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Neon: An R Package to Estimate Human Effective Population Size and Divergence Time from Patterns of Linkage Disequilibrium between SNPS

Objective: Estimating the effective population size (Ne) is crucial to understanding how populations evolved, expanded or shrunk. One possible approach is to compare DNA diversity, so as to obtain an average Ne over many past generations; however as the population sizes change over time, another possibility is to describe this change. Linkage Disequilibrium (LD) patterns contain information about these changes, and, whenever a large number of densely linked markers are available, can be used to monitor fluctuating population size through time. Here, we present a new R package, NeON that has been designed to explore population’s LD patterns to reconstruct two key parameters of human evolution: the effective population size and the divergence time between populations. 
Methods: NeON starts with binary or pairwise-LD PLINK files, and allows (a) to assign a genetic map position using HapMap (NCBI release 36 or 37) (b) to calculate the effective population size over time exploiting the relationship between Ne and the average squared correlation coefficient of LD (r2LD) within predefined recombination distance categories, and (c) to calculate the confidence interval about Ne based on the observed variation of the estimator across chromosomes; the outputs of the functions are both numerical and graphical. This package also offers the possibility to estimate the divergence time between populations given the Ne values calculated from the within-population LD data and a matrix of between-populations FST. These routines can be adapted to any species whenever genetic map positions are available. 
Results and Conclusion: The functions contained in the R package NeON provide reliable estimates of effective population sizes of human chromosomes from LD patterns of genome-wide SNPs data, as it is shown here for the populations contained in the CEPH panel. The NeON package enables to accommodate variable numbers of individuals, populations and genetic markers, allowing analyzing those using standard personal computers.