Publications by authors named "Bram Prins"

Article Synopsis
  • Genome-wide association studies have found numerous genetic loci linked to glycemic traits, but connecting these loci to specific genes and biological pathways remains a challenge.
  • Researchers conducted meta-analyses of exome-array studies across four glycemic traits, analyzing data from over 144,000 participants, which led to the identification of coding variant associations in more than 60 genes.
  • The study revealed significant pathways related to insulin secretion, zinc transport, and fatty acid metabolism, enhancing understanding of glycemic regulation and making data available for further research.
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Understanding the genetic basis of neuro-related proteins is essential for dissecting the molecular basis of human behavioural traits and the disease aetiology of neuropsychiatric disorders. Here the SCALLOP Consortium conducted a genome-wide association meta-analysis of over 12,000 individuals for 184 neuro-related proteins in human plasma. The analysis identified 125 cis-regulatory protein quantitative trait loci (cis-pQTL) and 164 trans-pQTL.

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  • Type 2 diabetes (T2D) is a complex disease influenced by various genetic factors and molecular mechanisms that vary by cell type and ancestry.
  • In a large study involving over 2.5 million individuals, researchers identified 1,289 significant genetic associations linked to T2D, including 145 new loci not previously reported.
  • The study categorized T2D signals into eight distinct clusters based on their connections to cardiometabolic traits and showed that these genetic profiles are linked to vascular complications, emphasizing the role of obesity-related processes across different ancestry groups.
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  • The Pharma Proteomics Project is a large-scale research initiative analyzing blood protein profiles from over 54,000 UK Biobank participants to understand links between genetics and health.
  • The project identifies significant genetic associations with proteins, revealing many novel interactions and highlighting ancestry-specific variations, along with insights into disease mechanisms and potential drug targets.
  • By making their findings publicly accessible, the consortium aims to advance research in biomarker development and therapeutic strategies, enhancing our understanding of how genetic factors influence health outcomes.
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Integrating human genomics and proteomics can help elucidate disease mechanisms, identify clinical biomarkers and discover drug targets. Because previous proteogenomic studies have focused on common variation via genome-wide association studies, the contribution of rare variants to the plasma proteome remains largely unknown. Here we identify associations between rare protein-coding variants and 2,923 plasma protein abundances measured in 49,736 UK Biobank individuals.

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  • The project is a partnership between the IHCC and DAC aimed at creating a genomic risk assessment tool for Alzheimer's disease that incorporates data from diverse ethnic backgrounds.
  • The GIRA model combines genetic factors, like polygenic risk scores and specific gene haplotypes, with non-genetic data such as age and sex.
  • Early findings show the GIRA model can effectively identify individuals at higher risk for Alzheimer's, with implications for future clinical use and research.
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Background: While polygenic risk scores hold significant promise in estimating an individual's risk of developing a complex trait such as obesity, their application in the clinic has, to date, been limited by a lack of data from non-European populations. As a collaboration model of the International Hundred K+ Cohorts Consortium (IHCC), we endeavored to develop a globally applicable trans-ethnic PRS for body mass index (BMI) through this relatively new international effort.

Methods: The polygenic risk score (PRS) model was developed, trained and tested at the Center for Applied Genomics (CAG) of The Children's Hospital of Philadelphia (CHOP) based on a BMI meta-analysis from the GIANT consortium.

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Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes. To characterise the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study (GWAS) data from 2,535,601 individuals (39.7% non-European ancestry), including 428,452 T2D cases.

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Article Synopsis
  • Understanding the genetic foundation of neuro-related proteins is vital for exploring human behavior and neuropsychiatric disorders.
  • The SCALLOP Consortium analyzed genetic data from over 12,500 individuals, identifying numerous cis- and trans-regulatory loci affecting neuro-related proteins.
  • Their findings also suggest potential causal relationships between these proteins and traits like sleep, smoking, mental health, and highlight new opportunities for drug repurposing and therapeutic targets.
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The use of omic modalities to dissect the molecular underpinnings of common diseases and traits is becoming increasingly common. But multi-omic traits can be genetically predicted, which enables highly cost-effective and powerful analyses for studies that do not have multi-omics. Here we examine a large cohort (the INTERVAL study; n = 50,000 participants) with extensive multi-omic data for plasma proteomics (SomaScan, n = 3,175; Olink, n = 4,822), plasma metabolomics (Metabolon HD4, n = 8,153), serum metabolomics (Nightingale, n = 37,359) and whole-blood Illumina RNA sequencing (n = 4,136), and use machine learning to train genetic scores for 17,227 molecular traits, including 10,521 that reach Bonferroni-adjusted significance.

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Understanding the genetic basis of neuro-related proteins is essential for dissecting the disease etiology of neuropsychiatric disorders and other complex traits and diseases. Here, the SCALLOP Consortium conducted a genome-wide association meta-analysis of over 12,500 individuals for 184 neuro-reiated proteins in human plasma. The analysis identified 117 cis-regulatory protein quantitative trait loci (cis-pQTL) and 166 trans-pQTL.

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Background: While polygenic risk scores hold significant promise in estimating an individual's risk of developing a complex trait such as obesity, their application in the clinic has, to date, been limited by a lack of data from non-European populations. As a collaboration model of the International Hundred K+ Cohorts Consortium (IHCC), we endeavored to develop a globally applicable trans-ethnic PRS for body mass index (BMI) through this relatively new international effort.

