Genetic Risk Scores and Dementia Risk Across Different Ethnic Groups in 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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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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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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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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Associations of coffee genetic risk scores with consumption of coffee, tea and other beverages in the UK Biobank

Addiction ◽

10.1111/add.13975 ◽

2017 ◽

Vol 113 (1) ◽

pp. 148-157 ◽

Cited By ~ 10

Author(s):

Amy E. Taylor ◽

George Davey Smith ◽

Marcus R. Munafò

Keyword(s):

Genetic Risk ◽

Risk Scores ◽

Uk Biobank ◽

Genetic Risk Scores ◽

The Uk

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Assessing the clinical utility of genetic risk scores for targeted cancer screening

Journal of Translational Medicine ◽

10.1186/s12967-020-02699-w ◽

2021 ◽

Vol 19 (1) ◽

Author(s):

Carly A. Conran ◽

Zhuqing Shi ◽

William Kyle Resurreccion ◽

Rong Na ◽

Brian T. Helfand ◽

...

Keyword(s):

Colorectal Cancer ◽

Primary Care ◽

High Risk ◽

Risk Score ◽

Genetic Risk ◽

Clinical Utility ◽

Low Risk ◽

Risk Scores ◽

Average Risk ◽

Genetic Risk Scores

Abstract Background Genome-wide association studies have identified thousands of disease-associated single nucleotide polymorphisms (SNPs). A subset of these SNPs may be additively combined to generate genetic risk scores (GRSs) that confer risk for a specific disease. Although the clinical validity of GRSs to predict risk of specific diseases has been well established, there is still a great need to determine their clinical utility by applying GRSs in primary care for cancer risk assessment and targeted intervention. Methods This clinical study involved 281 primary care patients without a personal history of breast, prostate or colorectal cancer who were 40–70 years old. DNA was obtained from a pre-existing biobank at NorthShore University HealthSystem. GRSs for colorectal cancer and breast or prostate cancer were calculated and shared with participants through their primary care provider. Additional data was gathered using questionnaires as well as electronic medical record information. A t-test or Chi-square test was applied for comparison of demographic and key clinical variables among different groups. Results The median age of the 281 participants was 58 years and the majority were female (66.6%). One hundred one (36.9%) participants received 2 low risk scores, 99 (35.2%) received 1 low risk and 1 average risk score, 37 (13.2%) received 1 low risk and 1 high risk score, 23 (8.2%) received 2 average risk scores, 21 (7.5%) received 1 average risk and 1 high risk score, and no one received 2 high risk scores. Before receiving GRSs, younger patients and women reported significantly more worry about risk of developing cancer. After receiving GRSs, those who received at least one high GRS reported significantly more worry about developing cancer. There were no significant differences found between gender, age, or GRS with regards to participants’ reported optimism about their future health neither before nor after receiving GRS results. Conclusions Genetic risk scores that quantify an individual’s risk of developing breast, prostate and colorectal cancers as compared with a race-defined population average risk have potential clinical utility as a tool for risk stratification and to guide cancer screening in a primary care setting.

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Has the time come to integrate genetic risk scores into clinical practice?

European Heart Journal ◽

10.1093/ehjci/ehaa946.3761 ◽

2020 ◽

Vol 41 (Supplement_2) ◽

Author(s):

F.V Moniz Mendonca ◽

M.I Mendonca ◽

A Pereira ◽

J Monteiro ◽

J Sousa ◽

...

Keyword(s):

