Deep learning based phenotyping of medical images improves power for gene discovery of complex disease.
Deep learning based phenotyping of medical images improves power for gene discovery of complex disease.
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DOI：
10.1038/s41746-023-00903-x
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发表时间：
2023-08-21
影响因子：
15.2
通讯作者：
中科院分区：
文献类型：
--
作者：
电子健康记录通常不完整，降低了遗传关联研究的能力。对于某些疾病，如膝关节骨关节炎，其中常规诊断过程涉及X射线，基于图像的表型分析提供了一种替代和公正的方法来确定疾病病例。我们通过训练深度学习模型来研究这一点，以从达到临床医生级别性能的膝关节DXA扫描中确定膝关节骨关节炎病例。使用我们的模型，我们确定了1931例（178%）比目前在健康记录中诊断的病例多。通过我们的模型诊断为病例的个体与对照个体相比，自我报告的膝关节疼痛发生率更高，持续时间更长，严重程度更高。我们训练了另一个深度学习模型来测量膝关节间隙宽度，这是一种与膝关节骨关节炎严重程度相关的定量表型。在进行遗传关联分析时，我们发现，尽管两种表型在遗传上高度相关，但与我们的病例和对照的二元模型相比，使用定量测量改善了我们发现的全基因组显著位点的数量。此外，我们发现了膝关节骨关节炎的定量测量与成人骨折风险增加之间的关联-这是老年人损伤相关死亡的主要原因-说明了基于图像的表型分析揭示电子健康记录中未捕获的流行病学关联的能力。对于具有放射学诊断的疾病，我们的研究结果证明了在生物库规模上使用深度学习进行表型的潜力，提高了遗传和流行病学关联分析的能力。
Electronic health records are often incomplete, reducing the power of genetic association studies. For some diseases, such as knee osteoarthritis where the routine course of diagnosis involves an X-ray, image-based phenotyping offers an alternate and unbiased way to ascertain disease cases. We investigated this by training a deep-learning model to ascertain knee osteoarthritis cases from knee DXA scans that achieved clinician-level performance. Using our model, we identified 1931 (178%) more cases than currently diagnosed in the health record. Individuals diagnosed as cases by our model had higher rates of self-reported knee pain, for longer durations and with increased severity compared to control individuals. We trained another deep-learning model to measure the knee joint space width, a quantitative phenotype linked to knee osteoarthritis severity. In performing genetic association analysis, we found that use of a quantitative measure improved the number of genome-wide significant loci we discovered by an order of magnitude compared with our binary model of cases and controls despite the two phenotypes being highly genetically correlated. In addition we discovered associations between our quantitative measure of knee osteoarthritis and increased risk of adult fractures- a leading cause of injury-related death in older individuals-, illustrating the capability of image-based phenotyping to reveal epidemiological associations not captured in the electronic health record. For diseases with radiographic diagnosis, our results demonstrate the potential for using deep learning to phenotype at biobank scale, improving power for both genetic and epidemiological association analysis.
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影响因子：
15.1
作者：
Birmpili, Panagiota;Atkins, Eleanor;Li, Qiuju;Johal, Amundeep S.;Waton, Sam;Williams, Robin;Pherwani, Arun D.;Cromwell, David A.
通讯作者：
Cromwell, David A.
影响因子：
30.8
作者：
Bulik-Sullivan, Brendan K.;Loh, Po-Ru;Finucane, Hilary K.;Ripke, Stephan;Yang, Jian;Patterson, Nick;Daly, Mark J.;Price, Alkes L.;Neale, Benjamin M.
通讯作者：
Neale, Benjamin M.
影响因子：
64.8
作者：
Harris CR;Millman KJ;van der Walt SJ;Gommers R;Virtanen P;Cournapeau D;Wieser E;Taylor J;Berg S;Smith NJ;Kern R;Picus M;Hoyer S;van Kerkwijk MH;Brett M;Haldane A;Del Río JF;Wiebe M;Peterson P;Gérard-Marchant P;Sheppard K;Reddy T;Weckesser W;Abbasi H;Gohlke C;Oliphant TE
通讯作者：
Oliphant TE
影响因子：
9.2
作者：
Chang CC;Chow CC;Tellier LC;Vattikuti S;Purcell SM;Lee JJ
通讯作者：
Lee JJ
影响因子：
64.8
作者：
Bycroft C;Freeman C;Petkova D;Band G;Elliott LT;Sharp K;Motyer A;Vukcevic D;Delaneau O;O'Connell J;Cortes A;Welsh S;Young A;Effingham M;McVean G;Leslie S;Allen N;Donnelly P;Marchini J
通讯作者：
Marchini J