Abstract
Knowledge of non-genomic inheritance of traits is currently limited. Although it is well established that maternal diet influences offspring inheritance of traits through DNA methylation, studies on the impact of prepubertal paternal diet on DNA methylation are rare. This study aimed to evaluate the impact of prepubertal diet in Polypay rams on complex traits, DNA methylation, and transmission of traits to offspring. A total of 10 littermate pairs of F0 rams were divided so that one ram was fed a control diet, and the other was fed the control diet with supplemental methionine. Diet was associated with earlier age at puberty in treatment vs. control F0 rams. F0 treatment rams tended to show decreased pubertal weight compared to control rams; however, no differences were detected in overall growth. A total of ten F0 rams were bred, and the entire F1 generation was fed a control diet. Diet of F0 rams had a significant association with scrotal circumference (SC) and weight at puberty of F1 offspring. The paternal diet was not significantly associated with F1 ram growth or age at puberty. The DNA methylation of F0 ram sperm was assessed, and genes related to both sexual development (e.g., DAZAP1, CHD7, TAB1, MTMR2, CELSR1, MGAT1) and body weight (e.g., DUOX2, DUOXA2) were prevalent in the data. These results provide novel information about the mechanisms through which the prepubertal paternal diet may alter body weight at puberty and sexual development.
Highlights
Genomic prediction is a pillar of animal breeding in the livestock industry
Our research demonstrated that the supplementation of methionine to cows at the preimplantation stage of embryo development significantly altered the expression of 276 genes in blastocysts, decreasing gene expression in most cases (Peñagaricano et al, 2013)
This study is a pioneering effort for understanding the transgenerational epigenetic influence of paternal diet over complex traits
Summary
Genomic prediction is a pillar of animal breeding in the livestock industry. Some traits are more challenging to predict due to their complexity. The inclusion of biological information and functional annotation data in genomic prediction models has been shown to improve the prediction of dairy bull fertility (Abdollahi-Arpanahi et al, 2017; Nani et al, 2019). Gaining a better understanding regulation of gene function and nongenomic inheritance is increasingly important and may serve as an avenue to explain variation in lowly-heritable traits. Maternal nutrition impacts the inheritance of traits, which are challenging to predict using only genomic data. Genomic data explain only 6% of heritability for type 2 diabetes (Zeggini et al, 2008)
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