Biomedical subjects

Alexander G Ioannidis

Publications and source records attributed to Alexander G Ioannidis.

3 recordsLinked to original sources

PubMed2026

The Landscape of Genomic and Socioeconomic Variables in Patients with Colorectal Cancer Based on Genetic Ancestry.

BACKGROUND: Despite differences in tumor alterations across genetic ancestries, investigations of the colorectal cancer molecular landscape have used self-reported ethnicity instead of genetic ancestry. METHODS: We used tumor and matched normal whole-exome sequencing data from 16,388 patients with stage I to IV colorectal cancer to investigate colorectal cancer's germline and somatic molecular landscape and the potential influence of socioeconomic factors (Distressed Communities Index, DCI) across diverse genetic ancestries. Genetic ancestry determined via supervised local ancestry inference included African (AFR, N = 1,697), Native American (AMR, N = 1,291), East Asian (EAS, N = 2,247), European (EUR, N = 9,726), Levantine Middle Eastern (LME, N = 1,192), and South Asian (SAS, N = 184). RESULTS: Microsatellite instability (MSI) was the most common form of hypermutation (80.8%), higher in the EUR genetic ancestry than in the AFR, AMR, and EAS genetic ancestry. Among germline findings, positive results were most common in high-penetrance genes associated with Lynch syndrome. Enrichment patterns included MLH1 (SAS) and PMS2 (AFR). There were significant differences in the frequency of driver mutations in APC, BRAF, KRAS, TP53, and PIK3CA between the EUR and other ancestry groups in both MSI and microsatellite stable tumors. Mutational signatures suggested enrichment of reactive oxygen species and POLE in AFR, colibactin in EAS, and aflatoxin and NTHL1 in SAS. DCI scores differed by ancestry (higher distress in AFR/AMR than in EUR), but driver mutation frequencies did not vary across DCI quintiles. CONCLUSIONS: Genetic ancestry shapes hereditary risk, tumor biology, and environmental exposures. IMPACT: These findings suggest that incorporating ancestry into screening, trials, and precision oncology may improve equity, though outcome-linked prospective studies and implementation research are warranted.

Aged↗

PubMed2026

ARGformer: learning on ancestral recombination graphs with transformers.

MOTIVATION: Recent advances in inference of the ancestral recombination graph (ARG), which describes how segments of chromosomes trace back through recombination and shared lineages, have made it possible to reconstruct genome-wide genealogies for large cohorts, but it remains difficult to summarize and use this information for population genetic analyses. RESULTS: We present ARGformer, an encoder-only transformer that learns context-dependent embeddings with a self-supervised masked objective finetuned with contrastive learning for downstream retrieval tasks. We train ARGformer on genealogies from coalescent simulations and on genealogies inferred from ancient and present-day Homo sapiens genomes. Using only these learned embeddings, without access to genotype matrices, ARGformer captures patterns of global population structure and supports ancestry inference through clustering and nearest-neighbor retrieval. On genealogies that include archaic hominins, ARGformer can highlight Denisovan-derived segments in Oceanian genomes and reveals Oceanian-like ancestry in South American Indigenous populations. AVAILABILITY AND IMPLEMENTATION: ARGformer is available at https://github.com/AI-sandbox/ARGformer.

Humans↗

PubMed2026

ADAMIXTURE: adaptive first-order optimization for biobank-scale genetic clustering.

MOTIVATION: Estimating genetic clusters from sequencing data is a fundamental task in population and medical genetics, enabling demographic inference and adjustment for population structure in association studies. ADMIXTURE, a widely used model-based clustering method, employs an accelerated Expectation-Maximization (EM) algorithm to infer population parameters; however, its computational demands scale poorly, limiting its usefulness for modern biobank-sized datasets. While recent EM acceleration strategies employing second-order quasi-Newton schemes preserve accuracy, they remain computationally intensive. Conversely, EM-free approaches that prioritize speed often compromise solution quality. RESULTS: We introduce ADAMIXTURE, a novel optimization framework that integrates the EM algorithm with Adaptive Moment Estimation (Adam). Unlike traditional acceleration methods, ADAMIXTURE utilizes first-order gradients with adaptive learning rates derived from raw and squared moments to approximate curvature information, bypassing the computational overhead of Hessian approximations. This approach surpasses the convergence efficiency of second-order methods while maintaining the low computational complexity of first-order updates. Across simulated and large-scale empirical datasets, ADAMIXTURE demonstrates substantial reductions in wall-clock runtime and enhanced scalability compared to state-of-the-art methods, while maintaining comparable or improved inference accuracy. Its GPU implementation runs in under 2 h on half a million samples and variants, a two order of magnitude speedup over current state-of-the-art. AVAILABILITY AND IMPLEMENTATION: Source code is available at: https://github.com/AI-sandbox/ADAMIXTURE.

Clustering Algorithms↗