Hidden genetic burdens shape litter size in rabbits, new study finds

Researchers have developed a new statistical framework that separates two distinct sources of inbreeding depression in rabbits, revealing that the genetic burden carried by embryos affects the number of kits born alive more strongly than the burden carried by their mothers. The study, published in Genetics Selection Evolution, analysed more than three decades of reproductive records from the Pannon White rabbit breed and showed that ancestors differ markedly in how much hidden genetic load they transmit to their descendants.

Genetic load refers to the burden of deleterious variants carried by a population. It has two components: a realized one, expressed as reduced fitness, and a hidden one, masked in heterozygotes. The hidden component arises from recessive or partially recessive deleterious alleles and from overdominant loci, and it constitutes what geneticists call the inbreeding load. When mating between relatives increases homozygosity, this hidden load is unmasked and appears as inbreeding depression, the decline in performance of reproductive and fitness-related traits with increasing inbreeding.

Conventional analyses estimate inbreeding depression by regressing phenotypic records on an individual’s inbreeding coefficient, which assumes that all ancestors contribute equally to deleterious variation. The new approach, building on the concept of individual inbreeding load, instead partitions an animal’s total inbreeding into partial contributions attributable to specific ancestors using a Mendelian decomposition based on Malécot’s tabular method. This allows the inbreeding load to be modelled as an additional additive genetic effect, so that every individual carries two genetic effects: a direct one expressed in its own phenotype, and an inbreeding load effect expressed only in its inbred descendants.

The study’s key innovation was applying this framework to a composite reproductive trait, number born alive, in which inbreeding can act through two biologically distinct pathways. The dam’s inbreeding load operates through maternal physiology such as ovulation rate and uterine capacity, while the litter’s inbreeding load operates through the genetic potential of the embryos themselves. The research team, led by C. Hervás-Rivero with colleagues including L. Varona, built Bayesian linear mixed models that jointly incorporated maternal and litter inbreeding loads, along with average inbreeding depression covariates for both doe and litter.

The dataset consisted of 33,521 kindling records collected between 1992 and 2024 from the Pannon White breed, which was developed at Kaposvár University in the late 1980s and officially recognized in Hungary in 1992. The average number of kits born alive per kindling was 9.02, with a standard deviation of 3.43. The pedigree, recorded between 1989 and 2024, included 43,858 individuals, of which 79.2 percent were inbred, with an average inbreeding coefficient of 0.057. For individuals with phenotypic records, the average pedigree depth was 18 generations.

The Mendelian decomposition of the pedigree produced more than 25.8 million partial inbreeding coefficients, but only 3,236 ancestors actually contributed to the inbreeding of at least one descendant, and most contributions were negligible. After excluding coefficients below 0.001, the researchers retained 152,687 partial inbreeding coefficients for decomposing litter inbreeding and 150,746 for dam inbreeding. The contribution of individual ancestors was highly heterogeneous: one individual contributed to as many as 57,825 inbreeding coefficients, while 498 individuals contributed to fewer than ten.

Ten competing models were compared using the Deviance Information Criterion. The best-fitting model included both maternal and litter inbreeding load effects but no genetic correlations between the additive and inbreeding load effects, which the authors attributed to over-parameterization in the more complex specifications. Inbreeding depression estimates were consistently negative. Litter inbreeding depression was estimated between −4.89 and −5.60 across models and was statistically robust, with highest posterior density intervals excluding zero in every model. Maternal inbreeding depression was also negative, ranging from −2.30 to −3.59, but with wider intervals and lower precision. Scaled to the trait mean, the preferred model corresponds to a reduction of 0.57 percent in kits born alive for litter inbreeding and 0.33 percent for maternal inbreeding per 1 percent increase in homozygosity, and the litter effect exceeded the maternal effect in 91 percent of the Gibbs samples.

Both inbreeding load variances were substantial, indicating marked heterogeneity among ancestors in their contributions to inbreeding depression. The authors noted that these variance estimates are large because they correspond to a hypothetical individual with complete inbreeding, and rescaling to more realistic inbreeding levels reduces them considerably. Estimates of genetic correlations between the additive genetic effect and the inbreeding load effects were generally low and imprecise, with wide posterior intervals that included zero, although the correlation between the additive effect and maternal inbreeding load was consistently negative in direction.

Analysis of genetic trends from 1987 to 2024 revealed sustained genetic progress for number born alive, even though the population has not been directly selected for this trait; selection has instead targeted litter weight at day 21 and average daily gain. This progress was accompanied by divergent trends in the two inbreeding load components: litter inbreeding load showed a continuous positive trend broadly similar to the additive genetic effect, while maternal inbreeding load remained relatively stable until about 2010 and then declined. The authors suggest this pattern may reflect indirect purging of deleterious alleles affecting embryonic viability on the litter side, alongside an unfavourable correlated response on the maternal side, and they propose that a weighted selection index accounting for both components could help mitigate undesirable changes in maternal inbreeding load.

The authors caution that the available data were insufficient to reliably estimate the genetic correlations between the components, and they note that confirmation of the difference between doe and litter inbreeding depression in larger datasets and additional populations would be valuable. Nevertheless, they argue that jointly modelling maternal and litter inbreeding loads provides a biologically coherent picture of the genetic architecture of litter size in rabbits, and that rabbit breeding programs could benefit from explicitly incorporating inbreeding load information when designing selection and mating schemes, identifying lineages that carry elevated hidden genetic load without substantially limiting genetic gain.

Subject of Research: Joint modelling of maternal and litter inbreeding load for number born alive in rabbits

Article Title: Quantifying genetic load through joint modelling of inbreeding depression and inbreeding load for litter size in rabbits

Article References: Hervás-Rivero, C., Nguyen, A. T., Kövér, G., Bokor, A., Nagy, I., Curik, I., & Varona, L. (2026). Quantifying genetic load through joint modelling of inbreeding depression and inbreeding load for litter size in rabbits. Genetics Selection Evolution, 58(1), Article 69. https://doi.org/10.1186/s12711-026-01090-5

Image Credits: AI Generated

DOI: 10.1186/s12711-026-01090-5

Keywords: inbreeding depression, inbreeding load, genetic load, rabbits, litter size, Pannon White, quantitative genetics, animal breeding, Bayesian models, number born alive, pedigree analysis, selection