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A generalizable deep learning framework for inferring fine-scale germline mutation rate maps

Nature Machine Intelligence volume 4, pages 1209–1223 (2022)Cite this article

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A preprint version of the article is available at bioRxiv.

Abstract

Germline mutation rates are essential for genetic and evolutionary analyses. Yet, estimating accurate fine-scale mutation rates across the genome is a great challenge, due to relatively few observed mutations and intricate relationships between predictors and mutation rates. Here, we present Mutation Rate Learner (MuRaL), a deep learning framework to predict mutation rates at the nucleotide level using only genomic sequences as input. Harnessing human germline variants for comprehensive assessment, we show that MuRaL achieves better predictive performance than current state-of-the-art methods. Moreover, MuRaL can build models with relatively few training mutations and a moderate number of sequenced individuals, and can leverage transfer learning to further reduce data and time demands. We apply MuRaL to produce genome-wide mutation rate maps for four representative species—Homo sapiens, Macaca mulatta, Drosophila melanogaster and Arabidopsis thaliana—demonstrating its high applicability. As an example, we use improved mutation rate estimates to stratify human genes into distinct groups that are enriched for different functions, and highlight that many developmental genes are subject to high mutational burden. The open-source software and generated mutation rate maps can greatly facilitate related research.

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Fig. 1: Schematics of the MuRaL model and evaluation strategies.
Fig. 2: Comparison of MuRaL models trained with different rare-variant data.
Fig. 3: Comparison of MuRaL and existing models.
Fig. 4: Training DNM models and transfer learning.
Fig. 5: Application of MuRaL to other species.
Fig. 6: Clustering human coding genes based on mutation rate profiles.

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Data availability

All the analyses in this study were based on published data. The variants of humans were from the gnomAD database (v2.1.1) (http://www.gnomad-sg.org/downloads/)15 and the gene4denovo database (http://www.genemed.tech/gene4denovo/download/)46. The variants of M. mulatta were from the UCSC Genome Browser (https://hgdownload.soe.ucsc.edu/gbdb/rheMac10/rhesusSNVs/)31. The variants of D. melanogaster were from the Drosophila Genetic Reference Panel (http://dgrp2.gnets.ncsu.edu/data/website/dgrp2.vcf)59. The variants of A. thaliana were from the 1001 Genomes project (https://1001genomes.org/)52. Human gene annotations were from GENCODE (v37) (https://www.gencodegenes.org/human/)55. The ClinVar variants were from NCBI (https://ftp.ncbi.nlm.nih.gov/pub/clinvar/vcf_GRCh37/clinvar_20220730.vcf.gz)60. The predicted mutation rate maps for genomes of human, M. mulatta, D. melanogaster and A. thaliana are available at the ScienceDB repository (https://doi.org/10.11922/sciencedb.01173)61. Source data are provided with this paper.

Code availability

Source code of the MuRaL package is available at https://github.com/CaiLiLab/MuRaL. The MuRaL version (v1.0.0) used for this publication is also available at Zenodo (https://doi.org/10.5281/zenodo.6989025)62.

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Acknowledgements

We thank X. He, X. Shen and N. Luscombe for insightful comments on the manuscript. We thank all lab members for discussion and help throughout this project. This work was supported by National Natural Science Foundation of China (32070593), Guangdong Basic and Applied Basic Research Foundation (2022A1515010888), and Science and Technology Planning Project of Guangzhou (202102020816).

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  1. These authors contributed equally: Yiyuan Fang, Shuyi Deng.

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  1. State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, China

    Yiyuan Fang, Shuyi Deng & Cai Li

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Contributions

C.L. designed and supervised the project. C.L. developed the MuRaL framework, with input from Y.F. and S.D. for detailed evaluation. Y.F. and S.D. performed comparative analyses and generated mutation rate maps. C.L, Y.F. and S.D. wrote the manuscript.

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Correspondence to Cai Li.

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Nature Machine Intelligence thanks Varun Aggarwala and Jedidiah Carlson for their contribution to the peer review of this work.

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Fang, Y., Deng, S. & Li, C. A generalizable deep learning framework for inferring fine-scale germline mutation rate maps. Nat Mach Intell 4, 1209–1223 (2022). https://doi.org/10.1038/s42256-022-00574-5

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