Fine-mapping from summary data with the “Sum of Single Effects” model

Zou, Yuxin; Carbonetto, Peter; Wang, Gao; Stephens, Matthew

doi:10.1371/journal.pgen.1010299

Cited by 429Genetic Associations and EpidemiologyGenetic Mapping and Diversity in Plants and AnimalsGenetic and phenotypic traits in livestock

Fine-mapping from summary data with the “Sum of Single Effects” model

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Zou, Yuxin, Carbonetto, Peter, Wang, Gao, Stephens, Matthew·2022·PLOS Genetics

DOI 10.1371/journal.pgen.1010299PMC PMC9337707

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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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LD consistency between discovery and reference samples in the EAS population was high; s values from SuSiE-RSS ranged from 0.007 to 0.027 across fine-mapped loci.
“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]
The Description of the method section explains how SuSiE-RSS assesses consistency between summary statistics and a reference-panel LD matrix, including diagnostic plots. But our copy does not report EAS fine-mapped loci, s values of 0.007–0.027, or the claimed high LD consistency for those samples; those specific results are not supported by this source. (LLM, conf=0.82)
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METAL: fast and efficient meta-analysis of genomewide association scans
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A well-conditioned estimator for large-dimensional covariance matrices
Ledoit, O., & Wolf, M. (2004) · Journal of Multivariate Analysis · doi:10.1016/s0047-259x(03)00096-4 · Cited by 3,046 · cited 1× · see citing works
“A common way to improve estimation of covariance matrices is to use regularization [38], replacing R̂ with R̂l, R̂l ≔ ð1 lÞR̂0 þ lI; ð23Þ”
Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits
Genetic Investigation of ANthropometric Traits (GIANT) Consortium, Yang, J., DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) Consortium, Ferreira, T., Morris, A. P., Medland, S. E., Madden, P. A. F., Heath, A. C., Martin, N. G., Montgomery, G. W., et al. (2012) · Nature Genetics · doi:10.1038/ng.2213 · Cited by 1,712 · see citing works

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Sex-specific genetic architecture of blood pressure
Yang, M., Xu, C., Gupte, T. P., Hoffmann, T. J., Iribarren, C., Zhou, X., & Ganesh, S. K. (2024) · Nature Medicine · doi:10.1038/s41591-024-02858-2 · Cited by 52 · see citing works
“For each LD-pruned BP-associated locus identified within a ±125-kb window, SuSieR 95,96 was applied for fine-mapping and identification of possible causal regions using in-sample LD estimation by PLINK Article https://doi.org/10.1038/s41591-024-02858-2 based on a posterior inclusion probability…”
Influential
Towards improved fine-mapping of candidate causal variants
Li, Z., & Zhou, X. (2025) · Nature Reviews Genetics · doi:10.1038/s41576-025-00869-4 · Cited by 38 · see citing works
“Specifically, DENTIST 33 and SuSiER (with the kriging_rss function) 30 assume that the effect of any single SNP is negligible.”
Influential
Genome-wide analysis of a model-derived binge-eating disorder phenotype identifies risk loci and implicates iron metabolism
Burstein, D., Griffen, T., Therrien, K., Bendl, J., Venkatesh, S., Dong, P., Modabbernia, A., Zeng, B., Mathur, D., Hoffman, G., et al. (2023) · Nature Genetics · doi:10.1038/s41588-023-01464-1 · Cited by 36 · see citing works
Genetic architecture distinguishes tinnitus from hearing loss
Clifford, R. E., Maihofer, A. X., Chatzinakos, C., Coleman, J. R. I., Daskalakis, N. P., Gasperi, M., Hogan, K., Mikita, E. A., Stein, M. B., Tcheandjieu, C., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-44842-x · Cited by 38 · see citing works
“Tinnitus risk loci were ﬁ ne-mapped using SUSIE 104 , with the maximum number of causal SNPs set to 2, per SNP herit-abilities used as priors, pre-computed UKB based summary LD information used as the LD reference, and loci start and end positions as determined by FUMA.”
