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Prioritizing effector genes at trait-associated loci using multimodal evidence

Nature Genetics volume 57, pages 323–333 (2025)Cite this article

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Abstract

Genome-wide association studies (GWAS) yield large numbers of genetic loci associated with traits and diseases. Predicting the effector genes that mediate these locus-trait associations remains challenging. Here we present the FLAMES (fine-mapped locus assessment model of effector genes) framework, which predicts the most likely effector gene in a locus. FLAMES creates machine learning predictions from biological data linking single-nucleotide polymorphisms to genes, and then evaluates these scores together with gene-centric evidence of convergence of the GWAS signal in functional networks. We benchmark FLAMES on gene-locus pairs derived by expert curation, rare variant implication and domain knowledge of molecular traits. We demonstrate that combining single-nucleotide-polymorphism-based and convergence-based modalities outperforms prioritization strategies using a single line of evidence. Applying FLAMES, we resolve the FSHB locus in the GWAS for dizygotic twinning and further leverage this framework to find schizophrenia risk genes that converge with rare coding evidence and are relevant in different stages of life.

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Fig. 1: Overview of the FLAMES framework.
Fig. 2: Overview of study design.
Fig. 3: Predictive value of SNP-to-gene annotations.
Fig. 4: Benchmarking of FLAMES versus state-of-the-art tools in different datasets.
Fig. 5: FLAMES mapped genes of the twinning FSHB locus.
Fig. 6: Functionality and temporal expression patterns of schizophrenia risk genes.

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Data availability

The reference data needed for running FLAMES and/or running (pathway-naive) PoPS is available via Zenodo at https://zenodo.org/records/12635505 (ref. 50). All annotations used are publicly available and the source of each individual annotation can be found in Supplementary Table 1. The BrainSpan expression data is available through https://www.brainspan.org/.

Code availability

The code for installing and running FLAMES can be accessed via https://github.com/Marijn-Schipper/FLAMES. A static version of the code used to perform the analyses in this paper is available via Zenodo at https://zenodo.org/records/14050681 (ref. 51).

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Acknowledgements

We thank M. de Hemptinne, K. Heilbron and the members of the Complex Trait Genetics laboratory at VU University for their input and discussions. This project was supported by NWO Gravitation: BRAINSCAPES: a roadmap from neurogenetics to neurobiology (grant no. 024.004.012), and European Research Council advanced grant ERC-2018-ADG 834057. This research has been conducted using the UK Biobank Resource under Application no. 16406.

Author information

Authors and Affiliations

  1. Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands

    Marijn Schipper, Christiaan A. de Leeuw, Bernardo A. P. C. Maciel, Douglas P. Wightman, Nikki Hubers, Dorret I. Boomsma & Danielle Posthuma

  2. Netherlands Twin Register, Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands

    Nikki Hubers

  3. Amsterdam Reproduction & Development (AR&D) research institute, Amsterdam, The Netherlands

    Nikki Hubers & Dorret I. Boomsma

  4. Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine & Clinical Neurosciences, Cardiff University, Cardiff, UK

    Michael C. O’Donovan

  5. Department of Child and Adolescent Psychiatry and Pediatric Psychology, Section Complex Trait Genetics, Amsterdam Neuroscience, Vrije Universiteit Medical Center, Amsterdam, The Netherlands

    Danielle Posthuma

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  1. Marijn Schipper
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Contributions

M.S. and D.P. conceived the study. M.S. developed the FLAMES framework and implemented the software. M.S. performed the analyses and wrote the manuscript with contributions from CA.d.L., B.A.P.C.M., D.P.W. and D.P. M.S., D.P., N.H, D.I.B. and M.C.O.ʼD. interpreted the results. All authors provided meaningful contributions at each stage of the project.

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Correspondence to Marijn Schipper.

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Extended data

Extended Data Fig. 1 Full feature impact by SHAP values.

Impact of features denoted by SHAP value. Blue denotes low feature values, red denote high feature values.

Extended Data Fig. 2 Ratio of annotation scores in L2G expert-curated causal genes.

Ratios of average annotation score per annotation in ExWAS implicated gene in GWAS locus versus the rest of the genes in the locus. Error bars represent 95% confidence intervals were calculated by bootstrapping 1000 times.

