G2DR: A Genotype-First Framework for Genetics-Informed Target Prioritization and Drug Repurposing

Muhammad Muneeb; David B. Ascher

q-bio.GNMar 20, 2026

G2DR: A Genotype-First Framework for Genetics-Informed Target Prioritization and Drug Repurposing

Authors: Muhammad Muneeb, David B. Ascher

Organizations: School of Chemistry and Molecular Biology, The University of Queensland, Queen Street, 4067, Queensland, Australia · Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Commercial Road, 3004, Victoria, Australia

Abstract

Human genetics offers a promising route to therapeutic discovery, yet practical frameworks translating genotype-derived signal into ranked target and drug hypotheses remain limited, particularly when matched disease transcriptomics are unavailable. Here we present G2DR, a genotype-first prioritization framework propagating inherited variation through genetically predicted expression, multi-method gene-level testing, pathway enrichment, network context, druggability, and multi-source drug--target evidence integration. In a migraine case study with 733 UK Biobank participants under stratified five-fold cross-validation, we imputed expression across seven transcriptome-weight resources and ranked genes using a reproducibility-aware discovery score from training and validation data, followed by a balanced integrated score for target selection. Discovery-based prioritization generalized to held-out data, achieving gene-level ROC-AUC of 0.775 and PR-AUC of 0.475, while retaining enrichment for curated migraine biology. Mapping prioritized genes to compounds via Open Targets, DGIdb, and ChEMBL yielded drug sets enriched for migraine-linked compounds relative to a global background, though recovery favoured broader mechanism-linked and off-label space over migraine-specific approved therapies. Directionality filtering separated broadly recovered compounds from mechanistically compatible candidates. G2DR is a modular framework for genetics-informed hypothesis generation, not a clinically actionable recommendation system. All outputs require independent experimental, pharmacological, and clinical validation.

Figures & tables

Figure 1: Overview of the G2DR framework. Genotype and phenotype data from the migraine cohort were partitioned by stratified cross-validation and propagated through genotype-based transcriptome imputation across multiple expression-weight resources. Covariate-adjusted predicted expression values were tested using multiple differential-expression and association methods, and significant results from the training and validation splits were aggregated into a discovery set. Genes were then ranked using a composite score that integrated reproducibility, effect magnitude, and statistical confidence, followed by pathway, network, and druggability annotation to generate an integrated target-prioritization score. Top-ranked genes were mapped to candidate compounds through Open Targets, DGIdb, and ChEMBL, and the resulting drug lists were evaluated against curated migraine-associated drug references.

Model	Source	Description	Gene–Tissue Pairs
MASHR	https://zenodo.org/records/7551845	Multivariate adaptive shrinkage models for cross-population transcriptome prediction, designed to improve TWAS performance in underrepresented populations Araujo et al. (2023)	686,241 (49 tissues)
JTI	https://zenodo.org/records/3842289	Joint-tissue imputation models that borrow information across tissues via shared genetic regulation, enabling both expression prediction and downstream causal inference Zhou et al. (2020)	533,141 (49 tissues)
CTIMP	https://github.com/yiminghu/CTIMP	Cross-tissue gene-expression imputation models trained with sparse group-LASSO to borrow information across GTEx tissues Hu et al. (2019)	404,450 (49 tissues)
UTMOST	https://zenodo.org/records/3842289	Cross-tissue transcriptome prediction models from the UTMOST framework, hosted alongside JTI models; treated as a separate model set in this study Hu et al. (2019)	340,104 (49 tissues)
EpiXcan	https://www.synapse.org/Synapse:syn52745052	Transcriptome imputation incorporating epigenomic annotations to weight SNP priors, improving prediction accuracy over standard elastic net Zhang et al. (2019)	143,609 (14 tissues)
FUSION	http://gusevlab.org/projects/fusion/	Bayesian sparse linear mixed model and penalized regression framework for transcriptome-wide association studies Gusev et al. (2016)	265,052 (48 tissues)

Table 1: Expression weight models used for genotype-based gene expression prediction.

Figure 2: Cross-database concordance of predicted gene expression across expression-weight databases. (A) Hierarchical clustering of databases using distance $(1-r)$ derived from the pairwise correlation matrix. (B) Pairwise correlation matrix (Pearson $r$ ).

