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Fine-mapping from summary data with the “Sum of Single Effects” model
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Abstract
In recent work, Wang et al introduced the “Sum of Single Effects” ( SuSiE ) model, and showed that it provides a simple and efficient approach to fine-mapping genetic variants from individual-level data. Here we present new methods for fitting the SuSiE model to summary data, for example to single-SNP z -scores from an association study and linkage disequilibrium (LD) values estimated from a suitable reference panel. To develop these new methods, we first describe a simple, generic strategy for extending any individual-level data method to deal with summary data. The key idea is to replace the usual regression likelihood with an analogous likelihood based on summary data. We show that existing fine-mapping methods such as FINEMAP and CAVIAR also (implicitly) use this strategy, but in different ways, and so this provides a common framework for understanding different methods for fine-mapping. We investigate other common practical issues in fine-mapping with summary data, including problems caused by inconsistencies between the z -scores and LD estimates, and we develop diagnostics to identify these inconsistencies. We also present a new refinement procedure that improves model fits in some data sets, and hence improves overall reliability of the SuSiE fine-mapping results. Detailed evaluations of fine-mapping methods in a range of simulated data sets show that SuSiE applied to summary data is competitive, in both speed and accuracy, with the best available fine-mapping methods for summary data.
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“The s values from SuSiE-RSS ranged from 0.007 to 0.027 across fine-mapped loci, and the diagnostic plots demonstrated high consistency of LD between the summary statistics and reference panel (Supplementary Fig. 19a–g).”
From Shared genetic architectures of educational attainment in East Asian and European populations (2024) · cites this paper as [34]
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Items 101–200 of 357
Generative prediction of causal gene sets responsible for complex traits
Kuznets-Speck, B., Ogonor, B. K., Wytock, T. P., & Motter, A. E. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2415071122 · Cited by 6
Zhong, X.-Y., Mitchell, R. M., Billstrand, C., Thompson, E., Sakabe, N., Aneas, I., Salamone, I. M., Gu, J., Sperling, A., Schoettler, N., et al. (2025) · medRxiv · doi:10.1101/2025.02.11.25322088 · Cited by 8
“Next, we used the summary statistics version of “sum of single effects” (SuSiE) [45,46] model to fine-map LD blocks at GWAS loci.”
Zheng, R., Li, Z., Fang, W., Lou, H., Liu, F., Sun, Q., Shi, X., Liu, H., Chen, Q., Shen, X., et al. (2025) · Cell Reports Medicine · doi:10.1016/j.xcrm.2024.101923 · Cited by 14
“21 For function study, all data were represented as the mean ± SEM.”
CAUSALdb2: an updated database for causal variants of complex traits
Wang, J., Ouyang, L., You, T., Yang, N., Xu, X., Zhang, W., Yang, H., Yi, X., Huang, D., Zhou, W., et al. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae1096 · Cited by 10
Robust fine-mapping in the presence of linkage disequilibrium mismatchPreprint
Zhang, W., Lu, T., SLADEK, R. M., Dupuis, J., & Lettre, G. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.10.29.620968 · Cited by 8
“SuSiE and SparsePro group correlated variants and jointly model their effects, significantly improving the efficiency and accuracy of fine-mapping [2, 3, 6].”
BackgroundMethodInfluentialGenetic and functional analysis of Raynaud’s syndrome implicates loci in vasculature and immunity
Tervi, A. M., Ramste, M. O., Abner, E., Cheng, P. P., Lane, J. M., Maher, M., Valliere, J., Lammi, V., Strausz, S. J., Riikonen, J., et al. (2024) · Cell Genomics · doi:10.1016/j.xgen.2024.100630 · Cited by 9
Wen, J., Sun, Q., Huang, L., Zhou, L., Doyle, M. F., Ekunwe, L., Durda, P., Olson, N. C., Reiner, A. P., Li, Y., et al. (2024) · Genetics · doi:10.1093/genetics/iyae098 · Cited by 11
Lu, Z., Wang, X., Carr, M., Kim, A., Gazal, S., Mohammadi, P., Wu, L., Gusev, A., Pirruccello, J. P., Kachuri, L., et al. (2024) · medRxiv · doi:10.1101/2024.04.15.24305836 · Cited by 5
“In addition, the limited accessibility to the individual-level complex trait data allows the extension of SuSiE-like models to be compatible with summary statistics 38,39,41,51 , which typically requires external LD reference panels.”
MethodLi, X., Sham, P. C., & Zhang, Y. (2024) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2023.12.007 · Cited by 10
Mapping of promoter usage QTL using RNA-seq data reveals their contributions to complex traits
Kubota, N., & Suyama, M. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010436 · Cited by 7
“We also performed fine-mapping of puQTL to estimate candidate causal variants using the SuSiE software [34,35].”
