Haliotismidae是南非商业水产养殖中最重要的软体动物之一。在这项研究中,构建了高分辨率的综合连锁图,和使用2b-RADseq对三个家族的SNP进行基因分型鉴定的QTL。最终的综合连锁图是通过合并各个家庭图组成的,导致3290个信息性SNP映射到18个连锁组,符合H.midae的已知单倍体染色体数。全图跨越1798.25cM,平均标记间隔为0.55cM,代表98.76%的基因组覆盖率。QTL分析,在所有三个家庭中,总共鉴定了五个生长相关性状的QTL,壳体宽度,壳体长度,和总体重。对于外壳宽度和总重量,每个性状分别鉴定出一个QTL,同时确定了三个QTL的壳长。鉴定的QTL分别解释了观察到的表型变异的7.20%和11.40%之间。所有三个性状均显着相关(r=0.862-0.970;p<0.01),并且共有重叠的QTL。将生长性状的QTL定位回H.midae草图基因组,BLAST搜索揭示了候选基因的身份,例如egf-1,megf10,megf6,tnx,sevp1,kcp,notch1和scube2在H.midae生长中可能具有功能作用。构建的高密度连锁图谱和定位的QTL为生长相关性状的遗传结构提供了宝贵的见解,并将成为标记辅助选择的重要遗传资源。它仍然存在,然而,未来通过连锁不平衡精细作图验证因果变异很重要。
Haliotis midae is one of the most important molluscs in South African commercial aquaculture. In this study, a high-resolution integrated linkage map was constructed, and QTL identified using 2b-RADseq for genotyping SNPs in three families. The final integrated linkage map was composed by merging the individual family maps, resulting in 3290 informative SNPs mapping to 18 linkage groups, conforming to the known haploid chromosome number for H. midae. The total map spanned 1798.25 cM with an average marker interval of 0.55 cM, representing a genome coverage of 98.76%. QTL analysis, across all three families, resulted in a total of five QTL identified for growth-related traits, shell width, shell length, and total body weight. For shell width and total body weight, one QTL was identified for each trait respectively, whilst three QTL were identified for shell length. The identified QTL respectively explained between 7.20% and 11.40% of the observed phenotypic variance. All three traits were significantly correlated (r = 0.862-0.970; p < 0.01) and shared overlapping QTL. The QTL for growth traits were mapped back to the H. midae draft genome and BLAST searches revealed the identity of candidate genes, such as egf-1, megf10, megf6, tnx, sevp1, kcp, notch1, and scube2 with possible functional roles in H. midae growth. The constructed high-density linkage map and mapped QTL have given valuable insights regarding the genetic architecture of growth-related traits and will be important genetic resources for marker-assisted selection. It remains, however, important to validate causal variants through linkage disequilibrium fine mapping in future.