Predicted set	TP	FP	FN	Precision	Recall
$G_{\mathrm{discovery}}$ (train $\cup$ val)	4,995	4,310	2,146	0.537	0.699
Top 50	39	11	7,102	0.780	0.005
Top 100	79	21	7,062	0.790	0.011
Top 200	156	44	6,985	0.780	0.022
Top 500	347	153	6,794	0.694	0.049
Top 1000	664	336	6,477	0.664	0.093

Table 2: Held-out test replication for the discovery set and top-

K

lists. Positives are test-significant genes (

T=7{,}141

). TP/FP/FN are computed relative to

T

Set	$N_{\mathrm{pred}}$	$k_{\mathrm{obs}}$	$k_{\mathrm{exp}}$	FE	$p$ -value	$N_{\mathrm{pred}}$	$k_{\mathrm{obs}}$	$k_{\mathrm{exp}}$	FE	$p$ -value
	Test positives ( $T=7{,}141$ )					Curated migraine genes ( $\|M\|=3{,}190$ )
$G_{\mathrm{discovery}}$ (train $\cup$ val)	9,305	4,995	1,934.13	2.58	$<10^{-300}$	9,305	1,189	864.01	1.38	$5.47\times 10^{-40}$
Top 50	50	39	10.39	3.75	$7.14\times 10^{-18}$	50	12	4.64	2.58	$1.71\times 10^{-3}$
Top 100	100	79	20.79	3.80	$1.55\times 10^{-35}$	100	21	9.29	2.26	$2.97\times 10^{-4}$
Top 200	200	156	41.57	3.75	$1.77\times 10^{-67}$	200	36	18.57	1.94	$8.72\times 10^{-5}$
Top 500	500	347	103.93	3.34	$6.94\times 10^{-123}$	500	74	46.43	1.59	$4.26\times 10^{-5}$

Table 3: Hypergeometric enrichment of predicted sets for held-out test positives and curated migraine genes. For test positives:

k_{\mathrm{exp}}=N_{\mathrm{pred}}\cdot(T/U)

T=7{,}141

U=34{,}355

. For migraine genes:

k_{\mathrm{exp}}=N_{\mathrm{pred}}\cdot(|M|/U)

|M\cap U|=3{,}190

. FE =

k_{\mathrm{obs}}/k_{\mathrm{exp}}

Tier	Top-20	Top-50	Top-100	Prec@100	Rec@100	FE@100	$p$ @100	Top-200
Tier 1: Migraine-specific approved	0	0	0	0.00	0.000	0.00	1.00	0
Tier 2: Guideline-supported	0	1	2	0.02	0.022	30.35	$2.04\times 10^{-3}$	2
Tier 3: Established off-label	1	1	1	0.01	0.034	48.14	$2.06\times 10^{-2}$	1
Tier 4: Broad literature-linked	4	18	48	0.48	0.011	14.67	$7.08\times 10^{-44}$	86

Table 4: Tiered migraine-evidence benchmark of the ranked drug list. Tier 1: migraine-specific approved therapies (

n_{\mathrm{ref}}=135

); Tier 2: guideline-supported therapies (

n_{\mathrm{ref}}=92

); Tier 3: established off-label therapies (

n_{\mathrm{ref}}=29

); Tier 4: broader literature-linked compounds (

n_{\mathrm{ref}}=4{,}568

). Fold enrichment (FE) and hypergeometric

p

-values are computed relative to a global background of 139,597 drugs. High FE values for Tiers 2–3 reflect low absolute counts and should be interpreted accordingly.

Directionality class	All pairs ( $n=4{,}861$ )		Migraine drugs only ( $n=614$ )
	Count	%	Count	%
Consistent	549	11.3%	50	8.1%
Inconsistent	618	12.7%	83	13.5%
Unclear	$3{,}694$	76.0%	481	78.3%
Total	$4{,}861$	100.0%	614	100.0%