Genetic correlates of vitamin D-binding protein and 25-hydroxyvitamin D in neonatal dried blood spots
Albiñana, C., Zhu, Z., Borbye‐Lorenzen, N., Boelt, S. G., Cohen, A. S., Skogstrand, K., Wray, N. R., Revez, J. A., Privé, F., Petersen, L. V., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-36392-5 · Cited by 35 · see citing works
“…of the GWAS summary statistic results was performed using a combination of (a) PolyFun 42 for computing prior causal probabilities based on functional annotations and (b) SuSiE 94 which ﬁ ne-maps the variants and provides posterior inclusion probabilities (PIPs) and credible sets of variants.”
Influential
Fast and flexible joint fine-mapping of multiple traits via the Sum of Single Effects model
Zou, Y., Carbonetto, P. S., Xie, D., Wang, G. T., & Stephens, M. (2026) · Nature Genetics · doi:10.1038/s41588-025-02486-7 · Cited by 41 · see citing works
“We compared mvSuSiE with existing multitrait fine-mapping meth-ods and a single-trait fine-mapping method, SuSiE 13,16 , in simulations.”
InfluentialSelf-citation
A flexible modeling and inference framework for estimating variant effect sizes from GWAS summary statisticsPreprint
Spence, J. P., Sinnott-Armstrong, N., Assimes, T. L., & Pritchard, J. K. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.04.18.488696 · Cited by 35 · see citing works
Cross-ancestry genome-wide association study and systems-level integrative analyses implicate new risk genes and therapeutic targets for depression
Li, Y., Dang, X., Chen, R., Teng, Z., Wang, J., Li, S., Yue, Y., Mitchell, B. L., Zeng, Y., Yao, Y.-G., et al. (2025) · Nature Human Behaviour · doi:10.1038/s41562-024-02073-6 · Cited by 30 · see citing works
Robust use of phenotypic heterogeneity at drug target genes for mechanistic insights: Application of cis‐multivariable Mendelian randomization to GLP1R gene region
Patel, A., Gill, D., Shungin, D., Mantzoros, C. S., Knudsen, L. B., Bowden, J., & Burgess, S. (2024) · Genetic Epidemiology · doi:10.1002/gepi.22551 · Cited by 29 · see citing works
“…using marginal genetic association estimates can suffer from numerical instability, as the matrix of correlations between genetic variants can be near singular (ill ‐ conditioned) even if pairwise correlations between the genetic variants are pruned at a threshold level (Zou et al., 2022).”
Background
XMAP: Cross-population fine-mapping by leveraging genetic diversity and accounting for confounding bias
Cai, M., Wang, Z., Xiao, J., Hu, X., Chen, G., & Yang, C. (2023) · Nature Communications · doi:10.1038/s41467-023-42614-7 · Cited by 26 · see citing works
A review of genetic risk in systemic lupus erythematosus
Guga, S., Wang, Y., Graham, D. S. C., & Vyse, T. J. (2023) · Expert Review of Clinical Immunology · doi:10.1080/1744666x.2023.2240959 · Cited by 35 · see citing works
“[26], we believe more data on applying this tool to fine map SNPs from GWAS in SLE will emerge in the future.”
Method
A spectrum of explainable and interpretable machine learning approaches for genomic studies
Conard, A. M., DenAdel, A., & Crawford, L. A. (2023) · Wiley Interdisciplinary Reviews Computational Statistics · doi:10.1002/wics.1617 · Cited by 30 · see citing works
“…intervals in the setting of highly collinear features (Du et al., 2021; Hormozdiari et al., 2014; Hutchinson et al., 2020; Wang et al., 2020; Zou et al., 2022), or simply taken to be T ¼ 0 : 5 to obtain an equivalence of the Bayesian “ median probability model ” (Barbieri et al., 2021;…”
Fast and Accurate Bayesian Polygenic Risk Modeling with Variational Inference
Zabad, S., Gravel, S., & Li, Y. (2022) · bioRxiv · doi:10.1101/2022.05.10.491396 · Cited by 29
“The weak performance of both VIPRS and SBayesR in this setting is notable, since both models iteratively estimate the residual variance s(2)e , whereas recent work recommended fixing this hyperparameter in the out-of-sample LD setting.(97) In our experiments, we did not see significant differences between VIPRS with f…”
BackgroundMethod
METRO: Multi-ancestry transcriptome-wide association studies for powerful gene-trait association detection.
