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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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“The SuSiE model has since been further extended to support fine mapping based on summary statistics, 13 to account for model mis-specification, 14 and to support cross-population fine mapping.”
Regulatory landscape of Alzheimer’s disease variants in human microgliaPreprint
Lee, C.-Y. J., Ravi, A., Huang, T.-C., Wyman, B., Liu, Z., Chriss, A., Humphrey, J., Knowles, D. A., Cotsapas, C. J., Huckins, L. M., et al. (2025) · medRxiv · doi:10.1101/2025.10.13.25337902 · Cited by 2
“21,101,114 variants passed quality control and variants below p=1x10-5 were selected.”
Genetic correlation analysis identifies TMEM106B , ACE , and ERC2 as genetic loci shared between Alzheimer's disease and primary psychiatric disorders
Kumar, A., Ray, N. R., Kurup, J. T., del Rosario, P., Manoochehri, M., Stein, C. M., Vito, A. N. D., Cholerton, B., Sweet, R. A., Jager, P. L. D., et al. (2026) · Alzheimer s & Dementia · doi:10.1002/alz.71278 · Cited by 2
“26,27 Variants and loci identified in these analyses were further scrutinized employing ADSP R4 whole-genome sequencing data, information from the AGORA portal (https://agora.adknowledgeportal.org/), 28 multi-omic data from ONTIME (https://ontime.wustl.edu/), methylation and histone modification…”
An integrated multi-tissue atlas of epigenomic landscapes and regulatory elements in the bovine genomePreprint
Guan, D., Bruscadin, J. J., Yang, W., Prowse-Wilkins, C., Wang, J., Petry, B., Li, H., Zhang, H., Xu, X., Wang, Y., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.08.21.671512 · Cited by 4
“(g) Fine-mapping a cattle GWAS locus for protein percentage in milk using regulatory magnitude as prior weights in SuSiE-adj 97,104 .”
Self-citation
Logica: A likelihood framework for cross-ancestry local genetic correlation estimation using summary statistics
Gao, B., Li, Z., & Zhou, X. (2025) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2025.10.001 · Cited by 2
“…from X EUR and 200 individuals of East Asian ancestry from X EAS to compute the SNP-SNP correlation matrices Σ EUR and Σ EAS following Zou et al. 31,32 and Weissbrod et al. 31,32 To further assess robustness with smaller reference panels, we additionally considered 503 European individuals and…”
MACHINE: a robust and scalable multi-ancestry fine-mapping method using a continuous global-local shrinkage priorPreprint
Li, X., Xiong, Z., Sham, P. C., & Zhang, Y. (2025) · medRxiv · doi:10.1101/2025.09.28.25336857 · Cited by 1
“Among these methods, XMAP, MESuSiE, SuSiEx, and MultiSuSiE are all multi-ancestry extensions of SuSiE 14,15 , differing in their modeling assumptions, prior specifications, and posterior inference approaches (Table 1).”
Observational and Experimental Evidence on the Interaction Between Fine Particulate Matter and Shared Genetic Variants Across Atherosclerotic Cardiovascular Disease Subtypes
Zhang, J., Ran, S., Chen, L., Cai, M., Tian, F., Ai, B., Qian, S. E., Tabet, M., Howard, S. W., Yang, Y., et al. (2025) · Circulation Genomic and Precision Medicine · doi:10.1161/circgen.124.004986 · Cited by 2
Genome-wide iterative fine-mapping for non-Gaussian phenotypes
Xu, S., Williams, J., Tegge, A., & Ferreira, M. A. R. (2025) · Scientific Reports · doi:10.1038/s41598-025-09270-x · Cited by 1
“Specifically, we compare GINA-X to SuSiE-RSS 12 because: (1) SuSiE-RSS is based on summary statistics and, thus, may be used as an approximate method for the analysis of non-Gaussian phenotypes; (2) SuSiE-RSS performs similarly 12 to its sister method SuSiE 14 ; and (3) SuSiE has better performance…”
Influential
Widespread but moderate genetic overlap between circulating polyunsaturated fatty acids and brain disorders
Xu, H., Sun, Y., Francis, M., Cheng, C. F., Modulla, N. T. R., Brenna, J. T., Chiang, C. W. K., & Ye, K. (2025) · Journal of Lipid Research · doi:10.1016/j.jlr.2025.100890 · Cited by 5
“12.27) (50).”
