IOCAS-IR  > 海洋生态与环境科学重点实验室
刺参生长性状分子标记开发及应用
崔玮
Subtype硕士
Thesis Advisor孙丽娜
2022-05-16
Degree Grantor中国科学院大学
Place of Conferral中国科学院海洋研究所
Degree Name工程硕士
Degree Discipline生物工程
Keyword刺参 生长性状 遗传连锁图谱 全基因组关联分析 液相芯片
Abstract

刺参(Apostichopus japonicus)是棘皮动物门中重要的经济品种。近些年,刺参养殖产业发展势头迅猛,2020年养殖面积达到24.28万hm2,占全国海水养殖面积的12.17%;年产量19.66万t,占全国海水养殖产量的0.92%,总产值达到600亿,引领了第五次海水养殖浪潮,成为我国又一关键支柱性产业。但在带来显著经济效益的同时,刺参种质退化、生长缓慢、病害频发、良种匮乏等问题日益凸显,制约着刺参产业持续健康发展。生长性状作为水产生物重要的经济性状之一,其遗传改良能够大大提升生物的生长速度,缩短育种年限,降低养殖成本。根据已有的研究报道,家畜的生长变异系数通常为 7-10%,大多数鱼类在 20-35%,而刺参群体的生长变异系数却超过 50%,高表型变异率意味着刺参具备巨大的选育潜力。生长性状属于复杂的数量性状,由多基因调控,受多种因素影响。随着测序技术的代代更迭,水产生物分子育种成为不可逆转的趋势,其中最首要的任务是对目标性状进行精准的遗传解析。本研究利用多种高通量手段筛选了刺参基因组范围内与生长性状相关的遗传变异,并基于此构建了刺参液相育种芯片,为刺参优良种质创制提供了重要的信息和技术支持。

1、刺参高密度遗传图谱构建及QTL定位

本章实验选用一对无亲缘关系且表型差异较大的刺参亲本构建了全同胞家系,采用GBS技术对132个刺参个体(2个亲本和130个F1子代)测序,构建了基于SNP的高密度遗传连锁图谱。该图谱共划分了22个遗传连锁群,上图标记达到6144个,图谱全长3181.54 cM,分辨率为0.52 cM。通过Pearson分析和QTL定位揭示了体重、体长和棘刺数目之间的强相关性,这意味着可以通过改良其中一个或几个指标达到提高生长速度的目的。我们一共鉴定到11个与生长性状相关的候选基因。其中,位于第18号连锁群的marker lm863和864有着最高的表型贡献率,其定位的鸟苷酸交换因子protein still life, isoforms C/SIF type 2 (sif)基因被认定为调控生长性状的关键候选基因。据qRT-PCR结果显示,sif基因在刺参幼体发育阶段显著高表达,但在成体组织内表达无差异,推测sif基因可能通过调控早期幼体发育影响刺参的生长性状。

2、刺参生长性状全基因组关联分析

本章实验选用4个遗传背景明确且同龄的刺参家系(Ⅰ速生雌 x 速生雄;Ⅱ慢生雌 x 慢生雄;Ⅲ速生雌 x 慢生雄;Ⅳ 慢生雌 x 速生雄)作为研究材料,利用逐只刺参隔离养殖实验设计排除环境等因素的影响,探究遗传因素对生长性状的调控作用。我们通过表型测量和统计分析构建了以体重数据为核心的生长性状表型数据库,利用全基因组关联分析技术筛选了与体重、特异生长率和棘刺数目3个与生长性状相关的表型指标的遗传变异,共鉴定出25个显著关联的SNP位点,挖掘出29个候选基因,功能分析表明这些基因大多富集到神经免疫、转录调控、信号转导、胚胎、物质代谢、细胞分化等相关通路。

3、刺参生长性状30k cGPS液相育种芯片构建

本章实验基于前两章内容以及国内8个不同海区的野生刺参群体的重测序数据构建了刺参cGPS液相育种芯片。该芯片共包含30776个SNPs位点,在基因组内的覆盖度相对比较均匀。通过123个重测序刺参群体基因分型测试,所有个体成功分型,平均位点检出率在98.38%,一致率达到95%以上,MAF频率分布较为平滑,利用该芯片可以实现对刺参群体优良性状基因型的早期鉴定,为良种选育提供技术支持。