InfluentialGenetic analysis of circulating metabolic traits in 619,372 individuals
Tambets, R., Jesse, M., Kronberg, J., van der Graaf, A., Abner, E., Võsa, U., Rahu, I., Taba, N., Kolde, A., Yarish, D., et al. (2026) · Nature · doi:10.1038/s41586-026-10532-5 · Cited by 28
Multi-ancestry genome-wide association study of severe pregnancy nausea and vomiting
Fejzo, M. S., Wang, X., Tan, Q., Zöllner, J., Pujol‐Gualdo, N., Laisk, T., Metspalu, A., Milani, L. A., Esko, T., Mägi, R., et al. (2026) · Nature Genetics · doi:10.1038/s41588-026-02564-4 · Cited by 9
“We extracted SNPs present in both 515 unrelated Europeans participants from the 1000 Genomes Project and meta-analysis summary statistics.”
InfluentialGodrich, D., Pasteris, J., Martin, E. R., Kunkle, B. W., Naj, A. C., Hamilton, K., Wang, H., Lee, W., Dumitrescu, L. C., Hohman, T. J., et al. (2025) · Alzheimer s & Dementia · doi:10.1002/alz.70760 · Cited by 8
Genomic analyses reveal new insights into Alzheimer's diseasePreprint
Uffelmann, E., Wightman, D. P., Bahrami, S., Shadrin, A. A., Fominykh, V., Ojima, T., Jiang, C., Benner, C., Moreno, E., Campos, A. I., et al. (2025) · medRxiv · doi:10.1101/2025.10.10.25337470 · Cited by 8
Borzoi-informed fine mapping improves causal variant prioritization in complex trait GWAS
Srivastava, D., Korsakova, A., Wang, Q. S., Luong, R., Yuan, H., & Kelley, D. R. (2025) · bioRxiv · doi:10.1101/2025.07.09.663936 · Cited by 7
“Bayesian fine-mapping approaches such as SuSiE [5, 6], FINEMAP [4], and CARMA [7] jointly model variant effects to estimate posterior inclusion probabilities (PIPs) for prioritizing likely causal variants.”
Lee, H., Fernandes, M., Lee, J., Merino, J., & Kwak, S. H. (2025) · Diabetologia · doi:10.1007/s00125-025-06403-9 · Cited by 9
“Conditional association analysis and Bayesian models such as sum of single effects (SuSIE) [89] and probabilistic annotation integrator (PAINTOR) [90] are commonly used for fine-mapping.”
A compendium of genetic variations associated with promoter usage across 49 human tissues
Yuan, J., Tong, Y., Wang, L., Yang, X., Liu, X., Shu, M., Li, Z., Jin, W., Guan, C., Wang, Y., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-53131-6 · Cited by 12
“Subsequently, the SuSiE R package (version 0.12.10) was utilized to identify the causal variants linked to each promoter by analyzing the summary statistics of puQTLs for individual promoters through the susie_rss function, while explicitly setting estimate_prior_variance to FALSE 67,68 .”
InfluentialPark, Y. J., Moon, S., Choi, J., Kim, J., Kim, H.-J., Son, H., Kim, J., & Kim, J. (2024) · Lipids in Health and Disease · doi:10.1186/s12944-024-02248-0 · Cited by 7
“Fine-mapping was performed using POLYgenic FUNctionally informed fine-mapping (PolyFUN), implementing the Sum of Single Effects(SuSiE) statistical model [25].”
MethodKeener, R., Chhetri, S. B., Connelly, C. J., Taub, M. A., Conomos, M. P., Weinstock, J. S., Ni, B., Strober, B. J., Aslibekyan, S. W., Auer, P. L., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-48394-y · Cited by 8
Karhunen, V., Launonen, I. K., Järvelin, M., Sebert, S. P., & Sillanpää, M. J. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad396 · Cited by 8
“…with or developed for GWAS summary statistics (The Wellcome Trust Case Control Consortium et al. 2012, Yang et al. 2012, Hormozdiari et al. 2014, Kichaev et al. 2014, Chen et al. 2015, Kichaev and Pasaniuc 2015, Benner et al. 2016, Newcombe et al. 2016, Wang et al. 2020, Zou et al. 2022).”
Morgante, F., Carbonetto, P. S., Wang, G., Zou, Y., Sarkar, A., & Stephens, M. (2023) · PLoS Genetics · doi:10.1371/journal.pgen.1010539 · Cited by 6
“mash to work 454 with “summary data” [25,61,62].”
BackgroundSelf-citationSpargo, T. P., Gilchrist, L., Hunt, G. P., Dobson, R., Proitsi, P., Al‐Chalabi, A., Pain, O., & Iacoangeli, A. (2023) · eLife · doi:10.7554/elife.88768 · Cited by 3
“Statistical fine-mapping is a common approach for dissecting complex LD structures and finding variants with implications for a given trait among the tens or hundreds that might be associated in the region ( Zou et al., 2022 ).”
InfluentialAttfield, K. E., Armen, A. P., Kuttikkatte, S. B., Farooq, R., Francis, A. G., Dendrou, C. A., Leite, M. I., Waters, P. J., Aktaş, O., Asgari, N., et al. (2026) · The Lancet Neurology · doi:10.1016/s1474-4422(26)00084-0 · Cited by 6
“…was performed using SuSiE 21,22 for each region around independently associated variants to estimate the probability of each variant in the region being causal (posterior inclusion probability) and to generate 95% credible sets, each corresponding to an independent signal (appendix 1 pp 22–23).”
