deepBreaks identifies and prioritizes genotype-phenotype associations using machine learning.

Mahdi Baghbanzadeh, Tyson Dawson, Bahar Sayoldin, Seth A Frazer, Todd H Oakley, Keith A Crandall, Ali Rahnavard

Abstract read

In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Mahdi BaghbanzadehComputational Biology Institute, Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC, 20052, USA.

Tyson DawsonComputational Biology Institute, Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC, 20052, USA.

Bahar SayoldinComputational Biology Institute, Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC, 20052, USA.

Seth A FrazerEcology, Evolution, and Marine Biology, University of California, Santa Barbara, CA, 93106, USA.

Todd H OakleyEcology, Evolution, and Marine Biology, University of California, Santa Barbara, CA, 93106, USA.

Keith A CrandallComputational Biology Institute, Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC, 20052, USA.

Ali RahnavardComputational Biology Institute, Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC, 20052, USA. rahnavard@gwu.edu.

Funding

National Science Foundation 1754770National Science Foundation 2109688

6 · The paper itself

Abstract

Sequence data, such as nucleotides or amino acids, are crucial in advancing our understanding of biology. However, investigating and analyzing sequencing data and genotype-phenotype associations present several challenges, including noise components that arise from the sequencing, nonlinear genotype-phenotype associations, collinearity between input features, and high dimensionality of the input data. Machine learning (ML) algorithms have proven to be effective in detecting intricate and nonstructural patterns, making them a valuable tool for studies focused on genotype-phenotype associations. Yet, there needs to be more user-friendly ML implementations that leverage the unique features of high-volume DNA sequence data. Here, we introduce deepBreaks, a generic approach that detects important positions (genotypes) in sequence data that are associated with phenotypic traits. deepBreaks compares the performance of multiple ML algorithms and prioritizes positions based on the best-fit models. It is open-source software with online documentation and examples available at https://github.com/omicsEye/deepBreaks .

Indexed as

Genetic Association StudiesMachine LearningSoftwareAlgorithmsComputational BiologyGenotypeHumansPhenotypeSequence Analysis, DNAMachine learning algorithmPhenotype-genotype associationSequence analysisSNP prioritization

Identifiers

PMID41203782

DOI10.1038/s41598-025-25580-6

PMCPMC12594848

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LicenceCC BY-NC-ND

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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.