Other Abstract

Sea cucumber (Apostichopus japonicus), which is an economically important mariculture species in Asia, is an edible echinoderm with medicinal properties. In recent years, the sea cucumber aquaculture industry has developed rapidly. In 2020, the aquaculture area is 242,800 hm2, accounting for 12.17% of the national marine aquaculture area; the annual output is 196,600 tons, accounting for 0.92% of the national marine aquaculture production. Its total output value reached 60 billion, leading the fifth wave of marine aquaculture and becoming another key pillar industry. However, increases in the scale of sea cucumber farming have highlighted specific problems, including germplasm degradation, slow growth, and frequent diseases, which restrict the sustainable and healthy development of the sea cucumber industry. Previous studies have shown that the coefficient of variation for growth is 7-10% for livestock and 20-35% for fish, while the coefficient of variation for growth in sea cucumber populations often exceeds 50%. Growth is the complex trait that is precisely regulated by multiple genes and influenced by other multiple factors. The genetic mechanism underlying growth has long attracted the attention of genetic breeders. In this study, a variety of high-throughput methods were used to screen the genetic variation related to growth traits in the A. japonicus genome, and based on this, the liquid-phase breeding chip was constructed, which provided an important genome resource for the creation of superior A. japonicus germplasm.

1. Construction of a high-density genetic linkage map and identification of QTL for the A. japonicus

In this study, a pair of unrelated parents with large phenotypic differences was used to construct a full-sib family. GBS technology was used to sequence 132 sea cucumber individuals (2 parents and 130 F1 offspring) to construct a SNP-based high-density genetic linkage map. The consensus map was 3181.54 cM long with a resolution of 0.52 cM and contained 6144 SNPs which were assigned to 22 linkage groups (LGs). The results of Pearson analysis and QTL mapping revealed the strong correlations among body length, body weight, and papillae number, which means that we may be able to improve the overall growth by manipulating one of these traits. We identified a total of 11 candidate genes associated with growth traits. Among them, the gene named protein still life, isoforms C/SIF type 2 (sif) located in LG18 with the highest percentage of the phenotypic variation, was identified as an important candidate gene for regulating growth trait which was reported to be a guanylate exchange factor. According to the results of qRT-PCR, the sif gene was significantly highly expressed during the larval developmental stages of A. japonicus, but there was no difference in the expression in adult tissues. It is speculated that the sif gene may affect the growth traits of A. japonicus by regulating the development of early larvae.

2. Genome-wide association analysis of growth traits in A. japonicus

In this chapter, four sea cucumber families with clear genetic background (I fast-growing female x fast-growing male; II slow-growing female x slow-growing male; III fast-growing female x slow-growing male; IV slow-growing female x fast-growing male) were selected as research materials. By culturing sea cucumber one by one to exclude the influence of environmental factors, we explored the regulatory effect of genetic factors on growth traits. We constructed a growth trait phenotype database with body weight data as the core through phenotypic measurement and statistical analysis, and screened the genetic variation loci for 4 phenotypic indicators related to growth traits, including body weight, specific growth rate, body wall production rate and papillae number using genome-wide association analysis technology. A total of 25 significant SNPs and 29 important candidate genes were identified. Gene functional analysis showed that most of these genes were involved in neuroimmunity, transcriptional regulation, signal transduction, embryonic, material metabolism, cell differentiation and other related pathways.

3. Construction of 30k cGPS liquid-phase breeding chip for growth traits of A. japonicus

Based on the results of the previous two chapters and the resequencing data of wild sea cucumber populations in 8 different sea areas in China, the cGPS liquid-phase breeding chip of A. japonicus was constructed. This chip contains a total of 30,776 SNPs, with relatively uniform coverage in the genome. Through the genotyping test of 103 sea cucumber, all individuals were successfully genotyped, the average call rate was 98.38%, the consensus rate was over 95%, and the MAF frequency distribution was relatively smooth, which indicate that the chip can be used to achieve sea cucumber genotyping of population. These results will contribute to the genetic analysis of the growth traits of sea cucumber, and provide valuable genomic resources for molecular marker-assisted breeding with excellent production traits.