Influential
Privacy-Preserving Multivariate Bayesian Regression Models for Overcoming Data Sharing Barriers in Health and GenomicsPreprint
Sørensen, I. F., & Sørensen, P. (2025) · medRxiv · doi:10.1101/2025.07.30.25332448
“This approach enables eﬀicient computation, reduces complexity, and preserves key relationships in the data 28,30 .”
A reference panel for linkage disequilibrium and genotype imputation using whole-genome sequencing data from 2,680 participants across India
Li, Z., Zhao, W., Zhou, X., Leung, Y.-Y., Schellenberg, G., Wang, L.-S., Dey, S., Lee, J., Smith, J. A., Dey, A., et al. (2025) · bioRxiv · doi:10.1101/2025.06.30.662450 · Cited by 1
A Genome-wide Association study of Buccal Mucosa Cancer in India and Multi-ancestry Meta-analysis Identifies Novel Risk Loci and Gene-environment InteractionsPreprint
Mhatre, S., Dutta, D., Iyer, A., Gholap, S., Mishra, A., Krishnatreya, M., George, G. S., Doibale, P., Dun, Y., Wang, Z.-Q., et al. (2025) · medRxiv · doi:10.1101/2025.04.16.25325815 · Cited by 1
Leveraging cell-type specificity and similarity improves single-cell eQTL fine-mapping
Lin, C., Lin, Y., Li, W., Xu, L., Zhang, X., & Zhao, H. (2026) · Nature Communications · doi:10.1038/s41467-026-72176-3 · Cited by 1
“(1) aligns with the well-studied single-trait ( C = 1) models 26 – 28 , as the diagonal elements are 1/ N c in V due to the standardized gene expressions.”
Psychiatric Genomics 2025
Liu, C., Zhang, C., & Glatt, S. J. (2025) · Psychiatric Clinics of North America · doi:10.1016/j.psc.2025.01.002 · Cited by 1
Diverse ancestry GWAS for advanced age-related macular degeneration in TOPMed-imputed and Ophthalmologically-confirmed 16,108 cases and 18,038 controlsPreprint
Gorski, M., Grunin, M., Herold, J. M., Fröhlich, B., Behr, M., Wheeler, N. R., Bush, W., Song, Y. E., Zhu, X.-F., Blanton, S., et al. (2024) · medRxiv · doi:10.1101/2024.11.08.24316962 · Cited by 1
“We also applied SuSiE for single-ancestry fine-mapping in EUR 20 to compare results.”
Method
Refining genetic discoveries of group knockoffs via a feature-level filter
Gu, J., Chen, Z., & He, Z. (2026) · Journal of the Royal Statistical Society Series C (Applied Statistics) · doi:10.1093/jrsssc/qlag006 · Cited by 3
“Another category of commonly used approaches are various Bayesian fine-mapping approaches (Wilson et al., 2010; Guan and Stephens, 2011; Hormozdiari et al., 2014; Chen et al., 2015; Benner et al., 2016; Wang et al., 2020; Zou et al., 2022; Li et al., 2024b).”
MR-GGI: accurate inference of gene–gene interactions using Mendelian randomization
Oh, W., Jung, J., & Joo, J. (2024) · BMC Bioinformatics · doi:10.1186/s12859-024-05808-4 · Cited by 1
“The F1-score is calculated as the harmonic mean of precision and recall [28], and it is a proper metric for evaluating model performance in imbalanced datasets.”
Method
A common NFKB1 variant detected through antibody analysis in UK Biobank predicts risk of infection and allergy
Chong, A. Y.-Y., Brenner, N., Jimenez-Kaufmann, A., Cortés, A., Hill, M., Littlejohns, T. J., Gilchrist, J. J., Fairfax, B. P., Knight, J. C., Hodel, F., et al. (2024) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2023.12.013 · Cited by 1
“11.92; Wang et al. and Zou et al. 45,46 ) and the intersection of variants from all GWAS phenotypes and the eQTL traits from the region immediately upstream of and across NFKB1.”
