| 大菱鲆体重和体尺性状联合GWAS分析 |
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| 作者姓名： | 高进 杨润清 |
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| 作者单位： | 南京农业大学无锡渔业学院 无锡 214081; 中国水产科学研究院生物技术研究中心 北京 100141 |
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| 基金项目： | 中国水产科学研究院基本科研业务费项目“鲆鲽鱼分子标记辅助配套选育技术”(2017A001)资助。 |
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| 摘 要： | 为了揭示大菱鲆(Scophthalmus maximus)体重和体尺性状的分子遗传机制,探寻用于改良目标性状的分子标记及候选基因,本研究以大菱鲆育种群体为研究对象,分别测量其体重、体长、体宽和尾柄宽性状的表型值,利用简化基因组测序技术(2b-RAD)获得相应基因型数据,进行全基因组关联研究(Genome-wide association study, GWAS),筛选与大菱鲆体重和体尺性状显著关联的数量性状核苷酸(Quantitative trait nucleotides, QTNs)遗传位点。结果显示,以多性状线性混合模型(mv LMM)对体重–体长和体长–体宽–尾柄宽2个性状组合进行多性状GWAS分析,分别检测到9个和2个一因多效QTNs;以单一性状线性混合模型(LMM)对各个性状进行GWAS分析,在体重性状中检测到4个与之显著关联的QTNs,在体长和体宽性状中各检测到1个QTN,而在尾柄宽性状中则没有检测到显著的遗传位点。比较2种模型的结果,发现mvLMM相较于LMM能够检测到更多QTNs,且检测到的QTNs为更具生物学意义的一因多效QTNs。本研究首次利用mvLMM和LMM对大菱鲆体重和体尺性状进行联合GWAS分析,共筛选到17个显著的QTNs,其中,有4个QTNs被重复检测到。以这些检测到的QTNs为探针,在大菱鲆全基因组上找到了距离其最近的12个候选基因,它们可能是影响大菱鲆体重和体尺性状的重要候选标记和功能基因,本研究为大菱鲆体重和体尺性状的分子标记辅助选育提供了理论素材和参考。
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| 关 键 词： | 大菱鲆 体重和体尺性状 全基因组关联分析 多性状线性混合模型 |
| 收稿时间： | 2020/2/2 0:00:00 |
| 修稿时间： | 2020/2/19 0:00:00 |
Joint genome-wide association study of body mass and morphological traits in turbot (Scophthalmus maximus) |
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| Authors: | GAO Jin YANG Runqing |
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| Institution: | Wuxi Fisheries College, Nanjing Agricultural University, Wuxi 214081; Research Centre for Aquatic Biotechnology, Chinese Academy of Fishery Sciences, Beijing 100141 |
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| Abstract: | To reveal the molecular genetic mechanisms of body mass and morphological traits in turbot (Scophthalmus maximus) and scan molecular markers and candidate genes, which can be used to improve the target traits, a genome-wide association study (GWAS) was carried out using specific-locus amplified fragment technology (restriction site-associated DNA, 2b-RAD). First, body mass (BM), body length (BL), body width (BW), and caudal peduncle width (CPW) of 441 individuals were measured at about 473 days of growth period in a turbot breeding population. Second, all individuals were genotyped using 2b-RAD, and 23,988 SNPs were obtained after strict quality control. Using a multivariate linear mixed model (mvLMM) for GWAS of traits of BM-BL and BL-BW-CPW, 9 and 2 pleiotropic QTNs were detected for each phenotypic combination, respectively. However, a single-trait linear mixed model (LMM) based on the FaST-LMM algorithm was used for the association analysis of each trait, and the results showed that 4 QTNs were detected in the BM trait, 1 QTN was associated with BL and BW traits, respectively, and no significant locus was found in the CPW trait. A comparison between results of mvLMM and LMM found that mvLMM could detect more QTNs than LMM in GWAS, and the pleiotropic QTNs detected by mvLMM were more biologically meaningful. This study applied mvLMM and LMM to the joint GWAS of body mass and morphological traits in turbot, 17 significant QTNs were detected both using mvLMM and LMM, and 4 of them were detected repeatedly. Furthermore, 12 candidate genes were found by searching the nearest gene of each detected QTN on the whole turbot genome. All of them might be important candidate markers and functional genes, which could influence turbot body mass and morphology. Our study also provided the theory and a reference for marker-assisted selection of body mass and morphological traits in turbot. |
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| Keywords: | Turbot Body mass and morphological traits Genome-wide Association Analysis Multivariate linear mixed models |
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