Polygenic Determinants of Arterial Stiffness: Implications for Hypertension

Establishing new approaches for the prevention and treatment of stroke relies on identifying modifiable risk factors that contribute to the development of this complex disease. Mendelian randomization (MR) studies, analogous to naturally occurring randomized trials, can assess causality of potentially modifiable biomarkers and offer new insights into biological pathways. Stroke is the second leading cause of death worldwide and the chief determinant of long-term disability. Stroke is a heterogeneous disease arising from several distinct underlying pathologies and is typically classified as ischemic or hemorrhagic, and further subclassified using imaging data. Ischemic stroke (IS), including its 3 main subtypes: small vessel disease, large vessel disease, and cardioembolic stroke, accounts for ≈80% of stroke and is the result of an interrupted blood supply, leading to localized areas of ischemia in the brain. Small vessel disease may be a consequence of nonatherosclerotic, as well as atherosclerotic, mechanisms that result in an occlusion of the small perforating arteries, whereas large vessel disease results from occlusions or emboli from plaque rupture in larger vessels, such as a carotid artery. Cardioembolic stroke arises typically from emboli from the heart. By contrast, hemorrhagic stroke is a consequence of intracerebral hemorrhage (bleeding into the brain) or subarachnoid hemorrhage (bleeding into the subarachnoid space). These diverse stroke subtypes have distinct underlying pathologies reflecting different risk factor distributions. MR studies, using genetic variants as instrumental variables, afford a powerful approach to assessing causality of risk factors and avoid biases inherent in observational studies, including confounding and reverse causation. This review considers the contribution of MR studies to stroke epidemiology and their relevance to understanding risk factors and new therapeutic targets for stroke. Meta-analyses of large prospective studies have enhanced our knowledge of classical and emerging risk factors for stroke.1–4 Classical risk factors for stroke include nonmodifiable characteristics, …

A Blueprint for Genetic Determinism

Background: Genome-wide association studies (GWAS) have advanced our understanding of the polygenic basis of common, complex disorders. However, translating GWAS findings into clinical applications remains challenging. Polygenic risk scores (PRS) hold promise for clinical use with recent methodologies enhancing predictive accuracy. This study compared two PRS (LDpred-funct which incorporates functional annotations into the score and a traditional linear scoring system) for traits with varying heritability in European- (EA) and African-ancestry (AA) populations, addressing a need to evaluate novel approaches across diverse conditions and populations. Hypothesis: This study hypothesized that integrating functional annotations into PRS derivation will enhance performance compared to assessing the genetic load for a trait. The addition will provide improvement for (1) traits with high (height) compared to moderate (systolic blood pressure, SBP) heritability and (2) individuals with EA relative to AA. Methods: This study used multi-ancestry summary statistics representing data from 4,080,687 individuals for height (GIANT consortium) and 436,099 individuals for SBP adjusted for antihypertensive medication use (UK Biobank). Two PRS for these traits were constructed using LDpred-funct: PRS funct which incorporates the functional annotation and PRS raw which is a simple linear scoring system. Optimization was performed in 8,916 AA and 1,716 EA participants from the Reasons for Geographic and Racial Differences in Stroke (REGARDS) study. PRS predictive performance was evaluated by calculating the proportion of trait variance explained by the PRS (R 2 ) after accounting for age, sex, and genetic ancestry. Results: For height, the PRS funct outperformed PRS raw among both race groups, with a greater improvement among EA (PRS funct R 2 =0.110 versus PRS raw R 2 =0.060, 5% difference) compared to AA (PRS funct R 2 =0.042 versus PRS raw R 2 =0.034, 0.8% difference). Similar findings were observed for medication-adjusted SBP, where PRS funct had better predictive performance than PRS raw , and with greater improvement observed in the EA (PRS funct R 2 =0.042 versus PRS raw R 2 =0.027, 1.5% difference) compared to AA participants (PRS funct R 2 =0.014 versus PRS raw R 2 =0.010, 0.4% difference). Conclusions: Incorporating functional priors into the PRS enhanced the predictive accuracy for both traits. A greater improvement was observed for the higher heritability (height) versus the moderate heritability (SBP) trait, as well as within individuals of EA compared to AA. These findings are currently undergoing validation, using data from the Electronic Medical Records and Genomics (eMERGE) Network, highlighting the potential of incorporating functional annotations into PRS development for broader clinical applications.

