Biology subjects

Kettunen, J.

Publications and source records attributed to Kettunen, J..

10 recordsLinked to original sources

bioRxiv2018

Unraveling the polygenic architecture of complex traits using blood eQTL meta-analysis

SummaryWhile many disease-associated variants have been identified through genome-wide association studies, their downstream molecular consequences remain unclear.\n\nTo identify these effects, we performed cis- and trans-expression quantitative trait locus (eQTL) analysis in blood from 31,684 individuals through the eQTLGen Consortium.\n\nWe observed that cis-eQTLs can be detected for 88% of the studied genes, but that they have a different genetic architecture compared to disease-associated variants, limiting our ability to use cis-eQTLs to pinpoint causal genes within susceptibility loci.\n\nIn contrast, trans-eQTLs (detected for 37% of 10,317 studied trait-associated variants) were more informative. Multiple unlinked variants, associated to the same complex trait, often converged on trans-genes that are known to play central roles in disease etiology.\n\nWe observed the same when ascertaining the effect of polygenic scores calculated for 1,263 genome-wide association study (GWAS) traits. Expression levels of 13% of the studied genes correlated with polygenic scores, and many resulting genes are known to drive these traits.

genomics↗

bioRxiv2018

Direct estimation of HDL-mediated cholesterol efflux capacity from serum

High-density lipoprotein mediated cholesterol efflux capacity (HDL-CEC) is a functional attribute that may have a protective role in atherogenesis. However, the estimation of HDL-CEC is based on in vitro cell assays that are laborious and hamper large-scale phenotyping. Here, we present a cost-effective high-throughput nuclear magnetic resonance (NMR) spectroscopy method to estimate HDL-CEC directly from serum. We applied the new method in a population-based study of 7,603 individuals including 574 who developed incident coronary heart disease (CHD) during 15 years of follow-up, making this the largest quantitative study for HDL-CEC. As estimated by NMR-spectroscopy, a 1-SD higher HDL-CEC was associated with a lower risk of incident CHD (hazards ratio 0.86; 95%CI 0.79-0.93, adjusted for traditional risk factors and HDL-C). These findings are consistent with published associations based on in vitro cell assays. These corroborative large-scale findings provide further support for a potential protective role of HDL-CEC in CHD, and substantiate this new method and its future applications.

epidemiology↗

bioRxiv2018

Elevated alpha 1 antitrypsin is a major component of GlycA-associated risk for future morbidity and mortality

Integration of electronic health records with systems-level biomolecular data has led to the discovery that GlycA, a complex nuclear magnetic resonance (NMR) spectroscopy biomarker, predicts long-term risk of disease onset and death from myriad causes. To determine the molecular underpinnings of the disease risk of the heterogeneous GlycA signal, we used machine learning to build imputation models for GlycAs constituent glycoproteins, then estimated glycoprotein levels in 11,861 adults across two population-based cohorts with long-term follow-up. While alpha-1-acid glycoprotein had the strongest correlation with GlycA, our analysis revealed that alpha-1 antitrypsin (AAT) was the most predictive of morbidity and mortality for the widest range of diseases, including heart failure (HR=1.60 per s.d., P=1x10-10), influenza and pneumonia (HR=1.37, P=6x10-10), and liver diseases (HR=1.81, P=1x10-6). Despite emerging evidence of AAT's role in suppressing inflammation, transcriptional analyses revealed elevated expression of diverse inflammatory immune pathways with elevated AAT levels, suggesting AAT is elevating to compensate for low-grade chronic inflammation. This study clarifies the molecular underpinnings of the GlycA biomarker and its associated disease risk, and indicates a previously unrecognised association between elevated AAT and severe disease onset and mortality.

systems biology↗

bioRxiv2018

Lipoprotein Signatures of Cholesteryl Ester Transfer Protein and HMG-CoA Reductase Inhibition

