Interactions of genetic variations in FAS, GJB2 and PTPRN2 are associated with noise-induced hearing loss: a case-control study in China.

BACKGROUND: This study aimed to screen and validate noise-induced hearing loss (NIHL) associated single nucleotide polymorphisms (SNPs), construct genetic risk prediction models, and evaluate higher-order gene-gene, gene-environment interactions for NIHL in Chinese population. METHODS: First, 83 cases and 83 controls were recruited and 60 candidate SNPs were genotyped. Then SNPs with promising results were validated in another case-control study (153 cases and 252 controls). NIHL-associated SNPs were identified by logistic regression analysis, and a genetic risk model was constructed based on the genetic risk score (GRS), and classification and regression tree (CART) analysis was used to evaluate interactions among gene-gene and gene-environment. RESULTS: Six SNPs in five genes were significantly associated with NIHL risk (p < 0.05). A positive dose-response relationship was found between GRS values and NIHL risk. CART analysis indicated that strongest interaction was among subjects with age ≥ 45 years and cumulative noise exposure ≥ 95 [dB(A)·years], without personal protective equipment, and carried GJB2 rs3751385 (AA/AB) and FAS rs1468063 (AA/AB) (OR = 10.038, 95% CI = 2.770, 47.792), compared with the referent group. CDH23, FAS, GJB2, PTPRN2 and SIK3 may be NIHL susceptibility genes. CONCLUSION: GRS values may be utilized in the evaluation of the cumulative effect of genetic risk for NIHL based on NIHL-associated SNPs. Gene-gene, gene-environment interaction patterns play an important role in the incidence of NIHL.

The Application of Genetic Risk Scores in Rheumatic Diseases: A Perspective.

Modest effect sizes have limited the clinical applicability of genetic associations with rheumatic diseases. Genetic risk scores (GRSs) have emerged as a promising solution to translate genetics into useful tools. In this review, we provide an overview of the recent literature on GRSs in rheumatic diseases. We describe six categories for which GRSs are used: (a) disease (outcome) prediction, (b) genetic commonalities between diseases, (c) disease differentiation, (d) interplay between genetics and environmental factors, (e) heritability and transferability, and (f) detecting causal relationships between traits. In our review of the literature, we identified current lacunas and opportunities for future work. First, the shortage of non-European genetic data restricts the application of many GRSs to European populations. Next, many GRSs are tested in settings enriched for cases that limit the transferability to real life. If intended for clinical application, GRSs are ideally tested in the relevant setting. Finally, there is much to elucidate regarding the co-occurrence of clinical traits to identify shared causal paths and elucidate relationships between the diseases. GRSs are useful instruments for this. Overall, the ever-continuing research on GRSs gives a hopeful outlook into the future of GRSs and indicates significant progress in their potential applications.

A partitioned 88-loci psoriasis genetic risk score reveals HLA and non-HLA contributions to clinical phenotypes in a Newfoundland psoriasis cohort.

Psoriasis is an immune-mediated inflammatory skin disease typically characterized by erythematous and scaly plaques. It affects 3% of the Newfoundland population while only affecting 1.7% of the general Canadian population. Recent genome-wide association studies (GWAS) in psoriasis have identified more than 63 genetic susceptibility loci that individually have modest effects. Prior studies have shown that a genetic risk score (GRS) combining multiple loci can improve psoriasis disease prediction. However, these prior GRS studies have not fully explored the association of GRS with patient clinical characteristics. In this study, we calculated three types of GRS: one using all known GWAS SNPs (GRS-ALL), one using a subset of SNPs from the HLA region (GRS-HLA), and the last using non-HLA SNPs (GRS-noHLA). We examined the relationship between these GRS and a number of psoriasis features within a well characterized Newfoundland psoriasis cohort. We found that both GRS-ALL and GRS-HLA were significantly associated with early age of psoriasis onset, psoriasis severity, first presentation of psoriasis at the elbow or knee, and the total number of body locations affected, while only GRS-ALL was associated with a positive family history of psoriasis. GRS-noHLA was uniquely associated with genital psoriasis. These findings clarify the relationship of the HLA and non-HLA components of GRS with important clinical features of psoriasis.

