Collective interaction effects associated with mammalian behavioral traits reveal genetic factors connecting fear and hemostasis

Hyung Jun Woo,Jaques Reifman

doi:10.1186/s12888-018-1753-4

Abstract

BackgroundInvestigation of the genetic architectures that influence the behavioral traits of animals can provide important insights into human neuropsychiatric phenotypes. These traits, however, are often highly polygenic, with individual loci contributing only small effects to the overall association. The polygenicity makes it challenging to explain, for example, the widely observed comorbidity between stress and cardiac disease.MethodsWe present an algorithm for inferring the collective association of a large number of interacting gene variants with a quantitative trait. Using simulated data, we demonstrate that by taking into account the non-uniform distribution of genotypes within a cohort, we can achieve greater power than regression-based methods for high-dimensional inference.ResultsWe analyzed genome-wide data sets of outbred mice and pet dogs, and found neurobiological pathways whose associations with behavioral traits arose primarily from interaction effects: γ-carboxylated coagulation factors and downstream neuronal signaling were highly associated with conditioned fear, consistent with our previous finding in human post-traumatic stress disorder (PTSD) data. Prepulse inhibition in mice was associated with serotonin transporter and platelet homeostasis, and noise-induced fear in dogs with hemostasis.ConclusionsOur findings suggest a novel explanation for the observed comorbidity between PTSD/anxiety and cardiovascular diseases: key coagulation factors modulating hemostasis also regulate synaptic plasticity affecting the learning and extinction of fear.

Highlights

Investigation of the genetic architectures that influence the behavioral traits of animals can provide important insights into human neuropsychiatric phenotypes
Animal models, which allow transgenic experiments and controlled phenotyping, help us understand the neurobiological bases of human psychiatric disorders, such as schizophrenia, autism, depression, anxiety, and post-traumatic stress disorder (PTSD)
Collective inference for quantitative traits We implemented the algorithm we termed the continuous discriminant analysis (CDA) procedure for quantitative traits, where the genotype-phenotype data for m single-nucleotide polymorphism (SNP) and n individuals were fit to a joint distribution model using the maximum likelihood method (Fig. 1)

Summary

Introduction

Investigation of the genetic architectures that influence the behavioral traits of animals can provide important insights into human neuropsychiatric phenotypes These traits, are often highly polygenic, with individual loci contributing only small effects to the overall association. The polygenicity makes it challenging to explain, for example, the widely observed comorbidity between stress and cardiac disease. Recent developments in genome-wide association studies have made it possible to perform unbiased, high-resolution interrogation of associated loci Such studies have largely been limited to human genetics, in which typical linkage disequilibrium (LD) between individuals is relatively small and high-quality reference panels of common variants are available [7]. Near gene-level mapping resolution has been achieved, levels of LD between variants in many associated loci remain substantially higher

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