scholarly journals Genetic Risk Prediction of COVID-19 Susceptibility and Severity in the Indian Population

2021 ◽  
Vol 12 ◽  
Author(s):  
P. Prakrithi ◽  
Priya Lakra ◽  
Durai Sundar ◽  
Manav Kapoor ◽  
Mitali Mukerji ◽  
...  
Keyword(s):  
Genetic Susceptibility ◽  
Genetic Risk ◽  
Genome Wide Association Study ◽  
Risk Groups ◽  
Risk Scores ◽  
Nucleotide Polymorphisms ◽  
Genetic Risk Prediction ◽  
Indian Genome ◽  
Vaccine Distribution ◽  
Effective Public Health

Host genetic variants can determine their susceptibility to COVID-19 infection and severity as noted in a recent Genome-wide Association Study (GWAS). Given the prominent genetic differences in Indian sub-populations as well as differential prevalence of COVID-19, here, we compute genetic risk scores in diverse Indian sub-populations that may predict differences in the severity of COVID-19 outcomes. We utilized the top 100 most significantly associated single-nucleotide polymorphisms (SNPs) from a GWAS by Pairo-Castineira et al. determining the genetic susceptibility to severe COVID-19 infection, to compute population-wise polygenic risk scores (PRS) for populations represented in the Indian Genome Variation Consortium (IGVC) database. Using a generalized linear model accounting for confounding variables, we found that median PRS was significantly associated (p < 2 x 10−16) with COVID-19 mortality in each district corresponding to the population studied and had the largest effect on mortality (regression coefficient = 10.25). As a control we repeated our analysis on randomly selected 100 non-associated SNPs several times and did not find significant association. Therefore, we conclude that genetic susceptibility may play a major role in determining the differences in COVID-19 outcomes and mortality across the Indian sub-continent. We suggest that combining PRS with other observed risk-factors in a Bayesian framework may provide a better prediction model for ascertaining high COVID-19 risk groups and to design more effective public health resource allocation and vaccine distribution schemes.

scholarly journals Genetic risk prediction of COVID-19 susceptibility and severity in the Indian population

2021 ◽  
Author(s):  
P Prakrithi ◽  
Priya Lakra ◽  
Durai Sundar ◽  
Manav Kapoor ◽  
Mitali Mukerji ◽  
...  
Keyword(s):  
Correlation Analysis ◽  
Genetic Risk ◽  
Indian Population ◽  
Genome Wide Association Study ◽  
Risk Groups ◽  
Risk Scores ◽  
Effective Vaccine ◽  
P Value ◽  
Nucleotide Polymorphisms ◽  
Genetic Risk Scores

Host genetic variants can determine the susceptibility to COVID-19 infection and severity as noted in a recent Genome-wide Association Study (GWAS) by Pairo-Castineira et al.1. Given the prominent genetic differences in Indian sub-populations as well as differential prevalence of COVID-19, here, we deploy the previous study and compute genetic risk scores in different Indian sub-populations that may predict the severity of COVID-19 outcomes in them. We computed polygenic risk scores (PRSs) in different Indian sub-populations with the top 100 single-nucleotide polymorphisms (SNPs) with a p-value cutoff of 10-6 derived from the previous GWAS summary statistics1. We selected SNPs overlapping with the Indian Genome Variation Consortium (IGVC) and with similar frequencies in the Indian population. For each population, median PRS was calculated, and a correlation analysis was performed to test the association of these genetic risk scores with COVID-19 mortality. We found a varying distribution of PRS in Indian sub-populations. Correlation analysis indicates a positive linear association between PRS and COVID-19 deaths. This was not observed with non-risk alleles in Indian sub-populations. Our analyses suggest that Indian sub-populations differ with respect to the genetic risk for developing COVID-19 mediated critical illness. Combining PRSs with other observed risk-factors in a Bayesian framework can provide a better prediction model for ascertaining high COVID-19 risk groups. This has a potential utility in the design of more effective vaccine disbursal schemes.

