Genome-wide association study of the backfat thickness trait in two pig populations

Zhu, Dandan; Max, Rothschild; Zhang, Zhiwu; Zhao, Shuhong; Fan, Bin

doi:10.15302/j-fase-2014005

Cited by 6 publications

(3 citation statements)

References 15 publications

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“…For example, Qiao et al (2015) performed a GWAS using a White Duroc × Erhualian F 2 intercross population and a Chinese Sutai population to reveal SNP and candidate genes related to growth and fatness traits, but no overlap of results was found in the 2 populations. Likewise, Zhu et al (2014) carried out separate GWAS in 2 populations, one population consisted of 820 Yorkshire pigs and Large White × Landrace intercrosses, and the other consisted of 208 Berkshire × Yorkshire F 2 intercrosses. Similarly, there were no overlapping significant SNPs identified in the 2 populations.…”

Section: Comparing the Single-population Analysis With The Combined Amentioning

confidence: 99%

A genome-wide association study of growth and fatness traits in two pig populations with different genetic backgrounds

Jiang

Tang

Wang

et al. 2018

Journal of Animal Science

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Improvement in growth and fatness traits are the main objectives in pig all breeding programs. Tenth rib backfat thickness (10RIBBFT) and days to 100 kg (D100), which are good predictors of carcass lean content and growth rate, respectively, are economically important traits and also main breeding target traits in pigs. To investigate the genetic mechanisms of 10RIBBFT and D100 of pigs, we sampled 1,137 and 888 pigs from 2 Yorkshire populations of American and British origin, respectively, and conducted genome-wide association study (GWAS) through combined analysis and meta-analysis, to identify SNPs associated with 10RIBBFT and D100. A total of 11 and 7 significant SNPs were identified by combined analysis for 10RIBBFT and D100, respectively. And in meta-analysis, 8 and 7 significant SNPs were identified for 10RIBBFT and D100, respectively. Among them, 6 and 5 common significant SNPs in two analysis results were, respectively, identified associated with 10RIBBFT and D100, and correspondingly explained 2.09% and 0.52% of the additive genetic variance of 10RIBBFT and D100. Further bioinformatics analysis revealed 10 genes harboring or close to these common significant SNPs, 5 for 10RIBBFT and 5 for D100. In particular, Gene Ontology analysis highlighted 6 genes, PCK1, ANGPTL3, EEF1A2, TNFAIP8L3, PITX2, and PLA2G12, as promising candidate genes relevant with backfat thickness and growth. PCK1, ANGPTL3, EEF1A2, and TNFAIP8L3 could influence backfat thickness through phospholipid transport, regulation of lipid metabolic process through the glycerophospholipid biosynthesis and metabolism pathway, the metabolism of lipids and lipoproteins pathway. PITX2 has a crucial role in skeletal muscle tissue development and animal organ morphogenesis, and PLA2G12A plays a role in the lipid catabolic and phospholipid catabolic processes, which both are involved in the body weight pathway. All these candidate genes could directly or indirectly influence fat production and growth in Yorkshire pigs. Our findings provide novel insights into the genetic basis of growth and fatness traits in pigs. The candidate genes for D100 and 10RIBBFT are worthy of further investigation.

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Section: Comparing the Single-population Analysis With The Combined Amentioning

confidence: 99%

A genome-wide association study of growth and fatness traits in two pig populations with different genetic backgrounds

Jiang

Tang

Wang

et al. 2018

Journal of Animal Science

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show abstract

“…Several studies have reported QTLs and candidate genes to possibly identify functional mutations in different loci associated with backfat in pigs ( Fontanesi et al, 2012 ; Zhu et al, 2014 ; Pant et al, 2015 ). We have identified one strong candidate SNP (rs80985094) located on SSC5:66103958 with very strong association with backfat across populations.…”