Relationship Between a Vitamin D Genetic Risk Score and Autoantibodies Among First-Degree Relatives of Probands With Rheumatoid Arthritis and Systemic Lupus Erythematosus.

Objective: Higher 25-hydroxyvitamin D (25(OH)D) levels have been associated with reduced risk for autoimmune diseases and are influenced by vitamin D metabolism genes. We estimated genetically-determined vitamin D levels by calculating a genetic risk score (GRS) and investigated whether the vitamin D GRS was associated with the presence of autoantibodies related to rheumatoid arthritis (RA) and systemic lupus erythematosus (SLE) in those at increased risk for developing RA and SLE, respectively. Methods: In this cross-sectional study, we selected autoantibody positive (aAb+) and autoantibody negative (aAb-) individuals from the Studies of the Etiologies of Rheumatoid Arthritis (SERA), a cohort study of first-degree relatives (FDRs) of individuals with RA (189 RA aAb+, 181 RA aAb-), and the Lupus Family Registry and Repository (LFRR), a cohort study of FDRs of individuals with SLE (157 SLE aAb+, 185 SLE aAb-). Five SNPs known to be associated with serum 25(OH)D levels were analyzed individually as well as in a GRS: rs4588 (GC), rs12785878 (NADSYN1), rs10741657 (CYP2R1), rs6538691 (AMDHD1), and rs8018720 (SEC23A). Results: Both cohorts had similar demographic characteristics, with significantly older and a higher proportion of males in the aAb+ FDRs. The vitamin D GRS was inversely associated with RA aAb+ (OR = 0.85, 95% CI = 0.74-0.99), suggesting a possible protective factor for RA aAb positivity in FDRs of RA probands. The vitamin D GRS was not associated with SLE aAb+ in the LFRR (OR = 1.09, 95% CI = 0.94-1.27). The SEC23A SNP was associated with RA aAb+ in SERA (OR = 0.65, 95% CI = 0.43-0.99); this SNP was not associated with SLE aAb+ in LFRR (OR = 1.41, 95% CI = 0.90 - 2.19). Conclusion: Genes associated with vitamin D levels may play a protective role in the development of RA aAbs in FDRs of RA probands, perhaps through affecting lifelong vitamin D status. The GRS and the SEC23A SNP may be of interest for future investigation in pre-clinical RA. In contrast, these results do not support a similar association in SLE FDRs, suggesting other mechanisms involved in the relationship between vitamin D and SLE aAbs not assessed in this study.

Genetically-Guided Medical Nutrition Therapy in Type 2 Diabetes Mellitus and Pre-diabetes: A Series of n-of-1 Superiority Trials.

Type 2 diabetes mellitus (T2DM) is a heterogeneous metabolic disorder of multifactorial etiology that includes genetic and dietary influences. By addressing the latter, medical nutrition therapy (MNT) contributes to the management of T2DM or pre-diabetes toward achieving glycaemic control and improved insulin sensitivity. However, the clinical outcomes of MNT vary and may further benefit from personalized nutritional plans that take into consideration genetic variations associated with individual responses to macronutrients. The aim of the present series of n-of-1 trials was to assess the effects of genetically-guided vs. conventional MNT on patients with pre-diabetes or T2DM. A quasi-experimental, cross-over design was adopted in three Caucasian adult men with either diagnosis. Complete diet, bioclinical and anthropometric assessment was performed and a conventional MNT, based on the clinical practice guidelines was applied for 8 weeks. After a week of "wash-out," a precision MNT was prescribed for an additional 8-week period, based on the genetic characteristics of each patient. Outcomes of interest included changes in body weight (BW), fasting plasma glucose (FPG), and blood pressure (BP). Collectively, the trials indicated improvements in BW, FPG, BP, and glycosylated hemoglobin (HbA1c) following the genetically-guided precision MNT intervention. Moreover, both patients with pre-diabetes experienced remission of the condition. We conclude that improved BW loss and glycemic control can be achieved in patients with pre-diabetes/T2DM, by coupling MNT to their genetic makeup, guiding optimal diet, macronutrient composition, exercise and oral nutrient supplementation in a personalized manner.

The Utility of Genetic Risk Score to Improve Performance of FRAX for Fracture Prediction in US Postmenopausal Women.

