Published in

Springer Nature [academic journals on nature.com], European Journal of Human Genetics, 9(23), p. 1223-1228, 2014

DOI: 10.1038/ejhg.2014.259

Links

Tools

Export citation

Search in Google Scholar

Heritability of liver enzyme levels estimated from genome-wide SNP data

This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

Full text: Download

Green circle
Preprint: archiving allowed
Orange circle
Postprint: archiving restricted
Red circle
Published version: archiving forbidden
Data provided by SHERPA/RoMEO

Abstract

Variation in the liver enzyme levels in humans is moderately heritable, as indicated by twin-family studies. At present, genome-wide association studies have traced <2% of the variance back to genome-wide significant single-nucleotide polymorphisms (SNPs). We estimated the SNP-based heritability of levels of three liver enzymes (gamma-glutamyl transferase (GGT); alanine aminotransferase (ALT); and aspartate aminotransferase (AST)) using genome-wide SNP data in a sample of 5421 unrelated Dutch individuals. Two estimation methods for SNP-based heritability were compared, one based on the distant genetic relatedness among all subjects as summarized in a Genetic Relatedness Matrix (GRM), and the other one based on density estimation (DE). The DE method was also applied to meta-analysis results on GGT and ALT. GRM-derived SNP-based heritability estimates were significant for GGT (16%) and AST (11%), but not for ALT (6%). DE estimates in the same sample varied as a function of pruning and were around 23% for all liver enzymes. Application of the DE approach to meta-analysis results for GGT and ALT gave SNP-based heritability estimates of 6 and 3%. The significant results in the Dutch sample indicate that genome-wide SNP platforms contain substantial information regarding the underlying genetic variation in the liver enzyme levels. A major part of this genetic variation remains however undetected. SNP-based heritability estimates, based on meta-analysis results, may point at substantial heterogeneity among cohorts contributing to the meta-analysis. This type of analysis may provide useful information to guide future gene searches.European Journal of Human Genetics advance online publication, 26 November 2014; doi:10.1038/ejhg.2014.259.