Associations of coffee genetic risk scores with consumption of coffee, tea and other beverages in the UK Biobank

AbstractInherited factors are thought to be responsible for a substantial fraction of many different forms of cancer. However, individual cancer risk cannot currently be well quantified by analyzing germ line DNA. Most analyses of germline DNA focus on the additive effects of single nucleotide polymorphisms (SNPs) found. Here we show that chromosomal-scale length variation of germline DNA can be used to predict whether a person will develop cancer. In two independent datasets, the Cancer Genome Atlas (TCGA) project and the UK Biobank, we could classify whether or not a patient had a certain cancer based solely on chromosomal scale length variation. In the TCGA data, we found that all 32 different types of cancer could be predicted better than chance using chromosomal scale length variation data. We found a model that could predict ovarian cancer in women with an area under the receiver operator curve, AUC=0.89. In the UK Biobank data, we could predict breast cancer in women with an AUC=0.83. This method could be used to develop genetic risk scores for other conditions known to have a substantial genetic component and complements genetic risk scores derived from SNPs.

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Associations of coffee genetic risk scores with coffee, tea and other beverages in the UK Biobank

10.1101/096214 ◽

2016 ◽

Cited By ~ 1

Author(s):

Amy E. Taylor ◽

Marcus R. Munafò

Keyword(s):

Genetic Risk ◽

Coffee Consumption ◽

Risk Scores ◽

Tea Consumption ◽

Uk Biobank ◽

Beverage Consumption ◽

Dietary Recall ◽

Genetic Risk Scores ◽

Decaffeinated Coffee ◽

The Uk

AbstractBackgroundGenetic variants which determine amount of coffee consumed have been identified in genome-wide association studies (GWAS) of coffee consumption; these may help to further understanding of the effects of coffee on health outcomes. However, there is limited information about how these variants relate to caffeinated beverage consumption more generally.AimsTo improve phenotype definition for coffee consumption related genetic risk scores by testing their association with coffee, tea and other beverages.MethodsWe tested the associations of genetic risk scores for coffee consumption with beverage consumption in 114,316 individuals of European ancestry from the UK Biobank. Drinks were self-reported in a baseline questionnaire and in detailed 24 dietary recall questionnaires in a subset.ResultsGenetic risk scores including two and eight single nucleotide polymorphisms (SNPs) explained up to 0.39%, 0.19% and 0.77% of the variance in coffee, tea and combined coffee and tea consumption respectively. A one standard deviation increase in the 8 SNP genetic risk score was associated with a 0.13 cup per day (95% CI: 0.12, 0.14), 0.12 cup per day (95%CI: 0.11, 0.14) and 0.25 cup per day (95% CI: 0.24, 0.27) increase in coffee, tea and combined tea and coffee consumption, respectively. Genetic risk scores also demonstrated positive associations with both caffeinated and decaffeinated coffee and tea consumption. In 48,692 individuals with dietary recall data, the genetic risk scores were positively associated with coffee and tea, (apart from herbal teas) consumption, but did not show clear evidence for positive associations with other beverages. However, there was evidence that the genetic risk scores were associated with lower daily water consumption and lower overall drink consumption.ConclusionsGenetic risk scores created from variants identified in coffee consumption GWAS associate more broadly with caffeinated beverage consumption and also with decaffeinated coffee and tea consumption.

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Polygenic prediction of breast cancer: comparison of genetic predictors and implications for screening

10.1101/448597 ◽

2018 ◽

Author(s):

Kristi Läll ◽

Maarja Lepamets ◽

Marili Palover ◽

Tõnu Esko ◽

Andres Metspalu ◽

...

