Transgenerational epigenetic heritability for growth, body composition, and reproductive traits in Landrace pigs

Authors: ARAUJO, ANDRE - Purdue University; Johnson, Jay; GRAHAM, JASON - Purdue University; HOWARD, JEREMY - Smithfield Foods, Inc; OLIVERIA, HINAYAH - Purdue University; BRITO, LUIZ - Purdue University

Submitted to: Frontiers in Genetics
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/24/2024
Publication Date: 1/23/2025
Citation: Araujo, A.C., Johnson, J.S., Graham, J.R., Howard, J., Oliveria, H.R., Brito, L.F. 2025. Transgenerational epigenetic heritability for growth, body composition, and reproductive traits in Landrace pigs. Frontiers in Genetics. https://doi.org/10.3389/fgene.2024.1526473.
DOI: https://doi.org/10.3389/fgene.2024.1526473

Interpretive Summary: Genomic selection for improved swine heat stress resilience is of utmost importance as climate change continues to have a reductive effect on the sustainability and profitability of global swine production. However, development of these genomic selection models must consider possible environmental influences that can result in variation in complex traits independent of DNA sequences. Therefore, the study objective was to evaluate the influence of transgenerational epigenetics on prediction models developed to breed more climactically resilient pigs. Based on this study, it was determined that epigenetics had a limited influence on the predictive performance of genomic breeding values. These data have positive implications towards improving climactic resilience in swine populations using current genomic prediction models.

Technical Abstract: Background Epigenetics is an important source of variation in complex traits that is not due to changes in DNA sequences. The magnitude of epigenetic effects depends on the environment in which individuals are exposed, especially under stressful conditions. Therefore, we aimed to estimate transgenerational epigenetic heritability, the percentage of the reset and transmissibility rate of epigenetic markers, and genetic parameters and prediction breeding values based on genetic and epigenetic models for growth, body composition, and reproductive traits in Landrace pigs based on routinely-recorded datasets. Results Bayesian estimation methods resulted in reliable genetic parameters when fitting models including epigenetic effects for all traits evaluated: birth and weaning weight, backfat, total number of piglets born, and number of piglets born alive (BW, WW, BF, TNB, and NBA, respectively). Models including epigenetic effects had a similar or better fit than genetic models. Including genomic information in epigenetic models resulted in large changes in the variance component estimates, indicating non-orthogonality of the effects, while no substantial changes were observed when using only pedigree information. The estimated percentage of the reset coefficient for epigenetic markers in the traits evaluated was between 80 to 90% . The heritability estimates for the direct additive genetic effects ranged from 0.040 (BW) to 0.502 (BF) while the ratios for maternal genetic and transgenerational epigenetic effects ranged from 0.034 (BF) to 0.134 (BW) and 0.042 (NBA) to 0.336 (BF). The repeatability of the reproductive traits ranged from 0.098 (NBA) to 0.148 (TNB). Prediction accuracies, bias, and dispersion ranged between 0.199 (BW) to 0.443 (BF), -0.080 (WW) to 0.034 (NBA), and -0.134 (WW) to 0.131 (TN), respectively. No substantial differences were observed on the prediction metrics between the genetic and epigenetic models. Conclusions Transgenerational epigenetic heritability estimates based on routinely-recorded data are moderate for growth and body composition traits while of lower magnitude for reproductive traits in North American Landrace pigs. However, including epigenetic effects in genetic models does not impact the predictive performance of breeding values. This work provides a comprehensive investigation of the impact of epigenetic effects on genetic and genomic models from a quantitative perspective using routinely-recorded data.