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Biology subjects

Theunert, C.

Publications and source records attributed to Theunert, C..

2 recordsLinked to original sources

Estimation of population divergence times from SNP data and a test for treeness

We present a method for estimating population divergence times from genome sequences when one individual is sampled from each population. Our method is a simplified version of one presented by Rassmussen et al. (2014) for testing for direct ancestry of an archaic genome. Our method does not require distinguishing ancestral from derived alleles or assumptions about demographic history before population divergence. We discuss the relationship of our method to two similar methods, one introduced by Green et al. (2010) and denoted by F(A | B) and the other introduced by Schlebusch et al. (2017) and called the TT method. When our method is applied to individuals from three or more populations, it provides a test of whether the population history is treelike. We illustrate the use of our method by applying it to three high-coverage archaic genomes, two Neanderthals (Vindija and Altai) and a Denisovan.

genomics

Joint estimation of relatedness coefficients and allele frequencies from ancient samples

We develop and test a method to address whether DNA samples sequenced from a group of fossil hominin bone or teeth fragments originate from the same individual or from closely related individuals. Our method assumes low amounts of retrievable DNA, significant levels of sequencing error and contamination from one or more present-day humans. We develop and implement a maximum likelihood method that estimates levels of contamination, sequencing error rates and pairwise relatedness co-efficients in a set of individuals. We assume there is no reference panel for the ancient population to provide allele and haplotype frequencies. Our approach makes use of single nucleotide polymorphisms and does not make assumptions about the underlying demographic model. By artificially mating individual genomes from the 1000 Genomes Project, we determine the numbers of individuals at a given genomic coverage that are required to detect different levels of genetic relatedness with confidence.

evolutionary biology