1, 2 or 3-years-old).Heritability (h2) and proportion of common littervariance (c2) for the traits were calculated as h2s2a=(s2as2c s2e ); and c2s2c =(s2as2c s2e );where s2a; s2c and s2e are trait variances of additivegenetic, common litter environment and residual,respectively. Linear animal model was used toanalyse PREG and FEL, although theoretically athreshold model would be more appropriate foranalysis of binary data (Gianola, 1982). Heritabilitycalculated on the observed binary scale varies withincidence because the amount of variance due tomeasurement error depends on the incidence. Toovercome this, heritabilities were converted from thebinary to the continous scale using Dempster andLerner (1950) formula:h2h201p(1p)=z2;where h2 is the heritability in the continuous scale,h201 is the corresponding heritability calculated on thebinary scale, p is the incidence of affected individualsin the population, and z is the ordinate of thestandard normal density function on the thresholdcorresponding to the incidence p.In addition to the genetic parameters, genetictrends for the studied traits were assessed byexamining standardised estimated breeding values(EBV). EBVs were calculated with MiX99(Stranden & Lidauer, 1999; Vuori et al., 2006).The largest subpopulation was used for EBV calcu-lations. The data included observations from395,233 animals and the pedigree had 451,643animals from years 19882006 in 59 farms. The2 M. Koivula et al.model used in the EBV calculation was the sameas in the variance component analysis but thevariances were the obtained REML estimates.Breeding values were standardised to year 2003with mean 100 and SD 10 in order to makecomparison of years and EBVs of different traitseasier.Results and discussionTable I gives the number of observations in each traitpair. Descriptive statistics for LS, PREG, FEL andAS are given in Table II. The mean LS was 5.47 andSD was 2.10. Mean PREG was 0.89, indicating that11% of the young mink females were barren.Average FEL was 0.96, indicating that 4% of femalesgetting pregnant lost or aborted their kits. Mean ASwas 4.06 in the current data. The recommendationgiven to grader is that the average for AS should beclose to three within a farmyear. However, themean 4.06 shows that higher scores are commonlyused. Males comprised 30% of the graded indivi-duals, their average of AS being 4.60, femalesaverage AS was 3.82, and that with unknown sex(3.5% of the individuals) 3.97.Proportion of litter variance was low for PREGand FEL (0.0032 and 0.0009, respectively). Thus,littermates do not have great impact on these traits.For the LS the c2 was 0.07 and AS 0.03. Koivulaet al. (2009b) suggested that litter effects were largerthan maternal heritabilities for litter size and animalsize. This implies that it is important to estimate alsoenvironmental effects common to littermates forthese traits.Heritability (h2) and litter variance proportion (c2)for the traits are in Table III. Heritability estimate ofLS was 0.11. In other studies heritability for LS inmink has varied from 0.02 to 0.20 (Berg, 1993;Lagerkvist et al., 1994; Rozempolska-Rucinsca,2004; Koivula et al., 2009b, 2010). Thus, theobtained heritability estimate in this study is withinthe range reported for mink. Heritability estimates forLS in other fur animals have also been similar to thosefor mink. In blue fox, heritability estimates of LShave varied from 0.03 to 0.17 (Kenttamies, 1996;Wierzbicki & Jagusiak, 2006; Peura et al., 2007;Koivula et al., 2009a), for raccoon dog LS heritabilityhas been 0.08 (Slaska, 2002).The heritabilities estimated on the observed bin-ary scale for the PREG and FEL were low, 0.032and 0.026, respectively. Because of the low herit-ability, genetic change in PREG and FEL will beexpected to be slow. In the underlying continuousscale (Dempster & Lerner, 1950), the heritabilityestimates were higher, 0.092 for PREG and 0.046for FEL. The results suggest some benefit for using athreshold model over a linear model so that thebinary nature of the response variable in PREG andFEL is accounted. However, the low heritabilitysuggests that both models give the same ranking ofanimals (Meijering & Gianola, 1985; Hoeschele,1988; Foulley et al., 1990).Reproductive traits like PREG and FEL have beenstudied in other production animals as well. In otherspecies heritability estimates for traits similar toPREG and FEL in mink have varied considerably.In blue fox, heritability of PREG and FEL were0.029 and 0.049 (estimated on continuous scale),respectively (Koivula et al., 2009a). Heritabilityestimates from threshold