Li, Z., Zhao, W., Shang, L.-L., Mosley, T., Kardia, S., Smith, J. A., & Zhou, X. (2022) · American Journal of Human Genetics · doi:10.1016/j.ajhg.2022.03.003 · Cited by 29 · see citing works
An efficient and robust tool for colocalisation: Pair-wise Conditional and Colocalisation (PWCoCo)Preprint
Robinson, J. W., Hemani, G., Babaei, M. S., Huang, Y., Baird, D. A., Tsai, E. A., Chen, C., Gaunt, T. R., & Zheng, J. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.08.08.503158 · Cited by 35 · see citing works
Population-specific polygenic risk scores for people of Han Chinese ancestry
Chen, H., Chen, C.-H., Hou, M., Fu, Y., Li, L., Chou, C.-Y., Yeh, E., Tsai, M.-F., Chen, C.-H., Yang, H., et al. (2025) · Nature · doi:10.1038/s41586-025-09350-y · Cited by 26 · see citing works
“SuSiE was conducted for this summary statistics-based fine mapping with linkage disequilibrium derived from our imputation reference panel 70 , which reflects our study population’s genetic architecture.”
Multi-ancestry GWAS meta-analyses of lung cancer reveal susceptibility loci and elucidate smoking-independent genetic risk
Gorman, B. R., Ji, S., Francis, M., Sendamarai, A. K., Shi, Y., Devineni, P., Saxena, U., Partan, E. S., DeVito, A. K., Byun, J., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-52129-4 · Cited by 35 · see citing works
SharePro: an accurate and efficient genetic colocalization method accounting for multiple causal signals
Zhang, W., Lu, T., SLADEK, R. M., Li, Y., Najafabadi, H. S., & Dupuis, J. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae295 · Cited by 28 · see citing works
“The eBMD measured at the heel using quantitative ultrasound is an important biomarker of osteoporosis and strongly predicts fracture risk (Morris et SharePro for colocalization 2021, 2022).”
BackgroundMethod
Diversity and Scale: Genetic Architecture of 2,068 Traits in the VA Million Veteran Program
Verma, A., Huffman, J., Rodriguez, A. A., Conery, M., Liu, M.-L., Ho, Y., Kim, Y., Heise, D., Guare, L., Panickan, V., et al. (2023) · medRxiv · doi:10.1101/2023.06.28.23291975 · Cited by 27
“Using fine-mapping with SuSiE, we identified 57,601 independent signals across 936 of the traits, including 14,405 signals that were fine-mapped to a single variant, enriched for annotations (e.g., coding variants) that a priori are likely causal.”
MethodInfluential
Sequenced-based GWAS for linear classification traits in Belgian Blue beef cattle reveals new coding variants in genes regulating body size in mammals
Duarte, J. L. G., Yuan, C., Gori, A.-S., Moreira, G. C. M., Takeda, H., Coppieters, W. R., Charlier, C., Georges, M., & Druet, T. (2023) · Genetics Selection Evolution · doi:10.1186/s12711-023-00857-4 · Cited by 30 · see citing works
“First, an iterative Bayesian step-wise selection (IBSS) approach implemented in SuSiE [46, 47], relying on summary statistics obtained from the ST-GWAS and on the linkage disequilibrium (LD) pattern among SNPs, was applied to identify a CS of SNPs with a probability higher than 0.95 of containing…”
MethodInfluential
GWAShug: a comprehensive platform for decoding the shared genetic basis between complex traits based on summary statistics
Cao, C., Tian, M., Li, Z., Zhu, W., Huang, P., & Yang, S. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae873 · Cited by 24 · see citing works
“These tools include genetic correlation ( 12 ,13 ), Mendelian randomization (MR) ( 14 ), fine mapping ( 15 ), transcriptome-wide association studies (TWAS) ( 16 ,17 ) and colocalization analysis ( 18 ,19 ).”