BackgroundInfluential
Sources of gene expression variation in a globally diverse human cohort
Taylor, D. J., Chhetri, S. B., Tassia, M. G., Biddanda, A., Battle, A., & McCoy, R. (2023) · bioRxiv · doi:10.1101/2023.11.04.565639 · Cited by 1
“With this goal in mind, we used SuSiE 24 to perform fine-mapping for all eGenes and the introns of all sGenes to identify causal variants driving each QTL signal.”
A novel Bayesian fine-mapping model using a continuous global-local shrinkage prior with applications in prostate cancer analysis
Li, X., Sham, P., & Zhang, Y. D. (2023) · medRxiv · doi:10.1101/2023.08.04.23293456 · Cited by 1
Validation of human telomere length trans-ancestry meta-analysis association signals identifies POP5 and KBTBD6 as novel human telomere length regulation genesPreprint
Keener, R., Chhetri, S. B., Connelly, C., Taub, M., Conomos, M. P., Weinstock, J., Ni, B., Strober, B., Aslibekyan, S., Auer, P., et al. (2023) · bioRxiv · doi:10.1101/2023.07.12.548702 · Cited by 1
“SuSiE identified two credible sets for the signal led by rs35510081 (Figure 2E).”
MethodInfluential
Genetic architecture and shared mechanisms of common 'neglected' diseases
Pietzner, M., Denaxas, S. C., Summaira, Y., WoÌrheide, M., Schmidt, H., Uluvar, B., Nepal, A., Koprulu, M., Carrasco-Zanini, J., Arnold, M., et al. (2023) · medRxiv · doi:10.1101/2023.05.23.23290408 · Cited by 1
Implication of FOXD2 dysfunction in syndromic congenital anomalies of the kidney and urinary tract (CAKUT)
Riedhammer, K., Nguyen, T.-M. T., Koşukcu, C., Calzada-Wack, J., Li, Y., Saygılı, S., Wimmers, V., Kim, G.-J., Chrysanthou, M., Bakey, Z., et al. (2023) · medRxiv · doi:10.1101/2023.03.21.23287206 · Cited by 1
“We therefore attempted to identify the 243 responsible variants underlying the association signal by using the statistical fine-mapping 244 method SuSiE.(34) SuSiE selects single nucleotide polymorphisms (SNPs) with a high probability 245 to causally affect a given trait - here the urinary albumin-to-creatinine ratio…”
Method
Inferring on Joint Associations From Marginal Associations and a Reference Sample
Frostig, T., & Heller, R. (2022) · Biometrical journal. Biometrische Zeitschrift · doi:10.1002/bimj.70114 · Cited by 1
Effects of introgressed Neanderthal alleles on present-day brain morphology
Zeloni, R., Amaolo, A., Morez Jacobs, A., Zapparoli, E., Akl, Y., Shafie, M., Huerta-Sanchez, E., Pizzagalli, F., Provero, P., Pagani, L., et al. (2026) · bioRxiv · doi:10.64898/2026.04.14.718380
“Fine-mapping was conducted using the Sum of Single Effects model implemented in the susieR R package 82 .”
