Genetics of 35 blood and urine biomarkers in the UK Biobank

Sinnott-Armstrong, Nasa; Tanigawa, Yosuke; Amar, David; Mars, Nina; Benner, Christian; Aguirre, Matthew; Venkataraman, Guhan; Wainberg, Michael; Ollila, Hanna; Kiiskinen, Tuomo; Havulinna, Aki S.; Pirruccello, James P.; Qian, Junyang; Shcherbina, Anna; FinnGen,; Rodríguez, Fátima; Assimes, Themistocles L.; Agarwala, Vineeta; Tibshirani, Robert; Hastie, Trevor; Ripatti, Samuli; Pritchard, Jonathan K.; Daly, Mark J.; Rivas, Manuel A.

doi:10.1038/s41588-020-00757-z

Cited by 512 publications

(504 citation statements)

References 98 publications

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“…In a two-sample MR approach, we first identified 115 single-nucleotide polymorphisms (SNPs) that were genome-wide associated (p < 5 × 10 -8 ) with circulating total bilirubin levels in a genome-wide association study (GWAS) that included 317,639 individuals of European ancestry (white British) from the UK Biobank (UKB) (non-British white, South Asian, African, and East Asian GWASs were excluded based on a combination of self-identification and refinement using population-specific genotype principal components) [19]. The UKB project, a long-term prospective cohort study, recruited about 500,000 people aged between 40 and 69 years in 2006-2010 from across the UK [20].…”

Section: Methodsmentioning

confidence: 99%

“…The UKB project, a long-term prospective cohort study, recruited about 500,000 people aged between 40 and 69 years in 2006-2010 from across the UK [20]. The raw total bilirubin levels were adjusted for age, sex, the top 40 principal components for population stratification, recruitment center, indicators of socioeconomic status, and potential technical confounders (blood and urine sampling time, fasting time, and sample dilution factor) [19]. SNPs were independently associated with total bilirubin levels, which were reflected by measures of linkage disequilibrium (LD R 2 < 0.001).…”

Section: Methodsmentioning

confidence: 99%

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Genetically Raised Circulating Bilirubin Levels and Risk of Ten Cancers: A Mendelian Randomization Study

Khoei

Carreras‐Torres

Murphy

et al. 2021

Cells

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Bilirubin, an endogenous antioxidant, may play a protective role in cancer development. We applied two-sample Mendelian randomization to investigate whether genetically raised bilirubin levels are causally associated with the risk of ten cancers (pancreas, kidney, endometrium, ovary, breast, prostate, lung, Hodgkin’s lymphoma, melanoma, and neuroblastoma). The number of cases and their matched controls of European descent ranged from 122,977 and 105,974 for breast cancer to 1200 and 6417 for Hodgkin’s lymphoma, respectively. A total of 115 single-nucleotide polymorphisms (SNPs) associated (p < 5 × 10−8) with circulating total bilirubin, extracted from a genome-wide association study in the UK Biobank, were used as instrumental variables. One SNP (rs6431625) in the promoter region of the uridine-diphosphoglucuronate glucuronosyltransferase1A1 (UGT1A1) gene explained 16.9% and the remaining 114 SNPs (non-UGT1A1 SNPs) explained 3.1% of phenotypic variance in circulating bilirubin levels. A one-standarddeviation increment in circulating bilirubin (≈ 4.4 µmol/L), predicted by non-UGT1A1 SNPs, was inversely associated with risk of squamous cell lung cancer and Hodgkin’s lymphoma (odds ratio (OR) 0.85, 95% confidence interval (CI) 0.73–0.99, P 0.04 and OR 0.64, 95% CI 0.42–0.99, p 0.04, respectively), which was confirmed after removing potential pleiotropic SNPs. In contrast, a positive association was observed with the risk of breast cancer after removing potential pleiotropic SNPs (OR 1.12, 95% CI 1.04–1.20, p 0.002). There was little evidence for robust associations with the other seven cancers investigated. Genetically raised bilirubin levels were inversely associated with risk of squamous cell lung cancer as well as Hodgkin’s lymphoma and positively associated with risk of breast cancer. Further studies are required to investigate the utility of bilirubin as a low-cost clinical marker to improve risk prediction for certain cancers.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Genetically Raised Circulating Bilirubin Levels and Risk of Ten Cancers: A Mendelian Randomization Study

Khoei

Carreras‐Torres

Murphy

et al. 2021

Cells

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“…We estimated the prevalence of statin use based on medication codes for self-reported use of any of 13 drugs (atorvastatin, crestor, eptastatin, fluvastatin, lescol, lipitor, lipostat, pravastatin, rosuvastatin, simvador, simvastatin, zocor, zocor heart-pro) as defined in previous genetic analyses of UK Biobank (42). We used this composite statin variable (yes/no) to estimate the effects of BMI and WHR on statin use using 2SLS models with logistic regression as the second stage, and evidence for interaction of BMI and WHR with statin use via product terms of genetically predicted BMI or WHR with observed statin use in relation to outcome traits, expecting interactions to be most evident for LDL cholesterol.…”

Section: Main Analyses: Mr Estimates Of Effectmentioning

confidence: 99%

Dominant role of abdominal adiposity in circulating lipoprotein, lipid, and metabolite levels in UK Biobank: Mendelian randomization study

