Department Evolutionary Genetics, Max-Planck Institute for Evolutionary Biology, 24306 Plön, Germany.
G3 (Bethesda). 2022 Feb 4;12(2). doi: 10.1093/g3journal/jkab443.
Various advances in 3D automatic phenotyping and landmark-based geometric morphometric methods have been made. While it is generally accepted that automatic landmarking compromises the capture of the biological variation, no studies have directly tested the actual impact of such landmarking approaches in analyses requiring a large number of specimens and for which the precision of phenotyping is crucial to extract an actual biological signal adequately. Here, we use a recently developed 3D atlas-based automatic landmarking method to test its accuracy in detecting QTLs associated with craniofacial development of the house mouse skull and lower jaws for a large number of specimens (circa 700) that were previously phenotyped via a semiautomatic landmarking method complemented with manual adjustment. We compare both landmarking methods with univariate and multivariate mapping of the skull and the lower jaws. We find that most significant SNPs and QTLs are not recovered based on the data derived from the automatic landmarking method. Our results thus confirm the notion that information is lost in the automated landmarking procedure although somewhat dependent on the analyzed structure. The automatic method seems to capture certain types of structures slightly better, such as lower jaws whose shape is almost entirely summarized by its outline and could be assimilated as a 2D flat object. By contrast, the more apparent 3D features exhibited by a structure such as the skull are not adequately captured by the automatic method. We conclude that using 3D atlas-based automatic landmarking methods requires careful consideration of the experimental question.
在 3D 自动表型分析和基于标志点的几何形态测量方法方面已经取得了各种进展。虽然人们普遍认为自动标志点定位会影响生物变异的捕捉,但尚无研究直接测试在需要大量标本进行分析且表型精度对于充分提取实际生物信号至关重要的情况下,这种标志点定位方法的实际影响。在这里,我们使用最近开发的基于 3D 图谱的自动标志点定位方法来测试其在检测与家鼠颅骨和下颌骨颅面发育相关的 QTL 的准确性,这些标本数量众多(约 700 个),之前已经通过半自动标志点定位方法进行了表型分析,并辅以手动调整。我们将这两种标志点定位方法与颅骨和下颌骨的单变量和多变量映射进行了比较。我们发现,大多数显著的 SNP 和 QTL 都无法根据自动标志点定位方法得出的数据进行回收。因此,我们的结果证实了这样一种观点,即在自动标志点定位过程中会丢失信息,尽管这在一定程度上取决于所分析的结构。尽管自动方法可以稍微更好地捕捉某些类型的结构,例如其形状几乎完全由轮廓概括的下颌骨,并且可以被视为二维扁平物体,但该方法似乎无法充分捕捉到结构的更明显的 3D 特征,例如颅骨。我们得出结论,使用基于 3D 图谱的自动标志点定位方法需要仔细考虑实验问题。