ssGBLUP Method Improves the Accuracy of Breeding Value Prediction in Huacaya Alpaca

Mancisidor, Betsy; Cruz, Alan; Gutiérrez, Gustavo; Burgos, A.; Morón, J.A.; Wurzinger, María; Gutiérrez, Juan Pablo

doi:10.3390/ani11113052

Cited by 10 publications

(4 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Pearson correlations between EBVs and GEBVs were statistically significant, with values of 0.98, 0.97, and 0.98 for AFC, CI, and PL78, respectively. Similarly, high correlations exceeding 80% were also reported by Mancusidor et al [21] in Huacaya Alpaca and Sharko et al [22] in the Black-and-White cattle population. The EBV and GEBV reliabilities are presented in Table 5.…”

Section: Resultssupporting

confidence: 67%

Improving Breeding Value Reliability with Genomic Data in Breeding Groups of Charolais

Brzáková,

Veselá,

Vařeka

et al. 2023

Genes

View full text Add to dashboard Cite

The aim of this study was to assess the impact of incorporating genomic data using the single-step genomic best linear unbiased prediction (ssGBLUP) method compared to the best linear unbiased prediction (BLUP) method on the reliability of breeding values for age at first calving, calving interval, and productive longevity at 78 months in Charolais cattle. The study included 48,590 purebred Charolais individuals classified into four subgroups based on genotyping and performance records. The results showed that considering genotypes significantly improved genomic estimated breeding values (GEBV) reliability across all categories except nongenotyped individuals. For young genotyped individuals, the increase in reliability was up to 27% for both sexes. The highest average reliability was achieved for genotyped proven bulls and cows with performance records, and the inclusion of genomic data further improved the reliability by up to 22% and 21% for cows and bulls, respectively. The gain in reliability was observed mainly during the first three calvings, and then the differences decreased. The imported individuals showed lower estimated breeding values (EBV) and GEBV reliabilities than the domestic population, probably due to the weak genetic connection with the domestic population. However, when the progeny of imported heifers were sired by domestic bulls, the reliability increased by up to 24%. For nongenotyped individuals, only a slight increase in reliability was observed; however, the number of genotyped individuals in the population was still relatively small.

show abstract

Section: Resultssupporting

confidence: 67%

Improving Breeding Value Reliability with Genomic Data in Breeding Groups of Charolais

Brzáková,

Veselá,

Vařeka

et al. 2023

Genes

View full text Add to dashboard Cite

show abstract

“…This approach combined with random‐regression‐based reaction norm models (RNM) allows the use of information from individuals even if they were recorded in just one environment (Hayes et al., 2016), since the response of each individual to environmental variation is modelled through a unique random regression curve that has its trajectory determined by the environmental gradient. In addition, the use of pedigree and genomic information in the ssGBLUP approach reflects the current reality of most livestock breeding programs, where not all individuals are genotyped, while providing similar or better results than other methods (Cesarani et al., 2021; Guarini et al., 2018; Mancisidor et al., 2021). In this context, the overall goal of this study was to evaluate the use of different threshold temperatures and their impact on the model predictive ability of genomic breeding values.…”

Section: Introductionmentioning

confidence: 99%

Genomic predictions and GWAS for heat tolerance in pigs based on reaction norm models with performance records and data from public weather stations considering alternative temperature thresholds

