No abstract

Nonalcoholic fatty liver (NAFL) and its sequelae are growing health problems. We performed a genome-wide association study of NAFL, cirrhosis and hepatocellular carcinoma, and integrated the findings with expression and proteomic data. For NAFL, we utilized 9,491 clinical cases and proton density fat fraction extracted from 36,116 liver magnetic resonance images. We identified 18 sequence variants associated with NAFL and 4 with cirrhosis, and found rare, protective, predicted loss-of-function variants in MTARC1 and GPAM, underscoring them as potential drug targets. We leveraged messenger RNA expression, splicing and predicted coding effects to identify 16 putative causal genes, of which many are implicated in lipid metabolism. We analyzed levels of 4,907 plasma proteins in 35,559 Icelanders and 1,459 proteins in 47,151 UK Biobank participants, identifying multiple proteins involved in disease pathogenesis. We show that proteomics can discriminate between NAFL and cirrhosis. The present study provides insights into the development of noninvasive evaluation of NAFL and new therapeutic options.

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.

High-throughput proteomics platforms measuring thousands of proteins in blood combined with genomic information have the power to bridge the gap between the genome and diseases and in that capture some of the environmental contributions to their risk and pathogenesis. Although such methods have already demonstrated their utility, the validation of their actual protein targets is lacking. Here we present a large-scale analysis of levels of proteins in plasma and protein quantitative trait loci (pQTLs) detected using the Olink Explore 1536 (1,459 immunoassays) in 47,151 European participants from the UK Biobank with 57.7 million imputed sequence variants. We compared the results with those of a large-scale SomaScan v4 study (35,559 participants and 4,907 aptamer-based assays) in order to assess and compare the qualities of these two platforms. The correlation between levels of proteins targeted by the two platforms is modest (median Spearman correlation 0.46). The vast majority of assays on the Olink Explore platform had cis pQTLs, evidence that they correctly target their intended proteins (84%), while the assays on the SomaScan v4 platform were half as likely to have cis pQTLs (38%). We also highlight novel pQTLs discovered using the Olink Explore platform, not captured by SomaScan v4, and describe their colocalization with disease-associated sequence variants as well as associations between protein levels and diseases. Our results further underscore the value of proteomics data and highlight the major differences in quality between the two most commonly used high-throughput proteomics platforms.