Molecular Ecology ResourcesBioinformaticsPeer-reviewed journal article
Estimating admixture pedigrees of recent hybrids without a contiguous reference genome
Genís Garcia‐ErilliD, Kristian HanghøjiD, Rasmus HelleriD, Carsten WiufiD, Anders AlbrechtseniD
Abstract
The genome of recently admixed individuals or hybrids have characteristic genetic patterns that can be used to learn about their recent admixture history. One of these are patterns of interancestry heterozygosity, which can be inferred from SNP data from either called genotypes or genotype likelihoods, without the need for information on genomic location. This makes them applicable to a wide range of data that are often used in evolutionary and conservation genomic studies, such as low-depth sequencing mapped to scaffolds and reduced representation sequencing. Here we implement maximum likelihood estimation of interancestry heterozygosity patterns using two complementary models. We furthermore develop apoh (Admixture Pedigrees Of Hybrids), a software that uses estimates of paired ancestry proportions to detect recently admixed individuals or hybrids, and to find the most compatible recent admixture pedigree. It furthermore calculates several hybrid indices that make it easier to identify and rank possible admixture pedigrees that could give rise to the estimated patterns. We implemented apoh both as a command line tool and as a Graphical User Interface that allows the user to automatically and interactively explore, rank and visualize compatible recent admixture pedigrees, and calculate the different summary indices. We validate the performance of the method using admixed family trios from the 1000 Genomes Project. In addition, we show its applicability on identifying recent hybrids from RAD-seq data of Grants gazelle (Nanger granti and Nanger petersii) and whole genome low depth data of waterbuck (Kobus ellipsiprymnus) which shows complex admixture of up to four populations.
- organism: human
- organism: non-human primate
- assay: bulk RNA-seq
- research focus: cell type annotation
- research focus: clustering
- research focus: trajectory inference
- approach: statistical model
- Citations
- 11
- FWCI
- 1.99
- Field percentile
- top 13%
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