Robust genome-wide ancestry inference for heterogeneous datasets: illustrated using the 1,000 genome project with 3D facial images

Ancestry testing of "Old Tom," a killer whale central to mutualistic interactions with human whalers

Cooperative hunting between humans and killer whales (Orcinus orca) targeting baleen whales was reported in Eden, New South Wales, Australia, for almost a century. By 1928, whaling operations had ceased, and local killer whale sightings became scarce. A killer whale from the group, known as "Old Tom," washed up dead in 1930 and his skeleton was preserved. How these killer whales from Eden relate to other populations globally and whether their genetic descendants persist today remains unknown. We extracted and sequenced DNA from Old Tom using ancient DNA techniques. Genomic sequences were then compared with a global dataset of mitochondrial and nuclear genomes. Old Tom shared a most recent common ancestor with killer whales from Australasia, the North Atlantic, and the North Pacific, having the highest genetic similarity with contemporary New Zealand killer whales. However, much of the variation found in Old Tom's genome was not shared with these widespread populations, suggesting ancestral rather than ongoing gene flow. Our genetic comparisons also failed to find any clear descendants of Tom, raising the possibility of local extinction of this group. We integrated Traditional Custodian knowledge to recapture the events in Eden and recognize that Indigenous Australians initiated the relationship with the killer whales before European colonization and the advent of commercial whaling locally. This study rectifies discrepancies in local records and provides new insight into the origins of the killer whales in Eden and the history of Australasian killer whales.

Hybrid autoencoder with orthogonal latent space for robust population structure inference

Analysis of population structure and genomic ancestry remains an important topic in human genetics and bioinformatics. Commonly used methods require high-quality genotype data to ensure accurate inference. However, in practice, laboratory artifacts and outliers are often present in the data. Moreover, existing methods are typically affected by the presence of related individuals in the dataset. In this work, we propose a novel hybrid method, called SAE-IBS, which combines the strengths of traditional matrix decomposition-based (e.g., principal component analysis) and more recent neural network-based (e.g., autoencoders) solutions. Namely, it yields an orthogonal latent space enhancing dimensionality selection while learning non-linear transformations. The proposed approach achieves higher accuracy than existing methods for projecting poor quality target samples (genotyping errors and missing data) onto a reference ancestry space and generates a robust ancestry space in the presence of relatedness. We introduce a new approach and an accompanying open-source program for robust ancestry inference in the presence of missing data, genotyping errors, and relatedness. The obtained ancestry space allows for non-linear projections and exhibits orthogonality with clearly separable population groups.

Principal Component Analyses (PCA)-based findings in population genetic studies are highly biased and must be reevaluated

Principal Component Analysis (PCA) is a multivariate analysis that reduces the complexity of datasets while preserving data covariance. The outcome can be visualized on colorful scatterplots, ideally with only a minimal loss of information. PCA applications, implemented in well-cited packages like EIGENSOFT and PLINK, are extensively used as the foremost analyses in population genetics and related fields (e.g., animal and plant or medical genetics). PCA outcomes are used to shape study design, identify, and characterize individuals and populations, and draw historical and ethnobiological conclusions on origins, evolution, dispersion, and relatedness. The replicability crisis in science has prompted us to evaluate whether PCA results are reliable, robust, and replicable. We analyzed twelve common test cases using an intuitive color-based model alongside human population data. We demonstrate that PCA results can be artifacts of the data and can be easily manipulated to generate desired outcomes. PCA adjustment also yielded unfavorable outcomes in association studies. PCA results may not be reliable, robust, or replicable as the field assumes. Our findings raise concerns about the validity of results reported in the population genetics literature and related fields that place a disproportionate reliance upon PCA outcomes and the insights derived from them. We conclude that PCA may have a biasing role in genetic investigations and that 32,000-216,000 genetic studies should be reevaluated. An alternative mixed-admixture population genetic model is discussed.

Genetic variants underlying differences in facial morphology in East Asian and European populations

Facial morphology-a conspicuous feature of human appearance-is highly heritable. Previous studies on the genetic basis of facial morphology were performed mainly in European-ancestry cohorts (EUR). Applying a data-driven phenotyping and multivariate genome-wide scanning protocol to a large collection of three-dimensional facial images of individuals with East Asian ancestry (EAS), we identified 244 variants in 166 loci (62 new) associated with typical-range facial variation. A newly proposed polygenic shape analysis indicates that the effects of the variants on facial shape in EAS can be generalized to EUR. Based on this, we further identified 13 variants related to differences between facial shape in EUR and EAS populations. Evolutionary analyses suggest that the difference in nose shape between EUR and EAS populations is caused by a directional selection, due mainly to a local adaptation in Europeans. Our results illustrate the underlying genetic basis for facial differences across populations.