A whole-genome association study for litter size and litter weight traits in pigs

Metodiev, S.; Thekkoot, Dinesh M.; Young, J. M.; Onteru, Suneel Kumar; Rothschild, Max F.; Dekkers, Jack C. M.

doi:10.1016/j.livsci.2018.03.004

Cited by 14 publications

(15 citation statements)

References 61 publications

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“…Several authors hypothesized that partly different genes control reproductive traits in successive parities (Hermesch, Luxford, & Graser, ; Noguera, Varona, Babot, & Estany, ; Roehe & Kennedy, ). Recently, several genome‐wide association and candidate gene approach studies have reported results supporting this hypothesis for NBA (Ma et al, ; Metodiev et al, ; Onteru et al, ; Sato, Kikuchi, Uemoto, Mikawa, & Suzuki, ). When the hypothesis is true, an alternative model, such as a multiple‐trait model that regards the records at different parities as genetically different traits, may be better choice (Satoh, Furukawa, Nishida, & Obata, ; Tartar & Bolet, ).…”

Section: Introductionmentioning

confidence: 92%

Genetic parameter estimation for number born alive at different parities in Landrace and Large White pigs

Ogawa

Konta

Kimata³

et al. 2019

Animal Science Journal

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We estimated genetic parameters for number born alive (NBA) from the first to the seventh parities in Landrace and Large White pigs using three models. Analyzing 55,160 farrowing records for 12,677 Landrace dams and 43,839 for 10,405 Large White dams, we used a single‐trait animal model to estimate the heritability of NBA at each parity and a two‐trait animal model and a single‐trait random regression model to estimate the genetic correlations between parities. Heritability estimates of NBA at each parity ranged from 0.08 to 0.13 for Landrace and from 0.05 to 0.16 for Large White. Estimated genetic correlations between parities in all cases were positive. Genetic correlations between the first and second parities were slightly lower than those between other neighboring parities. Genetic correlations between more distant parities tended to be lower, in some cases <0.8. The results indicate the necessity to investigate the applicability of evaluating NBA at different parities as different traits (e.g., the first and later parities), although a repeatability model might still be reasonable.

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Section: Introductionmentioning

confidence: 92%

Genetic parameter estimation for number born alive at different parities in Landrace and Large White pigs

Ogawa

Konta

Kimata³

et al. 2019

Animal Science Journal

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show abstract

“…Sow fertility is one of the most important economic parameters in the swine industry, and therefore improving reproduction rate can significantly increase the profitability for swine producers. Candidate genes and quantitative trait loci (QTL) associated with reproductive traits, such as total number (of piglets) born (TNB), have been identified in genome-wide association studies (GWAS) in the past [1][2][3][4][5][6]. Thus far, 377 quantitative trait loci (QTL) for reproductive traits were detected, of which 159 and 129 QTLs for TNB and number born alive (NBA) have already been identified, respectively [7].…”

Section: Introductionmentioning

confidence: 99%

Integrated analysis of lncRNA, miRNA and mRNA reveals novel insights into the fertility regulation of large white sows

Jia

et al. 2020

BMC Genomics

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Background Improving sow fertility is extremely important as it can lead to increased reproductive efficiency and thus profitability for swine producers. There are considerable differences in fertility rates among individual animals, but the underlying molecular mechanisms remain unclear. In this study, by using different types of RNA libraries, we investigated the complete transcriptome of ovarian tissue during the luteal (L) and follicular (F) phases of the estrous cycle in Large White pigs with high (H) and low (L) fecundity, and performed a comprehensive analysis of long noncoding RNAs (lncRNAs), mRNAs and micro RNAs (miRNAs) from 16 samples by combining RNA sequencing (RNA-seq) with bioinformatics. Results In total, 24,447 lncRNAs, 27,370 mRNAs, and 216 known miRNAs were identified in ovarian tissues. The genomic features of lncRNAs, such as length distribution and number of exons, were further analyzed. We selected a threshold of P < 0.05 and |log2 (fold change)| ≥ 1 to obtain the differentially expressed lncRNAs, miRNAs and mRNAs by pairwise comparison (LH vs. LL, FH vs. FL). Bioinformatics analysis of these differentially expressed RNAs revealed multiple significantly enriched pathways (P < 0.05) that were closely involved in the reproductive process, such as ovarian steroidogenesis, lysosome, steroid biosynthesis, and the estrogen and GnRH signaling pathways. Moreover, bioinformatics screening of differentially expressed miRNAs that share common miRNA response elements (MREs) with lncRNAs and their downstream mRNA targets were performed. Finally, we constructed lncRNA–miRNA–mRNA regulation networks. The key genes in these networks were verified by Reverse Transcription Real-time Quantitative PCR (RT-qRCR), which were consistent with the results from RNA-Seq data. Conclusions These results provide further insights into the fertility of pigs andcan contribute to further experimental investigation of the functions of these genes.

