Book Chapters

(trainees are marked with §)

Working Papers

(trainnes are marked with §)

Journal Publications

(trainnes are marked with §)

  1. Chen B§, Craiu RV, Sun L (to appear). Bayesian model averaging for the X-Chromosome inactivation dilemma in genetic association study. Biostatistics

    Earlier version available at arXiv:1704.01207

  2. Goncalves VF, Cappi C, ..., Kennedy JL, Sun L (2018). A comprehensive analysis of nuclear-encoded mitochondrial genes in schizophrenia. Biological Psychiatry 83(9)780-789.

  3. Panjwani N§, Xiao B§ et al. (2018). Improving imputation in disease-relevant regions: lessons from cystic fibrosis. npj Genomic Medicine 3:8;doi:10.1038/s41525-018-0047-6.

  4. Soave D§, Sun L (2017). A generalized Levene's scale test for variance heterogeneity in the presence of sample correlation and group uncertainty. Biometrics 73(3):960-971.
    gJLS implementation

  5. Yoo YJ, Sun L, Poirier J, Paterson AD, Bull SB (2017). Multiple-linear-combination (MLC) regression tests for common variants adapted to linkage disequilibrium structure. Genetic Epidemiology 41(2):108-121.

  6. Strug LJ et al. (2016). Cystic fibrosis gene modifier SLC26A9 modulates airway response to CFTR-directed therapies. Human Molecular Genetics 25(20):4590-4600.

  7. Xu L§, Craiu RV, Sun L, Paterson AD (2016). Parameter expanded algorithms for Bayesian latent variable modelling of genetic pleiotropy data. Journal of Computational and Graphical Statistics 25(2):405-425.

    Earlier version is arXiv:1211.1405 Bayesian latent variable modeling of longitudinal family data for genetic pleiotropy studies.

  8. Derkach A§, Lawless J, Sun L (2015). Score tests for association under response-dependent sampling designs for expensive covariates. Biometrika 102(4):988-994.

  9. Corvol et al. (2015). Genome-wide association meta-analysis identifies five modifier loci of lung disease severity in cystic fibrosis. Nature Communications 6. doi:10.1038/ncomms9382.

  10. Poirier J§, Faye LL§, Dimitromanolakis A§, Paterson AD, Sun L, Bull SB (2015). Resampling to address the winner's curse in genetic association analysis of time to event. Genetic Epidemiology 39(7):518-528.

  11. Soave D§, ..., Strug LJ, Sun L (2015). A joint location-scale test improves power to detect associated SNPs, gene-sets and pathways. The American Journal of Human Genetics 97:125-138.
    JLS implementation

    Selected for the inaugural Trainee Paper Spotlight that highlights outstanding publications and research done by trainees, by the American Society of Human Genetics' Training and Development Committee.

    Recommended by Faculty of 1000 In F1000Prime

    Earlier version is arXiv:1411.3690 A novel joint location-scale testing framework for improved detection of variants with main or interaction effects.

  12. Miller MR§ et al. (2015). Variants in solute carrier SLC26A9 modify prenatal exocrine pancreatic damage in cystic fibrosis. Jounral of Pediatrics 166(5):1152-1157.

  13. Hosseini SM§ et al. (2015). The association of previously reported polymorphisms for microvascular complications in a meta-analysis of diabetic retinopathy. Human Genetics 134(2):247-257.

  14. Soave D§ et al. (2014). Evidence for a causal relationship between early exocrine pancreatic disease and cystic fibrosis-related diabetes: a Mendelian randomization study. Diabetes 63(6):2114-2119.

  15. Goncalves VF§, ..., Sun L, Kennedy JL (2014). A hypothesis driven association study of 28 nuclear-encoded mitochondrial genes with antipsychotic-induced weight gain in schizophrenia. Neuropsychopharmacology 39:1347-1354.

  16. Derkach A§, Lawless J, Sun L (2014). Pooled association tests for rare genetic variants: a review and some new results. Statistical Science 29(2): 302-321.

    Earlier version is arXiv:1205.4079 Assessment of Pooled Association Tests for Rare Genetic Variants within a Unified Framework.

  17. Blue EM, Sun L, Tintle NL, Wijsman EM (2014). Value of Mendelian laws of segregation in families: data quality control, imputation and beyond. Genetic Epidemiology 38(S1):S21-S28.

  18. Xu L§, Craiu RV, Derkach A§, Paterson AD, Sun L (2014). Using a Bayesian latent variable approach to detect pleiotropy in the GAW18 Data. BMC Proceedings 8(S1):S77.

  19. Sun L, Dimitromanolakis A§ (2014). PREST-plus identifies pedigree errors and cryptic relatedness in the GAW18 sample using genome-wide SNP data. BMC Proceedings 8(S1):S23.

