Transcriptome-wide association studies identify candidate genes for carcass and meat traits in meat rabbits

Meat rabbits are a small herbivorous livestock and have been popularly raised in China for producing high-quality meat. Therefore, it is economically important to genetically improve both carcass performance and meat quality in meat rabbits. However, we still know less about the underlying candidate genes that may determine phenotypic variation on carcass and meat traits of meat rabbits. The main objective of this study was to identify candidate genes whose mRNA expression levels may be significantly involved in regulating carcass and meat traits of meat rabbits based on the transcriptome-wide association studies (TWAS). Five carcass traits of the carcass weight (CW), dressing out percentage (DP), cut weight of hind legs (LW), weight ratio of cut hind legs to carcass (RLW), and weight of visceral and interscapular fat (WF), as well as two meat traits of the drip loss (DL) and cooking loss (CL) were phenotyped in a F1 crossbred population (N = 119) between Zika rabbits and Sichuan White rabbits. The effects of mRNA expression levels of a total of 10,288 genome-wide genes on these seven traits were statistically estimated using the mixed linear model, in which the polygenic background effects were accounted for. Our results revealed two candidate genes (RDH5 and MTARC2) that were statistically significantly associated with LW trait (the adjusted p values <0.05), whereas no gene reached the statistically significant threshold for all the remaining six traits. Because of the relatively small sample size analyzed, we alternatively selected 20 candidate genes with the lowest p values for every trait and subjected them to functional enrichment analyses, which identified three Gene Ontology (GO) terms that were significantly enriched by the candidate genes of CW and RLW traits. In conclusion, this study used TWAS approach to successfully reveal several candidate genes whose mRNA expression levels may be involved in regulating carcass and meat traits in meat rabbits, which are helpful to explore the underlying molecular mechanism in the future studies.

Introduction

Domestic rabbits (Oryctolagus cuniculus) are a prolific small herbivorous livestock and have been popularly raised for producing meat, as well as wool and fur. China is the largest producer and consumer of rabbit meat around the world (1), with an approximate production of 543 thousand in 2023. Due to high contents of protein, unsaturated fatty acids, and essential amino acids, as well as low fat and cholesterol levels (2, 3), it has been becoming more and more popular to consume rabbit meat and products. In this context, it is desirable to genetically improve both carcass and meat traits in meat rabbits, which have been largely overlooked in comparison to other common livestock, such as pigs (4) and beef cattle (5).

On the whole, the estimated heritabilities were moderate to high for the growth-related traits in meat rabbits, which had been comprehensively reviewed by Blasco et al. (6), García et al. (7), and Helal et al. (8). Many studies have been already conducted to explore the candidate genetic markers and genes that may be significantly associated with growth traits in different breeds of meat rabbits. Gencheva et al. (9) revealed that single-nucleotide polymorphisms (SNPs) of growth hormone and growth hormone receptor genes were significantly associated with individual body weight during the growing periods measured at 35, 70 and 90 days of age in Californian breed rabbits. Using genotyping-by-sequencing technique, genome-wide SNPs were explored among five rabbit breeds, for which the associations were analyzed with various production and morphological traits (10). In contrast, both carcass and meat traits in meat rabbits have been less studied yet. Yang et al. (11) performed the genome-wide association studies (GWAS) of growth, carcass and meat quality traits in meat rabbits, and revealed some candidate SNPs and genes that were significantly associated with some of these traits. Based on GWAS approach, some candidate SNPs and genes were also suggested to be significantly associated with intramuscular fat and fatty acid composition traits in rabbits regarding animal additive genetic effects (12) and maternal genetic effects (13).

One of the foundations of GWAS is dependent on linkage disequilibrium between the genotyped SNPs and causative variants. Therefore, the detection power of GWAS will be influenced by both marker distribution density genotyped and population genomic architecture studied. Another limitation of GWAS is the gaps between the significant variants identified and the finally manifest phenotype, which may be further mediated by some transcriptional regulations. To overcome these limitations, gene expression level-based association analyses, i.e., the transcriptome-wide association studies (TWAS), were suggested to be a promising approach to identify candidate genes that significantly regulate the complex traits (14). Recently, we successfully employed the long-read sequencing technology approach for profiling genome-wide gene expression levels in Longissimus dorsi tissue and subjected them to association analyses with growth traits in meat rabbits (15). In this study, we further investigated candidate genes whose mRNA expression levels are significantly associated with seven carcass and meat traits in meat rabbits, which may be helpful us to better understand the functional genes and pathways that may be involved in regulating these complex traits.

