Biology subjects

Ioannidis, A. G.

Publications and source records attributed to Ioannidis, A. G..

21 records · Page 2Linked to original sources

bioRxiv2021

Admixture dynamics in colonial Mexico and the genetic legacy of the Manila Galleon

Mexico has considerable population substructure due to pre-Columbian diversity and subsequent variation in admixture levels from trans-oceanic migrations, primarily from Europe and Africa, but also, to a lesser extent, from Asia. Detailed analyses exploring sub-continental structure remain limited and post-Columbian demographic dynamics within Mexico have not been inferred with genomic data. We analyze the distribution of ancestry tracts to infer the timing and number of pulses of admixture in ten regions across Mexico, observing older admixture timings in the first colonial cities and more recent timings moving outward into southern and southeastern Mexico. We characterize the specific origin of the heterogeneous Native American ancestry in Mexico: a widespread western-central Native Mesoamerican component in northern Aridoamerican states and a central-eastern Nahua contribution in Guerrero (southern Mexico) and Veracruz to its north. Yucatan shows lowland Mayan ancestry, while Sonora exhibits a unique northwestern native Mexican ancestry matching no sampled reference, each consistent with localized indigenous cultures. Finally, in Acapulco, Guerrero a notable proportion of East Asian ancestry was observed, an understudied heritage in Mexico. We identified the source of this ancestry within Southeast Asia--specifically western Indonesian and non-Negrito Filipino--and dated its arrival to approximately thirteen generations ago (1620 CE). This points to a genetic legacy from the 17th century Manila Galleon trade between the colonial Spanish Philippines and the Pacific port of Acapulco in Spanish Mexico. Although this piece of the colonial Spanish trade route from China to Europe appears in historical records, it has been largely ignored as a source of genetic ancestry in Mexico, neglected due to slavery, assimilation as "Indios" and incomplete historical records.

genetics↗

bioRxiv2021

High Resolution Ancestry Deconvolution for Next Generation Genomic Data

As genome-wide association studies and genetic risk prediction models are extended to globally diverse and admixed cohorts, ancestry deconvolution has become an increasingly important tool. Also known as local ancestry inference (LAI), this technique identifies the ancestry of each region of an individuals genome, thus permitting downstream analyses to account for genetic effects that vary between ancestries. Since existing LAI methods were developed before the rise of massive, whole genome biobanks, they are computationally burdened by these large next generation datasets. Current LAI algorithms also fail to harness the potential of whole genome sequences, falling well short of the accuracy that such high variant densities can enable. Here we introduce Gnomix, a set of algorithms that address each of these points, achieving higher accuracy and swifter computational performance than any existing LAI method, while also enabling portable models that are particularly useful when training data are not shareable due to privacy or other restrictions. We demonstrate Gnomix (and its swift phase correction counterpart Gnofix) on worldwide whole-genome data from both humans and canids and utilize its high resolution accuracy to identify the location of ancient New World haplotypes in the Xoloitzcuintle, dating back over 100 generations. Code is available at https://github.com/AI-sandbox/gnomix.

genomics↗

bioRxiv2021

Neural ADMIXTURE: rapid population clustering with autoencoders

Characterizing the genetic substructure of large cohorts has become increasingly important as genetic association and prediction studies are extended to massive, increasingly diverse, biobanks. ADMIXTURE and STRUCTURE are widely used unsupervised clustering algorithms for characterizing such ancestral genetic structure. These methods decompose individual genomes into fractional cluster assignments with each cluster representing a vector of DNA marker frequencies. The assignments, and clusters, provide an interpretable representation for geneticists to describe population substructure at the sample level. However, with the rapidly increasing size of population biobanks and the growing numbers of variants genotyped (or sequenced) per sample, such traditional methods become computationally intractable. Furthermore, multiple runs with different hyperparameters are required to properly depict the population clustering using these traditional methods, increasing the computational burden. This can lead to days of compute. In this work we present Neural ADMIXTURE, a neural network autoencoder that follows the same modeling assumptions as ADMIXTURE, providing similar (or better) clustering, while reducing the compute time by orders of magnitude. Indeed, the equivalent of one month of continuous compute can be reduced to hours. In addition, Neural ADMIXTURE can include multiple outputs, providing the equivalent results as running the original ADMIXTURE algorithm many times with different numbers of clusters. Our models can also be stored, allowing later cluster assignment to be performed with a linear computational time. The software implementation of Neural ADMIXTURE can be found at https://github.com/ai-sandbox/neural-admixture.

genomics↗