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Mendelian Randomization studies indicate that BMI contributes to various diseases, but it's unclear if this is entirely mediated by BMI itself. This study examines whether disease risk from BMI-associated sequence variants is mediated through BMI or other mechanisms, using data from Iceland and the UK Biobank. The associations of BMI genetic risk score with diseases like fatty liver disease, knee replacement, and glucose intolerance were fully attenuated when conditioned on BMI, and largely for type 2 diabetes, heart failure, myocardial infarction, atrial fibrillation, and hip replacement. Similar attenuation was observed for chronic kidney disease and stroke, though results varied. Findings were consistent across sexes, except for myocardial infarction. Residual effects may result from temporal BMI changes, pleiotropy, measurement error, non-linear relationships, non-collapsibility, or confounding. The attenuation extent of BMI genetic risk score on disease associations suggests the potential impact of reducing BMI on disease risk., Competing Interests: Competing interests G.E., G.T., V.S., F.Z., H.Helgason, T.O., S.R., V.T., M.O.U., G.S., A.S.S., H.E., H.M.A., G.A.J., A.H., S.G., U.S., H.K.A., D.O.A., G.B., H.S., T.T., P.S., U.T., H.Holm, D.F.G. and K.S. are employees of deCODE Genetics/Amgen Inc. The remaining authors declare no competing interests., (© 2024. The Author(s).)

High-throughput proteomics platforms measuring thousands of proteins in plasma combined with genomic and phenotypic information have the power to bridge the gap between the genome and diseases. Here we performed association studies of Olink Explore 3072 data generated by the UK Biobank Pharma Proteomics Project 1 on plasma samples from more than 50,000 UK Biobank participants with phenotypic and genotypic data, stratifying on British or Irish, African and South Asian ancestries. We compared the results with those of a SomaScan v4 study on plasma from 36,000 Icelandic people 2 , for 1,514 of whom Olink data were also available. We found modest correlation between the two platforms. Although cis protein quantitative trait loci were detected for a similar absolute number of assays on the two platforms (2,101 on Olink versus 2,120 on SomaScan), the proportion of assays with such supporting evidence for assay performance was higher on the Olink platform (72% versus 43%). A considerable number of proteins had genomic associations that differed between the platforms. We provide examples where differences between platforms may influence conclusions drawn from the integration of protein levels with the study of diseases. We demonstrate how leveraging the diverse ancestries of participants in the UK Biobank helps to detect novel associations and refine genomic location. Our results show the value of the information provided by the two most commonly used high-throughput proteomics platforms and demonstrate the differences between them that at times provides useful complementarity., (© 2023. The Author(s).)

Importance: Whether protein risk scores derived from a single plasma sample could be useful for risk assessment for atherosclerotic cardiovascular disease (ASCVD), in conjunction with clinical risk factors and polygenic risk scores, is uncertain., Objective: To develop protein risk scores for ASCVD risk prediction and compare them to clinical risk factors and polygenic risk scores in primary and secondary event populations., Design, Setting, and Participants: The primary analysis was a retrospective study of primary events among 13 540 individuals in Iceland (aged 40-75 years) with proteomics data and no history of major ASCVD events at recruitment (study duration, August 23, 2000 until October 26, 2006; follow-up through 2018). We also analyzed a secondary event population from a randomized, double-blind lipid-lowering clinical trial (2013-2016), consisting of individuals with stable ASCVD receiving statin therapy and for whom proteomic data were available for 6791 individuals., Exposures: Protein risk scores (based on 4963 plasma protein levels and developed in a training set in the primary event population); polygenic risk scores for coronary artery disease and stroke; and clinical risk factors that included age, sex, statin use, hypertension treatment, type 2 diabetes, body mass index, and smoking status at the time of plasma sampling., Main Outcomes and Measures: Outcomes were composites of myocardial infarction, stroke, and coronary heart disease death or cardiovascular death. Performance was evaluated using Cox survival models and measures of discrimination and reclassification that accounted for the competing risk of non-ASCVD death., Results: In the primary event population test set (4018 individuals [59.0% women]; 465 events; median follow-up, 15.8 years), the protein risk score had a hazard ratio (HR) of 1.93 per SD (95% CI, 1.75 to 2.13). Addition of protein risk score and polygenic risk scores significantly increased the C index when added to a clinical risk factor model (C index change, 0.022 [95% CI, 0.007 to 0.038]). Addition of the protein risk score alone to a clinical risk factor model also led to a significantly increased C index (difference, 0.014 [95% CI, 0.002 to 0.028]). Among White individuals in the secondary event population (6307 participants; 432 events; median follow-up, 2.2 years), the protein risk score had an HR of 1.62 per SD (95% CI, 1.48 to 1.79) and significantly increased C index when added to a clinical risk factor model (C index change, 0.026 [95% CI, 0.011 to 0.042]). The protein risk score was significantly associated with major adverse cardiovascular events among individuals of African and Asian ancestries in the secondary event population., Conclusions and Relevance: A protein risk score was significantly associated with ASCVD events in primary and secondary event populations. When added to clinical risk factors, the protein risk score and polygenic risk score both provided statistically significant but modest improvement in discrimination.

