A method for low-coverage single-gamete sequence analysis demonstrates adherence to Mendel's first law across a large sample of human sperm

  1. Sara A Carioscia
  2. Kathryn J Weaver
  3. Andrew N Bortvin
  4. Hao Pan
  5. Daniel Ariad
  6. Avery Davis Bell
  7. Rajiv C McCoy  Is a corresponding author
  1. Johns Hopkins University, United States
  2. Georgia Institute of Technology, United States

Abstract

Recently published single-cell sequencing data from individual human sperm (n = 41,189; 969-3,377 cells from each of 25 donors) offer an opportunity to investigate questions of inheritance with improved statistical power, but require new methods tailored to these extremely low-coverage data (∼0.01 x per cell). To this end, we developed a method, named rhapsodi, that leverages sparse gamete genotype data to phase the diploid genomes of the donor individuals, impute missing gamete genotypes, and discover meiotic recombination breakpoints, benchmarking its performance across a wide range of study designs. Mendel's Law of Segregation states that the offspring of a diploid, heterozygous parent will inherit either allele with equal probability. While the vast majority of loci adhere to this rule, research in model and non-model organisms has uncovered numerous exceptions whereby 'selfish' alleles are disproportionately transmitted to the next generation. Evidence of such 'transmission distortion' (TD) in humans remains equivocal in part because scans of human pedigrees have been under-powered to detect small effects. After applying rhapsodi to the sperm sequencing data, we therefore scanned the gametes for evidence of TD. Our results exhibited close concordance with binomial expectations under balanced transmission. Together, our work demonstrates that rhapsodi can facilitate novel uses of inferred genotype data and meiotic recombination events, while offering a powerful quantitative framework for testing for TD in other cohorts and study systems.

Data availability

Data analysis scripts specific to our study are available at https://github.com/mccoy-lab/transmission-distortion. Our package rhapsodi is available at: https://github.com/mccoy-lab/rhapsodi.Raw sperm sequencing data from Bell et al. (2020) can be accessed via dbGaP (study accession number phs001887.v1.p1), as described in the original publication. Raw sperm sequencing data from Leung et al. (2021) was accessed upon request from the authors. We filtered the cells in our analysis using metadata published by Bell et al. (2020) at: https://zenodo.org/record/3561081#.YLAdO2ZKhb9. Analogous metadata from Leung et al. (2021) was obtained upon request from the authors.

The following previously published data sets were used

Article and author information

Author details

  1. Sara A Carioscia

    Department of Biology, Johns Hopkins University, Baltimore, United States
    Competing interests
    No competing interests declared.
  2. Kathryn J Weaver

    Department of Biology, Johns Hopkins University, Baltimore, United States
    Competing interests
    No competing interests declared.
  3. Andrew N Bortvin

    Department of Biology, Johns Hopkins University, Baltimore, United States
    Competing interests
    No competing interests declared.
  4. Hao Pan

    Department of Biology, Johns Hopkins University, Baltimore, United States
    Competing interests
    No competing interests declared.
  5. Daniel Ariad

    Department of Biology, Johns Hopkins University, Baltimore, United States
    Competing interests
    No competing interests declared.
  6. Avery Davis Bell

    School of Biological Sciences, Georgia Institute of Technology, Atlanta, United States
    Competing interests
    Avery Davis Bell, is an inventor on a US Patent Application (US20210230667A1, applicant: President and Fellows of Harvard College) relating to the Sperm-seq single-cell sequencing method. Was an occasional consultant for Ohana Biosciences between October 2019 and March 2020..
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1837-302X
  7. Rajiv C McCoy

    Department of Biology, Johns Hopkins University, Baltimore, United States
    For correspondence
    rajiv.mccoy@jhu.edu
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0615-146X

Funding

National Science Foundation (1746891)

  • Sara A Carioscia

National Institutes of Health (R35GM133747)

  • Rajiv C McCoy

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

Copyright

© 2022, Carioscia 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

  • 1,438
    views
  • 206
    downloads
  • 5
    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. Sara A Carioscia
  2. Kathryn J Weaver
  3. Andrew N Bortvin
  4. Hao Pan
  5. Daniel Ariad
  6. Avery Davis Bell
  7. Rajiv C McCoy
(2022)
A method for low-coverage single-gamete sequence analysis demonstrates adherence to Mendel's first law across a large sample of human sperm
eLife 11:e76383.
https://doi.org/10.7554/eLife.76383

Share this article

https://doi.org/10.7554/eLife.76383

Further reading

    1. Genetics and Genomics
    2. Neuroscience
    Thomas P Spargo, Lachlan Gilchrist ... Alfredo Iacoangeli
    Research Article

    Continued methodological advances have enabled numerous statistical approaches for the analysis of summary statistics from genome-wide association studies. Genetic correlation analysis within specific regions enables a new strategy for identifying pleiotropy. Genomic regions with significant ‘local’ genetic correlations can be investigated further using state-of-the-art methodologies for statistical fine-mapping and variant colocalisation. We explored the utility of a genome-wide local genetic correlation analysis approach for identifying genetic overlaps between the candidate neuropsychiatric disorders, Alzheimer’s disease (AD), amyotrophic lateral sclerosis (ALS), frontotemporal dementia, Parkinson’s disease, and schizophrenia. The correlation analysis identified several associations between traits, the majority of which were loci in the human leukocyte antigen region. Colocalisation analysis suggested that disease-implicated variants in these loci often differ between traits and, in one locus, indicated a shared causal variant between ALS and AD. Our study identified candidate loci that might play a role in multiple neuropsychiatric diseases and suggested the role of distinct mechanisms across diseases despite shared loci. The fine-mapping and colocalisation analysis protocol designed for this study has been implemented in a flexible analysis pipeline that produces HTML reports and is available at: https://github.com/ThomasPSpargo/COLOC-reporter.

    1. Chromosomes and Gene Expression
    2. Genetics and Genomics
    Arkadiy K Golov, Alexey A Gavrilov ... Sergey V Razin
    Research Article

    The enhancer-promoter looping model, in which enhancers activate their target genes via physical contact, has long dominated the field of gene regulation. However, the ubiquity of this model has been questioned due to evidence of alternative mechanisms and the lack of its systematic validation, primarily owing to the absence of suitable experimental techniques. In this study, we present a new MNase-based proximity ligation method called MChIP-C, allowing for the measurement of protein-mediated chromatin interactions at single-nucleosome resolution on a genome-wide scale. By applying MChIP-C to study H3K4me3 promoter-centered interactions in K562 cells, we found that it had greatly improved resolution and sensitivity compared to restriction endonuclease-based C-methods. This allowed us to identify EP300 histone acetyltransferase and the SWI/SNF remodeling complex as potential candidates for establishing and/or maintaining enhancer-promoter interactions. Finally, leveraging data from published CRISPRi screens, we found that most functionally verified enhancers do physically interact with their cognate promoters, supporting the enhancer-promoter looping model.