BackgroundCETP inhibition reduces vascular event rates but confusion surrounds its low-density lipoprotein (LDL)-cholesterol effects. We sought to clarify associations of genetic inhibition of CETP on detailed lipoproteins.\n\nMethods and ResultsWe used variants associated with CETP (rs247617) and HMGCR (rs12916) expression in 62,400 Europeans with detailed lipoprotein profiling from nuclear magnetic resonance spectroscopy. Genetic associations were scaled to 10% lower risk of coronary heart disease (CHD). Associations of lipoprotein measures with risk of incident CHD in three population-based cohorts (770 cases) were examined.\n\nCETP and HMGCR had near-identical associations with LDL-cholesterol concentration estimated by Friedewald-equation. HMGCR had a relatively consistent effect on cholesterol concentrations across all apolipoprotein B-containing lipoproteins. CETP had stronger effects on remnant and very-low-density lipoprotein cholesterol but no effect on cholesterol concentrations in LDL defined by particle size (diameter 18-26 nm) (-0.02SD 95%CI: -0.10, 0.05 for CETP versus -0.24SD, 95%CI -0.30, -0.18 for HMGCR). CETP had profound effects on lipid compositions of lipoproteins, with strong reductions in the triglyceride content of all highdensity lipoprotein (HDL) particles. These alterations in triglyceride composition within HDL subclasses were observationally associated with risk of CHD, independently of total cholesterol and triglycerides (strongest HR per 1-SD higher triglyceride composition in very-large HDL 1.35; 95%CI: 1.18, 1.54).\n\nConclusionCETP inhibition does not affect size-specific LDL cholesterol but may lower CHD risk by lowering cholesterol in other apolipoprotein-B containing lipoproteins and lowering triglyceride content of HDL particles. Conventional composite lipid assays may mask heterogeneous effects of lipid-altering therapies.

genetics↗

bioRxiv2018

Proof of concept for quantitative urine NMR metabolomics pipeline for large-scale epidemiology and genetics

BackgroundQuantitative molecular data from urine are rare in epidemiology and genetics. NMR spectroscopy could provide these data in high-throughput, and it has already been applied in epidemiological settings to analyse urine samples. However, quantitative protocols for large-scale applications are not available.\n\nMethodsWe describe in detail how to prepare urine samples and perform NMR experiments to obtain quantitative metabolic information. Semi-automated quantitative lineshape fitting analyses were set up for 43 metabolites and applied to data from various analytical test samples and from 1,004 individuals from a population-based epidemiological cohort. Novel analyses on how urine metabolites associate with quantitative serum NMR metabolomics data (61 metabolic measures; n=995) were performed. In addition, confirmatory genome-wide analyses of urine metabolites were conducted (n=578). The fully automated quantitative regression-based spectral analysis is demonstrated for creatinine and glucose (n= 4,548).\n\nResultsIntra-assay metabolite variations were mostly <5% indicating high robustness and accuracy of the urine NMR spectroscopy methodology per se. Intra-individual metabolite variations were large, ranging from 6% to 194%. However, population-based inter-individual metabolite variations were even larger (from 14% to 1655%), providing a sound base for epidemiological applications. Metabolic associations between urine and serum were found clearly weaker than those within serum and within urine, indicating that urinary metabolomics data provide independent metabolic information. Two previous genome-wide hits for formate and 2-hydroxyisobutyrate were replicated at genome-wide significance.\n\nConclusionsQuantitative urine metabolomics data suggest broad novelty for systems epidemiology. A roadmap for an open access methodology is provided.

epidemiology↗

bioRxiv2018

Metabolomic consequences of genetic inhibition of PCSK9 compared with statin treatment