Table 5: Summary of directionality classification for unique prioritized gene–drug pairs from the final integrated migraine ranking. Results are shown for all pairs derived from the top 200 ranked genes and separately for pairs involving drugs from the curated migraine reference set (

n_{\mathrm{mig}}=614

Drug rank	Drug	Gene	Gene rank	Disease direction	Drug action	Evidence source	Approved	Known migraine gene
26	Aspirin	GSTP1	31	Higher in cases	Inhibitor	ChEMBL	Yes	Yes
126	Digoxin	ATP1A4	2	Higher in cases	Inhibitor	DGIdb	Yes	Yes
133	Digitoxin	ATP1A4	2	Higher in cases	Inhibitor	DGIdb	Yes	Yes
142	Exenatide	ALPL	6	Lower in cases	Agonist	DGIdb	Yes	No
144	Liraglutide	ALPL	6	Lower in cases	Agonist	DGIdb	Yes	No
146	Semaglutide	ALPL	6	Lower in cases	Agonist	DGIdb	Yes	No

Table 6: Representative directionally consistent gene–drug pairs from the migraine reference set. Pairs were classified as consistent when the known drug action was mechanistically compatible with the inferred disease-associated direction of the target gene. Drug rank is from the integrated gene-to-drug scoring pipeline; gene rank is from the Combined_Score integrated gene prioritization. Evidence source indicates the database providing mechanism-of-action annotation. The MRPL36 antibiotic cluster (rows 3–9) reflects DGIdb-derived mitochondrial ribosomal inhibitor annotations and should be interpreted with caution.

Rank	Symbol	CombScore	ImpScore	Disease direction	KnMig	Confidence tier	Hits	Tissues	DBs	NDrugs	ApprDrug	DirBest
1	APOBEC3G	0.936	0.435	Higher in cases	No	Tier3_Exploratory	100	29	1	98	Yes	Unclear
2	ATP1A4	0.934	0.531	Higher in cases	Yes	Tier3_Exploratory	18	3	3	24	Yes	Consistent
3	HLA-DQB1	0.933	0.410	Lower in cases	Yes	Tier2_Moderate	32	15	2	55	Yes	Inconsistent
4	PSMB5	0.932	0.413	Lower in cases	No	Tier2_Moderate	40	13	2	33	Yes	Inconsistent
5	SLC1A1	0.932	0.491	Lower in cases	No	Tier2_Moderate	103	15	2	54	Yes	Inconsistent
6	ALPL	0.931	0.412	Lower in cases	No	Tier1_High	35	5	1	96	Yes	Consistent

Table 7: Top 10 prioritized genes from the final integrated migraine ranking. Genes are ranked by Combined_Score (integrated: DE + pathway + druggability + hub). KnMig = known migraine gene; Tier = confidence tier from primary composite ranking; NDrugs = number of unique linked drugs; ApprDrug = has at least one approved linked drug; DirBest = best directionality support across linked drug pairs.

Rank	Drug	Phase	KnMig	Approved	NTarg	Target genes	Disease directions	Directionality	Evidence sources
1	Ataluren	APPROVED	No	Yes	4	RPL28, RPS14, RPL12, RPS16	Unclear, lower	Unclear	DGIdb, OpenTargets
2	Metformin	APPROVED	Yes	Yes	2	NDUFV1, NDUFS6	Lower in cases	Inconsistent	OpenTargets, DGIdb
3	Voxelotor	APPROVED	No	Yes	1	HBB	Lower in cases	Unclear	OpenTargets
4	Dibotermin alfa	APPROVED	No	Yes	1	BMPR1B	Higher in cases	Inconsistent	OpenTargets
5	Amantadine	APPROVED	No	Yes	1	GRIN2B	Lower in cases	Inconsistent	OpenTargets
6	Lifitegrast	APPROVED	No	Yes	2	ITGB2, ICAM1	Higher in cases	Consistent	OpenTargets

Table 8: Top 10 prioritized drug candidates from the final integrated migraine repurposing analysis. Drugs are ranked by DrugScore ; target genes are merged per drug. KnMig = drug present in curated migraine reference set; NTarg = number of unique target genes among top 200; Dir = best directionality classification across gene–drug pairs.

Appendix figures & tables6 assets

Supplementary material from the paper’s appendix.