Coronary artery calcium score and polygenic risk score have each separately been proposed as novel markers to identify risk of coronary heart disease (CHD), but no prior studies have directly compared these markers in the same cohorts. To evaluate change in CHD risk prediction when a coronary artery calcium score, a polygenic risk score, or both are added to a traditional risk factor-based model. Two observational population-based studies involving individuals aged 45 years through 79 years of European ancestry and free of clinical CHD at baseline: the Multi-Ethnic Study of Atherosclerosis (MESA) study involved 1991 participants at 6 US centers and the Rotterdam Study (RS) involved 1217 in Rotterdam, the Netherlands. Traditional risk factors were used to calculate CHD risk (eg, pooled cohort equations [PCEs]), computed tomography for the coronary artery calcium score, and genotyped samples for a validated polygenic risk score. Model discrimination, calibration, and net reclassification improvement (at the recommended risk threshold of 7.5%) for prediction of incident CHD events were assessed. The median age was 61 years in MESA and 67 years in RS. Both log (coronary artery calcium+1) and polygenic risk score were significantly associated with 10-year risk of incident CHD (hazards ratio per SD, 2.60; 95% CI, 2.08-3.26 and 1.43; 95% CI, 1.20-1.71, respectively), in MESA. The C statistic for the coronary artery calcium score was 0.76 (95% CI, 0.71-0.79) and for the polygenic risk score, 0.69 (95% CI, 0.63-0.71). The change in the C statistic when each was added to the PCEs was 0.09 (95% CI, 0.06-0.13) for the coronary artery calcium score, 0.02 (95% CI, 0.00-0.04) for the polygenic risk score, and 0.10 (95% CI, 0.07-0.14) for both. Overall categorical net reclassification improvement was significant when the coronary artery calcium score (0.19; 95% CI, 0.06-0.28) but was not significant when the polygenic risk score (0.04; 95% CI, -0.05 to 0.10) was added to the PCEs. Calibration of the PCEs and models with coronary artery calcium and/or polygenic risk scores was adequate (all χ2<20). Subgroup analysis stratified by the median age demonstrated similar findings. Similar findings were observed for 10-year risk in RS and in longer-term follow-up in MESA (median, 16.0 years). In 2 cohorts of middle-aged to older adults from the US and the Netherlands, the coronary artery calcium score had better discrimination than the polygenic risk score for risk prediction of CHD. In addition, the coronary artery calcium score but not the polygenic risk score significantly improved risk discrimination and risk reclassification for CHD when added to traditional risk factors.

Objective: Single nucleotide polymorphisms (SNPs) can contribute to differences in the prevalence of common complex diseases such as cardiovascular disease (CVD). The arterial stiffness (AS) is well recognized as an intermediate phenotype of CVD. AS is complex in origin, with contributions from lifestyle and genetic factors. However, the association between SNPs and generally increased AS remains unclear. In the current study we assessed whether or not the multi-locus genetic risk score (GRS) based on selected SNPs are associated with AS in Polish hypertensive cohort. Design and method: The study population included subjects (N = 669, mean age 54,45 ± 13,53) from the Care North cohort. AS was assessed as carotid-femoral pulse wave velocity (PWV). All subjects were genotyped for 171 SNPs previously shown to be associated with CVD and its risk. Linear regression assuming an additive model was applied to identify and compare the effect size of tested SNPs. For GRS construction, SNPs significant at p-value <0.1 were selected. The association between GRS and PWV was analyzed using linear regression and Kruskall-Wallis test in unadjusted and adjusted models. Results: Factors such as age, gender, 24-hour pulse pressure, kidney function and obesity explained 41.41% PWV variance and were included into adjusted model. In unadjusted and adjusted model 15 and 17 SNPs were selected, respectively. SNPs were associated with blood pressure, lipid concentration and risk of myocardial infarction and coronary artery disease. Each genetic risk score explained a greater proportion of PWV variance than any individual SNP (from 0.24 to 0.89% variance explained). Adjusted and unadjusted GRS explained 8.42% and 6.83% PWV variance, respectively. Similar results were obtained by internal validation what confirmed the good quality of models. The linear association between GRS and PWV was observed. The differences between the PWV mean values in quartiles of GRS were highly statistically significant: p-value 2,48 × 10–11 and 8,06 × 10–9 for unadjusted and adjusted models, respectively (Fig.).Conclusions: Our findings indicate that GRS was associated with AS independently of age, gender, blood pressure, kidney function and obesity in Polish hypertensive population. This implies that GRS may help to identify patients at greater risk for increased AS.

