Institutional Repository of Key Laboratory of Experimental Marine Biology, Institute of Oceanology, Chinese Academy of Sciences
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|全基因组关联分析 基因组选择 生长性状 耐高盐性状 凡纳滨对虾
全基因组关联分析（Genome-wide association study, GWAS）和基因组选择（Genomic selection, GS）是育种3.0阶段重要的技术手段，极大地推动了动植物性状的遗传解析和分子育种技术的发展。凡纳滨对虾基因组的破译使对虾相关研究进入了后基因组时代，GWAS分析成为解析对虾经济性状遗传基础的重要手段。开展凡纳滨对虾分子育种技术研究，加速其遗传选育过程，是破解其“良种缺乏、种源受制于人”困境的重要技术手段。前期本课题组和国内外相关研究团队开展了凡纳滨对虾生长、抗弧菌等性状的GWAS和GS研究，但是进展相对比较缓慢。本研究针对凡纳滨对虾生长和耐高盐性状，利用高密度SNP芯片、比较转录组分析等开展了性状相关标记的筛选，鉴定了性状相关基因，并建立了适合水产动物进行基因组选择育种的策略，为对虾重要经济性状的遗传解析奠定了重要基础，并为GS在水产动物中进行应用提供了重要思路。论文主要进展如下：
1. 对虾生长性状相关标记和基因的筛选：利用凡纳滨对虾600 K高密度芯片对多家系混合群体进行了SNP分型，并进行了连锁不平衡衰减（LD decay）分析，结果表明当r2 = 0.2时，标记间的物理距离为20 Kb，说明该群体的衰减速率较快。利用GWAS分析鉴定了11个与体重性状显著相关的SNP标记，并在40号连锁群上鉴定到一个与生长性状显著相关的区段，在该区段内定位到候选基因c76794。对候选基因进行关联分析鉴定了11个与体重显著相关的变异位点，其中单个标记对体重表型的解释率达11.23%，11个标记的累积表型解释率为24%，表明其为生长性状密切相关基因，该基因的发现为对虾生长性状的遗传解析奠定了重要基础。
2. 对虾耐高盐性状相关基因及通路的筛选：通过耐高盐家系和敏感家系在正常盐度和高盐处理后的比较转录组分析，发现耐高盐家系和敏感家系在正常盐度和高盐环境下其差异基因均富集在 “response to stimulus”通路上，这个通路中包含多个编码甲壳蓝蛋白的基因，这些基因均在耐高盐家系中呈显著高表达的趋势，提示它们在对虾耐高盐过程中发挥重要作用。通过比较耐高盐家系和敏感家系对高盐处理后的应答反应差异，获得只在抗性家系发生响应的基因，这些基因主要富集在“serine-type endopeptidase activity”, “serine-type peptidase activity”和“serine hydrolase activity”这三个GO条目中。其中，丝氨酸蛋白酶家族的基因是三个GO条目中共有的基因，提示该类型的基因在对虾耐高盐过程中发挥着重要作用。在基因组水平上，通过整合GWAS和机器学习的方法对耐高盐性状进行关联分析，在21号连锁群上发现了一个与性状显著相关标记，在该标记上下游20 Kb范围内定位到一个功能基因，该基因恰恰与转录组数据分析发现的丝氨酸蛋白酶类基因相吻合，进一步说明其在对虾耐高盐过程中发挥重要作用。
4. 基于机器学习的对虾GS分析方法的建立：利用1565尾凡纳滨对虾的基因型和表型数据，对不同预测模型，包括传统GS模型中的GBLUP、BayesB、以及机器学习模型的KAML, Catboost, XGBoost, ExtraTrees, RF, KNN, LightGBM, NeuralNet, WE的预测能力进行了比较。表型数据包括体长、腹长与头胸部长的比值两个表型。预测能力的比较发现，NeuralNet在所有模型的对比中表现最好，与GBLUP比较，NeuralNet在两种表型中的预测能力都提高了约10%，说明机器学习中的NeuralNet模型在凡纳滨对虾基因组选育中有更好的应用前景。
5. 对虾耐高盐性状的基因组选择分析：采用课题组自主设计的对虾育种芯片评估了凡纳滨对虾耐高盐性状的遗传参数，并结合建立的GS方法开展了耐高盐性状的基因组选择分析。以耐高盐性状测试中的对虾存活时间、致死盐度以及存活状态这三个性状作为表型性状，利用分子标记评估的遗传力分别为0.38 ± 0.01，0.40 ± 0.01，0.5 ± 0.01。在不同GS模型中，NeuralNet的预测准确性最高，其在对虾存活时间、致死盐度以及存活状态预测的准确性分别为0.67 ± 0.05，0.66 ± 0.06，0.77 ± 0.03。相较于传统的系谱选择（PBLUP），基因组选择的预测准确性提高了13.8%。进一步的分析表明，当参考群体的数量达到1000左右时，其预测准确性可提高到0.9。说明基因组选择较传统的系谱选择具有明显的优势，研究结果为凡纳滨对虾耐高盐性状的基因组选择提供了重要指导。
Genome-wide association analysis (GWAS) and genomic selection (GS) are important techniques in the breeding 3.0 stage, which have greatly promoted genetic dissection of important traits and molecular breeding in animals and plants. With the decoding of Litopenaeus vannamei (L. vannamei) genome, studies on shrimp are going to the post-genome era. GWAS has become an important method to analyze the genetic basis of economic traits in shrimp. Molecular breeding technology is a fundamental approach to solve the problem faced by shrimp culture, “absence of high quality broodstocks of Litopenaeus vannamei due to the restriction from abroad”. Our group and other research teams have carried out some GWAS and GS research in L. vannamei, but the progress is still limited. In this study, high-density SNP chips and comparative transcriptome analysis were used to screen markers or genes related to growth and high salinity tolerance traits. Strategy for genomic selection breeding in aquatic animals were developed. All these works have laid an important foundation on the genetic analysis of important economic traits in shrimp, and will provide important theoretical guidance and technical support for the application of GS in aquatic animals, especially in shrimp. The main progresses are as follows:
1. Identification of markers and genes related to growth traits in L. vannamei: A mixed population composed of multiple families was genotyped by 600 K high-density chip. Linkage disequilibrium decay (LD decay) analysis showed that the estimated physical distance between markers was 20 Kb when r2 = 0.2, which indicated that the decay rate of the analyzed population was high. A total of 11 SNPs significantly correlated with body weight were identified by GWAS, and a chromosome region apparently related to growth trait was localized on linkage group 40. A functional gene named c76794 was identified in this region. Candidate gene association analysis further illustrated that 11 SNPs in this gene were significantly related to body weight. The phenotype explanation ratio of a single loci reached 11.23% for the body weight phenotype, and the cumulative phenotypic explanation rate of these 11 SNPs in this gene was 24%. These data suggested that the candidate gene c76794 should play important roles in growth trait of shrimp. The discovery of this gene laid an important foundation for the genetic dissection of growth trait in shrimp.
