# Estimating The Fraction of Variance Explained by Genetics and Neuroanatomy in Neuropsychiatric Conditions

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA, SAN DIEGO · 2024 · $556,500

## Abstract

Abstract
Mental health problems such as autism are highly prevalent in the population and incur great suffering and
financial costs. Yet there is currently a dearth of biomarkers that accurately predict their diagnosis or
prognosis. Characterizing the contributions of high-dimensional biomarkers to susceptibility of such complex
disorders is critically important for advancing our understanding of their etiology and for developing new
treatments. The fraction of variance explained (FVE) by a set of biomarkers is a measure of the total amount of
information for an outcome contained in the predictor variables. It is a fundamental quantity in much of mental
health-related research, e.g., human microbiome, proteomics, gene expression, etc. Canonical examples
where the FVE is of fundamental interest include Genome-Wide Association Studies (GWAS) and
neuroimaging, both crucial tools for understanding the biological basis of mental health disorders. GWAS have
successfully mapped thousands of genetic factors by mass-univariate association of millions of single
nucleotide polymorphisms (SNPs), but the top significant associations, even in aggregate, account for only a
small proportion of susceptibility. To assess the amount of information in GWAS, the SNP-heritability, h2SNP,
quantifies the FVE among all GWAS SNPs in aggregate, regardless of significance. Similarly, the FVE by brain
imaging measures captures variation in the brain related to mental illness, which again appears to be highly
distributed. In both the genetic and brain imaging domains, the number of predictors is extremely large, in the
order of thousands to millions, far larger than the number of subjects. As a result, the specific associations with
each predictor unit cannot be estimated, and effects of specific loci are extremely difficult to identify. In
contrast, the FVE can be reliably estimated from data, even if only univariate summary statistics are available.
Estimating FVE requires sophisticated statistical methods designed for these particular, high-dimensional data.
In this proposal, we propose a general framework for FVE estimation, applicable to high-dimensional data
including both GWAS and brain imaging settings. We develop foundational theory establishing the validity and
consistency of FVE estimation, develop new methods for evaluating the required conditions in real data, and
develop methods for partitioning FVE into more local components, allowing understanding of the distribution of
contributions to susceptibility in a top-down approach. We apply these methods to the Adolescent Brain
Cognitive Development (ABCD) Study, comprising longitudinal, multi-modal brain imaging, GWAS data, and
autism-related assessments for 11,875 participants aged 9-10 at baseline and continuing into early adulthood.

## Key facts

- **NIH application ID:** 10875541
- **Project number:** 5R01MH128923-03
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN DIEGO
- **Principal Investigator:** Armin Schwartzman
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $556,500
- **Award type:** 5
- **Project period:** 2022-08-15 → 2027-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10875541

## Citation

> US National Institutes of Health, RePORTER application 10875541, Estimating The Fraction of Variance Explained by Genetics and Neuroanatomy in Neuropsychiatric Conditions (5R01MH128923-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10875541. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*