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Fine human genetic map based on UK10K data set

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Abstract

Recombination is a major force that shapes genetic diversity. Determination of recombination rate is important and can theoretically be improved by increasing the sample size. However, it is nearly impossible to estimate recombination rates using traditional population genetics methods when the sample size is large because these methods are highly computationally demanding. In this study, we used a refined machine learning approach to estimate the recombination rate of the human genome using the UK10K human genomic dataset with 7,562 genomic sequences and its three subsets with 200, 400 and 2,000 genomic sequences. The estimation was performed under the human Out-of-Africa demographic model. We not only obtained an accurate human genetic map, but also found that the fluctuation of estimated recombination rate is reduced along the human genome when the sample size increases. The estimated UK10K recombination rate heterogeneity is less than that estimated from its subsets. Our results demonstrate how the sample size affects the estimated recombination rate, and analyses of a larger number of genomes result in a more precise estimation of recombination rate. The accurate genetic map based on UK10K data set is also expected to benefit other human biology researches.

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Acknowledgements

We thank the UK10K Project Consortium for sharing the data.

Funding

This work was supported by grants from the National Natural Science Foundation of China (nos. 31100273, 31172073, 91131010), the Strategic Priority Research Program of the Chinese Academy of Sciences (No. XDB38030100), the National Key Research and Development Project (No. 2020YFC0847000) and the funding from Shanghai Institute of Nutrition and Health (No. JBGSRWBD-SINH-2021-10).

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Author notes

  1. Ziqian Hao and Pengyuan Du contributed equally to this work.

Authors and Affiliations

  1. CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China

    Ziqian Hao, Pengyuan Du & Haipeng Li

  2. Key Laboratory of Brain Functional Genomics of Ministry of Education, School of Life Science, East China Normal University, Shanghai, 200062, China

    Yi-Hsuan Pan

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  1. Ziqian Hao
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Contributions

ZH, PD, YHP, and HL conceived and designed the research; ZH and HL wrote the code; ZH and PD analyzed the data; ZH, PD, YHP, and HL wrote the paper.

Corresponding authors

Correspondence to Yi-Hsuan Pan or Haipeng Li.

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Conflict of interest

The authors declare no competing interests.

Availability of data and material

The datasets used in this study are available at the UK10K Project Consortium (Walter et al. 2015) (https://www.uk10k.org/) The genetic maps of OMNI data set built by LDhat (Auton and McVean 2007) were downloaded from the 1000 Genomes Project (ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/working/20130507_omni_recombination_rates).

Code availability

FastEPRR 2.0 is written in R and integrated on the eGPS cloud (Yu et al. 2019) (http://www.egps-software.net). The desktop version and the genetic maps established in this study are freely available on the institute website (https://www.picb.ac.cn/evolgen/).

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Hao, Z., Du, P., Pan, YH. et al. Fine human genetic map based on UK10K data set. Hum Genet 141, 273–281 (2022). https://doi.org/10.1007/s00439-021-02415-8

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