Current applications and perspectives of genomic selection in Bos indicus (Nellore) cattle

Innovations in Cattle Breeding Technology: Prospects in the Era of Gene Editing

As a core species in the global livestock industry, cattle play an irreplaceable role in human food security and economic development. Beef cattle and dairy cattle meet the dietary needs of billions of people around the world by providing high-quality protein and dairy products, respectively. With the growth in population and the intensification of the pressure of climate change, traditional breeding techniques may be unsuitable to meet the increasingly growing demands for sustainable and highly adaptable processes. In recent years, the rapid development of genomics, bioinformatics, and gene-editing technologies has provided unprecedented tools and perspectives for the genetic improvement of cattle, driving the precise design and efficient development of new cattle breeds. However, the development of new cattle breeds still faces multiple bottlenecks pertaining to scientific, ethical, and industrialization aspects, which can be addressed through interdisciplinary collaboration. In this review, we will systematically assess the technological progress in the genetic breeding of beef cattle and dairy cattle, analyze the integration path of traditional breeding and modern biotechnology, and explore the future directions of cattle breeding research under the sustainable development goals, with the aim of providing theoretical support for cattle breeding.

Influence of variance component estimates on genomic predictions for growth and reproductive-related traits in Nellore cattle

This study aimed to estimate variance components (VCs) for growth and reproductive traits in Nellore cattle using two relationship matrices (pedigree relationship A matrix and pedigree plus genomic relationship H matrix), and records collected before and after genomic selection (GS) implementation. The study also evaluated how genomic breeding values (GEBV) are affected by variance components and discarding old records. The analysed traits were weight at 120 days (W120), weight and scrotal circumference at 450 days (W450 and SC450, respectively). Three datasets were used to estimate VCs, including all phenotypic information (All) or records for animals born before or after GS implementation (Before or After datasets, respectively). Both relationship matrices were considered for VC estimation, the A matrix was used in all three datasets and VC from each combination were named as A_Before, A_After, and A_All). The H was used in two datasets: H_All and H_After. Different VCs were used for GEBV prediction through ssGBLUP. This step used two possible Datasets, using all available phenotypic data (Dataset 1) or just records collected since GS implementation (Dataset 2). Validation was conducted using accuracy, bias and dispersion according to the LR method and prediction accuracy from corrected phenotypes. The heritability of all traits increased from A_Before to A_After, while estimates for A_All were intermediary. In the previous order, the estimates were 0.16, 0.17, and 0.15 for W120; 0.31, 0.39, and 0.35 for W450; 0.35, 0.47, and 0.41 for SC. For W450 and SC, using the H matrix reduced the heritability (0.33 and 0.32 for W450; 0.41 and 0.38 for SC for H_After and H_All, respectively). For W120, Dataset1 and VCs from A_After showed the highest accuracy for direct and maternal GEBV (0.953 and 0.868). For W450, Dataset 1 and VC from H_After allowed the highest accuracy (0.854) but use Dataset 2 and same VC source yield similar value (0.846). For SC, Dataset 2 with VC from H_After showed the highest accuracy (0.925). To use Dataset 2 does not cause important changes in bias or dispersion with respect to Dataset 1. The VC and genetic parameters changed for W120, W450, and SC450, using records before or after the GS implementation. For W450 and SC450, genetic variance and heritability estimates increased with the use of GS. For W120, genomic predictions were more accurate using A for VC estimation. Accuracy gains were observed for W450 and SC450 using H in VC estimation and/or discarding records before GS. It is possible to discard phenotypic records before GS implementation without generating bias or dispersion in the GEBV of young candidates.

Applying the algorithm for Proven and young in GWAS Reveals high polygenicity for key traits in Nellore cattle

Background

Identifying genomic regions associated with traits of interest and their biological processes provides valuable insights into the phenotypic variability of these traits. This study aimed to identify candidate genes and genomic regions associated with 16 traits currently evaluated by the Brazilian Association of Zebu Breeders (ABCZ). These traits include reproductive traits such as age at first calving (AFC), stayability (STAY), and scrotal circumference at 365 (SC365) and 450 days (SC450). Growth traits include birthweight (BW), expected progeny difference for weight at 120days of age (EPD120), as well as weight at 120 (W120), 210 (W210), 365 (W365), and 450 days of age (W450). Carcass traits include body conformation (BC), finishing score (FS), marbling (MARB), muscularity (MUSC), finishing precocity (FP), and ribeye area (REA).