scholarly journals Sleep patterns, genetic susceptibility, and incident cardiovascular disease: a prospective study of 385 292 UK biobank participants

2019 ◽  
Vol 41 (11) ◽  
pp. 1182-1189 ◽  
Author(s):  
Mengyu Fan ◽  
Dianjianyi Sun ◽  
Tao Zhou ◽  
Yoriko Heianza ◽  
Jun Lv ◽  
...  
Keyword(s):  
Cardiovascular Disease ◽  
Prospective Study ◽  
Genetic Susceptibility ◽  
Genetic Risk ◽  
Risk Groups ◽  
Sleep Pattern ◽  
Risk Scores ◽  
Poor Sleep ◽  
Uk Biobank ◽  
High Genetic Risk

Abstract Aims To quantify the association of combined sleep behaviours and genetic susceptibility with the incidence of cardiovascular disease (CVD). Methods and results This study included 385 292 participants initially free of CVD from UK Biobank. We newly created a healthy sleep score according to five sleep factors and defined the low-risk groups as follows: early chronotype, sleep 7–8 h per day, never/rarely insomnia, no snoring, and no frequent excessive daytime sleepiness. Weighted genetic risk scores of coronary heart disease (CHD) or stroke were calculated. During a median of 8.5 years of follow-up, we documented 7280 incident CVD cases including 4667 CHD and 2650 stroke cases. Compared to those with a sleep score of 0–1, participants with a score of 5 had a 35% (19–48%), 34% (22–44%), and 34% (25–42%) reduced risk of CVD, CHD, and stroke, respectively. Nearly 10% of cardiovascular events in this cohort could be attributed to poor sleep pattern. Participants with poor sleep pattern and high genetic risk showed the highest risk of CHD and stroke. Conclusion In this large prospective study, a healthy sleep pattern was associated with reduced risks of CVD, CHD, and stroke among participants with low, intermediate, or high genetic risk.

scholarly journals Applicability of Obesity-Related SNPs and Their Effect Size Measures Defined on Populations with European Ancestry for Genetic Risk Estimation among Roma

Genes ◽  
2020 ◽  
Vol 11 (5) ◽  
pp. 516 ◽  
Author(s):  
Erand Llanaj ◽  
Péter Pikó ◽  
Károly Nagy ◽  
Gábor Rácz ◽  
Sándor János ◽  
...  
Keyword(s):  
Effect Size ◽  
Genetic Risk ◽  
Association Studies ◽  
Strong Association ◽  
European Ancestry ◽  
Risk Scores ◽  
Nucleotide Polymorphisms ◽  
Continuous Variables ◽  
Roma Population ◽  
The Impact

Investigations on the impact of genetic factors on the development of obesity have been limited regarding the Roma population—the largest and most vulnerable ethnic minority in Europe of Asian origin. Genetic variants identified from genetic association studies are primarily from European populations. With that in mind, we investigated the applicability of data on selected obesity-related single nucleotide polymorphisms (SNPs), obtained from the Hungarian general (HG) population of European origin, on the Hungarian Roma (HR) population. Twenty preselected SNPs in susceptible alleles, known to be significantly associated with obesity-related phenotypes, were used to estimate the effect of these SNPs on body mass index (BMI) and waist circumference (WC) in HG (N = 1783) and HR (N = 1225) populations. Single SNP associations were tested using linear and logistic regression models, adjusted for known covariates. Out of 20 SNPs, four located in FTO (rs1121980, rs1558902, rs9939609, and rs9941349) showed strong association with BMI and WC as continuous variables in both samples. Computations based on Adult Treatment Panel III (ATPIII) and the International Diabetes Federation’s (IDF) European and Asian criteria showed rs9941349 in FTO to be associated only with WC among both populations, and two SNPs (rs2867125, rs6548238) in TMEM18 associated with WC only in HG population. A substantial difference (both in direction and effect size) was observed only in the case of rs1801282 in PPARγ on WC as a continuous outcome. Findings suggest that genetic risk scores based on counting SNPs with relatively high effect sizes, defined based on populations with European ancestry, can sufficiently allow estimation of genetic susceptibility for Roma. Further studies are needed to clarify the role of SNP(s) with protective effect(s).

