This page lists publications from the Statistical Functional Genomics Lab since 2020, when we started at Columbia University.

* Co-corresponding/co-last author

Highlighted Work

Genetics & Statistics

  1. McCreight A, Cho Y, Li R, Nachun D, Carbonetto P, Gan H, Stephens M*, Denault WRP*, Wang G*. SuSiE 2.0: improved methods and implementations for genetic fine-mapping and phenotype prediction. (2025). doi: 10.1101/2025.11.25.690514

  2. Liu A, Sun H, Carbonetto P, De Jager P, Bennett D, Stephens M, Wang G*, Denault WRP*. mfSuSiE Enables Multi-cell-type Fine-mapping and Multi-omic Integration of Chromatin Accessibility QTLs in Aging Brain. (2025). doi: 10.1101/2025.11.25.690439

  3. Denault WRP, Sun H, Carbonetto P, Liu A, De Jager PL, Bennett D, The Alzheimer’s Disease Functional Genomics Consortium, Wang G*, Stephens M*. fSuSiE enables fine-mapping of QTLs from genome-scale molecular profiles. Under review, Nature Methods (2025). doi: 10.1101/2025.08.17.670732

  4. Denault WRP, Carbonetto P, Li R, The Alzheimer’s Disease Functional Genomics Consortium, Wang G*, Stephens M*. Accounting for uncertainty in residual variances improves calibration of the Sum of Single Effects model for small sample sizes. Under review, Nature Methods (2025). doi: 10.1101/2025.05.16.654543

  5. Cao X, Sun H, Feng R, Mazumder R, Buen Abad Najar CF, Li YI, de Jager PL, Bennett D, The Alzheimer’s Disease Functional Genomics Consortium, Dey KK*, Wang G*. Integrative multi-omics QTL colocalization maps regulatory architecture in aging human brain. Under revision, Nature Genetics (2025). doi: 10.1101/2025.04.17.25326042

  6. Zou Y, Carbonetto P, Xie D, Wang G*, Stephens M*. Fast and flexible joint fine-mapping of multiple traits via the Sum of Single Effects model. Nature Genetics, accepted (2025). doi: 10.1101/2023.04.14.536893

  7. Qi Z, Pelletier A, Willwerscheid J, Cao X, Wen X, Cruchaga C, De Jager P, TCW J*, Wang G*. Refined Missing Data Imputation Approaches Enhance Quantitative Trait Loci Discovery in Multi-Omics Analysis. medrxiv (2023). doi: 10.1101/2023.11.29.23299181

  8. Zou Y, Carbonetto P, Wang G*, Stephens M*. Fine-mapping from summary data with the “Sum of Single Effects” model. PLoS Genet (2022). doi: 10.1371/journal.pgen.1010299

  9. Wang G, Sarkar A, Carbonetto P, Stephens M. A simple new approach to variable selection in regression, with application to genetic fine mapping. Journal of the Royal Statistical Society: Series B (Statistical Methodology) (2020). doi: 10.1111/rssb.12388

Genetics & Machine Learning / Bioinformatics

  1. Li R, Feng R, Liu A, Cao X, De Jager P, Bennett D, Jiang T, The Alzheimer’s Disease Functional Genomics Consortium, Wen J*, Wang G*. Neuroimaging-derived Brain Endophenotypes Link Molecular Mechanisms to Alzheimer’s Disease and Aging. (2025). doi: 10.1101/2025.11.25.25340884

  2. Liu A, Jiang R, Li R, Qi Z, Cao X, Dey KK, De Jager P, Bennett D, The Alzheimer’s Disease Functional Genomics Consortium, Wang T*, Wang G*. Distributional genetic effects reveal context-dependent molecular regulation in human brain aging and Alzheimer’s disease. (2025). doi: 10.21203/rs.3.rs-8219833

  3. Lakhani CM, Cavalca G, Liu A, Nidumbu R, Feng R, Raj T, The Alzheimer’s Disease Functional Genomics Consortium, Wang G*, Knowles DA*. Machine Learning-Based Prediction of Cell-type Resolved Brain eQTLs Enhances Discovery of Variants Explaining Alzheimer’s Disease Heritability. (2025). doi: 10.64898/2025.12.03.25341562

  4. Buen Abad Najar CF, Feng R, Dai C, Fair B, Hauck Q, Li J, Cao X, Dey KK, De Jager P, Bennett D, The Alzheimer’s Disease Sequencing Project Functional Genomics Consortium, Liu X, Wang G*, Li YI*. Genetic and functional analysis of unproductive splicing using LeafCutter2. Under revision, Nature Genetics (2025). doi: 10.1101/2025.04.06.646893

Under Submission

  1. Lee H, Sun H, Cao X, Karaahmet B, Li Z, Ulrich-Klein H, Taga M, Wang G, De Jager PL, Bennett DA, Pinello L, Jin X, Mazumder R, Dey KK. Mapping disease critical spatially variable gene programs by integrating spatial transcriptomics with human genetics. Under review, Nature Communications (2025). doi: 10.1101/2025.09.24.678397

  2. Zhang Y, Wang W, Tan ZY, Tong Y, Xu C, Liu A, Sim D, Jin S, Tian C, Roca X, Comandante Lou N, de Jager P, Bennett D, The Alzheimer’s Disease Functional Genomics Consortium, Wang G, Liu B. Single-cell splicing analysis with ISSAC links cell type-specific and cell state-dependent sQTLs to neurological disorders. Under review, Nature Genetics (2025).

