Inbreeding and Outcrossing on Reproductive and Growth Traits and How to Account for it in Genetic Evaluation

Matt Culbertson

Cotswold USA

John Mabry, Ignacy Misztal, and Keith Bertrand

Department of Animal Sciences

University of Georgia


Genetic evaluation for livestock species has improved dramatically in recent years with the widespread implementation of Best Linear Unbiased Prediction (BLUP) of genetic merit for many traits of economic importance. Due to the corresponding developments in computing technology it has become relatively common to performance test individual animals and subsequently, combine all available information, i.e. from the animal, ancestors, collateral relatives, and progeny, into a single value to represent genetic merit for a given trait. However, although these methods are a marked improvement over previous phenotypic selection schemes, the current methods are only concerned with the additive component of genetic variation. Therefore, if non-additive genetic effects are an important part of variation, the accuracy of evaluations, even for "high-accuracy" animals, is reduced.

Accounting for dominance genetic effects may contribute significantly to accurate evaluation of performance records. However, dominance effects have been ignored because evaluation methods were computationally unfeasible for larger populations and the importance of dominance effects have not been well established. Recent developments have made computations with dominance effects much more manageable. Hoeschele and VanRaden (1991) discovered rules for the inversion of the dominance relationship matrix. Further, refinements in computing have enabled evaluation with dominance in the model to require less than twice the memory and computing time as a model which includes only additive effects (Misztal, 1996). Finally, use of Method R (Reverter et al., 1994) has enabled estimation of dominance variance from large data sets.

The importance of dominance effects may prove larger in litter bearing species that have added economic importance, and corresponding selection programs, based on prolificacy. Falconer (1989) suggests that fitness traits, such as measures of reproductive performance, where the population mean is below the optimal value for fitness, may have large dominance effects. Traditionally, the genetic cause of crossbreeding and inbreeding has been thought to be dominance (Falconer, 1989; Hill, 1982). Various studies have shown the phenotypic results of crossbreeding and inbreeding, or heterosis and inbreeding depression, to be greatest in those traits with lower additive genetic variation, or typically reproductive traits (Warwick and Legates, 1979). Finally, Hoeschele (1991) showed that significant dominance effects appeared to exist in dairy cattle populations analyzed for measures of fertility.

Assuming that dominance variation does exist, accuracy of genetic evaluation for additive genetic merit will be improved by the inclusion of these effects into the prediction model (Uimari, 1990). In addition, the genetic merit of parental combinations (specific combining ability) can be estimated to enhance the productivity of purebred matings for fertility (DeStefano and Hoeschele, 1992). This, in turn, should improve the profitability of sections of a genetic pyramid which must utilize pure-line matings.

As mentioned previously, inbreeding also poses a problem in the genetic evaluation of animals for traits which are affected by inbreeding depression. For traits and populations affected by inbreeding, the genetic evaluation methods currently used are inaccurate. However, if inbreeding is included in the model, the genetic evaluation of inbred animals will be inflated if inbreeding is not included in mating decisions (Uimari, 1990). Theoretically, if mating pairs (dominance) and inbreeding are both included in the model the resulting information should be accurate for purebreds and crossbreds (Misztal et al., 1995). Therefore, in smaller nucleus populations attempting to make rapid genetic improvement and minimize inbreeding, the inclusion of dominance and inbreeding in the estimation model may offer significant improvement over current methods for evaluation of reproductive traits.


179,485 reproductive records and 239,354 growth records from purebred American Yorkshire swine were obtained from the National Swine Registry. Data sets were initially edited to insure connectedness and eliminate biological extremes and were adjusted to a constant basis (NSIF, 1987; Culbertson, 1997). NBA and LWT were analyzed separately with a model which included the fixed effects of contemporary group and regression on inbreeding percentage and the random effects of additive genetic, parental dominance, animal permanent environment, and mate within contemporary group. DAYS and BF were analyzed separately with a model which included the fixed effects of contemporary group, sex, and regression on inbreeding and the random effects of additive genetic, dominance genetic, litter of birth, and maternal permanent environment. Estimates were obtained by Method R (Reverter et al., 1994) following the procedures of Misztal (1997). Dominance variance was estimated as four times the parental dominance estimate. Each trait was analyzed with 6 samples of the data selected by a random number generator. The convergence criterion was ri = 1 ± .0001, where ri is the regression from the random effect i. Sampling standard deviation was defined as the standard deviation of the 6 estimates from the subsamples.

After estimating the relative variances, additive breeding values were predicted for all animals using single-trait procedures and three separate prediction models differing in the inclusion of inbreeding and dominance genetic effects. The first model contained only additive genetic effects, the second model contained additive genetic and regression on inbreeding, and the third model contained additive genetic, regression on inbreeding, and dominance genetic. Additive breeding values were compared between alternative models and trends and differences in specific groups of animals were analyzed.


Estimates of inbreeding depression and variances for NBA and LWT are presented in Table 1. All variances are expressed as a percentage of phenotypic variation. Estimates of inbreeding depression were found to be sizeable for both traits and similar to those found by Bereskin et al. (1968). For example, a sow with an own inbreeding level of F=.125 would have her record adjusted by approximately .3 of a pig for NBA. Dominance variance was found to be larger for LWT, 78% of additive variance, relative to NBA, 25% of additive variance.

