Automated image analysis of muscle phenotypes for genetic marker discovery in Atlantic salmon

现在每天吃的鱼中，大约每两条就有一条来自水产养殖。鲑鱼和其他养殖鱼类处于驯化的相对早期阶段，通过利用现代基因技术选择亲鱼，有相当大的空间来提高生产效率和质量。产生更多肌肉纤维的鲑鱼有更高的生长速度和更好的生长速度。为了发现与较高纤维数相关的遗传标记，首先有必要测量数百条鲑鱼中每条鱼的多达一百万条肌肉纤维。该项目将利用图像分析方面的进步，开发一种高度自动化的工作流程，以捕获、识别和量化肌肉纤维的图像，以促进这一任务。所获得的性状数据将与遗传分析相结合，以开发一种实用的方法来选择具有高纤维肌数的优质亲本。

Around one out of every two fish eaten each day now comes from aquaculture. Salmon and other farmed fish are at a relatively early stage of domestication and there is considerable scope for increasing production efficiency and quality by using modern genetic technologies to select broodstock. Salmon that produce more muscle fibres have higher growth rate and superior growth rate. In order to discover genetic markers associated with a higher fibre number it is first necessary to measure up to a million muscle fibres in each of hundreds of individual salmon. This project will exploit advances in image analysis to develop a highly automated workflow to capture, recognise and quantify images of muscle fibres to facilitate this task. The trait data obtained will be integrated with genetic analysis to develop a practical means of selecting superior quality broodstock with a high fibre muscle number.