BackgroundBoth statins and PCSK9 inhibitors lower blood low-density lipoprotein cholesterol (LDL-C) levels to reduce risk of cardiovascular events. To assess potential differences between metabolic effects of these two lipid-lowering therapies, we performed detailed lipid and metabolite profiling of a large randomized statin trial, and compared the results with the effects of genetic inhibition of PCSK9, acting as a naturally occurring trial.\n\nMethods228 circulating metabolic measures were quantified by nuclear magnetic resonance spectroscopy, including lipoprotein subclass concentrations and their lipid composition, fatty acids, and amino acids, for 5,359 individuals (2,659 on treatment) in the PROspective Study of Pravastatin in the Elderly at Risk (PROSPER) trial at 6-months post-randomization. The corresponding metabolic measures were analyzed in eight population cohorts (N=72,185) using PCSK9 rs11591147 as an unconfounded proxy to mimic the therapeutic effects of PCSK9 inhibitors.\n\nResultsScaled to an equivalent lowering of LDL-C, the effects of genetic inhibition of PCSK9 on 228 metabolic markers were generally consistent with those of statin therapy (R2=0.88). Alterations in lipoprotein lipid composition and fatty acid balance were similar. However, discrepancies were observed for very-low-density lipoprotein (VLDL) lipid measures. For instance, genetic inhibition of PCSK9 showed weaker effects on lowering of VLDL-cholesterol compared with statin therapy (54% vs. 77% reduction, relative to the lowering effect on LDL-C; P=2 x 10-7 for heterogeneity). Genetic inhibition of PCSK9 showed no robust effects on amino acids, ketones, and a marker of inflammation (GlycA); in contrast, statin treatment lowered GlycA levels.\n\nConclusionsGenetic inhibition of PCSK9 results in similar metabolic effects as statin therapy across a detailed lipid and metabolite profile. However, for the same lowering of LDL-C, PCSK9 inhibitors are predicted to be less efficacious than statins at lowering VLDL lipids, which could potentially translate into subtle differences in cardiovascular risk reduction.

epidemiology↗

bioRxiv2018

The landscape of incident disease risk for the biomarker GlycA and its mortality stratification in angiography patients

Integration of systems-level biomolecular information with electronic health records has led to the discovery of robust blood-based biomarkers predictive of future health and disease. Of recent intense interest is the GlycA biomarker, a complex nuclear magnetic resonance (NMR) spectroscopy signal reflective of acute and chronic inflammation, which predicts long term risk of diverse outcomes including cardiovascular disease, type 2 diabetes, and all-cause mortality. To systematically explore the specificity of the disease burden indicated by GlycA we analysed the risk for 468 common incident hospitalization and mortality outcomes occurring during an 8-year follow-up of 11,861 adults from Finland. Our analyses of GlycA replicated known associations, identified associations with specific cardiovascular disease outcomes, and uncovered new associations with risk of alcoholic liver disease (meta-analysed hazard ratio 2.94 per 1-SD, P=5x10-6), chronic renal failure (HR=2.47, P=3x10-6), glomerular diseases (HR=1.95, P=1x10-6), chronic obstructive pulmonary disease (HR=1.58, P=3x10-5), inflammatory polyarthropathies (HR=1.46, P=4x10-8), and hypertension (HR=1.21, P=5x10-5). We further evaluated GlycA as a biomarker in secondary prevention of 12-year cardiovascular mortality in 900 angiography patients with suspected coronary artery disease. We observed hazard ratios of 4.87 and 5.00 for 12-year mortality in angiography patients in the fourth and fifth quintiles by GlycA levels demonstrating the prognostic potential of GlycA for identification of high mortality-risk individuals. Both GlycA and C-reactive protein had shared as well as independent contributions to mortality hazard, emphasising the importance of chronic inflammation in secondary prevention of cardiovascular disease.

epidemiology↗

bioRxiv2017

Metabolic Effects Of Risk Alleles In PNPLA3, TM6SF2, GCKR, And LYPLAL1 Inform About Heterogeneity Of Non-Alcoholic Fatty Liver Disease

