Genome‐wide association study for feed efficiency in collective cage‐raised rabbits under full and restricted feeding

Abstract

Summary Feed efficiency (FE) is one of the most economically and environmentally relevant traits in the animal production sector. The objective of this study was to gain knowledge about the genetic control of FE in rabbits. To this end, GWASs were conducted for individual growth under two feeding regimes (full feeding and restricted) and FE traits collected from cage groups, using 114 604 autosome SNPs segregating in 438 rabbits. Two different models were implemented: (1) an animal model with a linear regression on each SNP allele for growth trait; and (2) a two‐trait animal model, jointly fitting the performance trait and each SNP allele content, for FE traits. This last modeling strategy is a new tool applied to GWAS and allows information to be considered from non‐genotyped individuals whose contribution is relevant in the group average traits. A total of 189 SNPs in 17 chromosomal regions were declared to be significantly associated with any of the five analyzed traits at a chromosome‐wide level. In 12 of these regions, 20 candidate genes were proposed to explain the variation of the analyzed traits, including genes such as FTO, NDUFAF6 and CEBPA previously associated with growth and FE traits in monogastric species. Candidate genes associated with behavioral patterns were also identified. Overall, our results can be considered as the foundation for future functional research to unravel the actual causal mutations regulating growth and FE in rabbits.

Disentangling the causal relationship between rabbit growth and cecal microbiota through structural equation models

“…This result is also supported by the estimate of the genetic correlation of 0.59 between these traits that was reported by Piles and Sánchez [5]. In line with results of previous research in human [8] and livestock [9][10][11] populations, a recent study conducted by Velasco-Galilea et al [12] in the same rabbit population as used in [7] suggested that, in rabbit, cecal microbial composition and diversity are heritable traits, and identified several host genomic regions that affect cecal microbial composition. However, since most of these studies are based on associations or correlations, causal relationships between the microbiome and the traits cannot be implied.…”

“…Thus, these traits have been successfully used as selection criteria in experiments carried out in pigs and rabbits [4,6]. In addition, a genome-wide association study (GWAS) on rabbits conducted by Sánchez et al [7] identified different quantitative trait loci (QTL) for both these traits, supporting the idea that the genetic control of the growth of animals fed under a restricted regime differs from that of animals fed ad libitum. This result is also supported by the estimate of the genetic correlation of 0.59 between these traits that was reported by Piles and Sánchez [5].…”

The value of gut microbiota to predict feed efficiency and growth of rabbits under different feeding regimes

“…Apart from that, the commercial application of feed restriction (i.e., a reduction in the amount of the feed provided to the animal) is common since it improves FE and reduces mortality and morbidity caused by enteric disorders 10 . Piles and Sánchez 11 estimated a low genetic correlation between ADG AL and ADG R , and the genome-wide association study conducted by Sánchez et al 12 identified different QTL regions for both traits. Such findings support the existence of different genetic backgrounds for these traits.…”

The Value of Gut Microbiota to Predict Feed Efficiency and Growth of Rabbits Under Different Feeding Regimes

“…The study of ADG has particular significance for rabbit breeding programs since this trait is commonly selected to indirectly improve FE. Apart from that, the commercial application of [12] identified different QTL regions for both traits. Such findings support the existence of different genetic backgrounds for these traits.…”

“…For each trait, the corresponding dataset was randomly divided into 5 folds, 4 of which constituted the learning dataset, and the remaining was used as the validation dataset. Before fitting the sPLSR on the learning dataset, optimal tuning parameters sparsity and number of latent components were chosen by an internal 5-fold cross-validation using cv.spls() function of the "spls" R package [64-Chung et al, 2019] within ranges (0.01-0.99) and (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20) for sparsity and number of latent components, respectively. With the tuning parameters returned by the cv.spls() function, the combination that resulted in the minimum mean squared prediction error (MSPE) was used to finally fit the sPLSR to the learning dataset by the function spls().…”