Optimized sample selection for cost-efficient long-read population sequencing
- T. Rhyker Ranallo-Benavidez1,
- Zachary Lemmon2,
- Sebastian Soyk3,
- Sergey Aganezov1,
- William J. Salerno4,
- Rajiv C. McCoy1,
- Zachary B. Lippman2,5,
- Michael C. Schatz1,2 and
- Fritz J. Sedlazeck4
- 1Johns Hopkins University, Baltimore, Maryland 21218, USA;
- 2Cold Spring Harbor Laboratory, Cold Spring Harbor, New York 11724, USA;
- 3Center for Integrative Genomics, University of Lausanne, Lausanne 1005, Switzerland;
- 4Human Genome Sequencing Center, Baylor College of Medicine, Houston, Texas 77030, USA;
- 5Howard Hughes Medical Institute, Cold Spring Harbor Laboratory, Cold Spring Harbor, New York 11724, USA
Abstract
An increasingly important scenario in population genetics is when a large cohort has been genotyped using a low-resolution approach (e.g., microarrays, exome capture, short-read WGS), from which a few individuals are resequenced using a more comprehensive approach, especially long-read sequencing. The subset of individuals selected should ensure that the captured genetic diversity is fully representative and includes variants across all subpopulations. For example, human variation has historically focused on individuals with European ancestry, but this represents a small fraction of the overall diversity. Addressing this, SVCollector identifies the optimal subset of individuals for resequencing by analyzing population-level VCF files from low-resolution genotyping studies. It then computes a ranked list of samples that maximizes the total number of variants present within a subset of a given size. To solve this optimization problem, SVCollector implements a fast, greedy heuristic and an exact algorithm using integer linear programming. We apply SVCollector on simulated data, 2504 human genomes from the 1000 Genomes Project, and 3024 genomes from the 3000 Rice Genomes Project and show the rankings it computes are more representative than alternative naive strategies. When selecting an optimal subset of 100 samples in these cohorts, SVCollector identifies individuals from every subpopulation, whereas naive methods yield an unbalanced selection. Finally, we show the number of variants present in cohorts selected using this approach follows a power-law distribution that is naturally related to the population genetic concept of the allele frequency spectrum, allowing us to estimate the diversity present with increasing numbers of samples.
Footnotes
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[Supplemental material is available for this article.]
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Article published online before print. Article, supplemental material, and publication date are at https://www.genome.org/cgi/doi/10.1101/gr.264879.120.
- Received August 6, 2020.
- Accepted March 30, 2021.
This article is distributed exclusively by Cold Spring Harbor Laboratory Press for the first six months after the full-issue publication date (see https://genome.cshlp.org/site/misc/terms.xhtml). After six months, it is available under a Creative Commons License (Attribution-NonCommercial 4.0 International), as described at http://creativecommons.org/licenses/by-nc/4.0/.