2. Screening of genes and pathways related to high salinity tolerance traits in L. vannamei: Comparing the transcriptomes between high salinity tolerant families and susceptible families, we found that the response to stimulus was the most enriched Gene Ontology (GO) term for biological process. Meanwhile, genes encoding crustacyanin (CRCN) showed apparently high expressions in tolerant families both under normal and high salinity conditions, which suggests its relevance in shrimp tolerance to high salinity stress. By comparing the responses of high salinity tolerant families and susceptible families under high salinity treatment, some genes specifically responsive to high salinity in tolerant families were obtained. These genes were mainly enriched in three GO terms, including serine-type endopeptidase activity, serine-type peptidase activity and serine hydrolase activity. The genes related to serine protease family were shared by these three GO terms, which suggested that these genes should play important roles in the shrimp resistance to high salinity. Through association analysis on the high salinity resistant trait by GWAS and random forest (RF) methods, a significantly associated marker was found in the linkage group 21. Around this marker, a candidate gene was localized, which is a kind of serine protease discovered in the transcriptome data. The data suggested that this gene might play a key role in shrimp tolerance to high salinity.
3. Evaluation on the accuracy of genomic selection using markers selected by GWAS: Due to the characteristics of aquatic animals, developing a GS breeding strategy suitable for aquatic animals and reducing the cost of genotyping are the basis for promoting the application of GS in aquatic animals. In this study, we proposed a strategy for GS using a subset of markers selected by genome wide association studies (GWAS), and evaluated the prediction accuracy for disease resistance traits in different aquaculture species. The results showed that the prediction accuracy using SNPs selected by GWAS was higher than that predicted by randomly selected SNPs and all SNPs. BayesB model presented a better performance than GBLUP model. Furthermore, the optimal SNP numbers necessary for GS were varied in different species for different traits. The proposed strategy of GS in the present study could not only reduce the genotyping cost, but also improve the prediction accuracy of GS, which will be very helpful to accelerate the application of GS in aquaculture breeding program.
4. Establishment of GS method based on machine learning for L. vannamei: In order to compare the prediction ability of different GS methods in genomic selection of Litopenaeus vannamei, we selected a population composed of 1565 individuals. The body length and ratio of abdomen length to cephalothorax length of shrimp were used as two phenotypes. The traditional GS models included GBLUP, BayesB, and machine learning models included KAML, Catboost, XGBoost, ExtraTrees, RF, KNN, LightGBM, NeuralNet, WE were used. Comparison among the prediction abilities of different models showed that NeuralNet had the highest prediction ability in the two growth phenotypees. The prediction ability of NeuralNet increased about 10% when compared to GBLUP for both phenotypes, which indicated that NeuralNet had a better application prospect for genomic selection breeding in shrimp.
5. Genomic selection of shrimp tolerance traits to high salinity: The genetic parameters and genomic prediction accuracy on high salinity tolerance trait were evaluated using the shrimp breeding chip designed by our research group. The survival time, lethal salinity and survival status of shrimp during the high salinity treatment were recorded for each individual and used as phenotypes. The heritability of three phenotypes were 0.38 ± 0.01, 0.40 ± 0.01, 0.5 ± 0.01, respectively. Among different GS models, NeuralNet showed the highest prediction accuracy, and its prediction accuracy for survival time, lethal salinity, and survival state of shrimp were 0.67 ± 0.05, 0.66 ± 0.06, and 0.77 ± 0.03, respectively. Compared with traditional pedigree selection (PBLUP), GS improved the prediction accuracy by up to 13.8%. In addition, the optimal training population size was predicted to be around 1000, in which the prediction accuracy can rise to around 0.9. These results indicate that the heritability of high salinity tolerance trait of shrimp is medium to high heritability, and the genomic selection is superior to traditional selection approach. This study provides important guidance for genomic selection of shrimp tolerant to high salinity.
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|罗正. 凡纳滨对虾生长和耐高盐性状相关标记的筛选及基因组育种技术研究[D]. 中国科学院海洋研究所. 中国科学院大学,2022.
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