Teslovich et al. Identification of seven novel loci associated with amino acid levels using single variant and gene-based tests in 8,545 Finnish men from the METSIM study. Human Molecular Genetics. 2018; DOI: 10.1093/hmg/ddy067
There is a growing body of evidence that suggests amino acids are heritable biomarkers for a number of chronic conditions, including type 2 diabetes, liver disease and Alzheimer’s disease. Previous studies have identified more than 200 common genetic variants affecting the variability of amino acid levels, but detecting rare genetic variation underlying these metabolites is challenging and represents an important area for investigation.
In a study of 8,545 diabetic Finnish men from the METabolic Syndrome in Men (METSIM) study, Teslovich and colleagues used Nightingale’s high-throughput NMR assay to quantify the levels of nine amino acids: alanine, glutamine, glycine, histidine, isoleucine, leucine, phenylalanine, tyrosine and valine. In addition to traditional genome-wide association study (GWAS), they used gene-based association analysis and conditional analysis of known loci to identify novel genetic variants for amino acid levels. These methods can be useful for detecting rare, protein-altering variants that cannot be detected with traditional single variant test. In total, seven novel amino acid loci were identified, five from single variant tests (LOC157273/PPP1R3B, ZFHX3, LIPC, WWOX and TRIB1) and two from gene-based associations (PYCR1 and BCAT2), harboring rare missense variants that were not detected with traditional GWAS. In a further analysis conditioned on previously identified GWAS signals, five additional novel signals were detected in known amino acid loci, including two rare variant signals.
Taken together, these results provide researchers with further information that can be used to help clarify molecular mechanisms of amino acid metabolism. For example, one identified association related to decreased glycine levels has previously been linked to decreased high-density lipoprotein cholesterol (HDL-C), total cholesterol levels and increased risk of type 2 diabetes and liver disease, demonstrating mechanistic links with lipid metabolism and chronic diseases.
This study is an example of Nightingale's NMR metabolomics platform being applied in human genetics research. Our blood analysis service can be utilized to investigate the molecular mechanisms underpinning a wide range of chronic diseases, using metabolic profiling to identify circulating biomarkers associated with disease risk.
In this study, our assay was used to quantify nine amino acid measures for 8,545 participants in the METSIM study. Nightingale's assay has been successfully used in a wide range of research applications and has featured in over 100 peer-reviewed studies.