A Random Regression Model Based on a Single-Step Method for Improving the Genomic Prediction Accuracy of Residual Feed Intake in Pigs

Wang, Ye; Diao, Chenguang; Kang, Hao; Hao, Wenjie; Mrode, Raphael; Chen, Junhai; Liu, Jianfeng; Zhou, Lei

doi:10.3389/fgene.2021.769849

Cited by 4 publications

(7 citation statements)

References 37 publications

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“…These results corroborate with Wang et al. (2022), who reported that models using LP of higher order were also better for genomic prediction of feed efficiency in pigs. The models that assumed heterogeneous residual variance also showed better goodness of fit, suggesting considerable changes in the residual variance over the feedlot period.…”

Section: Discussionsupporting

confidence: 92%

“…In contrast, Wang et al. (2022) related higher heritability estimates, ranging from 0.11 to 0.48, in a pig population using an RRM based on LPs.…”

Section: Discussionmentioning

confidence: 90%

“…In our study, we implement the analysis in one step as described by Wang et al. (2022). For the genetic parameters estimation of RFI, the mixed model assumed y as the vector of daily DMI observations, and b included the systematic effects of daily metabolic BW (

{DBW}^{0.75}

), daily BWG, CG and performance test group.…”

Section: Methodsmentioning

confidence: 99%

“…For RFI, as an example, a regression of DMI in the function of metabolic BW (DBW 0.75 ) and daily BWG is fitted and the residual is used in the genetic parameter estimation. In our study, we implement the analysis in one step as described by Wang et al (2022). For the genetic parameters estimation of RFI, the mixed model assumed y as the vector of daily DMI observations, and b included the systematic effects of daily metabolic BW (DBW 0.75 ), daily BWG, CG and performance test group.…”

Section: Statistical Analysesmentioning

confidence: 99%

“…Therefore, new methodologies capable of predicting breeding values with high accuracy for these traits become crucial for selecting efficient animals. Feed efficiency traits can be analysed longitudinally using random regression models (RRMs) (Wang et al., 2022). Unlike the traditional model, in which the average daily feed intake and weight gain are used, RRMs allow the fitting of random genetic and environmental effects over time.…”

Section: Introductionmentioning

confidence: 99%

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Genomic analysis of feed efficiency traits in beef cattle using random regression models

Ramos,

de Oliveira Menezes,

da Silva

et al. 2023

J Animal Breeding Genetics

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Feed efficiency plays a major role in the overall profitability and sustainability of the beef cattle industry, as it is directly related to the reduction of the animal demand for input and methane emissions. Traditionally, the average daily feed intake and weight gain are used to calculate feed efficiency traits. However, feed efficiency traits can be analysed longitudinally using random regression models (RRMs), which allow fitting random genetic and environmental effects over time by considering the covariance pattern between the daily records. Therefore, the objectives of this study were to: (1) propose genomic evaluations for dry matter intake (DMI), body weight gain (BWG), residual feed intake (RFI) and residual weight gain (RWG) data collected during an 84‐day feedlot test period via RRMs; (2) compare the goodness‐of‐fit of RRM using Legendre polynomials (LP) and B‐spline functions; (3) evaluate the genetic parameters behaviour for feed efficiency traits and their implication for new selection strategies. The datasets were provided by the EMBRAPA–GENEPLUS beef cattle breeding program and included 2920 records for DMI, 2696 records for BWG and 4675 genotyped animals. Genetic parameters and genomic breeding values (GEBVs) were estimated by RRMs under ssGBLUP for Nellore cattle using orthogonal LPs and B‐spline. Models were compared based on the deviance information criterion (DIC). The ranking of the average GEBV of each test week and the overall GEBV average were compared by the percentage of individuals in common and the Spearman correlation coefficient (top 1%, 5%, 10% and 100%). The highest goodness‐of‐fit was obtained with linear B‐Spline function considering heterogeneous residual variance. The heritability estimates across the test period for DMI, BWG, RFI and RWG ranged from 0.06 to 0.21, 0.11 to 0.30, 0.03 to 0.26 and 0.07 to 0.27, respectively. DMI and RFI presented within‐trait genetic correlations ranging from low to high magnitude across different performance test‐day. In contrast, BWG and RWG presented negative genetic correlations between the first 3 weeks and the other days of performance tests. DMI and RFI presented a high‐ranking similarity between the GEBV average of week eight and the overall GEBV average, with Spearman correlations and percentages of individuals selected in common ranging from 0.95 to 1.00 and 93 to 100, respectively. Week 11 presented the highest Spearman correlations (ranging from 0.94 to 0.98) and percentages of individuals selected in common (ranging from 85 to 94) of BWG and RWG with the average GEBV of the entire period of the test. In conclusion, the RRM using linear B‐splines is a feasible alternative for the genomic evaluation of feed efficiency. Heritability estimates of DMI, RFI, BWG and RWG indicate enough additive genetic variance to achieve a moderate response to selection. A new selection strategy can be adopted by reducing the performance test to 56 days for DMI and RFI selection and 77 days for BWG and RWG selection.

