Genomics and other -Omics

  1. Iliou, A., et al. A Multiomic Approach Integrating Genomic and Metabolomic Data Highlights Colorectal Cancer Pathways. Journal of proteome research, 10.1021/acs.jproteome.5c00459.. (2026) https://doi.org/10.1021/acs.jproteome.5c00459
  2. Luo, T. S., et al. MethylModes: Computationally Efficient Detection of Multimodal Distributions in DNA Methylation Data. Bioinformatics (Oxford, England), btag045. (2026) https://doi.org/10.1093/bioinformatics/btag045
  3. Smith, A., Pinto, R., Zagkos, L. et al. Cross-platform metabolomics imputation using importance-weighted autoencoders. npj Syst Biol Appl (2026) https://pubmed.ncbi.nlm.nih.gov/41513683/
  4. Xu, K et al. Assessing Metabolic Ageing via DNA Methylation Surrogate Markers: A Multicohort Study in Britain, Ireland and the USA. Aging cell, e14484. (2025) https://pubmed.ncbi.nlm.nih.gov/39829316/
  5. McIntosh, A.Met al. Trans-ancestry genome-wide study of depression identifies 697 associations implicating cell types and pharmacotherapies. . (2025) https://www.sciencedirect.com/science/article/pii/S0092867424014156
  6. Zhu T., et al. An improved epigenetic counter to track mitotic age in normal and precancerous tissues. Nat Commun. 2024 May 17;15(1):4211.. (2024) https://pubmed.ncbi.nlm.nih.gov/38760334/
  7. Ganna L., Bauermeister, S et al. Dementias Platform UK: Bringing genetics into life. Alzheimer's & dementia : the journal of the Alzheimer's Association, 20(5), 3281 3289.. (2024) https://pubmed.ncbi.nlm.nih.gov/38506636/
  8. Freire-Aradas, A., et al. Inference of tobacco and alcohol consumption habits from DNA methylation analysis of blood. Forensic Sci Int Genet. 2024 Jan 28;70:103022.. (2024) https://pubmed.ncbi.nlm.nih.gov/38309257/
  9. Luo, Q., et al. A meta-analysis of immune-cell fractions at high resolution reveals novel associations with common phenotypes and health outcomes.. Genome medicine, 15(1), 59.. (2023) https://pubmed.ncbi.nlm.nih.gov/37525279/
  10. Stebbing, J., et al. Comparison of phenomics and cfDNA in a large breast screening population: the Breast Screening and Monitoring Study (BSMS). Oncogene 42, 825–832. (2023) https://pubmed.ncbi.nlm.nih.gov/36693953/
  11. Pazoki R, et al. Genetic analysis in European ancestry individuals identifies 517 loci associated with liver enzymes. Nat Commun. 2021 May 10;12(1):2579. (2021) https://pubmed.ncbi.nlm.nih.gov/33972514/
  12. Yang JJ, et al. Circulating trimethylamine N-oxide in association with diet and cardiometabolic biomarkers: an international pooled analysis. Am J Clin Nutr. 2021 May 8;113(5):1145-1156. (2021) https://pubmed.ncbi.nlm.nih.gov/33826706/
  13. Chen MH, et al. Trans-ethnic and Ancestry-Specific Blood-Cell Genetics in 746,667 Individuals from 5 Global Populations. Cell. Sep 3;182(5):1198-1213.e14 (2020). (2020) https://pubmed.ncbi.nlm.nih.gov/32888493/
  14. Vuckovic D, et al. The Polygenic and Monogenic Basis of Blood Traits and Diseases. Cell. 2020 Sep 3;182(5):1214-1231.e11. (2020) https://pubmed.ncbi.nlm.nih.gov/32888494/
  15. Robinson, O., et al. Determinants of accelerated metabolomic and epigenetic aging in a UK cohort. Aging Cell 19, e13149. (2020) https://pubmed.ncbi.nlm.nih.gov/32363781/
  16. Noordam R, et al. Multi-ancestry sleep-by-SNP interaction analysis in 126,926 individuals reveals lipid loci stratified by sleep duration. Nat Commun. Nov 12; 10(1):5121. (2019) https://pubmed.ncbi.nlm.nih.gov/31719535/
  17. Wuttke, M., et al. A catalog of genetic loci associated with kidney function from analyses of a million individuals. Nat Genet 51, 957-972. (2019) https://www.ncbi.nlm.nih.gov/pubmed/31152163/
  18. Yu B, et al. The Consortium of Metabolomics Studies (COMETS): Metabolomics in 47 Prospective Cohort Studies. Am J Epidemiol. Jun 1;188(6):991-1012. (2019) https://pubmed.ncbi.nlm.nih.gov/31155658/
  19. Clark, D. W., et al. Associations of autozygosity with a broad range of human phenotypes. Nat Commun 10, 4957. (2019) https://pubmed.ncbi.nlm.nih.gov/31673082/
  20. Blaise, B. J., et al. Power Analysis and Sample Size Determination in Metabolic Phenotyping. Anal Chem 88, 5179-5188. (2016) https://www.ncbi.nlm.nih.gov/pubmed/27116637/
  21. Chami, N., et al. Exome Genotyping Identifies Pleiotropic Variants Associated with Red Blood Cell Traits. Am J Hum Genet 99, 8-21. (2016) https://www.ncbi.nlm.nih.gov/pubmed/27346685/
  22. Eicher, J. D., et al. Platelet-Related Variants Identified by Exomechip Meta-analysis in 157,293 Individuals. Am J Hum Genet 99, 40-55. (2016) https://www.ncbi.nlm.nih.gov/pubmed/27346686/
  23. Tajuddin, S. M., et al. Large-Scale Exome-wide Association Analysis Identifies Loci for White Blood Cell Traits and Pleiotropy with Immune-Mediated Diseases. Am J Hum Genet 99, 22-39. (2016) https://www.ncbi.nlm.nih.gov/pubmed/27346689/
  24. Lewis, M. R., et al. Development and Application of Ultra-Performance Liquid Chromatography-TOF MS for Precision Large Scale Urinary Metabolic Phenotyping. Anal Chem 88, 9004-9013. (2016) https://www.ncbi.nlm.nih.gov/pubmed/27479709/
Post date
Monday, February 2, 2026 - 00:00