scholarly journals Independent and cumulative coeliac disease-susceptibility loci are associated with distinct disease phenotypes

2021 ◽  
Author(s):  
Juliana X. M. Cerqueira ◽  
Päivi Saavalainen ◽  
Kalle Kurppa ◽  
Pilvi Laurikka ◽  
Heini Huhtala ◽  
...  
Keyword(s):  
Small Bowel ◽  
Coeliac Disease ◽  
Risk Score ◽  
Disease Susceptibility ◽  
Genetic Risk Score ◽  
Disease Onset ◽  
Mucosal Damage ◽  
Polygenic Risk Score ◽  
Susceptibility Loci ◽  
Weighted Genetic Risk Score

AbstractThe phenotype of coeliac disease varies considerably for incompletely understood reasons. We investigated whether established coeliac disease susceptibility variants (SNPs) are individually or cumulatively associated with distinct phenotypes. We also tested whether a polygenic risk score (PRS) based on genome-wide associated (GWA) data could explain the phenotypic variation. The phenotypic association of 39 non-HLA coeliac disease SNPs was tested in 625 thoroughly phenotyped coeliac disease patients and 1817 controls. To assess their cumulative effects a weighted genetic risk score (wGRS39) was built, and stratified by tertiles. In our PRS model in cases, we took the summary statistics from the largest GWA study in coeliac disease and tested their association at eight P value thresholds (PT) with phenotypes. Altogether ten SNPs were associated with distinct phenotypes after correction for multiple testing (PEMP2 ≤ 0.05). The TLR7/TLR8 locus was associated with disease onset before and the SH2B3/ATXN2, ITGA4/UBE2E3 and IL2/IL21 loci after 7 years of age. The latter three loci were associated with a more severe small bowel mucosal damage and SH2B3/ATXN2 with type 1 diabetes. Patients at the highest wGRS39 tertiles had OR > 1.62 for having coeliac disease-related symptoms during childhood, a more severe small bowel mucosal damage, malabsorption and anaemia. PRS was associated only with dermatitis herpetiformis (PT = 0.2, PEMP2 = 0.02). Independent coeliac disease-susceptibility loci are associated with distinct phenotypes, suggesting that genetic factors play a role in determining the disease presentation. Moreover, the increased number of coeliac disease susceptibility SNPs might predispose to a more severe disease course.

scholarly journals P820 An easy and rapid targeted next generation sequencing-based genotyping assay for the validated IBD risk loci

2020 ◽  
Vol 14 (Supplement_1) ◽  
pp. S637-S638
Author(s):  
S Verstockt ◽  
L Hannes ◽  
S Deman ◽  
W J Wollants ◽  
E Souche ◽  
...  
Keyword(s):  
Risk Score ◽  
Performance Metrics ◽  
Genetic Risk Score ◽  
Cost Effective ◽  
Polygenic Risk Score ◽  
Call Rate ◽  
Susceptibility Loci ◽  
Polygenic Risk ◽  
Genotyping Assay ◽  
Genotyping Call Rate

Abstract Background Inflammatory bowel diseases (IBD) are complex genetic diseases for which 242 susceptibility loci have been identified thus far. For translational or functional follow-up studies it can be of interest to know the genotype of specific variants. For other studies a composite genetic risk score–the polygenic risk score–is of value. There currently is a gap in technology to genotype a few hundred variants in a flexible and cost-effective way. We therefore developed a genotyping assay for the 242 validated IBD susceptibility loci. Methods Using MIPgen v.1.1, we designed molecular inversion probes (MIPs) covering 269 independent variants from the 242 IBD loci. MIP libraries were prepared according to Neveling et al. (Clin Chem. 2017), followed by paired-end sequencing using a MiSeq® System (Illumina). In the pilot studies, 16 IBD patients were genotyped, and results were compared with available immunochip (ichip) data. Genotypes for the covered variants were obtained using an in-house developed pipeline, and performance metrics were assessed (incl. genotyping call rate, percentage off-target reads and concordance with ichip-based genotypes). After optimisation, we genotyped 279 individuals (168 IBD patients and 111 non-IBD controls). We also calculated a weighted IBD polygenic risk score (PRSice 2.0) for these. Results Despite a genotyping call rate of 94.3%, the first pilot run suffered from a high rate of off-target reads (52.5%). After redesigning poorly-performing MIPs, off-target reads dropped to 9.4%, and the genotyping call rate increased to 97.5%. Concordance with genotypes previously obtained from ichip was 99.3%. When applying the optimised design on a larger scale (i.e. on the 279 individuals), we obtained similar performance metrics, with 8.0% off-target reads and a genotyping call rate of 97.3%. Moreover, upscaling resulted in a turnaround time of 2.5 working days/96 samples and a cost of €14/sample. The calculated IBD polygenic risk scores showed higher scores in patients as compared with controls (5.5E−03 vs. 4.0E−03, p = 8.80E−10; R² IBD polygenic risk score = 0.15, p = 1.28E−07), however with a large overlap between both groups. Quartile analysis showed that individuals within the highest quartile had an 8.1-fold (95% CI: 3.7–17.5) increase in risk towards IBD compared with individuals in the first quartile. Conclusion We developed a cost-effective genotyping assay for currently known IBD risk loci, with an integrated bioinformatics pipeline from raw sequencing data to individual genotypes and calculation of a polygenic risk score. Furthermore, this assay enables genotyping of individuals on a large scale while remaining flexible to implement newly identified genetic variants.

