The influence of rare variants in circulating metabolic biomarkers

Abstract

Circulating metabolite levels are biomarkers for cardiovascular disease (CVD). Here we studied, association of rare variants and 226 serum lipoproteins, lipids and amino acids in 7,142 (discovery plus follow-up) healthy participants. We leveraged the information from multiple metabolite measurements on the same participants to improve discovery in rare variant association analyses for gene-based and gene-set tests by incorporating correlated metabolites as covariates in the validation stage. Gene-based analysis corrected for the effective number of tests performed, confirmed established associations at APOB, APOC3, PAH, HAL and PCSK (p<1.32x10 -7 ) and identified novel gene-trait associations at a lower stringency threshold with ACSL1, MYCN, ). Regulation of the pyruvate dehydrogenase (PDH) complex was associated for the first time, in gene-set analyses also corrected for effective number of tests, with IDL and LDL parameters, as well as circulating cholesterol (p METASKAT <2.41x10 -6 ). In conclusion, using an approach that leverages metabolite measurements obtained in the same participants, we identified novel loci and pathways involved in the regulation of these important metabolic biomarkers. As large-scale biobanks continue to amass sequencing and phenotypic information, analytical approaches such as ours will be useful to fully exploit the copious amounts of biological data generated in these efforts.

Coupling of metabolomics and exome sequencing reveals graded effects of rare damaging heterozygous variants on gene function and resulting traits and diseases

“…Comparison of our results to those from published studies that focused on rare variant aggregation testing of metabolite levels [14][15][16][17][18][19][20]24 (Methods) showed that 32 of the 73 identified unique genes (44%) had not been reported as significant in any of these studies. Moreover, 115 of all 192 detected gene-metabolite associations (60%) were novel (Supplementary Table 4).…”

“…We compared our significant findings to the significant findings from eight published genetic studies of the plasma/serum or urine metabolome that focused on rare exonic variant aggregation testing and used sequencing and high-throughput metabolomics data [14][15][16][17][18][19][20]24 . We first assessed whether the genes identified in our study were reported as associated with any metabolite in any of the seven studies at their respective multiple-testing corrected significance threshold, after having mapped all gene names to their current version in Ensembl version 109 using https://www.ensembl.org/biomart/martview.…”

“…Gene-based aggregation testing of rare, putatively damaging variants in population studies can address this challenge. Previously, such studies have focused almost exclusively on the circulating metabolome [14][15][16][17][18][19][20] . We have recently shown that GWAS of paired plasma and urine metabolomes not only reveal many more associations, but also enable specific insights into renal metabolite handling 2 .…”

Contribution of rare whole-genome sequencing variants to plasma protein levels and the missing heritability

“…A number of gene-based studies using proteomics or other types of omics data have also been performed. For example, using WES data and a large set of metabolites, rare-variant associations were identified for five genes previously known to be associated with the same phenotypes 27 . Few gene-based studies have been performed with WGS data to discover associations with rare variants.…”

Genetic determinants of blood gene expression and splicing and their contribution to molecular phenotypes and health outcomes

“…Then, we integrate the QTL data with additional molecular QTL data derived from the same study (Figure 1). These data include plasma protein levels measured through an antibody-based proximity extension assay (Olink Target panels, n=4,662-4,981 individuals) 17,18 and an aptamer-based multiplex protein assay (SomaScan v3, n=3,301) 5 , as well as serum metabolite levels measured using an untargeted mass spectrometry platform (Metabolon HD4, n=14,296) 10 and a nuclear magnetic resonance spectroscopy platform (Nightingale Health, n=40,849) 19,20 .…”