Deep learning models have shown great promise in predicting regulatory effects from DNA sequence, but their informativeness for human complex diseases is not fully understood. Here, we evaluate genome-wide SNP annotations from two previous deep learning models, DeepSEA and Basenji, by applying stratified LD score regression to 41 diseases and traits (average N = 320K), conditioning on a broad set of coding, conserved and regulatory annotations. We aggregated annotations across all (respectively blood or brain) tissues/cell-types in meta-analyses across all (respectively 11 blood or 8 brain) traits. The annotations were highly enriched for disease heritability, but produced only limited conditionally significant results: non-tissue-specific and brain-specific Basenji-H3K4me3 for all traits and brain traits respectively. We conclude that deep learning models have yet to achieve their full potential to provide considerable unique information for complex disease, and that their conditional informativeness for disease cannot be inferred from their accuracy in predicting regulatory annotations.

深度学习模型在预测 DNA 序列的调节作用方面显示出巨大的希望，但它们对人类复杂疾病的信息性尚不完全清楚。在这里，我们通过将分层 LD 评分回归应用于 41 种疾病和特征（平均N = 320K），以广泛的编码、保守和监管注释为条件。我们在所有（分别为 11 个血液或 8 个脑）特征的荟萃分析中汇总了所有（分别为血液或脑）组织/细胞类型的注释。注释高度丰富了疾病遗传性，但仅产生了有限的条件显着性结果：分别针对所有性状和大脑性状的非组织特异性和大脑特异性 Basenji-H3K4me3。我们得出结论，深度学习模型尚未充分发挥其为复杂疾病提供大量独特信息的潜力，并且无法从其预测监管注释的准确性推断出它们对疾病的条件信息性。