The ability of the fracture risk assessment tool (FRAX) in discriminating fracture and non-fracture in postmenopausal women remains suboptimal. Adding a genetic profile may improve the performance of FRAX. Three genetic risk scores (GRSs) (GRS_fracture, GRS_BMD, GRS_eBMD) were calculated for each participant in the Women's Health Initiative Study (n = 23,981), based on the summary statistics of three comprehensive osteoporosis-related genome-wide association studies (GWAS). The primary outcomes were incident major osteoporotic fracture (MOF) and hip fracture (HF). The association between each GRS and fracture risk were evaluated in separate Cox Proportional Hazard models, with FRAX clinical risk factors adjusted for. The discrimination ability of each model was assessed using Area Under the Curve (AUC). The predictive improvement attributable to each GRSs was assessed using the net reclassification improvement (NRI) and the integrated discrimination improvement (IDI). GRS_BMD and GRS_eBMD were significantly associated with MOF and HF risk, independent of the base FRAX risk factors. Compare to the base FRAX model, the models with GRS_fracture, GRS_BMD, and GRS_eBMD improved the reclassification of MOF by 0.5% (95% CI, 0.2% to 0.9%, p = p < .01), 0.3% (95% CI, 0.1% to 0.6%, p = 0.01), and 2.1% (95% CI, 0.3% to 2.8%, p < .01), respectively. Similar results were also observed when using HF as an outcome. Our study suggested that the addition of genetic profiles provide limited improvements in the reclassification of FRAX for MOF and HF.

Prognostic value of leukocyte telomere length in renal cell carcinoma patients.

Telomeres play important roles in cancer initiation and progression. Leukocyte telomere length (LTL) can modulate cancer risk and outcome. We hypothesize that genetically predicted short LTL is associated with worse prognosis in renal cell carcinoma (RCC). A total of 1,086 histologically confirmed RCC patients were included in this study. A weighted genetic risk score (GRS) predictive of LTL was constructed using 10 confirmed LTL-associated single nucleotide polymorphisms (SNPs). The associations of individual SNPs and GRS with recurrence and survival were determined by multivariate Cox proportional hazards analysis. In individual SNP analysis, long LTL-associated allele of rs7675998 in NAF1 gene at chromosome 4 was significantly associated with a reduced risk of recurrence (HR=0.85, 95% CI, 0.73-0.99, P=0.043), while the long LTL-associated allele of rs10936599 in TERC at chromosome 3 conferred a reduced risk of death (HR=0.85, 95% CI, 0.73-1.00, P=0.047). More importantly, genetically predicted LTL was associated with both recurrence and survival. Dichotomized at the median value of GRS, patients with low GRS (indicating short LTL) exhibited significantly increased risks of recurrence (HR=1.26, 95% CI, 1.03-1.54, P=0.025) and death (HR=1.23, 95% CI, 1.00-1.50, P=0.045). Hence, we concluded that genetically predicted short LTL is associated with worse prognosis in RCC patients.

Multi-Locus Models to Address Hb F Variability in Portuguese ß-Thalassemia Carriers.

Hb F production is under the influence of major quantitative trait loci (QTL). The present study aims: i) to replicate the association with Hb F for representative genetic variants in the three major Hb F QTLs in a Portuguese sample of ß-thalassemia (ß-thal) carriers; and ii) to test different genetic multi-locus models to account for the genetic component of Hb F variation. A population sample of 79 Portuguese ß-thal carriers (39 males, 40 females), aged between 2 to 70 years old, were genotyped for polymorphisms in the locus control region (LCR)-5' hypersensitive site 4 (5'HS4) rs16912979, XmnI-HBG2 rs7482144, BCL11A rs1427407 and HMIP rs66650371, using standard biomolecular procedures. Univariate linear regression models were used to test for genetic associations with Hb F. The minor alleles of the individual variants BCL11A rs1427407 (T) (0.165), HMIP rs66650371 (3 bp del) (0.247) and XmnI-HBG2 rs7482144 (T) (0.196), were found to be significantly associated with increased levels of Hb F (p = 0.029, p = 0.002 and p = 0.0004, respectively), explaining about 6.0, 12.0 and 15.0% of Hb F variation, respectively. In a multiple linear regression approach, the three loci accounted for about 30.0% of Hb F variance. Two genetic risk scores (GRS), rationalizing the number of minor alleles into a single genetic variable, explained about 30.0 and 32.0% of the Hb F variation. In conclusion, we replicated in ß-thal carriers previously reported associations with Hb F. Multi-locus models combining three representative variants of Hb F influencing QTLs can explain a larger amount of Hb F variability.

