Optimized sample selection for cost-efficient long-read population sequencing.

Ranallo-Benavidez, T Rhyker, Lemmon, Zachary, Soyk, Sebastian, Aganezov, Sergey, Salerno, William J, McCoy, Rajiv C, Lippman, Zachary B, Schatz, Michael C, Sedlazeck, Fritz J (May 2021) Optimized sample selection for cost-efficient long-read population sequencing. Genome Research, 31 (5). pp. 910-918. ISSN 1088-9051

[img] PDF
2021.RanalloBenavidez.LongReadPopulationSequencing.pdf
Restricted to Repository staff only until 2 November 2021.
Available under License Creative Commons Attribution.

Download (2MB)
URL: https://www.ncbi.nlm.nih.gov/pubmed/33811084
DOI: 10.1101/gr.264879.120

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.

Item Type: Paper
Subjects: Investigative techniques and equipment > assays > long-read sequencing
organism description > plant
bioinformatics > genomics and proteomics > genetics & nucleic acid processing > population genetics
CSHL Authors:
Communities: CSHL labs > Lippman lab
CSHL labs > Schatz lab
SWORD Depositor: CSHL Elements
Depositing User: CSHL Elements
Date: May 2021
Date Deposited: 06 May 2021 16:23
Last Modified: 06 May 2021 16:23
URI: https://repository.cshl.edu/id/eprint/40012

Actions (login required)

Administrator's edit/view item Administrator's edit/view item
CSHL HomeAbout CSHLResearchEducationNews & FeaturesCampus & Public EventsCareersGiving