InfluentialBridging the variant-to-function gap in type 2 diabetes: advances and challenges
Maynard, A. G., Bhardwaj, R., Jones, T. R., & Claussnitzer, M. (2025) · Diabetologia · doi:10.1007/s00125-025-06600-6 · Cited by 7
An accurate genetic colocalization method for the HLA locusPreprint
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Funmap: integrating high-dimensional functional annotations to improve fine-mapping
Li, Y., Xiao, J., Ming, J., Zeng, Y., & Cai, M. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btaf017 · Cited by 6
“More recently, SuSiE [10, 11] proposes an efficient fine-mapping framework by decomposing causal signals into a sum of single causal effects.”
BackgroundCoPheScan: phenome-wide association studies accounting for linkage disequilibrium
Manipur, I., Reales, G., Sul, J. H., Shin, M. K., Longerich, S., Cortés, A., & Wallace, C. (2024) · Nature Communications · doi:10.1038/s41467-024-49990-8 · Cited by 5
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“Next, new functionalities will be supported, such as expanding the genetic association analysis beyond a single locus and integrating functional genomic and fine-mapping tools (29–32) into the analysis to facilitate variant prioritization and causal variant discovery.”
Integration of expression QTLs with fine mapping via SuSiE
Zhang, X., Jiang, W., & Zhao, H. (2024) · PLoS Genetics · doi:10.1371/journal.pgen.1010929 · Cited by 13
“We note that SuSiE has been extended for summary statistics [25], which makes it competitive with other well-developed fine-mapping methods.”
Riedhammer, K. M., Nguyen, T.-M. T., Koşukçu, C., Calzada‐Wack, J., Li, Y., Batzir, N. A., Saygılı, S., Wimmers, V., Kim, G.-J., Chrysanthou, M., et al. (2023) · Kidney International · doi:10.1016/j.kint.2023.11.032 · Cited by 11
Clifford, R. E., Munro, D. B., Dochtermann, D. R., Devineni, P., Pyarajan, S., Program, M. V., Muralidhar, S., Moser, J., Deen, J. E., Tsao, P. S., et al. (2023) · Journal of the Association for Research in Otolaryngology · doi:10.1007/s10162-023-00917-y · Cited by 7
A genome-wide association study of adults with community-acquired pneumonia
Suárez-Pajés, E., Marcelino-Rodriguez, I., Brito, E. H., González-Barbuzano, S., Ramirez-Falcon, M., Tosco-Herrera, E., Rubio-Rodríguez, L. A., Briones, M. L., Rajas, O., Borderías, L., et al. (2023) · Respiratory Research · doi:10.1186/s12931-024-03009-4 · Cited by 6
“To validate the results, we also inferred the cred-ible set with 95% confidence using the R package SuSiE [26] and assumed 3, 5, or 10 causal variants.”
MethodWang, X. M., Kho, P. F., Ramachandran, D., Bafligil, C., Amant, F. C., Goode, E. L., Scott, R. J., Tomlinson, I. P. M., Evans, D. G., Crosbie, E. J., et al. (2023) · iScience · doi:10.1016/j.isci.2023.106590 · Cited by 8
BEATRICE: Bayesian Fine-mapping from Summary Data using Deep Variational Inference
Ghosal, S., Schatz, M. C., & Venkataraman, A. (2023) · bioRxiv · doi:10.1101/2023.03.24.534116 · Cited by 6
“• SuSiE: [40, 34] introduced an iterative Bayesian selection approach for fine-mapping that represents the causal vector as a sum of “single-effect” vectors.”
InfluentialMultiSuSiE improves multi-ancestry fine-mapping in All of Us whole-genome sequencing data
Rossen, J., Shi, H., Strober, B. J., Zhang, M. J., Kanai, M., McCaw, Z. R., Liang, L., Weissbrod, O., & Price, A. L. (2026) · Nature Genetics · doi:10.1038/s41588-025-02450-5 · Cited by 24
“Fourth, as with all multiple causal variant fine-mapping methods, we recommend applying MultiSuSiE using in-sample LD 18,24 ; when in-sample LD is not available, an alternative is to use LD reference panels from each target population that span at least 10% of the target GWAS sample size, as…”
InfluentialCaro, I., Western, D., Namba, S., Sun, N., Kawaguchi, S., He, Y., Fujita, M., Roshchupkin, G. V., D’Aoust, T., Duperron, M., et al. (2025) · Nature Aging · doi:10.1038/s43587-025-01006-w · Cited by 22
“As a complementary approach, we used SuSiE colocalization 90 (susieR package) to perform fine-mapping based on z -scores and linkage disequilibrium matrices using pQTLs in sample linkage disequilibrium matrix, allowing for the presence of multiple signals.”
Yang, Y.-H., & Zhu, X.-F. (2025) · bioRxiv · doi:10.1101/2025.07.11.664423 · Cited by 5
“Although we use lasso as an example to illustrate the principle of BDSP, we actually use SuSiE (Wang et al., 2020; Zou et al., 2022) as the penalty ˜ p ( θ ) in the minimization (10) to identify non-zero entries of δ .”
