Forty-two and 19 SNPs with a Bayes factor greater than 150 were detected for HR and AFC, respectively (Figs. 1 and 2). As expected, all SNPs that were significant for AFC were also significant for HR (Table 1), since both traits are indicators of reproductive efficiency in beef cattle and should be, at least in part, under the control of the same groups of genes. Gene symbols and their respective names are in Table 2.
Table 1 Significant SNPs for heifer rebreeding and age at first calving
Table 2 Gene symbols and full names
The 42 SNPs that were significant for HR are located within or next to 35 genes that are distributed over 18 chromosomes; of these, 27 are protein-encoding genes, six are pseudogenes, and two are miscellaneous noncoding (nc) RNAs. A pseudogene is a nucleotide sequence that is similar to a normal gene, but is not expressed. Miscellaneous nc RNAs are small noncoding RNAs sequences that do not carry information for producing proteins but can have various important functions in the cell.
Genes that contained significant SNPs were listed (distance = 0); if the SNPs were not located within genes, the closest gene (either on the 5’ or 3’ end) was identified with the distance between SNP and gene. Espigolan et al. [20] reported that since LD in Nellore cattle was lower than in Bos taurus breeds, a distance of less than 30 kb was required for genomic prediction/association. However, in our study, although some genes were more than 30 kb away from a significant SNP, they were retained because they were the closest annotated genes.
The identified genes can be grouped according to metabolic function, including groups of genes that are involved in the formation and physiology of the central nervous system (LOC781274, NEUROD6, GPR98, GALC, MIR124A-2, MAPK8IP3, RBFOX1, and ODZ3); in the formation and physiology of the female reproductive system (FMRD3, CYP7B1, and LOC782102); in lipid metabolism (ELOVOL5) and in bone growth (DDR2 and COL24A1); genes that act as olfactory receptors (LOC514434) and genes involved in basal metabolism (LOC787257, ADAM22, LOC100295124, LOC100847971, LOC785763, SEL1L, HAAO, KIF16B, CAMK1D, ARMC4, SDCCAG8, LOC783434, GTF2H2, OCLN, LOC529061, LOC529061, FHIT, and LOC782601). The last group of genes plays a role in different metabolic pathways, including cell-cell signaling, protein synthesis and transport, oxygen transport, cell proliferation and survival, transcription and metabolism of nucleotides and histones, membrane transport, and the formation of the cell membrane and its receptors, among others.
Genes that are involved in the formation and physiology of the central nervous system play a role in reproduction, since they influence neuronal formation, differentiation and communication and the synthesis of reproductive hormones of the hypothalamus-pituitary axis by acting on the hormone cascade that coordinates the estrous cycle in females. Thus, because the genomic regions that are described here are biologically relevant to animal physiology, they are good candidates for marker-assisted selection. Fortes et al. [21–23] and Hawken et al. [24] identified other genes that belong to the same group, i.e., genes acting on the central nervous system, associated with puberty and fertility traits in Brahman cattle and tropical composite breeds.
Similarly, genes that are involved in the formation and physiology of the female reproductive system should affect the onset of the estrous cycle, conception, pregnancy establishment and maintenance, and calving. In general, the genes of this group that were detected here act on the formation of specific tissues and hormone synthesis. One example is the LOC782102 gene which encodes a component of the egg membrane that is responsible for sperm attraction. Polymorphisms in LOC782102 may result in a protein that is more or less functional for spermatozoid recognition and thus pregnancy will be either facilitated or impaired.
Lipid metabolism is intimately related to reproduction and according to [25], in dairy cattle, the success of postpartum rebreeding depends on the accumulation of fat reserves in the animal, which permits the cow to start cycling again. During the postpartum period, dairy cows enter a state of negative energy balance and mobilize body fat for milk production since they are unable to meet their energy requirement solely through feeding. As a consequence, the presence of favorable alleles of genes related to energy metabolism may be associated with better reproductive performance. Many genes related to fat metabolism and, consequently, to reproduction have been described in dairy cattle [26, 27]. This mechanism has already been demonstrated in beef cattle and genes that are part of this metabolic pathway have been reported [21].
It is known that growth influences reproduction as demonstrated by the observation that heavier animals reach sexual maturity later in life [28]. Growth-related genes have been associated with fertility and puberty in Zebu and tropical composite breeds [23, 24], in agreement with the results obtained here for genes related to bone growth.
Our results on the chromosome location of the identified SNPs are similar to those of previous investigations on the association between SNPs and reproductive traits. Sahana et al. [29] detected SNPs for fertility traits on most of the chromosomes in Danish and Swedish Holstein females, which were associated with pregnancy rate, interval from first to last insemination in cows, number of inseminations per conception in cows, and interval from calving to first insemination. The authors reported the presence of significant SNPs in a region between 28.5 and 68.06 Mb on BTA13 (BTA for Bos taurus chromosome), which is larger than the region that we identified here. However, the SNPs that we found significant for AFC and HR are within the region reported in [29].
Schulman et al. [30] identified significant SNPs on BTA27 between 6.08 and 21.65 Mb, which were associated with non-return rate for heifers in Finnish Ayrshire heifers. Here, we detected three significant SNPs on BTA27 in the region between 3.08 and 44.15 Mb, which, although much larger than that reported in [30], contains an SNP at 12.3 Mb very close to the SNP identified by [30], i.e., at 11.2 Mb. This result suggests the presence of a QTL on this chromosome.
Pausch et al. [31] described three significant SNPs for calving ease on BTA21 in Fleckvieh females at 2.15, 2.33, and 2.38 Mb. Although we also detected one SNP on BTA21, it is located at quite a large distance from those reported by Pausch et al. [31].
Hawken et al. [24] reported 66 significant SNPs (P < 0.001) for postpartum anestrous interval in Brahman cattle. Although their results showed that BTA3 and 14 contained the largest numbers of SNPs, the most significant SNP was on BTA6 at 118 Mb. In our case, we found no significant SNP on BTA6. The same authors also described 68 significant SNPs for first postpartum ovulation before weaning mainly on BTA3, 6, 14, 17 and 21 in Brahman cattle. The most significant SNP was at 112.3 Mb on BTA3. In our case, three significant SNPs were detected on BTA3, but in the region between 6.8 and 58.3 Mb.
Although several studies have reported significant SNPs associated with fertility traits in cattle on BTA14 [23, 24, 29–31], we did not detect any significant SNP associated with HR and AFC on this chromosome. This may be due to genetic differences between Bos taurus and Bos indicus, since Bos indicus females reach puberty later than Bos taurus females. Evidence from Hawken et al. [23], who reported that the number of significant SNPs on BTA14 associated with fertility traits was much smaller in Brahman cattle than that from other studies in Bos taurus [29–31], supports this hypothesis.
Taken together, the 42 SNPs significant for HR and the 19 SNPs significant for AFC explained 11.35 % (Table 3) and 6.42 % (Table 4) of the phenotypic variance of these traits, respectively. These SNPs will be useful to generate a specific panel for Nellore animals.
Table 3 Significant SNPs for heifer rebreeding with chromosome number, percentage of phenotypic variance explained by the SNP (%PV) and cumulative percentage (%CPV)
Table 4 Significant SNPs for age at first calving with chromosome number, percentage of phenotypic variance explained by the SNP (%PV) and cumulative percentage (%CPV)