IOCAS-IR  > 实验海洋生物学重点实验室
牡蛎生长与高温耐受性状的遗传解析
王金鹏1,2
Subtype博士
Thesis Advisor张国范
2016-05-21
Degree Grantor中国科学院大学
Place of Conferral北京
Degree Discipline理学博士海洋生物学专业
Keyword连锁分析 分组分离分析 数量性状位点定位 基因表达量数量性状位点定位
Abstract牡蛎是世界上养殖量最大的经济贝类,然而近些年来很多国家都面临着养殖牡蛎夏季大规模死亡的问题,研究发现高温是导致牡蛎夏季大规模死亡的重要环境因素之一。由于生长与存活是影响牡蛎产量的两个最重要因素,因此解析生长与高温耐受这两个性状的遗传机制,从而指导牡蛎的遗传改良,进而提高牡蛎产量、增加其经济价值是牡蛎养殖产业的迫切需求。本研究利用高通量测序技术开发SNP标记,对生长与高温耐受性状进行遗传定位,筛选与性状关联的分子标记,并定位候选基因。在对重要经济性状进行遗传解析的同时,又为分子标记辅助选育提供有价值的遗传资源,促进牡蛎的遗传改良工作,同时也为双壳贝类的生长发育和适应性进化机制研究提供了重要的遗传信息。
1 高密度遗传连锁图谱的构建
本研究利用分布于我国北方的长牡蛎(Crassostrea gigas gigas)与南方的福建牡蛎(Crassostrea gigas angulata)构建了杂交家系,并利用该杂交家系构建了牡蛎的高密度遗传连锁图谱。在整合图谱中,将1694个标记定位到10条连锁群(A1-A10)上,图谱总图距为1084.3 cM,标记间平均距离为0.8 cM,对于基因组的覆盖率为98.7%。连锁群的长度范围为94.7 cM (A9)到134.3 cM (A1),平均长度为108.4 cM。雌性图谱总图距为1034.6 cM,标记间平均距离为1.3 cM,对于基因组的覆盖率为97.5%。雄性图谱总图距为744.8 cM,标记间平均距离为1.7 cM,对于基因组的覆盖率为95.2%。雌性图谱与雄性图谱总的重组率之比是1.39:1。连锁群4表现出了最大的雌性雄性重组率之比,为2.4:1。连锁群9表现出了最小的雌性雄性重组率之比,为1.07:1。性别之间表现出了局部的重组率差异。在整合图谱的1694个标记中,836个标记表现出了偏离孟德尔分离比的现象(P<0.05)。偏分离标记倾向于成簇分布于连锁群上,而不是随机分布于连锁群上。在偏离孟德尔分离比的423个lm x ll标记中,183个表现出了纯合基因型缺失,而240个表现出了杂合基因型缺失;在偏离孟德尔分离比的238个nn x np标记中,120个表现出了纯合基因型缺失,118个表现出了杂合基因型缺失。
2 生长性状的遗传作图
对牡蛎的5个生长性状(壳高、壳长、壳宽、全湿重和软体重)进行QTL定位,共定位到27个QTL,其中7个与壳高性状有关,3个与壳长性状有关,10个与壳宽性状有关,1个与全湿重有关,6个与软体重有关。这27个QTL位点分布于整合图谱的8个连锁群上,连锁群A3与A6上没有检测到QTL位点。超过一半的(16个)QTL位点成簇分布于它们各自的连锁群上。在5个生长性状中,4个性状在连锁群A8上有QTL,表明连锁群A8上富集了调控牡蛎生长的基因。对于壳高性状来说,这7个QTL解释了总表型变异的35.3%。对于壳长性状来说,这3个QTL解释了总表型变异的20.1%。对于壳宽性状来说,这10个QTL解释了总表型变异的50.8%。对于全湿重来说,这1个QTL解释了总表型变异的7.7%。对于软体重来说,这6个QTL解释了总表型变异的34.1%。在这些QTL区域内鉴定到38个基因。这些基因中,有两个是参与碳水化合物代谢的关键基因(aglfbp1)。定位的其他基因中,有些参与了细胞骨架的组装与调控(avilfmn2specc1l),有些参与了信号转导通路(prgk1dusp6grk1),有些参与了细胞分化与发育的调控(tbatamegf8)。
3 高温耐受性状的遗传作图
本研究利用长牡蛎与福建牡蛎构建了杂交群体,通过持续的人工高温刺激(37℃)获得了高温耐受组牡蛎与高温敏感组牡蛎。采用极端表型混池重测序的方法获得两个组别中的SNP位点。抗性组共获得了6,389,699个SNP位点,敏感组共获得了6,516,028个SNP位点。对得到的SNP进行测序深度过滤,只保留测序深度介于30与100的SNP位点。过滤测序深度之后,剩余1,790,801个SNP位点。对这些SNP位点进行频率差异分析,定位频率差异显著(两组间频率差异大于等于0.4)的SNP位点所在基因。对基因组进行窗口滑动分析,取100 kb大小为窗口,10 kb为步移,计算窗口内的SNP频率差异的平均值,最大的SNP频率差异均值为0.33,最小的SNP频率差异均值值为0.1。取SNP频率差异均值的top 5%窗口作为受选择窗口。结合BSA分析得到的频率差异显著SNP位点、窗口Fst、以及高温刺激响应基因的表达谱数据,
本研究获得与牡蛎高温耐受性状相关的SNP位点95个,候选基因48个。本研究构建了9个牡蛎家系,通过人工高温刺激实验,确定了它们的高温耐受等级。它们表现出了高温耐受的分化。在这些家系的母本中进行高温耐受关联SNP位点频率的验证,最终得到了5个与BSA分析结果一致的SNP位点。
在本研究构建的牡蛎高密度遗传图谱基础上,针对热刺激下高表达的15个HSP70及HSP20基因进行eQTL定位研究。通过荧光定量PCR的方法测定基因的表达量。最终只有3个基因的表达量在定位群体中的分布接近于正态分布。对这3个基因进行eQTL定位,最终定位到两个QTL位点,在QTL位点内定位到20个候选基因。其中有研究报道forkhead box基因能够调控HSP基因的表达,这一基因位于BSA分析结果中的top 5% Fst窗口中。二者结果的一致表明调控该基因的SNP位点可能通过调控HSP基因表达量的变化来控制牡蛎的高温耐受。