Introduction: Heart Failure (HF) has shared genetic architecture with its risk factors, including atrial fibrillation (AF), body mass index (BMI), coronary heart disease (CHD), systolic blood pressure (SBP), and type 2 diabetes (T2D). The risk prediction performance of polygenic risk scores (PRS) for those HF risk factors and HF itself over an established risk equation warrants investigation. Methods: Within the Atherosclerosis Risk in Communities (ARIC) study, six PRSs were constructed for AF, BMI, CHD, SBP, T2D, and HF by summing the product of pre-computed weights from genome-wide association studies and SNP allele dosages in European and African Americans separately. The association between PRSs and incident HF was assessed using cox proportional hazard models, and the 10-, 20-, and 30-year risk prediction performance of PRS over the ARIC HF risk equation was assessed using C-statistics. Associations between AF PRS and HF subtypes, echocardiographic measures, and 4,877 proteins were examined. Results: Over 30 years follow-up, 1,922 (22%) and 735 (29%) HF cases developed in 8,624 European (mean age=54.2, 52% female) and 2,525 African (mean age=53.3, 61% female) Americans. The PRSs for AF and HF were associated with incident HF in both European and African Americans (P&lt;0.05). The AF PRS showed the greatest effect on HF risk in European (HR=1.47, 95% CI: 1.41 to 1.53) and African (HR=1.29, 95% CI: 1.20 to 1.39) Americans. Addition of AF PRS to the ARIC HF risk equation significantly improved the C-statistics for 10-year risk prediction in European (ΔC=0.017, 95% CI: 0.009 to 0.026) and African (ΔC=0.015, 95% CI: 0.004 to 0.026) Americans. The AF PRS was further associated with HF with reduced (HR=1.45, 95% CI: 1.29 to 1.64) and preserved (HR=1.49, 95% CI: 1.34 to 1.66) ejection fraction, and higher left atrial volume index (P=1.67х10 -4 ). Protein analyses revealed that 61 proteins were associated with AF PRS, where NT-proBNP and Coagulation factor X showed the strongest positive and negative associations respectively. Conclusions: The PRS of AF was associated with incident HF, and had significant incremental value over an established HF risk prediction equation. These findings suggest that PRS may be useful in identifying individuals with high risk of HF.