Loss of CFHR5 function reduces the risk for age-related macular degeneration
Reeve, M. P., Loomis, S. J., Nissilä, E., Soare, T. W., Rausch, T., Zheng, Z., Parolo, P. D. B., Ben-Isvy, D., Aho, E., Cesetti, E., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-61193-3 · Cited by 10
“Statistical fi ne-mapping of non-CFH regions was conducted using SuSIE 45 .”
Genetic contributions to epigenetic-defined endotypes of allergic phenotypes in children
Thompson, E. E., Zhong, X., Carbonetto, P. S., Morin, A., Willwerscheid, J., Visness, C. M., Bacharier, L. B., Kattan, M., O’Connor, G. T., Rivera‐Spoljaric, K., et al. (2025) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2025.05.006 · Cited by 7
“We then used a modification of the “Sum of Single Effects” (SuSiE) model, 34 SuSiE-RSS, 35 to fine-map each significant block, allowing up to five independent causal signals per block.”
Self-citationZhang, Y., Wang, J., Yi, C., Su, Y., Yin, Z. C., Zhang, S., Li, J., Stoneking, M., Yang, J., Wang, K., et al. (2025) · Cell Genomics · doi:10.1016/j.xgen.2025.100855 · Cited by 3
“A previous study reported a significant positive genetic correlation ( r g ) between height and BMR 19 ( r g = 0.61, standard error = 0.015, p = 1 × 10 − 308 ) using 337,199 unrelated UK Biobank (UKBB) individuals of European ancestry through cross-trait linkage disequilibrium (LD) score regression.”
InfluentialTandem repeat variation shapes immune cell type-specific gene expression
Tanudisastro, H. A., Cuomo, A. S. E., Weisburd, B., Welland, M. J., Spenceley, E., Franklin, M., Xue, A., Huang, H. L., Bowen, B., Fan, J., et al. (2026) · Nature · doi:10.1101/2024.11.02.621562 · Cited by 14
“0.2) 102 to perform ordinary least squares regression between the summed TR repeat length (across both alleles for each individual) and normalized methylation probability, while adjusting for known confounders: 𝑦 = 𝑊α + 𝑔β + ϵ where denotes normalized gene expression values, denotes the…”
InfluentialWhole‐genome sequencing study in Koreans identifies novel loci for Alzheimer's disease
Kang, M.-I., Farrell, J. J., Zhu, C., Park, H., Kang, S., Seo, E. H., Choi, K. Y., Jun, G., Won, S., Gim, J., et al. (2024) · Alzheimer s & Dementia · doi:10.1002/alz.14128 · Cited by 8
“67,68 We applied a Bayesian method for selecting true causal variants from a cluster of multiple highly correlated variants using the sum-of-single-effects (SuSiE) model 69,70 .”
A translational framework of genoproteomic studies for cardiovascular drug discovery
Yang, Z., Zhao, J. V., Qi, Y., Deng, X., Ji, Z., & Liu, J. (2024) · npj Cardiovascular Health · doi:10.1038/s44325-024-00015-9 · Cited by 6
Chen, D. M., Dong, R., Kachuri, L., Hoffmann, T. J., Jiang, Y., Berndt, S. I., Shelley, J. P., Schaffer, K. R., Machiela, M. J., Freedman, N. D., et al. (2024) · Human Genetics and Genomics Advances · doi:10.1016/j.xhgg.2024.100315 · Cited by 4
“35,36 A random subset of 10% of all variants was used during initial parameter estimation assuming at most five causal effects for each region, but regions with PIPs of 0.8 or greater were recomputed.”
MethodInfluentialLiang, Q.-M., Jiang, Y., Shieh, A. W., Zhou, D., Chen, R., Wang, F., Xu, M., Niu, M., Wang, X.-S., Pinto, D., et al. (2023) · bioRxiv · doi:10.1101/2023.06.04.543603 · Cited by 5
Pietzner, M., Williamson, A., Hunt, K. A., Koprulu, M., Kohleick, L., Demircan, K., van Heel, D. A., Finer, S., Carrasco-Zanini, J., van Heel, D. A., et al. (2026) · Nature Genetics · doi:10.1038/s41588-026-02697-6 · Cited by 6
“After collating associated regions across GWAS and ExWAS results files, we used statistical fine-mapping 88 as implemented in the R package susieR (v.0.14.2) to identify independent credible sets (maximum r 2 0.25).”
InfluentialShen, H., Zhang, L., Ma, X., Yin, Y., Wang, J., & Tan, B. (2026) · Science China Life Sciences · doi:10.1007/s11427-025-3163-6 · Cited by 5
“Coloc and related methods are most informative when coupled to credible-set fine-mapping (e.g., SuSiE) that resolves multi-signal loci in complex LD (Giambarto-lomei et al., 2014; Zou et al., 2022).”