Experimental benchmarking of in silico annotations at prostate cancer risk loci and hypothesis-generating cross-cancer prioritisation of ovarian risk variants
Al-Amili, W. A., Najm, M. A., Jumaah, S. S., & Suleiman, A. A. (2026) · Genomics & Informatics · doi:10.1186/s44342-026-00087-2
“Because coloc.abf assumes one causal variant per trait, we also ran coloc-SuSiE [22], fine-mapping each trait with SuSiE-RSS (susieR 0.14.2, L = 10) [23] using an LD matrix from 1000 Genomes phase 3 European samples (n = 503) [24], and we inter-sected GWAS 95% credible sets with GTEx v10 SuSiE eQTL…”
Influential
Alzheimer’s disease genetics converges on microglial and neuronal dysregulation
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. (2026) · Nature · doi:10.1038/s41586-026-11206-y
Unraveling the shared genetic architecture of metabolic bone diseases via genomic structural equation modeling and multi-omics integration
Yi, K., Wang, F., Hu, F., Tang, W., Liu, Y., Gao, B., Zhang, S., Jiang, L., Ding, J., Lei, D., et al. (2026) · PLoS ONE · doi:10.1371/journal.pone.0360386
“To prioritize the most probable putative candidate variants underlying the mvMBDs associations, we conducted rigorous statistical fine-mapping using the Sum of Single Effects (SuSiE) [19] and FINEMAP [20] algorithms, both implemented seamlessly within the echolocatoR R package (v.2.0.3) [21].”
Influential
Multivariate Genomic Analysis Reveals Shared Genetic Architecture and Potential Therapeutic Targets for Atherosclerotic Cardiovascular Disease
Xu, Y., Wang, J., Zhang, R., Hou, C., Sun, X., Li, Q., & Liu, C. (2026) · Journal of the American Heart Association · doi:10.1161/jaha.126.050689
Autoimmune hepatitis and primary biliary cholangitis are among a subset of autoimmune diseases defined by profound co-prevalence and genetic risk association with the IL12 pathway
Nakka, P., Hobbs, B. D., Sidore, C., Palmer, W. H., Murdock, D., Gurski, L., Zaretsky, A. G., Moll, K., Campos, A. I., Joshi, A., et al. (2026) · Hepatology · doi:10.1097/hep.0000000000001819
Engineered regulatory states resolve context-dependent genetics in macular degenerationPreprint
Metzner, E., Guruvayurappan, K., Southard, K. M., Ardy, R. C., Dey, K. K., & Norman, T. M. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.10.09.758047
“94,95 To nominate a larger set of variants for genetic epistasis testing, we ﬁrst performed a genome-wide ﬁne-mapping using SuSIE 96,97 on our AMD summary statistics (see Curation of AMD genetic association studies ).”
gRely: Relyability for genome-trained sequence-to-function model predictionsPreprint
Rafi, A. M., Eraslan, G., & Fletez‐Brant, K. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.05.23.727431
LinearPFN: Amortized Variable Selection for Linear Models with Interactions
Schiekiera, L., Zimmer, M., Roux, C., Arnold, M., Pokutta, S., & Günther, F. (2026) · doi:10.48550/arxiv.2610.06580
“First, when two predictors are nearly collinear, the posterior mass splits across them, both PIPs can fall below one half, and the MPM rule then selects neither (Zou et al., 2022).”
Multivariate GWAS Analysis Reveals a Common Genetic Factor Linking Dry Eye Disease to Gastrointestinal Comorbidities
Li, G., Lin, J., 唐帅, & Wang, Y. (2026) · Translational Vision Science & Technology · doi:10.1167/tvst.15.10.32
“To identify the most probable causal variants associated with mvDED-GI, we used Sum of Single Effects (SuSIE) and FINEMAP, 18,19 implemented in the R software package echolocatoR version 2.0.3.”
Influential
Applying AlphaGenome Variant Impact for SNV PrioritizationPreprint
Qu, H., & Hakonarson, H. H. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.09.26.754650
“SuSiE PIP reflects the posterior probability assigned to a variant as a causal contributor to an association signal under the fine-mapping model and local linkage disequilibrium (Wang et al. 2020; Zou et al. 2022), whereas AVI_QUANTILE reflects predicted biological and molecular effects.”
Marker Prioritization With Different Fine Mapping Strategies Improves Genomic Selection Performance.
Yao, D.-B., Tan, T.-T., Yang, C.-X., & Du, Z.-Q. (2026) · Animal Genetics · doi:10.1002/age.70220
Evidence-Calibrated Reanalysis of an Otosclerosis Locus Generates a Testable Myeloid State Hypothesis
Ma, W., Cheng, Y., Li, H., & Lv, H. (2026) · Genes · doi:10.3390/genes17101277
“SuSiE-RSS [20] was used to allow multiple regional signals and to generate credible sets from summary statistics.”
