Assessing the effect of sequencing depth and sample size in population genetics inferences.

Matteo Fumagalli,Ludovic Orlando

doi:10.1371/journal.pone.0079667

Matteo Fumagalli, Ludovic Orlando

Open Access

https://doi.org/10.1371/journal.pone.0079667

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Abstract

Next-Generation Sequencing (NGS) technologies have dramatically revolutionised research in many fields of genetics. The ability to sequence many individuals from one or multiple populations at a genomic scale has greatly enhanced population genetics studies and made it a data-driven discipline. Recently, researchers have proposed statistical modelling to address genotyping uncertainty associated with NGS data. However, an ongoing debate is whether it is more beneficial to increase the number of sequenced individuals or the per-sample sequencing depth for estimating genetic variation. Through extensive simulations, I assessed the accuracy of estimating nucleotide diversity, detecting polymorphic sites, and predicting population structure under different experimental scenarios. Results show that the greatest accuracy for estimating population genetics parameters is achieved by employing a large sample size, despite single individuals being sequenced at low depth. Under some circumstances, the minimum sequencing depth for obtaining accurate estimates of allele frequencies and to identify polymorphic sites is , where both alleles are more likely to have been sequenced. On the other hand, inferences of population structure are more accurate at very large sample sizes, even with extremely low sequencing depth. This all points to the conclusion that under various experimental scenarios, in cost-limited population genetics studies, large sample sizes at low sequencing depth are desirable to achieve high accuracy. These findings will help researchers design their experimental set-ups and guide further investigation on the effect of protocol design for genetic research.

Highlights

One primary aim of population genetics studies is understanding the relative role of neutral and selective forces in shaping the overall genetic diversity of populations
Until recently, studies relied on the analysis of sequencing data for short genomic regions or for a limited number of candidate genes, or on the analysis of genotypes from sparse Single Nucleotide Polymorphism (SNP) data
Sequence data was divided into 100 independent windows and the bias in the estimates for the population genetics statistics was computed for each region separately

Summary

Introduction

One primary aim of population genetics studies is understanding the relative role of neutral and selective forces in shaping the overall genetic diversity of populations. Until recently, studies relied on the analysis of sequencing data for short genomic regions or for a limited number of candidate genes, or on the analysis of genotypes from sparse Single Nucleotide Polymorphism (SNP) data. While the former approach produces accurate inferences, it targets a small fraction of the genome, and the latter provides insights at the genome-wide level but can be prone to considerable ascertainment bias, which has been shown to inflate certain results [1]. In the last few years, new high-throughput DNA sequencing technologies have allowed researchers to generate large amounts of genetic data. Individual genotypes are inferred from the allelic state of the reads covering the site of interest (a procedure called ‘‘genotype calling’’), while ‘‘SNP calling’’ refers to the process of identifying which sites are polymorphic in the sample, that is, have more than 1 base type at the site

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