Deep learning modeling of rare noncoding genetic variants in human motor neurons defines<i>CCDC146</i>as a therapeutic target for ALS

Zhang, Sai; Moll, Tobias; Rubin-Sigler, Jasper; Tu, Sharon; Li, Shuya; Yuan, Enming; Liu, Menghui; Butt, Afreen; Harvey, Calum; Gornall, Sarah; Alhalthli, Elham; Shaw, Allan; Souza, Cleide Dos Santos; Ferraiuolo, Laura; Hornstein, Eran; Shelkovnikova, Tatyana; van Dijk, Charlotte H.; Timpanaro, Ilia S.; Kenna, Kevin P.; Zeng, Jianyang; Tsao, Philip S.; Shaw, Pamela J.; Ichida, Justin K.; Cooper-Knock, Johnathan; Snyder, Michael P.

doi:10.1101/2024.03.30.24305115

Cited by 2 publications

References 83 publications

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Leveraging functional annotations to map rare variants associated with Alzheimer’s disease with gruyere

Das,

Lakhani,

Terwagne

et al. 2024

Preprint

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The increasing availability of whole-genome sequencing (WGS) has begun to elucidate the contribution of rare variants (RVs), both coding and non-coding, to complex disease. Multiple RV association tests are available to study the relationship between genotype and phenotype, but most are restricted to per-gene models and do not fully leverage the availability of variant-level functional annotations. We propose Genome-wide Rare Variant EnRichment Evaluation (gruyere), a Bayesian probabilistic model that complements existing methods by learning global, trait-specific weights for functional annotations to improve variant prioritization. We apply gruyere to WGS data from the Alzheimer's Disease (AD) Sequencing Project, consisting of 7,966 cases and 13,412 controls, to identify AD-associated genes and annotations. Growing evidence suggests that disruption of microglial regulation is a key contributor to AD risk, yet existing methods have not had sufficient power to examine rare non-coding effects that incorporate such cell-type specific information. To address this gap, we 1) use predicted enhancer and promoter regions in microglia and other potentially relevant cell types (oligodendrocytes, astrocytes, and neurons) to define per-gene non-coding RV test sets and 2) include cell-type specific variant effect predictions (VEPs) as functional annotations. gruyere identifies 15 significant genetic associations not detected by other RV methods and finds deep learning-based VEPs for splicing, transcription factor binding, and chromatin state are highly predictive of functional non-coding RVs. Our study establishes a novel and robust framework incorporating functional annotations, coding RVs, and cell-type associated non-coding RVs, to perform genome-wide association tests, uncovering AD-relevant genes and annotations.

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Leveraging functional annotations to map rare variants associated with Alzheimer’s disease with gruyere

Das,

Lakhani,

Terwagne

et al. 2024

Preprint

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Detection of motor nervous disease using deep learning based Duple feature extraction network

2024

Technology and Health Care

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Background A motor nervous disease (MND) is a debilitating nervous disease that affects motor neurons that regulates the muscular voluntary movements. The disease gradually destroys parts of the neurological system. Generally, MND develops owing to a grouping of genetic, behavioural, and natural features. Objective However, early detection of MND is challenging and manual identification requires a lot of time. Therefore, automated methods like deep learning structures are needed to detect MND quickly and more accurately than manual classification. In this work, a novel deep learning-based Duple feature extraction network is proposed for identifying MND in its early stages. Methods Initially, the input DTI images are pre-processed utilizing a Gaussian adaptive bilateral filter (GAB) to improve the quality of the image. Then the pre-processed DTI images are fed into the dual feature extraction phase for colour and structural conversion. The Colour Information Feature (CIF) with Local and Global sampling (LOG) is integrated into the LinkNet module to extract colour features. Moreover, the Local Binary Pattern (LBP) with Edge sampling models is integrated into the MobileNet module to extract edge features. Afterward, the extracted colour and texture features of images are flattered and given as the input to a Deep Neural Network for classifying the MND levels. Results From the test results, the proposed Duple feature extraction network has yielded a 99.62% accuracy rate. The proposed DNN improves its F1-score by 1.32%, 2.1%, and 3.18% better than FNN, GNN, and GRU respectively. The proposed Duple-feature extraction network improves overall accuracy by 6.15%, 5.56%, 5.96%, and 6.68% compared to CNN, SVM-RFE, MLP, and Tri-planar CNN respectively. Conclusion The novel deep learning-based Duple feature extraction framework shows promising results in early detection of motor nervous disease, significantly improving accuracy and f1-scores compared to existing models.

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Deep learning modeling of rare noncoding genetic variants in human motor neurons definesCCDC146as a therapeutic target for ALS

Cited by 2 publications

References 83 publications

Leveraging functional annotations to map rare variants associated with Alzheimer’s disease with gruyere

Leveraging functional annotations to map rare variants associated with Alzheimer’s disease with gruyere

Detection of motor nervous disease using deep learning based Duple feature extraction network

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