Genomic atlas of 7000 plasma proteins and their associations with diseases and traits in East Asian populations
Pozarickij, A., Wang, B., Mohamed, A., Lin, K., Morris, S., Kartsonaki, C., Wright, N., Fry, H., Chen, Y., Du, H., et al. (2026) · 中国科学通报：英文版 · doi:10.64898/2026.02.05.26345625 · Cited by 3
Human genetics implicates a BACH2–NRF2 axis in fetal haemoglobin activation
Guo, C., Arora, U. P., Cheng, X., Xu, W., Cato, L. D., Li, R., Lu, H. Y., Lee, A., Yu, F., Agarwal, G., et al. (2026) · Nature · doi:10.1038/s41586-026-11113-2 · Cited by 11
Genetic variation in HLA, IGKV, and HHEX loci influences mRNA vaccine-induced humoral protection against COVID-19.
Puzek, B., Wratil, P. R., Janke, C., Thi, T. L. L., Rubio-Acero, R., Lupoli, G., Schoof, I. C., Stern, M., Zhelyazkova, A., Rech, J., et al. (2026) · Cell Reports · doi:10.1016/j.celrep.2026.117981
Single-cell analysis of chromatin accessibility in the human intestine identifies regulatory programs and clarifies genetic associations in Crohn’s disease
Zhao, Y. N., Zhou, R., Mu, Z., Carbonetto, P. S., Zhong, X., Xie, B., Luo, K., Jiang, Z., Liu, J., Cham, C. M., et al. (2026) · Nature Genetics · doi:10.1038/s41588-026-02755-z · Cited by 2
“We conducted fine-mapping with summary statistics using the sum of single effects (SuSiE) method 65,66 , implemented in susieR (v0.”
Self-citation
ARCHER-LD: Rapid Long-Range Linkage Disequilibrium Calculations at Biobank Scale using GPU Acceleration
Kumar, R., Singhal, P., Zhang, D. Y., Carson, C., Conery, M., Rodriguez, A. A., Nandi, T., Ritchie, M. D., Thavappiragasam, M., Voight, B. F., et al. (2026) · bioRxiv · doi:10.64898/2026.09.15.751759
“This leads many researchers to use reference panels where LD has been precomputed in a different population, such as the 1000 Genomes dataset, rather than computing in-sample LD on their own dataset [6, 7].”
Genetic regulation of circulating metabolome in cattle
Teng, J., Li, H.-C., Yang, J., Lu, J.-S., Duan, C.-W., Chen, Z.-J., Zhang, X.-Y., Zhao, X.-X., Pei, F., Wu, X.-P., et al. (2026) · bioRxiv · doi:10.64898/2026.09.05.749592
Short-term, Long-term, and Genetic Determinants of Human Plasma Proteome VariabilityPreprint
Benacom, D., Specht, A., Nicholas, J. C., Guillard, R., Gillman, M. G., Dubin, R. F., Ganz, P., Rotter, J. I., Taylor, K. D., Rich, S. S., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.07.30.741635
Genetic Associations Among Appendicular Lean Mass, Alanine Aminotransferase, and Type 2 Diabetes: Multi-Dataset Mendelian Randomization and Colocalization at the SERPINA1 Candidate Locus
Wang, X., Yang, W., Song, S., Su, H., & Zhang, Y. (2026) · Genes · doi:10.3390/genes17091154
“The following supporting information can be downloaded at: https: //www.mdpi.com/article/10.3390/genes17091154/s1, Supplementary Methods S1–S12 [47,48,50,54– 64, 74”
Influential
Functional characterization of the 9q34.13 locus identifies RAPGEF1 as a candidate gene modulating risk for melanoma and nevi via RAS activation
Thakur, R., Xu, M., Thornock, A., Yon, J., Anyaso-Samuel, S., Lauss, M., Rehling, T., Bui, L. T., Sowards, H., Duncan, G., et al. (2026) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2026.08.014 · Cited by 1
Systematic benchmarking of multi-ancestry fine-mapping under variant heterogeneity and LD mismatch
Chen, G., Paschou, P., & Gao, B. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag574
GWAS Fine-Mapping for Soybean First Pod Insertion Height Identifies Candidate Genomic Regions Involved in Plant Development
Zorkoltseva, I. V., Kirichenko, A. V., Potapov, D. A., Kiryukhin, S. V., Гуринович, С. О., Panarina, V. I., Polyudina, R., Salina, E. A., & Svishcheva, G. R. (2026) · Genes · doi:10.3390/genes17090994
“To refine the localization of potentially causal variants, we performed fine-mapping using the SuSiE method implemented in the R package susieR (v.0.14.2) [6,7].”