Association between genetic risk scores and risk of narcolepsy: a case-control study.

BACKGROUND: Genome-wide association studies (GWASs) have identified a large number of single-nucleotide polymorphisms (SNPs) associated with narcolepsy. However, the sum impact of these SNPs on defining the genomic risk of narcolepsy remains unknown. In the present study, we investigated the associations between genetic risk scores (GRSs) and narcolepsy along with their predictive power. METHODS: A case-control study consisting of 903 narcolepsy patients and 1,981 healthy control subjects was performed. Thirty-two SNPs previously reported to confer susceptibility to narcolepsy were assessed for their association with narcolepsy risk. Subsequently, we constructed four GRS groups comprising reported narcolepsy susceptibility SNPs located in different genomic regions, and tested their association with narcolepsy risk using a regression model. Receiver operating characteristic (ROC) curves were used to examine the discriminatory power of the GRSs for predicting narcolepsy. RESULTS: Nine individual SNPs were significantly associated with narcolepsy after Bonferroni correction. All four GRSs were strongly associated with narcolepsy risk even when GRSs were constructed using SNPs located outside the previously implicated human leukocyte antigen (HLA) region on chromosome 6. The odds ratio (OR) for narcolepsy risk increased with the number of genetic loci implicated, ranging from an OR of 2.016 (95% CI, 1.657-2.456) to an OR of 4.298 (95% CI, 3.378-5.481). GRS4, constructed using the narcolepsy-associated SNPs identified in the Chinese population, was most closely associated with narcolepsy risk. CONCLUSIONS: The results suggest that the GRS method for combining common genetic variations can significantly associate GRS scores with narcolepsy risk and may facilitate narcolepsy risk stratification for prevention trials, both for HLA-DQB1*06:02-positive and -negative individuals.

Performance of FRAX in Predicting Fractures in US Postmenopausal Women with Varied Race and Genetic Profiles.

BACKGROUND: Whether the Fracture Risk Assessment Tool (FRAX) performed differently in estimating the 10-year fracture probability in women of different genetic profiling and race remained unclear. METHODS: The genomic data in the Women's Health Initiative (WHI) study was analyzed (n = 23,981). The genetic risk score (GRS) was calculated from 14 fracture-associated single nucleotide polymorphisms (SNPs) for each participant. FRAX without bone mineral density (BMD) was used to estimate fracture probability. RESULTS: FRAX significantly overestimated the risk of major osteoporotic fracture (MOF) in the WHI study. The most significant overestimation was observed in women with low GRS (predicted/observed ratio (POR): 1.61, 95% CI: 1.45-1.79) specifically Asian women (POR: 3.5, 95% CI 2.48-4.81) and in African American women (POR: 2.59, 95% CI: 2.33-2.87). Compared to the low GRS group, the 10-year probability of MOF adjusted for the FRAX score was 21% and 30% higher in the median GRS group and high GRS group, respectively. Asian, African American, and Hispanic women respectively had a 78%, 76%, and 56% lower hazard than Caucasian women after the FRAX score was adjusted. The results were similar for hip fractures. CONCLUSIONS: Our study suggested the FRAX performance varies significantly by both genetic profile and race in postmenopausal women.

Polygenetic risk for coronary artery disease increases hospitalization burden and mortality.