Methods

A dataset containing 304,782 Nellore cattle genotyped with 437,650 SNPs (after quality control) was used for this study. The Algorithm for Proven and Young (APY), implemented in the PREGSF90 software, was used to compute theG A P Y - 1 matrix using 36,000 core animals (which explained 98% of the variance in the genomic matrix). Subsequently, the SNP solutions were estimated by back-solving the Genomic Estimated Breeding Values (GEBVs) predicted by ABCZ using the single-step GBLUP method. Genomic regions were identified using sliding windows of 175 consecutive SNPs, and the top 1% genomic windows, ranked based on their proportion of the additive genetic variance, were used to annotate positional candidate genes and genomic regions associated with each of the 16 traits.

Results

The top 1% windows for all traits explained between 2.779% (STAY) to 3.158% (FP) of the additive genetic variance, highlighting the polygenic nature of these traits. Functional analysis of the candidate genes and genomic regions provided valuable insights into the genetic architecture underlying these traits in Nellore cattle. For instance, our results revealed genes with important functions for each trait, such as SERPINA14 (plays a key role for the endometrial epithelium) identified for AFC, HSPG2 (associated with morphological development and tissue differentiation) identified for BW, among others.

Conclusion

We identified genomic regions and candidate genes, some of which have been previously reported in the literature, while others are novel discoveries that warrant further investigation. These findings contribute to gene prioritization efforts, facilitating the identification of functional candidate genes that can enhance genomic selection strategies for economically important traits in Nellore cattle.

Selection signatures detection in Nelore, Gir, and Red Sindhi cattle breeds

Technological advances in genomics and bioinformatics made it possible to study the genetic structure of breeds and understand genome changes caused by selection over generations. Our objective was to evaluate selection signatures (SS) in Nelore, Gir, and Red Sindhi cattle from Brazil and the Asian continent to identify divergent variants due to the history of formation and selection of populations, with a focus on the SS of animals from Brazil. Extended haplotype homozygosities between populations (XP-EHH), the ratio of site-specific extended haplotype homozygosity between populations (Rsb), and the allelic fixation index (Fst) were used to detect SS. Considering a window size of 50-kb, a non-sliding window approach was used to define SS regions. A total of 62, 57, and 72 genes were co-located within SS regions for Nelore, Gir, and Red Sindhi, respectively, and used to perform functional analyses per breed. Most genes were associated with productive and reproductive traits, while others were related to thermotolerance, the immune system, temperament, and coat color. The identified SS demonstrate how animal breeding programs shape the genetic makeup of these breeds to meet production system requirements, given that animals from Brazil and the Asian continent have undergone different selection processes. The identification of genes related to thermotolerance, temperament, and the immune system suggests specific alleles have enabled animals to adapt to environmental conditions and selection criteria in Brazil. Understanding SS can support breeding strategies for Nelore, Gir, and Red Sindhi cattle, contributing to enhanced resistance, adaptation, and productivity to meet food production demands.

Multi-omics integration identifies molecular markers and biological pathways for carcass and meat quality traits in Nellore cattle