Materials and methods

Animals and traits

The animal population was established and first descripted in our previous study (15). In brief, a F1 crossbred population (N = 119) between Zika rabbits and Sichuan White rabbits was established in the research farm. All rabbits were weaned and finished at 35 and 84 days of age, respectively. After slaughter, a total of seven carcass and meat traits were measured and collected, including the carcass weight (CW, g), dressing out percentage (DP, %), cut weight of hind legs (LW, g), weight ratio of cut hind legs to carcass (RLW, %), weight of visceral and interscapular fat (WF, g), drip loss (DL, %), and cooking loss (CL, %). The raw phenotypic records were quality-controlled through removing the possible outliers that reside outside the median ± 3.5 × median absolute deviation (16). The definitions of these traits and statistical description are in Table 1.

Table 1

Table 1. Descriptive statistics of the carcass and meat traits analyzed in this study.

Gene expression dataset

The gene mRNA expression dataset was first reported in our previous study (15). In brief, all 119 rabbits were euthanized at 84 days of age and subjected to the collection of Longissimus dorsi tissue. About one μg of the quantified RNA sample was used for Oxford Nanopore single-molecule long-read sequencing. The raw sequencing reads were quality-controlled and normalized across samples using TMM method (17), after which the gene expression levels were quantified and measured as counts per million reads (CPM) using edge R R package v4.0.16 (18). Finally, the mRNA expression dataset consisted of a total of 10,288 genes that were effectively expressed in the Longissimus dorsi tissue among 115 samples (four samples were excluded after quality controls), and the effectively expressed genes were defined if they were present (the raw read count ≥2) within more than 30% of individuals.

Association analyses and statistical model

According to Mai et al. (19), associations of gene mRNA expression levels with carcass and meat traits were statistically analyzed using the mixed linear model (MLM), which adjusted the polygenic background effects and was defined as:

$\mathrm{y}={\mathrm{w}}_i{b}_i+C\beta +\mathrm{W}\mathrm{u}+\mathrm{e}$

where $\mathrm{y}$ is the phenotypic value vector of each trait studied; $b_i$ is the estimated effect of gene $i$ on the trait with its mRNA expression level vector ${\mathrm{w}}_i$ ; $\mathrm{C}$ is the incidence matrix of fixed effects, which included the gender (two levels) for LW, RLW, WF, and DL, and gender and birth season (three levels) for CW, DP, and CL, with the effect vector $\overrightarrow{\beta }$ . These fixed effects were selected for inclusion in the model based on the backward elimination procedure (p < 0.05) of the lm function in R software. $\mathrm{W}$ is the matrix consisting of the standardized expression levels for all included genes, $\mathrm{u}$ is the vector of the polygenic effects of all genes on the trait with $\mathrm{u}~\mathrm{N}\left(0,\mathrm{O}{\sigma }_o^2\right)$ , in which $\mathrm{O}$ is the expression level-based similarity matrix (ORM) and ${\sigma }_o^2$ is proportion of phenotype variance explained by all genes; $\mathrm{e}$ is the residual effect vector with $\mathrm{e}~\mathrm{N}\left(0,\mathrm{I}{\sigma }_e^2\right)$ , and the $\mathrm{I}$ is an identity matrix. The element of $\mathrm{O}$ matrix between individual $j$ and $k$ was computed as (20):

$O_{jk}=\frac{1}{m}{\displaystyle \sum }_i\left(x_{ij}-{\mu }_i\right)/\left(x_{ik}-{\mu }_i\right)/{\sigma }_i^2\text{,}$

Where $x_{ij}$ and $x_{ik}$ are the normalized expression levels of gene $i$ in the individual $j$ and $k$ , respectively; ${\mu }_i$ and ${\sigma }_i^2$ are the mean and variance of expression level for gene $i$ across all individuals, respectively. In order to avoid the double fitting of one target gene simultaneously considered as both fixed and random effects in the MLM, the MOMENT (multi-component MLM-based omic association excluding the target) module implemented in OSCA software v0.46 was used (20).

Multiple comparison adjustment and functional analyses

To explore possible issue of population stratification, quantile-quantile (QQ) plots were first visualized according to the raw p values obtained for all genes. Second, genomic inflation factor (λ) was calculated as the ratio of the median of the observed distribution of ${\chi }^2$ statistic to the expected median, as $\widehat{\lambda }=\mathrm{median}\left({\chi }^2\right)/0.4549$ , for which the 95% confidence interval (CI) was further derived. To address multiple comparison problem, false discovery rate (FDR) approach was used for adjusting the raw p values and 0.05 was set to the statistically significant threshold (21). The gene with p value lower than this FDR-adjusted threshold was considered to be statistically significant.

