Explainable multi-task learning improves the parallel estimation of polygenic risk scores for many diseases through shared genetic basis.
Explainable multi-task learning improves the parallel estimation of polygenic risk scores for many diseases through shared genetic basis.
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DOI：
10.1371/journal.pcbi.1011211
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发表时间：
2023-07
影响因子：
4.3
通讯作者：
中科院分区：
文献类型：
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许多复杂疾病具有共同的遗传决定因素，并且在人群中是共病的。我们假设疾病的共同发生及其重叠的遗传病因可以被利用来同时改善多种疾病的多基因风险评分（PRS）。使用基于可解释的神经网络架构的多任务学习（MTL）方法对这一假设进行了测试。我们发现，在泛癌症MTL模型中对17种流行癌症的PRS的平行估计通常比在可比的单任务学习（STL）模型中对个体癌症的独立估计更准确。在泛疾病MTL模型中，对于60种流行的非癌症疾病，也一致地观察到正迁移学习所赋予的这种性能改善。MTL模型的解释揭示了神经网络用于PRS估计的重要单核苷酸多态性集之间的显着遗传相关性。这表明一个具有共同遗传基础的疾病网络。为了预防或延迟复杂疾病的发作，一个人可以通过了解他/她通过遗传而易患的疾病以及他/她对遗传不太敏感的疾病而受益。使用多基因风险评分（PRS）量化一个人患复杂疾病的总体遗传风险。传统上，PRS是使用统计方法针对不同疾病独立开发的。在这项研究中，我们使用了多任务学习方法，并训练了一个深度学习模型来同时学习许多疾病的PRS。我们发现，新的多任务学习模型可以为这些疾病提供更准确的PRS估计，而不是分别针对个别疾病训练的相应单任务学习模型。多任务学习的性能提升表明，许多复杂的疾病可能在其中共享大量共同的遗传风险变体，这有助于在多任务学习期间实现知识的正向转移。
Many complex diseases share common genetic determinants and are comorbid in a population. We hypothesized that the co-occurrences of diseases and their overlapping genetic etiology can be exploited to simultaneously improve multiple diseases’ polygenic risk scores (PRS). This hypothesis was tested using a multi-task learning (MTL) approach based on an explainable neural network architecture. We found that parallel estimations of the PRS for 17 prevalent cancers in a pan-cancer MTL model were generally more accurate than independent estimations for individual cancers in comparable single-task learning (STL) models. Such performance improvement conferred by positive transfer learning was also observed consistently for 60 prevalent non-cancer diseases in a pan-disease MTL model. Interpretation of the MTL models revealed significant genetic correlations between the important sets of single nucleotide polymorphisms used by the neural network for PRS estimation. This suggested a well-connected network of diseases with shared genetic basis. To prevent or delay the onset of complex diseases, an individual can benefit from knowing which diseases he/she is predisposed to through heredity and which diseases he/she is less susceptible to genetically. The overall genetic risk of a person to a complex disease is quantified using a polygenic risk score (PRS). Traditionally, PRS were developed independently for different diseases using statistical approaches. In this study, we used a multi-task learning approach and trained a deep learning model to simultaneously learn the PRS of many diseases all together. We showed that the new multi-task learning model can provide more accurate estimation of PRS for these diseases than their corresponding single-task learning models that were trained for individual diseases separately. The performance boost by multi-task learning suggests that many complex diseases may share a large number of common genetic risk variants among them, which can contribute to the positive transfer of knowledge during multi-task learning.
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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
影响因子：
9.8
作者：
Maier R;Moser G;Chen GB;Ripke S;Cross-Disorder Working Group of the Psychiatric Genomics Consortium;Coryell W;Potash JB;Scheftner WA;Shi J;Weissman MM;Hultman CM;Landén M;Levinson DF;Kendler KS;Smoller JW;Wray NR;Lee SH
通讯作者：
Lee SH
影响因子：
9.8
作者：
DeBoever, Christopher;Tanigawa, Yosuke;Rivas, Manuel A.
通讯作者：
Rivas, Manuel A.
DOI：
10.1186/1297-9686-45-44
发表时间：
2013-10-30
期刊：
Genetics, selection, evolution : GSE
影响因子：
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作者：
Clark SA;Kinghorn BP;Hickey JM;van der Werf JH
通讯作者：
van der Werf JH
DOI：
10.1158/1055-9965.epi-20-1635
发表时间：
2021-06
期刊：
Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology
影响因子：
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