Table 1. Means (standard deviations) of estimates of inbreeding depression and additive, dominance, permanent environment (PE), and mate within contemporary group (mate) variances for NBA and LWT

Variance (% of Phenotypic)


Inbreeding Depression1







8.8 (.5)

2.2 (.7)

6.2 (.4)

3.4 (.2)



8.1 (1.1)

6.3 (.9)

4.6 (1.0)

4.0 (.5)

1 Per 10% of inbreeding

Estimates of inbreeding depression and variances for DAYS and BF are presented in Table 2. Inbreeding effects were found to be significant for DAYS and negligible for BF. Dominance variance for DAYS was found to be approximately 10% of phenotypic variation and a third of additive genetic variance.

Table 2. Means (standard deviations) of estimates of inbreeding depression and additive, dominance, litter, and maternal permanent environmental variances (PE) for days to 104.5 kg (DAYS) and backfat at 104.5 kg (BF)

Variance (% of Phenotypic Variation)


Inbreeding Depression1







33.2 (.4)

10.3 (1.5)

12.7 (.6)

1.2 (.4)



43.6 (.9)

4.8 (.7)

7.3 (.3)

1.0 (.2)

1 Per 10% of inbreeding

Additive breeding values were obtained after fitting of the three alternative prediction models. For all 4 traits, the correlation between additive breeding values after fitting of the three models were > .99. This would suggest, on average, that accounting for inbreeding and dominance effects has little effect on prediction of additive genetic merit. However, by identifying groups of animals which are the most affected it may be possible to determine types of populations where dominance models are most beneficial. Table 3 presents changes in sires' additive breeding for LWT due to inclusion of regression of inbreeding with classes based upon number and average inbreeding of daughters. Table 4 shows changes in additive breeding value for inclusion of dominance genetic effects with classes based upon the number of full-sibs, i.e. the amount of available dominance information. Changes for individual animals were noticeable for all traits except BF. Inbreeding caused greatest change in prediction of additive merit for inbred animals and those with inbred progeny. Changes in prediction due to dominance genetic effects were generally smaller than those due to inbreeding effects and were found to increase as the amount of dominance information available for a given family increases.

Table 3. Change in sire additive breeding value for LWT due to inclusion of inbreeding in the genetic prediction model with classes based on average inbreeding of daughters (Number of sires in parentheses)

Average Inbreeding of Daughters

Number of Daughters




³ 0.200


0.05 (4,191)

0.06 (564)

0.09 (378)

0.20 (56)


0.05 (1,349)

0.10 (199)

0.19 (64)

0.29 (7)


0.06 (1,703)

0.14 (245)

0.30 (51)

0.59 (5)

Table 4. Change in additive breeding value for LWT due to inclusion of dominance genetic effects in the genetic prediction model (number of animals in parenthesis)

Number of Full-Sibs





> 10


0.06 (3,519)

0.06 (4,529)

0.07 (695)

0.11 (69)


0.05 (25,009)

0.05 (42,203)

0.05 (4,550)

0.07 (344)

In addition to improving prediction of additive genetic merit, utilization of a more complete prediction model with inbreeding and dominance genetic effects may allow for increased phenotypic performance through a mate allocation program. A small simulation study determined that utilizing conservative assumptions of 1 sire to 15 dams, no parental combinations previously tested, and an average off-diagonal of the parental dominance relationship matrix of .05, the expectation for increase would be approximately 5% of the parental dominance standard deviation. However, if 20% of the parental combinations are previously tested the increase then becomes approximately 35% of the parental dominance standard deviation. Utilizing these results and economic values of $17 per additional pig, $1.10 per kilogram of litter weight, $.17 per day to 104.5 kg and $.60 per millimeter of BF, increased revenue may be up to $1.24 per litter for NBA, $0.69 per litter LWT, $0.11 per pig for DAYS, and $0.09 per pig for BF.


The inclusion of dominance genetic and inbreeding effects in a genetic prediction model may prove more beneficial for smaller, closed populations of swine similar to those maintained by most commercial breeding organizations. In addition, future research should be conducted to look at mating programs and genetic system designs which may be able to maximize the potential return of a mating scheme which incorporates these effects.


Bereskin, B., Shelby, C.E., Rowe, K.E., Urban, W.E., Jr., Blunn, C.T., Chapman, A.B., Garwood, V.A., Hazel, L.N., Lasley, J.G., Magee, W.T., McCarty, J.W., and Whatley, J.A., Jr. (1968) J. Anim. Sci. 27:339-350.

Culbertson, M.S., Mabry, J.W., Bertrand, J.K., and Nelson, A.H. (1997) J. Anim. Sci. accepted.

DeStefano and Hoeschele. (1992) J. Dairy Sci. 75:1680-1690.

Falconer (1989) Introduction to quantitative genetics.

Hill (1982) Z. Tierz. Zuechtungsbiol. 99:161-168.

Henderson, C.R. (1985) J. Anim. Sci. 60:111-117.

Henderson, C.R. (1989) J. Anim. Sci. 72: 2592-2605.

Hoeschele (1991) J. Dairy Sci. 74:1743-1752.

Misztal, I., Fernando, R.L., Grossman, M., Lawlor, T.J., Lukaszewicz, M. (1995) Animal Science Papers and Reports 13:251-265.

Misztal, I. (1997) J. Dairy Sci. 80:965-974.

NSIF (1987) "Guidelines for uniform swine improvement programs." National Pork Producers Council, Des Moines.

Reverter, A., Golden, B.L., Bourdon, R.M., and Brinks, J.S. (1994) J. Anim. Sci. 72:2247-2253.