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SNP- and haplotype-based genome-wide association studies for growth, carcass, and meat quality traits in a Duroc multigenerational population.

Sato S, Uemoto Y, Kikuchi T, Egawa S, Kohira K, Saito T, Sakuma H, Miyashita S, Arata S, Kojima T, Suzuki K - BMC Genet. (2016)

Bottom Line: Four regions detected by SNP-based GWAS were significantly associated with multiple traits: on Sus scrofa chromosome (SSC) 1 at 304 Mb; and on SSC7 at 35-39 Mb, 41-42 Mb, and 103 Mb.The vertnin gene (VRTN) in particular, was located on SSC7 at 103 Mb and was significantly associated with vertebrae number and carcass lengths.In addition, a novel significant region could be detected by SNP-based GWAS as opposed to haplotype-based GWAS.

View Article: PubMed Central - PubMed

Affiliation: National Livestock Breeding Center, Nishigo, Fukushima, 961-8511, Japan. s0sato@nlbc.go.jp.

ABSTRACT

Background: The aim of the present study was to compare the power of single nucleotide polymorphism (SNP)-based genome-wide association study (GWAS) and haplotype-based GWAS for quantitative trait loci (QTL) detection, and to detect novel candidate genes affecting economically important traits in a purebred Duroc population comprising seven-generation pedigree. First, we performed a simulation analysis using real genotype data of this population to compare the power (based on the hypothesis) of the two methods. We then performed GWAS using both methods and real phenotype data comprising 52 traits, which included growth, carcass, and meat quality traits.

Results: In total, 836 animals were genotyped using the Illumina PorcineSNP60 BeadChip and 14 customized SNPs from regions of known candidate genes related to the traits of interest. The power of SNP-based GWAS was greater than that of haplotype-based GWAS in a simulation analysis. In real data analysis, a larger number of significant regions was obtained by SNP-based GWAS than by haplotype-based GWAS. For SNP-based GWAS, 23 genome-wide significant SNP regions were detected for 17 traits, and 120 genome-wide suggestive SNP regions were detected for 27 traits. For haplotype-based GWAS, 6 genome-wide significant SNP regions were detected for four traits, and 11 genome-wide suggestive SNP regions were detected for eight traits. All genome-wide significant SNP regions detected by haplotype-based GWAS were located in regions also detected by SNP-based GWAS. Four regions detected by SNP-based GWAS were significantly associated with multiple traits: on Sus scrofa chromosome (SSC) 1 at 304 Mb; and on SSC7 at 35-39 Mb, 41-42 Mb, and 103 Mb. The vertnin gene (VRTN) in particular, was located on SSC7 at 103 Mb and was significantly associated with vertebrae number and carcass lengths. Mapped QTL regions contain some candidate genes involved in skeletal formation (FUBP3; far upstream element binding protein 3) and fat deposition (METTL3; methyltransferase like 3).

Conclusion: Our results show that a multigenerational pig population is useful for detecting QTL, which are typically segregated in a purebred population. In addition, a novel significant region could be detected by SNP-based GWAS as opposed to haplotype-based GWAS.

No MeSH data available.


Related in: MedlinePlus

Power to achieve 5 % genome-wide significance within different ranges around selected QTL in simulation analysis. The y-axis represents the power to detect QTL. The results of the SNP-based genome-wide association study (GWAS) in high and low minor allele frequency (MAF) scenarios and those of haplotype-based GWAS in a high MAF scenario are shown. Three different ranges around the selected QTL were evaluated. QTL ± 0.5 Mb: The region ranged from ±0.5 Mb apart from the selected QTL. QTL ± 0.5–1.0 Mb: The region ranged from ±0.5 to 1.0 Mb apart from the selected QTL. QTL ± 1.0–2.0 Mb: The region ranged from ±1.0 to 2.0 Mb apart from the selected QTL
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Fig2: Power to achieve 5 % genome-wide significance within different ranges around selected QTL in simulation analysis. The y-axis represents the power to detect QTL. The results of the SNP-based genome-wide association study (GWAS) in high and low minor allele frequency (MAF) scenarios and those of haplotype-based GWAS in a high MAF scenario are shown. Three different ranges around the selected QTL were evaluated. QTL ± 0.5 Mb: The region ranged from ±0.5 Mb apart from the selected QTL. QTL ± 0.5–1.0 Mb: The region ranged from ±0.5 to 1.0 Mb apart from the selected QTL. QTL ± 1.0–2.0 Mb: The region ranged from ±1.0 to 2.0 Mb apart from the selected QTL

Mentions: For the practicability of finer mapping by SNP- and haplotype-based GWAS, the power among three different region apart from the selected QTL were calculated by SNP- and haplotype-based GWAS, and are shown in Fig. 2. The power of haplotype-based GWAS in a low-MAF scenario are not shown, because of very low power (see Fig. 1). For SNP-based GWAS, the similar trend of the results was observed in the power to detect QTL with high and low MAFs. The power decreased from the region (QTL ± 0.5 Mb) to the region (QTL ± 0.5–1.0 Mb) was a greater extent than it did from the region (QTL ± 0.5–1.0 Mb) to the region (QTL ± 1.0–2.0 Mb), and the decreased power was 0.26 and 0.04, respectively On the other hand, the power of the haplotype-based GWAS showed a constant decrease, and the decreased power of the region of interest was 0.07 and 0.05, respectively.Fig. 2


SNP- and haplotype-based genome-wide association studies for growth, carcass, and meat quality traits in a Duroc multigenerational population.