Zhang, W., Lan, F., Han, Y., Cai, R., Zhang, J., Wu, G., Li, G., Yan, Y., Yang, N., Yin, H., et al. (2026) · Journal of Advanced Research · doi:10.1016/j.jare.2026.03.021 · Cited by 3
Multi-ancestry GWAS of age-related hearing loss identifies 140 loci and key cellular mechanisms
Shi, L., He, H., Li, J., Gai, K., Li, W., Zhao, Y., Yuan, H., & Wu, Y. (2026) · Nature Communications · doi:10.1038/s41467-026-69894-z · Cited by 3
Hautakangas, H. M., Kartau, J., FinnGen, Palotie, A. V., & Pirinen, M. (2026) · Nature Communications · doi:10.1038/s41467-025-64880-3 · Cited by 10
Complex genetic effects linked to plasma protein abundance in the UK Biobank
Sigurdsson, A. I., Gräf, J. F., Yang, Z., Ravn, K., Meisner, J., Thielemann, R., Webel, H. E., Smit, R. A. J., Niu, L., Mann, M., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-67235-0 · Cited by 11
“12.35) 62 with settings estimate_- prior_variance=TRUE and estimate_prior_method = “ optim ” .”
InfluentialSuSiE 2.0: improved methods and implementations for genetic fine-mapping and phenotype prediction
McCreight, A., Cho, Y., Li, R.-X., Nachun, D., Gan, H., Carbonetto, P., Stephens, M., Denault, W., & Wang, G. (2025) · bioRxiv · doi:10.1101/2025.11.25.690514 · Cited by 4
Self-citationFine-mapping methods for complex traits: essential adaptations for samples of related individuals
Wang, J., Tiezzi, F., Huang, Y., See, G. M., Schwab, C. R., Wei, J., Maltecca, C., & Jiang, J. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf614 · Cited by 11
“12.35) [12, 13], FINEMAP-inf v1.”
InfluentialMhatre, S., Dutta, D., Iyer, A., Golapkar, S. V., Mishra, A., Krishnatreya, M., George, G. S., Doibale, P., Dun, Y., Wang, Z.-Q., et al. (2025) · EBioMedicine · doi:10.1016/j.ebiom.2025.106042 · Cited by 4
“50,51 The analysis at 5p15.”
Galimberti, M., Levey, D. F., Deak, J. D., Adhikari, K., Overstreet, C. M., Gupta, P. D., Nitin, R., Zhou, H., Lake, N. J., Harrington, K. M., et al. (2025) · Nature Genetics · doi:10.1038/s41588-025-02260-9 · Cited by 8
“These were necessary together with the summary statistics and EUR genotypes from 1000 Genomes Project Phase 3 to perform functionally informed fine-mapping with the sum of single effects 84,85 .”
A calcium-sensing receptor allelic series and underdiagnosis of genetically driven hypocalcemia
Chang, J. B., Barnhill, C. P., Apostolov, A. M., Soliai, M. M., Hecker, J., Nierenberg, J. L., Stapleton, L. M., Mathew, A. S., Zeng, X., Diao, J., et al. (2025) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2025.06.013 · Cited by 3
“53 We conducted fine mapping of these common variants using SuSiE 54 identified 10 that distinct from ADH1-associated variants (Table S17).”
A Genome-Wide Association Study of Taste Liking in the Danish Population
Haydar, S., Karlsson, C. C., Linneberg, A. R., Kårhus, L. L., Ängquist, L. H., Pedersen, O. B., Bredie, W. L. P., Hansen, T. F., & Grarup, N. (2025) · Journal of Nutrition · doi:10.1016/j.tjnut.2025.06.001 · Cited by 4
“Fine mapping using the Sum of Single Effect model was performed as an alternative, to identify a credible set of causal variants [31].”
Toward whole-genome inference of polygenic scores with fast and memory-efficient algorithms
Zabad, S., Haryan, C. A., Gravel, S., Misra, S., & Li, Y. (2025) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2025.05.002 · Cited by 5
“LD matrices are an integral part of modern statistical genetics toolkits, playing an essential role in fine-mapping, 23,53 PRS inference, 8,10–13, 15,25 and imputation or simulation of summary statistics, 19 , 54 among other Furthermore, our analysis highlighted the importance of the spectral…”
Kumasaka, N., Mezawa, H., Sakamoto, K., Isobe, T., Sato, M., Saito-Abe, M., Yang, L., Harama, D., Miyaji, Y., Nishizato, M., et al. (2025) · medRxiv · doi:10.1101/2025.02.18.25322458 · Cited by 4
regionalpcs improve discovery of DNA methylation associations with complex traits
Eulalio, T. Y., Sun, M., Gevaert, O., Greicius, M. D., Montine, T. J., Nachun, D. C., & Montgomery, S. B. (2025) · Nature Communications · doi:10.1038/s41467-024-55698-6 · Cited by 8
“DAP-G uses a Bayesian framework to estimate the posterior probabilities of each genetic variant associated with a molecular trait such as methylation.”
InfluentialTrang, B. K., Sharma, P. K., Cook, L. E., Mount, Z., Thomas, R. M., Kulkarni, N. N., Cabrera, E. C., Rachimi, S., Pahl, M. C., Pippin, J. A., et al. (2024) · medRxiv · doi:10.1101/2024.08.12.24311676 · Cited by 2
Kaplan, S. J., Wong, W., Yan, J., Pulecio, J., Cho, H., Li, Q., Zhao, J., Leslie-Iyer, J., Kazakov, J., Murphy, D., et al. (2024) · Cell Reports · doi:10.1016/j.celrep.2024.114640 · Cited by 15
“27,82 To predict whether rs528350911 would affect enhancer activation in pancreatic development, we trained a ChromBPNet 83,84 model on PFG-stage ATAC-seq data.”
