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High-density single nucleotide polymorphism (SNP) genotyping data at varying depths were obtained through whole genome sequencing (WGS). The accuracy of genotyping was evaluated, and methods for forensic SNP genealogy inference using WGS data were explored. The impact of sequencing depth on the accuracy of forensic genealogy inference was also assessed. Samples were sequenced at autosomal depths of 30 × , 14 × , 8 × , and 4 × using the MGISEQ-200RS platform, extracting 645,199 autosomal SNP loci referring the SNP chip panel. After quality control, the Identity by Descent (IBD) algorithm was used to calculate kinship and analyze the biogeographic origin of the samples. The consistency rate of SNP genotyping between sequencing data and SNP chip data exceeded 96.00 %. The IBD algorithm accurately predicted kinship from 1st to 7th degree using autosomal depths of 30 × , 14 × , and 8 × , with one false negative at the 7th degree in 8 × data. The accuracy of SNP genealogy inference from 30 × , 14 × , and 8 × WGS data was not significantly different from that obtained from the SNP chip (p-values: 0.93, 0.83, and 0.54). For 4 × depth data, improvements in quality control and algorithm optimization are needed to enhance genealogy inference accuracy. Additionally, SNP-based biogeographic inference from WGS data were consistent with survey results.
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http://dx.doi.org/10.1016/j.fsigen.2025.103296 | DOI Listing |
Syst Biol
September 2025
Department of Ecology, Evolution, and Environmental Biology, Columbia University, New York, NY 10027, USA.
Genomes are composed of a mosaic of segments inherited from different ancestors, each separated by past recombination events. Consequently, genealogical relationships among multiple genomes vary spatially across different genomic regions. Genealogical variation among unlinked (uncorrelated) genomic regions is well described for either a single population (coalescent) or multiple structured populations (multispecies coalescent).
View Article and Find Full Text PDFNat Genet
September 2025
Department of Statistics, University of California, Berkeley, CA, USA.
The Ancestral Recombination Graph (ARG), which describes the genealogical history of a sample of genomes, is a vital tool in population genomics and biomedical research. Recent advancements have substantially increased ARG reconstruction scalability, but they rely on approximations that can reduce accuracy, especially under model misspecification. Moreover, they reconstruct only a single ARG topology and cannot quantify the considerable uncertainty associated with ARG inferences.
View Article and Find Full Text PDFGenetics
September 2025
Institute of Ecology and Evolution, School of Biological Sciences, The University of Edinburgh, Edinburgh, EH9 3FL, United Kingdom.
Recent advances in methods to infer and analyse ancestral recombination graphs (ARGs) are providing powerful new insights in evolutionary biology and beyond. Existing inference approaches tend to be designed for use with fully-phased datasets, and some rely on model assumptions about demography and recombination rate. Here I describe a simple model-free approach for genealogical inference along the genome from unphased genotype data called Sequential Tree Inference by Collecting Compatible Sites (sticcs).
View Article and Find Full Text PDFGenetics
September 2025
Department of Statistics, University of Oxford, Oxford OX1 3LB, UK.
Phantom epistasis arises when, in the course of testing for gene-by-gene interactions, the omission of a causal variant with a purely additive effect on the phenotype causes the spurious inference of a significant interaction between two SNPs. This is more likely to arise when the two SNPs are in relatively close proximity, so while true epistasis between nearby variants could be commonplace, in practice there is no reliable way of telling apart true epistatic signals from false positives. By considering the causes of phantom epistasis from a genealogy-based perspective, we leverage the rich information contained within reconstructed genealogies (in the form of ancestral recombination graphs) to address this problem.
View Article and Find Full Text PDFForensic Sci Int
August 2025
Section of Forensic Genetics, Department of Forensic Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Denmark.
Historically, forensic genetics research has focused on increasing sensitivity of DNA analyses, improving mixture profile deconvolution, and advancing forensic DNA intelligence methodologies. The aim of this study was to quantify the relevance of these areas using empirical data from forensic genetic casework in Danish criminal cases. We present a retrospective analysis, primarily covering the years 2016-2022, with additional data from 2023 to 2024.
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