Genomics and other -Omics

  1. 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/
  2. 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/
  3. 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/
  4. 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/
  5. 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/
  6. 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/
  7. 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/
  8. 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/
  9. 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/
  10. 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/
  11. 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/
  12. 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/
  13. 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/
  14. 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/
  15. 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/
  16. 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
Tuesday, September 24, 2024 - 10:46