Dating genomic variants and shared ancestry in population-scale sequencing data

Abstract

The origin and fate of new mutations within species is the fundamental process underlying evolution. However, while much attention has been focused on characterizing the presence, frequency, and phenotypic impact of genetic variation, the evolutionary histories of most variants are largely unexplored. We have developed a nonparametric approach for estimating the date of origin of genetic variants in large-scale sequencing data sets. The accuracy and robustness of the approach is demonstrated through simulation. Using data from two publicly available human genomic diversity resources, we estimated the age of more than 45 million single nucleotide polymorphisms (SNPs) in the human genome and release the Atlas of Variant Age as a public online database. We characterize the relationship between variant age and frequency in different geographical regions, and demonstrate the value of age information in interpreting variants of functional and selective importance. Finally, we use allele age estimates to power a rapid approach for inferring the ancestry shared between individual genomes, to quantify genealogical relationships at different points in the past, as well as describe and explore the evolutionary history of modern human populations.

Genomic basis of parallel adaptation varies with divergence inArabidopsisand its relatives

“…In contrast, the overall low contribution of de-novo originated parallel alleles and generally large and variable outcrossing Arabidopsis populations suggest a minor role of mutation limitation, at least within our genomic Arabidopsis dataset. In general, our study demonstrates the importance of a quantitative understanding of divergence for the assessment of evolutionary predictability 54 and brings support to the emerging view of ubiquitous influence of divergence scale on different evolutionary and ecological mechanisms 34 . .…”

“…Such risk is, however, mitigated at least in our Arabidopsis dataset, as the genomically investigated alpine populations share very similar niches 42 In contrast, no relationship between the probability of gene reuse and divergence was shown in experimental evolution of different populations of yeast 56 raising a question about the generality of our findings. Our study addresses a complex selective agent (a multihazard alpine environment 57 ) in order to provide insights into an ecologically realistic scenario relevant for adaptation in natural environments 14,53,54 .. Results might differ in systems with a high degree of self-fertilization or recent bottlenecks, as these might decrease the probability of gene reuse even among closely related lineages by reducing the pool of shared standing variation 58,59 .…”

An approximate full-likelihood method for inferring selection and allele frequency trajectories from DNA sequence data

“…It may also possible to make use of recent advances in inferring pairwise coalescence times 613 (e.g., [71]) to build an approximation to the full likelihood. Recently, Albers & McVean proposed 614 a composite likelihood method to estimate allele age by "sandwiching" the age using identity-by-615 descent tracts at the site of interest [72]. However, their method does not extend to inferring how 616 the allele frequency changed over time, and does not explicitly model selection.…”

Inferring the ancestry of everyone

“…A degree of error is inevitable in DNA sequence data and methods must be reasonably robust to be relevant to empirical data. We therefore impose a genotyping error process derived from an empirical analysis [Albers and McVean, 2018] on the simulated haplotypes (leading to an observed error of around 0.35%), and assess the performance of tools with and without the presence of these simulated errors.…”

“…Classical results in population genetics provide a theoretical expectation for the age of an allele based on its frequency [Kimura andOta, 1973, Griffiths andTavaré, 1998]. There are several existing methods for estimating allele age, but are either computationally expensive or require detailed knowledge about historical population processes [Ormond et al, 2015, Nakagome et al, 2016, Smith et al, 2018, although a more efficient non-parametric method has recently been introduced [Albers and McVean, 2018].…”