Large-scale machine-learning-based phenotyping significantly improves genomic discovery for optic nerve head morphology.
Large-scale machine-learning-based phenotyping significantly improves genomic discovery for optic nerve head morphology.
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DOI：
10.1016/j.ajhg.2021.05.004
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发表时间：
2021-07-01
影响因子：
9.8
通讯作者：
McLean CY
中科院分区：
文献类型：
--
作者：
Alipanahi B;Hormozdiari F;Behsaz B;Cosentino J;McCaw ZR;Schorsch E;Sculley D;Dorfman EH;Foster PJ;Peng LH;Phene S;Hammel N;Carroll A;Khawaja AP;McLean CY
全基因组关联研究（GWAS）需要准确的队列表型，但专家标记可能是昂贵的，时间密集的和可变的。在这里，我们开发了一个机器学习（ML）模型来预测彩色眼底照片中的昏迷性视神经乳头特征。我们使用该模型预测垂直杯盘比（VCDR），这是青光眼的诊断参数和主要内在表型，在英国生物银行（UKB）的65，680名欧洲人中进行。基于ML的VCDR的GWAS在156个基因座中识别出299个独立的全基因组显著（GWS; p ≤ 5 × 10−8）命中。基于ML的GWAS复制了UKB最近VCDR GWAS中65个GWS位点中的62个，其中两名眼科医生手动标记了67，040名欧洲人的图像。基于ML的GWAS还确定了93个新位点，显著扩展了我们对青光眼和VCDR遗传病因学的理解。通路分析支持VCDR的新命中的生物学意义：已知神经元和突触生物学中涉及的基因附近的选择基因座或窝藏变体会导致严重的孟德尔眼科疾病。最后，在独立EPIC-Norfolk队列中，基于ML的GWAS结果显著改善了VCDR和原发性开角型青光眼的多基因预测。
Genome-wide association studies (GWASs) require accurate cohort phenotyping, but expert labeling can be costly, time intensive, and variable. Here, we develop a machine learning (ML) model to predict glaucomatous optic nerve head features from color fundus photographs. We used the model to predict vertical cup-to-disc ratio (VCDR), a diagnostic parameter and cardinal endophenotype for glaucoma, in 65,680 Europeans in the UK Biobank (UKB). A GWAS of ML-based VCDR identified 299 independent genome-wide significant (GWS; p ≤ 5 × 10−8) hits in 156 loci. The ML-based GWAS replicated 62 of 65 GWS loci from a recent VCDR GWAS in the UKB for which two ophthalmologists manually labeled images for 67,040 Europeans. The ML-based GWAS also identified 93 novel loci, significantly expanding our understanding of the genetic etiologies of glaucoma and VCDR. Pathway analyses support the biological significance of the novel hits to VCDR: select loci near genes involved in neuronal and synaptic biology or harboring variants are known to cause severe Mendelian ophthalmic disease. Finally, the ML-based GWAS results significantly improve polygenic prediction of VCDR and primary open-angle glaucoma in the independent EPIC-Norfolk cohort.
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影响因子：
3.5
作者：
Gao, X. Raymond;Huang, Hua;Kim, Heejin
通讯作者：
Kim, Heejin
影响因子：
2
作者：
Bowden J;Del Greco M F;Minelli C;Davey Smith G;Sheehan N;Thompson J
通讯作者：
Thompson J
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：
4.1
作者：
Foster, PJ;Buhrmann, R;Johnson, GJ
通讯作者：
Johnson, GJ
影响因子：
4.1
作者：
Garway-Heath, DF;Rudnicka, AR;Hitchings, RA
通讯作者：
Hitchings, RA