HIBLUP: an integration of statistical models on the BLUP framework for efficient genetic evaluation using big genomic data

Yin, Lilin; Zhang, Haohao; Tang, Zhenshuang; Ding, Yin; Fu, Yuhua; Yuan, Xiaohui; Li, Xinyun; Liu, X.; Zhao, Shuhong

doi:10.1093/nar/gkad074

Cited by 31 publications

(16 citation statements)

References 53 publications

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“…In this study, hiblup (v1.1.0) (Yin et al., 2023) software was used to calculate the estimated breeding value and heritability based on chip data and phenotypic data. The narrow heritability formula is as follows:

h^{2} = σ_{a}^{2} / σ_{P}^{2}

, where

σ_{a}^{2}

is the additive variance and

σ_{P}^{2}

is the phenotypic variance.…”

Section: Methodsmentioning

confidence: 99%

Genome‐wide association analysis revealed new QTL and candidate genes affecting the teat number in Dutch Large White pigs

Deng,

Qiu,

Liu

et al. 2024

Animal Genetics

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Teat number (TNUM) is an important reproductive trait of sows, which affects the weaning survival rate of piglets. In this study, 1166 Dutch Large White pigs with TNUM phenotype were used as the research object. These pigs were genotyped by 50K SNP chip and the chip data were further imputed to the resequencing level. The estimated heritabilities of left teat number (LTN), right teat number (RTN) and total teat number (TTN) were 0.21, 0.19 and 0.3, respectively. Based on chip data, significant SNPs for RTN on SSC2, SSC5, SSC9 and SSC13 were identified using genome‐wide association analysis (GWAS). Significant SNPs for TTN were identified on SSC2, SSC5 and SSC7. Based on imputed data, the GWAS identified a significant SNP (rs329158522) for LTN on SSC17, two significant SNPs (rs342855242 and rs80813115) for RTN on SSC2 and SSC9, and two significant SNPs (rs327003548 and rs326943811) for TTN on SSC5 and SSC6. Among them, four novel QTL were discovered. The Bayesian fine‐mapping method was used to fine map the QTL identified in the GWAS of the imputed data, and the confidence intervals of QTL affecting LTN (SSC17: 45.22–46.20 Mb), RTN (SSC9: 122.18–122.80 Mb) and TTN (SSC5: 14.01–15.91 Mb, SSC6: 120.06–121.25 Mb) were detected. A total of 52 candidate genes were obtained. Furthermore, we identified five candidate genes, WNT10B, AQP5, FMNL3, NUAK1 and CKAP4, for the first time, which involved in breast development and other related functions by gene annotation. Overall, this study provides new molecular markers for the breeding of teat number in pigs.

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h^{2} = σ_{a}^{2} / σ_{P}^{2}

, where

σ_{a}^{2}

is the additive variance and

σ_{P}^{2}

is the phenotypic variance.…”

Section: Methodsmentioning

confidence: 99%

Genome‐wide association analysis revealed new QTL and candidate genes affecting the teat number in Dutch Large White pigs

Deng,

Qiu,

Liu

et al. 2024

Animal Genetics

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show abstract

“…Two prediction models, genomic best linear unbiased prediction (GBLUP) and the Bayesian sparse linear mixed model (BSLMM), were used for genetic evaluation. In the GBLUP model, the genetic values correspond to the random effect, which is represented by , where is a vector of breeding values of all individuals, following the distribution ), is an incidence matrix relating observations in to the corresponding random genetic effects, is the additive genetic variance, is the genomic relationship matrix based on SNP genotypes of the animals, which was calculated using VanRaden's method [ 35 ] as: ; the GBLUP analysis was implemented using the HiBLUP software [ 36 ].…”

