Multi-ancestry study of blood lipid levels identifies four loci interacting with physical activity

Abstract

Many genetic loci affect circulating lipid levels, but it remains unknown whether lifestyle factors, such as physical activity, modify these genetic effects. To identify lipid loci interacting with physical activity, we performed genome-wide analyses of circulating HDL cholesterol, LDL cholesterol, and triglyceride levels in up to 120,979 individuals of European, African, Asian, Hispanic, and Brazilian ancestry, with follow-up of suggestive associations in an additional 131,012 individuals. We find four loci, in/near CLASP1, LHX1, SNTA1, and CNTNAP2, that are associated with circulating lipid levels through interaction with physical activity; higher levels of physical activity enhance the HDL cholesterol-increasing effects of the CLASP1, LHX1, and SNTA1 loci and attenuate the LDL cholesterol-increasing effect of the CNTNAP2 locus. The CLASP1, LHX1, and SNTA1 regions harbor genes linked to muscle function and lipid metabolism. Our results elucidate the role of physical activity interactions in the genetic contribution to blood lipid levels.

The Missing Diversity in Human Genetic Studies

“…These interactions were discovered in a transethnic mapping study that included European, African, Asian, and Hispanic populations. Two out of the four loci (SNTA1 and CNTNAP2) were identified because Africans and Hispanics showed a relatively high frequency of the variants associated with lipid levels compared to other populations (Kilpelä inen et al, 2019). Had the study only been performed in Europeans, these effects would not have been discovered.…”

Lifestyle Risk Score for aggregating multiple lifestyle factors: Handling missingness of individual lifestyle components in meta-analysis of gene-by-lifestyle interactions

“…Next, we compared summary statistics (e.g., mean, standard deviation, minimum, maximum) of estimated effect sizes, standard errors, and p-values across cohorts to identify potential outliers, and reviewed SE-N (i.e., inverse of the median standard error versus the square root of the sample size) plots to look for possible problems with phenotype or covariates (Winkler et al, 2014). Finally, SNPs were excluded if imputation quality score was <0.5, or if the product of the imputation quality and minor allele count was <20 (de las Fuentes et al, 2020;Kilpelä inen et al, 2019). No genomic control was applied after filtering as there were little to no problems with inflation (genomic control inflation λ ranged from 1.020 to 1.071).…”

“…In addition to genetics, environmental factors and gene-environment interactions may contribute to this unexplained trait heritability (Manolio et al, 2009;Rao et al, 2017). Recently, genomewide gene-environment interaction studies have been conducted to further explore the potential mechanisms underlying an array of diseases or disease traits of interest (de las Fuentes et al, 2020;Graff et al, 2017;Liu et al, 2012;Noordam et al, 2019;Wu et al, 2020). Thus far, these collaborative efforts have largely focused on a single environmental or lifestyle factor, such as smoking Justice et al, 2017;Yun J.…”

“…Thus far, these collaborative efforts have largely focused on a single environmental or lifestyle factor, such as smoking Justice et al, 2017;Yun J. Sung et al, 2018;Yun Ju Sung et al, 2019;Wu et al, 2020), physical activity Kilpelä inen et al, 2019), alcohol intake (De Vries et al, 2019), educational attainment (de las Fuentes et al, 2020) and others (Jiang et al, 2018;Noordam et al, 2019). By accounting for the environmental risk factor, these efforts identified several novel loci beyond those identified by the traditional main effects-only GWAS.…”

Revealing grand-paternal programming of lipid metabolism using a novel computational tool

“…We wanted to test the hypothesis that a higher carbohydrate intake in (grand)sires alters lipid metabolism in offspring as this contributes to our understanding of dietary intake and metabolic programming 25,26 and the effects of metabolic disease across generations 5 in a model system. This gives us an insight into possible interventions to improve human metabolic health in familial circumstances.…”