LocusMatch: Defining Novelty in GWAS Locus Discovery

1. Overview

Genome-wide association studies (GWAS) have been instrumental in identifying loci associated with complex, polygenic disorders, but there is no consensus on how to define or track novelty in locus discovery. This limits the interpretability and reproducibility of GWAS findings, particularly in highly polygenic diseases like schizophrenia. This project proposes the development of LocusMatch, an open-source web tool for comparing genetic loci across GWAS and defining novel associations in a systematic, LD-aware framework. An additional deliverable will be a case study of schizophrenia GWAS using our tool.

The development will occur over 8 months:

3 months for design and implementation
3 months for evaluation in schizophrenia datasets
2 months for deployment and manuscript preparation

The project will be hosted at the Federal University of Rio Grande do Norte (UFRN), with computing resources provided by the institution. All code will be released on GitHub, and results will be deposited in Zenodo for public access.

2. Introduction

Genome-wide association studies (GWAS) have advanced our understanding of complex disorders, yet the interpretation of GWAS loci is often ambiguous due to linkage disequilibrium (LD) and the possibility of the presence of multiple causal variants within a region¹. Typically, associated loci are defined using proximity to the lead SNP or by fixed genomic windows, with limited consideration for LD structure or multi-causal signals, and often omitting the methodology used ^2,3. As a result, distinguishing between novel discoveries and replications remains inconsistent across studies.

Tools like LDTrait⁴ have attempted to incorporate LD information, but they assume a single causal variant per region and do not provide outputs optimized for novel locus interpretation. This problem is especially pronounced in highly polygenic disorders such as schizophrenia⁵, where genomic regions are densely populated with overlapping signals.

We propose the development of LocusMatch, a web tool that compares loci across GWAS datasets and systematically defines novelty. As a case study, we will apply LocusMatch to schizophrenia GWAS spanning from 2008 to 2022^6–14 to reconstruct the trajectory of locus discovery.

Hypotheses

H1: There are insufficient criteria for evaluating whether a locus in a GWAS finding is novel

H2: Methodologies such as LD and colocalisation coupled with existing public data could provide a more reproducible way of determining the novelty of a locus

H3: An accessible web application will reduce barrier to entry for researchers working with GWAS to define novelty in their findings

Impact Statement

By introducing a transparent and standardized approach to locus comparison, LocusMatch will improve GWAS reproducibility and facilitate targeted follow-up studies. Even if the approach does not fully resolve the novelty problem, it will highlight gaps in current reporting practices and guide future methodological improvements.

3. Methods

Participants/Sample

This is a computational study. Publicly available summary statistics from schizophrenia GWAS will be used, including datasets from 2008 to 2022^6–14. No new subjects will be recruited.

Inclusion criteria:

GWAS datasets with genome-wide summary statistics and sufficient SNP coverage for LD-based analysis.
Studies with population-matched LD reference data available (e.g., 1000 Genomes).

Materials and Procedures

A brief overview of the analysis steps for the web application is shown on figure 1:

^{Fig. 1 - Main diagram with analysis steps.}

Region Extraction

Use PLINK’s clumping algorithm¹⁵ to define independent loci based on LD (r² < 0.1 within 500 kb).
Merge overlapping loci (>200 kb) and split minor overlaps for consistency.

LD Proxy Investigation

For each lead SNP, retrieve LD proxies via LDlink API¹⁶.
Filter proxies based on significance in user and reference GWAS.

Colocalization Analysis

Use the coloc.susie function from the Coloc R package¹⁷ to identify shared causal signals.
Run pairwise comparisons between user input and reference studies.

Reporting and Visualization

Generate interactive plots (Manhattan, UpSet, LocusZoom).
Output tables of significant proxies and colocalized signals.

Web Application

Backend: Python FastAPI + Celery task queue, PostgreSQL DB to store analysis results for up to a month.
Frontend: Vue.js.

^{Fig 2 - System architecture and data flow of the LocusMatch web tool.}

Planned Analyses

We will:

Quantify overlap and novelty across schizophrenia GWAS.
Compare tool-defined novel loci with those reported in original studies.

