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

2016 ◽  
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.

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

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

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

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

Chromosomal scale length variation of germline DNA can predict individual cancer risk

2018 ◽  
Author(s):  
Chris Toh ◽  
James P. Brody
Keyword(s):  
Cancer Risk ◽  
Genetic Risk ◽  
Germ Line ◽  
The Cancer Genome Atlas ◽  
Risk Scores ◽  
Length Variation ◽  
Uk Biobank ◽  
Genetic Risk Scores ◽  
The Uk ◽  
Scale Length

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.

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

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.

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

2016 ◽  
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.

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

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.

scholarly journals Carbohydrate and sodium intake and physical activity interact with genetic risk scores of four genetic variants mainly related to lipid metabolism to modulate metabolic syndrome risk in Korean middle-aged adults

2019 ◽  
Vol 122 (8) ◽  
pp. 919-927 ◽  
Author(s):  
Jun-Yu Zhou ◽  
Mi Young Song ◽  
Sunmin Park
Keyword(s):  
Physical Activity ◽  
Metabolic Syndrome ◽  
Genetic Variants ◽  
Genetic Risk ◽  
Risk Scores ◽  
Moderate Activity ◽  
Middle Aged ◽  
Genetic Risk Scores ◽  
Daily Physical Activities ◽  
Middle Aged Adults

AbstractMetabolic syndrome (MetS) risk is influenced by genetic and environmental factors. The present study explored genetic risk scores (GRS) of genetic variants that influence the MetS and the effect of interactions between GRS and nutrient intake on MetS risk. The genetic variants that influence MetS risk were selected by genome-wide association study after adjusting for age, sex, area of residence and BMI in 8840 middle-aged adults. GRS were calculated by summing the risk alleles of the selected SNP and divided into low (0–1), medium (2–3) and high (4–7) risk groups, and the relationships between the MetS and GRS were determined by logistic regression after adjusting covariates involved in MetS risk. We also analysed the interaction between GRS and lifestyles. Four genetic variants (APOA5_rs651821, EFCAB4B_rs4766165, ZNF259_rs2160669 and APOBEC1_rs10845640) were selected because they increased MetS risk after adjusting for covariates. Individuals with medium-GRS and high-GRS alleles had a higher MetS risk by 1·48- and 2·23-fold, respectively, compared with those with low-GRS after adjusting for covariates. The increase in MetS risk was mainly related to serum TAG and HDL-cholesterol concentrations. The GRS had an interaction with carbohydrate (CHO) and Na intakes and daily physical activities for MetS risk. In conclusion, Asian middle-aged adults with high-GRS alleles were at increased MetS risk mainly due to dyslipidaemia. High daily physical activity (≥1 h moderate activity per d) reduced the MetS risk but a low-CHO diet (<65 % of total energy intake) increased the risk in carriers with high-GRS alleles. Low Na intake (<1·6 g Na intake/4 MJ) did not decrease its risk.

scholarly journals Genetic Risk Scores and Blood Pressure — The Heart is What Matters

Kidney360 ◽  
2021 ◽  
Vol 2 (8) ◽  
pp. 1209-1211
Author(s):  
Mirna Boumitri ◽  
Nayanjot K. Rai ◽  
Paul E. Drawz
Keyword(s):  
Blood Pressure ◽  
Genetic Risk ◽  
Risk Scores ◽  
Genetic Risk Scores ◽  
What Matters
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