In addition to the candidate genes that were revealed to be statistically significant according to the threshold defined, the 20 genes with lowest p values were selected for every trait and subjected to functional enrichment analyses. Because of the relatively small sample size included in this study, this more flexible approach may identify the biologically relevant genes, whereas whose p values did not reach the defined threshold. The enrichment analyses were conducted using the g:GOSt function of the g:Profiler web server (22), including the target data sets of the Gene Ontology (GO) terms (23) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways (24). The default parameters and methods for adjusting for multiple comparison testing (i.e., the build-in g:SCS method) were used, and 0.05 was set to the statistically significant threshold.

Results

The descriptive statistics of all seven traits are in Table 1. The mean (± SD) of CW and DP was 917.7 ± 132.5 g and 46.2 ± 2.6%, respectively. The absolute and relative weight of hind legs was 340.7 g (LW, SD = 50.0 g) and 37.5% (RLW, SD = 1.5%). The WF ranged from 0.9 to 23.3 g, with the mean (± SD) of 9.2 ± 6.1 g. The two meat traits had the means (± SD) of 6.1 ± 2.8% for DL and 65.9 ± 5.7% for CL. The phenotypic correlations were further analyzed among all seven traits (Figure 1). CW had the significant positive correlations with DP (r = 0.598, p < 0.001), LW (r = 0.745, p < 0.001), and WF (r = 0.304, p < 0.01), whereas weak negative correlations were observed with RLW (r = −0.293, p < 0.01) and DL (r = −0.361, p < 0.001). There was no significant correlation between LW and RLW. With an exception of CW, WF did not show significant correlation with other five traits. There was weak but significant negative correlation between DL and CL (r = −0.214, p < 0.05).

Figure 1

Figure 1. Phenotypic correlations among seven carcass traits and meat traits. CW, carcass weight; DP, dressing out percentage; LW, cut weight of hind legs; RLW, weight ratio of cut hind legs to carcass; WF, weight of visceral and interscapular fat; DL, drip loss; CL, cooking loss. *, **, and *** indicate the statistical significance at 0.05, 0.01, and 0.001, respectively.

The association analysis results of gene mRNA expression levels with carcass and meat traits are in Figure 2. Based on the visualization of QQ plots and the quantitative results of λ factors that ranged from 0.93 (95% CI: 0.74–1.12) for LW to 1.03 (95% CI: 0.84–1.22) for CW and DP traits (Supplementary Figure S1), it can be concluded that there was no obvious population stratification. According to the statistically significant threshold defined, we identified two protein-encoding genes that were significantly associated with LW trait, including the retinol dehydrogenase 5 (RDH5) gene located on Chr4 (p = 1.61 × 10⁻⁶), and the mitochondrial amidoxime reducing component 2 (MTARC2) gene located on Chr16 (p = 6.80 × 10⁻⁶). However, no gene reached the statistically significant threshold for other six traits. The lowest p values of genes were 3.11 × 10⁻⁵ for CW, 1.82 × 10⁻⁴ for DP, 5.02 × 10⁻⁴ for RLW, 4.08 × 10⁻⁴ for WF, 9.21 × 10⁻⁵ for DL, and 1.05 × 10⁻⁴ for CL, respectively (Supplementary Table S1).

Figure 2

Figure 2. Manhattan plots for the gene expression level-based association analyses. CW, carcass weight; DP, dressing out percentage; LW, cut weight of hind legs; RLW, weight ratio of cut hind legs to carcass; WF, weight of visceral and interscapular fat; DL, drip loss; CL, cooking loss. The significance thresholds are denoted by the dashed lines for every trait. Each point represents each gene, and the significant candidate genes revealed in the present study were further denoted by their gene names.

For every trait, we alternatively selected 20 protein-coding genes with the lowest p values and subjected them to functional enrichment analyses; the detailed information of these genes, such as raw p values and genomic positions, are in Supplementary Table S1. Among them, we revealed two significant GO terms for these candidate genes of CW trait, of CW trait (Table 2), including the molecular function of “glycosaminoglycan binding” (GO:0005539) and cellular component of “cell junction” (GO:0030054), which were significantly enriched by three genes (proprotein convertase subtilisin/kexin type 6, PCSK6; layilin, LAYN; decorin, DCN) and five genes (sterile alpha motif domain containing 4A, SAMD4A; ribosomal protein L14, RPL14; WD repeat and FYVE domain containing 1, WDFY1; cadherin 8, CDH8; Yes1 associated transcriptional regulator, YAP1), respectively. Furthermore, there were 12 genes significantly enriched into a cellular component of “cytoplasm” for RLW trait, including calumenin (CALU), Cbl proto-oncogene B (CBLB), ribosomal protein L7 (RPL7), inorganic pyrophosphatase 1 (PPA1), cathepsin C (CTSC), TATA-box binding protein associated factor 5 like (TAF5L), protein O-glucosyltransferase 1 (POGLUT1), ATPase phospholipid transporting 8A1 (ATP8A1), integrator complex subunit 7 (INTS7), component of oligomeric golgi complex 3 (COG3), family with sequence similarity 114 member A1 (FAM114A1), and synaptophysin like 1 (SYPL1). There was no significantly enriched biological function for other five traits, and no KEGG was significantly enriched for any of all traits analyzed.