Predicting all-cause mortality risk is challenging and requires extensive medical data. Recently, large-scale proteomics datasets have proven useful for predicting health-related outcomes. Here, we use measurements of levels of 4,684 plasma proteins in 22,913 Icelanders to develop all-cause mortality predictors both for short- and long-term risk. The participants were 18-101 years old with a mean follow up of 13.7 (sd. 4.7) years. During the study period, 7,061 participants died. Our proposed predictor outperformed, in survival prediction, a predictor based on conventional mortality risk factors. We could identify the 5% at highest risk in a group of 60-80 years old, where 88% died within ten years and 5% at the lowest risk where only 1% died. Furthermore, the predicted risk of death correlates with measures of frailty in an independent dataset. Our results show that the plasma proteome can be used to assess general health and estimate the risk of death.

Olfactory receptor (OR) genes in humans form a special class characterized by unusually high DNA sequence diversity, which should give rise to differences in perception and behavior. In the largest genome-wide association study to date based on olfactory testing, we investigated odor perception and naming with smell tasks performed by 9,122 Icelanders, with replication in a separate sample of 2,204 individuals. We discovered an association between a low-frequency missense variant in TAAR5 and reduced intensity rating of fish odor containing trimethylamine (p.Ser95Pro, p combined  = 5.6 × 10 -15 ). We demonstrate that TAAR5 genotype affects aversion to fish odor, reflected by linguistic descriptions of the odor and pleasantness ratings. We also discovered common sequence variants in two canonical olfactory receptor loci that associate with increased intensity and naming of licorice odor (trans-anethole: lead variant p.Lys233Asn in OR6C70, p combined  = 8.8 × 10 -16 and p combined  = 1.4 × 10 -9 ) and enhanced naming of cinnamon (trans-cinnamaldehyde; intergenic variant rs317787-T, p combined  = 5.0 × 10 -17 ). Together, our results show that TAAR5 genotype variation influences human odor responses and highlight that sequence diversity in canonical OR genes can lead to enhanced olfactory ability, in contrast to the view that greater tolerance for mutations in the human OR repertoire leads to diminished function., Competing Interests: Declaration of Interests R.S.G., E.V.I., A.H., N.K.H., G.R., G.A.A., A.S., B.A.J., G.L.N., M.O.U., H. Helgason, B.V.H., M.S.N., V.T., G.S., T.T., A.O., R.P.K., G.B., G. T., I. J., H. Holm, D.F.G., U.T., H.S., P.S., and K.S. are employees of deCODE genetics/Amgen, Inc., (Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.)

Machine learning algorithms can be trained to estimate age from brain structural MRI. The difference between an individual's predicted and chronological age, predicted age difference (PAD), is a phenotype of relevance to aging and brain disease. Here, we present a new deep learning approach to predict brain age from a T1-weighted MRI. The method was trained on a dataset of healthy Icelanders and tested on two datasets, IXI and UK Biobank, utilizing transfer learning to improve accuracy on new sites. A genome-wide association study (GWAS) of PAD in the UK Biobank data (discovery set: [Formula: see text], replication set: [Formula: see text]) yielded two sequence variants, rs1452628-T ([Formula: see text], [Formula: see text]) and rs2435204-G ([Formula: see text], [Formula: see text]). The former is near KCNK2 and correlates with reduced sulcal width, whereas the latter correlates with reduced white matter surface area and tags a well-known inversion at 17q21.31 (H2).