Identifying disease-associated genes enables the development of precision medicine and the understanding of biological processes. Genome-wide association studies (GWAS), gene expression data, biological pathway analysis, and protein network analysis are among the techniques used to identify causal genes. We propose a machine-learning (ML) and deep-learning (DL) pipeline to identify genes associated with a phenotype. The proposed pipeline consists of two interrelated processes. The first is classifying people into case/control based on the genotype data. The second is calculating feature importance to identify genes associated with a particular phenotype. We considered 30 phenotypes from the openSNP data for analysis, 21 ML algorithms, and 80 DL algorithms and variants. The best-performing ML and DL models, evaluated by the area under the curve (AUC), F1 score, and Matthews correlation coefficient (MCC), were used to identify important single-nucleotide polymorphisms (SNPs), and the identified SNPs were compared with the phenotype-associated SNPs from the GWAS Catalog. The mean per-phenotype gene identification ratio (GIR) was 0.84. These results suggest that SNPs selected by ML/DL algorithms that maximize classification performance can help prioritise phenotype-associated SNPs and genes, potentially supporting downstream studies aimed at understanding disease mechanisms and identifying candidate therapeutic targets.
Unsupervised ensemble-based phenotyping helps enhance the discoverability of genes related to heart morphology
University of Leeds（利兹大学）；Research Institute for Signals, Systems and Computational Intelligence, sinc(i)（信号、系统与计算智能研究所（sinc(i)））；FICH-UNL（阿根廷国立拉普拉塔大学计算学院）；CONICET（阿根廷国家科学技术研究委员会）；University of Manchester（曼彻斯特大学）；Manchester University NHS Foundation Trust（曼彻斯特大学国民保健服务基金会信托）；Manchester Academic Health Science Centre（曼彻斯特学术健康科学中心）；IBM Almaden Research Center（IBM阿尔马登研究中心）
Benchmarking end-to-end genotype-to-phenotype prediction workflows across 80 openSNP phenotypes
The University of Queensland（昆士兰大学）；Baker Heart and Diabetes Institute（贝克心脏与糖尿病研究所）；Sorbonne University Abu Dhabi（阿布扎比索邦大学）；Khalifa University of Science and Technology（哈利法科学与技术大学）
GenDMR: A dynamic multimodal role-swapping network for identifying risk gene phenotypes
Southwest Minzu University（西南民族大学）；Beijing Normal University（北京师范大学）；Peking University Health Science Center（北京大学医学部）；University of Electronic Science and Technology of China（电子科技大学）
PhenoLinker: Phenotype-Gene Link Prediction and Explanation using Heterogeneous Graph Neural Networks
Departamento de Ingeniería de la Información y las Comunicaciones, Universidad de Murcia（信息与通信工程系，穆尔西亚大学）；Genetics and Genomic Medicine, Great Ormond Street Institute of Child Health, University College London（遗传学与基因组医学，儿童健康大奥蒙街研究所，伦敦大学学院）；Department of Neurodegenerative Diseases, UCL Institute of Neurology（神经退行性疾病部门，伦敦大学学院神经学研究所）；NIMGenetics, Genómica y Medicina, SL, Madrid, Spain（NIMGenetics，基因组学与医学，SL，马德里，西班牙）
LSTM Autoencoder-based Deep Neural Networks for Barley Genotype-to-Phenotype Prediction
School of Information Technology, Murdoch University（默多克大学信息技术学院）；School of Medical, Molecular and Forensic Sciences, Murdoch University（默多克大学医学、分子与法医学学院）；Western Crop Genetics Alliance, Murdoch University（默多克大学西部作物遗传学联盟）；Western Australian State Agricultural Biotechnology Centre（西澳大利亚州农业生物技术中心）；Department of Primary Industries and Regional Development（初级产业与区域发展部）
Identifying Genetic Risk Factors via Sparse Group Lasso with Group Graph Structure
Arizona State University（亚利桑那州立大学）；Baidu Research（百度研究院）；University of Southern California（南加州大学）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of Michigan（密歇根大学）
