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Autozygosity, meaning inheritance of an ancestral allele in the homozygous state is known to lead bi-allelic mutations that manifest their effects through the autosomal recessive inheritance pattern. Autosomal recessive mutations are known to be the underlying cause of several Mendelian metabolic diseases, especially among the offspring of related individuals. In line with this, inbreeding coefficient of an individual as a measure of cryptic autozygosity among the general population is known to lead adverse metabolic outcomes including type 2 diabetes (T2DM), a multifactorial metabolic disease for which the recessive genetic causes remain unknown. In order to unravel such effects for multiple metabolic facades of the disease, we investigated the relationship between the excess of homozygosity and the metabolic signature of T2DM. We included a set of heritable 143 circulating markers associated with fasting glucose in a Dutch genetic isolate Erasmus Rucphen Family (ERF) of up to 2,580 individuals. We calculated individual whole genome-based, exome-based and pedigree-based inbreeding coefficients and tested their influence on the T2DM-related metabolites as well as T2DM risk factors. We also performed model supervised genome-wide association analysis (GWAS) for the metabolites which significantly correlate with inbreeding values. Inbreeding value of the population significantly and positively correlated with associated with risk factors of T2DM: body-mass index (BMI), glucose, insulin resistance, fasting insulin and waist-hip ratio. We found that inbreeding influenced 32.9% of the T2DM-related metabolites, clustering among chemical groups of lipoproteins, amino-acids and phosphatidylcholines, whereas 80 % of these significant associations were independent of the BMI. The most remarkable effect of inbreeding is observed for S-HDL-ApoA1, for which we show evidence of the novel DISP1 genetic region discovered by model supervised GWAS, in the ERF population. In conclusion, we show that inbreeding effects human metabolism and genetic models other than the globally used additive model is worth considering for study of metabolic phenotypes.

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