Table 2

Table 2. The significantly enriched GO terms for the 20 candidate genes with the lowest p values for every trait.

Discussion

A large number of GWAS have been successfully used for identifying causative genetic variants and genes that are significantly associated with diverse production traits in all kinds of livestock (25). However, there are some gaps from GWAS results to biological insights. Despite of the improved detection power of gene expression level-based association analysis as illustrated in human genetic studies (26), TWAS approach has not been widely employed yet in livestock mainly because of the relatively high cost on quantifying transcriptomic expression in a large-scale sample. Based on the imputed gene expression data, Chhotaray et al. (27) used the TWAS approach for identifying significant causative loci for milk production and its composition in 136 Murrah buffaloes. In Huaxi cattle, both GWAS and TWAS approaches were integrated to reveal several candidate genes that are significantly associated with the weight of Longissimus dorsi muscle (28). We recently profiled the transcriptomic landscape using long-read RNA sequencing technology and successfully performed TWAS for several growth traits of meat rabbits (15). In the present study, we similarly used TWAS approach for further revealing the candidate genes for carcass and meat traits in meat rabbits.

In meat rabbits, carcass performance has the important economic values. The differential carcass traits were previously reported between two rabbit genotypes (29). Based on 186 microsatellite and three SNP markers, a highly significant quantitative trait loci (QTL) located on Chr7 was revealed to be associated with carcass weights in rabbits (30). Using GWAS approach, Yang et al. (11) identified tens of genome-wide SNPs that are significantly associated with the carcass traits in rabbits, and suggested the most possible causative gene of lipase A, lysosomal acid type (LIPA) that encodes the lipase A, the lysosomal acid lipase (cholesterol ester hydrolase). In the present study, our TWAS did not identify any gene that is statistically significantly associated with the carcass weight. However, two functional GO terms were significantly enriched among the eight candidate genes that had the relatively low p values. In both of them, the GO term of “glycosaminoglycan binding” was previously suggested to be involved in the regulation of water holding capacity in Chinese Simmental beef cattle (31). The involved DCN gene that encodes a member of the small leucine-rich proteoglycan family of proteins was also found to be differentially expressed in vitamin A-restricted Japanese Black steers (32). Furthermore, SAMD4A and CDH8 genes found in this study were previously suggested to be candidate genes associated with drip loss (33) and carcass traits (34) in pigs, respectively.

We found in the present study that CW had the moderate and high positive correlations with DP and LW. However, there was no gene or functional gene set revealed to be significantly associated with DP, for which several candidate genes were previously reported (11). Regarding the important carcass cut trait of LW in meat rabbits (35), we identified two statistically significant genes of RDH5 and MTARC2. MARC2 is involved in adipogenesis and regulated by nutritional status (36). The knock-out mice of MARC2 were resistant to high-fat diet-induced obesity through being responsible for N-reductive biotransformation (37). MTARC2 was also reported to be a candidate gene significantly associated with the fatness in pigs (38) and meat performance in quails (39). RDH5 is involved in the retinol metabolism pathways, and the increased retinol metabolism promotes lipolysis in adipose tissue (40). Based on copy number variations, RDH5 was previously suggested to be associated with feed conversion rate in Nellore cattle (41). In this context, we suggested that both RDH5 and MTARC2 may be highly possibly involved in the regulation on muscle growth in meat rabbits, which can be subjected to selection with aiming to genetically improve the carcass or meat quality traits. Here, we did not detect genes that are significantly associated with the simple measure of WF trait in meat rabbits, whereas there were studies that previously suggested a common genetic regulation between fat deposition in muscle and carcass performance (42). Furthermore, we cannot exclude possible association of RDH5 and MTARC2 genes with other intramuscular fat traits.