Language中文
Table of Contents

第一章   绪论     1

1.1  水产生物分子育种研究进展      1

1.1.1  遗传标记研究概述   1

1.1.2  水产生物育种发展趋势   2

1.2  遗传连锁图谱构建进展      4

1.2.1  遗传连锁图谱构建理论基础   4

1.2.2  遗传连锁图谱作图群体   5

1.2.3  水产生物遗传连锁图谱构建进展   5

1.3  全基因组关联分析研究进展      7

1.3.1  全基因组关联分析原理   7

1.3.2  群体选取和结构分析       7

1.3.3  关联分析模型和结果矫正       8

1.3.4  水产生物全基因组关联分析研究进展   9

1.4  液相芯片研究进展      10

1.4.1  基因型检测平台的变革   10

1.4.2  Luminex液相芯片原理及应用 10

1.4.3  GBTS液相芯片原理及应用     11

1.5  刺参育种研究现状      12

1.5.1  产业现状   12

1.5.2  分子育种研究进展   13

1.6  立项依据与科学问题   14

1.6.1  目的意义   14

1.6.2  科学问题   15

1.6.3  技术路线   15

1.7  本章小结      16

第二章   刺参高密度遗传图谱构建及QTL定位     17

2.1  前言      17

2.2  材料与方法   17

2.2.1  实验材料   17

2.2.2  DNA、RNA和蛋白质提取      18

2.2.3  GBS文库构建   20

2.2.4  参考基因组比对       20

2.2.5  SNP 检测与标记开发      21

2.2.6  遗传连锁图谱构建   21

2.2.7  生长性状QTL定位   21

2.2.8  关键候选基因分型   21

2.2.9  实时荧光定量PCR   22

2.2.10  体内RNAi实验      23

2.3  实验结果      24

2.3.1  GBS文库构建和测序       24

2.3.2  参考基因组比对       24

2.3.3  SNP 检测及标记开发      25

2.3.4  遗传连锁图谱构建   25

2.3.5  生长性状QTL定位及关键基因注释      28

2.3.6  生长性状关键基因表达特征分析   34

2.4  讨论      37

2.4.1  遗传连锁图谱构建   37

2.4.2  QTL定位及关键基因注释       38

2.4.3  候选基因表达特征分析   39

2.4.4  关键基因体内RNAi  39

2.5  本章小结      40

第三章   刺参生长性状全基因组关联分析     41

3.1  前言      41

3.2  材料与方法   41

3.2.1 实验材料     41

3.2.2 表型数据统计     42

3.2.3 DNA文库构建     43

3.2.4 数据质控     43

3.2.5 变异检测与过滤  43

3.2.6 群体结构分析     43

3.2.7 全基因组关联分析     44

3.2.8 候选基因功能注释     44

3.2.9 关联位点后续验证     44

3.3  实验结果      45

3.3.1 表型数据统计分析     45

3.3.2 DNA文库构建和数据质控  46

3.3.3遗传变异检测      47

3.3.4 群体遗传结构分析     49

3.3.5 全基因组关联分析     52

3.3.6 关联位点后续验证     68

3.4  讨论      70

3.5  本章小结      72

第四章  刺参生长性状30k cGPS液相育种芯片构建     73

4.1  前言      73

4.2  材料与方法   73

4.2.1  芯片设计材料来源   73

4.2.2  位点筛选   73

4.2.3  芯片探针设计与制备       74

4.2.4  芯片测试   74

4.2.5  生物信息和数据分析       74

4.3  实验结果      74

4.3.1  cGPS标记设计合成  74

4.3.2  基因组覆盖深度       75

4.3.3  位点检出率       75

4.3.4  位点MAF统计  76

4.4  讨论      77

4.5  本章小结      78

第五章  结论与展望    79

5.1  研究总结      79

5.2  存在问题      80

5.3  研究展望      80

参考文献       81

致  谢    93

作者简历及攻读学位期间发表的学术论文与研究成果    95

 

Document Type学位论文
Identifierhttp://ir.qdio.ac.cn/handle/337002/178359
Collection海洋生态与环境科学重点实验室
Recommended Citation
GB/T 7714
崔玮. 刺参生长性状分子标记开发及应用[D]. 中国科学院海洋研究所. 中国科学院大学,2022.
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