Keyword(s):

Breast Cancer ◽

Genetic Risk ◽

Odds Ratio ◽

Population Based ◽

Risk Scores ◽

Full Potential ◽

Genome Wide Association Studies ◽

Uk Biobank ◽

Genetic Risk Scores ◽

The Uk

AbstractBackgroundPublished genetic risk scores for breast cancer (BC) so far have been based on a relatively small number of markers and are not necessarily using the full potential of large-scale Genome-Wide Association Studies. This study aims to identify an efficient polygenic predictor for BC based on best available evidence and to assess its potential for personalized risk prediction and screening strategies.MethodsFour different genetic risk scores (two already published and two newly developed) and their combinations (metaGRS) are compared in the subsets of two population-based biobank cohorts: the UK Biobank (UKBB, 3157 BC cases, 43,827 controls) and Estonian Biobank (EstBB, 317 prevalent and 308 incident BC cases in 32,557 women). In addition, correlations between different genetic risk scores and their associations with BC risk factors are studied in both cohorts.ResultsThe metaGRS that combines two genetic risk scores (metaGRS2 - based on 75 and 898 Single Nucleotide Polymorphisms, respectively) has the strongest association with prevalent BC status in both cohorts. One standard deviation difference in the metaGRS2 corresponds to an Odds Ratio = 1.6 (95% CI 1.54 to 1.66, p = 9.7*10-135) in the UK Biobank and accounting for family history marginally attenuates the effect (Odds Ratio = 1.58, 95% CI 1.53 to 1.64, p = 9.1*10-129). In the EstBB cohort, the hazard ratio of incident BC for the women in the top 5% of the metaGRS2 compared to women in the lowest 50% is 4.2 (95% CI 2.8 to 6.2, p = 8.1*10-13). The different GRSs are only moderately correlated with each other and are associated with different known predictors of BC. The classification of genetic risk for the same individual may vary considerably depending on the chosen GRS.ConclusionsWe have shown that metaGRS2 that combines on the effects of more than 900 SNPs provides best predictive ability for breast cancer in two different population-based cohorts. The strength of the effect of metaGRS2 indicates that the GRS could potentially be used to develop more efficient strategies for breast cancer screening for genotyped women.

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Genetic Risk Scores and Dementia Risk Across Different Ethnic Groups in UK Biobank

SSRN Electronic Journal ◽

10.2139/ssrn.3964607 ◽

2021 ◽

Author(s):

Naaheed Mukadam ◽

Olga Giannakopoulou ◽

Nick Bass ◽

Karoline Kuchenbaecker ◽

Andrew McQuillin

Keyword(s):

Ethnic Groups ◽

Genetic Risk ◽

Risk Scores ◽

Uk Biobank ◽

Genetic Risk Scores ◽

Dementia Risk

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Genetic analysis of the PCSK9 locus in psychological, psychiatric, metabolic and cardiovascular traits in UK Biobank.

10.31234/osf.io/hktmu ◽

2021 ◽

Author(s):

Rachel Hay ◽

Breda Cullen ◽

Nicholas Graham ◽

Donald Lyall ◽

Alisha Aman ◽

...

Keyword(s):

Linkage Disequilibrium ◽

Genetic Risk ◽

Statistical Significance ◽

Lipid Lowering ◽

Risk Scores ◽

Central Adiposity ◽

Uk Biobank ◽

Genetic Risk Scores ◽

Mood Instability ◽

Wide Range

The association between severe mental illness (SMI) and cardiovascular and metabolic disease (CMD) is poorly understood. PCSK9 is expressed in systems critical to both SMI and CMD and influences lipid homeostasis and brain function. We systematically investigated relationships between genetic variation within the PCSK9 locus and risk for both CMD and SMI. UK Biobank recruited ~500,000 volunteers and assessed a wide range of SMI and CMD phenotypes. Initially we used genetic data from white British ancestry individuals of UK Biobank. Genetic association analyses were conducted in PLINK, with statistical significance defined by the number of independent SNPs. Conditional analyses and linkage disequilibrium assessed the independence of SNPs and the presence of multiple signals. Two genetic risk scores of lipid-lowering alleles were calculated and used as proxies for putative lipid-lowering effects of PCSK9. PCSK9 variants were associated with central adiposity, venous thrombosis embolism, systolic blood pressure, mood instability, and neuroticism (all p<1.16x10-4). No secondary signals were identified. Conditional analyses and high linkage disequilibrium (r2 =0.98) indicated that mood instability and central obesity may share a genetic signal, which was confirmed using trans-ancestry data from UK Biobank. Genetic risk scores suggested that the lipid-lowering effects of PCSK9 may be causal for greater mood instability and higher neuroticism. This is the first study to implicate the PCSK9 locus in mood-disorder symptoms and related traits, as well as the shared pathology of SMI and CMD. PCSK9 effects on mood may occur via lipid-lowering mechanisms. Further work is needed to understand whether repurposing PCSK9-targeting therapies might improve SMI symptoms and prevent CMD.

