Research

The Evans lab has several areas of research, all centered on the genetics of complex traits.

Funding:

HEV~NI23013 Hevolution/AFAR. PI Evans. Hevolution/AFAR New Investigator Awards in Aging Biology and Geroscience Research, "Gene-gene interaction associations with frailty to identify core genes of aging and their biological context." Project duration 01/01/2024-12/31/2026. Total award $375,000. Role: Primary Investigator.

CU Boulder Research & Innovation Seed Grant, “Proteome-wide associations and changes with age-related frailty." Project duration 07/01/2025-06/30/2026. Total award $60,000. Role: PI.

CU Boulder Research & Innovation Seed Grant, “Molecular validation of core genes in Alzheimer's Disease and Related Dementias.” Project duration 04/01/2024-03/31/2025. Total award $50,000. Role: Co-Investigator.

NIH/NIEHS R21ES035826-01. PI Harry Smolker. Long-term Effects of Developmental Air Pollution Exposure on Adult Mental Health: Sensitive Periods, Neural Correlates, and Genetic Risk Factors. Project duration 04/01/2023-03/31-2025, total award $391,250. Role: Co-Investigator.

NIH/NIA 1 R01 AG046938-06 (PI: Reynolds) "Colorado Adoption/Twin Study of Lifespan Behavioral Development & Cognitive Aging (CATSLife2).” Project duration 06/01/15-05/31/25, total award $9,254,749  (Role: Co-Investigator)

NIH/NIDA 1 R01 DA044283-01, (PI: Vrieze) "Deep sequencing, phenotyping, and imputation in large-scale biobanks: A novel and cost-effective framework to identify rare mutations associated with addiction." Project duration 05/01/19-02/28/24, total award $1,236,721 (Role: Co-Investigator)

NIH/NIMH 5 R01 MH100141-06 (PI: Keller) "Estimating the genetic and environmental architecture of psychiatric disorders" project duration: 7/1/18-6/30/23, total award: $3,192,862 (Role: Co-Investigator)

NIH/NIA 1 R01 AG046938-05 (MPI: Reynolds/Wadsworth), "Colorado Adoption/Twin Study of Lifespan Behavioral Development & Cognitive Aging (CATSLife) Subcontract to Institute for Behavioral Genetics" (Wadsworth), project duration 06/01/15-02/28/20, total award $5,831,433 (Role: Co-Investigator)

Projects/Research Interests

My work focuses on the development and improvement of statistical genetic methods to explore the genetic basis and architecture of complex traits, with emphasis on psychiatric and aging phenotypes. I use large-scale, genome-wide datasets to understand the causes of behavioral traits, including aging-related traits such as frailty and dementia, and psychiatric traits such as internalizing disorders (depression & anxiety) and substance use (alcohol and nicotine use and problematic use). This has included applying a wide range of analytical methods to estimate total genetic contributions, identifying individual loci that affect these traits, and understanding how the genetic influences are shared across traits. Importantly, while association studies have yielded valuable knowledge of loci that influence complex traits, interpretation of single-SNP associations to identify functional variation is rarely straightforward. My lab group links genetics to other omics as a key step towards understanding possible functional effects of loci. Utilizing and improving analytical methods that statistically link omics and phenotype data, including through analyses of genetic architecture, are ways forward, and are foci of the proposed research.

Trait Genetic Architecture

The genetic architecture of a trait describes the number of variants that influence that trait, their frequency, their location in the genome, and their interactions, including interactions with the environment. Understanding and describing the architecture of complex traits, including substance use and anxiety disorders, is important from a basic, evolutionary standpoint to determine what forces shape these traits, as well as from a practical and potentially clinical perspective, because the architecture of a trait may have implications for the etiology of disease and possibility of risk assessment and prediction. I am now applying these tools and methods to psychiatric disorder and substance use datasets, specifically focusing on aging and internalizing disorders.

Genetic Interactions

Identifying genetic interactions can elucidate underlying biology of complex traits. Understanding the context-dependency of genetic associations, the interactions of genes with each other or with their environment, is critical to characterizing genetic influences. I have led or contributed to numerous studies to test for such interactions, developing a new approach to exhaustively and efficiently statistically test for all genome-wide gene-gene interactions. This includes developing novel enrichment tests to quantify the contribution of gene sets, such as from known molecular pathways, to understand the biological context of genetic influences. Rigorously testing for such interactions, we have identified novel and replicated interactions in some cases and for some traits, and in other situations do not find such context-dependency.

Heritability

Narrow-sense heritability (h²) remains a fundamental parameter of medical and evolutionary genetics. In addition to providing an understanding of the genetic basis of traits, h² determines the response to selection, the potential utility of individual genetic risk and trait prediction, and how much of the phenotypic variability could theoretically be accounted for in genome-wide association studies (GWAS). Multiple methods have now been developed to estimate h² from marker data in unrelated individuals, avoiding possible confounding factors in family-based estimation and accounting for a greater proportion of the phenotypic variance than significantly-associated loci from GWAS. However, a comprehensive evaluation of these methods across a wide range of conditions, confounding factors, and genetic architectures has not been performed, leading to discrepancy in the literature and sometimes conflicting estimates. I used thousands of real whole genome sequences to perform the most comprehensive, thorough, and realistic evaluation to-date, determining under which conditions the many heritability-estimation methods produce biased estimates. A key outcome from was providing guidance for understanding published estimates and in best practices as the field moves toward estimating the near full additive genetic variation of traits using whole-genome sequence datasets and large imputation reference panels. I am now applying these tools and methods to substance use and psychiatric disorder datasets from large-scale biobanks.

Evolution

Understanding the forces that shape genetic variation is a fundamental goal of genetics. Much of my work has included empirical studies of what demographic and selective forces shape genetic variation, using human, mice, and plants as study systems. These included estimating the effect of inbreeding on complex traits, gene-environment interactions, exploring population genetic structure, estimating gene flow and past demography (including effective population size through time), and identifying signatures of recent positive and purifying selection throughout the genome. This experience will be relevant to the proposed research exploring whole genome data across multiple traits and understanding how their shared genetic architecture impacts trait heterogeneity, trait associations, and functional enrichment. Examples of this work are included below.