Based on the GWAS, several genes, such as EWS RNA binding protein 1 (EWSR1), apolipoprotein L domain containing 1 (APOLD1), and phospholipase B domain containing 1 (PLBD1), were suggested to be significantly associated with intramuscular fat traits in the divergently selected lines of rabbits (43). Similarly, 15 genome-wide significant SNPs were detected to affect feed conversion rate and residual feed intake in rabbits (44). The genetic polymorphisms of five genes of insulin-like growth factor-binding proteins (IGFBPs) were scanned in four rabbit breeds and revealed to significantly affect the acidity, color, and shear force traits of rabbit meat (45). For the two meat traits analyzed in the present study, we revealed that CW has a weak negative correlation with DL, which is favorable as the individuals that have a greater carcass weight will have the decreased drip loss. Similarly, no significant gene or functional gene set was identified in the present study to affect the two meat traits.

There are two practical implications regarding the results obtained in the present study. First, it revealed several candidate genes of carcass and meat traits in meat rabbits based on the association analyses of gene mRNA expression level, which will facilitate the follow-up studies to dissect underlying molecular regulation mechanism. Second, the candidate genes identified in the present study can be included for optimizing the genomic evaluation models, such as through assigning the greater weights to the genetic variants located in or tightly linked to these candidate genes. On the other hand, we acknowledged that one limitation of this study is the relatively small sample size in comparison with the ordinary GWAS, which will decrease the detection power. Another limitation is that only two simple traits of meat quality were phenotyped, and more desirable meat characteristics, such as the intramuscular fatty acid contents and composition traits, remain to be investigated.

Conclusion

In this study, we measured seven carcass and meat traits in meat rabbits and conducted the association analyses using transcriptomic data. Several candidate genes, such as RDH5 and MTARC2, were suggested to be significantly associated with some of these traits. The results are helpful to explore underlying molecular mechanism and optimize genomic evaluation models to genetically improve the carcass performance and meat quality in meat rabbits.

Data availability statement

The original contributions presented in the study are publicly available. This raw 295 sequencing reads can be found here: https://ngdc.cncb.ac.cn/gsa/; CRA013219. The 296 normalized and quantified gene expression data can be found here: 297 https://doi.org/10.6084/m9.figshare.27804465.v1.

Ethics statement

The animal study was approved by Institutional Animal Care and Use Committee of Sichuan Agricultural University. The study was conducted in accordance with the local legislation and institutional requirements.

Author contributions

HH: Formal analysis, Methodology, Writing – review & editing, Resources. XT: Methodology, Writing – review & editing, Formal analysis, Writing – original draft. ZK: Methodology, Writing – review & editing. GW: Methodology, Writing – review & editing. XJ: Methodology, Resources, Writing – review & editing. WS: Methodology, Resources, Writing – review & editing. S-JL: Methodology, Resources, Writing – review & editing. S-YC: Formal analysis, Funding acquisition, Methodology, Resources, Writing – original draft, Writing – review & editing.

Funding

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This study was financially supported by National Natural Science Foundation of China (32072684), Earmarked Fund for China Agriculture Research System (CARS-44-A-2), and Science & Technology Department of Sichuan Province (2021YFYZ0033).

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fvets.2024.1453196/full#supplementary-material

References

1. Zamaratskaia, G, Havrysh, O, Korzeniowska, M, and Getya, A. Potential and limitations of rabbit meat in maintaining food security in Ukraine. Meat Sci. (2023) 204:109293. doi: 10.1016/j.meatsci.2023.109293

Crossref Full Text | Google Scholar

2. Li, S, Zeng, W, Li, R, Hoffman, LC, He, Z, Sun, Q, et al. Rabbit meat production and processing in China. Meat Sci. (2018) 145:320–8. doi: 10.1016/j.meatsci.2018.06.037

PubMed Abstract | Crossref Full Text | Google Scholar

3. Siddiqui, SA, Gerini, F, Ikram, A, Saeed, F, Feng, X, and Chen, Y. Rabbit meat—production, consumption and consumers’ attitudes and behavior. Sustain For. (2023) 15:2008. doi: 10.3390/su15032008

Crossref Full Text | Google Scholar

4. Chen, SY, Freitas, PHF, Oliveira, HR, Lazaro, SF, Huang, YJ, Howard, JT, et al. Genotype-by-environment interactions for reproduction, body composition, and growth traits in maternal-line pigs based on single-step genomic reaction norms. Genet Sel Evol. (2021) 53:51. doi: 10.1186/s12711-021-00645-y

PubMed Abstract | Crossref Full Text | Google Scholar

5. Berton, MP, de Antunes Lemos, MV, Seleguim Chud, TC, Bonvino Stafuzza, N, Kluska, S, Amorim, ST, et al. Genome-wide association study between copy number variation regions and carcass- and meat-quality traits in Nellore cattle. Anim Prod Sci. (2021) 61:731–44. doi: 10.1071/an20275

