Genetic associations vary across the spectrum of fasting serum insulin: results from the European IDEFICS/I.Family children's cohort

Abstract

Aims/hypothesis: There is increasing evidence for the existence of shared genetic predictors of metabolic traits and neurodegenerative disease. We previously observed a U-shaped association between fasting insulin in middle-aged women and dementia up to 34 years later. In the present study, we performed genome-wide association (GWA) analyses for fasting serum insulin in European children with a focus on variants associated with the tails of the insulin distribution.

Methods: Genotyping was successful in 2825 children aged 2-14 years at the time of insulin measurement. Because insulin levels vary during childhood, GWA analyses were based on age- and sex-specific z scores. Five percentile ranks of z-insulin were selected and modelled using logistic regression, i.e. the 15th, 25th, 50th, 75th and 85th percentile ranks (P15-P85). Additive genetic models were adjusted for age, sex, BMI, survey year, survey country and principal components derived from genetic data to account for ethnic heterogeneity. Quantile regression was used to determine whether associations with variants identified by GWA analyses differed across quantiles of log-insulin.

Results: A variant in the SLC28A1 gene (rs2122859) was associated with the 85th percentile rank of the insulin z score (P85, p value=3×10^-8). Two variants associated with low z-insulin (P15, p value <5×10^-6) were located on the RBFOX1 and SH3RF3 genes. These genes have previously been associated with both metabolic traits and dementia phenotypes. While variants associated with P50 showed stable associations across the insulin spectrum, we found that associations with variants identified through GWA analyses of P15 and P85 varied across quantiles of log-insulin.

Conclusions/interpretation: The above results support the notion of a shared genetic architecture for dementia and metabolic traits. Our approach identified genetic variants that were associated with the tails of the insulin spectrum only. Because traditional heritability estimates assume that genetic effects are constant throughout the phenotype distribution, the new findings may have implications for understanding the discrepancy in heritability estimates from GWA and family studies and for the study of U-shaped biomarker-disease associations.

Keywords: BMI; Biomarkers; Dementia; Genetics; Genome-wide association analysis; Insulin; Metabolic traits; Obesity; Quantile regression; SNP; Type 2 diabetes.

Fig. 1

Manhattan plot illustrating GWA analysis results for the 85th percentile rank of the age- and sex-specific insulin distribution, indicating rs-numbers for SNPs with associations with p<10⁻⁵ (blue line) or p<5×10⁻⁸ (red line)

Fig. 2

Quantile regression plots for selected SNPs identified by GWA analyses of insulin percentile ranks P15 (a), P25 (b), P50 (c), P75 (d) and P85 (e) (Table 2) as well as for the APOE-4 genotype (f), including a test for heteroscedasticity across quantile levels (p_hs value). Quantile regression of log-insulin was performed for selected SNPs and adjusted for age, age², sex, BMI, survey, country and principal components (regression parameters with 95% confidence bands). Regression models for APOE-4 were not further adjusted for BMI

Fig. 3

Improvement in coefficient of determination for quantile models of log-insulin adjusted for SNPs identified in GWA analyses for insulin percentiles P15, P25, P50, P75 and P85 relative to a model without genetic predictors. Quantile regression of log-insulin was performed for groups of SNPs and adjusted for age, sex, BMI, survey, country and principal components. Adjusted R² (R²_adj) calculated as described by Koenker and Machado [22]