Genetic Variation in the Social Environment Contributes to Health and Disease

Abstract

Assessing the impact of the social environment on health and disease is challenging. As social effects are in part determined by the genetic makeup of social partners, they can be studied from associations between genotypes of one individual and phenotype of another (social genetic effects, SGE, also called indirect genetic effects). For the first time we quantified the contribution of SGE to more than 100 organismal phenotypes and genome-wide gene expression measured in laboratory mice. We find that genetic variation in cage mates (i.e. SGE) contributes to variation in organismal and molecular measures related to anxiety, wound healing, immune function, and body weight. Social genetic effects explained up to 29% of phenotypic variance, and for several traits their contribution exceeded that of direct genetic effects (effects of an individual’s genotypes on its own phenotype). Importantly, we show that ignoring SGE can severely bias estimates of direct genetic effects (heritability). Thus SGE may be an important source of “missing heritability” in studies of complex traits in human populations. In summary, our study uncovers an important contribution of the social environment to phenotypic variation, sets the basis for using SGE to dissect social effects, and identifies an opportunity to improve studies of direct genetic effects.

Indirect genetic effects in behavioral ecology: does behavior play a special role in evolution?

“…In their first experiment using 2 inbred lines of mice, the authors detected IGEs in 11 out of 50 phenotypes assayed. In a separate experiment with an outbred population, the authors detected IGEs in 43 out of 117 traits assayed, and in 8 of those 43, IGEs explained a greater proportion of variance than DGEs (Baud et al 2017). The design of the experiment did not enable a conclusion regarding the likelihood of behavioral traits experiencing IGEs versus other sorts of traits, owing to the nonrandom selection of assayed phenotypes.…”

“…The strength and direction of IGEs can also evolve over time and across populations (Chenoweth et al 2010;Bailey and Zuk 2012;Kazancioğlu et al 2012;Edenbrow et al 2017), and associated social selection is predicted to vary accordingly (McGlothlin et al 2010). In recent years, IGEs have been incorporated into animal and plant breeding studies to more accurately predict evolutionary responses in agriculturally valuable traits, such as growth rate, thermal tolerance, and infection risk (Camerlink et al 2013(Camerlink et al , 2014(Camerlink et al , 2015Costa e Silva et al 2013;Anche et al 2014;Muñoz et al 2014;Alemu et al 2016;Baud et al 2017). A substantial literature explores the mathematical approaches used to study IGEs.…”

“…Baud et al (2017) assayed over 100 phenotypes related to health and disease after housing mice of different genotypes together. In their first experiment using 2 inbred lines of mice, the authors detected IGEs in 11 out of 50 phenotypes assayed.…”

“…To make this comparison, we advocate calculating evolvability, which is the coefficient of variation standardized by the mean. This approach allows comparison of the relative importance of IGEs and DGEs under standard quantitative genetic assumptions, and is similar to that used by Baud et al (2017) in controlled experiments with laboratory mice. A caveat is that transforming phenotypic values prior to calculating genetic variances, as was done using a Box-Cox power transformation to satisfy model assumption in Baud et al (2017), can complicate subsequent comparison of evolvabilities (Garcia-Gonzalez et al 2012).…”

“…This approach allows comparison of the relative importance of IGEs and DGEs under standard quantitative genetic assumptions, and is similar to that used by Baud et al (2017) in controlled experiments with laboratory mice. A caveat is that transforming phenotypic values prior to calculating genetic variances, as was done using a Box-Cox power transformation to satisfy model assumption in Baud et al (2017), can complicate subsequent comparison of evolvabilities (Garcia-Gonzalez et al 2012). Thus, to enable inference about both the relative influence of IGEs, and to compare differences in absolute evolutionary potential contributed by IGEs across traits or organisms, we advocate comparing mean-standardized variances (Hansen et al 2011;Garcia-Gonzalez et al 2012).…”