Integrating a Genome-Wide Association Study With Transcriptome Analyses to Identify Candidate Genes and Pathways for Feed Conversion Ratio in Yorkshire Pigs

Miao, Yuanxin; q, mei; c, fu; Liao, Ming’an; Song, Zhao; Xiang, Tao

doi:10.21203/rs.3.rs-104076/v1

Cited by 1 publication

(1 citation statement)

References 66 publications

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“…For FCR, genes that largely affected FCR were not consistent in LL and YY [21] and thus, the linked SNP markers captured different proportion of total genetic variances.…”

Section: Predictive Abilitiesmentioning

confidence: 99%

Single-Step Genomic Evaluation With a Metafounder for Feed Conversion Ratio and Average Daily Gain in Danish Landrace and Yorkshire Pigs

Ostersen

Xiang

et al. 2021

Preprint

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Background Single-step genomic best linear unbiased prediction (SSGBLUP) method is an effective approach for genetic evaluation. To overcome the issue that pedigree and genomic relationship matrices refer to different base populations, a single-step genomic method with a metafounder (MF-SSGBLUP) was put forward. The aim of this study was to compare the predictive abilities and bias of genomic evaluation based on MF-SSGBLUP compared with standard SSGBLUP. We did this for feed conversion ratio (FCR) and average daily gain (ADG) in DanBred Landrace (LL) and DanBred Yorkshire (YY) pigs using both univariate and bivariate models. Additionally, optimal weighting factors (ω) on pedigree-based relationship matrices for ADG and FCR in both SSGBLUP and MF-SSGBLUP were also investigated. Results In general, SSGBLUP and MF-SSGBLUP methods showed similar predictive abilities and biasedness of GEBVs. In LL, for ADG, the predictive ability for SSGBLUP was up to 0.355, while MF-SSGBLUP reached a highest predictive ability of 0.356; for FCR, the predictive ability for MF-SSGBLUP together with a bivariate model was highest (0.196) among all the different scenarios. In YY, performances of genomic prediction for ADG were almost similar among different scenarios and predictive ability for ADG was up to 0.347; for FCR, the highest predictive ability (0.355) was obtained in MF-SSGBLUP. In biasedness, SSGBLUP and MF-SSGBLUP performed nearly the same, and bivariate models generally had less biasedness than univariate models. Additionally, in terms of the optimal weighting factors (ω), in LL, regardless of a univariate or a bivariate model was used, the optimal ω for ADG were around 0.2 in both SSGBLUP and MF-SSGBLUP; for FCR, 0.70 and 0.55 were the optimal ω in SSGBLUP and MF-SSGBLUP method, respectively. In YY, the optimal ω ranged from 0.25 to 0.35 for ADG and from 0.10 to 0.30 for FCR in different scenarios. Conclusions This study indicated that MF-SSGBLUP method performed at least as good as SSGBLUP for genomic evaluation. The optimal weighting factors (ω) in SSGBLUP and MF-SSGBLUP are not dramatically different. Overall, MF-SSGBLUP in combination with a bivariate model was recommended for single-step genomic evaluation for ADG and FCR in DanBred Landrace and Yorkshire pigs.

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“…For FCR, genes that largely affected FCR were not consistent in LL and YY [21] and thus, the linked SNP markers captured different proportion of total genetic variances.…”

Section: Predictive Abilitiesmentioning

confidence: 99%