scholarly journals The Association between Fasting Glucose and Sugar Sweetened Beverages Intake Is Greater in Latin Americans with a High Polygenic Risk Score for Type 2 Diabetes Mellitus

Nutrients ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 69
Author(s):  
María Lourdes López-Portillo ◽  
Andrea Huidobro ◽  
Eduardo Tobar-Calfucoy ◽  
Cristian Yáñez ◽  
Rocío Retamales-Ortega ◽  
...  
Keyword(s):  
Risk Score ◽  
Fasting Glucose ◽  
Genetic Risk Score ◽  
Polygenic Risk Score ◽  
Sugar Sweetened Beverages ◽  
Sweetened Beverages ◽  
Weighted Genetic Risk Score ◽  
Risk Snps ◽  
Four Levels ◽  
Chilean Population

Chile has the highest per capita intake of sugar-sweetened beverages (SSB) world-wide. However, it is unknown whether the effects from this highly industrialized food will mimic those reported in industrialized countries or whether they will be modified by local lifestyle or population genetics. Our goal was to evaluate the association between SSB intake and fasting glucose in the Chilean population. We calculated a weighted genetic risk score (GRSw) based on 16 T2D risk SNPs in 2828 non-diabetic participants of the MAUCO cohort. SSB intake was categorized in four levels using a food frequency questionnaire. Log-fasting glucose was regressed on SSB and GRSw tertiles while accounting for socio-demography, lifestyle, obesity, and Amerindian ancestry. Fasting glucose increased systematically per unit of GRSw (β = 0.02 ± 0.006, p = 0.00002) and by SSB intake (β[cat4] = 0.04 ± 0.009, p = 0.0001), showing a significant interaction, where the strongest effect was observed in the highest GRSw-tertile and in the highest SSB consumption category (β = 0.05 ± 0.02, p = 0.02). SNP-wise, SSB interacted with additive effects of rs7903146 (TCF7L2) (β = 0.05 ± 0.01, p = 0.002) and with the G/G genotype of rs10830963 (MTNRB1B) (β = 0.19 ± 0.05, p = 0.001). Conclusions: The association between SSB intake and fasting glucose in the Chilean population without diabetes is modified by T2D genetic susceptibility.

Genetic Risk, a Healthy Lifestyle, and Type 2 Diabetes: the Dongfeng-Tongji Cohort Study

2020 ◽  
Vol 105 (4) ◽  
pp. 1242-1250
Author(s):  
Xu Han ◽  
Yue Wei ◽  
Hua Hu ◽  
Jing Wang ◽  
Zhaoyang Li ◽  
...  
Keyword(s):  
Cohort Study ◽  
Risk Score ◽  
Genetic Risk ◽  
Healthy Lifestyle ◽  
Genetic Risk Score ◽  
Lifestyle Factors ◽  
Diabetes Risk ◽  
Incident Diabetes ◽  
Polygenic Risk Score ◽  
Weighted Genetic Risk Score