Appendix

Scheme	DE	Path	Drug	Hub	Spearman $\rho$	Top-100 overlap (%)	DISC ROC-AUC	FULL ROC-AUC
(A) Reasonable alternative schemes
Default (reference)	0.45	0.25	0.25	0.05	1.000	100.0	0.546	0.776
DE-heavy	0.60	0.15	0.20	0.05	0.978	84.0	0.546	0.776
Pathway-heavy	0.30	0.40	0.25	0.05	0.951	78.0	0.545	0.775
Drug-heavy	0.30	0.25	0.40	0.05	0.962	82.0	0.546	0.775
Equal weights (no hub)	0.33	0.33	0.33	0.00	0.941	77.0	0.545	0.775

Appendix

Table S1: Supplementary Table S1. Weight stability analysis of the integrated scoring framework across 17 alternative weighting schemes. Nine reasonable alternative schemes (panel A) and eight extreme single-component stress tests (panel B) were evaluated against the default integrated weights (DE

=

0.45, Pathway

=

0.25, Drug

=

0.25, Hub

=

0.05). For each scheme, Spearman

\rho

against the default ranking, mean Top-100 gene overlap (%), DISC-universe test ROC-AUC (discovery set only,

n=9{,}305

genes), and FULL-universe test ROC-AUC (all

U=34{,}355

tested genes) are reported. Across reasonable alternatives, mean

\rho=0.963

(minimum

0.866

) and FULL-universe ROC-AUC ranged from

0.775

0.776

, confirming stability within the biologically justified parameter space. Extreme single-component schemes diverged substantially (mean

\rho=0.682

; minimum

\rho=0.292

for hub-only), confirming that the integrated score draws on genuine multi-source signal.

Threshold rule	Train	Validation	Test	$G_{\mathrm{discovery}}$	ROC-AUC	PR-AUC	FE test	FE migraine
FDR $<0.10$ , $\|\log_{2}\mathrm{FC}\|\geq 0.50$	1,046	9,107	7,141	9,305	0.7753	0.4754	2.58	1.38
FDR $<0.05$ , $\|\log_{2}\mathrm{FC}\|\geq 0.50$	958	7,861	5,313	8,135	0.7314	0.3412	2.62	1.39
FDR $<0.10$ , $\|\log_{2}\mathrm{FC}\|\geq 0.75$	263	6,241	4,599	6,303	0.7339	0.3289	3.20	1.39
FDR $<0.05$ , $\|\log_{2}\mathrm{FC}\|\geq 0.75$	248	5,709	3,772	5,794	0.7025	0.2497	3.12	1.41

Appendix

Table S2: Supplementary Table S2. Sensitivity analysis of gene prioritization under stricter significance thresholds. For each threshold rule, we report the number of significant genes in the training, validation, and held-out test splits; the size of the discovery set (

G_{\mathrm{discovery}}

); held-out ranking performance (ROC-AUC and PR-AUC evaluated over all

U=34{,}355

tested genes); enrichment of discovery genes for held-out test positives (FE test,

T=7{,}141

); and enrichment for curated migraine genes (FE migraine,

|M\cap U|=3{,}190

). The primary analysis uses FDR

<

0.10 and

|\log_{2}\mathrm{FC}|\geq 0.50

(first row). All stricter settings preserved the main prioritization conclusions: held-out ROC-AUC remained above

0.70

, fold-enrichment for test positives ranged from

2.58

-fold to

3.20

-fold, and fold-enrichment for curated migraine genes ranged from

1.38

-fold to

1.41

-fold. These results indicate that the main gene-prioritization findings are not driven by permissive significance filtering.

Component	$N_{\mathrm{pred}}$	$k_{\mathrm{obs}}$	$k_{\mathrm{exp}}$	FE	$p_{\mathrm{emp}}$
(A) Expression-weight databases
MASHR	187	40	17.40	2.30	$1.00\times 10^{-4}$
JTI	104	18	9.68	1.86	$6.799\times 10^{-3}$
FUSION	9,146	1,156	851.16	1.36	$1.00\times 10^{-4}$
EpiXcan	34	5	3.16	1.58	$2.06\times 10^{-1}$
CTIMP	16	2	1.49	1.34	$4.51\times 10^{-1}$

Appendix

Table S3: Supplementary Table S3. Discovery disease-enrichment for curated migraine genes across expression-weight databases, tissues, and analytical methods. For each component, the discovery set comprises unique genes significant at least once in training and validation within that component. Enrichment is computed against curated migraine genes (