BACKGROUND: Coronary artery disease (CAD) is a leading cause of death worldwide and increasing cost for society. Genome wide association studies (GWAS) have identified common variants associated with CAD. Combining single nucleotide polymorphisms (SNPs) into a genetic risk score (GRS) can estimate an individual's genetic burden. OBJECTIVES: To investigate whether GRS for CAD can predict hospitalization and mortality. METHODS: 23,594 individuals without CAD at baseline and with full data for all covariates from the population based prospective study Malmö diet and cancer study were investigated. The association between hospitalizations was calculated by negative binomial regression and risk of mortality was calculated by Cox proportional hazards regression. The GRS was constructed from 50 SNPs. RESULTS: The study population was divided into quintiles according to the value of GRS. During the mean follow-up time of 17.8 years, 17,254 individuals were hospitalized at least once. Individuals in the highest quintile of GRS were hospitalized 10% more often than individuals in the lowest quintile (IRR: 1.10 [95% CI 1.04-1.16], p = 0.001), mainly for cardiovascular reasons (IRR: 1.31 [95% CI 1.20-1.43], p = 5.17 × 10-10). These individuals had highly increased risk of CVD mortality (HR: 1.44 [1.25-1.66], p = 6.56 × 10-7) but not the risk of mortality due to other causes. CONCLUSION: Our results suggest that genetic predisposition for CAD can predict hospitalization burden and mortality, especially due to cardiovascular causes, independently of traditional risk factors. As the risk conferred by the GRS is partially modifiable, our results may help to reduce societal costs, individual suffering and prolong life.

A Weighted Genetic Risk Score Based on Four APOE-Independent Alzheimer's Disease Risk Loci May Supplement APOE E4 for Better Disease Prediction.

Recent genome-wide association studies (GWAS) in European descent population have identified more than 30 independent single-nucleotide polymorphisms (SNPs) associated with Alzheimer's disease (AD). In the present study, we genotyped 39 AD-risk SNPs in 499 sporadic AD patients and 760 matched healthy controls of Mongolian ethnicity from the Inner Mongolia, China. Further, we investigated whether genetic risk score (GRS) combining multiple AD-risk loci confirmed in our study population could improve AD prediction. Two approaches were used for GRS calculation: a simple risk allele count (cGRS) and a weighted approach (wGRS). The area under the receiver operating characteristic curve (AUC) was used to compare the discriminatory ability of the GRS models. Seven SNPs were confirmed associated with AD and four SNPs were associated with AD risk independent of APOE genotypes in our population. GRS based on either seven SNPs or four APOE-independent SNPs were significantly associated with AD risk (P = 2.3E-17~2.0E-6). The AUC for wGRS was significantly greater than for cGRS (0.6416 versus 0.6339, P = 0.0049 for seven SNPs; 0.5857 versus 0.5765, P = 0.0047 for four APOE-independent SNPs). Furthermore, we found that wGRS combining four APOE-independent SNPs and APOE E4 genotypes reached AUC 0.7023, significantly better than the discriminate ability of APOE E4 genotypes alone (AUC = 0.6699, P = 0.0379). The combined model, with an AUC of 0.6989, significantly higher than that of APOE E4 alone (0.6529) (P = 0.0284), for subjects in a validation cohort comprising 250 cases and 380 controls, randomly selected from our original cohort. In summary, we found that wGRS based on four APOE-independent AD risk SNPs may supplement APOE E4 for better assessing individual risk for AD in Mongolian population in China.

Identification of female-specific genetic variants for metabolic syndrome and its component traits to improve the prediction of metabolic syndrome in females.

BACKGROUND: Metabolic syndrome (MetS), defined as a cluster of metabolic risk factors including dyslipidemia, insulin-resistance, and elevated blood pressure, has been known as partly heritable. MetS effects the lives of many people worldwide, yet females have been reported to be more vulnerable to this cluster of risks. METHODS: To elucidate genetic variants underlying MetS specifically in females, we performed a genome-wide association study (GWAS) for MetS as well as its component traits in a total of 9932 Korean female subjects (including 2276 MetS cases and 1692 controls). To facilitate the prediction of MetS in females, we calculated a genetic risk score (GRS) combining 14 SNPs detected in our GWA analyses specific for MetS. RESULTS: GWA analyses identified 14 moderate signals (Pmeta < 5X10- 5) specific to females for MetS. In addition, two genome-wide significant female-specific associations (Pmeta < 5X10- 8) were detected for rs455489 in DSCAM for fasting plasma glucose (FPG) and for rs7115583 in SIK3 for high-density lipoprotein cholesterol (HDLC). Logistic regression analyses (adjusted for area and age) between the GRS and MetS in females indicated that the GRS was associated with increased prevalence of MetS in females (P = 5.28 × 10- 14), but not in males (P = 3.27 × 10- 1). Furthermore, in the MetS prediction models using GRS, the area under the curve (AUC) of the receiver operating characteristics (ROC) curve was higher in females (AUC = 0.85) than in males (AUC = 0.57). CONCLUSION: This study highlights new female-specific genetic variants associated with MetS and its component traits and suggests that the GRS of MetS variants is a likely useful predictor of MetS in females.

