PT  - JOURNAL ARTICLE
AU  - Albert Dominguez Mantes
AU  - Daniel Mas Montserrat
AU  - Carlos D. Bustamante
AU  - Xavier Giró-i-Nieto
AU  - Alexander G. Ioannidis
TI  - Neural ADMIXTURE: rapid population clustering with autoencoders
AID  - 10.1101/2021.06.27.450081
DP  - 2021 Jan 01
TA  - bioRxiv
PG  - 2021.06.27.450081
4099  - http://biorxiv.org/content/early/2021/06/28/2021.06.27.450081.short
4100  - http://biorxiv.org/content/early/2021/06/28/2021.06.27.450081.full
AB  - 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. In addition, this network can include multiple outputs, providing the equivalent results as running the original ADMIXTURE algorithm many times with different numbers of clusters. These models can also be stored, allowing later cluster assignment to be performed with a linear computational time.Competing Interest StatementThe authors have declared no competing interest.