Characterizing the effect of background selection on the polygenicity of brain-related traits

Frank R Wendt; Gita A Pathak; Cassie Overstreet; Daniel S Tylee; Joel Gelernter; Elizabeth G Atkinson; Renato Polimanti

doi:10.1016/j.ygeno.2020.11.032

Characterizing the effect of background selection on the polygenicity of brain-related traits

Genomics. 2021 Jan;113(1 Pt 1):111-119. doi: 10.1016/j.ygeno.2020.11.032. Epub 2020 Dec 2.

Authors

Frank R Wendt¹, Gita A Pathak¹, Cassie Overstreet², Daniel S Tylee¹, Joel Gelernter³, Elizabeth G Atkinson⁴, Renato Polimanti⁵

Affiliations

¹ Department of Psychiatry, Yale School of Medicine and VA CT Healthcare System, West Haven, CT 06516, USA.
² National Center for Posttraumatic Stress Disorder, Clinical Neurosciences Division, VA CT Healthcare System and Department of Psychiatry, Yale University School of Medicine, USA.
³ Department of Psychiatry, Yale School of Medicine and VA CT Healthcare System, West Haven, CT 06516, USA; Departments of Genetics and Neuroscience, Yale University School of Medicine, New Haven, CT 06510, USA.
⁴ Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
⁵ Department of Psychiatry, Yale School of Medicine and VA CT Healthcare System, West Haven, CT 06516, USA. Electronic address: renato.polimanti@yale.edu.

Abstract

Background: Genome-wide association studies (GWAS) have demonstrated that psychopathology phenotypes are affected by many risk alleles with small effect (polygenicity). It is unclear how ubiquitously evolutionary pressures influence the genetic architecture of these traits.

Methods: We partitioned SNP heritability to assess the contribution of background (BGS) and positive selection, Neanderthal local ancestry, functional significance, and genotype networks in 75 brain-related traits (8411 ≤ N ≤ 1,131,181, mean N = 205,289). We applied binary annotations by dichotomizing each measure based on top 2%, 1%, and 0.5% of all scores genome-wide. Effect size distribution features were calculated using GENESIS. We tested the relationship between effect size distribution descriptive statistics and natural selection. In a subset of traits, we explore the inclusion of diagnostic heterogeneity (e.g., number of diagnostic combinations and total symptoms) in the tested relationship.

Results: SNP-heritability was enriched (false discovery rate q < 0.05) for loci with elevated BGS (7 phenotypes) and in genic (34 phenotypes) and loss-of-function (LoF)-intolerant regions (67 phenotypes). These effects were strongest in GWAS of schizophrenia (1.90-fold BGS, 1.16-fold genic, and 1.92-fold LoF), educational attainment (1.86-fold BGS, 1.12-fold genic, and 1.79-fold LoF), and cognitive performance (2.29-fold BGS, 1.12-fold genic, and 1.79-fold LoF). BGS (top 2%) significantly predicted effect size variance for trait-associated loci (σ² parameter) in 75 brain-related traits (β = 4.39 × 10^-5, p = 1.43 × 10^-5, model r² = 0.548). Considering the number of DSM-5 diagnostic combinations per psychiatric disorder improved model fit (σ² ~ B_Top2% × Genic × diagnostic combinations; model r² = 0.661).

Conclusions: Brain-related phenotypes with larger variance in risk locus effect sizes are associated with loci under BGS. We show exploratory results suggesting that diagnostic complexity may also contribute to the increased polygenicity of psychiatric disorders.

Keywords: Background selection; Complex traits; Diagnostic heterogeneity; Natural selection; Partitioned heritability; Psychiatry.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

Brain / metabolism*
Genetic Background*
Genetic Heterogeneity*
Humans
Mental Disorders / diagnosis
Mental Disorders / genetics*
Multifactorial Inheritance*
Phenotype
Polymorphism, Single Nucleotide
Selection, Genetic*

Abstract

Publication types

MeSH terms

Grants and funding