arXiv subjects

Javier Blanco-Portillo

Publications and source records attributed to Javier Blanco-Portillo.

3 recordsLinked to original sources

arXiv2024

Bayesian estimation of the number of significant principal components for cultural data

Principal component analysis (PCA) is often used to analyze multivariate data together with cluster analysis, which depends on the number of principal components used. It is therefore important to determine the number of significant principal components (PCs) extracted from a data set. Here we use a variational Bayesian version of classical PCA, to develop a new method for estimating the number of significant PCs in contexts where the number of samples is of a similar to or greater than the number of features. This eliminates guesswork and potential bias in manually determining the number of principal components and avoids overestimation of variance by filtering noise. This framework can be applied to datasets of different shapes (number of rows and columns), different data types (binary, ordinal, categorical, continuous), and with noisy and missing data. Therefore, it is especially useful for data with arbitrary encodings and similar numbers of rows and columns, such as cultural, ecological, morphological, and behavioral datasets. We tested our method on both synthetic data and empirical datasets and found that it may underestimate but not overestimate the number of principal components for the synthetic data. A small number of components was found for each empirical dataset. These results suggest that it is broadly applicable across the life sciences.

stat.AP↗

arXiv2024

The Human Genomic Landscape of Oceania

Oceania and Island Southeast Asia have a rich, yet understudied, human genomic landscape. This region encompasses some of the first areas inhabited by humans following the out-of-Africa expansion, includes populations with the highest levels of archaic hominin introgression, and contains Pacific islands that are among the most remote continuously inhabited locations in the world. Here, we describe the first region-wide analysis of individuals from population groups spanning Oceania and its broad perimeter. In total we generate and analyze genome-wide data from 92 different populations, 58 separate islands, and 30 countries, covering one third of the planet. Leveraging this diverse dataset, we resolve genetic connections among islands, providing a detailed view of regional population structure and identifying the island groups involved in the settlement of several Polynesian Outliers. Ancestry-specific analyses allow us to deconvolve different layers of history, from tracing groups deriving their Austronesian ancestry via the Lapita expansion to quantifying variable archaic introgression across the basal Papuan component of Oceanians and Southeast Asians. Finally, we map biomedically relevant variants across Oceania and Southeast Asia, observing pronounced allele-frequency differences between population groups. Together, these findings refine models of oceanic settlement and admixture and establish a comprehensive reference that will advance global efforts to ensure broad and equitable representation in human genomics.

q-bio.PE↗

arXiv2022

Ancestry-specific analyses of genome-wide data confirm the settlement sequence of Polynesia

By demonstrating the role that historical population replacements and waves of admixture have played around the world, the genetics work of Reich and colleagues has provided a paradigm for understanding human history [Reich et al. 2009; Reich et al. 2012; Patterson et al. 2012]. Although we show in Ioannidis et al. [2021] that the peopling of Polynesia was a range expansion, and not, as suggested by Huang et al. [2022], yet another example of waves of admixture and large-scale gene flow between populations, we believe that our result in this recently settled oceanic expanse is the exception that proves the rule.

q-bio.PE↗