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

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. We 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 was providing guidance for understanding published estimates and in best practices as the field moves toward estimating the near full additive genetic variance of traits using whole-genome sequence datasets and large imputation reference panels, now published in several papers. We are now applying these tools and methods to psychiatric and substance use datasets from the Psychiatric Genomics Consortium and the UK Biobank.

The genetic architecture of a trait.

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 nicotine and alcohol use, 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. My current work focuses on applying genome-wide, marker-based methods to explore the genetic architecture of behavior phenotypes. We are now applying these tools and methods to psychiatric and substance use phenotypes.

Understanding the evolutionary forces that shape genetic variation is a fundamental goal of genetics.

Much of my previous work included empirical studies of what demographic and selective forces shape naturally-occurring genetic variation, using human and plants as study systems. These included estimating the effect of inbreeding on complex traits, 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. These approaches are key to understanding complex trait genetic architecture from an evolutionary perspective.