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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 in simulation analysis. The x-axis indicates QTL heritability and the y-axis represents the power to detect QTL. Results of varying minor allele frequency (MAF) categories (low and high) and models (SNP-based and haplotype-based genome-wide association studies) are shown
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Fig1: Power to achieve 5 % genome-wide significance in simulation analysis. The x-axis indicates QTL heritability and the y-axis represents the power to detect QTL. Results of varying minor allele frequency (MAF) categories (low and high) and models (SNP-based and haplotype-based genome-wide association studies) are shown

Mentions: The powers to detect QTL by SNP-based GWAS and haplotype-based GWAS in simulation analysis are presented in Fig. 1. With regard to the impact of QTL MAF on QTL detection, the difference in power between SNP-based GWAS and haplotype-based GWAS was evident. For SNP-based GWAS, the similar trend of the results was observed in the power to detect QTL with high and low MAFs. For haplotype-based GWAS, the power to detect QTL with high MAF was greater, as the QTL heritability increased to more than 0.05. However, the power to detect QTL with low MAF was quite low at all QTL heritabilities (the maximum value of power was 0.03). The power of SNP-based GWAS was greater than that of haplotype-based GWAS under all simulation conditions. For SNP-based GWAS, as the QTL heritability increased, the power to detect QTL also increased and was almost constant at higher QTL heritabilities (more than 0.10). In addition, the power to detect QTL with heritability 0.05 was 0.50 in a high-MAF scenario and 0.45 in a low-MAF scenario. Thus, QTL with smaller heritabilities and both MAFs could be detected by SNP-based GWAS. For haplotype-based GWAS, the power to detect QTL with heritability less than 0.05 was very low (less than 0.03) in a high MAF scenario, but increased as the QTL heritability increased to more than 0.05.Fig. 1


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 in simulation analysis. The x-axis indicates QTL heritability and the y-axis represents the power to detect QTL. Results of varying minor allele frequency (MAF) categories (low and high) and models (SNP-based and haplotype-based genome-wide association studies) are shown
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig1: Power to achieve 5 % genome-wide significance in simulation analysis. The x-axis indicates QTL heritability and the y-axis represents the power to detect QTL. Results of varying minor allele frequency (MAF) categories (low and high) and models (SNP-based and haplotype-based genome-wide association studies) are shown
Mentions: The powers to detect QTL by SNP-based GWAS and haplotype-based GWAS in simulation analysis are presented in Fig. 1. With regard to the impact of QTL MAF on QTL detection, the difference in power between SNP-based GWAS and haplotype-based GWAS was evident. For SNP-based GWAS, the similar trend of the results was observed in the power to detect QTL with high and low MAFs. For haplotype-based GWAS, the power to detect QTL with high MAF was greater, as the QTL heritability increased to more than 0.05. However, the power to detect QTL with low MAF was quite low at all QTL heritabilities (the maximum value of power was 0.03). The power of SNP-based GWAS was greater than that of haplotype-based GWAS under all simulation conditions. For SNP-based GWAS, as the QTL heritability increased, the power to detect QTL also increased and was almost constant at higher QTL heritabilities (more than 0.10). In addition, the power to detect QTL with heritability 0.05 was 0.50 in a high-MAF scenario and 0.45 in a low-MAF scenario. Thus, QTL with smaller heritabilities and both MAFs could be detected by SNP-based GWAS. For haplotype-based GWAS, the power to detect QTL with heritability less than 0.05 was very low (less than 0.03) in a high MAF scenario, but increased as the QTL heritability increased to more than 0.05.Fig. 1

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