Estimation of the contemporary effective population size from SNP data while accounting for mating structure.

A new method is developed to estimate the contemporary effective population size (Ne ) from linkage disequilibrium (LD) between SNPs without information on their location, which is the usual scenario in non-model species. The general theory of linkage disequilibrium is extended to include the contribution of full-sibs to the measure of LD, leading naturally to the estimation of Ne in monogamous and polygamous mating systems, as well as in multiparous species, and with non-random distributions of full-sib family size due to selection or other causes. Prediction of confidence intervals for Ne estimates was solved using a small artificial neural network trained on a dataset of over 105 simulation results. The method, implemented in a user-friendly and fast software (currentNe), is able to estimate Ne even in problematic scenarios with large population sizes or small sample sizes and provides confidence intervals that are more consistent than resampling methods.

Impact of population structure in the estimation of recent historical effective population size by the software GONE.

BACKGROUND: Effective population size (Ne) is a crucial parameter in conservation genetics and animal breeding. A recent method, implemented by the software GONE, has been shown to be rather accurate in estimating recent historical changes in Ne from a single sample of individuals. However, GONE estimations assume that the population being studied has remained isolated for a period of time, that is, without migration or confluence of other populations. If this occurs, the estimates of Ne can be heavily biased. In this paper, we evaluate the impact of migration and admixture on the estimates of historical Ne provided by GONE through a series of computer simulations considering several scenarios: (a) the mixture of two or more ancestral populations; (b) subpopulations that continuously exchange individuals through migration; (c) populations receiving migrants from a large source; and (d) populations with balanced systems of chromosomal inversions, which also generate genetic structure. RESULTS: Our results indicate that the estimates of historical Ne provided by GONE may be substantially biased when there has been a recent mixture of populations that were previously separated for a long period of time. Similarly, biases may occur when the rate of continued migration between populations is low, or when chromosomal inversions are present at high frequencies. However, some biases due to population structuring can be eliminated by conducting population structure analyses and restricting the estimation to the differentiated groups. In addition, disregarding the genomic regions that are involved in inversions can also remove biases in the estimates of Ne. CONCLUSIONS: Different kinds of deviations from isolation and panmixia of the populations can generate biases in the recent historical estimates of Ne. Therefore, estimation of past demography could benefit from performing population structure analyses beforehand, by mitigating the impact of these biases on historical Ne estimates.

An empirical test of the estimation of historical effective population size using Drosophila melanogaster.

The availability of a large number of high-density markers (SNPs) allows the estimation of historical effective population size (Ne ) from linkage disequilibrium between loci. A recent refinement of methods to estimate historical Ne from the recent past has been shown to be rather accurate with simulation data. The method has also been applied to real data for numerous species. However, the simulation data cannot encompass all the complexities of real genomes, and the performance of any estimation method with real data is always uncertain, as the true demography of the populations is not known. Here, we carried out an experimental design with Drosophila melanogaster to test the method with real data following a known demographic history. We used a population maintained in the laboratory with a constant census size of about 2800 individuals and subjected the population to a drastic decline to a size of 100 individuals. After a few generations, the population was expanded back to the previous size and after a few further generations again expanded to twice the initial size. Estimates of historical Ne were obtained with the software GONE both for autosomal and X chromosomes from samples of 17 individuals sequenced for the whole genome. Estimates of the historical effective size were able to infer the patterns of changes that occurred in the populations showing generally good performance of the method. We discuss the limitations of the method and the application of the software carried out so far.

The estimates of effective population size based on linkage disequilibrium are virtually unaffected by natural selection.

The effective population size (Ne) is a key parameter to quantify the magnitude of genetic drift and inbreeding, with important implications in human evolution. The increasing availability of high-density genetic markers allows the estimation of historical changes in Ne across time using measures of genome diversity or linkage disequilibrium between markers. Directional selection is expected to reduce diversity and Ne, and this reduction is modulated by the heterogeneity of the genome in terms of recombination rate. Here we investigate by computer simulations the consequences of selection (both positive and negative) and recombination rate heterogeneity in the estimation of historical Ne. We also investigate the relationship between diversity parameters and Ne across the different regions of the genome using human marker data. We show that the estimates of historical Ne obtained from linkage disequilibrium between markers (NeLD) are virtually unaffected by selection. In contrast, those estimates obtained by coalescence mutation-recombination-based methods can be strongly affected by it, which could have important consequences for the estimation of human demography. The simulation results are supported by the analysis of human data. The estimates of NeLD obtained for particular genomic regions do not correlate, or they do it very weakly, with recombination rate, nucleotide diversity, proportion of polymorphic sites, background selection statistic, minor allele frequency of SNPs, loss of function and missense variants and gene density. This suggests that NeLD measures mainly reflect demographic changes in population size across generations.

Highly pleiotropic variants of human traits are enriched in genomic regions with strong background selection.

Recent studies have shown the ubiquity of pleiotropy for variants affecting human complex traits. These studies also show that rare variants tend to be less pleiotropic than common ones, suggesting that purifying natural selection acts against highly pleiotropic variants of large effect. Here, we investigate the mean frequency, effect size and recombination rate associated with pleiotropic variants, and focus particularly on whether highly pleiotropic variants are enriched in regions with putative strong background selection. We evaluate variants for 41 human traits using data from the NHGRI-EBI GWAS Catalog, as well as data from other three studies. Our results show that variants involving a higher degree of pleiotropy tend to be more common, have larger mean effect sizes, and contribute more to heritability than variants with a lower degree of pleiotropy. This is consistent with the fact that variants of large effect and frequency are more likely detected by GWAS. Using data from four different studies, we also show that more pleiotropic variants are enriched in genome regions with stronger background selection than less pleiotropic variants, suggesting that highly pleiotropic variants are subjected to strong purifying selection. From the above results, we hypothesized that a number of highly pleiotropic variants of low effect/frequency may pass undetected by GWAS.

Recent Demographic History Inferred by High-Resolution Analysis of Linkage Disequilibrium.

Inferring changes in effective population size (Ne) in the recent past is of special interest for conservation of endangered species and for human history research. Current methods for estimating the very recent historical Ne are unable to detect complex demographic trajectories involving multiple episodes of bottlenecks, drops, and expansions. We develop a theoretical and computational framework to infer the demographic history of a population within the past 100 generations from the observed spectrum of linkage disequilibrium (LD) of pairs of loci over a wide range of recombination rates in a sample of contemporary individuals. The cumulative contributions of all of the previous generations to the observed LD are included in our model, and a genetic algorithm is used to search for the sequence of historical Ne values that best explains the observed LD spectrum. The method can be applied from large samples to samples of fewer than ten individuals using a variety of genotyping and DNA sequencing data: haploid, diploid with phased or unphased genotypes and pseudohaploid data from low-coverage sequencing. The method was tested by computer simulation for sensitivity to genotyping errors, temporal heterogeneity of samples, population admixture, and structural division into subpopulations, showing high tolerance to deviations from the assumptions of the model. Computer simulations also show that the proposed method outperforms other leading approaches when the inference concerns recent timeframes. Analysis of data from a variety of human and animal populations gave results in agreement with previous estimations by other methods or with records of historical events.