# Solar-Eclipse Computational Tools for Imaging Genetics

> **NIH NIH R01** · UNIVERSITY OF MARYLAND BALTIMORE · 2021 · $532,952

## Abstract

Abstract
This application will provide urgently needed analytical methods to develop the field of imaging genetics.
SOLAR-Eclipse is an integrated suite of resources for genetic and epigenetic analyses such as heritability,
pleiotropy, high-resolution genome-wide association (GWA) and Whole-Genome Analyses (WGA), gene
expression, quantitative trait loci-linkage (QTL-L) and methylation analyses optimized for traits derived from
structural and functional neuroimaging data. Our focus is on phase 3 development to support full-resolution
voxel-and-vertex-wise analyses of imaging genetics networks involved in complex polygenic illnesses. During
the first two funding periods, we demonstrated the utility of SOLAR-Eclipse for imaging genetics applications
and developed strong “Pull/Push” collaboration with three major NIH brain imaging initiatives: the NIH Big Data
2 Knowledge (BD2K) Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA), Human
Connectome Project (HCP), MRI-GENIE, CHARGE and UK Bio Bank. During these funding periods, we
released over 20 software updates and this support was acknowledged in ~250 manuscripts. We focused the
next phase of SE development on the needs identified by our Big Data partners. The first aim is to revise our
software to support “Repeat, Rerun, Replicate (R3)” initiative, including Python based application programming
interface for straightforward integration of SOLAR-Eclipse into modern processing workflows. It will include a
new data format optimized for voxel-and-vertex wise imaging genetic analyses including GWA and WGA, as
well as recording the provenance of imaging genetics data analysis workflows. Aim 2 is focused on enabling
GWA and WGA at full voxel-vertex-and- genetic resolution. This will derive and perform causality testing and
annotation for the imaging-genetic networks while accounting for linkage disequilibrium (LD) and spatial
dependency patterns and correcting for multiple comparisons. We will perform causative inference testing for
the vertical and horizontal pleiotropies - the two main mechanisms that govern genetic risk factors for complex
polygenic illnesses such as schizophrenia. Aim 3, we will execute collaborative studies to tune novel methods
in large and diverse samples assembled by our Big Data partners: ENIGMA, HCP, SiGN, UKBB and others.
This collaborative piloting and honing of novel methods will serve to popularize and disseminate our
developments for individual imaging genetics labs.

## Key facts

- **NIH application ID:** 10363130
- **Project number:** 2R01EB015611-08
- **Recipient organization:** UNIVERSITY OF MARYLAND BALTIMORE
- **Principal Investigator:** PETER V. KOCHUNOV
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $532,952
- **Award type:** 2
- **Project period:** 2012-08-01 → 2025-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10363130

## Citation

> US National Institutes of Health, RePORTER application 10363130, Solar-Eclipse Computational Tools for Imaging Genetics (2R01EB015611-08). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10363130. Licensed CC0.

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