Publications by authors named "Frayling T"

The contribution of rare noncoding genetic variation to common phenotypes is largely unknown, as a result of a historical lack of population-scale whole-genome sequencing data and the difficulty of categorizing noncoding variants into functionally similar groups. To begin addressing these challenges, we performed a cis association analysis using whole-genome sequencing data, consisting of 1.1 billion variants, 123 million noncoding aggregate-based tests and 2,907 circulating protein levels in ~50,000 UK Biobank participants.

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Background: Multimorbidity, the presence of two or more conditions in one person, is common but studies are often limited to observational data and single datasets. We address this gap by integrating large-scale primary-care and genetic data from multiple studies to interrogate multimorbidity patterns and producing digital resources to support future research.

Methods: We defined chronic, common, and heritable conditions in individuals aged ≥65 years, using two large primary-care databases [CPRD (UK) N = 2,425,014 and SIDIAP (Spain) N = 1,053,640], and estimated heritability using the same definitions in UK Biobank (N = 451,197).

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Obesity and cardiometabolic disease often, but not always, coincide. Distinguishing subpopulations within which cardiometabolic risk diverges from the risk expected for a given body mass index (BMI) may facilitate precision prevention of cardiometabolic diseases. Accordingly, we performed unsupervised clustering in four European population-based cohorts (N ≈ 173,000).

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Article Synopsis
  • The study investigates how rare non-coding genetic variations affect complex traits, specifically focusing on human height by analyzing data from over 333,100 individuals across three large datasets.
  • Researchers found 29 significant rare variants linked to height, with impacts ranging from a decrease of 7 cm to an increase of 4.7 cm, after considering previously known variants.
  • The team also identified specific non-coding variants near key genes associated with height, demonstrating a new method for understanding the effects of rare variants in regulatory regions using whole-genome sequencing.
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  • Blood-derived DNA methylation shows potential for early detection of dementia risk, linking biological factors with lifestyle and environmental influences.
  • A multivariate methylation risk score (MMRS) was developed, predicting mild cognitive impairment independently of age and sex, alongside significant future risk of cognitive decline in Alzheimer’s and Parkinson’s diseases.
  • The study highlights the integration of machine learning and omics data to enhance dementia risk prediction at the population level.
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Mendelian randomization (MR) is an epidemiological approach that utilizes genetic variants as instrumental variables to estimate the causal effect of an exposure on a health outcome. This paper investigates an MR scenario in which genetic variants aggregate into clusters that identify heterogeneous causal effects. Such variant clusters are likely to emerge if they affect the exposure and outcome via distinct biological pathways.

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Self-reported shorter/longer sleep duration, insomnia, and evening preference are associated with hyperglycaemia in observational analyses, with similar observations in small studies using accelerometer-derived sleep traits. Mendelian randomization (MR) studies support an effect of self-reported insomnia, but not others, on glycated haemoglobin (HbA1c). To explore potential effects, we used MR methods to assess effects of accelerometer-derived sleep traits (duration, mid-point least active 5-h, mid-point most active 10-h, sleep fragmentation, and efficiency) on HbA1c/glucose in European adults from the UK Biobank (UKB) (n = 73,797) and the MAGIC consortium (n = 146,806).

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  • Obesity is often viewed as a lifestyle choice rather than a disease, leading to initiatives like the IMI SOPHIA project, which aims to better categorize individuals with obesity based on their disease risk and treatment responses.
  • SOPHIA faces challenges due to siloed clinical cohorts, which limit data sharing for biomarker discovery, but tackles this by using a federated database built on open-source DataSHIELD technology that integrates 16 different data sources.
  • The project allows secure analysis of combined data without revealing individual patient information, demonstrated through a proof-of-concept analysis linking BMI and blood pressure, which showed results similar to traditional meta-analyses, setting a standard for safe collaborative research.
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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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Background: Diabetes (regardless of type) and obesity are associated with a range of musculoskeletal disorders. The causal mechanisms driving these associations are unknown for many upper limb pathologies. We used genetic techniques to test the causal link between glycemia, obesity and musculoskeletal conditions.

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Diseases diagnosed in adulthood may have antecedents throughout (including prenatal) life. Gaining a better understanding of how exposures at different stages in the lifecourse influence health outcomes is key to elucidating the potential benefits of disease prevention strategies. Mendelian randomisation (MR) is increasingly used to estimate causal effects of exposures across the lifecourse on later life outcomes.

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Background: Vasomotor symptoms (VMS) can often significantly impact women's quality of life at menopause. In vivo studies have shown that increased neurokinin B (NKB) / neurokinin 3 receptor (NK3R) signalling contributes to VMS, with previous genetic studies implicating the TACR3 gene locus that encodes NK3R. Large-scale genomic analyses offer the possibility of biological insights but few such studies have collected data on VMS, while proxy phenotypes such as hormone replacement therapy (HRT) use are likely to be affected by changes in clinical practice.

