Estimating effective population size using RADseq: Effects of SNP selection and sample size. Issue 4 (11th February 2020)

• Record Type: Journal Article
• Authors: Marandel, Florianne; Charrier, Grégory; Lamy, Jean‐Baptiste; Le Cam, Sabrina; Lorance, Pascal; Trenkel, Verena M.
• Is Part Of: Ecology and evolution. Volume 10:Issue 4(2020)
• Page Start: 1929
• Page End: 1937
• Publication Date: 2020-02-11
• Subjects: effective population size -- linkage disequilibrium -- NeEstimator -- RADseq -- skates and rays; Ecology -- Periodicals; Evolution -- Periodicals; 577.05
• DOI: 10.1002/ece3.6016
• Languages: English
• ISSNs: 2045-7758

Abstract

Abstract: Effective population size ( Ne ) is a key parameter of population genetics. However, N e remains challenging to estimate for natural populations as several factors are likely to bias estimates. These factors include sampling design, sequencing method, and data filtering. One issue inherent to the restriction site‐associated DNA sequencing (RADseq) protocol is missing data and SNP selection criteria (e.g., minimum minor allele frequency, number of SNPs). To evaluate the potential impact of SNP selection criteria on Ne estimates (Linkage Disequilibrium method) we used RADseq data for a nonmodel species, the thornback ray. In this data set, the inbreeding coefficient F IS was positively correlated with the amount of missing data, implying data were missing nonrandomly. The precision of Ne estimates decreased with the number of SNPs. Mean Ne estimates (averaged across 50 random data sets with2000 SNPs) ranged between 237 and 1784. Increasing the percentage of missing data from 25% to 50% increased Ne estimates between 82% and 120%, while increasing the minor allele frequency (MAF) threshold from 0.01 to 0.1 decreased estimates between 71% and 75%. Considering these effects is important when interpreting RADseq data‐derived estimates of effective population size in empirical studies. Abstract : SNP‐based estimates of effective population size were found to be sensitive to SNP filtering threshold values. Increasing the percentage of missing data from 25% to 50% increased Ne estimates between 82% and 120%, while increasing the minor allele frequency (MAF) threshold from 0.01 to 0.1 decreased estimates by around 70%. Sample size had a small effect beyond a minimum number.