Genomic analysis of stayability in Nellore cattle

Teixeira, Daniela Barreto Amaral; Júnior, Gerardo Alves Fernandes; Silva, Danielly Beraldo dos Santos; Costa, Raphael Bermal; Takada, Luciana; Gordo, Daniel Gustavo Mansan; Bresolin, Tiago; Carvalheiro, Roberto; Baldi, Fernando; Albuquerque, Lúcia Galvão de

doi:10.1371/journal.pone.0179076

Cited by 12 publications

(10 citation statements)

References 44 publications

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“…who presented heritability estimates of STAY63 in Nellore cattle ranging from 0.03 to 0.07. In contrast, the results of the present study corroborate withTeixeira et al (2017) andBonamy et al (2018) who reported heritability for STAY63 of 0.14 and 0.16, respectively. Prediction accuracy (r) of estimated breeding values (EBVs), genomic EBVs (GEBVs) and bias obtained through the BLUP and ssGBLUP methods for four stayability traits, and the percentage of increase in prediction accuracy (>Acc) obtained by the ssGBLUP method, in Nellore cattle…”

supporting

confidence: 91%

Genomic evaluation for novel stayability traits in Nellore cattle

Ramos

Silva

et al. 2020

Reprod Domestic Animals

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Cow stayability plays a major role on the overall profitability of the beef cattle industry, as it is directly related to reproductive efficiency and cow's longevity. Stayability (STAY63) is usually defined as the ability of the cow to calve at least three times until 76 months of age. This is a late‐measured and lowly heritable trait, which consequently constrains genetic progress per time unit. Thus, the use of genomic information associated with novel stayability traits measured earlier in life will likely result in higher prediction accuracy and faster genetic progress for cow longevity. In this study, we aimed to compare pedigree‐based and single‐step GBLUP (ssGBLUP) methods as well as to estimate genetic correlations between the proposed stayability traits: STAY42, STAY53 and STAY64, which are measured at 52, 64 and 76 months of cow's age, considering at least 2, 3 and 4 calving, respectively. ssGBLUP yielded the highest prediction accuracy for all traits. The heritability estimates for STAY42, STAY53, STAY63 and STAY64 were 0.090, 0.151, 0.152 and 0.143, respectively. The genetic correlations between traits ranged from 0.899 (STAY42 and STAY53) to 0.985 (STAY53 and STAY63). The high genetic correlation between STAY42 and STAY53 suggests that besides being related to cow longevity, STAY53 is also associated with the early‐stage reproductive efficiency. Thus, STAY53 is recommended as a suitable selection criterion for reproductive efficiency due to its higher heritability, favourable genetic correlation with other traits, and measured earlier in life, compared with the conventional stayability trait, that is STAY63.

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supporting

confidence: 91%

Genomic evaluation for novel stayability traits in Nellore cattle

Ramos

Silva

et al. 2020

Reprod Domestic Animals

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“…Positive values observed for CC top 5%, CC top 10%, and RC 100% indicate agreement between the models to select the best animals. These results may be due to the advantage of using H −1 matrix intead of A −1 when, for example, the farms adopt the multiple-sire mating system, which is common in Brazil (Teixeira et al, 2017). In such a situation, the relationship matrix based on pedigree is much less informative than the genomic relationship matrix due to the occurrence of many offspring with unknown paternity.…”

Section: Resultsmentioning

confidence: 99%

Reaction norm for yearling weight in beef cattle using single-step genomic evaluation1

Oliveira

Lourenço

Tsuruta

et al. 2018

Journal of Animal Science

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When the environment on which the animals are raised is very diverse, selecting the best sires for different environments may require the use of models that account for genotype by environment interaction (G × E). The main objective of this study was to evaluate the existence of G × E for yearling weight (YW) in Nellore cattle using reaction norm models with only pedigree and pedigree combined with genomic relationships. Additionally, genomic regions associated with each environment gradient were identified. A total of 67,996 YW records were used in reaction norm models to calculate EBV and genomic EBV. The method of choice for genomic evaluations was single-step genomic BLUP (ssGBLUP). Traditional and genomic models were tested on the ability to predict future animal performance. Genetic parameters for YW were obtained with the average information restricted maximum likelihood method, with and without adding genomic information for 5,091 animals. Additive genetic variances explained by windows of 200 adjacent SNP were used to identify genomic regions associated with the environmental gradient. Estimated variance components for the intercept and the slope in traditional and genomic models were similar. In both models, the observed changes in heritabilities and genetic correlations for YW across environments indicate the occurrence of genotype by environment interactions. Both traditional and genomic models were capable of identifying the genotype by environment interaction; however, the inclusion of genomic information in reaction norm models improved the ability to predict animals' future performance by 7.9% on average. The proportion of genetic variance explained by the top SNP window was 0.77% for the regression intercept (BTA5) and 0.82% for the slope (BTA14). Single-step GBLUP seems to be a suitable model to predict genetic values for YW in different production environments.

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“…In addition, one drawback of ssGWAS approaches is the inability to determine SNP significance levels (Wang et al, 2012). Despite that, various important genomic regions have been unraveled by using this method in a variety of traits in cattle (Barreto Amaral Teixeira et al, 2017;Medeiros de Oliveira Silva et al, 2017;Melo et al, 2017), aquaculture (Vallejo et al, 2017), and pigs (Wu et al, 2018).…”

Section: Gwas and Functional Analysesmentioning

confidence: 99%

Genetics and genomics of reproductive disorders in Canadian Holstein cattle

Guarini

Lourenço

Brito

et al. 2019

Journal of Dairy Science

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In Canada, reproductive disorders known to affect the profitability of dairy cattle herds have been recorded by producers on a voluntary basis since 2007. Previous studies have shown the feasibility of using producer-recorded health data for genetic evaluations. Despite low heritability estimates and limited availability of phenotypic information, sufficient genetic variation has been observed for those traits to indicate that genetic progress, although slow, can be achieved. Pedigree-and genomic-based analyses were performed on producer-recorded health data of reproductive disorders, including retained placenta (RETP), metritis (METR), and cystic ovaries (CYST) using traditional BLUP and single-step genomic BLUP. Genome-wide association studies and functional analyses were carried out to unravel significant genomic regions and biological pathways, and to better understand the genetic mechanisms underlying RETP, METR, and CYST. Heritability estimates (posterior standard deviation in parentheses) were 0.02 (0.003), 0.01 (0.004), and 0.02 (0.003) for CYST, METR, and RETP, respectively. A moderate to strong genetic correlation of 0.69 (0.102) was found between METR and RETP. Averaged over all traits, sire proof reliabilities increased by approximately 11 percentage points with the incorporation of genomic data using a multiple-trait linear model. Biological pathways and associated genes underlying the studied traits were identified and will contribute to a better understanding of the biology of these 3 health disorders in dairy cattle.

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Genomic analysis of stayability in Nellore cattle

Cited by 12 publications

References 44 publications

Genomic evaluation for novel stayability traits in Nellore cattle

Genomic evaluation for novel stayability traits in Nellore cattle

Reaction norm for yearling weight in beef cattle using single-step genomic evaluation1

Genetics and genomics of reproductive disorders in Canadian Holstein cattle

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