A method for low-coverage single-gamete sequence analysis demonstrates adherence to Mendel's first law across a large sample of human sperm
- Johns Hopkins University, United States
- 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.
Article and author information
Author details
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.
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