95. Kharaghani A*, Tio E, Milic M, Bennett DA, De Jager PL, Schneider JA, Sun L, Felsky D (Accepted). Association of whole-person polygenic component scores with Alzheimer's disease pathology. Human Molecular Genetics.
94. Lin BX*, Paterson AD, Sun L (Accepted). Better together against genetic heterogeneity: a sex-combined joint main and interaction analysis of 290 quantitative traits in the UK Biobank. PLoS Genetics.
93. Wang Z*, Paterson AD, Sun L (2024). A population-aware retrospective regression to detect genome-wide variants with sex difference in allele frequency. Annals of Applied Statistics18(2):1113-1136.
92. Garg E*, Arguello-Pascualli P, Vishnyakova O, Halevy AR, Yoo S, Brooks JD, Bull SB, Gagnon F, Greenwood CMT, Huang RJ, Lawless JF, Lerner-Ellis J, Dennis JK, Abraham RJS, Garant JM, Thiruvahindrapuram B, Jones SJM, HostSeq Implementation Committee, Strug LJ, Paterson AD, Sun L, Elliott LT (2024). Canadian COVID-19 host genetics cohort replicates known severity associations. PLoS Genetics 20(3):e1011192.
91. Sugolov A*, Emmenegger E*, Paterson AD, Sun L (2024) Statistical learning of large-scale genetic data: How to run a genome-wide association study of gene-expression data using the 1000 Genomes Project data. Statistics in Biosciences16:250-264. (Open Source Data and Analysis Guide)
90. Zhao Y*, Sun L (2024). A stable and adaptive polygenic signal detection method based on repeated sampling. Canadian Journal of Statistics52(1):79-97.
89. Chen DZ*, Roshandel D, Wang Z*, Sun L, Paterson AD (2024). Comprehensive whole-genome analyses of the UK Biobank reveal significant sex differences in both genotype missingness and allele frequency on the X chromosome. Human Molecular Genetics33(6):543-551.
88. The COVID-19 Host Genetics Initiative (2023). A second update on mapping the human genetic architecture of COVID-19. Nature 621(7977):E7-E26.
87. Sun L, Wang Z*, Lu T*, Manolio TA, Paterson AD (2023). eXclusionarY: Ten years later, where are the sex chromosomes in GWAS? American Journal of Human Genetics110(6):903-912. (Inside AJHG by the American Society of Human Genetics)
86. Yoo S, Garget E*, et al. (2023). HostSeq: a Canadian whole genome sequencing and clinical data resource. BMC Genomic Data 24(1):26.
85. Zhang L*, Strug L, Sun L (2023). Leveraging Hardy-Weinberg disequilibrium for association testing in case-control studies. Annals of Applied Statistics 17(2):1764-1781.
84. Zhang Z*, Sun L (2023). The hidden factor: accounting for covariate effects in power and sample size computation for a binary trait. Bioinformatics 39(4):btad139.
83. Mastromatteo S* et al. (2023). High-quality read-based phasing of cystic fibrosis cohort informs genetic understanding of disease modification. Human Genetics and Genomics Advances 4(1):100156.
82. Zhang L*, Sun L (2022). Unifying genetic association tests via regression: Prospective and retrospective, parametric and non-parametric, and genotype- and allele-based tests. Canadian Journal of Statistics 50(4):1321- 1339.
81. Gao J*, Espin-Garcia O, Paterson AD, Sun L (2022). Integrating variant functional annotations scores have varied abilities to improve power of genome-wide association studies. Scientific Reports 12(1):10720.
80. Zhang L*, Sun L (2022). A generalized robust allele-based genetic association test. Biometrics 78(2):487-498.
79. Wang Z*, Sun L, Paterson AD (2022). Major sex differences in allele frequencies for X chromosome variants in both the 1000 Genomes Project and and gnomAD. PLoS Genetics 18(5): e1010231.
78. Zhang L*, Sun L (2022). Linear mixed-effect models through the lens of Hardy-Weinberg disequilibrium. Frontier in Genetics - Statistical Genetics and Methodology 13:1-8.
77. Gong J* et al. (2022). Genetic evidence supports the development of SLC26A9 targeting therapies for the treatment of lung disease. npj Genomic Medicine 7(1):1-15.
76. Deng WQ*, Sun L (2022). gJLS2: A generalized joint location and scale analysis tool for X-inclusive genome-wide discoveries. G3: Genes, Genomes, Genetics 12(4):1-6. gJLS2 implementation.
75. Wang F*, Naim P, Cheng W, Sun L, Strug L (2022). A flexible summary statistics-based colocalization method with application to the mucin Cystic Fibrosis lung disease modifier locus. The American Journal of Human Genetics 109(2):253-269.
74. Chen B*, Craiu RV, Strug LJ, Sun L (2021). The X factor: a robust and powerful approach to X-chromosome-inclusive whole-genome association studies. Genetic Epidemiology 45(7):694-709.
73. Zhao Y*, Sun L (2021). On set-based association tests: insights from a regression using summary statistics. Canadian Journal of Statistics 49(3):754-770.
72. Lin Y* et al. (2021). Cystic fibrosis-related diabetes onset can be predicted using biomarkers measured at birth. Genetics in Medicine 23(5):927-933.