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“…Litter size, such as TNB, is one of the most important reproductive traits that are di cult to improve by selection because of its low heritability [1]. Meanwhile, it is a highly complex trait, which is controlled by multiple genes and is affected by parity, environmental factors and sire [3][4][5][6]. More importantly, ovulation rate is also a major factor that affects the litter size of sows [35].…”

Section: Discussionmentioning

confidence: 99%

“…Sow fertility is one of the most important economic parameters in the swine industry, and therefore improving reproduction rate can signi cantly increase the pro tability for swine producers. Candidate genes and quantitative trait loci (QTL) associated with reproductive traits, such as total number (of piglets) born (TNB), have been identi ed in genome-wide association studies (GWAS) in the past [1][2][3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Integrated Analysis of lncRNA, miRNA and mRNA Reveals Novel Insights into the Fertility Regulation of Large White Sows

Jia²,

et al. 2020

Preprint

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Background: Improving sow fertility is extremely important as it can lead to increased reproductive efficiency and thus profitability for swine producers. There are considerable differences in fertility rates among individual animals, but the underlying molecular mechanisms remain unclear. In this study, by using different types of RNA libraries, we investigated the complete transcriptome of ovarian tissue during the luteal (L) and follicular (F) phases of the estrous cycle in Large White pigs with high (H) and low (L) fecundity, and performed a comprehensive analysis of long noncoding RNAs (lncRNAs), mRNAs and micro RNAs (miRNAs) from 16 samples by combining RNA sequencing (RNA-seq) with bioinformatics.Results: In total, 24,447 lncRNAs, 27,370 mRNAs, and 216 known miRNAs were identified in ovarian tissues. The genomic features of lncRNAs, such as length distribution and number of exons, were further analyzed. We selected a threshold of P <0.05 and |log2 (fold change)| ≥ 1 to obtain the differentially expressed lncRNAs, miRNAs and mRNAs by pairwise comparison (LH vs. LL, FH vs. FL). Bioinformatics analysis of these differentially expressed RNAs revealed multiple significantly enriched pathways (P <0.05) that were closely involved in the reproductive process, such as ovarian steroidogenesis, lysosome, steroid biosynthesis, and the estrogen and GnRH signaling pathways. Moreover, bioinformatics screening of differentially expressed miRNAs that share common miRNA response elements (MREs) with lncRNAs and their downstream mRNA targets were performed. Finally, we constructed lncRNA–miRNA–mRNA regulation networks. The key genes in these networks were verified by Reverse Transcription Real-time Quantitative PCR (RT-qRCR), which were consistent with the results from RNA-Seq data.Conclusions: These results provide further insights into the fertility of pigs andcan contribute to further experimental investigation of the functions of these genes.

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A whole-genome association study for litter size and litter weight traits in pigs

Cited by 14 publications

References 61 publications

Genetic parameter estimation for number born alive at different parities in Landrace and Large White pigs

Genetic parameter estimation for number born alive at different parities in Landrace and Large White pigs

Integrated analysis of lncRNA, miRNA and mRNA reveals novel insights into the fertility regulation of large white sows

Integrated Analysis of lncRNA, miRNA and mRNA Reveals Novel Insights into the Fertility Regulation of Large White Sows

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