Crossref Full Text | Google Scholar

6. Blasco, A, Nagy, I, and Hernandez, P. Genetics of growth, carcass and meat quality in rabbits. Meat Sci. (2018) 145:178–85. doi: 10.1016/j.meatsci.2018.06.030

PubMed Abstract | Crossref Full Text | Google Scholar

7. María-Luz, G, and María-José, A. The genetic improvement in meat rabbits. In: A María-José, María Luz de la, P García, and PD Kevin, editors. Lagomorpha characteristics. Rijeka: IntechOpen (2020). p. Ch. 5.

Google Scholar

8. Helal, M, Hany, N, Maged, M, Abdelaziz, M, Osama, N, Younan, YW, et al. Candidate genes for marker-assisted selection for growth, carcass and meat quality traits in rabbits. Anim Biotechnol. (2022) 33:1691–710. doi: 10.1080/10495398.2021.1908315

PubMed Abstract | Crossref Full Text | Google Scholar

9. Gencheva, DG, Velikov, KP, and Veleva, PM. Association analysis of nucleotide polymorphisms in growth hormone (GH) and its receptor (GHR) with body weight in Californian rabbits. World Rabbit Sci. (2022) 30:95–102. doi: 10.4995/wrs.2022.13127

Crossref Full Text | Google Scholar

10. Li, C, Li, Y, Zheng, J, Guo, Z, Mei, X, Lei, M, et al. Trait analysis in domestic rabbits (Oryctolagus cuniculus f. domesticus) using SNP markers from genotyping-by-sequencing data. Animals. (2022) 12. doi: 10.3390/ani12162052

PubMed Abstract | Crossref Full Text | Google Scholar

11. Yang, X, Deng, F, Wu, Z, Chen, SY, Shi, Y, Jia, X, et al. A genome-wide association study identifying genetic variants associated with growth, carcass and meat quality traits in rabbits. Animals. (2022) 12:2052. doi: 10.3390/ani10061068

PubMed Abstract | Crossref Full Text | Google Scholar

12. El Nagar, AG, Heddi, I, Sosa-Madrid, BS, Blasco, A, Hernandez, P, and Ibanez-Escriche, N. Genome-wide association study of maternal genetic effects on intramuscular fat and fatty acid composition in rabbits. Animals. (2023) 13:3071. doi: 10.3390/ani13193071

PubMed Abstract | Crossref Full Text | Google Scholar

13. Laghouaouta, H, Sosa-Madrid, BS, Zubiri-Gaitan, A, Hernandez, P, and Blasco, A. Novel genomic regions associated with intramuscular fatty acid composition in rabbits. Animals. (2020) 10:2090. doi: 10.3390/ani10112090

PubMed Abstract | Crossref Full Text | Google Scholar

14. Wainberg, M, Sinnott-Armstrong, N, Mancuso, N, Barbeira, AN, Knowles, DA, Golan, D, et al. Opportunities and challenges for transcriptome-wide association studies. Nat Genet. (2019) 51:592–9. doi: 10.1038/s41588-019-0385-z

Crossref Full Text | Google Scholar

15. Jia, X, Kang, Z, Wang, G, Zhang, K, Fu, X, Li, C, et al. Long-read sequencing-based transcriptomic landscape in longissimus dorsi and transcriptome-wide association studies for growth traits of meat rabbits. Front Vet Sci. (2024) 11:1320484. doi: 10.3389/fvets.2024.1320484

PubMed Abstract | Crossref Full Text | Google Scholar

16. Leys, C, Ley, C, Klein, O, Bernard, P, and Licata, L. Detecting outliers: do not use standard deviation around the mean, use absolute deviation around the median. J Exp Soc Psychol. (2013) 49:764–6. doi: 10.1016/j.jesp.2013.03.013

Crossref Full Text | Google Scholar

17. Robinson, MD, and Oshlack, A. A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol. (2010) 11:R25. doi: 10.1186/gb-2010-11-3-r25

PubMed Abstract | Crossref Full Text | Google Scholar

18. Robinson, MD, McCarthy, DJ, and Smyth, GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. (2010) 26:139–40. doi: 10.1093/bioinformatics/btp616

PubMed Abstract | Crossref Full Text | Google Scholar

19. Mai, J, Lu, M, Gao, Q, Zeng, J, and Xiao, J. Transcriptome-wide association studies: recent advances in methods, applications and available databases. Commun Biol. (2023) 6:899. doi: 10.1038/s42003-023-05279-y