A multi-ancestry GWAS of Fuchs corneal dystrophy highlights the contributions of laminins, collagen, and endothelial cell regulation
Gorman, B. R., Francis, M., Nealon, C. L., Halladay, C. W., Duro, N., Markianos, K., Genovese, G., Hysi, P. G., Choquet, H., Afshari, N. A., et al. (2024) · Communications Biology · doi:10.1038/s42003-024-06046-3 · Cited by 23 · see citing works
Multi-ancestry GWAS of Fuchs corneal dystrophy highlights roles of laminins, collagen, and endothelial cell regulation
(2023) · doi:10.21203/rs.3.rs-2762003/v1 · Cited by 23 · see citing works
Cross-ancestry analysis of brain QTLs enhances interpretation of schizophrenia genome-wide association studies
Chen, Y., Liu, S., Ren, Z., Wang, F., Liang, Q.-M., Jiang, Y., Dai, R.-J., Duan, F., Han, C., Ning, Z.-L., et al. (2024) · American Journal of Human Genetics · doi:10.1016/j.ajhg.2024.09.001 · Cited by 22 · see citing works
Whole-genome sequencing analysis reveals new susceptibility loci and structural variants associated with progressive supranuclear palsy
Wang, H., Chang, T. S., Dombroski, B. A., Cheng, P., Patil, V., Valiente‐Banuet, L., Farrell, K. W., McLean, C., Molina‐Porcel, L., Rajput, A. H., et al. (2024) · Molecular Neurodegeneration · doi:10.1186/s13024-024-00747-3 · Cited by 35 · see citing works
“For H1/H2 region, fine-mapping were analyzed using SuSie [26].”
Method
Proteogenomic network analysis reveals dysregulated mechanisms and potential mediators in Parkinson’s disease
Torshizi, A. D., Truong, D. T., Hou, L., Smets, B., Whelan, C. D., & Li, S. (2024) · Nature Communications · doi:10.1038/s41467-024-50718-x · Cited by 26 · see citing works
Exploring the genetic basis of coronary artery disease using functional genomics
Rodríguez, M. L., Arasu, U. T., & Kaikkonen, M. U. (2023) · Atherosclerosis · doi:10.1016/j.atherosclerosis.2023.01.019 · Cited by 24 · see citing works
Single-cell atlas of the transcriptome and chromatin accessibility in the human retina
Li, J., Wang, J., Ibarra, I. L., Cheng, X., Luecken, M. D., Lu, J., Monavarfeshani, A., Yan, W., Zheng, Y., Zuo, Z., et al. (2026) · Nature Genetics · doi:10.1038/s41588-025-02454-1 · Cited by 62 · see citing works
“Using GWAS summary statistics and the ‘Sum of Single Effects’ statistical model (susieR) 61,62 , we identified 18,805 variants within 95% credible sets.”
Trans-ancestry GWAS identifies 59 loci and improves risk prediction and fine-mapping for kidney stone disease
Cao, X., Jiang, M.-H., Guan, Y., Li, S., Duan, C., Gong, Y., Kong, Y., Shao, Z., Wu, H., Yao, X., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-58782-7 · Cited by 21 · see citing works
Joint genotypic and phenotypic outcome modeling improves base editing variant effect quantification
Ryu, J., Barkal, S., Yu, T., Jankowiak, M., Zhou, Y., Francoeur, M., Phan, Q. V., Li, Z., Tognon, M., Brown, L., et al. (2024) · Nature Genetics · doi:10.1038/s41588-024-01726-6 · Cited by 33 · see citing works
Variants in tubule epithelial regulatory elements mediate most heritable differences in human kidney function
Loeb, G. B., Kathail, P., Shuai, R. W., Chung, R., Grona, R. J., Peddada, S., Sevi̇m, V., Federman, S., Mader, K., Chu, A. Y., et al. (2024) · Nature Genetics · doi:10.1038/s41588-024-01904-6 · Cited by 32 · see citing works
“Fine-mapping without functional annotations was performed using PolyFun with SuSiE 116 (option, non-funct).”
