European population genetic substructure was examined in a diverse set of >1,000 individuals of European descent, each genotyped with >300 K SNPs. Both STRUCTURE and principal component analyses (PCA) showed the largest division/principal component (PC) differentiated northern from southern European ancestry. A second PC further separated Italian, Spanish, and Greek individuals from those of Ashkenazi Jewish ancestry as well as distinguishing among northern European populations. In separate analyses of northern European participants other substructure relationships were discerned showing a west to east gradient. Application of this substructure information was critical in examining a real dataset in whole genome association (WGA) analyses for rheumatoid arthritis in European Americans to reduce false positive signals. In addition, two sets of European substructure ancestry informative markers (ESAIMs) were identified that provide substantial substructure information. The results provide further insight into European population genetic substructure and show that this information can be used for improving error rates in association testing of candidate genes and in replication studies of WGA scans.
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http://dx.doi.org/10.1371/journal.pgen.0040004 | DOI Listing |
Am J Hum Genet
January 2025
Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA; Human Medical Genetics and Genomics Program, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA; Mathematical and Statistical Sciences, University of Colorado Denver, Denver, CO 80204, USA; Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA. Electronic address:
Genetic summary data are broadly accessible and highly useful, including for risk prediction, causal inference, fine mapping, and incorporation of external controls. However, collapsing individual-level data into summary data, such as allele frequencies, masks intra- and inter-sample heterogeneity, leading to confounding, reduced power, and bias. Ultimately, unaccounted-for substructure limits summary data usability, especially for understudied or admixed populations.
View Article and Find Full Text PDFSci Adv
January 2025
Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
The pathophysiology of neurodevelopmental disorders involves vulnerable neural populations, including striatal circuitry, and convergent molecular nodes, including chromatin regulation and synapse function. Despite this, how epigenetic regulation regulates striatal development is understudied. Recurrent de novo mutations in are associated with intellectual disability and autism.
View Article and Find Full Text PDFJ Cheminform
January 2025
Oxford Protein Informatics Group, Department of Statistics, University of Oxford, Oxford, UK.
Current strategies centred on either merging or linking initial hits from fragment-based drug design (FBDD) crystallographic screens generally do not fully leaverage 3D structural information. We show that an algorithmic approach (Fragmenstein) that 'stitches' the ligand atoms from this structural information together can provide more accurate and reliable predictions for protein-ligand complex conformation than general methods such as pharmacophore-constrained docking. This approach works under the assumption of conserved binding: when a larger molecule is designed containing the initial fragment hit, the common substructure between the two will adopt the same binding mode.
View Article and Find Full Text PDFiScience
January 2025
Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan.
Drugs that interact with multiple therapeutic targets are potential high-value products in polypharmacology-based drug discovery, but the rational design remains a formidable challenge. Here, we present artificial intelligence (AI)-based methods to design the chemical structures of compounds that interact with multiple therapeutic target proteins. The molecular structure generation is performed by a fragment-based approach using a genetic algorithm with chemical substructures and a deep learning approach using reinforcement learning with stochastic policy gradients in the framework of generative adversarial networks.
View Article and Find Full Text PDFInt J Legal Med
January 2025
Institute of Forensic and Anthropological Science, Seoul National University Medical Research Center, 103 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea.
Inferring the ancestral origin of DNA evidence recovered from crime scenes is crucial in forensic investigations, especially in the absence of a direct suspect match. Ancestry informative markers (AIMs) have been widely researched and commercially developed into panels targeting multiple continental regions. However, existing forensic ancestry inference panels typically group East Asian individuals into a homogenous category without further differentiation.
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