  3. Zeng L, Atlas K, Lama T, Chitnis T, Weiner H, Wang G, Fujita M, Zipp F, Taga M, Kiryluk K, et al. GWAS highlights the neuronal contribution to multiple sclerosis susceptibility. Under revision, Nature Genetics (2025).

Published

  1. Wang L, Gao S, Chen S, Markus H, Carrel L, Wang G, Zhan X, Liu DJ, Jiang B. Looking beyond cell types: integrative analysis of axis-QTL and GWAS unveils novel genomic and translational insights into brain related traits. Nature Communications (2025). doi: 10.1038/s41467-025-65643-w

  2. Fujita M, Gao Z, Zeng L, McCabe C, White CC, Ng B, Green GS, Rozenblatt-Rosen O, Phillips D, Amir-Zilberstein L, et al. Cell subtype-specific effects of genetic variation in the Alzheimer’s disease brain. Nat Genet (2024) 56(4):605-614. PMID: 38514782

  3. Yang J, Xu Y, Yao M, Wang G, Liu Z. ERStruct: A Fast Python Package for Inferring the Number of Top Principal Components from Whole Genome Sequencing Data. BMC Bioinformatics (2023). doi: 10.1186/s12859-023-05305-0

  4. Morgante F, Carbonetto P, Wang G, Zou Y, Sarkar A, Stephens M. A flexible empirical Bayes approach to multivariate multiple regression, and its improved accuracy in predicting multi-tissue gene expression from genotypes. PLoS Genet (2023). doi: 10.1101/2022.11.22.517471

  5. Ding R, Zou X, Qin Y, Chen H, Ma X, Yu C, Wang G, Li L. xQTLbiolinks: a comprehensive and scalable tool for integrative analysis of molecular QTLs. biorxiv (2023). doi: 10.1101/2023.04.28.538654

  6. Naderi E, Cornejo-Sanchez DM, Li G, Schrauwen I, Wang G, Dewan AT, Leal SM. The genetic contribution of the X chromosome in age-related hearing loss. Front Genet (2023). doi: 10.3389/fgene.2023.1106328

  7. Cornejo-Sanchez DM, Li G, Fabiha T, Wang R, Acharya A, Everard JL, Kadlubowska MK, Huang Y, Schrauwen I, Wang G, DeWan AT, Leal SM. Rare-variant association analysis reveals known and new age-related hearing loss genes. Eur J Hum Genet (2023). doi: 10.1038/s41431-023-01302-2

  8. Wang G, Ott J. Digenic analysis finds highly interactive genetic variants underlying polygenic traits. Medical Research Archives (2023). doi: 10.18103/mra.v11i10.4604

  9. Clark LN, Gao Y, Wang G, Hernandez N, Ashley-Koch A, Jankovic J, Ottman R, Leal SM, Rodriguez SMB, Louis ED. Whole genome sequencing identifies candidate genes for familial essential tremor and reveals biological pathways implicated in essential tremor aetiology. eBioMedicine (2022). doi: 10.1016/j.ebiom.2022.104290

  10. Qian T, Gong Q, Shen H, Li C, Wang G, Xu X, Schrauwen I, Wang W. Novel variants in the RDH5 Gene in a Chinese Han family with fundus albipunctatus. BMC Ophthalmol (2022). doi: 10.1186/s12886-022-02301-5

  11. Olayinka OA, O’Neill NK, Farrer LA, Wang G, Zhang X. Molecular Quantitative Trait Locus Mapping in Human Complex Diseases. Curr Protoc (2022). doi: 10.1002/cpz1.426

  12. Zou X, Ding R, Chen W, Wang G, Cheng S, Wang Q, Li W, Li L. Using population-scale transcriptomic and genomic data to map 3’ UTR alternative polyadenylation quantitative trait loci. STAR Protoc (2022). doi: 10.1016/j.xpro.2022.101566

  13. Wu T, Wang Y, Wang YL, Zhao E, Wang G. OA-MedSQL: Order-Aware Medical Sequence Learning for Clinical Outcome Prediction. 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (2021). doi: 10.1109/BIBM52615.2021.9669367

  14. Qian T, Chen C, Li C, Gong Q, Liu K, Wang G, Schrauwen I, Xu X. A novel 4.25 kb heterozygous deletion in PAX6 in a Chinese Han family with congenital aniridia combined with cataract and nystagmus. BMC Ophthalmol (2021). doi: 10.1186/s12886-021-02120-0

  15. Xie Z, Sun C, Liu Y, Yu M, Zheng Y, Meng L, Wang G, Cornejo-Sanchez DM, Bharadwaj T, Yan J, Zhang L, Pineda-Trujillo N, Zhang W, Leal SM, Schrauwen I, Wang Z, Yuan Y. Practical approach to the genetic diagnosis of unsolved dystrophinopathies: a stepwise strategy in the genomic era. J Med Genet (2021). doi: 10.1136/jmedgenet-2020-107113

