Meta-analysis of genome-wide association studies uncovers shared candidate genes across breeds for pig fatness trait

Zeng, Haonan; Zhong, Zhanming; Xu, Zhiting; Teng, Jinyan; Chen, Wei; Chen, Zitao; Zhang, Wenjing; Ding, Xiangdong; Li, Jiaqi; Zhang, Zhe

doi:10.1186/s12864-022-09036-z

Cited by 9 publications

(8 citation statements)

References 66 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…ALGA0006623 has been reported as a significant marker and gene associated with lean meat percentage (LMP) and ADG in American Duroc and Canadian Duroc pigs [ 20 ]. Furthermore, it was identified as the most significant SNP in relation to the fatness trait in pigs [ 21 ]. The marker ASGA0038525 was found to be common for AGE, BF, and EMA, with significance above the Bonferroni threshold observed for BF.…”

Section: Resultsmentioning

confidence: 99%

Genome-wide association study to reveal new candidate genes using single-step approaches for productive traits of Yorkshire pig in Korea

Park

2024

Anim Biosci

View full text Add to dashboard Cite

Objective: The objective is to identify genomic regions and candidate genes associated with age to 105 kg (AGE), average daily gain (ADG), backfat thickness (BF), and eye muscle area (EMA) in Yorkshire pig.Methods: This study used a total of 104,380 records and 11,854 single nucleotide polymorphism (SNP) data obtained from Illumina porcine 60K chip. The estimated genomic breeding values (GEBVs) and SNP effects were estimated by single-step genomic best linear unbiased prediction (ssGBLUP).Results: The heritabilities of AGE, ADG, BF, and EMA were 0.50, 0.49, 0.49, and 0.23, respectively. We identified significant SNP markers surpassing the Bonferroni correction threshold (1.68×10<sup>-6</sup>), with a total of 9 markers associated with both AGE and ADG, and 4 markers associated with BF and EMA. Genome-wide association study (GWAS) analyses revealed notable chromosomal regions linked to AGE and ADG on <i>Sus scrofa</i> chromosome (SSC) 1, 6, 8, and 16; BF on SSC 2, 5, and 8; and EMA on SSC 1. Additionally, we observed strong linkage disequilibrium on SSC 1. Finally, we performed enrichment analyses using gene ontology and Kyoto encyclopedia of genes and genomes (KEGG), which revealed significant enrichments in eight biological processes, one cellular component, one molecular function, and one KEGG pathway.Conclusion: The identified SNP markers for productive traits are expected to provide valuable information for genetic improvement as an understanding of their expression.

show abstract

Section: Resultsmentioning

confidence: 99%

Genome-wide association study to reveal new candidate genes using single-step approaches for productive traits of Yorkshire pig in Korea

Park

2024

Anim Biosci

View full text Add to dashboard Cite

show abstract

“…3f). MC4R has been extensively demonstrated to be linked to muscle and fat deposition in pigs 45–47 . Notably, similar traits shared a greater number of lead SNPs, such as TNUM-related traits (Fig.…”

Section: Resultsmentioning

confidence: 99%

“…3f). MC4R has been extensively demonstrated to be linked to muscle and fat deposition in pigs [45][46][47] .…”

Section: Pleiotropy Of Genetic Variants In Complex Traitsmentioning

confidence: 99%

Integrating large-scale meta-GWAS and PigGTEx resources to decipher the genetic basis of complex traits in pig

Xu,

Lin,

Cai

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

Understanding the molecular and cellular mechanisms that underlie complex traits in pigs is crucial for enhancing their genetic improvement program and unleashing their substantial potentials in human biomedicine research. Here, we conducted a meta-GWAS analysis for 232 complex traits with 28.3 million imputed whole-genome sequence variants in 70,328 individuals from 14 pig breeds. We identified a total of 6,878 genomic regions associated with 139 complex traits. By integrating with the Pig Genotype-Tissue Expression (PigGTEx) resource, we systemically explored the biological context and regulatory circuits through which these trait-associated variants act and finally prioritized 16,664 variant-gene-tissue-trait circuits. For instance, rs344053754 regulates the expression of UGT2B31 in the liver by affecting the activity of regulatory elements and ultimately affects litter weight at weaning. Furthermore, we investigated the conservation of genetic and regulatory mechanisms underlying 136 human traits and 232 pig traits. Overall, our multi-breed meta-GWAS in pigs provides invaluable resources and novel insights for understanding the regulatory and evolutionary mechanisms of complex traits in pigs and humans.

show abstract

“…Although the single‐GWAS was a common approach for association analysis between each SNP and phenotype in a single population, some earlier reports suggested that the use of a single‐GWAS analysis has limitations to resolve potential effects caused by multiple tests and pleiotropic effects (Xue et al., 2021). Recently, meta1‐analyses have been wildly reported in humans (Soranzo et al., 2009), cattle (Bouwman et al., 2018), and pigs (Zeng et al., 2022; Zhou et al., 2021). Similar to mega‐GWAS, the detection power was enhanced in the meta‐analyses compared to the single‐GWAS.…”

Section: Discussionmentioning

confidence: 99%

Evaluating three strategies of genome‐wide association analysis for integrating data from multiple populations

Zhong,

Li,

et al. 2024

Animal Genetics

View full text Add to dashboard Cite

In livestock, genome‐wide association studies (GWAS) are usually conducted in a single population (single‐GWAS) with limited sample size and detection power. To enhance the detection power of GWAS, meta‐analysis of GWAS (meta‐GWAS) and mega‐analysis of GWAS (mega‐GWAS) have been proposed to integrate data from multiple populations at the level of summary statistics or individual data, respectively. However, there is a lack of comparison for these different strategies, which makes it difficult to guide the best practice of GWAS integrating data from multiple study populations. To maximize the comparison of different association analysis strategies across multiple populations, we conducted single‐GWAS, meta‐GWAS, and mega‐GWAS for the backfat thickness of 100 kg (BFT_100) and days to 100 kg (DAYS_100) within each of the three commercial pig breeds (Duroc, Yorkshire, and Landrace). Based on controlling the genome inflation factor to one, we calculated corrected p‐values (pC). In Yorkshire, with the largest sample size, mega‐GWAS, meta‐GWAS and single‐GWAS detected 149, 38 and 20 significant SNPs (pC < 1E‐5) associated with BFT_100, as well as 26, four, and one QTL, respectively. Among them, pC of SNPs from mega‐GWAS was the lowest, followed by meta‐GWAS and single‐GWAS. The correlation of pC among the three GWAS strategies ranged from 0.60 to 0.75 and the correlation of SNP effect values between meta‐GWAS and mega‐GWAS was 0.74, all showing good agreement. Collectively, even though there are differences in the integration of individual data or summary statistics, integrating data from multiple populations is an effective means of genetic argument for complex traits, especially mega‐GWAS versus single‐GWAS.

show abstract

Meta-analysis of genome-wide association studies uncovers shared candidate genes across breeds for pig fatness trait

Cited by 9 publications

References 66 publications

Genome-wide association study to reveal new candidate genes using single-step approaches for productive traits of Yorkshire pig in Korea

Genome-wide association study to reveal new candidate genes using single-step approaches for productive traits of Yorkshire pig in Korea

Integrating large-scale meta-GWAS and PigGTEx resources to decipher the genetic basis of complex traits in pig

Evaluating three strategies of genome‐wide association analysis for integrating data from multiple populations

Contact Info

Product

Resources

About