Methods: The PRS model was developed trained and tested at the Center for Applied Genomics (CAG) of The Children's Hospital of Philadelphia (CHOP) based on a BMI meta-analysis from the GIANT consortium.

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Stroke is the second leading cause of death with substantial unmet therapeutic needs. To identify potential stroke therapeutic targets, we estimate the causal effects of 308 plasma proteins on stroke outcomes in a two-sample Mendelian randomization framework and assess mediation effects by stroke risk factors. We find associations between genetically predicted plasma levels of six proteins and stroke (P ≤ 1.

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We assembled an ancestrally diverse collection of genome-wide association studies (GWAS) of type 2 diabetes (T2D) in 180,834 affected individuals and 1,159,055 controls (48.9% non-European descent) through the Diabetes Meta-Analysis of Trans-Ethnic association studies (DIAMANTE) Consortium. Multi-ancestry GWAS meta-analysis identified 237 loci attaining stringent genome-wide significance (P < 5 × 10), which were delineated to 338 distinct association signals.

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Article Synopsis
  • SARS-CoV-2 uses the ACE2 protein to enter human cells, making ACE2 crucial for COVID-19 infection and treatment; its levels vary significantly across different individuals but are not fully understood genetically.
  • In a large study involving over 28,000 individuals, researchers found genetic factors influencing plasma ACE2 levels and discovered 10 genetic loci linked to ACE2, explaining 30% of its heritability.
  • The study also indicated that higher ACE2 levels are causally associated with increased severity of COVID-19, hospitalization, and risk of infection, along with genetic links to vascular diseases and other complex health conditions.
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The current paradigm of personalized medicine envisages the use of genomic data to provide predictive information on the health course of an individual with the aim of prevention and individualized care. However, substantial efforts are required to realize the concept: enhanced genetic discoveries, translation into intervention strategies, and a systematic implementation in healthcare. Here we review how further genetic discoveries are improving personalized prediction and advance functional insights into the link between genetics and disease.

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Severe COVID-19 is characterised by immunopathology and epithelial injury. Proteomic studies have identified circulating proteins that are biomarkers of severe COVID-19, but cannot distinguish correlation from causation. To address this, we performed Mendelian randomisation (MR) to identify proteins that mediate severe COVID-19.

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Drug repurposing provides a rapid approach to meet the urgent need for therapeutics to address COVID-19. To identify therapeutic targets relevant to COVID-19, we conducted Mendelian randomization analyses, deriving genetic instruments based on transcriptomic and proteomic data for 1,263 actionable proteins that are targeted by approved drugs or in clinical phase of drug development. Using summary statistics from the Host Genetics Initiative and the Million Veteran Program, we studied 7,554 patients hospitalized with COVID-19 and >1 million controls.

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Interleukin 6 (IL-6) is a multifunctional cytokine with both pro- and anti-inflammatory properties with a heritability estimate of up to 61%. The circulating levels of IL-6 in blood have been associated with an increased risk of complex disease pathogenesis. We conducted a two-staged, discovery and replication meta genome-wide association study (GWAS) of circulating serum IL-6 levels comprising up to 67 428 (ndiscovery = 52 654 and nreplication = 14 774) individuals of European ancestry.

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Genetic studies of blood pressure (BP) to date have mainly analyzed common variants (minor allele frequency > 0.05). In a meta-analysis of up to ~1.

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Article Synopsis
  • Circulating proteins play a key role in human health and are used as biomarkers for disease and drug targets; this study maps protein quantitative trait loci (pQTLs) for 90 cardiovascular proteins in over 30,000 people, discovering 451 pQTLs for 85 proteins.
  • The researchers verified their findings with mouse studies and clinical trials, establishing the regulatory roles of certain genes on these proteins.
  • They also identified 11 proteins with potential causal links to diseases, suggesting new drug targets and opportunities for repositioning existing drugs, thus enhancing the understanding of the genetics of proteins in relation to health.
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Identifying biological correlates of late life cognitive function is important if we are to ascertain biomarkers for, and develop treatments to help reduce, age-related cognitive decline. Here, we investigated the associations between plasma levels of 90 neurology-related proteins (Olink® Proteomics) and general fluid cognitive ability in the Lothian Birth Cohort 1936 (LBC1936, N = 798), Lothian Birth Cohort 1921 (LBC1921, N = 165), and the INTERVAL BioResource (N = 4451). In the LBC1936, 22 of the proteins were significantly associated with general fluid cognitive ability (β between -0.

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Elevated serum urate levels cause gout and correlate with cardiometabolic diseases via poorly understood mechanisms. We performed a trans-ancestry genome-wide association study of serum urate in 457,690 individuals, identifying 183 loci (147 previously unknown) that improve the prediction of gout in an independent cohort of 334,880 individuals. Serum urate showed significant genetic correlations with many cardiometabolic traits, with genetic causality analyses supporting a substantial role for pleiotropy.

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Article Synopsis
  • Increased urinary albumin-to-creatinine ratio (UACR) is linked to higher risks of kidney disease and cardiovascular issues, yet the underlying causes are not fully understood.
  • A large meta-analysis identified 68 genetic loci associated with UACR, highlighting connections to conditions like proteinuria, hyperlipidemia, and hypertension.
  • Specific genes (such as TGFB1 and PRKCI) were implicated in kidney function, and experiments showed that disrupting these genes in fruit flies affects albumin processing, suggesting new avenues for research to lower albumin levels.
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