Andrew Grotzinger

  • Adjunct Professor
  • PSYCHOLOGY & NEUROSCIENCE
  • INSTITUTE FOR BEHAVIORAL GENETICS

Research Interests:

Multivariate genomic methods development and their application to psychiatric and cognitive traits.

Active Grants:

  • HD027802 (PI: Willcutt), 7/1/23-6/30/28, "Differential Diagnosis in Learning Disabilities", Role: Co-Investigator on Data Core
  • NIA U01 AG083829 (Grotzinger), 9/30/2024 - 6/30/29, Longitudinal multi-omic biomarkers for neurocognitive decline prior to dementia onset, Role: Subcontract PI

Additional Resources:

Lab website

Current CV

GenomicSEM on github

Grotzinger Current lab

Publications

Grotzinger News

Highlighted publications

Uncovering the multivariate genetic architecture of frailty with genomic structural equation modeling

Frailty is a heterogenous clinical state that predicts rapid aging and poor prognosis. Using Genomic SEM, we model the genetic architecture across 30 frailty deficits to identify six subclusters of deficits with particularly high genetic overlap. Follow-up analyses demonstrate the clinical and etiological relevance of these subclusters by revealing divergent associations with clinical outcomes (e.g., Alzheimer's disease) and biology linked to aging, including telomere function, amyloid fiber formation and oxidative stress.

(Nature Genetics, 2025)

Characterizing Genetic Pathways Unique to Autism Spectrum Disorder at Multiple Levels of Biological Analysis

This paper characterizes the genetic pathways for autism spectrum disorder (ASD) that are unique from the psychiatric disorder it has the highest genetic correlation with, ADHD. Results revealed clinical correlates (e.g., cognitive functions), functional annotations, and patterns of gene expression associated with this unique genetic signal in ASD.

(Molecular Autism, 2024)

Transcriptome-Wide Structural Equation Modeling of 13 Major Psychiatric Disorders for Cross-Disorder Risk and Drug Repurposing

Transcriptome-wide structural equation modeling (T-SEM) is applied to identify genes whose expression is associated with clusters of psychiatric disorders (e.g., internalizing disorders). Existing drugs that target these gene products are then identified for possible repurposing. As these pharmacological interventions target the genetic signal shared across multiple psychiatric disorders they could aid in reducing growing levels of polypharmacy, where several drugs are given to a single individual with comorbid presentations.

(JAMA Psychiatry, 2023)

Genetic architecture of 11 major psychiatric disorders at biobehavioral, functional genomic and molecular genetic levels of analysis

Here we apply Genomic SEM to model the genetic architecture across 11 psychiatric disorders to find four genomic factors (Compulsive, Thought, Neurodevelopmental, Internalizing) defined by subclusters of disorders. We also introduce and validate Stratified Genomic SEM which can be used to model enrichment in a multivariate space.

(Nature Genetics, 2022)

Shared genetic architecture across psychiatric disorders

This article provides an overview of recent findings in cross-disorder psychiatric genomics. This includes considering results at the genome-wide, functional, and genetic variant level of analysis along with possible future directions for the field.

(Psychological Medicine, 2021)

A general dimension of genetic sharing across diverse cognitive traits inferred from molecular data
This article applies Genomic SEM to examine the genetic risk sharing across seven different cognitive traits from UK Biobank.
 
(Nature Human Behavior, 2020)

Genomic structural equation modelling provides insights into the multivariate genetic architecture of complex traits
In this publication we introduce and validate Genomic Structural Equation Modeling (Genomic SEM). Genomic SEM is a flexible, open-source, multivariate framework for modeling genetic overlap as estimated from GWAS summary statistics.
 
(Nature Human Behavior, 2019)