# Delineating the network effects of mental disorder-associated variants using convex optimization methods

> **NIH NIH R01** · NEW YORK GENOME CENTER · 2022 · $799,148

## Abstract

PROJECT SUMMARY/ABSTRACT
Driven by international open scientific collaboration through groups such as the Psychiatric Genomics
Consortium (PGC, in which co-I Mullins is a leading analyst) both genome-wide association studies
(GWAS) and whole exome and genome sequencing studies of neuropsychiatric disorders (NPDs) are
rapidly increasing in sample size. With this increased sample size comes increased statistical power to
detect many more, smaller genetic effects on disease risk, known as the polygenic component. The
challenge now is to understand what these findings tell us about NPD risk, etiology and biology. Here we
propose a suite of methods for multi-trait analysis to determine underlying latent structure, causal
networks of genes and traits, and enriched data-derived regulatory pathways. We make extensive use of
convex optimization methods that allow both computational efficiency and guarantees on reproducibility.
In Aim 1 we will decompose a wide range of NPDs and their subphenotypes into shared and unique
genetic components using a novel convex formulation of observed-weighted principal components
analysis (PCA) and develop extensions to handle sample overlap, linkage disequilibrium (LD), and
different ancestries. In Aim 2 we will extend and customize our existing work on causal network inference
using biconvex optimization to estimate both cis and trans gene regulatory networks in the brain using
large-scale uniformly processed chromatin accessibility and expression quantitative trait loci (QTLs). We
will regularize estimates of cis interactions using chromatin conformation data, model latent genetic
confounders in these networks using an expectation-maximization (EM) approach and estimate networks
over both genes and NPDs in order to determine the most direct causes (“core” genes in the omnigenic
model). In Aim 3 we will analyze both rare and common genetic associations in their gene regulatory
network context. Borrowing from cancer genomics, we will use heat diffusion models to propagate
statistical information on the local network over both genes and regulatory elements (REs) and then use
graph clustering algorithms to extract “hot” subnetworks, corresponding to pathways implicated in the
NPD under study. The methods we develop for these analyses will be made publicly available under
source licenses with extensive support in terms of documentation, tutorials, and vignettes. Through this
we hope to empower future “post-GWAS” analyses that can leverage the genetic, subphenotype and trait
networks underlying human neuropsychiatric health, and eventually point the way to therapeutic
interventions.

## Key facts

- **NIH application ID:** 10504516
- **Project number:** 1R01MH130879-01
- **Recipient organization:** NEW YORK GENOME CENTER
- **Principal Investigator:** David Arthur Knowles
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $799,148
- **Award type:** 1
- **Project period:** 2022-08-01 → 2027-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10504516

## Citation

> US National Institutes of Health, RePORTER application 10504516, Delineating the network effects of mental disorder-associated variants using convex optimization methods (1R01MH130879-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10504516. Licensed CC0.

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