Influential
Integrated proteomic, transcriptomic, and epigenomic profiling identifies SRA1 as a novel therapeutic target for postpartum depression
Chen, M., Wei, Q., Li, H., Ruan, H., Fu, L., Ma, J., Xia, X., Liao, Y., Hou, C., & Huang, H. (2026) · npj Mental Health Research · doi:10.1038/s44184-026-00232-3
“For each shared locus, we extracted summary statistics for variants located within 1 Mb of the target gene and computed the posterior probability for H4 (PPH4) (association with both traits through a shared SNP) using both the coloc.abf and coloc.susie algorithms 29,30 .”
High-resolution promoter interaction analysis implicates genes involved in activation of type 3 innate lymphoid cells in immune disease risk
Malysheva, V., Ray-Jones, H. F., Lakes, N., Brown, R. A., Cazares, T. A., Clay, O., Ohayon, D. E., Artemov, P., Wayman, J. A., Yang, Z. F., et al. (2026) · Nature Genetics · doi:10.1038/s41588-026-02681-0 · Cited by 6
“For blocks with appreciable association signals (minimum P 10 −6 ), we used the susieR package 37,38 to fine-map the data.”
Multi-omic characterization of the Hispanic/Latino blood lipidome reveals an additional locus and attenuated genetic prediction
Zhu, W., Chen, H., Highland, H. M., Giles, C., Meikle, P. J., Lin, P., Petty, L. E., Polikowsky, H. G., Petty, A. S., Anwar, M. Y., et al. (2026) · Human Genetics and Genomics Advances · doi:10.1016/j.xhgg.2026.100664
“50,51 Integrating the lead SNPs and variants from the fine-mapped CSs, we mapped 577 associations to 208 unique genes using the Open Targets Genetics platform, 61,62 which prioritizes potential target genes using genetic location, molecular QTLs, chromatin interaction, and in silico functional…”
Unraveling the dual role of fibroblast growth factor 5 (FGF5) in cardiorenal diseases through multi-omics causal inference
Zan, T., Wen, Z., Jia, H., Dong, X., & Shen, N. (2026) · Cardiovascular Diabetology · doi:10.1186/s12933-026-03309-7
Exome-wide association study reveals common and rare coding variants shaping chronic pain in 327,642 UK biobank participants
Ao, X., Kolifarhood, G., Parisien, M., Bortsov, A. V., Grant, A. V., & Diatchenko, L. B. (2026) · Genome Medicine · doi:10.1186/s13073-026-01732-9
“12.35) [37, 38], which leveraged ExWAS sum-mary statistics together with in-sample WES genotypes.”
Influential
A genetic signal at 8q12.3 modulates GGT levels via the Runx1–CYP7B1 axis in female ethnic minorities from Guizhou
Yao, Q., Chen, J., Yang, T., Lei, C., Zeng, S., Luo, F., Hong, F., & Jin, X. (2026) · Molecular Genetics and Genomics · doi:10.1007/s00438-026-02484-2
“3 locus, we conducted fine-mapping analyses using susieR (Wang et al. 2020; Zou et al. 2022).”