scholarly journals Do genetic risk scores for body mass index predict risk of phobic anxiety? Evidence for a shared genetic risk factor

2014 ◽  
Vol 45 (1) ◽  
pp. 181-191 ◽  
Author(s):  
S. Walter ◽  
M. M. Glymour ◽  
K. Koenen ◽  
L. Liang ◽  
E. J. Tchetgen Tchetgen ◽  
...  
Keyword(s):  
Body Mass Index ◽  
Body Mass ◽  
Genetic Risk ◽  
Genetic Risk Score ◽  
Conclusive Evidence ◽  
Anxiety Symptoms ◽  
Risk Scores ◽  
Health Study ◽  
Nucleotide Polymorphisms ◽  
Phobic Anxiety

BackgroundObesity and anxiety are often linked but the direction of effects is not clear.MethodUsing genetic instrumental variable (IV) analyses in 5911 female participants from the Nurses' Health Study (NHS, initiated 1976) and 3697 male participants from the Health Professional Follow-up Study (HPFS, initiated 1986), we aimed to determine whether obesity increases symptoms of phobic anxiety. As instrumental variables we used the fat mass and obesity-associated (FTO) gene, the melanocortin 4 receptor (MC4R) gene and a genetic risk score (GRS) based on 32 single nucleotide polymorphisms (SNPs) that significantly predict body mass index (BMI). ‘Functional’ GRSs corresponding with specific biological pathways that shape BMI (adipogenesis, appetite and cardiopulmonary) were considered. The main outcome was phobic anxiety measured by the Crown Crisp Index (CCI) in 2004 in the NHS and in 2000 in the HPFS.ResultsIn observational analysis, a 1-unit higher BMI was associated with higher phobic anxiety symptoms [women:β = 0.05, 95% confidence interval (CI) 0.030–0.068; men:β = 0.04, 95% CI 0.016–0.071). IV analyses showed that BMI was associated with higher phobic anxiety symptoms in theFTO-instrumented analysis (p = 0.005) but not in the GRS-instrumented analysis (p = 0.256). Functional GRSs showed heterogeneous, non-significant effects of BMI on phobic anxiety symptoms.ConclusionsOur findings do not provide conclusive evidence in favor of the hypothesis that higher BMI leads to higher levels of phobic anxiety, but rather suggest that genes that influence obesity, in particularFTO, may have direct effects on phobic anxiety, and hence that obesity and phobic anxiety may share common genetic determinants.

scholarly journals Roma socioeconomic status has higher impact on smoking behaviour than genetic susceptibility

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
M A Merzah ◽  
P Pikó ◽  
R Ádány ◽  
S Fiatal
Keyword(s):  
Socioeconomic Status ◽  
Genetic Susceptibility ◽  
Risk Allele ◽  
Smoking Behaviour ◽  
Allele Frequencies ◽  
Risk Scores ◽  
Heavy Smoker ◽  
Nucleotide Polymorphisms ◽  
Cross Sectional ◽  
Smoking Behaviours