71. Lin Y*, Brooks J, Bull SB, Gagnon F, Greenwood CMT, Hung RJ, Lawless JF, Paterson A, Sun L, Strug LJ (2020). Statistical power in COVID-19 case-control host genomic study design. Genome Medicine12(1):115.
70. Panjwani N*, Wang Fan*, Rommens J, Sun L, Strug L (2020). LocusFocus: Web-based colocalization for the annotation and functional follow-up of GWAS. PLoS Computational Biology 6(10):e1008366. LocusFocus implementation.
69. Chen B*, Craiu RV, Sun L (2020). Bayesian model averaging for the X-chromosome inactivation dilemma in genetic association study. Biostatistics 21(2) 319-335.
68. Deng WQ*, Mao S, Kalnapenkis A, Esko T, Magi R, Pare G, Sun L (2019). Analytical strategies to include the X-chromosome in variance heterogeneity analyses: evidence for trait-specific polygenic variance structure. Genetic Epidemiology 43(7):815-830.
67. Cutter AD, Garrett R*, Mark S, Wang W, Sun L (2019). Molecular evolution across developmental time reveals rapid divergence in early embryogenesis. Evolution Letters 3(4):359-373.
66. Dimitromanolakis A*, Paterson A, Sun L (2019). Fast and accurate shared segment detection and relatedness estimation in un-phased genetic data using TRUFFLE. The American Journal of Human Genetics 105(1):78-88.
65. Kachuri L* et al. (2019). Investigation of leukocyte telomere length and genetic variants in chromosome 5p15.33 as prognostic markers in lung cancer. Cancer Epidemiology, Biomarkers & Prevention 28(7):1228-1237.
64. Gong J*, Wang F*, Xiao B*, ..., Rommens J, Sun L, Strug L (2019). Genetic Association and transcriptome integration identify contributing genes and tissues at cystic fibrosis modifier loci. PLoS Genetics 15(2):e1008007.
63. Zhang T*, Sun L (2019). Beyond the traditional simulation design for evaluating type 1 error control: from the 'theoretical' null to 'empirical' null. Genetic Epidemiology 43:166-179.
62. 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.
61. Panjwani N* et al. (2018). Improving imputation in disease-relevant regions: lessons from cystic fibrosis. npj Genomic Medicine 3:8.
60. 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.
59. 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.
58. Strug LJ et al. (2016). Cystic fibrosis gene modifier SLC26A9 modulates airway response to CFTR-directed therapies. Human Molecular Genetics 25(20):4590-4600.
57. 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.
56. Derkach A*, Lawless J, Sun L (2015). Score tests for association under response-dependent sampling designs for expensive covariates. Biometrika 102(4):988-994.
55. 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.
54. 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.
53. 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.
52. Miller MR* et al. (2015). Variants in solute carrier SLC26A9 modify prenatal exocrine pancreatic damage in cystic fibrosis. Journal of Pediatrics 166(5):1152-1157.
51. 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.
50. 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.
49. 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.
48. 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.
47. 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.
46. 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.
45. 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.
44. 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.
43. 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.
42. 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.
41. 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.
40. Blackman S et al. (2013) Genetic modifiers of cystic fibrosis-related diabetes. Diabetes 62(10):3627-35.
39. 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.
38. 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
37. 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.
36. Goncalves VF*, ..., Sun L, Kennedy JL (2012). DRD4 VNTR polymorphism and age at onset of severe mental illnesses. Neuroscience Letters 519(1):9-13.
35. 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
34. 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 Epidemiology176(1):70-79.
33. 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.
31. 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.
30. 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.
29. 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.
28. Li W*, Sun L, ..., Strug LJ (2011). Understanding the population structure of North American patients with Cystic Fibrosis. Clinical Genetics 79:136-146.
27. 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.
26. 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.
25. 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.
24. 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.
23. 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.
22. 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.
21. 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.
20. 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.
19. Craiu RV, Sun L (2008). Choosing the lesser evil: trade-off between false discovery rate and non-discovery rate. Statistica Sinica 18:861-879.
18. 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.
17. 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.
16. 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.
15. 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.
14. 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.
13. Greenwood C, Rangreg J*, Sun L (2007). Optimal selection of markers for validation from genome-wide association studies. Genetic Epidemiology 31:396-407.
12. 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.
11. 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.
10. 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.
9. Biernacka J*, Sun L, Bull SB (2005). Tests for the presence of two linked disease susceptibility genes. Genetic Epidemiology 29:389-401.
8. Sun L, Bull SB (2005). Reduction of selection bias in genome-wide genetic studies by resampling. Genetic Epidemiology 28:352-367.
7. Biernacka J*, Sun L, Bull SB (2005). Simultaneous localization of two linked disease susceptibility genes. Genetic Epidemiology 28:33-47.
6. 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.
5. 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.
4. Sun L, Wilder K, McPeek MS (2002). Enhanced pedigree error detection. Human Heredity 54:99-110.
3. Sun L, Cox NJ, McPeek MS (2002). A statistical method for identification of polymorphisms that explain a linkage result. The American Journal of Human Genetics 70:399-411.
2. 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.
1. McPeek MS, Sun L (2000). Statistical tests for detection of misspecified relationships by use of genome-screen data. The American Journal of Human Genetics 66:1076-1094.
0. 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.