Influential
Integrating genomic structural equation modeling and experimental validation to unravel the genetic basis of male genital lichen sclerosus
Chen, J., Zhang, Z., Fu, Q., Hou, H., Xu, G., Li, Z., Lu, Z., Qiu, J., Wu, K., Gao, X., et al. (2026) · Frontiers in Immunology · doi:10.3389/fimmu.2026.1857945
“To identify the most likely causal variants associated with our Genomic-SEM, we applied two ﬁ ne-mapping tools — Sum of Single Effects (SuSIE) and Fine-Mapping (FINEMAP) (11, 12) — implemented via the R package echolocatoR v.2.0.3.”
Influential
Integrative multi-omics reveals MHC class II-mediated neuroinflammation and systemic metabolic dysregulation as transdiagnostic drivers in major brain disorders
Xu, X., Fan, S.-S., Wu, H., Du, Y., Bo, Y., He, J., He, Y., Cheng, N., & Meng, X. (2026) · Translational Psychiatry · doi:10.1038/s41398-026-04280-3 · Cited by 1
“This approach models the genetic architecture as a sparse sum of independent causal effects, allowing for the calculation of Posterior Inclusion Probabilities (PIPs) and the execution of conditional analyses using summary data (susie_rss) [26].”
Current challenges in GWAS integration and fine-mapping for variant interpretationPreprint
Ahmed, O. Y., Saravanan, N., Rovsing, A. B., Simpson, D., Devarajan, A., Gunn, S., Singh, T., Lappalainen, T., & Sanjana, N. E. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.07.04.736511 · Cited by 1
“We then performed fine-mapping using Sum of Single Effects regression (SuSiE) 30 with the simulated summary statistics and ancestry-matched LD matrix from PanUKBB 31 .”
Linkage disequilibrium and allelic heterogeneity explain variation in coronary artery disease risk at 9p21 across populations and reduced effect in Africans
Alkhairo, H., Koyama, S., Iyer, K. R., Hilliard, A. T., Kho, P. F., Clarke, S. L., Abdulwahab, F., Assimes, T. L., Lynch, J. A., Ramsay, M., et al. (2026) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2026.06.007
Multilayer Genomic Characterization of a Shared Genetic Factor Linking Depression-Related Liability and Reduced Physical Function
Zeng, W., Yang, X., & Xu, Y. (2026) · Genes · doi:10.3390/genes17070813
“Fine-mapping was performed using summary-statistics-based Bayesian fine-mapping with SuSiE RSS [23].”
Multi-ancestry colocalization approaches
Shen, C., Dupuis, J., & Zhang, Q. (2026) · PLoS Genetics · doi:10.1371/journal.pgen.1012221
Engineered Heart Tissues Facilitate Noncoding Variant Studies in Cardiomyopathy
Weber, Z., Monroe, T. O., Holgren, C. W., Mitchell, R. M., Zhang, L., Thornburg, A. G., Salamone, I. M., Chychula, I. A., Karthik, F., Fullenkamp, D. E., et al. (2026) · Circulation Research · doi:10.1161/circresaha.125.327506
Multi-ancestry multi-trait analysis reveals shared genetics across major psychiatric disorders and Alzheimer’s disease
Zheng, X., Feng, H., Ren, L., & Zhang, Y. (2026) · Translational Psychiatry · doi:10.1038/s41398-026-04213-0
Relational biological structure improves fine-mapping of causal GWAS variants under weak signal
Estaji, E., Zhao, S., Chen, Z., Nie, S., & Mao, J.-F. (2026) · bioRxiv · doi:10.64898/2026.05.15.725513
“Under pure statistical conditions—strong signal, random causal variant, no informative annotations— SuSiE[7, 36] and FINEMAP[8] retain a small advantage in mean rank[37] and should remain the default.”
Genetic Susceptibility to Nephrolithiasis: A Genome-Phenome Approach with Polygenic Risk Score Analysis
Chen, C.-Y., Lee, S., Lin, C.-M., Tsai, C.-Y., Wu, C.-C., Lin, Y.-F., Ho, C.-H., Huang, S., Hsu, W.-T., & Lee, C.-C. (2026) · Kidney Diseases · doi:10.1159/000553005
“Multiple causal variants were assumed using the sum of single effects (SuSiE) model [23].”

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
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: 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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