Understanding the biological factors that influence carcass and meat quality traits in Nellore cattle requires a clear interpretation of molecular complexity and its variations at different levels of expression. Hence, this study aimed to elucidate the biological control of carcass and meat quality traits in Nellore cattle by integrating genome-wide association study (GWAS), transcriptomic, and proteomic data, focusing on identifying key genes and pathways. GWAS analysis was performed using weighted single-step GBLUP with two iterations. RNA-Seq and proteomic analyses were performed on 24 muscle samples from animals with divergent adjusted phenotypic values (12 for high and 12 for low), for meat tenderness, ribeye area (REA), marbling, and backfat thickness (BFT). The phenotypic values were adjusted for the systematic effects of contemporary groups and age. Differential expression analyses indicated that genes associated with the promotion of growth processes, such as FRZB, IGFBP5 and SEMA6C, exhibited overexpression within the group characterized by higher meat tenderness that inhibits cellular cycles and growth (RTN4 and RB1) were downregulated. Proteins related to heat shock, structural functions, and metabolic regulation also affected the higher meat tenderness group. For marbling, actin-binding proteins, microtubule-forming proteins, and structural proteins were downregulated, while genes involved in fatty acid composition and synthesis were upregulated, with the key genes and transcripts CAND1, ACTN4, FGFR2, and NCOR2 identified. For BFT, neuronal genes, transcripts, and proteins associated with actin cytoskeleton organization and microtubule formation were found. Key genes related to ubiquitination, regulation of energy metabolism, and tissue remodeling were also identified. These findings provide a better understanding of genes, transcripts, proteins, and metabolic pathways involved in carcass and meat quality traits in Nellore cattle.

A review of sustainable cattle genetic improvement in the Peruvian Highlands

Cattle breeding in the highlands of Peru is an important economic activity at the level of the entire rural extension, because it serves as an economic reserve for rural families and forms an integral part of the agricultural producer's culture. This review aimed to provide a literature- and research-based approach to the fundamental aspects of a national genetic improvement plan, emphasising the efficacy of using a bovine germplasm of high genetic quality as an initiative to implement genetic improvement programmes. The concepts to be implemented in national livestock farming include high yield, feed conversion efficiency, and minimum greenhouse gas emissions. The use of a dual-purpose germplasm to maximise the usefulness of livestock farmers and the implementation of improvement programmes, with the expectation of achieving a differential increase in genetic merit, were also considered. In addition to aspects related to milk and carcase quality, there is a territorial approach, such as the case of breeds that adapt to terminal crossbreeding and consider reproductive aspects. One of the final aspects considered is the conservation and valuation of local animals within conservation and improvement plans owing to their resistance and adaptation to temperature and altitude conditions.

Genotype-by-environment interactions in beef and dairy cattle populations: A review of methodologies and perspectives on research and applications

Modern livestock production systems are characterized by a greater focus on intensification, involving managing larger numbers of animals to achieve higher productive efficiency and animal health and welfare within herds. Therefore, animal breeding programs need to be strategically designed to select animals that can effectively enhance production performance and animal welfare across a range of environmental conditions. Thus, this review summarizes the main methodologies used for assessing the levels of genotype-by-environment interaction (G × E) in cattle populations. In addition, we explored the importance of integrating genomic and phenotypic information to quantify and account for G × E in breeding programs. An overview of the structure of cattle breeding programs is provided to give insights into the potential outcomes and challenges faced when considering G × E to optimize genetic gains in breeding programs. The role of nutrigenomics and its impact on gene expression related to metabolism in cattle are also discussed, along with an examination of current research findings and their potential implications for future research and practical applications. Out of the 116 studies examined, 60 and 56 focused on beef and dairy cattle, respectively. A total of 83.62% of these studies reported genetic correlations across environmental gradients below 0.80, indicating the presence of G × E. For beef cattle, 69.33%, 24%, 2.67%, 2.67%, and 1.33% of the studies evaluated growth, reproduction, carcass and meat quality, survival, and feed efficiency traits, respectively. By contrast, G × E research in dairy cattle populations predominantly focused on milk yield and milk composition (79.36% of the studies), followed by reproduction and fertility (19.05%), and survival (1.59%) traits. The importance of G × E becomes particularly evident when considering complex traits such as heat tolerance, disease resistance, reproductive performance, and feed efficiency, as highlighted in this review. Genomic models provide a valuable avenue for studying these traits in greater depth, allowing for the identification of candidate genes and metabolic pathways associated with animal fitness, adaptation, and environmental efficiency. Nutrigenetics and nutrigenomics are emerging fields that require extensive investigation to maximize our understanding of gene-nutrient interactions. By studying various transcription factors, we can potentially improve animal metabolism, improving performance, health, and quality of products such as meat and milk.

