ArticleBMC bioinformatics2023
Improving variant calling using population data and deep learning.
Article in BMC bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
13 citing papers in PubMed.
- Genetic analysis of neurodegenerative diseases.The Journal of clinical investigation · 2026Review
- Perspectives on the Current and Future State of Artificial Intelligence in Medical Genetics.American journal of medical genetics. Part A · 2025Review
- Performance comparison of germline variant calling tools in sporadic disease cohorts.Molecular genetics and genomics : MGG · 2025Article
- varCADD: large sets of standing genetic variation enable genome-wide pathogenicity prediction.Genome medicine · 2025Article
- Overcoming limitations to customize DeepVariant for domesticated animals with TrioTrain.Genome research · 2025Article
- Pangenome-aware DeepVariant.bioRxiv : the preprint server for biology · 2025Article
- Article
- Artificial intelligence in variant calling: a review.Frontiers in bioinformatics · 2025Review
- Beyond the genome: the role of functional markers in contemporary plant breeding.Frontiers in plant science · 2025Review
- DeepSomatic: Accurate somatic small variant discovery for multiple sequencing technologies.bioRxiv : the preprint server for biology · 2024Article
- Machine learning: a new era for cardiovascular pregnancy physiology and cardio-obstetrics research.American journal of physiology. Heart and circulatory physiology · 2024Review
- Overcoming Limitations to Deep Learning in Domesticated Animals with TrioTrain.bioRxiv : the preprint server for biology · 2024Article
- Translation of genome-wide association study: from genomic signals to biological insights.Frontiers in genetics · 2024Review
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Large-scale population variant data is often used to filter and aid interpretation of variant calls in a single sample. These approaches do not incorporate population information directly into the process of variant calling, and are often limited to filtering which trades recall for precision. In this study, we develop population-aware DeepVariant models with a new channel encoding allele frequencies from the 1000 Genomes Project. This model reduces variant calling errors, improving both precision and recall in single samples, and reduces rare homozygous and pathogenic clinvar calls cohort-wide. We assess the use of population-specific or diverse reference panels, finding the greatest accuracy with diverse panels, suggesting that large, diverse panels are preferable to individual populations, even when the population matches sample ancestry. Finally, we show that this benefit generalizes to samples with different ancestry from the training data even when the ancestry is also excluded from the reference panel.
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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.