Section: Methodsmentioning

confidence: 99%

Genomic prediction in pigs using data from a commercial crossbred population: insights from the Duroc x (Landrace x Yorkshire) three-way crossbreeding system

et al. 2023

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Background Genomic selection is widely applied for genetic improvement in livestock crossbreeding systems to select excellent nucleus purebred (PB) animals and to improve the performance of commercial crossbred (CB) animals. Most current predictions are based solely on PB performance. Our objective was to explore the potential application of genomic selection of PB animals using genotypes of CB animals with extreme phenotypes in a three-way crossbreeding system as the reference population. Using real genotyped PB as ancestors, we simulated the production of 100,000 pigs for a Duroc x (Landrace x Yorkshire) DLY crossbreeding system. The predictive performance of breeding values of PB animals for CB performance using genotypes and phenotypes of (1) PB animals, (2) DLY animals with extreme phenotypes, and (3) random DLY animals for traits of different heritabilities ($${h}^{2}$$ h 2 = 0.1, 0.3, and 0.5) was compared across different reference population sizes (500 to 6500) and prediction models (genomic best linear unbiased prediction (GBLUP) and Bayesian sparse linear mixed model (BSLMM)). Results Using a reference population consisting of CB animals with extreme phenotypes showed a definite predictive advantage for medium- and low-heritability traits and, in combination with the BSLMM model, significantly improved selection response for CB performance. For high-heritability traits, the predictive performance of a reference population of extreme CB phenotypes was comparable to that of PB phenotypes when the effect of the genetic correlation between PB and CB performance ($${r}_{pc}$$ r pc ) on the accuracy obtained with a PB reference population was considered, and the former could exceed the latter if the reference size was large enough. For the selection of the first and terminal sires in a three-way crossbreeding system, prediction using extreme CB phenotypes outperformed the use of PB phenotypes, while the optimal design of the reference group for the first dam depended on the percentage of individuals from the corresponding breed that the PB reference data comprised and on the heritability of the target trait. Conclusions A commercial crossbred population is promising for the design of the reference population for genomic prediction, and selective genotyping of CB animals with extreme phenotypes has the potential for maximizing genetic improvement for CB performance in the pig industry.

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“…The heritabilities and genetic correlations for the studied traits were estimated using the multiple-traits model of the HIBLUP software [21]. The model follows [21]:…”

Section: Estimation Of Genetic Parametersmentioning

confidence: 99%

Identifying Genetic Architecture of Carcass and Meat Quality Traits in a Ningxiang Indigenous Pig Population

Yin

Song

Gao

et al. 2023

Genes

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Ningxiang pig is a breed renowned for its exceptional meat quality, but it possesses suboptimal carcass traits. To elucidate the genetic architecture of meat quality and carcass traits in Ningxiang pigs, we assessed heritability and executed a genome-wide association study (GWAS) concerning carcass length, backfat thickness, meat color parameters (L.LD, a.LD, b.LD), and pH at two postmortem intervals (45 min and 24 h) within a Ningxiang pig population. Heritability estimates ranged from moderate to high (0.30~0.80) for carcass traits and from low to high (0.11~0.48) for meat quality traits. We identified 21 significant SNPs, the majority of which were situated within previously documented QTL regions. Furthermore, the GRM4 gene emerged as a pleiotropic gene that correlated with carcass length and backfat thickness. The ADGRF1, FKBP5, and PRIM2 genes were associated with carcass length, while the NIPBL gene was linked to backfat thickness. These genes hold the potential for use in selective breeding programs targeting carcass traits in Ningxiang pigs.

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HIBLUP: an integration of statistical models on the BLUP framework for efficient genetic evaluation using big genomic data

Cited by 31 publications

References 53 publications

Genome‐wide association analysis revealed new QTL and candidate genes affecting the teat number in Dutch Large White pigs

Genome‐wide association analysis revealed new QTL and candidate genes affecting the teat number in Dutch Large White pigs

Genomic prediction in pigs using data from a commercial crossbred population: insights from the Duroc x (Landrace x Yorkshire) three-way crossbreeding system

Identifying Genetic Architecture of Carcass and Meat Quality Traits in a Ningxiang Indigenous Pig Population

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