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利用低深度测序和 SNP 芯片数据进行白来航鸡产蛋品质性状的全基因组关联研究。

Genome-wide association studies for egg quality traits in White Leghorn layers using low-pass sequencing and SNP chip data.

机构信息

Department of Animal Science, University of California, Davis, California, USA.

Hy-Line International, Dallas Center, Iowa, USA.

出版信息

J Anim Breed Genet. 2022 Jul;139(4):380-397. doi: 10.1111/jbg.12679. Epub 2022 Apr 11.

Abstract

Low-pass sequencing data have been proposed as an alternative to single nucleotide polymorphism (SNP) chips in genome-wide association studies (GWAS) of several species. However, it has not been used in layer chickens yet. This study aims at comparing the GWAS results of White Leghorn chickens using low-pass sequencing data (1×) and 54 k SNP chip data. Ten commercially relevant egg quality traits including albumen height, shell strength, shell colour, egg weight and yolk weight collected from up to 1,420 White Leghorn chickens were analysed. The results showed that the genomic heritability estimates based on low-pass sequencing data were higher than those based on SNP chip data. Although two GWAS analyses showed similar overall landscape for most traits, low-pass sequencing captured some significant SNPs that were not on the SNP chip. In GWAS analysis using 54 k SNP chip data, after including more individuals (up to 5,700), additional significant SNPs not detected by low-pass sequencing data were found. In conclusion, GWAS using low-pass sequencing data showed similar results to those with SNP chip data and may require much larger sample sizes to show measurable advantages.

摘要

低深度测序数据已被提议作为全基因组关联研究(GWAS)中替代单核苷酸多态性(SNP)芯片的一种方法,用于几种物种。然而,它尚未在层鸡中使用。本研究旨在比较使用低深度测序数据(1×)和 54k SNP 芯片数据的白来航鸡的 GWAS 结果。分析了来自多达 1420 只白来航鸡的 10 种与商业相关的蛋品质性状,包括蛋白高度、蛋壳强度、蛋壳颜色、蛋重和蛋黄重。结果表明,基于低深度测序数据的基因组遗传力估计值高于基于 SNP 芯片数据的遗传力估计值。尽管两项 GWAS 分析显示大多数性状的总体景观相似,但低深度测序数据捕捉到了一些 SNP 芯片上没有的显著 SNP。在使用 54k SNP 芯片数据的 GWAS 分析中,在包括更多个体(多达 5700 个)后,发现了低深度测序数据未检测到的其他显著 SNP。总之,使用低深度测序数据的 GWAS 显示出与 SNP 芯片数据相似的结果,并且可能需要更大的样本量才能显示可衡量的优势。

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