Vuollo Ville, Sidlauskas Mantas, Sidlauskas Antanas, Harila Virpi, Salomskiene Loreta, Zhurov Alexei, Holmström Lasse, Pirttiniemi Pertti, Heikkinen Tuomo
Institute of Dentistry,University of Oulu,Oulu,Finland.
Clinic of Orthodontics,Lithuanian University of Health Sciences,Kaunas,Lithuania.
Twin Res Hum Genet. 2015 Jun;18(3):306-13. doi: 10.1017/thg.2015.16. Epub 2015 Apr 14.
The aim of this study was to compare facial 3D analysis to DNA testing in twin zygosity determinations. Facial 3D images of 106 pairs of young adult Lithuanian twins were taken with a stereophotogrammetric device (3dMD, Atlanta, Georgia) and zygosity was determined according to similarity of facial form. Statistical pattern recognition methodology was used for classification. The results showed that in 75% to 90% of the cases, zygosity determinations were similar to DNA-based results. There were 81 different classification scenarios, including 3 groups, 3 features, 3 different scaling methods, and 3 threshold levels. It appeared that coincidence with 0.5 mm tolerance is the most suitable feature for classification. Also, leaving out scaling improves results in most cases. Scaling was expected to equalize the magnitude of differences and therefore lead to better recognition performance. Still, better classification features and a more effective scaling method or classification in different facial areas could further improve the results. In most of the cases, male pair zygosity recognition was at a higher level compared with females. Erroneously classified twin pairs appear to be obvious outliers in the sample. In particular, faces of young dizygotic (DZ) twins may be so similar that it is very hard to define a feature that would help classify the pair as DZ. Correspondingly, monozygotic (MZ) twins may have faces with quite different shapes. Such anomalous twin pairs are interesting exceptions, but they form a considerable portion in both zygosity groups.
本研究的目的是比较面部三维分析与DNA检测在双胎合子性判定中的应用。使用立体摄影测量设备（3dMD，佐治亚州亚特兰大）采集了106对立陶宛年轻成年双胞胎的面部三维图像，并根据面部形态的相似性确定合子性。采用统计模式识别方法进行分类。结果显示，在75%至90%的病例中，合子性判定结果与基于DNA的结果相似。共有81种不同的分类方案，包括3组、3个特征、3种不同的缩放方法和3个阈值水平。结果表明，0.5毫米容差的一致性是最适合分类的特征。此外，在大多数情况下，不进行缩放可提高结果。缩放预期会使差异大小相等，从而带来更好的识别性能。尽管如此，更好的分类特征、更有效的缩放方法或在不同面部区域进行分类可能会进一步改善结果。在大多数情况下，男性双胞胎对的合子性识别水平高于女性。错误分类的双胞胎对在样本中似乎是明显的异常值。特别是，年轻的异卵（DZ）双胞胎的面部可能非常相似，以至于很难定义一个有助于将该对双胞胎分类为DZ的特征。相应地，同卵（MZ）双胞胎的面部形状可能有很大差异。这种异常的双胞胎对是有趣的例外情况，但在两个合子性组中都占了相当大的比例。