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  • Genome-wide association studies have enabled the development of genetic predictors for common human traits, prompting the need to re-evaluate individuals who deviate from these predictions for deeper insights.
  • In a study involving 158,951 individuals from the UK Biobank, researchers identified individuals misaligned with their genetically predicted standing height and LDL cholesterol levels, with only 0.15% and 0.12% classified as misaligned, respectively.
  • The misaligned individuals showed significant health-related patterns, such as being enriched for past growth issues or rare genetic variants linked to health risks, indicating that deviations from genetic predictions can help identify those at higher risk for health problems like coronary artery disease and type-two diabetes.
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The extent to which genetic variations contribute to interindividual differences in weight loss and metabolic outcomes after bariatric surgery is unknown. Identifying genetic variants that impact surgery outcomes may contribute to clinical decision making. This review evaluates current evidence addressing the association of genetic variants with weight loss and changes in metabolic parameters after bariatric surgery.

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Background: The mechanisms underlying genetic predisposition to higher body mass index (BMI) remain unclear.

Methods: We hypothesized that the relationship between BMI-genetic risk score (BMI-GRS) and BMI was mediated via disinhibition, emotional eating and hunger, and moderated by flexible (but not rigid) restraint within two UK cohorts: the Genetics of Appetite Study (GATE) (n = 2101, 2010-16) and the Avon Longitudinal Study of Parents and Children (ALSPAC) (n = 1679, 2014-18). Eating behaviour was measured by the Adult Eating Behaviour Questionnaire and Three-Factor Eating Questionaire-51.

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  • The study aimed to understand the link between different types of adiposity (metabolically unfavorable and favorable) and the risk of aggressive prostate cancer using Mendelian randomization.
  • Researchers analyzed data from the PRACTICAL consortium, including over 15,000 aggressive prostate cancer cases, to evaluate the genetic influence of various adiposity traits on cancer risk.
  • The findings indicated no strong associations between either type of adiposity or body mass index (BMI) and aggressive prostate cancer, suggesting metabolic factors aren't the primary influencers of prostate cancer risk, but further research is needed to investigate other potential links.
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  • Researchers examined genetic factors influencing insulin levels after a glucose challenge in over 55,000 people from different ancestry groups, identifying ten new genetic locations linked to postprandial insulin resistance.
  • * They found that many of these loci share genetics with type 2 diabetes, suggesting a common underlying mechanism.
  • * The study also highlighted nine candidate genes affecting GLUT4, a key glucose transporter, which plays an important role in glucose uptake during the post-meal state.
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Aims/hypothesis: Determining how high BMI at different time points influences the risk of developing type 2 diabetes and affects insulin secretion and insulin sensitivity is critical.

Methods: By estimating childhood BMI in 441,761 individuals in the UK Biobank, we identified which genetic variants had larger effects on adulthood BMI than on childhood BMI, and vice versa. All genome-wide significant genetic variants were then used to separate the independent genetic effects of high childhood BMI from those of high adulthood BMI on the risk of type 2 diabetes and insulin-related phenotypes using Mendelian randomisation.

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Musculoskeletal conditions, including fractures, can have severe and long-lasting consequences. Higher body mass index in adulthood is widely acknowledged to be protective for most fracture sites. However, sources of bias induced by confounding factors may have distorted previous findings.

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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
  • Researchers studied the impact of body-mass index (BMI) at different life stages on the risk of developing type 2 diabetes (T2D) and its relationship with insulin levels in a large UK Biobank sample.
  • They found that higher childhood BMI could potentially enhance insulin sensitivity and secretion, but did not show a strong link to a reduced risk of T2D when accounting for adulthood BMI effects.
  • The study emphasizes that despite interesting findings, caution should be taken in interpreting results for public health advice due to uncertainties in understanding the biological processes involved.
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  • Genetic studies have created predictors for traits like height and LDL cholesterol, highlighting the potential for identifying individuals who deviate from genetic expectations.
  • Among 158,951 UK Biobank individuals, only a small percentage (0.15% for height, 0.12% for LDL cholesterol) were found misaligned with their genetic predictions, showing connections to health issues and developmental anomalies.
  • These findings suggest that individuals deviating from genetic predictions may warrant further health evaluations, as their deviations correlate with higher risks for conditions like coronary artery disease and type-two diabetes.
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Insulin secretion is critical for glucose homeostasis, and increased levels of the precursor proinsulin relative to insulin indicate pancreatic islet beta-cell stress and insufficient insulin secretory capacity in the setting of insulin resistance. We conducted meta-analyses of genome-wide association results for fasting proinsulin from 16 European-ancestry studies in 45,861 individuals. We found 36 independent signals at 30 loci (p value < 5 × 10), which validated 12 previously reported loci for proinsulin and ten additional loci previously identified for another glycemic trait.

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