Evaluation of Bayesian Linear Regression Models as a Fine Mapping toolPreprint
Shrestha, M., Bai, Z., Gholipourshahraki, T., Hjelholt, A. J., Hu, S., Kjølby, M., Rohde, P. D., & Sørensen, P. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.09.01.555889 · Cited by 3
Stanzick, K. J., Stark, K. J., Gorski, M., Schödel, J., Krüger, R., Kronenberg, F., Warth, R., Heid, I. M., & Winkler, T. W. (2023) · BMC Bioinformatics · doi:10.1186/s12859-023-05472-0 · Cited by 8
A disease-associated gene desert orchestrates macrophage inflammatory responses via ETS2
Stankey, C., Bourges, C., Turner-Stokes, T., Piedade, A., Palmer-Jones, C., Papa, I., dos Santos, M. S., Randzavola, L., Speidel, L., Parkes, E., et al. (2023) · bioRxiv · doi:10.1101/2023.05.05.539522 · Cited by 4
Controlled Discovery and Localization of Signals via Bayesian Linear Programming
Spector, A., & Janson, L. (2024) · Journal of the American Statistical Association · doi:10.1080/01621459.2024.2347667 · Cited by 2
“(Note that BLiP can also apply on top of the refined SuSiE procedure suggested in Zou et al. 2021).”
GWAS on short tandem repeats identifies genetic mechanisms in Alzheimer’s disease
Gmelin, D., Ohlei, O., Aslam, M. M., Junge, M. P., Parkkinen, L., Mullin, K., Prokopenko, D., Lill, C. M., Tanzi, R. E., Dobričić, V., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-73902-7 · Cited by 4
“Additionally, we used sum of single effects regression (SuSiE-RSS) 61,62 for fi ne mapping based on summary statistics.”
Yang, Y., & Zhu, X. (2026) · Genome biology · doi:10.1186/s13059-026-04051-9 · Cited by 2
“Although we use lasso as an example to illustrate the principle of BDSP, we actually use SuSiE [53, 58] as the penalty ˜ p (θ) in the minimization (14) to identify non-zero entries of δ .”
Single-nucleus multi-omics identifies shared and distinct pathways in Pick’s and Alzheimer’s disease
Shi, Z., Das, S., Morabito, S., Stocksdale, J. T., Miyoshi, E., Srinivasan, S. S., Emerson, N., Shahin, A., Rahimzadeh, N., Cao, Z., et al. (2025) · Science Advances · doi:10.1126/sciadv.ads7973 · Cited by 9
Functional Determinants and Evolutionary Consequences of Pleiotropy in Complex and Mendelian Traits
Barbitoff, Y. A., Богайчук, П., Pavlova, N. S., Malysheva, P. V., & Predeus, A. V. (2025) · Molecular Biology and Evolution · doi:10.1093/molbev/msaf232 · Cited by 16
Shrestha, M., Bai, Z., Gholipourshahraki, T., Hjelholt, A. J., Hu, S., Kjølby, M., Rohde, P. D., & Sørensen, P. (2025) · PLoS Genetics · doi:10.1371/journal.pgen.1011783 · Cited by 4
Lawrence, K. A., Gjorgjieva, T., & Montgomery, S. (2025) · bioRxiv · doi:10.1101/2025.06.06.658175 · Cited by 3
“We used coloc-SuSiE (v5) 34 to colocalize pcQTL, eQTL, and GWAS signals.”
Human genetic variation determines 24-hour rhythmic gene expression and disease risk
Chen, Y., Liu, P., Sabo, A., & Guan, D. (2025) · Nature Communications · doi:10.1038/s41467-025-59524-5 · Cited by 7
“As a result, fi ne-mapping tools like SuSiE 50,51 , which are effective for separating association signals and estimating causal probabilities for SNPs, are not directly applicable to rhyQTLs.”
Mews, M., Naj, A. C., Griswold, A. J., Below, J. E., & Bush, W. S. (2025) · Journal of Alzheimer s Disease · doi:10.1177/13872877251326288 · Cited by 6
“In Stage V: Fine-mapping , we identify the speci fi c eQTLs driving the signi fi cant TWAS results by applying the SuSiE (Sum of Single Effects) 43,44 approach ( susieR package v.0.12.35) to the original summary statistics while also using the same LD reference panel used in the OTTERS analysis.”
InfluentialGenetic predisposition to altered blood cell homeostasis is associated with glioma risk and survival
Kachuri, L., Guerra, G., Nakase, T. C., Wendt, G. A., Hansen, H. M., Molinaro, A. M., Bracci, P. M., Mccoy, L. S., Rice, T., Wiencke, J. K., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-55919-6 · Cited by 3
“33 and genetic regulation of neutrophils, platelets, PLR, and telomere length.”
Semiparametric efficient estimation of small genetic effects in large-scale population cohorts
Labayle, O., Roskams-Hieter, B., Slaughter, J., Tetley-Campbell, K., van der Laan, M. J., Ponting, C. P., Beentjes, S. V., & Khamseh, A. (2024) · Biostatistics · doi:10.1093/biostatistics/kxaf030 · Cited by 4
“And fourth, we show how TarGene can investigate higher-order interactions using multiple loci related to vitamin D receptor (VDR) function.”