Concept and benchmarks for assessing narrow-sense validity of genetic risk score values.

BACKGROUND: While higher genetic risk score (GRS) has been statistically associated with increased disease risk (broad-sense validity), the concept and tools for assessing the validity of reported GRS values from tests (narrow-sense validity) are underdeveloped. METHODS: We propose two benchmarks for assessing the narrow-sense validity of GRS. The baseline benchmark requires that the mean GRS value in a general population approximates 1.0. The calibration benchmark assesses the agreement between observed risks and estimated risks (GRS values). We assessed benchmark performance for three prostate cancer (PCa) GRS tests, derived from three SNP panels with increasing stringency of selection criteria, in a PCa chemoprevention trial where 714 of 3225 men were diagnosed with PCa during the 4-year follow-up. RESULTS: GRS from Panels 1, 2, and 3 were all statistically associated with PCa risk; P = 5.58 × 10-3 , P = 1 × 10-3 , and P = 1.5 × 10-13 , respectively (broad-sense validity). For narrow-sense validity, the mean GRS value among men without PCa was 1.33, 1.09, and 0.98 for Panels 1, 2, and 3, respectively (baseline benchmark). For assessing the calibration benchmark, observed risks were calculated for seven groups of men with GRS values <0.3, 0.3-0.79, 0.8-1.19, 1.2-1.49, 1.5-1.99, 2-2.99, and ≥3. The calibration slope (higher is better) was 0.15, 0.12, and 0.60, and the bias score (lower is better) between the observed risks and GRS values was 0.08, 0.08, and 0.02 for Panels 1, 2, and 3, respectively. CONCLUSION: Performance differed considerably among GRS tests. We recommend that all GRS tests be evaluated using the two benchmarks before clinical implementation for individual risk assessment.

Screening of noise-induced hearing loss (NIHL)-associated SNPs and the assessment of its genetic susceptibility.

BACKGROUND: The aim of this study was to screen for noise-induced hearing loss (NIHL)-associated single nucleotide polymorphisms (SNPs) and to construct genetic risk prediction models for NIHL in a Chinese population. METHODS: Four hundred seventy-six subjects with NIHL and 476 matched controls were recruited from a cross-sectional survey on NIHL in China. A total of 83 candidate SNPs were genotyped using nanofluidic dynamic arrays on a Fluidigm platform. NIHL-associated SNPs were screened with a multiple logistic model, and a genetic risk model was constructed based on the genetic risk score (GRS). The results were validated using a prospective cohort population. RESULTS: Seven SNPs in the CDH23, PCDH15, EYA4, MYO1A, KCNMA1, and OTOG genes were significantly (P < 0.05) associated with the risk of NIHL, whereas seven other SNPs were marginally (P > 0.05 and P < 0.1) associated with the risk of NIHL. A positive correlation was observed between GRS values and odds ratio (OR) for NIHL. Two SNPs, namely, rs212769 and rs7910544, were validated in the cohort study. Subjects with higher GRS (≧9) showed a higher risk of NIHL incidence with an OR of 2.00 (95% CI = 1.04, 3.86). CONCLUSIONS: Genetic susceptibility plays an important role in the incidence of NIHL. GRS values, which are based on NIHL-associated SNPs. GRS may be utilized in the evaluation of genetic risk for NIHL and in the determination of NIHL susceptibility.

Genetic risk score combining six genetic variants associated with the cellular NRF2 expression levels correlates with Type 2 diabetes in the human population.

