# Statistical methods for identifying pleiotropy between complex human traits

> **NIH NIH R21** · JOHNS HOPKINS UNIVERSITY · 2024 · $204,688

## Abstract

PROJECT SUMMARY
 Years of genetic research on various complex human traits have implicated several genetic
variants as risk factors for two or more diseases/traits, including seemingly unrelated traits. A
recent systematic evaluation of >500 traits from >4100 genome-wide association studies (GWAS)
has revealed that 90% of the variants associated with these traits influence at least two traits.
This phenomenon where a genetic region or locus confers risk to more than one trait is known as
pleiotropy. Discovering patterns of pleiotropy is crucial for a comprehensive understanding of
biological mechanisms of human diseases and traits (e.g. understand how genetic variation leads
to trait variation and inter-trait correlations, and how traits may be causally related to each other),
and can have translational impact in the long run (e.g. guide identification of molecular targets or
help predict side-effects in drug development). While the scientific significance of studying
pleiotropy or genetic overlap is well-understood, statistical methods for identifying common
genetic basis between traits are still lacking. This is especially true for traits sampled under a
family-based design. To address methodological challenges in investigating pleiotropy, in Aim 1,
we propose a novel, innovative statistical method for identifying common genetic variants
influencing two possibly correlated traits. In Aim 2, we non-trivially extend our method in Aim 1 to
identify rare genetic variants influencing two independent traits. We use only aggregate-level
genotype-phenotype association results (or GWAS summary statistics), thus helping protect
human subjects data and facilitating global collaborations. The proposed methods, while having
the potential to substantially outperform current approaches in the area, will be more general and
distinct from existing research efforts in the following ways: applicability to correlated traits (Aim
1; e.g. a disease-related endophenotype and a molecular trait, or two different -omics traits), to
traits measured on independent sets of individuals (Aims 1, 2; e.g. separate case-control studies
on two diseases), to traits sharing some samples (Aim 1; e.g. case-control studies with shared
controls), to study designs where individuals may not be randomly sampled or unrelated (Aims
1, 2; e.g. case-parent trio design), to rare variants (Aim 2), and our open-access tools will allow
genomics researchers across the world to readily adopt and apply our methods even in resource-
poor environments (Aims 1, 2). Successful implementation of these aims will provide the broader
scientific community with novel, powerful and scalable methods, along with well-documented free
software, to statistically investigate questions of pleiotropy between complex human diseases/
traits across the entire allele frequency spectrum using GWAS summary statistics only.

## Key facts

- **NIH application ID:** 10874524
- **Project number:** 5R21HG012978-02
- **Recipient organization:** JOHNS HOPKINS UNIVERSITY
- **Principal Investigator:** Debashree Ray
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $204,688
- **Award type:** 5
- **Project period:** 2023-06-23 → 2026-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10874524

## Citation

> US National Institutes of Health, RePORTER application 10874524, Statistical methods for identifying pleiotropy between complex human traits (5R21HG012978-02). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10874524. Licensed CC0.

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