Abstract：
Predicting the outward appearance of dogs via their DNA, also known as Canine DNA Phenotyping, is a young, emerging field of research in forensic genetics. The few previous studies published in this respect were restricted to the consecutive analysis of single DNA markers, a process that is time- and sample-consuming and therefore not a viable option for limited forensic specimens. Here, we report on the development and evaluation of a Massively Parallel Sequencing (MPS) based molecular genetic assay, the LASSIE MPS Panel. This panel aims to predict externally visible as well as skeletal traits, which include coat color, coat pattern, coat structure, tail morphology, skull shape, ear shape, eye color and body size from DNA using 44 genetic markers in a single molecular genetic assay. A biostatistical naïve Bayes classification approach was applied to identify the most informative marker combinations for predicting phenotypes. Overall, the predictive performance was characterized by a very high classification success for some of the trait categories, and high to moderate success for others. The performance of the developed predictive framework was further evaluated using blind samples from three randomly selected dog individuals, whose appearance was well predicted.
摘要：
通过它们的DNA预测狗的外观，也被称为犬DNA表型分析，是一个年轻人,法医遗传学研究的新兴领域。在这方面发表的少数以前的研究仅限于单个DNA标记的连续分析，这是一个耗时和耗时的过程，因此对于有限的法医标本来说不是一个可行的选择。这里,我们报告了基于大规模平行测序（MPS）的分子遗传测定的开发和评估，LASSIEMPS面板。该小组旨在预测外部可见以及骨骼特征，其中包括外套颜色，外套图案,涂层结构,尾部形态,头骨形状,耳朵形状,在单个分子遗传分析中，使用44个遗传标记从DNA中获得眼睛颜色和身体大小。应用生物统计学朴素贝叶斯分类方法来鉴定用于预测表型的信息最丰富的标记组合。总的来说,预测性能的特点是某些特征类别的分类成功率很高，对其他人来说是高到中等的成功。使用来自三个随机选择的狗个体的盲样本进一步评估了开发的预测框架的性能，他的外表被很好地预测了。