Assessment of inbreeding coefficients and inbreeding depression on complex traits from genomic and pedigree data in Nelore cattle

BACKGROUND

Nelore cattle play a key role in tropical production systems due to their resilience to harsh conditions, such as heat stress and seasonally poor nutrition. Monitoring their genetic diversity is essential to manage the negative impacts of inbreeding. Traditionally, inbreeding and inbreeding depression are assessed by pedigree-based coefficients (F), but recently, genetic markers have been preferred for their precision in capturing the inbreeding level and identifying animals at risk of reduced productive and reproductive performance. Hence, we compared the inbreeding and inbreeding depression for productive and reproductive performance traits in Nelore cattle using different inbreeding coefficient estimation methods from pedigree information (FPed), the genomic relationship matrix (FGRM), runs of homozygosity (FROH) of different lengths (> 1 Mb (genome), between 1 and 2 Mb - FROH 1-2; 2-4 Mb FROH 2-4 or > 8 Mb FROH >8) and excess homozygosity (FSNP).

RESULTS

The correlation between FPed and FROH was lower when the latter was based on shorter segments (r = 0.15 with FROH 1-2, r = 0.20 with FROH 2-4 and r = 0.28 with FROH 4-8). Meanwhile, the FPed had a moderate correlation with FSNP (r = 0.47) and high correlation with FROH >8 (r = 0.58) and FROH-genome (r = 0.60). The FROH-genome was highly correlated with inbreeding based on FROH>8 (r = 0.93) and FSNP (r = 0.88). The FGRM exhibited a high correlation with FROH-genome (r = 0.55) and FROH >8 (r = 0.51) and a lower correlation with other inbreeding estimators varying from 0.30 for FROH 2-4 to 0.37 for FROH 1-2. Increased levels of inbreeding had a negative impact on the productive and reproductive performance of Nelore cattle. The unfavorable inbreeding effect on productive and reproductive traits ranged from 0.12 to 0.51 for FPed, 0.19-0.59 for FGRM, 0.21-0.58 for FROH-genome, and 0.19-0.54 for FSNP per 1% of inbreeding scaled on the percentage of the mean. When scaling the linear regression coefficients on the standard deviation, the unfavorable inbreeding effect varied from 0.43 to 1.56% for FPed, 0.49-1.97% for FGRM, 0.34-2.2% for FROH-genome, and 0.50-1.62% for FSNP per 1% of inbreeding. The impact of the homozygous segments on reproductive and performance traits varied based on the chromosomes. This shows that specific homozygous chromosome segments can be signs of positive selection due to their beneficial effects on the traits.

CONCLUSIONS

The low correlation observed between FPed and genomic-based inbreeding estimates suggests that the presence of animals with one unknown parent (sire or dam) in the pedigree does not account for ancient inbreeding. The ROH hotspots surround genes related to reproduction, growth, meat quality, and adaptation to environmental stress. Inbreeding depression has adverse effects on productive and reproductive traits in Nelore cattle, particularly on age at puberty in young bulls and heifer calving at 30 months, as well as on scrotal circumference and body weight when scaled on the standard deviation of the trait.

Genetic correlation estimates between calving ease in primiparous cows and economically important traits in Nellore cattle

This study aimed to estimate (co)variance components and genetic parameters for calving ease (CE) and their genetic correlations with growth, reproductive, carcass, and feed efficiency traits in Nellore cattle. Phenotypes for CE are scored in two categories: normal calving and assisted calving. The traits considered were probability of precocious calving, age at first calving, stayability, adjusted scrotal circumference at 365 days of age, accumulated cow productivity, age at puberty of males, gestation length, birth weight, adjusted weights at 210 and 450 days of age, adult cow weight, frame score, hip height, rib eye area, subcutaneous backfat thickness, rump fat thickness, intramuscular fat percentage, residual feed intake and dry matter intake. The estimation of genetic parameters was performed using a two-trait threshold-linear animal model, except for CE, stayability, and probability of precocious calving, which were evaluated through a two-trait threshold animal model. The direct (0.27) and maternal (0.19) heritability estimates for CE in heifers primiparous Nellore indicated that selecting for this trait is feasible. The selection to improve the female sexual precocity should consider CE during the selection and mating decisions to reduce calving problems. Genetic correlation estimates between CE and BW suggest that selecting low birth weight to reduce calving problems is not an appropriate strategy to improve calving ease in heifers Nellore. Therefore, adopting a multi-trait selection model with CE and BW in the Nellore breed would reduce calving difficulties, particularly in sexually precocious heifers, without impairing the growth, reproductive, feed efficiency conversion, and carcass indicator traits.

