Background selection and biased gene conversion affect more than 95% of the human genome and bias demographic inferences

Abstract

Disentangling the effect on genomic diversity of natural selection from that of demography is notoriously difficult, but necessary to properly reconstruct the history of species. Here, we use high-quality human genomic data to show that purifying selection at linked sites (i.e. background selection, BGS) and GC-biased gene conversion (gBGC) together affect as much as 95% of the variants of our genome. We find that the magnitude and relative importance of BGS and gBGC are largely determined by variation in recombination rate and base composition. Importantly, synonymous sites and non-transcribed regions are also affected, albeit to different degrees. Their use for demographic inference can lead to strong biases. However, by conditioning on genomic regions with recombination rates above 1.5 cM/Mb and mutation types (C↔G, A↔T), we identify a set of SNPs that is mostly unaffected by BGS or gBGC, and that avoids these biases in the reconstruction of human history.

Data availability

All data generated and script to analyse them is provided on the dryad repesitory: http://datadryad.org/review?doi=doi:10.5061/dryad.t76fk80

The following data sets were generated
The following previously published data sets were used

Article and author information

Author details

  1. Fanny Pouyet

    Institute of Ecology and Evolution, University of Bern, Berne, Switzerland
    For correspondence
    fanny.pouyet@iee.unibe.ch
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5614-6998
  2. Simon Aeschbacher

    Institute of Ecology and Evolution, University of Bern, Bern, Switzerland
    Competing interests
    The authors declare that no competing interests exist.
  3. Alexandre Thiéry

    Institute of Ecology and Evolution, University of Bern, Bern, Switzerland
    Competing interests
    The authors declare that no competing interests exist.
  4. Laurent Excoffier

    Institute of Ecology and Evolution, University of Bern, Bern, Switzerland
    For correspondence
    laurent.excoffier@iee.unibe.ch
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-7507-6494

Funding

Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (310030B-166605)

  • Laurent Excoffier

University of Berkeley (Visiting Miller Professorship)

  • Laurent Excoffier

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Reviewing Editor

  1. Krishna Veeramah, Stony Brook University, United States

Version history

  1. Received: March 1, 2018
  2. Accepted: August 17, 2018
  3. Accepted Manuscript published: August 20, 2018 (version 1)
  4. Accepted Manuscript updated: August 23, 2018 (version 2)
  5. Version of Record published: October 9, 2018 (version 3)

Copyright

© 2018, Pouyet et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 8,400
    views
  • 1,017
    downloads
  • 113
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Fanny Pouyet
  2. Simon Aeschbacher
  3. Alexandre Thiéry
  4. Laurent Excoffier
(2018)
Background selection and biased gene conversion affect more than 95% of the human genome and bias demographic inferences
eLife 7:e36317.
https://doi.org/10.7554/eLife.36317

Share this article

https://doi.org/10.7554/eLife.36317

Further reading

    1. Computational and Systems Biology
    2. Genetics and Genomics
    Chananchida Sang-aram, Robin Browaeys ... Yvan Saeys
    Research Article

    Spatial transcriptomics (ST) technologies allow the profiling of the transcriptome of cells while keeping their spatial context. Since most commercial untargeted ST technologies do not yet operate at single-cell resolution, computational methods such as deconvolution are often used to infer the cell type composition of each sequenced spot. We benchmarked 11 deconvolution methods using 63 silver standards, 3 gold standards, and 2 case studies on liver and melanoma tissues. We developed a simulation engine called synthspot to generate silver standards from single-cell RNA-sequencing data, while gold standards are generated by pooling single cells from targeted ST data. We evaluated methods based on their performance, stability across different reference datasets, and scalability. We found that cell2location and RCTD are the top-performing methods, but surprisingly, a simple regression model outperforms almost half of the dedicated spatial deconvolution methods. Furthermore, we observe that the performance of all methods significantly decreased in datasets with highly abundant or rare cell types. Our results are reproducible in a Nextflow pipeline, which also allows users to generate synthetic data, run deconvolution methods and optionally benchmark them on their dataset (https://github.com/saeyslab/spotless-benchmark).

    1. Genetics and Genomics
    2. Neuroscience
    Donghui Yan, Bowen Hu ... Qiongshi Lu
    Research Article

    Rich data from large biobanks, coupled with increasingly accessible association statistics from genome-wide association studies (GWAS), provide great opportunities to dissect the complex relationships among human traits and diseases. We introduce BADGERS, a powerful method to perform polygenic score-based biobank-wide association scans. Compared to traditional approaches, BADGERS uses GWAS summary statistics as input and does not require multiple traits to be measured in the same cohort. We applied BADGERS to two independent datasets for late-onset Alzheimer’s disease (AD; n=61,212). Among 1738 traits in the UK biobank, we identified 48 significant associations for AD. Family history, high cholesterol, and numerous traits related to intelligence and education showed strong and independent associations with AD. Furthermore, we identified 41 significant associations for a variety of AD endophenotypes. While family history and high cholesterol were strongly associated with AD subgroups and pathologies, only intelligence and education-related traits predicted pre-clinical cognitive phenotypes. These results provide novel insights into the distinct biological processes underlying various risk factors for AD.