Finemap-MiXeR: A variational Bayesian approach for genetic finemapping
Akdeniz, B. C., Frei, O., Shadrin, A. A., Vetrov, D., Kropotov, D., Hovig, E. J., Andreassen, O. A., & Dale, A. M. (2024) · PLoS Genetics · doi:10.1371/journal.pgen.1011372 · Cited by 21 · see citing works
“The remaining authorshave no competinginterest. algorithm requires individual-level genotype and phenotype data as input, it has been expanded to SuSiE Regression Summary Statistics (RSS) method which only requires sum-mary statistics-level data [17].”
Method
Latin American Trans‐ancestry INitiative for OCD genomics (LATINO): Study protocol
Crowley, J. J., Cappi, C., Ochoa-Panaifo, M. E., Frederick, R. M., Kook, M., Wiese, A. D., Rancourt, D., Atkinson, E. G., Giusti‐Rodríguez, P., Anderberg, J. L., et al. (2023) · American Journal of Medical Genetics Part B Neuropsychiatric Genetics · doi:10.1002/ajmg.b.32962 · Cited by 20 · see citing works
“We will utilize SuSiEx (SUm of SIngle Effects trans-ancestry), which is a new cross-population fine-mapping extension of SuSiE (Zou et al., 2022).”
BackgroundMethod
Shared genetic architectures of educational attainment in East Asian and European populations
Chen, T.-T., Kim, J., Lam, M., Chuang, Y.-F., Chiu, Y.-L., Lin, S.-C., Jung, S.-H., Kim, B., Kim, S., Cho, C., et al. (2024) · Nature Human Behaviour · doi:10.1038/s41562-023-01781-9 · Cited by 18 · see citing works
“Moreover, as the mismatch of the LD patterns between the reference panel and the GWAS discovery sample may bias the results for fine-mapping, we applied the SuSiE-RSS model 34 to evaluate the consistency of the LD using the susieR package v0.”
Influential
The goldmine of GWAS summary statistics: a systematic review of methods and tools
Kontou, P. I., & Bagos, P. G. (2024) · BioData Mining · doi:10.1186/s13040-024-00385-x · Cited by 21 · see citing works
“SusieR [221], BVS-PICA [222] and JAM [223], operate also in a Bayesian regression framework performing variable selection and penalized regression.”
Method
Liver eQTL meta-analysis illuminates potential molecular mechanisms of cardiometabolic traits.
Broadaway, K. A., Brotman, S. M., Rosen, J. D., Currin, K. W., Alkhawaja, A. A., Etheridge, A., Wright, F., Gallins, P., Jima, D. D., Zhou, Y.-H., et al. (2024) · American Journal of Human Genetics · doi:10.1016/j.ajhg.2024.07.017 · Cited by 18 · see citing works
A noncoding regulatory variant in IKZF1 increases acute lymphoblastic leukemia risk in Hispanic/Latino children
de Smith, A. J., Wahlster, L., Jeon, S., Kachuri, L., Black, S., Langie, J., Cato, L. D., Nakatsuka, N. J., Chan, T. F., Xia, G., et al. (2024) · Cell Genomics · doi:10.1016/j.xgen.2024.100526 · Cited by 22 · see citing works
“To ensure our GWAS test statistics were robust to residualized LD estimates and reduce false positives due to misassigned ref/alt alleles (i.e., allele ﬂips), we performed a kriging analysis using SuSiE.”