M=3{,}190

) within the analysis universe (

U^{\ast}

). FE denotes fold-enrichment (

k_{\mathrm{obs}}/k_{\mathrm{exp}}

). Empirical

p

-values are from size-matched random gene-set sampling (

n_{\mathrm{perm}}=10{,}000

	AUC metrics				Top-200 test		Top-200 migraine
Ranking	T ROC	T PR	K ROC	K PR	Obs	FE	Obs	FE
(A) Full universe ( $U=34{,}355$ genes; non-discovery genes scored as 0)
Significance only	0.776	0.477	0.557	0.107	146	3.51	25	1.35
Effect only	0.790	0.526	0.558	0.111	145	3.49	37	1.99
Primary composite ( $S_{g}$ )	0.775	0.475	0.557	0.109	156	3.75	36	1.94
Pathway only	0.549	0.265	0.520	0.108	150	3.61	66	3.55

Appendix

Table S4: Supplementary Table S4. Comparison of individual evidence components and composite scores across two evaluation universes. Full universe (

U=34{,}355

genes): non-discovery genes receive score

=0

; measures pipeline-level separation of discovery-relevant genes from the entire tested space. Discovery universe (

n=9{,}305

genes): discovery set only (train

\cup

val); measures within-set ranking quality. T ROC = test-replication ROC-AUC; T PR = test-replication PR-AUC; K ROC = known-migraine ROC-AUC; K PR = known-migraine PR-AUC. Top-200 FE test and FE mig denote fold-enrichment relative to random expectation. Expected Top-200 overlap: full-universe test

=

41.57, full-universe migraine

=

18.57; disc-universe test

=

107.36, disc-universe migraine

=

25.56. All raw components were percentile-normalized before ranking.

$N$	Universe	$K$	$\|\mathcal{U}\|$	$\|\mathcal{R}\|$	Overlap	Prec@K	Rec@K	F1@K	Expected	FE
Top- $N=200$ input genes
200	ALL_DRUGS	20	139,597	4,824	5	0.250	0.001036	0.002064	0.691	7.235
200	ALL_DRUGS	50	139,597	4,824	20	0.400	0.004146	0.008207	1.728	11.575
200	ALL_DRUGS	100	139,597	4,824	51	0.510	0.010572	0.020715	3.456	14.758
200	ALL_DRUGS	200	139,597	4,824	89	0.445	0.018449	0.035430	6.911	12.877
200	ALL_DRUGS	500	139,597	4,824	205	0.410	0.042496	0.077010	17.278	11.865

Appendix

Table S5: Supplementary Table S5. Multi-

K

overlap evaluation of predicted drugs against the curated migraine reference drug set (

|\mathcal{R}|=4{,}824

normalized drugs). Results are shown for top-

N

input genes (

N=200

and

N=500

) under two evaluation universes. ALL : global background of 139,597 drugs; hypergeometric enrichment test is valid. PREDICTED : returned candidate pool only; FE values below 1.0 reflect the fact that the reference drug set exceeds the returned candidate pool in size, making standard hypergeometric enrichment interpretation inapplicable — AUROC and AUPRC are the appropriate metrics in this frame. Within-set performance: for

N=200

, AUROC

=

0.8004 and AUPRC

=

0.3528 (353 curated migraine drugs among 3,963 predicted drugs); for

N=500

, AUROC

=

0.8152 and AUPRC

=

0.3311 (527 curated migraine drugs among 7,981 predicted drugs).

Metric	G2DR (all genes)	G2DR (target-filtered)	Open Targets
Ranked / returned genes	9,305	823	2,376
Reference genes recovered	1,189	464	1,228
Reference recovery (%)	37.27	14.55	38.50
Shared with Open Targets	823	823	–
Shared with Open Targets and reference	464	464	464
Reference genes recovered but absent from Open Targets	725	0	–

Appendix

Table S6: Supplementary Table S6. External contextual comparison of G2DR with the Open Targets migraine disease-target resource for migraine gene recovery. The full G2DR analysis was evaluated across the complete ranked gene universe (

n=9{,}305

genes). The target-filtered G2DR analysis was restricted to genes represented in the Open Targets migraine target space (

n=823

genes). Overall recovery is shown against the curated migraine reference set (

M=3{,}190

genes), together with Top-

K

precision before and after filtering. The substantially higher precision of standalone Open Targets (Top-50: 92.00%) reflects its disease-curated, pre-filtered design rather than a direct performance comparison with G2DR. G2DR operates across a wider gene universe and recovered 725 curated migraine reference genes absent from Open Targets entirely, indicating that the two resources are complementary rather than directly competitive.

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