  20. Derkach A§, Lawless J, Merico D, Paterson AD, Sun L (2014). Evaluation of gene-based association tests for analyzing rare variants using Genetic Analysis Workshop 18 data. BMC Proceedings 8(S1):S9.

  21. Bickeboller et al. (2014). Genetic Analysis Workshop 18: Methods and strategies for analyzing human sequence and phenotype data in members of extended pedigrees. BMC Proceedings 8(S1):S1.

  22. Li W§, ..., Sun L, Strug LJ (2014) Unraveling the complex genetic model for Cystic Fibrosis: pleiotropic effects of modifier genes on early CF-related morbidities. Human Genetics 133(2):151-161.

  23. Yoo YJ, Sun L, Bull SB (2013). Gene-based multiple regression association testing for combined examination of common and low frequency variants in quantitative trait analysis. Frontiers in Genetics 4:233. doi: 10.3389/fgene.2013.00233.

  24. Blackman S et al. (2013) Genetic modifiers of cystic fibrosis-related diabetes. Diabetes 62(10):3627-35.

  25. Faye LL§, Machiela MJ, Kraft P, Bull SB, Sun L (2013). Re-ranking sequencing variants in the post-GWAS era. PLoS Genetics 98(8):e1003609.

  26. Acar E§, Sun L (2013). A generalized Kruskal-Wallis test incorporating group uncertainty with application to genetic association studies. Biometrics. 69(2):427-435.
    GKW.test implementation

  27. Derkach A§, Lawless J, Sun L (2013). Robust and powerful tests for rare variants using Fisher's method to combine evidence of association from two or more complementary tests. Genetic Epidemiology 37(1):110-121.

  28. Goncalves VF§, ..., Sun L, Kennedy JL (2012). DRD4 VNTR polymorphism and age at onset of severe mental illnesses. Neuroscience Letters 519(1):9-13.

  29. Sun L, Rommens J, ..., Strug LJ (2012). Multiple apical plasma membrane constituents are associated with susceptibility to meconium ileus in individuals with cystic fibrosis. Nature Genetics 44:562-569. Newsroom Newsroom

  30. Mirea L§, Infante-Rivard C, Sun L, Bull SB (2012). Strategies for genetic association analyses combining unrelated case-control individuals and family trios. American Journal of Epidemiology 176(1):70-79.

  31. Wright F et al. (2011). Genome-wide association and linkage identify modifier loci of lung disease severity in cystic fibrosis at 11p13 and 20q13.2. Nature Genetics 43:539-548.

  32. Faye L§, Sun L, Dimitromanolakis A§, Bull SB (2011). A flexible genome-wide bootstrap method that accounts for ranking- and threshold-selection bias in GWAS interpretation and replication study design. Statistics in Medicine 30:1898-1912.

  33. Sun L (2011). On the efficiency of genome-wide scans: a multiple hypothesis testing perspective. U.P.B. Sci. Bull., Series A. , 73(1):19-26.

  34. Sun L, Dimitromanolakis A§, Faye L§, Paterson AD, Waggott D, the DCCT/EDIC Research Group, Bull SB (2011). BRsquared: a practical solution to the winner's curse in genome-wide scans. Human Genetics 129:545-552

  35. Dorfman R§ et al. (2011). Modulatory effect of the SLC9A3 gene on susceptibility to infections and pulmonary function in children with cystic fibrosis. Pediatric Pulmonology 46(4):385-392.

  36. Li W§, Sun L, ..., Strug LJ (2011). Understanding the population structure of North American patients with Cystic Fibrosis. Clinical Genetics 79:136-146.

  37. Xu L§, Craiu RV, Sun L (2011). Bayesian methods to overcome the winner's curse in genetic studies. Annals of Applied Statistics 5(1):201-231.

  38. Mirea L§, Sun L, Stafford JE, Bull SB (2010). Using evidence for population stratification bias in combined individual- and family-level genetic association analyses of quantitative traits. Genetic Epidemiology 34:502-511.

  39. Paterson AD et al. (2010). A genome-wide association study identifies a novel major locus for glycemic control in type 1 diabetes, as measured by both HbA1c and glucose. Diabetes 59:539-549.

  40. Yoo YJ§, Bull SB, Paterson AD, Waggott D§, The DCCT/EDIC Research Group, Sun L (2010). Were genome-wide linkage studies a waste of time? Exploiting candidate regions within genome-wide association studies. Genetic Epidemiology 34:107-118.

  41. Paterson AD et al. (2009). Genome-wide association identifies the ABO blood group as a major locus associated with serum levels of soluble E-Selectin. Arteriosclerosis, Thrombosis, and Vascular Biology 29:1958-1967

  42. Dorfman R§, Li W§, Sun L, ..., Strug LJ (2009). Modifier gene study of Meconium Ileus in Cystic Fibrosis: statistical considerations and gene mapping results. Human Genetics 126:763-778.