Freitas,

Johnson,

Tiezzi

et al. 2023

J Animal Breeding Genetics

View full text Add to dashboard Cite

Genetic improvement of livestock productivity has resulted in greater production of metabolic heat and potentially greater susceptibility to heat stress. Various studies have demonstrated that there is genetic variability for heat tolerance and genetic selection for more heat tolerant individuals is possible. The rate of genetic progress tends to be greater when genomic information is incorporated into the analyses as more accurate breeding values can be obtained for young individuals. Therefore, this study aimed (1) to evaluate the predictive ability of genomic breeding values for heat tolerance based on routinely recorded traits, and (2) to investigate the genetic background of heat tolerance based on single‐step genome‐wide association studies for economically important traits related to body composition, growth and reproduction in Large White pigs. Pedigree information was available for 265,943 animals and genotypes for 8686 animals. The studied traits included ultrasound backfat thickness (BFT), ultrasound muscle depth (MDP), piglet weaning weight (WW), off‐test weight (OTW), interval between farrowing (IBF), total number of piglets born (TNB), number of piglets born alive (NBA), number of piglets born dead (NBD), number of piglets weaned (WN) and weaning‐to‐estrus interval (IWE). The number of phenotypic records ranged from 6059 (WN) to 172,984 (TNB). Single‐step genomic reaction norm predictions were used to calculate the genomic estimated breeding values for each individual. Predictions of breeding values for the validation population individuals were compared between datasets containing phenotypic records measured in the whole range of temperatures (WR) and datasets containing only phenotypic records measured when the weather station temperature was above 10°C (10C) or 15°C (15C), to evaluate the usefulness of these datasets that may better reflect the within‐barn temperature. The use of homogeneous or heterogeneous residual variance was found to be trait‐dependent, where homogeneous variance presented the best fit for MDP, BFT, OTW, TNB, NBA, WN and IBF, while the other traits (WW and IWE) had better fit with heterogeneous variance. The average prediction accuracy, dispersion and bias values considering all traits for WR were 0.36 ± 0.05, −0.07 ± 0.13 and 0.76 ± 0.10, respectively; for 10C were 0.39 ± 0.02, −0.05 ± 0.07 and 0.81 ± 0.05, respectively; and for 15C were 0.32 ± 0.05, −0.05 ± 0.11 and 0.84 ± 0.10, respectively. Based on the studied traits, using phenotypic records collected when the outside temperature (from public weather stations) was above 10°C provided better predictions for most of the traits. Forty‐three and 62 candidate genomic regions were associated with the intercept (overall performance level) and slope term (specific biological mechanisms related to environmental sensitivity), respectively. Our results contribute to improve genomic predictions using existing datasets and better understand the genetic background of heat tolerance in pigs. Furthermore, the genomic regions and candidate genes identified will contribute to future genomic studies and breeding applications.

show abstract

“…HBLUP has been widely used in the genetic evaluation of livestock and has been employed in the national genetic evaluation program in many countries ( Gao et al, 2012 ; McMillan and Swan, 2017 ; Brown et al, 2018 ; Chung et al, 2018 ; Johnston et al, 2018 ; Meyer et al, 2018 ; Teissier et al, 2018 ; Oliveira et al, 2019 ; Mäntysaari et al, 2020 ; Alkhoder and Liu, 2021 ). There are numerous studies reporting that HBLUP outperforms traditional GBLUP ( Baloche et al, 2014 ; Gao et al, 2018 ; Gowane et al, 2019 ; Mancisidor et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

An effective hyper-parameter can increase the prediction accuracy in a single-step genetic evaluation

et al. 2023

View full text Add to dashboard Cite

The H-matrix best linear unbiased prediction (HBLUP) method has been widely used in livestock breeding programs. It can integrate all information, including pedigree, genotypes, and phenotypes on both genotyped and non-genotyped individuals into one single evaluation that can provide reliable predictions of breeding values. The existing HBLUP method requires hyper-parameters that should be adequately optimised as otherwise the genomic prediction accuracy may decrease. In this study, we assess the performance of HBLUP using various hyper-parameters such as blending, tuning, and scale factor in simulated and real data on Hanwoo cattle. In both simulated and cattle data, we show that blending is not necessary, indicating that the prediction accuracy decreases when using a blending hyper-parameter <1. The tuning process (adjusting genomic relationships accounting for base allele frequencies) improves prediction accuracy in the simulated data, confirming previous studies, although the improvement is not statistically significant in the Hanwoo cattle data. We also demonstrate that a scale factor, α, which determines the relationship between allele frequency and per-allele effect size, can improve the HBLUP accuracy in both simulated and real data. Our findings suggest that an optimal scale factor should be considered to increase prediction accuracy, in addition to blending and tuning processes, when using HBLUP.

show abstract

ssGBLUP Method Improves the Accuracy of Breeding Value Prediction in Huacaya Alpaca

Cited by 10 publications

References 40 publications

Improving Breeding Value Reliability with Genomic Data in Breeding Groups of Charolais

Improving Breeding Value Reliability with Genomic Data in Breeding Groups of Charolais

Genomic predictions and GWAS for heat tolerance in pigs based on reaction norm models with performance records and data from public weather stations considering alternative temperature thresholds

An effective hyper-parameter can increase the prediction accuracy in a single-step genetic evaluation

Contact Info

Product

Resources

About