Sato S, Uemoto Y, Kikuchi T, Egawa S, Kohira K, Saito T, Sakuma H, Miyashita S, Arata S, Kojima T, Suzuki K - BMC Genet. (2016)

Power to achieve 5 % genome-wide significance within different ranges around selected QTL in simulation analysis. The y-axis represents the power to detect QTL. The results of the SNP-based genome-wide association study (GWAS) in high and low minor allele frequency (MAF) scenarios and those of haplotype-based GWAS in a high MAF scenario are shown. Three different ranges around the selected QTL were evaluated. QTL ± 0.5 Mb: The region ranged from ±0.5 Mb apart from the selected QTL. QTL ± 0.5–1.0 Mb: The region ranged from ±0.5 to 1.0 Mb apart from the selected QTL. QTL ± 1.0–2.0 Mb: The region ranged from ±1.0 to 2.0 Mb apart from the selected QTL
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4837538&req=5

Fig2: Power to achieve 5 % genome-wide significance within different ranges around selected QTL in simulation analysis. The y-axis represents the power to detect QTL. The results of the SNP-based genome-wide association study (GWAS) in high and low minor allele frequency (MAF) scenarios and those of haplotype-based GWAS in a high MAF scenario are shown. Three different ranges around the selected QTL were evaluated. QTL ± 0.5 Mb: The region ranged from ±0.5 Mb apart from the selected QTL. QTL ± 0.5–1.0 Mb: The region ranged from ±0.5 to 1.0 Mb apart from the selected QTL. QTL ± 1.0–2.0 Mb: The region ranged from ±1.0 to 2.0 Mb apart from the selected QTL
Mentions: For the practicability of finer mapping by SNP- and haplotype-based GWAS, the power among three different region apart from the selected QTL were calculated by SNP- and haplotype-based GWAS, and are shown in Fig. 2. The power of haplotype-based GWAS in a low-MAF scenario are not shown, because of very low power (see Fig. 1). For SNP-based GWAS, the similar trend of the results was observed in the power to detect QTL with high and low MAFs. The power decreased from the region (QTL ± 0.5 Mb) to the region (QTL ± 0.5–1.0 Mb) was a greater extent than it did from the region (QTL ± 0.5–1.0 Mb) to the region (QTL ± 1.0–2.0 Mb), and the decreased power was 0.26 and 0.04, respectively On the other hand, the power of the haplotype-based GWAS showed a constant decrease, and the decreased power of the region of interest was 0.07 and 0.05, respectively.Fig. 2

Bottom Line: Four regions detected by SNP-based GWAS were significantly associated with multiple traits: on Sus scrofa chromosome (SSC) 1 at 304 Mb; and on SSC7 at 35-39 Mb, 41-42 Mb, and 103 Mb.The vertnin gene (VRTN) in particular, was located on SSC7 at 103 Mb and was significantly associated with vertebrae number and carcass lengths.In addition, a novel significant region could be detected by SNP-based GWAS as opposed to haplotype-based GWAS.

View Article: PubMed Central - PubMed

Affiliation: National Livestock Breeding Center, Nishigo, Fukushima, 961-8511, Japan. s0sato@nlbc.go.jp.

ABSTRACT

Background: The aim of the present study was to compare the power of single nucleotide polymorphism (SNP)-based genome-wide association study (GWAS) and haplotype-based GWAS for quantitative trait loci (QTL) detection, and to detect novel candidate genes affecting economically important traits in a purebred Duroc population comprising seven-generation pedigree. First, we performed a simulation analysis using real genotype data of this population to compare the power (based on the hypothesis) of the two methods. We then performed GWAS using both methods and real phenotype data comprising 52 traits, which included growth, carcass, and meat quality traits.

Results: In total, 836 animals were genotyped using the Illumina PorcineSNP60 BeadChip and 14 customized SNPs from regions of known candidate genes related to the traits of interest. The power of SNP-based GWAS was greater than that of haplotype-based GWAS in a simulation analysis. In real data analysis, a larger number of significant regions was obtained by SNP-based GWAS than by haplotype-based GWAS. For SNP-based GWAS, 23 genome-wide significant SNP regions were detected for 17 traits, and 120 genome-wide suggestive SNP regions were detected for 27 traits. For haplotype-based GWAS, 6 genome-wide significant SNP regions were detected for four traits, and 11 genome-wide suggestive SNP regions were detected for eight traits. All genome-wide significant SNP regions detected by haplotype-based GWAS were located in regions also detected by SNP-based GWAS. Four regions detected by SNP-based GWAS were significantly associated with multiple traits: on Sus scrofa chromosome (SSC) 1 at 304 Mb; and on SSC7 at 35-39 Mb, 41-42 Mb, and 103 Mb. The vertnin gene (VRTN) in particular, was located on SSC7 at 103 Mb and was significantly associated with vertebrae number and carcass lengths. Mapped QTL regions contain some candidate genes involved in skeletal formation (FUBP3; far upstream element binding protein 3) and fat deposition (METTL3; methyltransferase like 3).

Conclusion: Our results show that a multigenerational pig population is useful for detecting QTL, which are typically segregated in a purebred population. In addition, a novel significant region could be detected by SNP-based GWAS as opposed to haplotype-based GWAS.

No MeSH data available.


Related in: MedlinePlus