Lin Chen

Associate Professor
Research Summary
Dr. Chen's overall research interests focus on developing statistical methods for analyzing 'big' integrative genomics data. Her lab has developed statistical methods for ‘omics’ studies that involve multiple types of large-scale high-dimensional data sets, for example, genetic data, gene transcriptional expression data, proteomic data and complex phenotypes. Besides methodology development, her lab has also developed many R packages.
big data, integrative genomics, statistical methods, software development
  • Fred Hutchinson Cancer Research Center, Seattle, Postdoctoral Training Statistical genomics 08/2010
  • University of Washington, Seattle, PhD Biostatistics 01/2008
  • Peking University , Beijing, BS Economics 06/2002
Biosciences Graduate Program Association
Awards & Honors
  • 2017 - 2018 The Departmental Best Dissertation Award (for Dr. Chen's PhD student) Department of Public Health Sciences
  • 2018 - Associate Editor for Biometrics
  • 2020 - 2021 Program Chair-Elect for the Section on Statistics in Genomics and Genetics American Statistical Association
  • 2020 - Reviewer for BMRD study section NIH
  1. Integrated Proteogenomic Characterization of Clear Cell Renal Cell Carcinoma. Cell. 2020 Jan 09; 180(1):207. View in: PubMed

  2. Insights into Impact of DNA Copy Number Alteration and Methylation on the Proteogenomic Landscape of Human Ovarian Cancer via a Multi-omics Integrative Analysis. Mol Cell Proteomics. 2019 08 09; 18(8 suppl 1):S52-S65. View in: PubMed

  3. Using multivariate mixed-effects selection models for analyzing batch-processed proteomics data with non-ignorable missingness. Biostatistics. 2019 10 01; 20(4):648-665. View in: PubMed


  5. Co-occurring expression and methylation QTLs allow detection of common causal variants and shared biological mechanisms. Nat Commun. 2018 02 23; 9(1):804. View in: PubMed

  6. A meta-analysis approach with filtering for identifying gene-level gene-environment interactions. Genet Epidemiol. 2018 07; 42(5):434-446. View in: PubMed

  7. Identifying cis-mediators for trans-eQTLs across many human tissues using genomic mediation analysis. Genome Res. 2017 11; 27(11):1859-1871. View in: PubMed

  8. Risk-based Breast Cancer Screening: Implications of Breast Density. Med Clin North Am. 2017 Jul; 101(4):725-741. View in: PubMed

  9. Imputing Gene Expression in Uncollected Tissues Within and Beyond GTEx. Am J Hum Genet. 2016 Apr 07; 98(4):697-708. View in: PubMed

  10. A unified set-based test with adaptive filtering for gene-environment interaction analyses. Biometrics. 2016 06; 72(2):629-38. View in: PubMed

  11. Mediation analysis demonstrates that trans-eQTLs are often explained by cis-mediation: a genome-wide analysis among 1,800 South Asians. PLoS Genet. 2014 Dec; 10(12):e1004818. View in: PubMed

  12. A penalized EM algorithm incorporating missing data mechanism for Gaussian parameter estimation. Biometrics. 2014 Jun; 70(2):312-22. View in: PubMed

  13. A regularized Hotelling's T2 test for pathway analysis in proteomic studies. J Am Stat Assoc. 2011 Dec; 106(496):1345-1360. View in: PubMed

  14. Marbled inflation from population structure in gene-based association studies with rare variants. Genet Epidemiol. 2013 Apr; 37(3):286-92. View in: PubMed

  15. An exponential combination procedure for set-based association tests in sequencing studies. Am J Hum Genet. 2012 Dec 07; 91(6):977-86. View in: PubMed

  16. Insights into colon cancer etiology via a regularized approach to gene set analysis of GWAS data. Am J Hum Genet. 2010 Jun 11; 86(6):860-71. View in: PubMed

  17. Harnessing naturally randomized transcription to infer regulatory relationships among genes. Genome Biol. 2007; 8(10):R219. View in: PubMed

  18. Relaxed significance criteria for linkage analysis. Genetics. 2006 Aug; 173(4):2371-81. View in: PubMed

  19. CCmed: Cross-condition mediation analysis for identifying robust trans-eQTLs and assessing their effects on human traits. ::::

  20. Primo: integration of multiple GWAS and omics QTL summary statistics for elucidation of molecular mechanisms of trait-associated SNPs and detection of pleiotropy in complex traits. ::::