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HUMAN LONGEVITY IS INFLUENCED BY MANY GENETIC VARIANTS: EVIDENCE FROM 75,000 UK BIOBANK PARTICIPANTS

10.1101/038430 ◽

2016 ◽

Cited By ~ 4

Author(s):

Luke C Pilling ◽

Janice L Atkins ◽

Kirsty Bowman ◽

Samuel E Jones ◽

Jessica Tyrrell ◽

...

Keyword(s):

Blood Pressure ◽

Genetic Variants ◽

Genetic Risk ◽

Paternal Age ◽

Risk Scores ◽

Human Longevity ◽

Uk Biobank ◽

Age At Death ◽

Genetic Risk Scores ◽

A Genome

Variation in human lifespan is 20 to 30% heritable but few genetic variants have been identified. We undertook a Genome Wide Association Study (GWAS) using age at death of parents of middle-aged UK Biobank participants of European decent (n=75,244 with father's and/or mother's data). Genetic risk scores for 19 phenotypes (n=777 proven variants) were also tested. Genotyped variants (n=845,997) explained 10.2% (SD=1.3%) of combined parental longevity. In GWAS, a locus in the nicotine receptor CHRNA3 - previously associated with increased smoking and lung cancer - was associated with paternal age at death, with each protective allele (rs1051730[G]) being associated with 0.03 years later age at father's death (p=3x10-8). Offspring of longer lived parents had more protective alleles (lower genetic risk scores) for coronary artery disease, systolic blood pressure, body mass index, cholesterol and triglyceride levels, type-1 diabetes, inflammatory bowel disease and Alzheimer's disease. In candidate gene analyses, variants in the TOMM40/APOE locus were associated with longevity (including rs429358, p=3x10-5), but FOXO variants were not associated. These results support a multiple protective factors model for achieving longer lifespans in humans, with a prominent role for cardiovascular-related pathways. Several of these genetically influenced risks, including blood pressure and tobacco exposure, are potentially modifiable.

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Abstract P879: Differences in Statistical Performance of Polygenic Risk Scores for Cardiovascular Disease Across Different Race/Ethnicities

Stroke ◽

10.1161/str.52.suppl_1.p879 ◽

2021 ◽

Vol 52 (Suppl_1) ◽

Author(s):

Julian N Acosta ◽

Cameron Both ◽

Natalia Szejko ◽

Stacy Brown ◽

Kevin N Sheth ◽

...

Keyword(s):

Cardiovascular Disease ◽

Logistic Regression ◽

Genetic Risk ◽

Regression Models ◽

Risk Scores ◽

Uk Biobank ◽

Polygenic Risk ◽

Logistic Regression Models ◽

The Uk ◽

Significant Health

Introduction: Genome-wide association studies have identified numerous genetic risk variants for stroke and myocardial infarction (MI) in Europeans. However, the limited applicability of these results to non-Europeans due to racial/ethnic differences in the genetic architecture of cardiovascular disease (CVD), coupled with the limited availability of genomic data in non-Europeans, may create significant health disparities now that genomic-based precision medicine is a reality. We tested the hypothesis that the performance of polygenic risk scores (PRS) for CVD differ in Europeans versus non-Europeans. Methods: We conducted a nested study within the UK Biobank, a prospective, population-based study that enrolled ~500,000 participants across the UK. For this study, we identified self-reported black participants and randomly matched them 1:1 by age and sex with white participants. We created a PRS using previously discovered loci for stroke and MI. We then tested whether this PRS representing the aggregate polygenic susceptibility to CVD yielded similar precision in black versus white participants in logistic regression models. Results: Of the 502,536 participants enrolled in the UK Biobank, 8,061 were self-reported blacks, with 7,644 having available data for our analyses. We randomly matched these participants with white individuals, leading to a total sample size of 15,288 (mean age 51.9 [SD 8.1], female 8,722 [57%]). The total number of events was 741 overall, with 363 happening in blacks and 378 happening in whites. In logistic regression models including age, sex, and 5 principal components, the statistical precision (e.g. narrower confidence intervals) for the PRS was substantially higher for whites (OR 1.22, 95%CI 1.08 - 1.37; p<0.0001) compared to blacks (OR 1.24, 95%CI 1.05-1.47; p=0.01). Secondary analyses using genetically-determined ancestry yielded similar results. Conclusion: Because CVD-related PRSs are derived mainly using genetic risk factors identified in populations of European ancestry, their statistical performance is lower in non-European populations. This asymmetry can lead to significant health disparities now that these tools are being evaluated in multiple precision medicine approaches.

