College of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangdong, 510642, P.R. China.
Guangdong Wens Breeding Swine Technology Co., Ltd, Guangdong, 527439, P.R. China.
BMC Genomics. 2022 Aug 13;23(1):590. doi: 10.1186/s12864-022-08804-1.
Carcass traits are important in pig breeding programs for improving pork production. Understanding the genetic variants underlies complex phenotypes can help explain trait variation in pigs. In this study, we integrated a weighted single-step genome-wide association study (wssGWAS) and copy number variation (CNV) analyses to map genetic variations and genes associated with loin muscle area (LMA), loin muscle depth (LMD) and lean meat percentage (LMP) in Duroc pigs.
Firstly, we performed a genome-wide analysis for CNV detection using GeneSeek Porcine SNP50 Bead chip data of 3770 pigs. A total of 11,100 CNVs were detected, which were aggregated by overlapping 695 CNV regions (CNVRs). Next, we investigated CNVs of pigs from the same population by whole-genome resequencing. A genome-wide analysis of 21 pigs revealed 23,856 CNVRs that were further divided into three categories (851 gain, 22,279 loss, and 726 mixed), which covered 190.8 Mb (~ 8.42%) of the pig autosomal genome. Further, the identified CNVRs were used to determine an overall validation rate of 68.5% for the CNV detection accuracy of chip data. CNVR association analyses identified one CNVR associated with LMA, one with LMD and eight with LMP after applying stringent Bonferroni correction. The wssGWAS identified eight, six and five regions explaining more than 1% of the additive genetic variance for LMA, LMD and LMP, respectively. The CNVR analyses and wssGWAS identified five common regions, of which three regions were associated with LMA and two with LMP. Four genes (DOK7, ARAP1, ELMO2 and SLC13A3) were highlighted as promising candidates according to their function.
We determined an overall validation rate for the CNV detection accuracy of low-density chip data and constructed a genomic CNV map for Duroc pigs using resequencing, thereby proving a value genetic variation resource for pig genome research. Furthermore, our study utilized a composite genetic strategy for complex traits in pigs, which will contribute to the study for elucidating the genetic architecture that may be influenced and regulated by multiple forms of variations.
胴体性状在猪的育种计划中很重要，可用于提高猪肉产量。了解复杂表型背后的遗传变异有助于解释猪的性状变异。本研究整合了一种加权单步全基因组关联研究（wssGWAS）和拷贝数变异（CNV）分析方法，以定位与杜洛克猪的腰肌肉面积（LMA）、腰肌肉深度（LMD）和瘦肉百分比（LMP）相关的遗传变异和基因。
首先，我们使用 3770 头猪的 GeneSeek 猪 SNP50 Bead 芯片数据进行了全基因组 CNV 检测分析。共检测到 11100 个 CNV，通过重叠 695 个 CNV 区域（CNVRs）进行了汇总。接下来，我们通过全基因组重测序研究了来自同一群体的猪的 CNV。对 21 头猪的全基因组分析揭示了 23856 个 CNVR，进一步分为 3 类（851 个增益、22279 个缺失和 726 个混合），涵盖了猪常染色体基因组的 190.8Mb（约 8.42%）。进一步，使用芯片数据的 CNV 检测准确性的总体验证率为 68.5%来确定所识别的 CNVR。经过严格的 Bonferroni 校正，CNVR 关联分析确定了一个与 LMA 相关的 CNVR、一个与 LMD 相关的 CNVR 和 8 个与 LMP 相关的 CNVR。wssGWAS 分别鉴定出了 8、6 和 5 个区域，分别解释了 LMA、LMD 和 LMP 加性遗传方差的 1%以上。CNVR 分析和 wssGWAS 鉴定出了 5 个共同区域，其中 3 个区域与 LMA 相关，2 个区域与 LMP 相关。根据其功能，确定了四个有前景的候选基因（DOK7、ARAP1、ELMO2 和 SLC13A3）。
我们确定了低密度芯片数据的 CNV 检测准确性的总体验证率，并使用重测序构建了杜洛克猪的基因组 CNV 图谱，从而为猪基因组研究提供了有价值的遗传变异资源。此外，我们的研究利用了一种复合遗传策略来研究猪的复杂性状，这将有助于研究可能受到多种形式变异影响和调节的遗传结构。
