偏分离条件下牙鲆生长性状QTL的主成分定位

本研究利用有丝分裂雌核发育建立牙鲆(Paralichthys olivaceus)双单倍体(Doubled Haploids,DH)群体,对牙鲆体重、全长、背鳍长、腹鳍长、体高、尾柄高、头高和躯干长共8组表型性状标准化处理后,进行主成分分析,得到可解释全部性状90.4%的表型主成分性状。然后,基于JoinMap 4、MapDisto、JoinMap 4-DistortedMap、MapDisto-DistortedMap构建4个连锁图谱,用偏分离标记矫正数量性状位点(Quantitative Trait Locus,QTL)基因型条件概率,采用Bayesian模型选择方法定位牙鲆表型主成分性状的加性QTL和上位性QTL。结果表明,用不同方法构建的遗传图谱和表型主成分性状QTL定位结果均有所不同。在图谱构建中,与基于JoinMap 4构建的图谱相比,基于MapDisto构建的图谱中偏分离标记的相对位置和遗传距离都发生了变化,甚至有5个偏分离标记没有被定位到相应的连锁群上;相对于基于JoinMap 4和MapDisto构建的图谱,经DistortedMap校正后的图谱中偏分离标记的相对位置没有发生变化,但是偏分离标记间遗传距离发生了变化。另外,在4个图谱中都检测到3个加性QTL,分别位于6号连锁群上,可解释表型变异的12.95%、14.85%、11.56%和11.76%,9号、22号连锁群上,具有负向加性效应;9号连锁群上,可解释表型变异的13.86%、13.27%、11.17%和11.25%,具有负向加性效应;22号连锁群上,可解释表型变异的5.68%、4.36%、4.97%和3.58%,具有正向加性效应。同时,分别检测到28对、19对、29对和20对上位性QTL,主要分布在6号、7号、9号、17号、20号和22号连锁群上,可解释表型主成分性状变异的2.19%~17.62%、2.40%~22.26%、2.08%~26.0%、3.16%~22.05%。研究结果表明,基于MapDisto软件构建、经DistortedMap软件包矫正后的图谱,定位结果更加准确,但仍需进一步验证。

Mapping QTLs for principle components of growth traits using distorted markers in Japanese flounder Paralichthys olivaceus

For this study, a double haploid population of Japanese flounder Paralichthys olivaceus was produced using mitotic gynogenetics. Body weight and 7 morphological traits (i.e., total length, dorsal-fin length, pelvic-fin length, body height, caudal peduncle width, head width and trunk length) were measured. After normalizing the measures, we acquired a data set that could explain 90.4% of these principal-component analysis traits (PPCTs), and accordingly we denoted these PPCTs. Next, by modifying conditional probabilities of quantitative trait loci (QTL) genotypes on the distorted flanking markers, Bayesian model selection was used to dissect the genetic arc-hitecture of the PPCTs with four genetic linkage maps, created with the software JoinMap4, MapDisto, JoinMap- DistortedMap and MapDisto-DistortedMap. The different methods used to construct the genetic maps and QTL mapping of the PPCTs produced variable results. Comparing the map produced with JoinMap4, the relative posi-tions and the genetic map distances of the partial separation markers differed from the map produced with Map-Disto. Furthermore, five separation markers were not located at the corresponding linkage groups. Comparing the maps based on JoinMap 4 and MapDisto and after correction by DistortedMap, the positions were not changed, but the genetic map distances of the partial separation markers were changed. In the overall maps, three additive- ef-fect QTL were detected in linkage groups 6, 9 and 22, of which a negative effect could account for 12.95%, 14.85%, 11.56% and 11.76% in linkage group 6; a negative effect could account for 13.86%, 13.27%, 11.17% and 11.25% in linkage group 9; and a positive effect could account for 5.68%, 4.36%, 4.97% and 3.58% in linkage group 22. At the same time, 28, 19, 29 and 20 pairs of additive-additive interactions were identified, mainly distri-buted in linkage groups 6, 7, 9, 17, 20 and 22, and these interactions could account for approximately 2.19%? 17.62%, 2.40%?22.26%, 2.08%?26.0%, and 3.16%?22.05% of the variance in the PPCTs, respectively. We believe that the results of QTL mapping were more accurate in the linkage map using DistortedMap based on JoinMap4.