Genome-wide association study using haplotype alleles for the evaluation of reproductive traits in Nelore cattle

Nascimento, André Vieira do; Romero, Andrea; Utsunomiya, Yuri Tani; Utsunomiya, Adam Taiti Harth; Cardoso, Diércles Francisco; Neves, Haroldo Henrique Rezende; Carvalheiro, Roberto; Garcia, José Fernando; Barufatti, Alexéia

doi:10.1371/journal.pone.0201876

Cited by 13 publications

(12 citation statements)

References 73 publications

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“…Researchers have used a number of different statistical methodologies to exploit these associations. These methods included single-SNP GWAS analysis, haplotype GWAS analysis, the Identical By Descent approach, and GWAS analysis fitting all markers simultaneously (Hoggart et al ., 2008; Hayes, 2013; Wu et al ., 2014; Richardson et al ., 2016; Abo-Ismail et al ., 2017; Akanno et al ., 2018; Chen et al ., 2018; do Nascimento et al ., 2018). The GWAS method, however, comes with its own challenges.…”

Section: Traditional Animal Breeding Prediction Methodsmentioning

confidence: 99%

A review of traditional and machine learning methods applied to animal breeding

Nayeri

Sargolzaei

Tulpan

2019

Anim. Health. Res. Rev.

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The current livestock management landscape is transitioning to a high-throughput digital era where large amounts of information captured by systems of electro-optical, acoustical, mechanical, and biosensors is stored and analyzed on a daily and hourly basis, and actionable decisions are made based on quantitative and qualitative analytic results. While traditional animal breeding prediction methods have been used with great success until recently, the deluge of information starts to create a computational and storage bottleneck that could lead to negative long-term impacts on herd management strategies if not handled properly. A plethora of machine learning approaches, successfully used in various industrial and scientific applications, made their way in the mainstream approaches for livestock breeding techniques, and current results show that such methods have the potential to match or surpass the traditional approaches, while most of the time they are more scalable from a computational and storage perspective. This article provides a succinct view on what traditional and novel prediction methods are currently used in the livestock breeding field, how successful they are, and how the future of the field looks in the new digital agriculture era.

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Section: Traditional Animal Breeding Prediction Methodsmentioning

confidence: 99%

A review of traditional and machine learning methods applied to animal breeding

Nayeri

Sargolzaei

Tulpan

2019

Anim. Health. Res. Rev.

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“…Principal component analysis was applied to the GRM to investigate the population sub-structure. Additionally, we performed PCA after adding publicly available genotypes of Holstein 80 and Nelore 81 to compare the stratification level between the subpopulations of a single breed with the stratification across different breeds ( Supplementary Fig. S1).…”

Section: Genomic Relationships and Inbreeding Coefficientsmentioning

confidence: 99%

Uncovering Sub-Structure and Genomic Profiles in Across-Countries Subpopulations of Angus Cattle

Cardoso

Júnior

Scalez

et al. 2020

Sci Rep

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Highlighting genomic profiles for geographically distinct subpopulations of the same breed may provide insights into adaptation mechanisms to different environments, reveal genomic regions divergently selected, and offer initial guidance to joint genomic analysis. Here, we characterized similarities and differences between the genomic patterns of Angus subpopulations, born and raised in Canada (N = 382) and Brazil (N = 566). Furthermore, we systematically scanned for selection signatures based on the detection of autozygosity islands common between the two subpopulations, and signals of divergent selection, via FST and varLD tests. The principal component analysis revealed a sub-structure with a close connection between the two subpopulations. The averages of genomic relationships, inbreeding coefficients, and linkage disequilibrium at varying genomic distances were rather similar across them, suggesting non-accentuated differences in overall genomic diversity. Autozygosity islands revealed selection signatures common to both subpopulations at chromosomes 13 (63.77–65.25 Mb) and 14 (22.81–23.57 Mb), which are notably known regions affecting growth traits. Nevertheless, further autozygosity islands along with FST and varLD tests unravel particular sites with accentuated population subdivision at BTAs 7 and 18 overlapping with known QTL and candidate genes of reproductive performance, thermoregulation, and resistance to infectious diseases. Our findings indicate overall genomic similarity between Angus subpopulations, with noticeable signals of divergent selection in genomic regions associated with the adaptation in different environments.

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“…TIAL1 acts on post-transcriptional regulation encoding protein members of the RNA binding family, and has also been associated to the development of primordial germ cells of gametes (i.e., sperm and oocytes) in rodents [63]. In addition, BAG3 was annotated in proximity to a QTL affecting daughter pregnancy rate in Nellore beef cattle [3].…”

Section: Discussionmentioning

confidence: 99%

“…Reproductive performance directly impacts the profitability of the beef cattle industry [1,2]. Precocity and fertility rates are lower in Zebu cattle (Bos taurus indicus) compared to Taurine breeds (Bos taurus taurus) [3]. The majority of beef cattle breeding programs focus on growth and carcass traits, which are usually easier to measure in comparison with female reproductive traits [4].…”

Section: Introductionmentioning

confidence: 99%

Genetic Parameters and Genome-Wide Association Studies for Anti-Müllerian Hormone Levels and Antral Follicle Populations Measured After Estrus Synchronization in Nellore Cattle

Grigoletto

Santana

Bressan

et al. 2020

Animals

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Reproductive efficiency plays a major role in the long-term sustainability of livestock industries and can be improved through genetic and genomic selection. This study aimed to estimate genetic parameters (heritability and genetic correlation) and identify genomic regions and candidate genes associated with anti-Müllerian hormone levels (AMH) and antral follicle populations measured after estrous synchronization (AFP) in Nellore cattle. The datasets included phenotypic records for 1099 and 289 Nellore females for AFP and AMH, respectively, high-density single nucleotide polymorphism (SNP) genotypes for 944 animals, and 4129 individuals in the pedigree. The heritability estimates for AMH and AFP were 0.28 ± 0.07 and 0.30 ± 0.09, and the traits were highly and positively genetically correlated (rG = 0.81 ± 0.02). These findings indicated that these traits can be improved through selective breeding, and substantial indirect genetic gains are expected by selecting for only one of the two traits. A total of 31 genomic regions were shown to be associated with AMH or AFP, and two genomic regions located on BTA1 (64.9–65.0 Mb and 109.1–109.2 Mb) overlapped between the traits. Various candidate genes were identified to be potentially linked to important biological processes such as ovulation, tissue remodeling, and the immune system. Our findings support the use of AMH and AFP as indicator traits to genetically improve fertility rates in Nellore cattle and identify better oocyte donors.

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Genome-wide association study using haplotype alleles for the evaluation of reproductive traits in Nelore cattle

Cited by 13 publications

References 73 publications

A review of traditional and machine learning methods applied to animal breeding

A review of traditional and machine learning methods applied to animal breeding

Uncovering Sub-Structure and Genomic Profiles in Across-Countries Subpopulations of Angus Cattle

Genetic Parameters and Genome-Wide Association Studies for Anti-Müllerian Hormone Levels and Antral Follicle Populations Measured After Estrus Synchronization in Nellore Cattle

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