A method for genome-wide genealogy estimation for thousands of samples

By Leo Speidel, Marie Forest, Sinan Shi, S. Myers. Nature Genetics, 2019

488 citations recorded by the source corpus.

Knowledge of genome-wide genealogies for thousands of individuals would simplify most evolutionary analyses for humans and other species, but has remained computationally infeasible. We have developed a method, Relate, scaling to >10,000 sequences while simultaneously estimating branch lengths, mutational ages and variable historical population sizes, as well as allowing for data errors. Application to 1,000 Genomes Project haplotypes produces joint genealogical histories for 26 human populations. Highly diverged lineages are present in all groups, but most frequent in Africa. Outside Africa, these mainly reflect ancient introgression from groups related to Neanderthals and Denisovans, while African signals instead reflect unknown events unique to that continent. Our approach allows more powerful inferences of natural selection than has previously been possible. We identify multiple regions under strong positive selection, and multi-allelic traits including hair color, body mass index and blood pressure, showing strong evidence of directional selection, varying among human groups. Relate is a new method for evolutionary analysis of large genetic datasets that can estimate branch lengths, mutational ages and variable historical population sizes.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/f250b479f7216b42e392b9f8997005af1f9afaaf

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Citations in the corpus

Efficient ancestry and mutation simulation with msprime 1.0 (2021, methodology): “Thanks to this interoperability, msprime ’s mutation generator can work with any tskit tree sequence, be it simulated using SLiM (Haller and Messer 2019) or fwdpy11 (Thornton 2014), or estimated from real data (Kelleher et al. 2019; Speidel et al. 2019; Wohns et al. 2021).”