Extended Data Fig. 3 Odds ratio of expert-curated causal gene having the highest annotation scores in the locus.

Odds ratio of highest annotation score in the GWAS locus belonging to the ExWAS implicated gene in the locus versus the rest of the genes in the locus. Error bars represent 95% confidence intervals were calculated by bootstrapping 1000 times.

Extended Data Fig. 4 Benchmark of FLAMES versus Ei.

Benchmarking results of FLAMES versus Ei on the ExWAS implicated benchmarking set. Precision-recall curves of FLAMES (red line), our XGBoost model (purple line) and Ei (blue line) are visualized, with corresponding area under the precision recall curve (AUPRC) denoted in legend. For methods see Supplementary Note.

Extended Data Fig. 5 Benchmark of FLAMES on three molecular traits with within-sample-LD fine-mapping.

Benchmark of FLAMES in loci of three molecular traits, using fine-mapping results from within-sample fine-mapping6. Precision recall curves of FLAMES raw score (green line), with corresponding area under the precision-recall curve (AUPRC) denoted in legend. FLAMES recommended (orange diamond) denotes FLAMES prioritizations at our recommended threshold. Highest FLAMES (green diamond), MAGMA (cyan pentagon) and PoPS (white triangle) represent prioritizing the gene with the highest corresponding score in the locus. Closest gene (bold black cross) denotes taking the closest gene(s) to the fine-mapped credible sets. Closest + PoPS (grey triangle) denotes prioritizing a gene if it is the closest gene and has the highest PoPS score in the locus. Random predictor (black cross) represents prioritizing a random gene in the locus. FLAMES is scored using the more conservative raw FLAMES score due to multiple signals present per locus (see Supplementary Note).

Extended Data Fig. 6 Raw FLAMES score of prioritized vs non-prioritized genes.

Scores were derived from ExWAS implicated benchmarking set. Prioritized genes in blue, not prioritized in red. To prioritize genes we used the recommended FLAMES threshold.

Extended Data Fig. 7 Decision plot of DOCK5 locus.

Decision plot of FLAMES XGB scores of DOCK5 locus highlighting that DOCK5 is prioritized mostly due to high MAGMA Z-scores, distance and eQTL evidence.

Extended Data Fig. 8 Benchmarking results when including pathway naïve FLAMES.

Precision and recall of state-of-the-art gene prioritization methods in different benchmarking sets. Precision recall curves of FLAMES (green line), FLAMES scores using PoPS scores generated excluding pathway information (red line), our XGBoost model (purple line), L2G (blue line) and cS2G (yellow line) are visualized, with corresponding area under the precision-recall curve (AUPRC) denoted in legend. FLAMES recommended (orange diamond) denotes FLAMES prioritizations at our recommended threshold. Highest FLAMES (green diamond), L2G (blue circle), MAGMA (cyan pentagon) and PoPS (white triangle) represent prioritizing the gene with the highest corresponding score in the locus. Closest gene (bold black cross) denotes taking the closest gene(s) to the fine-mapped credible sets. Closest + PoPS (grey triangle) denotes prioritizing a gene if it is the closest gene and has the highest PoPS score in the locus. Random predictor (black cross) represents prioritizing a random gene in the locus. a, Benchmarking on all loci-gene pairs with available GWAS summary statistics in the expert-curated dataset3. b, Benchmarking of interpretable loci for GWAS of urate, IGF-1 levels and testosterone levels in blood6. c, Benchmarking on high confidence ExWAS implicated genes in nine traits5.

Extended Data Fig. 9 Brain expressed genes expression profile in BRAINSPAN.

Expression profile of BRAINSPAN brain expressed genes after k-means clustering. Gene expression is mean scaled per gene, and averaged across all genes in the cluster per timepoint. The expression of the two separate clusters is represented in orange and blue ± 95% confidence intervals in grey.

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Schipper, M., de Leeuw, C.A., Maciel, B.A.P.C. et al. Prioritizing effector genes at trait-associated loci using multimodal evidence. Nat Genet 57, 323–333 (2025). https://doi.org/10.1038/s41588-025-02084-7

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