Ethics and Data Management

No new data collection will occur. All data used are publicly available. Data processing will comply with FAIR principles, and all scripts will be open-source (GitHub). Results will be archived on Zenodo.

5. Budget

Budget estimated considering discounts for researchers from upper-middle income countries.

Item	Item Estimated Cost (USD)
Open-Access publication fees*	$2,850
Participation in an international conference**	$2,000
Additional costs	$150
Total	$5,000

* Briefings in Bioinformatics or similar journal

** ISMB/ECCB or event of a similar reach (registration + travel costs)

6. References

Long, E., Williams, J., Zhang, H. & Choi, J. An evolving understanding of multiple causal variants underlying genetic association signals. The American Journal of Human Genetics 112, 741–750 (2025).
Andrews, S. J. et al. The complex genetic architecture of Alzheimer’s disease: novel insights and future directions. eBioMedicine 90, (2023).
Wang, K.-S., Liu, X.-F. & Aragam, N. A genome-wide meta-analysis identifies novel loci associated with schizophrenia and bipolar disorder. Schizophrenia Research 124, 192–199 (2010).
Lin, S.-H., Brown, D. & Machiela, M. J. LDtrait: An Online Tool for Identifying Published Phenotype Associations in Linkage Disequilibrium. Cancer Research 80, 3443–3446 (2020).
Owen, M. J., Legge, S. E., Rees, E., Walters, J. T. R. & O’Donovan, M. C. Genomic findings in schizophrenia and their implications. Molecular Psychiatry 28, 1–10 (2023).
O’Donovan, M. C. et al. Identification of loci associated with schizophrenia by genome-wide association and follow-up. Nature Genetics 40, 1053–1055 (2008).
Potkin, S. G. et al. A Genome-Wide Association Study of Schizophrenia Using Brain Activation as a Quantitative Phenotype. Schizophrenia Bulletin 35, 96–108 (2008).
The Schizophrenia Psychiatric Genome-Wide Association Study (GWAS) Consortium. Genome-wide association study identifies five new schizophrenia loci. Nature Genetics 43, 969–976 (2011).
Need, A. C. et al. A Genome-Wide Investigation of SNPs and CNVs in Schizophrenia. PLoS Genetics 5, (2009).
Ripke, S. et al. Genome-wide association analysis identifies 13 new risk loci for schizophrenia. Nature Genetics 45, 1150–1159 (2013).
Schizophrenia Working Group of the Psychiatric Genomics Consortium. Biological insights from 108 schizophrenia-associated genetic loci. Nature 511, 421–427 (2014).
Li, Z. et al. Genome-wide association analysis identifies 30 new susceptibility loci for schizophrenia. Nature Genetics 49, 1576–1583 (2017).
Amare, A. T. et al. Association of Polygenic Score for Schizophrenia and HLA Antigen and Inflammation Genes With Response to Lithium in Bipolar Affective Disorder. JAMA Psychiatry (2017).
Trubetskoy, V. et al. Mapping genomic loci implicates genes and synaptic biology in schizophrenia. Nature 604, 502–508 (2022).
Purcell, S. et al. PLINK: A Tool Set for Whole-Genome Association and Population-Based Linkage Analyses. The American Journal of Human Genetics 81, 559–575 (2007).
Machiela, M. J. & Chanock, S. J. LDlink: a web-based application for exploring population-specific haplotype structure and linking correlated alleles of possible functional variants: Fig. 1. Bioinformatics 31, 3555–3557 (2015).
Giambartolomei, C. et al. Bayesian Test for Colocalisation between Pairs of Genetic Association Studies Using Summary Statistics. PLoS Genetics 10, e1004383 (2014).

LocusMatch: Defining Novelty in GWAS Locus Discovery

LocusMatch: Defining Novelty in GWAS Locus Discovery

Funding Progress

Related Funding

Request for Proposals - Bioinformatics & Genomics Research

Nonprofit Recipient

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