Fatty liver has been associated with unfavourable metabolic changes in circulation and is considered as a risk factor for cardiometabolic complications such as type 2 diabetes and cardiovascular disease. We aimed to provide insights in fatty liver related metabolic deviations by studying the resemblance between the metabolic profile associated with fatty liver observationally and metabolic profiles of non-alcoholic fatty liver disease (NAFLD) risk increasing genotypes. We determined cross-sectional associations of ultrasound-ascertained fatty liver status with 123 metabolic traits in 1,810 individuals aged 34-49 years from The Cardiovascular Risk in Young Finns Study. The cross-sectional associations were compared with the association profiles of NAFLD risk alleles in PNPLA3, TM6SF2, GCKR, and LYPLAL1 with the corresponding metabolic traits obtained from a publicly available genome-wide association study including up to 24,925 European individuals. The analysis revealed substantially different metabolic effects of the risk alleles. PNPLA3 rs738409-G, the strongest genetic risk factor to NAFLD, did not associate with metabolic changes. GCKR rs1260326-T resulted in an association profile similar to the observational fatty liver associations. Metabolic effects of LYPLAL1 rs12137855-C were similar, but statistically less robust, to the effects of GCKR rs1260326-T. In contrast, NAFLD risk allele TM6SF2 rs58542926-T displayed opposite metabolic associations when compared with the observational association pattern.\n\nConclusionThe divergent effects of the risk alleles on circulating lipids and metabolites underline involvement of several metabolic pathways in NAFLD and suggest that there are pathogenically different subtypes of NAFLD with alternate metabolic consequences. NAFLD risk alleles may have neutral or even cardioprotective effect on circulating lipids and metabolites providing evidence that hepatic lipid accumulation by itself would not necessarily cause the metabolic deviations associated observationally with fatty liver.

epidemiology↗

bioRxiv2017

Heterozygous RFX6 protein truncating variants cause Maturity-Onset Diabetes of the Young (MODY) with reduced penetrance

Finding new genetic causes of monogenic diabetes can help to understand development and function of the human pancreas. We aimed to find novel protein-truncating variants causing Maturity-Onset Diabetes of the Young (MODY), a subtype of monogenic diabetes. We used a combination of next-generation sequencing of MODY cases with unknown aetiology along with comparisons to the ExAC database to identify new MODY genes. In the discovery cohort of 36 European patients, we identified two probands with novel RFX6 heterozygous nonsense variants. RFX6 protein truncating variants were enriched in the MODY discovery cohort compared to the European control population within ExAC (odds ratio, OR=131, P=lxl0-4). We found similar results in non-Finnish European (n=348, OR=43, P=5xl0-5) and Finnish (n=80, OR=22, P=1xl0-6) replication cohorts. The overall meta-analysis OR was 34 (P=lxl0-16). RFX6 heterozygotes had reduced penetrance of diabetes compared to common HNF1A and HNF4A-MODY mutations (27%, 70% and 55% at 25 years of age, respectively). The hyperglycaemia resulted from beta-cell dysfunction and was associated with lower fasting and stimulated gastric inhibitory polypeptide (GIP) levels. Our study demonstrates that heterozygous RFX6 protein truncating variants are associated with MODY with reduced penetrance.

genetics↗

bioRxiv2016

An interaction map of circulating metabolites, immune gene networks and their genetic regulation

The interaction between metabolism and the immune system plays a central role in many cardiometabolic diseases. We integrated blood transcriptomic, metabolomic, and genomic profiles from two population-based cohorts, including a subset with 7-year follow-up sampling. We identified topologically robust gene networks enriched for diverse immune functions including cytotoxicity, viral response, B cell, platelet, neutrophil, and mast cell/basophil activity. These immune gene modules showed complex patterns of association with 158 circulating metabolites, including lipoprotein subclasses, lipids, fatty acids, amino acids, and CRP. Genome-wide scans for module expression quantitative trait loci (mQTLs) revealed five modules with mQTLs of both cis and trans effects. The strongest mQTL was in ARHGEF3 (rs1354034) and affected a module enriched for platelet function. Mast cell/basophil and neutrophil function modules maintained their metabolite associations during 7-year follow-up, while our strongest mQTL in ARHGEF3 also displayed clear temporal stability. This study provides a detailed map of natural variation at the blood immuno-metabolic interface and its genetic basis, and facilitates subsequent studies to explain inter-individual variation in cardiometabolic disease.

genomics↗