Participant overlap can induce overfitting bias into Mendelian randomization (MR) and polygenic risk score (PRS) studies. Here, we evaluated a block jackknife resampling framework for genome-wide association studies (GWAS) and PRS construction to mitigate overfitting bias in MR analyses and implemented this study design in a causal inference setting using data from the UK Biobank. We simulated PRS and MR under three scenarios: (1) using weighted SNP estimates from an external GWAS, (2) using weighted SNP estimates from an overlapping GWAS sample and (3) using a block jackknife resampling framework. Based on a P-value threshold to derive genetic instruments for MR studies (P < 5 × 10−8) and a 10% variance in the exposure explained by all SNPs, block-jackknifing PRS did not suffer from overfitting bias (mean R2 = 0.034) compared with the externally weighted PRS (mean R2 = 0.040). In contrast, genetic instruments derived from overlapping samples explained a higher variance (mean R2 = 0.048) compared with the externally derived score. Overfitting became considerably more severe when using a more liberal P-value threshold to construct PRS (e.g. P < 0.05, overlapping sample PRS mean R2 = 0.103, externally weighted PRS mean R2 = 0.086), whereas estimates using jackknife score remained robust to overfitting (mean R2 = 0.084). Using block jackknife resampling MR in an applied analysis, we examined the effects of body mass index on circulating biomarkers which provided comparable estimates to an externally weighted instrument, whereas the overfitted scores typically provided narrower confidence intervals. Furthermore, we extended this framework into sex-stratified, multivariate and bidirectional settings to investigate the effect of childhood body size on adult testosterone levels.

Background:Postoperative nausea and vomiting (PONV) is a key driver of unplanned admission and patient satisfaction after surgery. Because traditional risk factors do not completely explain variability in risk, this study hypothesized that genetics may contribute to the overall risk for this complication. The objective of this research is to perform a genome-wide association study of PONV, derive a polygenic risk score for PONV, assess associations between the risk score and PONV in a validation cohort, and compare any genetic contributions to known clinical risks for PONV.Methods:Surgeries with integrated genetic and perioperative data performed under general anesthesia at Michigan Medicine (Ann Arbor, Michigan) and Vanderbilt University Medical Center (Nashville, Tennessee) were studied. PONV was defined as nausea or emesis occurring and documented in the postanesthesia care unit. In the discovery phase, genome-wide association studies were performed on each genetic cohort, and the results were meta-analyzed. Next, the polygenic phase assessed whether a polygenic score, derived from genome-wide association study in a derivation cohort from Vanderbilt University Medical Center, improved prediction within a validation cohort from Michigan Medicine, as quantified by discrimination (c-statistic) and net reclassification index.Results:Of 64,523 total patients, 5,703 developed PONV (8.8%). The study identified 46 genetic variants exceeding the threshold of P < 1 × 10−5, occurring with minor allele frequency greater than 1%, and demonstrating concordant effects in both cohorts. Standardized polygenic score was associated with PONV in a basic model, controlling for age and sex (adjusted odds ratio, 1.027 per SD increase in overall genetic risk; 95% CI, 1.001 to 1.053; P = 0.044), a model based on known clinical risks (adjusted odds ratio, 1.029; 95% CI, 1.003 to 1.055; P = 0.030), and a full clinical regression, controlling for 21 demographic, surgical, and anesthetic factors, (adjusted odds ratio, 1.029; 95% CI, 1.002 to 1.056; P = 0.033). The addition of polygenic score improved overall discrimination in models based on known clinical risk factors (c-statistic, 0.616 compared to 0.613; P = 0.028) and improved net reclassification of 4.6% of cases.Conclusions:Standardized polygenic risk was associated with PONV in all three of the study’s models, but the genetic influence was smaller than exerted by clinical risk factors. Specifically, a patient with a polygenic risk score greater than 1 SD above the mean has 2 to 3% greater odds of developing PONV when compared to the baseline population, which is at least an order of magnitude smaller than the increase associated with having prior PONV or motion sickness (55%), having a history of migraines (17%), or being female (83%) and is not clinically significant. Furthermore, the use of a polygenic risk score does not meaningfully improve discrimination compared to clinical risk factors and is not clinically useful.

