My research focuses on social genetic effects (SGE, also called indirect genetic effects) and their impact on complex traits, health and disease. SGE are associations between genotypes of one individual and phenotype of another. SGE can arise when two individuals interact and heritable traits of one individual influence the phenotype of the other. SGE are an important component of the genotype to phenotype path, and can be used as a tool to study social effects.
In the first paper I published on SGE [Baud et al. Genetic variation in the social environment influences health and disease, PLoS Genetics, 2017], I reported evidence that SGE, in aggregate, affect a broad range of traits in laboratory mice. I showed that the contribution of SGE to phenotypic variation could be larger than that of direct genetic effects (DGE, effect of an individual's own genes on its phenotype) in some cases. I also explained why failing to account for SGE might lead to biased DGE heritability estimates. The press release associated with this article can be found at: http://blogs.plos.org/biologue/2017/03/16/understanding-images-how-genetic-makeup-of-a-roommate-can-influence-health. This article was runner-up for PLOS Genetics Research Prize 2018: https://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1007649.
The second step of my research into SGE focused on the comparative architecture of SGE and DGE [Baud et al. Comparative architectures of direct and social genetic effects from the genome-wide association study of 170 phenotypes in outbred laboratory mice, bioRxiv, 2018]. The two key results of this study are the partial overlap between SGE and DGE loci acting on the same phenotype and the relatively smaller effect sizes of SGE loci compared to DGE loci. Both have important implications for the design of studies of SGE. We also made available software to run similar analyses.
My doctoral work was on the genetic basis of complex traits - DGE only - in outbred rats [Baud et al. Combined sequence-based and genetic mapping analysis of complex traits in outbred rats, Nature Genetics, 2013]. I worked with the EURATRANS consortium, which was collecting sequence, genotype, and phenotype data on a population of outbred rats with unique genetic characteristics that made it possible to ask important quantitative genetics questions. I demonstrated the following:
(a) A large proportion of genotype-phenotype associations (~40%) are not caused by single variants but rather by haplotypes (allelic heterogeneity). Allelic heterogeneity has now been shown to be widespread in humans too [e.g. Hormozdiari et al. Widespread allelic heterogeneity in complex traits, American Journal of Human Genetics, 2017]. (b) At 35 loci harbouring a single causal variant, I identified the likely causal variant and/or gene, providing novel insights into the aetiology of anxiety, type 2 diabetes, osteoporosis, and multiple sclerosis. Association between one of these causal genes (Ctnnd2) and anxiety has been replicated in a human study [Nivard et al. Further confirmation of the association between anxiety and CTNND2: replication in humans, Genes Brain and Behaviour, 2014]. (c) I showed that different genome-wide significant loci contribute to variation in the same traits in mice and rats.
I decided to focus on SGE after hearing Piter Bijma's talk (given by Bruce Walsh) at the International Conference on Quantitative Genetics 2012.