Section: Discussionmentioning

confidence: 99%

Fine Mapping of a Major Backfat QTL Reveals a Causal Regulatory Variant Affecting the CCND2 Gene

et al. 2022

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Backfat is an important trait in pork production, and it has been included in the breeding objectives of genetic companies for decades. Although adipose tissue is a good energy storage, excessive fat results in reduced efficiency and economical losses. A large QTL for backfat thickness on chromosome 5 is still segregating in different commercial pig breeds. We fine mapped this QTL region using a genome-wide association analysis (GWAS) with 133,358 genotyped animals from five commercial populations (Landrace, Pietrain, Large White, Synthetic, and Duroc) imputed to the porcine 660K SNP chip. The lead SNP was located at 5:66103958 (G/A) within the third intron of the CCND2 gene, with the G allele associated with more backfat, while the A allele is associated with less backfat. We further phased the QTL region to discover a core haplotype of five SNPs associated with low backfat across three breeds. Linkage disequilibrium analysis using whole-genome sequence data revealed three candidate causal variants within intronic regions and downstream of the CCND2 gene, including the lead SNP. We evaluated the association of the lead SNP with the expression of the genes in the QTL region (including CCND2) in a large cohort of 100 crossbred samples, sequenced in four different tissues (lung, spleen, liver, muscle). Results show that the A allele increases the expression of CCND2 in an additive way in three out of four tissues. Our findings indicate that the causal variant for this QTL region is a regulatory variant within the third intron of the CCND2 gene affecting the expression of CCND2.

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“…The associated study results, such as identifying individual genes and large chromosomal segments obtained by different research groups, are often not entirely consistent with each other. This is due to several objective and subjective reasons: trait polygenicity, low marker effect values, the insufficient information content of traditional phenotypic measurements, the significant influence of paratype factors on the phenotype, which cannot always be taken into account, and the utilization of different models and approaches [13][14][15][16][17][18][19][20][21][22]. At the same time, other studies have identified the same candidate genes associated with trait variability in different animal species [23].…”

Section: Introductionmentioning

confidence: 99%

Weighted Single-Step Genomic Best Linear Unbiased Prediction Method Application for Assessing Pigs on Meat Productivity and Reproduction Traits

Кабанов

Melnikova

Nikitin

et al. 2022

Animals

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Changes in the accuracy of the genomic estimates obtained by the ssGBLUP and wssGBLUP methods were evaluated using different reference groups. The weighting procedure’s reasonableness of application Pwas considered to improve the accuracy of genomic predictions for meat, fattening and reproduction traits in pigs. Six reference groups were formed to assess the genomic data quantity impact on the accuracy of predicted values (groups of genotyped animals). The datasets included 62,927 records of meat and fattening productivity (fat thickness over 6–7 ribs (BF1, mm)), muscle depth (MD, mm) and precocity up to 100 kg (age, days) and 16,070 observations of reproductive qualities (the number of all born piglets (TNB) and the number of live-born piglets (NBA), according to the results of the first farrowing). The wssGBLUP method has an advantage over ssGBLUP in terms of estimation reliability. When using a small reference group, the difference in the accuracy of ssGBLUP over BLUP AM is from −1.9 to +7.3 percent points, while for wssGBLUP, the change in accuracy varies from +18.2 to +87.3 percent points. Furthermore, the superiority of the wssGBLUP is also maintained for the largest group of genotyped animals: from +4.7 to +15.9 percent points for ssGBLUP and from +21.1 to +90.5 percent points for wssGBLUP. However, for all analyzed traits, the number of markers explaining 5% of genetic variability varied from 71 to 108, and the number of such SNPs varied depending on the size of the reference group (79–88 for BF1, 72–81 for MD, 71–108 for age). The results of the genetic variation distribution have the greatest similarity between groups of about 1000 and about 1500 individuals. Thus, the size of the reference group of more than 1000 individuals gives more stable results for the estimation based on the wssGBLUP method, while using the reference group of 500 individuals can lead to distorted results of GEBV.

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Genome-wide association study of the backfat thickness trait in two pig populations

Cited by 6 publications

References 15 publications

A genome-wide association study of growth and fatness traits in two pig populations with different genetic backgrounds

A genome-wide association study of growth and fatness traits in two pig populations with different genetic backgrounds

Fine Mapping of a Major Backfat QTL Reveals a Causal Regulatory Variant Affecting the CCND2 Gene

Weighted Single-Step Genomic Best Linear Unbiased Prediction Method Application for Assessing Pigs on Meat Productivity and Reproduction Traits

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