Identification of genes shared between sedentary behaviour and inflammation: a bivariate genetic correlation analysis

Abstract Sedentary behaviour is associated with increased risk of several chronic conditions and all-cause mortality. Leisure screen time (LST) represents a substantial component of sedentary behaviour and is linked with higher levels of inflammation and increased body mass index (BMI), which in turn predispose to different disorders and negative health outcomes, both in the general population as well as in people with high levels of physical activity. Sedentary behaviour and inflammation are complex traits characterized by the interaction between genetic and environmental factors. While some of the genetic factors underlying LST have been suggested to be shared with body composition, to our knowledge the pleiotropy between LST and inflammation has not been investigated. In this study, we used global and local genetic correlation to identify genetic determinants shared between LST and two inflammatory markers, C-reactive protein (CRP) and interleukin 6 (IL6), using large genomic datasets. We compare results obtained with the definition of genomic loci based on the genome partitioning algorithm proposed in LAVA and a modified version in which loci are defined based on gene coordinates. We found a significant global genetic correlation between LST and CRP ( $$r_g$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>r</mml:mi> <mml:mi>g</mml:mi> </mml:msub> </mml:math> = 0.37, p = 1.6E-34) or IL6 ( $$r_g$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>r</mml:mi> <mml:mi>g</mml:mi> </mml:msub> </mml:math> = 0.41, p = 1.1E-08) and identified several genes shared between LST and inflammatory markers, dissecting loci for which the association was mediated by, or independent from, BMI. Our results provide novel knowledge on shared genetic determinants between sedentary behaviour and inflammation and suggest that different definitions of genomic loci can allow to obtain complementary information when using local genetic correlation analysis.