Abstract Objective The objective of this study is to examine whether healthy lifestyle could reduce diabetes risk among individuals with different genetic profiles. Design A prospective cohort study with a median follow-up of 4.6 years from the Dongfeng-Tongji cohort was performed. Participants A total of 19 005 individuals without diabetes at baseline participated in the study. Main Variable Measure A healthy lifestyle was determined based on 6 factors: nonsmoker, nondrinker, healthy diet, body mass index of 18.5 to 23.9 kg/m2, waist circumference less than 85 cm for men and less than 80 cm for women, and higher level of physical activity. Associations of combined lifestyle factors and incident diabetes were estimated using Cox proportional hazard regression. A polygenic risk score of 88 single-nucleotide polymorphisms previously associated with diabetes was constructed to test for association with diabetes risk among 7344 individuals, using logistic regression. Results A total of 1555 incident diabetes were ascertained. Per SD increment of simple and weighted genetic risk score was associated with a 1.39- and 1.34-fold higher diabetes risk, respectively. Compared with poor lifestyle, intermediate and ideal lifestyle were reduced to a 23% and 46% risk of incident diabetes, respectively. Association of lifestyle with diabetes risk was independent of genetic risk. Even among individuals with high genetic risk, intermediate and ideal lifestyle were separately associated with a 29% and 49% lower risk of diabetes. Conclusion Genetic and combined lifestyle factors were independently associated with diabetes risk. A healthy lifestyle could lower diabetes risk across different genetic risk categories, emphasizing the benefit of entire populations adhering to a healthy lifestyle.

scholarly journals A weighted genetic risk score using all known susceptibility variants to estimate rheumatoid arthritis risk

2013 ◽  
Vol 74 (1) ◽  
pp. 170-176 ◽  
Author(s):  
Annie Yarwood ◽  
Buhm Han ◽  
Soumya Raychaudhuri ◽  
John Bowes ◽  
Mark Lunt ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Amino Acids ◽  
Genetic Susceptibility ◽  
Risk Prediction ◽  
Risk Score ◽  
Genetic Risk ◽  
Risk Group ◽  
Genetic Risk Score ◽  
Susceptibility Loci ◽  
Weighted Genetic Risk Score

BackgroundThere is currently great interest in the incorporation of genetic susceptibility loci into screening models to identify individuals at high risk of disease. Here, we present the first risk prediction model including all 46 known genetic loci associated with rheumatoid arthritis (RA).MethodsA weighted genetic risk score (wGRS) was created using 45 RA non-human leucocyte antigen (HLA) susceptibility loci, imputed amino acids at HLA-DRB1 (11, 71 and 74), HLA-DPB1 (position 9) HLA-B (position 9) and gender. The wGRS was tested in 11 366 RA cases and 15 489 healthy controls. The risk of developing RA was estimated using logistic regression by dividing the wGRS into quintiles. The ability of the wGRS to discriminate between cases and controls was assessed by receiver operator characteristic analysis and discrimination improvement tests.ResultsIndividuals in the highest risk group showed significantly increased odds of developing anti-cyclic citrullinated peptide-positive RA compared to the lowest risk group (OR 27.13, 95% CI 23.70 to 31.05). The wGRS was validated in an independent cohort that showed similar results (area under the curve 0.78, OR 18.00, 95% CI 13.67 to 23.71). Comparison of the full wGRS with a wGRS in which HLA amino acids were replaced by a HLA tag single-nucleotide polymorphism showed a significant loss of sensitivity and specificity.ConclusionsOur study suggests that in RA, even when using all known genetic susceptibility variants, prediction performance remains modest; while this is insufficiently accurate for general population screening, it may prove of more use in targeted studies. Our study has also highlighted the importance of including HLA variation in risk prediction models.

scholarly journals Genome-wide gene-air pollution interaction analysis of lung function in 300,000 individuals

2021 ◽  
Author(s):  
Carl A. Melbourne ◽  
A. Mesut Erzurumluoglu ◽  
Nick Shrine ◽  
Jing Chen ◽  
Martin D. Tobin ◽  
...  
Keyword(s):  
Air Pollution ◽  
Lung Function ◽  
Risk Score ◽  
Interaction Analysis ◽  
Genetic Risk Score ◽  
Chronic Obstructive ◽  
Polygenic Risk Score ◽  
Genome Wide ◽  
A Genome ◽  
Weighted Genetic Risk Score