models for heifer PREGhave ranged from 0.13 to 0.66 (Evans et al., 1999;Bormann et al., 2006; Eler et al., 2006). In pig,heritability estimate of return rate has been 0.03 witha threshold model (Holm et al., 2005), and in sheepheritability estimate of fertility (ewes lambing perewes joined) has been 0.02, estimated on continuousscale (Brash et al., 1994). Thus, the heritabilityestimates of mink fertility traits are similar toestimates from other species, although estimatesfrom the linear model depend on frequency andare not directly comparable to threshold modelestimates.The genetic correlations between the traits are inTable IV. The genetic correlation between AS andLS was antagonistic (0.26). This result is sup-ported by the earlier studies in mink (Lagerkvistet al., 1993, 1994; Rozempolska-Rucinsca, 2004;Koivula et al., 2009b), and blue fox (Peura et al.,2007; Koivula et al., 2009a). Negative correlationbetween body size and reproduction is seen also inTable I. Number of observations in each trait pair in multi-traitanalysis. Pregnancy rate (PREG), felicity (FEL), first parity littersize (LS) and animal size (AS).PREG FEL LS, no AS, scorePREG 50,200FEL 45,154 45,154LS, no 40,523 40,523 40,523AS, score 21,756 19,421 15,961 40,983Table II. Number of observations (n), mean and standarddeviation (SD) for the pregnancy rate (PREG), felicity (FEL),first parity litter size (LS) and animal size (AS).Trait n Mean SDPREG 50,200 0.89 0.30FEL 45,154 0.96 0.20LS, no 40,523 5.47 2.10AS, score 40,983 4.06 0.72New breeding value evaluation of fertility traits 3practise because phenotypic LS has somewhat de-creased at the same time as AS has increased(Figure 1). The negative genetic correlation betweenAS and PREG was low (0.13), and between ASand FEL moderate (0.27), indicating that largeanimals will more likely lose their kits duringpregnancy or immediately after birth. Similar inter-action has been observed in blue foxes (Koivulaet al., 2009a), and in HolsteinFriesian cows wherelarger animals tend to be relatively less fertile thansmaller animals (Haile-Mariam et al., 2004).The genetic correlations between LS and PREG,and LS and FEL were 0.33 and 0.53, respectively.Thus, females having genetically larger LS havelower risk to be barren or abort/lose their kits. Thepositive genetic correlation is favourable when selec-tion goal is to increase LS: the results from our studyindicate that selection for increased LS could in-crease the PREG or decrease kit loss. Similarcorrelation has also been observed in pigs, wheregenetic correlation between return rate of gilts andnumber of piglets born alive in the first litter was0.22 (Holm et al., 2005). The genetic correlationbetween PREG and FEL was positive (0.46).Despite reasonably high genetic correlation be-tween LS and other fertility traits and betweenPREG and FEL, correlations were clearly less than1. Therefore, LS, PREG and FEL are undoubtedlydifferent traits, and accuracy of fertility evaluationswill increase when more traits are included into thebreeding programme. Females without LS observa-tion are expected to gain more than females with LSobservation from including PREG and FEL infor-mation into genetic evaluation. A simple exampleillustrates this. Assume phenotypic selection. Iffemale has no LS observation, accuracy of EBVincreases from zero to 0.11 when PREG and FELobservations are available. For a female with a LSobservation, accuracy increases from 0.33 to 0.34. Inpractice, increase in accuracy is not as large becauseno relationship information was accounted in thecalculations. Note that LS was observed only foranimals for which PREG and FEL had value 1, i.e.female was pregnant and did not lose/abort all kits. Asimilar consecutive relationship exists for PREG andFEL. Thus, some model assumptions are violated,Table IV. Estimated genetic correlations (rg) between the firstparity litter size (LS), pregnancy rate (PREG), felicity (FEL) andanimal size (AS) with standard errors, and phenotypic (rp)correlations with AS.rg rpTrait FEL LS AS ASPREG 0.4690.13 0.3490.08 0.1390.09 0.02FEL 0.5390.08 0.2790.10 0.05LS 0.2690.06 0.05R2 = 0.54R2 = 0.594. 