Unsupervised ensemble-based phenotyping helps enhance the discoverability of genes related to heart morphology
Unsupervised ensemble-based phenotyping helps enhance the discoverability of genes related to heart morphology
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DOI：
10.48550/arxiv.2301.02916
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发表时间：
2023-01
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影响因子：
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通讯作者：
Rodrigo Bonazzola;Enzo Ferrante;N. Ravikumar;Yan Xia;B. Keavney;S. Plein;T. Syeda-Mahmood;Alejandro F Frangi
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文献类型：
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作者：
Rodrigo Bonazzola;Enzo Ferrante;N. Ravikumar;Yan Xia;B. Keavney;S. Plein;T. Syeda-Mahmood;Alejandro F Frangi
最近的全基因组关联研究已经成功地确定了遗传变异和来自心脏磁共振(CMR)图像的简单心脏参数之间的关联。然而，包括与CMR相关的遗传数据在内的大型数据库的出现，促进了对形状变异更细微模式的研究。在这里，我们提出了一个新的基因发现框架，称为无监督表型集成(UPE)。UPE通过使用用不同超参数训练的深度学习模型，将以无监督方式学习的一组表型汇集在一起，构建了一个冗余但高度表达的表示。这些表型然后通过(GWAS)进行分析，仅保留整个群体中高度自信和稳定的关联。我们将我们的方法应用于英国生物库数据库，从图像衍生的三维网格中提取左心室(LV)几何特征。我们证明，我们的方法极大地提高了影响左室形态的基因的可发现性，识别出11个具有广泛研究意义的基因座和8个具有提示意义的基因座。我们认为，我们的方法将使我们能够更广泛地发现与其他器官或成像方式的成像表型相关的基因。
Recent genome-wide association studies (GWAS) have been successful in identifying associations between genetic variants and simple cardiac parameters derived from cardiac magnetic resonance (CMR) images. However, the emergence of big databases including genetic data linked to CMR, facilitates investigation of more nuanced patterns of shape variability. Here, we propose a new framework for gene discovery entitled Unsupervised Phenotype Ensembles (UPE). UPE builds a redundant yet highly expressive representation by pooling a set of phenotypes learned in an unsupervised manner, using deep learning models trained with different hyperparameters. These phenotypes are then analyzed via (GWAS), retaining only highly confident and stable associations across the ensemble. We apply our approach to the UK Biobank database to extract left-ventricular (LV) geometric features from image-derived three-dimensional meshes. We demonstrate that our approach greatly improves the discoverability of genes influencing LV shape, identifying 11 loci with study-wide significance and 8 with suggestive significance. We argue that our approach would enable more extensive discovery of gene associations with image-derived phenotypes for other organs or image modalities.