Cataloging telomeric variation across diverse human populations, and its utility in predicting cancer using supervised machine learning

Dr. Arun Sethuraman is a theoretical and applied population geneticist who develops new statistical methods, software, and pipelines for estimating evolutionary history from large population genomic data. His lab at San Diego State University is specifically interested in the genomics of structured populations, with ongoing methodological developments to estimate (1) population structure in the presence of missing genomic data, (2) signatures of linked natural selection versus adaptive introgression, (3) archaic introgression, and (4) relatedness in admixed/inbred populations. Methods we have recently developed include PPP, SpecKs, DemographiKs, InRelate, IMa2p, CoalMiner, and methylMapR. His lab is currently also working on several applied genomics projects to study (a) the evolutionary history of domestication in hops (Humulus lupulus L.), (b) the genomics of invasiveness in introduced beneficial insects (e.g. Coccinellid beetles) and agricultural pests (e.g. pink stem borer moths), (c) the evolution of modern human genomes in the face of "ghost" hybridization, and (d) estimating evolutionary demographic history along the polyploidy continuum. His lab has been supported by the NSF (CAREER-2021, ABI-2016, REU-2018), USDA-NIFA (REEU-2017, HSI-2022), US-DOE (2025), and NIH (R15-2022).

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