Genetic interactions affecting human gene expression identified by variance association mapping
Andrew Anand Brown,
Alfonso Buil,
Ana Viñuela,
Tuuli Lappalainen,
Hou-Feng Zheng,
J Brent Richards,
Kerrin S Small,
Timothy D Spector,
Emmanouil T Dermitzakis,
Richard Durbin
Affiliations
Andrew Anand Brown
Human Genetics, Wellcome Trust Sanger Institute, Cambridge, United Kingdom; NORMENT, KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
Alfonso Buil
Department of Genetic Medicine and Development, University of Geneva, Geneva, Switzerland; Institute of Genetics and Genomics in Geneva, University of Geneva Medical School, Geneva, Switzerland; Swiss Institute of Bioinformatics, Geneva, Switzerland
Ana Viñuela
Department of Twin Research and Genetic Epidemiology, King’s College London, London, United Kingdom
Tuuli Lappalainen
Department of Genetic Medicine and Development, University of Geneva, Geneva, Switzerland; Institute of Genetics and Genomics in Geneva, University of Geneva Medical School, Geneva, Switzerland; Swiss Institute of Bioinformatics, Geneva, Switzerland
Hou-Feng Zheng
Department of Medicine, Human Genetics, Epidemiology and Biostatistics, McGill University, Montreal, Canada
J Brent Richards
Department of Twin Research and Genetic Epidemiology, King’s College London, London, United Kingdom; Department of Medicine, Human Genetics, Epidemiology and Biostatistics, McGill University, Montreal, Canada
Kerrin S Small
Department of Twin Research and Genetic Epidemiology, King’s College London, London, United Kingdom
Timothy D Spector
Department of Twin Research and Genetic Epidemiology, King’s College London, London, United Kingdom
Emmanouil T Dermitzakis
Department of Genetic Medicine and Development, University of Geneva, Geneva, Switzerland; Institute of Genetics and Genomics in Geneva, University of Geneva Medical School, Geneva, Switzerland; Swiss Institute of Bioinformatics, Geneva, Switzerland
Richard Durbin
Human Genetics, Wellcome Trust Sanger Institute, Cambridge, United Kingdom
Non-additive interaction between genetic variants, or epistasis, is a possible explanation for the gap between heritability of complex traits and the variation explained by identified genetic loci. Interactions give rise to genotype dependent variance, and therefore the identification of variance quantitative trait loci can be an intermediate step to discover both epistasis and gene by environment effects (GxE). Using RNA-sequence data from lymphoblastoid cell lines (LCLs) from the TwinsUK cohort, we identify a candidate set of 508 variance associated SNPs. Exploiting the twin design we show that GxE plays a role in ∼70% of these associations. Further investigation of these loci reveals 57 epistatic interactions that replicated in a smaller dataset, explaining on average 4.3% of phenotypic variance. In 24 cases, more variance is explained by the interaction than their additive contributions. Using molecular phenotypes in this way may provide a route to uncovering genetic interactions underlying more complex traits.