Genetic Foundations of Nellore Traits: A Gene Prioritization and Functional Analyses of Genome-Wide Association Study Results

The main goal of this study was to pinpoint functional candidate genes associated with multiple economically important traits in Nellore cattle. After quality control, 1830 genomic regions sourced from 52 scientific peer-reviewed publications were used in this study. From these, a total of 8569 positional candidate genes were annotated for reproduction, 11,195 for carcass, 5239 for growth, and 3483 for morphological traits, and used in an over-representation analysis. The significant genes (adjusted p-values < 0.05) identified in the over-representation analysis underwent prioritization analyses, and enrichment analysis of the prioritized over-represented candidate genes was performed. The prioritized candidate genes were GFRA4, RFWD3, SERTAD2, KIZ, REM2, and ANKRD34B for reproduction; RFWD3, TMEM120A, MIEF2, FOXRED2, DUSP29, CARHSP1, OBI1, JOSD1, NOP58, and LOXL1-AS1 for the carcass; ANKRD34B and JOSD1 for growth traits; and no genes were prioritized for morphological traits. The functional analysis pinpointed the following genes: KIZ (plays a crucial role in spindle organization, which is essential in forming a robust mitotic centrosome), DUSP29 (involved in muscle cell differentiation), and JOSD1 (involved in protein deubiquitination, thereby improving growth). The enrichment of the functional candidate genes identified in this study highlights that these genes play an important role in the expression of reproduction, carcass, and growth traits in Nellore cattle.

Leveraging Functional Genomics for Understanding Beef Quality Complexities and Breeding Beef Cattle for Improved Meat Quality

Consumer perception of beef is heavily influenced by overall meat quality, a critical factor in the cattle industry. Genomics has the potential to improve important beef quality traits and identify genetic markers and causal variants associated with these traits through genomic selection (GS) and genome-wide association studies (GWAS) approaches. Transcriptomics, proteomics, and metabolomics provide insights into underlying genetic mechanisms by identifying differentially expressed genes, proteins, and metabolic pathways linked to quality traits, complementing GWAS data. Leveraging these functional genomics techniques can optimize beef cattle breeding for enhanced quality traits to meet high-quality beef demand. This paper provides a comprehensive overview of the current state of applications of omics technologies in uncovering functional variants underlying beef quality complexities. By highlighting the latest findings from GWAS, GS, transcriptomics, proteomics, and metabolomics studies, this work seeks to serve as a valuable resource for fostering a deeper understanding of the complex relationships between genetics, gene expression, protein dynamics, and metabolic pathways in shaping beef quality.

Detection and characterization of copy number variation in three differentially-selected Nellore cattle populations