Other AbstractOysters are some kind of the most important aquaculture species with the highest yield. Recently, massive summer mortality in oysters has been observed around the world. It has been reported that high water temperature is one of the key factors resulting in the massive summer mortality in oysters. As growth and survive are the most important factors affecting the yield of oysters, dissecting the genetic mechanism of them and then directing the genetic improvement is the urgent need of the oyster culture industry. The present study performed SNP calling with high-throughput sequencing technology, genetic mapping of growth-related and thermo-tolerant traits, and identifying SNP markers and genes associated with these traits. The results provided valueable genetic resources for marker-assisted breeding and dissecting the genetic mechnism of the economically important traits in oysters.
1 Construction of dense linkage map
The present study built a hybrid family of Crassostrea gigas gigas and Crassostrea gigas angulata which were distributed in north and south coast of China separately. The present study constructed a dense linakge map for oysters. For the sex-average map, 1,694 markers fell into 10 linkage groups (the linkage groups were named A1–A10). For the female linkage map, 1,102 markers fell into 12 linkage groups (the linkage groups were named F1–F10). For the male linkage map, 689 markers fell into 12 linkage groups (the linkage groups were named M1–M10) . The sex-average map spanned a total genetic distance of 1084.3 cM, with an average spacing of 0.8 cM and a Coa of 98.7%. The female map spanned a total genetic distance of 1034.6 cM, with an average spacing of 1.3 cM and a Coa of 97.5%. The male map spanned a total genetic distance of 744.8 cM, with an average spacing of 1.7 cM and a Coa of 95.2%. We observed an overall female-to-male recombination ratio of 1.39:1. The largest recombination ratio between sexes was found for linkage group 4, with a female:male ratio of 2.40:1, and the smallest recombination ratio between sexes was found for linkage group 9, with a female:male ratio of 1.07:1. Localized differences in recombination rates between the sexes were observed.   Of the 1,694 markers on the sex-average map, 836 (49.4%) showed significant (P < 0.05) segregation distortion. For the sex-average linkage map, LG A8 and LG A5 presented the most severe segregation distortion, with 77.2% and 73.5% of markers distorted, respectively. LG A10 presented the lowest segregation distortion, with 12.4% of markers distorted. Distorted markers tended to be clustered, rather than randomly distributed across a linkage group For the distorted markers in the lmxll type (423), 183 showed homozygote deficiency while 240 showed heterozygote deficiency. For the distorted markers in the nnxnp type (238), 120 showed homozygote deficiency while 118 showed heterozygote deficiency.