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Development and validation of decision rules models to stratify coronary artery disease, diabetes, and hypertension risk in preventive care: cohort study of returning UK Biobank participants

10.1101/2021.03.01.21252657 ◽

2021 ◽

Author(s):

José Castela Forte ◽

Pytrik Folkertsma ◽

Rahul Gannamani ◽

Sridhar Kumaraswamy ◽

Sarah Mount ◽

...

Keyword(s):

Lifestyle Intervention ◽

Genetic Risk ◽

Risk Group ◽

Decision Rules ◽

High Risk Group ◽

Risk Scores ◽

Uk Biobank ◽

Physical Measurements ◽

The Uk

AbstractBackgroundA wide range of predictive models exist that predict risk of common lifestyle conditions. However, these have not focused on identifying pre-clinical higher risk groups that would benefit from lifestyle interventions and do not include genetic risk scores. In this study, we developed, validated, and compared the performance of three decision rule algorithms including biomarkers, physical measurements and genetic risk scores for incident coronary artery disease (CAD), diabetes (T2D), and hypertension in the general population against commonly used clinical risk scoring tools.Methods and findingsOf all individuals recruited between 2006 and 2010 from the UK Biobank study for whom re-measurement data were available, 60782 were included in the analyses (mean age 56.3 (7.59), 51.2% female). Follow-up data were available until 2016. Three decision rules models with three risk strata were developed and tested for an association with incident disease. Hazard ratios (HR with 95% confidence interval) for incident CAD, T2D, and hypertension were calculated from survival models. Model performance in discriminating between higher risk individuals suitable for lifestyle intervention and individuals at low risk was assessed using the area under the receiver operating characteristic curve (AUROC).From the initial baseline measurement until the follow-up re-measurements, 500 incident CAD cases, 1005 incident T2D cases, and 2379 incident hypertension cases were ascertained. The higher risk group in the decision rules model had a 40-, 40.9-, and 21.6-fold increase in risk of CAD, T2D, and hypertension, respectively (P < 0.001 for all), and the risk increased significantly between the three strata for all three conditions (P < 0.05). Risk stratification based on decision rules identified both a low risk group which would not have benefited from lifestyle intervention (only 1.3% incident disease across all models), as well as a high risk group where 72%, 81.5%, and 74% of those who developed disease within 8 years would have been recommended lifestyle intervention. Based on genetic risk alone, we identified not only a high risk group, but also a group at elevated risk for all health conditions. This study was limited by the restricted number of participants with follow-up data, and the lack of ethnic diversity in the UK Biobank cohort.ConclusionIn this analysis of returning UK Biobank participants, we found that decision rule models comprising blood biomarkers, physical measurements, and polygenic risk scores were superior at identifying individuals likely to benefit from lifestyle intervention for three of the most common lifestyle-related chronic health conditions compared to commonly used clinical risk scores.

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Exploring Various Polygenic Risk Scores for Skin Cancer in the Phenomes of the Michigan Genomics Initiative and the UK Biobank with a Visual Catalog:PRSWeb

10.1101/384909 ◽

2018 ◽

Cited By ~ 1

Author(s):

Lars G. Fritsche ◽

Lauren J. Beesley ◽

Peter VandeHaar ◽

Robert B. Peng ◽

Maxwell Salvatore ◽

...