Objective: To explore the association between genetic variants of matrix metalloproteinase enzyme 2 (MMP2) gene and the blood pressure of children and adolescents. Methods: This cross-sectional study was performed in 2016 and included 4 155 children and adolescents in the urban area of Guangzhou. Physical examinations (including body height, weight, and blood pressure), questionnaires (including general characteristics, physical exercise, parental educational level, household income, etc.), and blood sampling were performed. Multivariable linear regression models were used to investigate the associations of MMP2 genetic variations (rs243865, rs7201) and the genetic risk score (GRS) level with standardized blood pressure. Mediating effect of standardized body mass index (BMI) was further assessed by process analysis in the association between GRS level and blood pressure, and potential additive interaction between physical activity and GRS level was analyzed using the product term in the regression model. Results: A total of 4 155 primary and secondary schoolchildren were finally included in the analysis, consisting of 1 401 (33.7%) second grade pupils of primary school, 1 422 (34.2%) first grade pupils of middle school, and 1 332 (32.1%) first-grade students of senior high school. After adjusting for age, sex, parental educational level, and family income, as compared to the rs243865 TT genotype, the CC/CT genotype increased diastolic blood pressure (DBP) by 0.461 standard deviations (SD) (β for dominant model=0.461, 95%CI 0.199-0.723). When compared to the rs7201 CC genotype, the AA/AC genotype showed 0.147 SD higher systolic blood pressure (SBP) (β for recessive model=0.147, 95%CI 0.014-0.279) and 0.171 SD increased DBP (β for recessive model=0.171, 95%CI 0.039-0.304). For each increment of GRS level, SBP and DBP increased by 0.151 SD (β for dominant model=0.151, 95%CI 0.029-0.272) and 0.242 SD (β=0.242, 95%CI 0.120-0.363), respectively. The mediating effect of BMI accounted for 28.3% and 12.6% of the total effect of GRS on SBP and DBP, respectively. After controlling BMI, the direct effect of GRS on DBP remained statistically significant (P<0.001). The insufficient moderate-to-vigorous physical activity (<0.5 h/d) showed a significant interaction with GRS on SBP under additive scale (β for interaction=0.518, 95%CI 0.088-0.949, P=0.018). Conclusions: rs243865 and rs7201 variants in MMP2 gene are associated with the elevated blood pressure of children and adolescents. Obesity may yield a mediation role in the associations, while insufficient physical activity may have a positively additive interaction with MMP2 genetic variants.

Background To investigate the difference of carotid structural abnormality and stiffness, and the relationship between carotid structural abnormality and stiffness in prehypertensive and normotensive subjects. Methods A total of 581 participants (270 with prehypertension, 311 normotensive control subjects) from the First Affiliated Hospital of Fujian Medical University were enrolled from January 2017 to March 2019. Body height/weight, resting heart rate, and blood pressure were recorded. Blood biochemical indexes and carotid–femoral pulse wave velocity were determined, and carotid ultrasonography was performed. Carotid intima–media thickness ≥1.0 mm and carotid–femoral pulse wave velocity ≥10.0 m/s were defined as carotid structural abnormality and arterial stiffness, respectively. Results The percentage of patients with carotid structural abnormality (60.7% vs. 51.4%), carotid–femoral pulse wave velocity [(8.78 ± 1.48) vs. (7.92 ± 1.30) m/s], and the percentage of patients with arterial stiffness (13.7% vs. 5.8%) were increased in the prehypertension group compared with the control group (all P &amp;lt; 0.05). In the prehypertension group, the percentage of patients with arterial stiffness in the abnormal carotid structure subgroup (n = 164) was higher than that in the normal carotid structure subgroup (n = 106) (20.1% vs. 3.8%, χ2 = 14.551, P &amp;lt; 0.001). Furthermore, carotid structural abnormality, age, diabetes mellitus, resting heart rate, systolic blood pressure, fasting blood glucose, glycosylated hemoglobin A1c, and hypoglycemic therapy were all correlated with arterial stiffness (all P &amp;lt; 0.05). After adjusting for other cardiovascular risk factors, multiple logistic regression analysis showed that carotid structural abnormality, age, diabetes mellitus, and resting heart rate were independently correlated with arterial stiffness, and patients with carotid structural abnormality had 5.25-fold higher risk of arterial stiffness than those with a normal carotid arterial structure (odds ratio = 5.250, 95% confidence interval 1.580–17.448, P = 0.007). However, in the normotensive group, no such relationships were observed between carotid artery structural abnormality and stiffness. Conclusions In prehypertensives but not normotensives, carotid artery structural abnormalities and stiffness are common, and independently correlated.