Influential
150 risk variants for diverticular disease of intestine prioritize cell types and enable polygenic prediction of disease susceptibility
Wu, Y., Goleva, S. B., Breidenbach, L. B., Kim, M., MacGregor, S., Gandal, M. J., Davis, L. K., & Wray, N. R. (2023) · Cell Genomics · doi:10.1016/j.xgen.2023.100326 · Cited by 17 · see citing works
Impact of loss-of-function in angiopoietin-like 4 on the human phenome
Gagnon, E., Bourgault, J., Gobeil, É., Thériault, S., & Arsenault, B. J. (2024) · Atherosclerosis · doi:10.1016/j.atherosclerosis.2024.117558 · Cited by 17 · see citing works
Impact of loss-of-function in angiopoietin-like 4 on the human phenome
Gagnon, É., Bourgault, J., Gobeil, É., Thériault, S., & Arsenault, B. (2024) · medRxiv · doi:10.1101/2024.02.13.24302776 · Cited by 17
“SuSIE was computed with a LD matrix obtained using the 1000-128 genome reference panel of individuals of European ancestry (Zou et al. 2022).”
Method
Accurate and efficient estimation of local heritability using summary statistics and the linkage disequilibrium matrix
Li, H., Mazumder, R., & Lin, X. (2023) · Nature Communications · doi:10.1038/s41467-023-43565-9 · Cited by 16 · see citing works
The ChickenGTEx pilot analysis: a reference of regulatory variants across 28 chicken tissues
Guan, D., Bai, Z., Zhu, X.-N., Zhong, C., Hou, Y., Lan, F.-R., Diao, S., Yao, Y., Zhao, B.-R., Zhu, D., et al. (2023) · bioRxiv · doi:10.1101/2023.06.27.546670 · Cited by 16
Meta-analysis of ACE inhibitor-induced angioedema identifies novel risk locus.
Mathey, C. M., Maj, C., Eriksson, N., Krebs, K., Westmeier, J., David, F. S., Koromina, M., Scheer, A. B., Szabo, N., Wedi, B., et al. (2024) · Journal of Allergy and Clinical Immunology · doi:10.1016/j.jaci.2023.11.921 · Cited by 15 · see citing works
Biological insights into schizophrenia from ancestrally diverse populations
Bigdeli, T. B., Chatzinakos, C., Bendl, J. M., Barr, P. B., Venkatesh, S., Gorman, B. R., Clarence, T., Genovese, G., Iyegbe, C. O., Peterson, R. E., et al. (2026) · Nature · doi:10.1038/s41586-025-10000-6 · Cited by 21 · see citing works
Higher eQTL power reveals signals that boost GWAS colocalization
Rosen, J. D., Broadaway, K. A., Brotman, S. M., Mohlke, K., & Love, M. I. (2025) · bioRxiv · doi:10.1016/j.ajhg.2026.02.009 · Cited by 15 · see citing works
Exome-wide analysis of copy number variation shows association of the human leukocyte antigen region with asthma in UK Biobank
Fawcett, K. A., Demidov, G. M., Shrine, N. R. G., Paynton, M. L., Ossowski, S., Sayers, I., Wain, L. V., & Hollox, E. J. (2022) · BMC Medical Genomics · doi:10.1186/s12920-022-01268-y · Cited by 16 · see citing works
“These imputed genotypes were then used alongside imputed SNPs and CNVs of interest to fine-map the signals of genetic association within the HLA region using a Bayesian method (Susie) [16].”
Method
Leveraging information between multiple population groups and traits improves fine-mapping resolution
Zhou, F., Soremekun, O. S., Chikowore, T., Fatumo, S., Barroso, I., Morris, A. P., & Asimit, J. (2023) · bioRxiv · doi:10.1038/s41467-023-43159-5 · Cited by 14
Deciphering the Genetic Architecture of Parkinson’s Disease in IndiaPreprint
Kishore, A., Sreelatha, A. A. K., Tenghe, A., Borgohain, R., Puthanveedu, D. K., Rajan, R., Urulangodi, M., Gonzalez-Ricardo, L.-G., Pal, P. K., Kandadai, R. M., et al. (2025) · medRxiv · doi:10.1101/2025.02.17.25322132 · Cited by 13 · see citing works
“The largest East Asian meta-analyses identified two novel Asian-specific loci for PD, SV2C (rs246814) and WBSCR17 (rs9638616) 25 .”