Risk Factors ◽

Clinical Practice ◽

Genetic Variants ◽

Genetic Risk ◽

Odds Ratio ◽

Risk Scores ◽

Cumulative Number ◽

Genetic Risk Scores ◽

Risk Alleles ◽

Cad Risk

Abstract Background The risk for Coronary Artery Disease (CAD) is determined by both genetic and environmental factors, as well as by the interaction between them. It is estimated that genetic factors could account for 40% to 55% of the existing variability among the population (inheritability). Therefore, some authors have advised that it is time we integrated genetic risk scores into clinical practice. Aim The aim of this study was to evaluate the magnitude of the association between an additive genetic risk score (aGRS) and CAD based on the cumulative number of risk alleles in these variants, and to estimate whether their use is valuable in clinical practice. Methods A case-control study was performed in a Portuguese population. We enrolled 3120 participants, of whom 1687 were CAD patients and 1433 were normal controls. Controls were paired to cases with respect to gender and age. 33 genetic variants known to be associated with CAD were selected, and an aGRS was calculated for each individual. The aGRS was further subdivided into deciles groups, in order to estimate the CAD risk in each decile, defined by the number of risk alleles. The magnitude of the risk (odds ratio) was calculated for each group by multiple logistic regression using the 5th decile as the reference group (median). In order to evaluate the ability of the aGRS to discriminate susceptibility to CAD, two genetic models were performed, the first with traditional risk factors (TRF) and second with TRF plus aGRS. The AUC of the two ROC curves was calculated. Results A higher prevalence of cases over controls became apparent from the 6th decile of the aGRS, reflecting the higher number of risk alleles present (see figure). The difference in CAD risk was only significant from the 6th decile, increasing gradually until the 10th decile. The odds ratio (OR) for the last decile related to 5th decile (median) was 1.87 (95% CI:1.36–2.56; p<0.0001). The first model yielded an AUC=0.738 (95% CI:0.720–0.755) and the second model was slightly more discriminative for CAD risk (AUC=0.748; 95% CI:0.730–0.765). The DeLong test was significant (p=0.0002). Conclusion Adding an aGRS to the non-genetic risk factors resulted in a modest improvement in the ability to discriminate the risk of CAD. Such improvement, even if statistically significant, does not appear to be of real value in clinical practice yet. We anticipate that with the development of further knowledge about different SNPs and their complex interactions, and with the inclusion of rare genetic variants, genetic risk scores will be better suited for use in a clinical setting. Funding Acknowledgement Type of funding source: None

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Predictive Value of Genetic Risk Scores in the Development of Colorectal Adenomas

Digestive Diseases and Sciences ◽

10.1007/s10620-021-07218-5 ◽

2021 ◽

Author(s):

Carla J. Gargallo-Puyuelo ◽

Rocío Aznar-Gimeno ◽

Patricia Carrera-Lasfuentes ◽

Ángel Lanas ◽

Ángel Ferrández ◽

...

Keyword(s):

Predictive Value ◽

Genetic Risk ◽

Colorectal Adenomas ◽

Risk Scores ◽

Genetic Risk Scores

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The patterns of family genetic risk scores for eleven major psychiatric and substance use disorders in a Swedish national sample

Translational Psychiatry ◽

10.1038/s41398-021-01454-z ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Kenneth S. Kendler ◽

Henrik Ohlsson ◽

Jan Sundquist ◽

Kristina Sundquist

Keyword(s):

Genetic Risk ◽

Molecular Genetic ◽

National Sample ◽

Autism Spectrum ◽

Risk Scores ◽

Pedigree Data ◽

Obsessive Compulsive ◽

Swedish Population ◽

Compulsive Disorder ◽

Genetic Risk Scores

AbstractTo clarify the structure of genetic risks for 11 major psychiatric disorders, we calculated, from morbidity risks for disorders in 1st–5th degree relatives controlling for cohabitation effects, in the Swedish population born between 1932 and 1995 (n = 5,830,014), the family genetic risk scores (FGRS) for major depression (MD), anxiety disorders (AD), obsessive-compulsive disorder (OCD), bipolar disorder (BD), schizophrenia (SZ), bulimia (BUL), anorexia nervosa (AN), alcohol use disorder (AUD), drug use disorder (DUD), ADHD, and autism-spectrum disorder (ASD). For all affected individuals, we calculated their mean standardized FGRS for each disorder. The patterns of FGRS were quite similar for MD and AD, and for AUD and DUD, but substantially less similar for BUL and AN, BD and SZ, and ADHD and ASD. While OCD had high levels of FGRS for MD and AD, the overall FGRS profile differed considerably from MD and AD. ADHD FGRS scores were substantially elevated in AUD and DUD. FGRS scores for BD, OCD, AN, ASD, ADHD, and especially SZ were relatively disorder-specific while genetic risk for MD and AD had more generalized effects. The levels of FGRS for BMI, coronary artery disease, and educational attainment across our disorders replicated prior associations found using molecular genetic methods. All diagnostic categories examined had elevated FGRS for many disorders producing, for each condition, an informative FGRS profile. Using a novel method which approximates, from pedigree data, aggregate genetic risk, we have replicated and extended prior insights into the structure of genetic risk factors for key psychiatric illnesses.

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