Bell

Richardson

et al. 2021

Preprint

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Background: The causal impact of excess adiposity on systemic metabolism is unclear. We used multivariable Mendelian randomization to compare the direct effects of total adiposity (using body mass index (BMI)) and abdominal adiposity (using waist-to-hip-ratio (WHR)) on circulating lipoproteins, lipids, and metabolites with a five-fold increase in sample size over previous studies. Methods: We used new metabolic data on 109,532 UK Biobank participants. BMI and WHR were measured in 2006-2010, during which EDTA plasma was collected. Plasma samples were used in 2019-2020 to quantify 249 metabolic traits with high-throughput nuclear magnetic resonance spectroscopy including subclass-specific lipoprotein concentrations, apolipoprotein B, cholesterol and triglycerides, plus pre-glycemic and inflammatory metabolites. We used two-stage least squares regression models with genetic risk scores for BMI and WHR as instruments to estimate the total (unadjusted) and direct (mutually adjusted) effects of BMI and WHR on metabolic traits. We also estimated the effects of BMI and WHR on statin use, and examined interaction of main effects by sex, statin use, and age as a proxy for medication use. Results: Higher BMI (per standard deviation (SD) or 4.8 kg/m2) was estimated to moderately decrease apolipoprotein B and low-density lipoprotein (LDL) cholesterol before and after adjustment for WHR, whilst higher BMI increased triglycerides before but not after WHR adjustment. Estimated effects of higher WHR (per SD, or 0.090 ratio-unit) on lipoproteins, lipids, and metabolites were often larger than those of BMI, but null for LDL cholesterol, and attenuations were minimal upon adjustment for BMI. Patterns of effect estimates differed by sex, e.g., only BMI independently increased triglycerides among men, whereas only WHR independently increased triglycerides among women. Higher BMI and WHR (per SD) were each estimated to directly increase the relative odds of using statins (by 3.49 (95% CI = 3.42, 3.57) times higher for WHR). These patterns were most pronounced among women, and there was strong evidence that the effects of BMI and WHR on metabolic traits differed by statin use and age. Among the youngest adults (38-53 years, statin use 5%), higher BMI and WHR (per SD) each modestly increased LDL cholesterol (0.04 SD, 95% CI = -0.01, 0.08 for total effect of BMI and 0.10 SD, 95% CI = 0.02, 0.17 for total effect of WHR). This estimate for BMI fully attenuated, and the estimate for WHR remained unchanged, upon mutual adjustment. These direct effects on LDL cholesterol were more inverse for BMI and less positive for WHR at intermediate ages (54-62 years, statins 17%) and older ages (63-73 years, statins 29%) where the mutually adjusted effects of BMI and WHR on LDL cholesterol had reversed to -0.19 SD (95% CI = -0.27, -0.11) and -0.05 SD (95% CI = -0.16, 0.06), respectively. Conclusions: Our results suggest that abdominal adiposity has a dominant role in driving the metabolic harms of excess adiposity, particularly among women. Our findings also suggest that apparent effects of adiposity on lowering LDL cholesterol are explained by an effect of adiposity on statin use.

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“…There are a number of common disorders involving immune system and inflammatory response disregulation (immune mediated inflammatory disease, IMID), such as allergy, asthma, diabetes and psoriasis, among others [14]. Blood is both an easily accessible tissue and a heterogeneous mixture of numerous cell types with relevance to inflammatory and immune response, so there is a strong interest in intermediate blood biomarkers of IMIDs for measuring disease risk, monitoring progression, and developing treatments [14,15]. The UK Biobank contains measurements of clinical laboratory biomarkers, as well as blood cell-type composition and disease phenotype data for > 480, 000 individuals [16].…”

Section: Application To Blood Traits and Immune Disorders In The Uk Biobankmentioning

confidence: 99%

Welch-weighted Egger regression reduces false positives due to correlated pleiotropy in Mendelian randomization

Brown

Knowles

2021

Preprint

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Modern population-scale biobanks contain simultaneous measurements of many phenotypes, providing unprecedented opportunity to study the relationship between biomarkers and disease. However, inferring causal effects from observational data is notoriously challenging. Mendelian randomization (MR) has recently received increased attention as a class of methods for estimating causal effects using genetic associations. However, standard methods result in pervasive false positives when two traits share a heritable, unobserved common cause. This is the problem of correlated pleiotropy. Here, we introduce a flexible framework for simulating traits with a common genetic confounder that generalizes recently proposed models, as well as simple approach we call Welch-weighted Egger regression (WWER) for estimating causal effects. We show in comprehensive simulations that our method substantially reduces false positives due to correlated pleiotropy while being fast enough to apply to hundreds of phenotypes. We first apply our method to a subset of the UK Biobank consisting of blood traits in inflammatorydisease, and then a broader set of 411 heritable phenotypes. We detect many effects with strong literaturesupport, as well as numerous behavioral effects that appear to stem from physician advice given to peopleat high risk for disease. We conclude that WWER is a powerful tool for exploratory data analysis inever-growing databases of genotypes and phenotypes

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Genetics of 35 blood and urine biomarkers in the UK Biobank

Cited by 512 publications

References 98 publications

Genetically Raised Circulating Bilirubin Levels and Risk of Ten Cancers: A Mendelian Randomization Study

Genetically Raised Circulating Bilirubin Levels and Risk of Ten Cancers: A Mendelian Randomization Study

Dominant role of abdominal adiposity in circulating lipoprotein, lipid, and metabolite levels in UK Biobank: Mendelian randomization study

Welch-weighted Egger regression reduces false positives due to correlated pleiotropy in Mendelian randomization

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