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- W4386135325 abstract "With the availability of high quality full genome polymorphism (SNPs) data, it becomes feasible to study the past demographic and selective history of populations in exquisite detail. However, such inferences still suffer from a lack of statistical resolution for recent, e.g. bottlenecks, events, and/or for populations with small nucleotide diversity. Additional heritable (epi)genetic markers, such as indels, transposable elements, microsatellites or cytosine methylation, may provide further, yet untapped, information on the recent past population history. We extend the Sequential Markovian Coalescent (SMC) framework to jointly use SNPs and other hyper-mutable markers. We are able to 1) improve the accuracy of demographic inference in recent times, 2) uncover past demographic events hidden to SNP-based inference methods, and 3) infer the hyper-mutable marker mutation rates under a finite site model. As a proof of principle, we focus on demo-graphic inference in A. thaliana using DNA methylation diversity data from 10 European natural accessions. We demonstrate that segregat-ing Single Methylated Polymorphisms (SMPs) satisfy the modelling assumptions of the SMC framework, while Differentially Methylated Regions (DMRs) are not suitable as their length exceeds that of the genomic distance between two recombination events. Combining SNPs and SMPs while accounting for site- and region-level epimutation processes, we provide new estimates of the glacial age bottleneck and post glacial population expansion of the European A. thaliana population. Our SMC framework readily accounts for a wide range of heritable genomic markers, thus paving the way for next generation inference of evolutionary history by combining information from several genetic and epigenetic markers." @default.
- W4386135325 created "2023-08-25" @default.
- W4386135325 creator A5083430943 @default.
- W4386135325 date "2023-08-24" @default.
- W4386135325 modified "2023-09-25" @default.
- W4386135325 title "eLife assessment: Improved inference of population histories by integrating genomic and epigenomic data" @default.
- W4386135325 doi "https://doi.org/10.7554/elife.89470.1.sa0" @default.
- W4386135325 hasPublicationYear "2023" @default.
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