speed‐ne: Software to simulate and estimate genetic effective population size (Ne) from linkage disequilibrium observed in single samples

• Published in: Molecular ecology resources Vol. 18; no. 3; pp. 714 - 728
• Main Authors: Hamilton, Matthew B., Tartakovsky, Maria, Battocletti, Amy
• Format: Journal Article
• Language: English
• Published: England Wiley Subscription Services, Inc 01-05-2018
• Subjects: Comparative studies; Computer simulation; confidence interval; Confidence intervals; effective population size; Estimators; Genotypes; Graphical user interface; jackknife; linkage disequilibrium; Loci; Microsatellites; Missing data; Nucleotides; null alleles; Polymorphism; simulation; jackknife; effective population size; null alleles; confidence interval; simulation; linkage disequilibrium
• ISSN: 1755-098X; 1755-0998
• DOI: 10.1111/1755-0998.12759

The genetic effective population size, Ne, can be estimated from the average gametic disequilibrium (r2^) between pairs of loci, but such estimates require evaluation of assumptions and currently have few methods to estimate confidence intervals. speed‐ne is a suite of matlab computer code functions to estimate Ne^ from r2^ with a graphical user interface and a rich set of outputs that aid in understanding data patterns and comparing multiple estimators. speed‐ne includes functions to either generate or input simulated genotype data to facilitate comparative studies of Ne^ estimators under various population genetic scenarios. speed‐ne was validated with data simulated under both time‐forward and time‐backward coalescent models of genetic drift. Three classes of estimators were compared with simulated data to examine several general questions: what are the impacts of microsatellite null alleles on Ne^, how should missing data be treated, and does disequilibrium contributed by reduced recombination among some loci in a sample impact Ne^. Estimators differed greatly in precision in the scenarios examined, and a widely employed Ne^ estimator exhibited the largest variances among replicate data sets. speed‐ne implements several jackknife approaches to estimate confidence intervals, and simulated data showed that jackknifing over loci and jackknifing over individuals provided ~95% confidence interval coverage for some estimators and should be useful for empirical studies. speed‐ne provides an open‐source extensible tool for estimation of Ne^ from empirical genotype data and to conduct simulations of both microsatellite and single nucleotide polymorphism (SNP) data types to develop expectations and to compare Ne^ estimators.