Impaired lung function is predictive of mortality and is a key component in the diagnosis of chronic obstructive pulmonary disease. Lung function has a strong genetic component but is also affected by environmental factors such as increased exposure to air pollution. How genetic factors and air pollution interact to affect lung function is however less understood. We conducted a genome-wide gene-air pollution interaction analysis of spirometry measures with three measures of air pollution at home address: particulate matter (PM2.5 & PM10) and nitrogen dioxide (NO2), in approximately 300,000 unrelated European individuals from UK Biobank. We explored air pollution interactions with previously identified lung function signals and determined their combined interaction effect using a polygenic risk score (PRS). We identified seven genome-wide interaction signals (P < 5 x 10 -8), and a further ten suggestive interaction signals (P < 5 x 10 -7). We found statistical evidence of interaction with PM2.5 for previous lung function signal, rs10841302, near AEBP2, suggesting increased susceptibility of FEV1/FVC to PM2.5, as copies of the G allele increased (interaction beta: -0.073 percentage points, 95%CI: -0.105,-0.041). There was no observed interaction between air pollutants and the weighted genetic risk score. We carried out the largest genome-wide gene-air pollution interaction study of lung function and identified effects of clinically relevant size and significance. We observed up to 440ml lower lung function for certain genotypes associated with mean levels of outdoor air pollution at baseline, which is approximately equivalent to nine years of normal loss of lung function.

scholarly journals A Weighted Genetic Risk Score Predicts Surgical Recurrence Independent of High-Risk Clinical Features in Dupuytren’s Disease

2019 ◽  
Vol 143 (2) ◽  
pp. 512-518 ◽  
Author(s):  
Sophie A. Riesmeijer ◽  
Oliver W. G. Manley ◽  
Michael Ng ◽  
Ilja M. Nolte ◽  
Dieuwke C. Broekstra ◽  
...  
Keyword(s):  
High Risk ◽  
Clinical Features ◽  
Risk Score ◽  
Genetic Risk ◽  
Genetic Risk Score ◽  
Dupuytren’S Disease ◽  
Dupuytren's Disease ◽  
Surgical Recurrence ◽  
Weighted Genetic Risk Score

scholarly journals OWE-16 Development and clinical validation of a genetic risk score for coeliac disease

2019 ◽  
Author(s):  
Seth Sharp ◽  
Samuel Jones ◽  
Robert Kimmitt ◽  
Michael Weedon ◽  
Anne Halpin ◽  
...  
Keyword(s):  
Coeliac Disease ◽  
Risk Score ◽  
Genetic Risk ◽  
Genetic Risk Score ◽  
Clinical Validation

scholarly journals Evaluation of a weighted genetic risk score for the prediction of biomarkers of CYP2A6 activity

2019 ◽  
Vol 25 (1) ◽  
Author(s):  
Ahmed El‐Boraie ◽  
Taraneh Taghavi ◽  
Meghan J. Chenoweth ◽  
Koya Fukunaga ◽  
Taisei Mushiroda ◽  
...  
Keyword(s):  
Risk Score ◽  
Genetic Risk ◽  
Genetic Risk Score ◽  
Weighted Genetic Risk Score

scholarly journals PCA-based GRS analysis enhances the effectiveness for genetic correlation detection

2018 ◽  
Vol 20 (6) ◽  
pp. 2291-2298
Author(s):  
Yan Zhao ◽  
Yujie Ning ◽  
Feng Zhang ◽  
Miao Ding ◽  
Yan Wen ◽  
...  
Keyword(s):  
Risk Score ◽  
Statistical Power ◽  
Genome Wide Association Study ◽  
Genetic Risk Score ◽  
Complex Diseases ◽  
Principal Component ◽  
Analysis Approach ◽  
Polygenic Risk Score ◽  
Multiple Testing Correction ◽  
Improved Performance

Abstract Genetic risk score (GRS, also known as polygenic risk score) analysis is an increasingly popular method for exploring genetic architectures and relationships of complex diseases. However, complex diseases are usually measured by multiple correlated phenotypes. Analyzing each disease phenotype individually is likely to reduce statistical power due to multiple testing correction. In order to conquer the disadvantage, we proposed a principal component analysis (PCA)–based GRS analysis approach. Extensive simulation studies were conducted to compare the performance of PCA-based GRS analysis and traditional GRS analysis approach. Simulation results observed significantly improved performance of PCA-based GRS analysis compared to traditional GRS analysis under various scenarios. For the sake of verification, we also applied both PCA-based GRS analysis and traditional GRS analysis to a real Caucasian genome-wide association study (GWAS) data of bone geometry. Real data analysis results further confirmed the improved performance of PCA-based GRS analysis. Given that GWAS have flourished in the past decades, our approach may help researchers to explore the genetic architectures and relationships of complex diseases or traits.

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