Population-Specific Polygenic Risk Scores Developed for the Han ChinesePreprint
Chen, H., Chen, C.-H., Hou, M., Fu, Y., Li, L., Chou, C.-Y., Yeh, E., Tsai, M.-F., Chen, C., Yang, H., et al. (2024) · medRxiv · doi:10.1101/2024.10.14.24315279
Gjoka, A., & Cordell, H. J. (2024) · Genetic Epidemiology · doi:10.1002/gepi.22592 · Cited by 3
MDA5 variants trade antiviral activity for protection from autoimmune disease
Wallace, C., Singh, R., & Modis, Y. (2025) · BMC Medical Genomics · doi:10.1186/s12920-025-02171-y · Cited by 4
Single-nucleus multi-omics identifies shared and distinct pathways in Pick’s and Alzheimer’s disease
Shi, Z.-C., Das, S., Morabito, S., Miyoshi, E., Stocksdale, J. T., Emerson, N., Srinivasan, S. S., Shahin, A., Rahimzadeh, N., Cao, Z.-K., et al. (2024) · bioRxiv · doi:10.1101/2024.09.06.611761 · Cited by 3
Kunkel, D., Sørensen, P., Shankar, V., & Morgante, F. (2025) · PLoS Genetics · doi:10.1371/journal.pgen.1011519 · Cited by 5
“However, this quantity is not strictly necessary, unless V is estimated within the mr.mash-rss algorithm [27].”
InfluentialShen, J., Jiang, L., Wang, K., Wang, A., Chen, F., Newcombe, P. J., Haiman, C., & Conti, D. (2024) · Genetic Epidemiology · doi:10.1002/gepi.22562 · Cited by 3
Proteomic associations with forced expiratory volume: a Mendelian randomisation study
Axelsson, G. T., Jónmundsson, Þ., Woo, Y. J., Frick, E. A., Aspelund, T., Loureiro, J., Orth, A. P., Jennings, L. L., Guðmundsson, G., Emilsson, V., et al. (2024) · Respiratory Research · doi:10.1186/s12931-023-02587-z · Cited by 6
“95% credible-sets were filtered and colocalization between GWAS and QTLs was performed using fastENLOC using the posterior inclusion probabilities estimated in SuSiE [33, 35].”
MethodProteomic associations with forced expiratory volume - a Mendelian randomisation study
Axelsson, G., Jonmundsson, T., Woo, Y., Frick, E., Aspelund, T., Loureiro, J., Orth, A., Jennings, L., Gudmundsson, G., Emilsson, V., et al. (2023) · medRxiv · doi:10.1101/2023.06.30.23292035 · Cited by 3
“95% credible-sets were filtered and colocalization between GWAS and QTLs was 170 performed using fastENLOC using the posterior inclusion probabilities estimated in SuSiE (31, 33).”
MethodShen, J., Jiang, L., Wang, K., Wang, A., Chen, F., Newcombe, P., Haiman, C., & Conti, D. (2022) · bioRxiv · doi:10.1101/2022.12.22.521659 · Cited by 3
Zhao, S., Crouse, W. L., Qian, S., Luo, K., Stephens, M., & He, X. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.09.27.509700 · Cited by 5
“521 Following the literature [61, 21], and particularly, the summary statistics version of SuSiE (SuSiE522 RSS) [21], we have the following model of ẑ, 523”
BackgroundMethodInfluentialSelf-citationLocal genetic sex differences in quantitative traits
Uffelmann, E., de Leeuw, C., Schipper, M., & Posthuma, D. (2025) · Nature Communications · doi:10.1038/s41467-025-62504-4 · Cited by 4
Burstein, D., Griffen, T. C., Therrien, K., Bendl, J., Venkatesh, S., Dong, P., Modabbernia, A., Zeng, B., Mathur, D., Hoffman, G., et al. (2022) · medRxiv · doi:10.1101/2022.04.28.22274437 · Cited by 3
Single-cell multiomics of neuron activation reveals context-specific genetics of brain disorders
Liang, L., Zhang, S., Wang, Z., Zhang, H., Li, C., Thapa, C., Oh, E., Sirkin, D., Sun, X., Barishman, A., et al. (2026) · Science · doi:10.1126/science.adw3949 · Cited by 3
Methods for Prioritizing Causal Genes in Molecular Studies of Human Disease: The State of the Art
Patasova, K., Sedaghati-Khayat, B., Knevel, R., Cordell, H., & Pratt, A. G. (2026) · Genetic Epidemiology · doi:10.1002/gepi.70037 · Cited by 2
“SuSiE separates the statistical evidence of association for each variant while conditioning on causal signal at the locus (Zou et al. 2022).”
