Given genomic variation data from multiple individuals, computing the likelihood of complex population genetic models is often infeasible. To circumvent this problem, we introduce a novel likelihood-free inference framework by applying deep learning,...
Detecting the targets of adaptive natural selection from whole genome sequencing data is a central problem for population genetics. However, to date most methods have shown sub-optimal performance under realistic demographic scenarios. Moreover, over...
Using a powerful machine learning approach, a recent study of human genomes has revealed widespread footprints of recent positive selection on standing genetic variation.
As population genomic datasets grow in size, researchers are faced with the daunting task of making sense of a flood of information. To keep pace with this explosion of data, computational methodologies for population genetic inference are rapidly be...
Hybridization and gene flow between species appears to be common. Even though it is clear that hybridization is widespread across all surveyed taxonomic groups, the magnitude and consequences of introgression are still largely unknown. Thus it is cru...
Forensic science, medicine, and pathology
30649693
Single nucleotide polymorphism (SNP) profiling is an effective means of individual identification and ancestry inferences in forensic genetics. This study established a SNP panel for the simultaneous individual identification and ancestry assignment ...
Population-scale genomic data sets have given researchers incredible amounts of information from which to infer evolutionary histories. Concomitant with this flood of data, theoretical and methodological advances have sought to extract information fr...
Identifying genomic locations of natural selection from sequence data is an ongoing challenge in population genetics. Current methods utilizing information combined from several summary statistics typically assume no correlation of summary statistics...
A central challenge in human genomics is to understand the cellular, evolutionary, and clinical significance of genetic variants. Here, we introduce a unified population-genetic and machine-learning model, called inear llele-pecific election nferenc ...
IEEE/ACM transactions on computational biology and bioinformatics
31150342
Single nucleotide polymorphisms (SNPs) are one type of genetic variations and each SNP represents a difference in a single DNA building block, namely a nucleotide. Previous research demonstrated that SNPs can be used to identify the correct source po...