BACKGROUND: Type 2 diabetes (T2D) is known as an inflammatory disease. NRF2 (Nuclear Factor Erythroid 2 Like2) encodes a transcription factor that binds to antioxidant response elements (AREs) and regulates the expression of genes involved in many antioxidant responses. OBJECTIVE: This study aimed to gain insight into individual anti-inflammatory activity to prevent T2D development in humans. METHODS: We performed a genome-wide association study (GWAS) to identify genetic variants influencing NRF2 expression in LCLs (lymphoblastoid cell lines) generated from 74 different individuals. Association analyses between T2D or its related traits and genetic risk score (GRS) calculated by combining genetic variants detected from GWAS for cellular NRF2 expression were performed using data from 8715 subjects. The T2D prediction model using GRS was evaluated by measuring the area under the curve (AUC) of the receiver operating characteristics (ROC) curve. RESULTS: Our GWAS identified six genetic variants (SNP) showing suggestive evidence of associations with cellular NRF2 expression (P < 10- 6). Logistic regression analysis demonstrated that GRS was associated with an increased risk of T2D (P value = 0.003, OR = 1.13). In addition, linear regression analyses showed positive associations between GRS and fasting glucose (P value = 0.028, ß = 0.62), 2-h glucose (P value = 0.0004, ß = 1.13) and HbA1C (P value = 0.033, ß = 0.03). In the T2D prediction model using GRS, the AUC of the ROC curve was 0.69. CONCLUSION: This study highlights genetic variants associated with cellular NRF2 expression and suggests that the GRS of NRF2 expression-associated variants is likely to be a useful indicator of T2D development in the human population.

The combined effects of cardiovascular disease related SNPs on ischemic stroke.

PURPOSE: Previous studies have revealed multiple common variants associated with known risk factors for cardiovascular disease (CVD). Ischemic stroke (IS) and CVD share several risk factors with each having substantial heritability. We aimed to generate a multi-locus genetic risk score (GRS) for IS based on CVD related SNPs to evaluate their combined effects on IS. METHODS: A total of 851 patients and 977 controls were selected from Beijing, Tianjin, Shandong, Shanxi, Shaanxi and Heilongjiang communities. The candidate genes were genotyped by PCR-hybridization. Information about demographic factors, history of disease (such as hypertension), and lifestyle was obtained using structured questionnaires. A GRS model weighted by the absolute value of regression coefficient ß was established to comprehensively assess the association between candidate SNPs and IS. Using the area under the receiver operating characteristic curve (AUC) to evaluate the value of GRS on predicting IS. RESULTS: The GRS of cases was 2.87 ±â€¯0.28, which was significantly higher than controls' GRS (2.78 ±â€¯0.30) (P < 0.000). With the increase of the GRS, the risk of IS became higher (Ptrend < 0.000). Subjects in the top quartile of the GRS had about 1.9-fold increased risk of IS compared with subjects in the lowest quartile (OR adjusted = 1.880, 95%CI = 1.442-2.452, P < 0.000). The AUC = 0.580, P < 0.000. CONCLUSION: 13 CVD related SNPs had combined effects on IS. The GRS of cases was significantly higher than controls' GRS. As the GRS increased, the risk of IS increased. The GRS model has some value for the prediction of IS.

Modeling Gene-Environment Interaction for the Risk of Non-hodgkin Lymphoma.

Background: Non-hodgkin lymphoma (NHL) is one of the most common and deadly cancers. There is limited analysis of gene-environment interactions for the risk of NHL. This study intends to explore the interactions between genetic variants and environmental factors, and how they contribute to NHL risk. Methods: A case-control study was performed in Shanghai, China. The cases were diagnosed between 2003 and 2008 with patients aged 18 years or older. Samples and SNPs which did not satisfy quality control were excluded from the analysis. Weighted and unweighted genetic risk scores (GRS) and environmental risk scores were generated using clustering analysis algorithm. Univariate and multivariable logistic regression analyses were conducted. Moreover, genetics and environment interactions (G × E) were tested on the NHL cases and controls. Results: After quality control, there are 22 SNPs, 11 environmental variables and 5 demographical variables to be explored. For logistic regression analyses, 5 SNPs (rs1800893, rs4251961, rs1800630, rs13306698, rs1799931) and environmental tobacco smoking showed statistically significant associations with the risk of NHL. Odds ratio (OR) and 95% confidence interval (CI) was 10.82 (4.34-28.88) for rs13306698, 2.84 (1.66-4.95) for rs1800893, and 2.54 (1.43-4.58) for rs4251961. For G × E analysis, the interaction between smoking and dichotomized weighted GRS showed statistically significant association with NHL (OR = 0.23, 95% CI = [0.09, 0.61]). Conclusions: Several genetic and environmental risk factors and their interactions associated with the risk of NHL have been identified. Replication in other cohorts is needed to validate the results.