Yuan Meng, Goovaerts Seppe, Vanneste Michiel, Matthews Harold, Hoskens Hanne, Richmond Stephen, Klein Ophir D, Spritz Richard A, Hallgrimsson Benedikt, Walsh Susan, Shriver Mark D, Shaffer John R, Weinberg Seth M, Peeters Hilde, Claes Peter
Department of Electrical Engineering, ESAT/PSI, KU Leuven, Leuven, Belgium.
Department of Human Genetics, KU Leuven, Leuven, Belgium.
PLoS Comput Biol. 2024 Dec 2;20(12):e1012617. doi: 10.1371/journal.pcbi.1012617. eCollection 2024 Dec.
Human facial shape, while strongly heritable, involves both genetic and structural complexity, necessitating precise phenotyping for accurate assessment. Common phenotyping strategies include simplifying 3D facial features into univariate traits such as anthropometric measurements (e.g., inter-landmark distances), unsupervised dimensionality reductions (e.g., principal component analysis (PCA) and auto-encoder (AE) approaches), and assessing resemblance to particular facial gestalts (e.g., syndromic facial archetypes). This study provides a comparative assessment of these strategies in genome-wide association studies (GWASs) of 3D facial shape. Specifically, we investigated inter-landmark distances, PCA and AE-derived latent dimensions, and facial resemblance to random, extreme, and syndromic gestalts within a GWAS of 8,426 individuals of recent European ancestry. Inter-landmark distances exhibit the highest SNP-based heritability as estimated via LD score regression, followed by AE dimensions. Conversely, resemblance scores to extreme and syndromic facial gestalts display the lowest heritability, in line with expectations. Notably, the aggregation of multiple GWASs on facial resemblance to random gestalts reveals the highest number of independent genetic loci. This novel, easy-to-implement phenotyping approach holds significant promise for capturing genetically relevant morphological traits derived from complex biomedical imaging datasets, and its applications extend beyond faces. Nevertheless, these different phenotyping strategies capture different genetic influences on craniofacial shape. Thus, it remains valuable to explore these strategies individually and in combination to gain a more comprehensive understanding of the genetic factors underlying craniofacial shape and related traits.
人类面部形状虽然具有很强的遗传性，但涉及遗传和结构复杂性，因此需要进行精确的表型分析以进行准确评估。常见的表型分析策略包括将三维面部特征简化为单变量特征，如人体测量学指标（如地标间距离）、无监督降维方法（如主成分分析（PCA）和自动编码器（AE）方法），以及评估与特定面部整体形态的相似性（如综合征性面部原型）。本研究对这些策略在三维面部形状的全基因组关联研究（GWAS）中的应用进行了比较评估。具体而言，我们在一项针对8426名近期欧洲血统个体的GWAS中，研究了地标间距离、PCA和AE衍生的潜在维度，以及与随机、极端和综合征性整体形态的面部相似性。通过连锁不平衡评分回归估计，地标间距离显示出基于单核苷酸多态性（SNP）的最高遗传率，其次是AE维度。相反，与极端和综合征性面部整体形态的相似性评分显示出最低的遗传率，符合预期。值得注意的是，对与随机整体形态的面部相似性进行多项GWAS汇总后，发现了数量最多的独立基因座。这种新颖且易于实施的表型分析方法在捕获源自复杂生物医学成像数据集的与遗传相关的形态特征方面具有巨大潜力，其应用范围不仅限于面部。然而，这些不同的表型分析策略捕捉到了对面部形状的不同遗传影响。因此，单独或组合探索这些策略，以更全面地了解颅面形状及相关特征的遗传因素，仍然具有重要价值。