Introduction: Nellore cattle (Bos taurus indicus) is the main beef cattle breed raised in Brazil. This breed is well adapted to tropical conditions and, more recently, has experienced intensive genetic selection for multiple performance traits. Over the past 43 years, an experimental breeding program has been developed in the Institute of Animal Science (IZ, Sertaozinho, SP, Brazil), which resulted in three differentially-selected lines known as Nellore Control (NeC), Nellore Selection (NeS), and Nellore Traditional (NeT). The primary goal of this selection experiment was to determine the response to selection for yearling weight (YW) and residual feed intake (RFI) on Nellore cattle. The main objectives of this study were to: 1) identify copy number variation (CNVs) in Nellore cattle from three selection lines; 2) identify and characterize CNV regions (CNVR) on these three lines; and 3) perform functional enrichment analyses of the CNVR identified. Results: A total of 14,914 unique CNVs and 1,884 CNVRs were identified when considering all lines as a single population. The CNVRs were non-uniformly distributed across the chromosomes of the three selection lines included in the study. The NeT line had the highest number of CNVRs (n = 1,493), followed by the NeS (n = 823) and NeC (n = 482) lines. The CNVRs covered 23,449,890 bp (0.94%), 40,175,556 bp (1.61%), and 63,212,273 bp (2.54%) of the genome of the NeC, NeS, and NeT lines, respectively. Two CNVRs were commonly identified between the three lines, and six, two, and four exclusive regions were identified for NeC, NeS, and NeT, respectively. All the exclusive regions overlap with important genes, such as SMARCD3, SLC15A1, and MAPK1. Key biological processes associated with the candidate genes were identified, including pathways related to growth and metabolism. Conclusion: This study revealed large variability in CNVs and CNVRs across three Nellore lines differentially selected for YW and RFI. Gene annotation and gene ontology analyses of the exclusive CNVRs to each line revealed specific genes and biological processes involved in the expression of growth and feed efficiency traits. These findings contribute to the understanding of the genetic mechanisms underlying the phenotypic differences among the three Nellore selection lines.

Challenges and opportunities of using Bos indicus cattle to meet consumers' demand for quality beef

Beef consumption is expected to increase worldwide, which necessitates the use of Bos indicus cattle that are well-adapted to harsher climates, like the tropics. Yet, beef from these cattle is considered inferior to that of Bos taurus breeds, primarily due to lowered tenderness values and reduced intramuscular fat content. However, the benefits of using Bos indicus genetics are numerous and undeniable. Herein, we explore how decreases in meat quality in these cattle may be offset by increases in livability. Further, we review the knowledge surrounding beef tenderness and explore the processes occurring during the early events of the transformation of muscle to meat that are different in this biological type and may be altered by stress. Growth rate, calpastatin activity and mitochondrial function will be discussed as they relate to tenderness. The opportunities of using Bos indicus cattle are of great interest to the beef industry worldwide, especially given the pressures for enhancing the overall sustainability and carbon footprint of this sector. Delivering a consistently high-quality product for consumers by exploiting Bos indicus genetics in a more sustainable manner will be proposed. Information on novel factors that influence the conversion of muscle to meat is explored to provide insights into opportunities for maximizing beef tenderization and maturation across all cattle. Exploring the use of Bos indicus cattle in modern production schemes, while addressing the mechanisms undergirding meat tenderness should provide the industry with a path forward for building greater demand through producing higher quality beef.

Identification of candidate lethal haplotypes and genomic association with post-natal mortality and reproductive traits in Nellore cattle

The wide use of genomic information has enabled the identification of lethal recessive alleles that are the major genetic causes of reduced conception rates, longer calving intervals, or lower survival for live-born animals. This study was carried out to screen the Nellore cattle genome for lethal recessive haplotypes based on deviation from the expected population homozygosity, and to test SNP markers surrounding the lethal haplotypes region for association with heifer rebreeding (HR), post-natal mortality (PNM) and stayability (STAY). This approach requires genotypes only from apparently normal individuals and not from affected embryos. A total of 62,022 animals were genotyped and imputed to a high-density panel (777,962 SNP markers). Expected numbers of homozygous individuals were calculated, and the probabilities of observing 0 homozygotes was obtained. Deregressed genomic breeding values [(G)EBVs] were used in a GWAS to identify candidate genes and biological mechanisms affecting HR, STAY and PNM. In the functional analyses, genes within 100 kb down and upstream of each significant SNP marker, were researched. Thirty haplotypes had high expected frequency, while no homozygotes were observed. Most of the alleles present in these haplotypes had a negative mean effect for PNM, HR and STAY. The GWAS revealed significant SNP markers involved in different physiological mechanisms, leading to harmful effect on the three traits. The functional analysis revealed 26 genes enriched for 19 GO terms. Most of the GO terms found for biological processes, molecular functions and pathways were related to tissue development and the immune system. More phenotypes underlying these putative regions in this population could be the subject of future investigation. Tests to find putative lethal haplotype carriers could help breeders to eliminate them from the population or manage matings in order to avoid homozygous.