European and African ancestry-specific plasma protein-QTL and metabolite-QTL analyses identify ancestry-specific T2D effector proteins and metabolites
Yang, C., Gorijala, P., Timsina, J., Wang, L., Liu, M., Wang, C., Brock, W., Wang, Y., Urano, F., Sung, Y. J., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-62463-w · Cited by 13 · see citing works
Integration of biobank-scale genetics and plasma proteomics reveals evidence for causal processes in asthma risk and heterogeneity
Donoghue, L. J., Benner, C., Chang, D., Irudayanathan, F. J., Pendergrass, R. K., Yaspan, B. L., Mahajan, A., & McCarthy, M. I. (2025) · Cell Genomics · doi:10.1016/j.xgen.2025.100840 · Cited by 8 · see citing works
“Using coloc-SuSiE, 53–55 which allows for the existence of multiple causal variants at a locus, we identiﬁed at least one pQTL colocalizing with asthma risk for 7 of the 16 proteins (H4 PP > 0.80).”
Influential
Fine-mapping causal tissues and genes at disease-associated loci
Strober, B. J., Zhang, M. J., Amariuta, T., Rossen, J., & Price, A. L. (2025) · Nature Genetics · doi:10.1038/s41588-024-01994-2 · Cited by 22 · see citing works
“TGFM uses a custom implementation of the SuSiE 30,31 algorithm that provides efficient estimation of PIPs across 100 parallel runs in which each of 100 parallel SuSiE runs differ only in their cis -predicted expression models.”
Influential
Discovery of 95 PTSD loci provides insight into genetic architecture and neurobiology of trauma and stress-related disordersPreprint
Nievergelt, C. M., Maihofer, A. X., Atkinson, E. G., Chen, C., Choi, K. W., Coleman, J. R. I., Daskalakis, N. P., Duncan, L. E., Polimanti, R. A., Aaronson, C. J., et al. (2023) · medRxiv · doi:10.1101/2023.08.31.23294915 · Cited by 11 · see citing works
Fine-mapping of retinal vascular complexity loci identifies Notch regulation as a shared mechanism with myocardial infarction outcomes
Villaplana-Velasco, A., Pigeyre, M., Engelmann, J., Rawlik, K., Canela‐Xandri, O., Tochel, C., Lona‐Durazo, F., Mookiah, M. R. K., Doney, A. S. F., Parra, E. J., et al. (2023) · Communications Biology · doi:10.1038/s42003-023-04836-9 · Cited by 11 · see citing works
“Fine-mapping of signi ﬁ cant D f SNPs was completed with SusieR v.0.11.42 R package 79 .”
Influential
Genome-Wide Association Study of COVID-19 Outcomes Reveals Novel Host Genetic Risk Loci in the Serbian Population
Zecevic, M., Kotur, N., Ristivojević, B., Gašić, V., Škodrić-Trifunović, V., Stjepanović, M., Stevanović, G., Lavadinović, L., Zukić, B., Pavlović, S., et al. (2022) · Frontiers in Genetics · doi:10.3389/fgene.2022.911010 · Cited by 17 · see citing works
SparsePro: An efficient fine-mapping method integrating summary statistics and functional annotations
Zhang, W., Najafabadi, H., & Li, Y. (2021) · bioRxiv · doi:10.1371/journal.pgen.1011104 · Cited by 12
“SparsePro, similar to Zou et al [14], does not require a full-rank LD matrix as it does not require matrix inversion throughout the algorithm.”