BackgroundArterial stiffness is associated with pathological changes underlying Alzheimer disease and related dementias. Total pulse wave velocity can be subdivided into 2 main mechanisms: structural arterial stiffness (S‐PWV) due to intrinsic remodeling of the artery wall and load‐dependent arterial stiffness due to increased blood pressure.Methods and ResultsIn this prospective cohort study, MESA (Multi‐Ethnic Study of Atherosclerosis) participants completed B‐mode carotid ultrasounds from which carotid total pulse wave velocity was calculated. S‐PWV was calculated by adjusting pulse wave velocity to 120/80 mmHg using a nonlinear pressure‐diameter relationship, and load‐dependent arterial stiffness was derived by subtracting S‐PWV from total pulse wave velocity. Participants had repeated cognitive assessments with the Cognitive Abilities Screening Instrument, Digit Symbol Coding, and Digit Span combined into a global cognitive composite (N=2489). Brain magnetic resonance imaging was used to generate total gray matter volume (N=906), white matter hyperintensity volume (N=896), and total white matter fractional anisotropy (N=810). Multivariable linear fixed and mixed effects regression models related standardized pulse wave velocity components to neuroimaging and cognitive decline parameters, respectively. Greater S‐PWV was associated with greater longitudinal cognitive decline in global cognitive composite score (β=–0.05, P=0.002) and subtests, whereas greater load‐dependent arterial stiffness was not associated with longitudinal cognitive decline. Greater S‐PWV was associated with lower gray matter volume (β=–3183.4, P=0.013) and higher log white matter hyperintensity volume (β=0.20, P<0.001), whereas load‐dependent arterial stiffness was associated with lower total white matter fractional anisotropy (β=–0.004, P≤0.001).ConclusionsHigher structural stiffness of the carotid artery is associated with cognitive decline, whereas both structural and load‐dependent stiffness are associated with brain structural abnormalities common in Alzheimer disease‐related dementias.

Growing research suggests that posttraumatic stress disorder (PTSD) may be a risk factor for poor cardiovascular health, and yet our understanding of who might be at greatest risk of adverse cardiovascular outcomes after trauma is limited. In this study, we conducted the first examination of the individual and synergistic contributions of PTSD symptoms and blood pressure genetics to continuous blood pressure levels. We harnessed the power of the Psychiatric Genomics Consortium-PTSD Physical Health Working Group and investigated these associations across 11 studies of 72,224 trauma-exposed individuals of European (n = 70,870) and African (n = 1,354) ancestry. Genetic contributions to blood pressure were modeled via polygenic scores (PGS) for systolic blood pressure (SBP) and diastolic blood pressure (DBP) that were derived from a prior trans-ethnic blood pressure genome-wide association study (GWAS). Results of trans-ethnic meta-analyses revealed significant main effects of the PGS on blood pressure levels [SBP: β = 2.83, standard error (SE) = 0.06, p < 1E-20; DBP: β = 1.32, SE = 0.04, p < 1E-20]. Significant main effects of PTSD symptoms were also detected for SBP and DBP in trans-ethnic meta-analyses, though there was significant heterogeneity in these results. When including data from the largest contributing study – United Kingdom Biobank – PTSD symptoms were negatively associated with SBP levels (β = −1.46, SE = 0.44, p = 9.8E-4) and positively associated with DBP levels (β = 0.70, SE = 0.26, p = 8.1E-3). However, when excluding the United Kingdom Biobank cohort in trans-ethnic meta-analyses, there was a nominally significant positive association between PTSD symptoms and SBP levels (β = 2.81, SE = 1.13, p = 0.01); no significant association was observed for DBP (β = 0.43, SE = 0.78, p = 0.58). Blood pressure PGS did not significantly moderate the associations between PTSD symptoms and blood pressure levels in meta-analyses. Additional research is needed to better understand the extent to which PTSD is associated with high blood pressure and how genetic as well as contextual factors may play a role in influencing cardiovascular risk.