Lawrence, K. A., Gjorgjieva, T., Nachun, D., & Montgomery, S. B. (2026) · American Journal of Human Genetics · doi:10.1016/j.ajhg.2026.02.022 · Cited by 2
Trang, B. K., Sharma, P. K., Cook, L. E., Mount, Z., Thomas, R. M., Kulkarni, N. N., Cabrera, E. C., Rachimi, S., Pahl, M. C., Pippin, J. A., et al. (2025) · Genome biology · doi:10.1186/s13059-025-03880-4 · Cited by 7
“We used the approximate Bayes factor ( abf ) framework of COLOC [48] and the iterative Bayesian step-wise selection regression of SuSiE [49] to investigate the colocalization between each autoimmune trait with each cell type eQTL from each resource for each identified eGenes.”
Impact of common variants on brain gene expression from RNA to protein to schizophrenia risk
Liang, Q., Jiang, Y., Shieh, A. W., Zhou, D., Chen, R., Wang, F., Xu, M. M., Niu, M., Wang, X., Pinto, D., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-65818-5 · Cited by 9
FINEMAP-miss: fine-mapping genome-wide association studies with missing genotype information
Kartau, J., & Pirinen, M. (2025) · medRxiv · doi:10.1093/bioinformatics/btaf616 · Cited by 2
“…practice to refine the GWAS results by fine-mapping methods, such as CAVIAR ( Hormozdiari et al. 2014), FINEMAP (Benner et al. 2016) or SuSiE (Zou et al. 2022), that jointly model the GWAS summary statistics of multiple variants and their LD to provide probabilistic inference on the causal…”
InfluentialGorski, M., Grunin, M. A., Herold, J. M., Fröhlich, B., Behr, M., Wheeler, N. R., Bush, W. S., Song, Y. E., Zhu, X., Blanton, S. H., et al. (2025) · Investigative Ophthalmology & Visual Science · doi:10.1167/iovs.66.13.51 · Cited by 3
Réal, A., Bp, K., Cuddleston, W., Muller, B., Jang, B., Tokolyi, A., Won, H.-H., Humphrey, J., Raj, T., & Knowles, D. (2025) · medRxiv · doi:10.1101/2025.09.25.25336663 · Cited by 2
“To identify putative causal variants underlying splicing regulation, we next applied Bayesian fine-mapping with SuSiE 2f shows genome-wide PIPs as a Manhattan-like plot).”
InfluentialGüler, M., & Canzian, F. (2025) · Blood Cancer Journal · doi:10.1038/s41408-025-01351-4 · Cited by 2
“To identify putative causal variants within associated loci, we performed statistical fi ne-mapping using the SuSiE (Sum of Single Effects) method [27] with both individual subtypes and phenocluster-level GWAS results.”
Yang, Y., Lorincz‐Comi, N. J., & Zhu, X. (2025) · Nature Communications · doi:10.1038/s41467-025-61423-8 · Cited by 1
Shared loci but distinct variants underlie genetic architecture of allergic diseasesPreprint
Salamone, I. M., Zhong, X., Sakabe, N. J., Mitchell, R., Wentworth-Sheilds, W., Zhang, L., Thompson, E. E., Decker, D. C., Rische, C. H., Liu, X., et al. (2025) · medRxiv · doi:10.1101/2025.06.06.25329154
“Next, we fine-mapped significant loci for each trait using SuSiE-RSS ( 31, 32 ).”
Comprehensive molecular impact mapping of common and rare variants at GWAS loci
Balderson, B., Tule, S., Okino, M.-L., Rieger, W. J., Corban, S., Jaureguy, J., Palpant, N., Gaulton, K., Yengo, L., Bodén, M., et al. (2025) · bioRxiv · doi:10.1101/2025.06.05.658079 · Cited by 2
“Moreover, how the causal variants affect genome function in specific cellular contexts is largely unknown 2 .”
Metadata
- Authors
- Zou, Y., Carbonetto, P., Wang, G., & Stephens, M.
- Affiliations
- University of Chicago (US)
- Columbia University (US)
- Corresponding author
- Stephens, Matthew
- Venue
- PLOS Genetics
- Publisher
- Public Library of Science (PLoS)
- Published
- 2022-07-19
- Volume
- 18
- Issue
- 7
- Pages
- e1010299
- ISSN
- 1553-7390 (ISSN-L)
- 1553-7404
- PMC
- PMC9337707
- OpenAlex
- W4286218496
- Semantic Scholar
- 9397075def54e53ddb3d934e9e45679c4cdb0422
- Cited by
- 429
- References
- 52
- Citations by year
- 2026: 134 · 2025: 123 · 2024: 103 · 2023: 50 · 2022: 18
- FWCI
- 45.68
- Type
- journal article
- Language
- en
- Update notices
- new_version (2022-07-29)
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS Genetics (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Knowledge@UChicago (University of Chicago) (submittedVersion, cc-by-nc-nd)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- University of Chicago (cc-by)
- Funders
- National Human Genome Research Institute (R01HG002585)
- Gordon and Betty Moore Foundation
- National Institute on Aging (U01AG072572)
- Thompson Family Foundation Initative (TAME-AD)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic and phenotypic traits in livestock
- Keywords
- fine mapping, summary data, linkage disequilibrium, caviar, model fitting, model refinement, genetic association studies
- MeSH terms
- Models, Genetic, Reproducibility of Results, Linkage Disequilibrium, Likelihood Functions, Polymorphism, Single Nucleotide
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