ArticlePLoS genetics2021
Multi-scale inference of genetic trait architecture using biologically annotated neural networks.
Article in PLoS genetics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Translating functional molecular knowledge into crop-breeding success.Nature reviews. Genetics · 2026Review
- Scalable nonparametric clustering with unified marker gene selection for single-cell RNA-seq data.Cell reports methods · 2026Article
- Decoding ancestry-specific genetic risk: interpretable deep feature selection reveals prostate cancer SNP disparities in diverse populations.BioData mining · 2025Article
- Scalable nonparametric clustering with unified marker gene selection for single-cell RNA-seq data.bioRxiv : the preprint server for biology · 2025Article
- A deep learning strategy for accurate identification of purebred and hybrid pigs across SNP chips.Journal of animal science and biotechnology · 2025Article
- Epistasis regulates genetic control of cardiac hypertrophy.Nature cardiovascular research · 2025Article
- Deciphering epistatic genetic regulation of cardiac hypertrophy.medRxiv : the preprint server for health sciences · 2025Article
- A deep learning model for prediction of autism status using whole-exome sequencing data.PLoS computational biology · 2024Article
- Multioviz: an interactive platform for in silico perturbation and interrogation of gene regulatory networks.BMC bioinformatics · 2024Article
- Designing interpretable deep learning applications for functional genomics: a quantitative analysis.Briefings in bioinformatics · 2024Review
- Improving the accuracy of genomic prediction in dairy cattle using the biologically annotated neural networks framework.Journal of animal science and biotechnology · 2024Article
- Article
- Factorizing polygenic epistasis improves prediction and uncovers biological pathways in complex traits.American journal of human genetics · 2023Article
- Fast and accurate Bayesian polygenic risk modeling with variational inference.American journal of human genetics · 2023Article
- PAUSE: principled feature attribution for unsupervised gene expression analysis.Genome biology · 2023Article
- Uncertainty quantification in variable selection for genetic fine-mapping using bayesian neural networks.iScience · 2022Article
- Predicting phenotypes from genetic, environment, management, and historical data using CNNs.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2021Article
- Interpretable artificial neural networks incorporating Bayesian alphabet models for genome-wide prediction and association studies.G3 (Bethesda, Md.) · 2021Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
In this article, we present Biologically Annotated Neural Networks (BANNs), a nonlinear probabilistic framework for association mapping in genome-wide association (GWA) studies. BANNs are feedforward models with partially connected architectures that are based on biological annotations. This setup yields a fully interpretable neural network where the input layer encodes SNP-level effects, and the hidden layer models the aggregated effects among SNP-sets. We treat the weights and connections of the network as random variables with prior distributions that reflect how genetic effects manifest at different genomic scales. The BANNs software uses variational inference to provide posterior summaries which allow researchers to simultaneously perform (i) mapping with SNPs and (ii) enrichment analyses with SNP-sets on complex traits. Through simulations, we show that our method improves upon state-of-the-art association mapping and enrichment approaches across a wide range of genetic architectures. We then further illustrate the benefits of BANNs by analyzing real GWA data assayed in approximately 2,000 heterogenous stock of mice from the Wellcome Trust Centre for Human Genetics and approximately 7,000 individuals from the Framingham Heart Study. Lastly, using a random subset of individuals of European ancestry from the UK Biobank, we show that BANNs is able to replicate known associations in high and low-density lipoprotein cholesterol content.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.