  16. Li L, Huang KL, Gao Y, Cui Y, Wang G, Elrod ND, Li Y, Chen YE, Ji P, Peng F, Russell WK, Wagner EJ, Li W. An atlas of alternative polyadenylation quantitative trait loci contributing to complex trait and disease heritability. Nat Genet (2021). doi: 10.1038/s41588-021-00864-5

  17. Roychowdhury T, Lu H, Hornsby WE, Crone B, Wang G, Guo DC, Sendamarai AK, Devineni P, Lin M, Zhou W, Graham SE, Wolford BN, Surakka I, Wang Z, Chang L, Zhang J, Mathis M, Brummett CM, Melendez TL, Shea MJ, Kim KM, Deeb GM, Patel HJ, Eliason J, Eagle KA, Yang B, Ganesh SK, Brumpton B, Åsvold BO, Skogholt AH, Hveem K; VA Million Veteran Program, Pyarajan S, Klarin D, Tsao PS, Damrauer SM, Leal SM, Milewicz DM, Chen YE, Garcia-Barrio MT, Willer CJ. Regulatory variants in TCF7L2 are associated with thoracic aortic aneurysm. Am J Hum Genet (2021). doi: 10.1016/j.ajhg.2021.06.016

  18. Barbeira AN, Bonazzola R, Gamazon ER, Liang Y, Park Y, Kim-Hellmuth S, Wang G, Jiang Z, Zhou D, Hormozdiari F, Liu B, Rao A, Hamel AR, Pividori MD, Aguet F; GTEx GWAS Working Group, Bastarache L, Jordan DM, Verbanck M, Do R; GTEx Consortium, Stephens M, Ardlie K, McCarthy M, Montgomery SB, Segrè AV, Brown CD, Lappalainen T, Wen X, Im HK. Exploiting the GTEx resources to decipher the mechanisms at GWAS loci. Genome Biol (2021). doi: 10.1186/s13059-020-02252-4

  19. Zhang S, Zhang H, Zhou Y, Qiao M, Zhao S, Kozlova A, Shi J, Sanders AR, Wang G, Luo K, Sengupta S, West S, Qian S, Streit M, Avramopoulos D, Cowan CA, Chen M, Pang ZP, Gejman PV, He X, Duan J. Allele-specific open chromatin in human iPSC neurons elucidates functional disease variants. Science (2020). doi: 10.1126/science.aay3983

  20. Zhao L, Zhang Z, Rodriguez SMB, Vardarajan BN, Renton AE, Goate AM, Mayeux R, Wang G, Leal SM. A quantitative trait rare variant nonparametric linkage method with application to age-at-onset of Alzheimer’s disease. Eur J Hum Genet (2020). doi: 10.1038/s41431-020-0703-z

  21. Zhang Z, Luo K, Zou Z, Qiu M, Tian J, Sieh L, Shi H, Zou Y, Wang G, Morrison J, Zhu AC, Qiao M, Li Z, Stephens M, He X, He C. Genetic analyses support the contribution of mRNA N6-methyladenosine (m6A) modification to human disease heritability. Nat Genet (2020). doi: 10.1038/s41588-020-0644-z

  22. Kim-Hellmuth S, Aguet F, Oliva M, Muñoz-Aguirre M, Kasela S, Wucher V, Castel SE, Hamel AR, Viñuela A, Roberts AL, Mangul S, Wen X, Wang G, Barbeira AN, Garrido-Martín D, Nadel BB, Zou Y, Bonazzola R, Quan J, Brown A, Martinez-Perez A, Soria JM; GTEx Consortium, Getz G, Dermitzakis ET, Small KS, Stephens M, Xi HS, Im HK, Guigó R, Segrè AV, Stranger BE, Ardlie KG, Lappalainen T. Cell type-specific genetic regulation of gene expression across human tissues. Science (2020). doi: 10.1126/science.aaz8528

  23. Barbeira AN, Melia OJ, Liang Y, Bonazzola R, Wang G, Wheeler HE, Aguet F, Ardlie KG, Wen X, Im HK. Fine-mapping and QTL tissue-sharing information improves the reliability of causal gene identification. Genet Epidemiol (2020). doi: 10.1002/gepi.22346

  24. Xie Z, Sun C, Zhang S, Liu Y, Yu M, Zheng Y, Meng L, Acharya A, Cornejo-Sanchez DM, Wang G, Zhang W, Schrauwen I, Leal SM, Wang Z, Yuan Y. Long-read whole-genome sequencing for the genetic diagnosis of dystrophinopathies. Ann Clin Transl Neurol (2020). doi: 10.1002/acn3.51201

  25. Wang Y, Wu T, Wang YL, Wang G. Enhancing Model Interpretability and Accuracy for Disease Progression Prediction via Phenotype-Based Patient Similarity Learning. Biocomputing 2020 (2020). doi: 10.1142/9789811215636_0045