| Title: | Effects of protein-coding variants on blood metabolite measurements and clinical biomarkers in the UK Biobank |
| Journal: | American Journal of Human Genetics |
| Published: | 20 Feb 2023 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/36809768/ |
| DOI: | https://doi.org/10.1016/j.ajhg.2023.02.002 |
| URL: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10027475 |
| Citations: | 11 (11 in last 2 years) as of 8 Aug 2024 |
Publication 11108
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Abstract
Genome-wide association studies (GWASs) have established the contribution of common and low-frequency variants to metabolic blood measurements in the UK Biobank (UKB). To complement existing GWAS findings, we assessed the contribution of rare protein-coding variants in relation to 355 metabolic blood measurements-including 325 predominantly lipid-related nuclear magnetic resonance (NMR)-derived blood metabolite measurements (Nightingale Health Plc) and 30 clinical blood biomarkers-using 412,393 exome sequences from four genetically diverse ancestries in the UKB. Gene-level collapsing analyses were conducted to evaluate a diverse range of rare-variant architectures for the metabolic blood measurements. Altogether, we identified significant associations (p < 1 × 10-8) for 205 distinct genes that involved 1,968 significant relationships for the Nightingale blood metabolite measurements and 331 for the clinical blood biomarkers. These include associations for rare non-synonymous variants in PLIN1 and CREB3L3 with lipid metabolite measurements and SYT7 with creatinine, among others, which may not only provide insights into novel biology but also deepen our understanding of established disease mechanisms. Of the study-wide significant clinical biomarker associations, 40% were not previously detected on analyzing coding variants in a GWAS in the same cohort, reinforcing the importance of studying rare variation to fully understand the genetic architecture of metabolic blood measurements.</p>
15 Authors
- Abhishek Nag
- Ryan S. Dhindsa
- Lawrence Middleton
- Xiao Jiang
- Dimitrios Vitsios
- Eleanor Wigmore
- Erik L. Allman
- Anna Reznichenko
- Keren Carss
- Katherine R. Smith
- Quanli Wang
- Benjamin Challis
- Dirk S. Paul
- Andrew R. Harper
- Slavé Petrovski
1 Application
| Application ID | Title |
|---|---|
| 26041 | Large-Scale Sequencing in the UK Biobank to Facilitate Gene Discovery, Genome Sciences, and Precision Medicine |
Enabling scientific discoveries that improve human health