Abstract Background Prevalence of smoking in Hungarian Roma (HR) population is two to five times higher than in Hungarian general (HG) population. Our study aims to examine genetic susceptibility and other possible determinants associated to smoking behaviours in these populations. Methods A questionnaire based cross-sectional study was designed in HG (N = 412) and HR (N = 402) populations. Ten Single Nucleotide Polymorphisms (SNPs) were genotyped known to be robustly linked to smoking behaviours. Risk allele frequencies were compared. Additive genetic risk scores (unweighted GRS and weighted GRS) were constructed to compare genetic load from SNPs in genes NRXN1, CHRNA5/4, AGPHD1, MAOA, TRPC7, KCNJ6, GABRA4, and CYP2A6. Smoking behaviour were associated with GRSs and confounders (age, gender, BMI, socioeconomic status-SES) in several regression models. SES was calculated based on Modified Kuppuswamy scale 2019. Results Risk allele frequencies of four SNPs were found to be different between populations (p < 0.01). Median of GRS was equivalent among in populations; whilst wGRS median was slightly higher among Roma (5.2 compared to Hungarian 4.9; P = 0.02). In Roma both genders were more likely to be heavy smoker (OR = 2.05, 95%CI: 1.47-2.86; OR = 1.89, 95%CI: 1.58-2.25, for males and females, respectively) compared to counterparts from general population. GRS were higher among heavy smokers of both populations compared to other smoking behaviours (ORRoma= 1.06, 95%CI:0.98-1.15; ORHungarian=1.05, 95%CI=0.91-1.2). Strong reversible relationship was found between SES and smoking behaviours among study populations (p < 0.0001). Heavy, moderate, and former smokers were having lower SES compared to never smokers of both populations (SES β=-0.037, P = 0.04 for Hungarian; β=-0.039, P = 0.02 for Roma). Conclusions Socioeconomic status was shown as a priority indicator based on multifactorial regression analysis. The highest efforts should be focused on improving the SES of Roma population. Key messages Result of this study indicate the priority impact of SES instead of genetic susceptibility on Roma smoking behaviours variations. Interventions on improving socioeconomic status of the Roma might result in decreasing their cigarette consumption.

scholarly journals Inclusion of Variants Discovered from Diverse Populations Improves Polygenic Risk Score Transferability

2020 ◽  
Author(s):  
Taylor B. Cavazos ◽  
John S. Witte
Keyword(s):  
Genetic Risk ◽  
African Ancestry ◽  
European Ancestry ◽  
Risk Scores ◽  
Polygenic Risk Score ◽  
Polygenic Risk ◽  
Genetic Risk Prediction ◽  
Causal Variants ◽  
The Uk ◽  
The Relationship

ABSTRACTThe majority of polygenic risk scores (PRS) have been developed and optimized in individuals of European ancestry and may have limited generalizability across other ancestral populations. Understanding aspects of PRS that contribute to this issue and determining solutions is complicated by disease-specific genetic architecture and limited knowledge of sharing of causal variants and effect sizes across populations. Motivated by these challenges, we undertook a simulation study to assess the relationship between ancestry and the potential bias in PRS developed in European ancestry populations. Our simulations show that the magnitude of this bias increases with increasing divergence from European ancestry, and this is attributed to population differences in linkage disequilibrium and allele frequencies of European discovered variants, likely as a result of genetic drift. Importantly, we find that including into the PRS variants discovered in African ancestry individuals has the potential to achieve unbiased estimates of genetic risk across global populations and admixed individuals. We confirm our simulation findings in an analysis of HbA1c, asthma, and prostate cancer in the UK Biobank. Given the demonstrated improvement in PRS prediction accuracy, recruiting larger diverse cohorts will be crucial—and potentially even necessary—for enabling accurate and equitable genetic risk prediction across populations.

scholarly journals Association of Polygenetic Risk Scores Related to Immunity and Inflammation with Hyperthyroidism Risk and Interactions between the Polygenetic Scores and Dietary Factors in a Large Cohort

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Mi Young Song ◽  
Sunmin Park
Keyword(s):  
Metabolic Syndrome ◽  
Genome Wide Association Study ◽  
Human Leukocyte ◽  
Dietary Factors ◽  
Risk Scores ◽  
Nucleotide Polymorphisms ◽  
Dietary Intakes ◽  
Urban Hospital ◽  
Coffee Intake ◽  
A Genome

