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Genomic Analysis Reveals Significant Non-Additive Genetic Effects for Growth and Reproduction Traits in Landrace and Yorkshire Pigs.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Genomic Analysis Reveals Significant Non-Additive Genetic Effects for Growth and Reproduction Traits in Landrace and Yorkshire Pigs./
Author:	O'neill, Shauneen.
Description:	1 online resource (60 pages)
Notes:	Source: Masters Abstracts International, Volume: 84-02.
Contained By:	Masters Abstracts International84-02.
Subject:	Software. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29228622click for full text (PQDT)
ISBN:	9798841533351

Genomic Analysis Reveals Significant Non-Additive Genetic Effects for Growth and Reproduction Traits in Landrace and Yorkshire Pigs.
O'neill, Shauneen.

Genomic Analysis Reveals Significant Non-Additive Genetic Effects for Growth and Reproduction Traits in Landrace and Yorkshire Pigs. - 1 online resource (60 pages)

Source: Masters Abstracts International, Volume: 84-02.

Thesis (M.Sc.)--North Carolina State University, 2022.

Includes bibliographical references

Decision-making when designing breeding programs in the swine industry typically consists of considering the proportion of phenotypic variance explained by additive genetic effects, or narrow-sense heritability, of economically-favored traits. This study aims to demonstrate to which extent non-additive genetic effects contribute to the growth and maternal traits in swine and to investigate if the addition of non-additive genetic effects can improve the accuracy of phenotype prediction. We hypothesized that non-additive genetic effects would explain a higher proportion of total phenotypic variance in reproductive traits than growth traits, and also that considering non-additive genetic effects would result in higher genomic prediction accuracy. After quality control, we analyzed a population of 20,504 Landrace pigs and 19,475 Yorkshire pigs from Acuity Ag Solutions, LLC (Carlyle, IL). Approximately 50K SNPs were kept after quality control, and missing genotypes were imputed using findhap.f90. Genotypes were used to construct genomic relationship matrices (GRMs) corresponding to additive (A), dominance (D), additive-by-additive (AA), additive-by-dominance (AD), and dominanceby-dominance (DD). These were employed as covariance matrices in a linear mixed model consisting of six variance components: additive effect, dominance effect, epistatic effects (AA, AD, DD), and a permanent environmental effect pertaining to the sow (p). We estimated variance components (VCs) for six growth traits (birthweight, average daily gain, average daily gain at the nursery stage, average daily gain at the finisher stage, back fat, and loin area), six maternal traits recorded on the first parity only (average birthweight, average weaning weight, pre-weaning piglet losses, number born alive, number weaned, and total number born), six maternal traits recorded on all parities available (average birthweight, average weaning weight, pre-weaning piglet losses, number born alive, number weaned, and total number born), and two teat count traits (total teat count of female and male piglets at birth) using REML in MMAP for both breeds (https://mmap.github.io/).Ten-fold cross-validation was performed for each trait for genomic prediction with a reduced (only additive genetic effect) and full model (all variance components). Comparisons were made between the total genetic value (TGV) and the estimated breeding value (EBV) from the full model, as well as the EBV from the reduced model. Results show that considering the full model significantly improves the prediction accuracy of genetic values, based on a significance level of 0.05, for the following traits in Landrace: number born alive, number of piglets weaned, piglet losses, and back fat. In Yorkshire, considering the full model significantly improves the prediction accuracy of genetic values, based on a significance level of 0.05, for the following traits: birthweight, total number born, and backfat.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798841533351Subjects--Topical Terms:

619355
Software.
Index Terms--Genre/Form:

542853
Electronic books.

Genomic Analysis Reveals Significant Non-Additive Genetic Effects for Growth and Reproduction Traits in Landrace and Yorkshire Pigs.
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100 1 $a O'neill, Shauneen. $3 3704359
245 1 0 $a Genomic Analysis Reveals Significant Non-Additive Genetic Effects for Growth and Reproduction Traits in Landrace and Yorkshire Pigs.
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300 $a 1 online resource (60 pages)
336 $a text $b txt $2 rdacontent
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500 $a Source: Masters Abstracts International, Volume: 84-02.
500 $a Advisor: Jiang, Jicai;Maltecca, Christian;Mia, Francesco Tiezzi Mazzoni Della Stella;Breen, Matthew.
502 $a Thesis (M.Sc.)--North Carolina State University, 2022.
504 $a Includes bibliographical references
520 $a Decision-making when designing breeding programs in the swine industry typically consists of considering the proportion of phenotypic variance explained by additive genetic effects, or narrow-sense heritability, of economically-favored traits. This study aims to demonstrate to which extent non-additive genetic effects contribute to the growth and maternal traits in swine and to investigate if the addition of non-additive genetic effects can improve the accuracy of phenotype prediction. We hypothesized that non-additive genetic effects would explain a higher proportion of total phenotypic variance in reproductive traits than growth traits, and also that considering non-additive genetic effects would result in higher genomic prediction accuracy. After quality control, we analyzed a population of 20,504 Landrace pigs and 19,475 Yorkshire pigs from Acuity Ag Solutions, LLC (Carlyle, IL). Approximately 50K SNPs were kept after quality control, and missing genotypes were imputed using findhap.f90. Genotypes were used to construct genomic relationship matrices (GRMs) corresponding to additive (A), dominance (D), additive-by-additive (AA), additive-by-dominance (AD), and dominanceby-dominance (DD). These were employed as covariance matrices in a linear mixed model consisting of six variance components: additive effect, dominance effect, epistatic effects (AA, AD, DD), and a permanent environmental effect pertaining to the sow (p). We estimated variance components (VCs) for six growth traits (birthweight, average daily gain, average daily gain at the nursery stage, average daily gain at the finisher stage, back fat, and loin area), six maternal traits recorded on the first parity only (average birthweight, average weaning weight, pre-weaning piglet losses, number born alive, number weaned, and total number born), six maternal traits recorded on all parities available (average birthweight, average weaning weight, pre-weaning piglet losses, number born alive, number weaned, and total number born), and two teat count traits (total teat count of female and male piglets at birth) using REML in MMAP for both breeds (https://mmap.github.io/).Ten-fold cross-validation was performed for each trait for genomic prediction with a reduced (only additive genetic effect) and full model (all variance components). Comparisons were made between the total genetic value (TGV) and the estimated breeding value (EBV) from the full model, as well as the EBV from the reduced model. Results show that considering the full model significantly improves the prediction accuracy of genetic values, based on a significance level of 0.05, for the following traits in Landrace: number born alive, number of piglets weaned, piglet losses, and back fat. In Yorkshire, considering the full model significantly improves the prediction accuracy of genetic values, based on a significance level of 0.05, for the following traits: birthweight, total number born, and backfat.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2023
538 $a Mode of access: World Wide Web
650 4 $a Software. $2 gtt. $3 619355
650 4 $a Livestock. $3 539534
650 4 $a Bioinformatics. $3 553671
650 4 $a Biometrics. $3 898232
650 4 $a Decomposition. $3 3561186
650 4 $a Hogs. $3 3554056
650 4 $a Genomes. $3 592593
650 4 $a Biology. $3 522710
650 4 $a Gene loci. $3 3561757
650 4 $a Genotype & phenotype. $3 3561790
650 4 $a Breeding of animals. $3 3691003
650 4 $a Gene expression. $3 643979
650 4 $a Birth weight. $3 3685767
650 4 $a Polymorphism. $3 3560380
650 4 $a Parameter estimation. $3 567557
650 4 $a Weaning. $3 3685019
650 4 $a Animal sciences. $3 3174829
650 4 $a Computer science. $3 523869
650 4 $a Genetics. $3 530508
655 7 $a Electronic books. $2 lcsh $3 542853
690 $a 0306
690 $a 0715
690 $a 0475
690 $a 0984
690 $a 0369
710 2 $a ProQuest Information and Learning Co. $3 783688
710 2 $a North Carolina State University. $3 1018772
773 0 $t Masters Abstracts International $g 84-02.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29228622 $z click for full text (PQDT)