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006Birth yearMean litter size, no3. animal size, scoreFigure 1. Phenotypic trend and coefficient of determination R2 in litter size (--j--) and animal size (score, 15) (--I--) by birth year inFinnish minkTable III. Estimated additive genetic variance (s2a); litter variance (s2c ); phenotypic variance (s2p ); and proportion of litter variance (c2) andheritability (h2) with standard error for the first parity litter size (LS), pregnancy rate (PREG), felicity (FEL) and animal size (AS).Trait s2a s2c s2p c2 h2PREG 0.0028 0.0032 0.0877 0.0490.006 0.0390.01FEL 0.0010 0.0009 0.0398 0.0290.007 0.0390.01LS 0.4683 0.0704 4.2789 0.0290.008 0.1190.01AS 0.0617 0.0296 0.3271 0.0990.006 0.1990.014 M. Koivula et al.or at least interpretation of genetic correlationparameters is not as straightforward as described.The standardised breeding value estimates for LS,PREG and FEL show similar trends between years1988 and 2001 (Figure 2). Genetic trends werenegative during 19882001 for all the fertility traits.After 2002 the genetic trend for LS has been slightlypositive, but still decreasing for PREG and FEL.PREG and FEL have not been included in thebreeding programme and selection for better LS hasnot improved other fertility traits. The genetic trendfor AS has been positive (Figure 2). Increase in ASwas also clear in the phenotypic trend (Figure 1).The phenotypic trend in LS has been decreasing butlately the decrease seems to have ceased. Thus, itseems that in spite of the negative genetic correlationbetween AS and LS, there has been genetic im-provement in LS, although this is not as clearly seenin phenotypic trend.Traits measuring reproduction and survival mayshow a negative genetic trend in spite of theirimportance to profitability (Goddard, 2009). Thisoccurs due to inbreeding depression and selectionfor other correlated traits. To overcome this pro-blem, fertility traits as well as other fitness traitsshould be included in the breeding objectives andthe selection index. Genetic trends for the fertilitytraits in the Finnish mink population have beennegative although fertility through LS has beenincluded in the breeding goal for several generations.Only since year 2002 the genetic trend for LS hasbeen slightly positive. However, in Finland basicallywhole fur animal breeding programmes operate onfarm level and farmers are responsible for finalbreeding selection. Thus, farmers may also decidethemselves if they want to weight more or lessfertility traits and less AS. The most importantreason to the negative trends is the antagonisticgenetic correlation between AS and the fertilitytraits, and selection for large AS. When selectionmainly focuses on increasing AS, the genetic level ofLS and the other fertility traits will probablydecrease. However, phenotypic impact may not bevery dramatic because heritabilities of the fertilitytraits are quite low.ConclusionsPREG and FEL are new fertility traits to the Finnishmink breeding evaluation where LS has been theonly measure for fertility. Negative genetic correla-tions were estimated between the three studiedfertility traits and AS. These antagonistic relation-ships would be reasonable to take into account inbreeding value evaluations by using a multi-traitmodel. Including the new fertility traits into thebreeding programme would make selection of breed-ing animals with good fertility more reliable.ReferencesBerg, P. (1993). Present knowledge about heritability of different traitsin mink, NJF Utredning/Rapport 90, NJF Workshop Viborg,Denmark. 10 pp.Bormann, J. M., Totir, L. R., Kachman, S. D., Fernando, R. L., &Wilson, D. E. (2006). Pregnancy rate and first-serviceconception rate in Angus heifers. Journal of Animal Science,84, 20222025.Brash, L. D., Fogarty, N. M., & Gilmour, A. R. (1994).Reproductive performance and genetic parameters for Aus-tralian Dorset sheep. Australian Journal of AgriculturalResearch, 45, 427441.Dempster, E. R. & Lerner, I. M. (1950). Heritability of thresholdcharacters. Genetics, 35, 212236.Eler, J. P., Ferraz, J. B. S., Balieiro, J. C. C., Mattos, E. C., &Mourao, G. B. (2006). Genetic correlation between heiferpregnancy and scrotal circumference measured at 15 and 18months of age in Nellore cattle. Genetics and MolecularResearch, 5, 569580.Evans, J. L., Golden, B. L., Bourdon, R. M., & Long, K. L.(1999). Additive genetic relationships between heifer preg-nancy and scrotal circumference in Hereford cattle. Journalof Animal Science, 77, 26212628.Foulley, J. L., Gianola, D., & Im, S. (1990). Genetic evaluationfor discrete polygenic traits in animal breeding. In D. Gianolaand K. Hammond (eds.) Advances in statistical methods forgenetic improvement of livestock, pp. 361410. Berlin, Heidel-berg: Springer-Verlag.Gianola, D. (1982). Theory and analysis of threshold characters.Journal of Animal Science, 54, 10791096.Goddard, M. (2009). Fitness traits in animal breeding programs.In J. van der Werf, H.