Institute for Vision Research, University of Iowa Hospitals and Clinics, 200 Hawkins Drive, Iowa City, IA 52242, USA.
Invest Ophthalmol Vis Sci. 2011 Nov 25;52(12):9195-206. doi: 10.1167/iovs.10-6793.
Determining the relationships between phenotype and genotype of many disorders can improve clinical diagnoses, identify disease mechanisms, and enhance therapy. Most genetic disorders result from interaction of many genes that obscure the discovery of such relationships. The hypothesis for this study was that image analysis has the potential to enable formalized discovery of new visible phenotypes. It was tested in twins affected with age-related macular degeneration (AMD).
Fundus images from 43 monozygotic (MZ) and 32 dizygotic (DZ) twin pairs with AMD were examined. First, soft and hard drusen were segmented. Then newly defined phenotypes were identified by using drusen distribution statistics that significantly separate MZ from DZ twins. The ACE model was used to identify the contributions of additive genetic (A), common environmental (C), and nonshared environmental (E) effects on drusen distribution phenotypes.
Four drusen distribution characteristics significantly separated MZ from DZ twin pairs. One encoded the quantity, and the remaining three encoded the spatial distribution of drusen, achieving a zygosity prediction accuracy of 76%, 74%, 68%, and 68%. Three of the four phenotypes had a 55% to 77% genetic effect in an AE model, and the fourth phenotype showed a nonshared environmental effect (E model).
Computational discovery of genetically determined features can reveal quantifiable AMD phenotypes that are genetically determined without explicitly linking them to specific genes. In addition, it can identify phenotypes that appear to result predominantly from environmental exposure. The approach is rapid and unbiased, suitable for large datasets, and can be used to reveal unknown phenotype-genotype relationships.
确定许多疾病的表型和基因型之间的关系可以改善临床诊断，确定疾病机制，并增强治疗效果。大多数遗传疾病是由许多基因相互作用引起的，这使得这些关系的发现变得模糊。本研究的假设是，图像分析有可能使新的可见表型的发现正式化。该假说在年龄相关性黄斑变性（AMD）的双胞胎患者中进行了测试。
对 43 对同卵（MZ）和 32 对异卵（DZ）双胞胎 AMD 患者的眼底图像进行了检查。首先，对软性和硬性玻璃膜疣进行了分割。然后，通过使用显著区分 MZ 和 DZ 双胞胎的玻璃膜疣分布统计数据来识别新的定义表型。使用 ACE 模型来识别加性遗传（A）、共同环境（C）和非共享环境（E）对玻璃膜疣分布表型的影响。
四个玻璃膜疣分布特征可显著区分 MZ 和 DZ 双胞胎。其中一个编码了玻璃膜疣的数量，其余三个编码了玻璃膜疣的空间分布，其同卵双胞胎预测准确率为 76%、74%、68%和 68%。在 AE 模型中，四个表型中的三个具有 55%至 77%的遗传效应，第四个表型则表现出非共享环境效应（E 模型）。
计算发现的遗传决定特征可以揭示可量化的 AMD 表型，这些表型是遗传决定的，而无需明确将其与特定基因联系起来。此外，它还可以识别似乎主要由环境暴露引起的表型。该方法快速且无偏倚，适用于大数据集，并可用于揭示未知的表型-基因型关系。