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Section: Discussionsupporting

confidence: 92%

“…In contrast, Wang et al. (2022) related higher heritability estimates, ranging from 0.11 to 0.48, in a pig population using an RRM based on LPs.…”

Section: Discussionmentioning

confidence: 90%

{DBW}^{0.75}

), daily BWG, CG and performance test group.…”

Section: Methodsmentioning

confidence: 99%

Section: Statistical Analysesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Genomic analysis of feed efficiency traits in beef cattle using random regression models

Ramos,

de Oliveira Menezes,

da Silva

et al. 2023

J Animal Breeding Genetics

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Use of dry-matter intake recorded at multiple time periods during lactation increases the accuracy of genomic prediction for dry-matter intake and residual feed intake in dairy cattle

et al. 2023

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Context Feed is the largest expense on a dairy farm, therefore improving feed efficiency is important. Recording dry-matter intake (DMI) is a prerequisite for calculating feed efficiency. Genetic variation of feed intake and feed efficiency varies across lactation stages and parities. DMI is an expensive and difficult-to-measure trait. This raises the question of which time periods during lactation would be most appropriate to measure DMI. Aims The aim was to evaluate whether sequence variants selected from genome-wide association studies (GWAS) for DMI recorded at multiple lactation time periods and parities would increase the accuracy of genomic estimated breeding values (GEBVs) for DMI and residual feed intake (RFI). Methods Data of 2274 overseas lactating cows were used for the GWAS to select sequence variants. GWAS was performed using the average of the DMI phenotypes in a 30-day window of six different time periods across the lactation. The most significant sequence variants were selected from the GWAS at each time period for either first or later parities. GEBVs for DMI and RFI in Australian lactating cows were estimated using BayesRC with 50 k single nucleotide polymorphisms (SNPs) and selected GWAS sequence variants. Key results There were differences in DMI genomic correlations and heritabilities between first and later parities and within parity across lactation time periods. Compared with using 50 k single-nucleotide polymorphisms (SNPs) only, the accuracy of DMI GEBVs increased by up to 11% by using the 50 k SNPs plus the selected sequence variants. Compared with DMI, the increase in accuracy for RFI was lower (by 6%) likely because the sequence variants were selected from GWAS for DMI not RFI. The accuracies for DMI and RFI GEBVs were highest by using selected sequence variants from the DMI GWAS in the mid- to late-lactation periods in later parity. Conclusions Our results showed that DMI phenotypes in late lactation time periods could capture more genetic variation and increase genomic prediction accuracy through the use of custom genotype panels in genomic selection. Implications Collecting DMI at the optimal time period(s) of lactation may help develop more accurate and cost-effective breeding values for feed efficiency in dairy cattle.

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Approximation of reliabilities for random-regression single-step genomic best linear unbiased predictor models

Bermann,

Aguilar,

Munera

et al. 2024

JDS Communications

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A Random Regression Model Based on a Single-Step Method for Improving the Genomic Prediction Accuracy of Residual Feed Intake in Pigs

Cited by 4 publications

References 37 publications

Genomic analysis of feed efficiency traits in beef cattle using random regression models

Genomic analysis of feed efficiency traits in beef cattle using random regression models

Use of dry-matter intake recorded at multiple time periods during lactation increases the accuracy of genomic prediction for dry-matter intake and residual feed intake in dairy cattle

Approximation of reliabilities for random-regression single-step genomic best linear unbiased predictor models

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