-U. Graser, R. Frankham, & C. Gondro(eds.) Adaptation and fitness in animal populations, evolutionaryand breeding perspectives on genetic resource management, pp.4152. Netherlands: Springer.Haile-Mariam, M., Bowman, B. J., & Goddard, M. E. (2004).Genetic parameters of fertility traits and their correlationwith production, type, workability, liveweight, survival index,and cell count. Australian Journal of Agricultural Research, 55,7787.9193959799101103105Birth yearIndexLS Preg Fel AS19981988 1990 1992 1994 1996 2000 2002 2004 2006Figure 2. Average genetic level by birth year according to meanstandardised estimated breeding value for the first parity litter size(LS), pregnancy rate (PREG), felicity (FEL) and animal size (AS)in Finnish blue foxNew breeding value evaluation of fertility traits 5Hansen, B. K. (2009). Litter size and kit survival. Scientifur, 33,1921.Hansen, B. K. & Berg, P. (2007). Low kit survival consequencesof selection for high body weight. Scientifur, 31, 104Hansen, B. K. & Berg, P. (2008). Reduced litter size and percentkits alive is a consequences of selecting for high body weight.Scientifur, 32, 15.Hoeschele, I. (1988). Comparison of maximum a-posterioriestimation and quasi best linear unbiased predictionwith threshold characters. Journal of Animal Breeding andGenetics, 105, 327361.Holm, B., Bakken, M., Vangen, O., & Rekaya, R. (2005). Geneticanalysis of age at first service, return rate, litter size, andweaning-to-first service interval of gilts and sows. Journal ofAnimal Science, 83, 4148.Kenttamies, H. (1996). Genetics and environmental factorsaffecting fertility traits in foxes. Animal Production ReviewApplied Science, 27, 6366.Koivula, M., Mantysaari, E. A., & Stranden, I. (2009a). Newfertility traits in breeding value evaluation of Finnish bluefox. Acta Agriculturae Scandinavica. Section A AnimalScience, 59, 131136.Koivula, M., Stranden, I., & Mantysaari, E. A. (2009b). Directand maternal genetic effects on first litter size, maturationage, and animal size in Finnish minks. Journal of AnimalScience, 87, 30833088.Koivula, M., Stranden, I., & Mantysaari, E. A. (2010). Geneticand phenotypic parameters of age at first mating, litter sizeand animal size in Finnish mink. Animal, 4, 183188.Lagerkvist, G., Johansson, K., & Lundeheim, N. (1993). Selectionfor litter size, body weight and pelt quality in mink (Mustelavison): Experimental design and direct response of each trait.Journal of Animal Science, 71, 32613272.Lagerkvist, G., Johansson, K., & Lundeheim, N. (1994). Selectionfor litter size, body weight, and pelt quality in mink (Mustelavison): Correlated responses. Journal of Animal Science, 72,11261137.Madsen, P. & Jensen, J. (2000). A users guide to DMU, a packagefor analyzing multivariate mixed models, Mimeo 22 p. Tjele,Denmark: Danish Institute of Agricultural Sciences (DIAS).Meijering, A. & Gianola, D. (1985). Linear versus nonlinearmethods of sire evaluation for categorical traits: A simulationstudy. Genetics Selection Evolution, 17, 115132.Peura, J., Stranden, I., & Mantysaari, E. A. (2007). Geneticparameters for Finnish blue fox population: Litter size, age atfirst insemination and pelt size. Agricultural and Food Science,16, 136146.Rozempolska-Rucinsca, I. (2004). Genetic background of perfor-mance and functional traits in mink. Electronic Journal ofPolish Agricultural Universities, Animal Husbandry. Accessed3 September 2010, available at: (2004). Statistical analysis system. Release 9.1 ed. Cary, NC:SAS Institute.Slaska, B. (2002). Genetic and environmental factors of raccoondog reproduction traits. Electronic Journal of PolishAgricultural Universities, Animal Husbandry. Available at: 3 September 2010)Stranden, I. & Lidauer, M. (1999). Solving large mixed modelsusing preconditioned conjugate gradient iteration. Journal ofDairy Science, 82, 27792787.Stranden, I. & Vuori, K. (2006). RelaX2: Pedigree analysisprogram. In Proceedings of the 8th world congress on GeneticsApplied to Livestock Production. 1318 August, 2006, BeloHorizonte, MG, Brazil, CD-ROM communication No.27-30.Vuori, K., Stranden, I., Lidauer, M. & Mantysaari, E. A. (2006).MiX99-Effective solver for large and complex linear mixedmodels. In Proceedings of the 8th world congress on GeneticsApplied to Livestock Production. 1318 August, 2006, BeloHorizonte, MG, Brazil, CD-ROM communication No.27-33.Wierzbicki, H. & Jagusiak, W. (2006). Breeding value evaluationin Polish fur animals: Estimates of (co)variances due to directand litter effects for fur coat and reproduction traits. CzechJournal of Animal Science, 51, 3946.6 M. Koivula et al.


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