2 Genetic mapping for growth-related traits
A total of 27 QTLs were identified, including seven for shell height, three for shell length, 10 for shell width, one for mass weight, and six for soft tissue weight . These 27 QTLs were distributed across eight linkage groups on the sex-average map, except for LG A3 and LG A6. More than half (16) of the QTLs were clustered in their respective LGs. The phenotypic variance explained by these QTLs ranged from 4.2% to 7.7%, with an average of 5.4%. The low phenotypic variance explained by these QTLs indicated that no major loci (explaining >20% of the total variation) were detected. For shell height, the seven QTLs explained 35.3% of the total phenotypic variance. For shell length, the three QTLs explained 20.1% of the total phenotypic variance. For shell width, the 10 QTLs explained 50.8% of the total phenotypic variance. For mass weight, the QTL explained 7.7% of the total phenotypic variance. For soft tissue weight, the six QTLs explained 34.1% of the phenotypic variance. A number of genes related to growth-related traits were identified from the higher-density map based on the Pacific oyster genome assembly. As shown, 38 annotated genes were identified in the QTL regions. Among the annotated genes, two were found to be growth-related (agl and fbp1), which function as key factors in carbohydrate metabolism, and others were found to participate in the assembly and regulation of the actin cytoskeleton (avil, fmn2, and specc1l), signal transduction pathways (prkg1, dusp6, and grk1), and the regulation of cell differentiation and development (tbata and megh8).
3 Genetic mapping for thermo-tolerant trait
The present study built a hybrid population of C. gigas gigas and C. gigas angulata and obtained two bulks of oyster with thermo-sensitive and thermo-tolerant traits by man-made heat stress. By whole genome resequencing, a total of 6,389,699 SNPs were identified in the thermo-tolerant bulk and 6,516,028 SNPs were identified in the thermo-susceptive bulk. SNPs identified in both pools (3,497,748) were used to calculate frequency difference. After filtering the depth of SNPs (with depth between 30-100 fold remained), 1,790,801 SNPs were retained. Those with frequency difference bigger than 0.4 were assumed as candidate SNPs. The average of frequency difference of all SNPs within the slide window was used as the Fst value of the slide window. The window size was set as 100 kb and the step wise as 10 kb. The biggest Fst value was 0.33 and the least Fst value was 0.1. The top 5% windows were selected as candidate windows. A total of 95 SNPs were identified as candidate SNPs and 48 genes as candidate genes by combining the data from transcription profile after heat stress of oyster. The verification of candidate SNPs was performed in nine oysters. The degree of thermo-tolerance of these nine oysters was identified by man-made heat stress. Finally, five SNPs from the bulks analysis were verified. However, none of SNPs from the transcription profile data was verified.
 A total of 15 HSP genes were quantified with quantitative PCR (qPCR) to map eQTLs controling their expression. However, only three of them presented differential expression among the mapping population. The eQTL mapping of the three genes detected two QTLs in total and 20 candidate genes were identified in the QTL regions. Of the candidate genes, forkhead box gene was reported to play an important role in cell under heat stress. The expression of forkhead box gene was regulated by the heat shock transcription factor (HSF). The forkhead box gene improved the ability of the cell to survive under heat stress. And forkhead box gene regulated the expression of HSP70 genes. The forkhead box genes was located in a slide window with a near value of Fst with the top 5% window. Our result implied that forkhead box gene may influence the thermo-tolerance of oysters by affecting the expression level of HSP genes.
Subject Area海洋生物学
Language中文
Document Type学位论文
Identifierhttp://ir.qdio.ac.cn/handle/337002/112512
Collection实验海洋生物学重点实验室
海洋生物技术研发中心
Affiliation1.中国科学院海洋研究所实验海洋生物学重点实验室
2.中国科学院大学
Recommended Citation
GB/T 7714
王金鹏. 牡蛎生长与高温耐受性状的遗传解析[D]. 北京. 中国科学院大学,2016.
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