Graves’s disease and thyroiditis induce hyperthyroidism, the causes of which remain unclear, although they are involved with genetic and environmental factors. We aimed to evaluate polygenetic variants for hyperthyroidism risk and their interaction with metabolic parameters and nutritional intakes in an urban hospital-based cohort. A genome-wide association study (GWAS) of participants with (cases; n = 842) and without (controls, n = 38,799) hyperthyroidism was used to identify and select genetic variants. In clinical and lifestyle interaction with PRS, 312 participants cured of hyperthyroidism were excluded. Single nucleotide polymorphisms (SNPs) associated with gene-gene interactions were selected by hyperthyroidism generalized multifactor dimensionality reduction. Polygenic risk scores (PRSs) were generated by summing the numbers of selected SNP risk alleles. The best gene-gene interaction model included tumor-necrosis factor (TNF)_rs1800610, mucin 22 (MUC22)_rs1304322089, tribbles pseudokinase 2 (TRIB2)_rs1881145, cytotoxic T-lymphocyte-associated antigen 4 (CTLA4)_rs231775, lipoma-preferred partner (LPP)_rs6780858, and human leukocyte antigen (HLA)-J_ rs767861647. The PRS of the best model was positively associated with hyperthyroidism risk by 1.939-fold (1.317–2.854) after adjusting for covariates. PRSs interacted with age, metabolic syndrome, and dietary inflammatory index (DII), while hyperthyroidism risk interacted with energy, calcium, seaweed, milk, and coffee intake ( P < 0.05 ). The PRS impact on hyperthyroidism risk was observed in younger (<55 years) participants and adults without metabolic syndrome. PRSs were positively associated with hyperthyroidism risk in participants with low dietary intakes of energy (OR = 2.74), calcium (OR = 2.84), seaweed (OR = 3.43), milk (OR = 2.91), coffee (OR = 2.44), and DII (OR = 3.45). In conclusion, adults with high PRS involved in inflammation and immunity had a high hyperthyroidism risk exacerbated under low intakes of energy, calcium, seaweed, milk, or coffee. These results can be applied to personalized nutrition in a clinical setting.

scholarly journals Lower Dietary Intake of Plant Protein Is Associated with Genetic Risk of Diabetes-Related Traits in Urban Asian Indian Adults

Nutrients ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 3064
Author(s):  
Sooad Alsulami ◽  
Dhanasekaran Bodhini ◽  
Vasudevan Sudha ◽  
Coimbatore Subramanian Shanthi Rani ◽  
Rajendra Pradeepa ◽  
...  
Keyword(s):  
Genetic Risk ◽  
Protein Intake ◽  
Asian Indians ◽  
Genetic Factors ◽  
Risk Allele ◽  
Plant Protein ◽  
Risk Scores ◽  
Nucleotide Polymorphisms ◽  
The Impact ◽  
Lower Plant

The increasing prevalence of type 2 diabetes among South Asians is caused by a complex interplay between environmental and genetic factors. We aimed to examine the impact of dietary and genetic factors on metabolic traits in 1062 Asian Indians. Dietary assessment was performed using a validated semi-quantitative food frequency questionnaire. Seven single nucleotide polymorphisms (SNPs) from the Transcription factor 7-like 2 and fat mass and obesity-associated genes were used to construct two metabolic genetic risk scores (GRS): 7-SNP and 3-SNP GRSs. Both 7-SNP GRS and 3-SNP GRS were associated with a higher risk of T2D (p = 0.0000134 and 0.008, respectively). The 3-SNP GRS was associated with higher waist circumference (p = 0.010), fasting plasma glucose (FPG) (p = 0.002) and glycated haemoglobin (HbA1c) (p = 0.000066). There were significant interactions between 3-SNP GRS and protein intake (% of total energy intake) on FPG (Pinteraction = 0.011) and HbA1c (Pinteraction = 0.007), where among individuals with lower plant protein intake (<39 g/day) and those with >1 risk allele had higher FPG (p = 0.001) and HbA1c (p = 0.00006) than individuals with ≤1 risk allele. Our findings suggest that lower plant protein intake may be a contributor to the increased ethnic susceptibility to diabetes described in Asian Indians. Randomised clinical trials with increased plant protein in the diets of this population are needed to see whether the reduction of diabetes risk occurs in individuals with prediabetes.

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