PubMed Abstract | Crossref Full Text | Google Scholar

20. Zhang, F, Chen, W, Zhu, Z, Zhang, Q, Nabais, MF, Qi, T, et al. OSCA: a tool for omic-data-based complex trait analysis. Genome Biol. (2019) 20:107. doi: 10.1186/s13059-019-1718-z

PubMed Abstract | Crossref Full Text | Google Scholar

21. Benjamini, Y, and Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc B. (1995) 57:289–300. doi: 10.1111/j.2517-6161.1995.tb02031.x

Crossref Full Text | Google Scholar

22. Kolberg, L, Raudvere, U, Kuzmin, I, Adler, P, Vilo, J, and Peterson, H. G:profiler-interoperable web service for functional enrichment analysis and gene identifier mapping (2023 update). Nucleic Acids Res. (2023) 51:W207–12. doi: 10.1093/nar/gkad347

PubMed Abstract | Crossref Full Text | Google Scholar

23. Consortium GO. The gene ontology resource: 20 years and still GOing strong. Nucleic Acids Res. (2019) 47:D330–8. doi: 10.1093/nar/gky1055

Crossref Full Text | Google Scholar

24. Kanehisa, M, Sato, Y, Furumichi, M, Morishima, K, and Tanabe, M. New approach for understanding genome variations in KEGG. Nucleic Acids Res. (2019) 47:D590–5. doi: 10.1093/nar/gky962

PubMed Abstract | Crossref Full Text | Google Scholar

25. Sharma, A, Lee, JS, Dang, CG, Sudrajad, P, Kim, HC, Yeon, SH, et al. Stories and challenges of genome wide association studies in livestock—a review. Asian Australas J Anim Sci. (2015) 28:1371–9. doi: 10.5713/ajas.14.0715

PubMed Abstract | Crossref Full Text | Google Scholar

26. Bosse, Y, Li, Z, Xia, J, Manem, V, Carreras-Torres, R, Gabriel, A, et al. Transcriptome-wide association study reveals candidate causal genes for lung cancer. Int J Cancer. (2020) 146:1862–78. doi: 10.1002/ijc.32771

PubMed Abstract | Crossref Full Text | Google Scholar

27. Chhotaray, S, Vohra, V, Uttam, V, Santhosh, A, Saxena, P, Gahlyan, RK, et al. TWAS revealed significant causal loci for milk production and its composition in Murrah buffaloes. Sci Rep. (2023) 13:22401. doi: 10.1038/s41598-023-49767-x

PubMed Abstract | Crossref Full Text | Google Scholar

28. Liang, M, An, B, Deng, T, Du, L, Li, K, Cao, S, et al. Incorporating genome-wide and transcriptome-wide association studies to identify genetic elements of longissimus dorsi muscle in Huaxi cattle. Front Genet. (2023) 13:982433. doi: 10.3389/fgene.2022.982433

PubMed Abstract | Crossref Full Text | Google Scholar

29. Paci, G, Cecchi, F, Preziuso, G, Ciampolini, R, and D’Agata, M. Carcass traits and meat quality of two different rabbit genotypes. Ital J Anim Sci. (2012) 11:e45. doi: 10.4081/ijas.2012.e45

Crossref Full Text | Google Scholar

30. Sternstein, I, Reissmann, M, Maj, D, Bieniek, J, and Brockmann, GA. A comprehensive linkage map and QTL map for carcass traits in a cross between Giant Grey and New Zealand white rabbits. BMC Genet. (2015) 16:16–2. doi: 10.1186/s12863-015-0168-1

PubMed Abstract | Crossref Full Text | Google Scholar

31. Du, L, Chang, T, An, B, Liang, M, Duan, X, Cai, W, et al. Transcriptome profiling analysis of muscle tissue reveals potential candidate genes affecting water holding capacity in Chinese Simmental beef cattle. Sci Rep. (2021) 11:11897. doi: 10.1038/s41598-021-91373-2

PubMed Abstract | Crossref Full Text | Google Scholar

32. Hayashi, M, Kido, K, and Hodate, K. Microarray analysis of longissimus thoracis muscle gene expressions in vitamin A-restricted Japanese black steers in middle fattening stage. Anim Sci J. (2018) 89:88–96. doi: 10.1111/asj.12898

PubMed Abstract | Crossref Full Text | Google Scholar

33. Welzenbach, J, Neuhoff, C, Heidt, H, Cinar, MU, Looft, C, Schellander, K, et al. Integrative analysis of metabolomic, proteomic and genomic data to reveal functional pathways and candidate genes for drip loss in pigs. Int J Mol Sci. (2016) 17:1426. doi: 10.3390/ijms17091426