We aimed to detect cross-sectional phenotype and polygenic risk score (PRS) associations between sleep duration and prevalent diseases using the Partners Biobank, a hospital-based cohort study linking electronic medical records (EMR) with genetic information. Disease prevalence was determined from EMR, and sleep duration was self-reported. A PRS for sleep duration was derived using 78 previously associated SNPs from genome-wide association studies (GWAS) for self-reported sleep duration. We tested for associations between (1) self-reported sleep duration and 22 prevalent diseases (n = 30 251), (2) the PRS and self-reported sleep duration (n = 6903), and (3) the PRS and the 22 prevalent diseases (n = 16 033). For observed PRS-disease associations, we tested causality using two-sample Mendelian randomization (MR). In the age-, sex-, and race-adjusted model, U-shaped associations were observed for sleep duration and asthma, depression, hypertension, insomnia, obesity, obstructive sleep apnea, and type 2 diabetes, where both short and long sleepers had higher odds for these diseases than normal sleepers (p < 2.27 × 10-3). Next, we confirmed associations between the PRS and longer sleep duration (0.65 ± 0.19 SD minutes per effect allele; p = 7.32 × 10-04). The PRS collectively explained 1.4% of the phenotypic variance in sleep duration. After adjusting for age, sex, genotyping array, and principal components of ancestry, we observed that the PRS was also associated with congestive heart failure (CHF; p = 0.015), obesity (p = 0.019), hypertension (p = 0.039), restless legs syndrome (RLS; p = 0.041), and insomnia (p = 0.049). Associations were maintained following additional adjustment for obesity status, except for hypertension and insomnia. For all diseases, except RLS, carrying a higher genetic burden of the 78 sleep duration-increasing alleles (i.e. higher sleep duration PRS) associated with lower odds for prevalent disease. In MR, we estimated causal associations between genetically defined longer sleep duration with decreased risk of CHF (inverse variance weighted [IVW] OR per minute of sleep [95% CI] = 0.978 [0.961-0.996]; p = 0.019) and hypertension (IVW OR [95% CI] = 0.993 [0.986-1.000]; p = 0.049), and increased risk of RLS (IVW OR [95% CI] = 1.018 [1.000-1.036]; p = 0.045). By validating the PRS for sleep duration and identifying cross-phenotype associations, we lay the groundwork for future investigations on the intersection between sleep, genetics, clinical measures, and diseases using large EMR datasets.

Polygenic Risk Scores for Follow Up After Colonoscopy and Polypectomy: Another Tool for Risk Stratification and Planning Surveillance?

Mendelian randomization (MR), polygenic risk score (PRS), Geno Ontology (GO), and the Kyoto Encyclopedia of Genes and Genomes (KEGG) are powerful bioinformatic analysis tools. However, the analysis of MR, PRS, GO, and KEGG may pose a challenge for novices. This article intends to introduce a program that adeptly guides beginners in implementing these analysis functions, ensuring that even those new to the field can confidently use them. The MPGK program was developed to run on the command line. It conveniently implements the MR, PRS, GO, and KEGG analysis functions by calling well-written R programs. The results of our analyses were validated using genome-wide association study (GWAS) summary data for diabetes and psoriasis, as well as gene sequencing data for diabetes. Three demo analyses using the MPGK program demonstrate the comprehensive capabilities of the MPGK program in conducting advanced bioinformatics analysis. First, the MPGK program revealed a causal relationship between diabetes and psoriasis. Additionally, the PRS analysis generated polygenic risk scores for diabetes, demonstrating the implementation of PRS analysis within the MPGK framework. Furthermore, the GO and KEGG analyses indicated that psoriasis is associated with infection and T helper 17 cells. These findings are consistent with the previous literature. MPGK can be easily used to perform comprehensive analysis, including MR, PRS, GO, and KEGG analyses, by both beginners and researchers.