  43. Yoo YJ§, Pinnaduwage D, Waggott D, Bull SB, Sun L (2009). Genome-wide association analyses of North American Rheumatoid Arthritis Consortium and Framingham Heart Study data utilizing genome-wide linkage results. BMC Proceedings 3:S103.

  44. Asimit J, Yoo YJ§, Waggott D, Sun L, Bull SB (2009). Region-based analysis in genome-wide association study of Framingham Heart Study blood lipid phenotypes. BMC Proceedings 3:S127.

  45. Craiu RV, Sun L (2008). Choosing the lesser evil: trade-off between false discovery rate and non-discovery rate. Statistica Sinica 18:861-879.

  46. Lee SSF§, Sun L, Kustra R, Bull SB (2008). EM-random forest and new measures of variable importance for multi-Locus quantitative trait linkage analysis. Bioinformatics 24:1603-1610.

  47. Dorfman R§ et al. (2008). Complex two-gene modulation of lung disease severity in children with cystic fibrosis. Journal of Clinical Investigation 118:1040-1049.

  48. Al-Kateb H§ et al. (2008). Multiple SOD1 / SFRS15 variants are associated with the development and progression of diabetic nephropathy: The DCCT/EDIC Genetics study. Diabetes 57:218-228.

  49. Al-Kateb H§ et al. (2007). Multiple variants in Vascular Endothelial Growth Factor (VEGF) are risk factors for time to severe retinopathy in type 1 diabetes: The DCCT/EDIC genetics study. Diabetes 56:2161-2168.

  50. Huang B§, Rangreg J§, Paterson AD, Sun L (2007). The multiplicity problem in linkage analysis of gene expression data - the power of differentiating cis- and trans-acting regulators. BMC Proceedings 1:S142. Supplementary material: Figures 1 and 2.

  51. Greenwood C, Rangreg J§, Sun L (2007). Optimal selection of markers for validation from genome-wide association studies. Genetic Epidemiology 31:396-407.

  52. Wu LY§, Sun L, Bull SB (2006). Locus-specific heritability estimation via the bootstrap in linkage scans for quantitative trait loci. Human Heredity 62:84-96.

  53. Sun L, Craiu RV, Paterson AD and Bull SB (2006). Stratified false discovery control for large-scale hypothesis testing with application to genome-wide association studies. Genetic Epidemiology 30:519-530.

  54. Wu LY§, Lee SSF§, Shi HS, Sun L, Bull SB (2005). Resampling methods to reduce the selection bias in genetic effect estimation in genome-wide scans. Genetic Analysis Workshop 14: Microsatellite and single-nucleotide polymorphism. BMC Genetics 6:S24.

  55. Biernacka J§, Sun L, Bull SB (2005). Tests for the presence of two linked disease susceptibility genes. Genetic Epidemiology 29:389-401.

  56. Sun L, Bull SB (2005). Reduction of selection bias in genomewide genetic studies by resampling. Genetic Epidemiology 28:352-367.

  57. Biernacka J§, Sun L, Bull SB (2005). Simultaneous localization of two linked disease susceptibility genes. Genetic Epidemiology 28:33-47.

  58. Paterson AD, Sun L, Liu XQ§ (2003). Transmission ratio distortion in families from the Framingham Heart Study. Genetic Analysis Workshop 13: Analysis of longitudinal family data for complex diseases and related risk factors. BMC Genetics 4:S48.

  59. Strug L, Sun L, Corey M (2003). The Genetics of Cross-Sectional and Longitudinal BMI. Genetic Analysis Workshop 13: Analysis of longitudinal family data for complex diseases and related risk factors. BMC Genetics 4:S14.

  60. Sun L, Wilder K, McPeek MS (2002). Enhanced pedigree error detection. Human Heredity 54:99-110.

  61. Sun L, Cox NJ, McPeek MS (2002). A statistical method for identification of polymorphisms that explain a linkage result. American Journal of Human Genetics 70:399-411.

  62. Sun L, Abney M, McPeek MS (2001). Detection of misspecified relationships in inbred and outbred pedigrees. Genetic Analysis Workshop 12: Analysis of complex genetic traits: Applications to asthma and simulated data. Genetic Epidemiology 21:S36-41.

  63. McPeek MS, Sun L (2000). Statistical tests for detection of misspecified relationships by use of genome-screen data. American Journal of Human Genetics 66:1076-1094.

  64. Sun L (2001). Two statistical problems in human genetics: I. Detection of pedigree errors; II. Identification of polymorphisms PhD Thesis, Department of Statistics, University of Chicago.