PubMed Abstract | Crossref Full Text | Google Scholar

34. Park, J. Genome-wide association study to reveal new candidate genes using single-step approaches for productive traits of Yorkshire pig in Korea. Anim Biosc. (2024) 37:451–60. doi: 10.5713/ab.23.0255

PubMed Abstract | Crossref Full Text | Google Scholar

35. Szendrő, Z, Metzger, S, Romvári, R, Szabó, A, Locsmándi, L, Petrási, Z, et al. Effect of divergent selection based on CT measured hind leg muscle volume on productive and carcass traits of rabbits In: 9th world rabbit congress. Z Szendrő. (ed.) Verona: World Rabbit Science Association (2008). 249–53.

Google Scholar

36. Neve, EP, Kofeler, H, Hendriks, DF, Nordling, A, Gogvadze, V, Mkrtchian, S, et al. Expression and function of mARC: roles in lipogenesis and metabolic activation of ximelagatran. PLoS One. (2015) 10:e0138487. doi: 10.1371/journal.pone.0138487

Crossref Full Text | Google Scholar

37. Rixen, S, Havemeyer, A, Tyl-Bielicka, A, Pysniak, K, Gajewska, M, Kulecka, M, et al. Mitochondrial amidoxime-reducing component 2 (MARC2) has a significant role in N-reductive activity and energy metabolism. J Biol Chem. (2019) 294:17593–602. doi: 10.1074/jbc.ra119.007606

PubMed Abstract | Crossref Full Text | Google Scholar

38. Kogelman, LJ, Cirera, S, Zhernakova, DV, Fredholm, M, Franke, L, and Kadarmideen, HN. Identification of co-expression gene networks, regulatory genes and pathways for obesity based on adipose tissue RNA sequencing in a porcine model. BMC Med Genet. (2014) 7:57. doi: 10.1186/1755-8794-7-57

PubMed Abstract | Crossref Full Text | Google Scholar

39. Volkova, NA, Romanov, MN, Abdelmanova, AS, Larionova, PV, German, NY, Vetokh, AN, et al. Genome-wide association study revealed putative SNPs and candidate genes associated with growth and meat traits in Japanese quail. Genes. (2024) 15:294. doi: 10.3390/genes15030294

PubMed Abstract | Crossref Full Text | Google Scholar

40. Flenkenthaler, F, Landstrom, E, Shashikadze, B, Backman, M, Blutke, A, Philippou-Massier, J, et al. Differential effects of insulin-deficient diabetes mellitus on visceral vs. subcutaneous adipose tissue-multi-omics insights from the Munich MIDY pig model. Front Med. (2021) 8:751277. doi: 10.3389/fmed.2021.751277

Crossref Full Text | Google Scholar

41. de Almeida Santana, MH, Junior, GA, Cesar, AS, Freua, MC, da Costa, GR, da Luz, ESS, et al. Copy number variations and genome-wide associations reveal putative genes and metabolic pathways involved with the feed conversion ratio in beef cattle. J Appl Genet. (2016) 57:495–504. doi: 10.1007/s13353-016-0344-7

PubMed Abstract | Crossref Full Text | Google Scholar

42. Zubiri-Gaitan, A, Blasco, A, Ccalta, R, Satue, K, and Hernandez, P. Intramuscular fat selection in rabbits modifies the fatty acid composition of muscle and liver tissues. Animals. (2022) 12:893. doi: 10.3390/ani12070893

PubMed Abstract | Crossref Full Text | Google Scholar

43. Sosa-Madrid, BS, Hernandez, P, Blasco, A, Haley, CS, Fontanesi, L, Santacreu, MA, et al. Genomic regions influencing intramuscular fat in divergently selected rabbit lines. Anim Genet. (2020) 51:58–69. doi: 10.1111/age.12873

Crossref Full Text | Google Scholar

44. Garreau, H, Labrune, Y, Chapuis, H, Ruesche, J, Riquet, J, Demars, J, et al. Genome wide association study of growth and feed efficiency traits in rabbits. World Rabbit Sci. (2023) 31:163–9. doi: 10.4995/wrs.2023.18215

Crossref Full Text | Google Scholar

45. Migdał, Ł, Migdał, A, Pałka, S, Kmiecik, M, Otwinowska-Mindur, A, Semik-Gurgul, E, et al. Polymorphism within IGFBP genes affects the acidity, colour, and shear force of rabbit meat. Animals. (2023) 13:3743. doi: 10.3390/ani13233743

PubMed Abstract | Crossref Full Text | Google Scholar