Keyword(s):

Skin Cancer ◽

Cell Carcinoma ◽

Genetic Risk ◽

Population Based ◽

Risk Scores ◽

Uk Biobank ◽

Polygenic Risk ◽

Cancer Subtypes ◽

Construction Methods ◽

The Uk

AbstractPolygenic risk scores (PRS) are designed to serve as a single summary measure, condensing information from a large number of genetic variants associated with a disease. They have been used for stratification and prediction of disease risk. The construction of a PRS often depends on the purpose of the study, the available data/summary estimates, and the underlying genetic architecture of a disease. In this paper, we consider several choices for constructing a PRS using summary data obtained from various publicly-available sources including the UK Biobank and evaluate their abilities to predict outcomes derived from electronic health records (EHR). Weexamine the three most common skin cancer subtypes in the USA: basal cellcarcinoma, cutaneous squamous cell carcinoma, and melanoma. The genetic risk profiles of subtypes may consist of both shared and unique elements and we construct PRS to understand the common versus distinct etiology. This study is conducted using data from 30,702 unrelated, genotyped patients of recent European descent from the Michigan Genomics Initiative (MGI), a longitudinal biorepository effort within Michigan Medicine. Using these PRS for various skin cancer subtypes, we conduct a phenome-wide association study (PheWAS) within the MGI data to evaluate their association with secondary traits. PheWAS results are then replicated using population-based UK Biobank data. We develop an accompanying visual catalog calledPRSwebthat provides detailed PheWAS results and allows users to directly compare different PRS construction methods. The results of this study can provide guidance regarding PRS construction in future PRS-PheWAS studies using EHR data involving disease subtypes.Author summaryIn the study of genetically complex diseases, polygenic risk scores synthesize information from multiple genetic risk factors to provide insight into a patient’s risk of developing a disease based on his/her genetic profile. These risk scores can be explored in conjunction with health and disease information available in the electronic medical records. They may be associated with diseases that may be related to or precursors of the underlying disease of interest. Limited work is available guiding risk score construction when the goal is to identify associations across the medical phenome. In this paper, we compare different polygenic risk score construction methods in terms of their relationships with the medical phenome. We further propose methods for using these risk scores to decouple the shared and unique genetic profiles of related diseases and to explore related diseases’ shared and unique secondary associations. Leveraging and harnessing the rich data resources of the Michigan Genomics Initiative, a biorepository effort at Michigan Medicine, and the larger population-based UK Biobank study, we investigated the performance of genetic risk profiling methods for the three most common types of skin cancer: melanoma, basal cell carcinoma and squamous cell carcinoma.

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Fine-scale population structure confounds genetic risk scores in the ascertainment population

10.1101/2020.08.09.243287 ◽

2020 ◽

Author(s):

Holly Trochet ◽

Julie Hussin

Keyword(s):

Population Structure ◽

Genetic Risk ◽

Complex Disease ◽

Predictive Ability ◽

Principal Component ◽

Risk Scores ◽

Genetic Risk Scores ◽

Scale Population ◽

The Uk ◽

Trait Measurement

AbstractGenetic risk scores (GRS), also known as polygenic risk scores, are a tool to estimate individuals’ liabilities to a disease or trait measurement based solely on genetic information. They have value in clinical applications [1] as well as for assessing relationships between traits and discovering causal determinants of complex disease [2, 3]. However, it has been shown that these scores are not robust to differences across continental populations [4, 5] and may not be portable within them either [6]. Even within a single population, they may have variable predictive ability across sexes and socioeconomic strata [7], raising questions about their potential biases. In this paper, we investigated the accuracy of two different GRS across population strata of the UK Biobank [8], separated along principal component (PC) axes, considering different approaches to account for social and environmental confounders. We found that these scores did not predict the real differences in phenotypes observed along the first principal component, with evidence of discrepancies on axes as high as PC45. These results demonstrate that the measures currently taken for correcting for population structure are not sufficient, and the need for social and environmental confounders to be factored into the creation of GRS.

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