Genetic determinants of plasma protein levels in the Estonian population
Kalnapenkis, A., Jõeloo, M., Lepik, K., Kukuškina, V., Kals, M., Alasoo, K., Estonian Biobank Research Team, Metspalu, A., Milani, L., Nelis, M., et al. (2024) · Scientific Reports · doi:10.1038/s41598-024-57966-3 · Cited by 11 · see citing works
Rare genetic variation in PTPRB is associated with central serous chorioretinopathy, varicose veins and glaucoma
Rämö, J. T., Gorman, B. R., Weng, L.-C. C., Jurgens, S. J., Singhanetr, P., Tieger, M. G., van Dijk, E. H. C., Halladay, C. W., Wang, X., Hauser, B. M., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-58686-6 · Cited by 12 · see citing works
Depression symptom-specific genetic associations in clinically diagnosed and proxy case Alzheimer’s disease
Gilchrist, L., Spargo, T. P., Green, R., Coleman, J. R. I., Howard, D. M., Thorp, J. G., Adey, B. N., Lord, J., Davies, H. L., Mundy, J., et al. (2025) · Nature Mental Health · doi:10.1038/s44220-024-00369-0 · Cited by 12 · see citing works
ProteoNexus: an integrative database to characterize genetic architecture, estimate mediation effects, and construct and evaluate prediction models of the plasma proteome
Shao, K., Luo, Z., Huang, P., & Yang, S. (2025) · Nucleic Acids Research · doi:10.1093/nar/gkaf1058 · Cited by 12 · see citing works
“Furthermore, for summary statistics of each protein, we used SuSiE to estimate the PIP of each SNP within any credible set [ 32 ].”
Genome-wide association study and fine-mapping using imputed sequences to prioritize candidate genes for 30 complex traits in 50,309 Holstein bulls.
Wang, J.-J., Gao, Y., Toghiani, S., Cole, J., Maltecca, C., Ma, L., & Jiang, J.-C. (2025) · Journal of Dairy Science · doi:10.3168/jds.2025-27058 · Cited by 10 · see citing works
Genomic loci and molecular genetic mechanisms for hidradenitis suppurativa
Broadaway, K. A., Sun, Q., Edmiston, S., Currin, K. W., Vadlamudi, S., Miller-Fleming, T., Shi, Y., Fajgenbaum, K., Melendez-Gonzalez, M., Bui, H., et al. (2025) · British Journal of Dermatology · doi:10.1093/bjd/ljaf277 · Cited by 10
“19 To match a signal to a credible set, we required that the lead variant be in the credible set.”
Integration of functional genomics and statistical fine-mapping systematically characterizes adult-onset and childhood-onset asthma genetic associations
Zhong, X., Mitchell, R., Billstrand, C., Thompson, E. E., Sakabe, N. J., Aneas, I., Salamone, I. M., Gu, J., Sperling, A. I., Schoettler, N., et al. (2025) · Genome Medicine · doi:10.1186/s13073-025-01459-z · Cited by 16 · see citing works
“We ran the susie_rss() function from susieR R package [8,9] version 0.12.35 on each significant block, which takes GWAS z-scores and correlations of SNPs in the LD block as input and estimates a posterior inclusion probability (PIP) for each SNP.”
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Influential

Authors: Zou, Y., Carbonetto, P., Wang, G., & Stephens, M.
Affiliations: University of Chicago (US)
Columbia University (US)
Corresponding author: Stephens, Matthew
ORCID: Zou, Yuxin
Carbonetto, Peter
Wang, Gao
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
DOI: 10.1371/journal.pgen.1010299
PMC: PMC9337707
OpenAlex: W4286218496
Cited by: 429
References: 52
Citations by year: 2026: 134 · 2025: 123 · 2024: 103 · 2023: 50 · 2022: 18 · 2021: 1
FWCI: 45.68
Type: journal article
Language: en
Update notices: new_version (2022-07-29)
Open access: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0/ (vor)
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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Fine-mapping from summary data with the “Sum of Single Effects” model

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