Prediction of Kv11.1 potassium channel PAS-domain variants trafficking via machine learning.

Congenital long QT syndrome (LQTS) is characterized by a prolonged QT-interval on an electrocardiogram (ECG). An abnormal prolongation in the QT-interval increases the risk for fatal arrhythmias. Genetic variants in several different cardiac ion channel genes, including KCNH2, are known to cause LQTS. Here, we evaluated whether structure-based molecular dynamics (MD) simulations and machine learning (ML) could improve the identification of missense variants in LQTS-linked genes. To do this, we investigated KCNH2 missense variants in the Kv11.1 channel protein shown to have wild type (WT) like or class II (trafficking-deficient) phenotypes in vitro. We focused on KCNH2 missense variants that disrupt normal Kv11.1 channel protein trafficking, as it is the most common phenotype for LQTS-associated variants. Specifically, we used computational techniques to correlate structural and dynamic changes in the Kv11.1 channel protein PAS domain (PASD) with Kv11.1 channel protein trafficking phenotypes. These simulations unveiled several molecular features, including the numbers of hydrating waters and hydrogen bonding pairs, as well as folding free energy scores, that are predictive of trafficking. We then used statistical and machine learning (ML) (Decision tree (DT), Random forest (RF), and Support vector machine (SVM)) techniques to classify variants using these simulation-derived features. Together with bioinformatics data, such as sequence conservation and folding energies, we were able to predict with reasonable accuracy (≈75%) which KCNH2 variants do not traffic normally. We conclude that structure-based simulations of KCNH2 variants localized to the Kv11.1 channel PASD led to an improvement in classification accuracy. Therefore, this approach should be considered to complement the classification of variant of unknown significance (VUS) in the Kv11.1 channel PASD.

Associations of genome-wide and regional autozygosity with 96 complex traits in old order Amish.

BACKGROUND: Autozygosity, the proportion of the genome that is homozygous by descent, has been associated with variation in multiple health-related traits impacting evolutionary fitness. Autozygosity (FROH) is typically measured from runs of homozygosity (ROHs) that arise when identical-by-descent (IBD) haplotypes are inherited from each parent. Population isolates with a small set of common founders have elevated autozygosity relative to outbred populations. METHODS: In this study, we examined whether degree of autozygosity was associated with variation in 96 cardiometabolic traits among 7221 Old Order Amish individuals residing in Lancaster County, PA. We estimated the average length of an ROH segment to be 6350 KB, with each individual having on average 17.2 segments 1.5 KB or larger. Measurements of genome-wide and regional FROH were used as the primary predictors of trait variation in association analysis. RESULTS: In genome-wide FROH analysis, we did not identify any associations that withstood Bonferroni-correction (p = 0.0005). However, on regional FROH analysis, we identified associations exceeding genome-wide thresholds for two traits: serum bilirubin levels, which were significantly associated with a region on chromosome 2 localized to a region surrounding UGT1A10 (p = 1 × 10- 43), and HbA1c levels, which were significantly associated with a region on chromosome 8 localized near CHRNB3 (p = 8 × 10- 10). CONCLUSIONS: These analyses highlight the potential value of autozygosity mapping in founder populations.

Polygenic scores for low lung function and the future risk of adverse health outcomes.

AIMS: Reduced lung function and adverse health outcomes are often observed. This study characterizes genetic susceptibility for reduced lung function and risk of developing a range of adverse health outcomes. METHODS: We studied 27,438 middle-aged adults from the Malmö Diet and Cancer study (MDCS), followed up to 28.8 years. Trait-specific Polygenic scores (PGS) for forced expiratory volume in 1 s (FEV1) and forced vital capacity (FVC) were constructed for each participant using MDCS genetic data and summary statistics from the latest GWAS of lung function. Linear regression models and cox proportional hazards regression models were used to assess associations between adverse health outcomes and lung function-PGS. RESULTS: FEV1-PGS and FVC-PGS were significantly associated with mean sBP at baseline after adjustments (FEV1-PGS Q1 (highest PGS = highest lung function): 140.7mmHg vs. Q4: 141.5mmHg, p-value 0.008). A low FVC-PGS was significantly associated with the risk of future diabetic events after adjustments (Q4 vs. Q1 HR: 1.22 (CI 1.12-1.32), p-trend < 0.001) and had added value to risk prediction models for diabetes. Low FEV1-PGS was significantly associated with future coronary events (Q4 vs. Q1 HR: 1.13 (CI: 1.04-1.22), p-trend 0.008). No significant association was found between PGS and sudden cardiac death, chronic kidney disease or all-cause mortality. Results remained largely unchanged in a subgroup of subjects when further adjusted for apolipoproteins. CONCLUSION: Genetic susceptibility for reduced lung function is associated with higher sBP, increased risk of diabetes and to a lesser extent, future coronary events, suggesting etiological roles of lung function on these outcomes. Using PGS, high-risk groups could be early detected to implement early lifestyle changes to mitigate the risk.

Phenome-wide association analysis suggests the APOL1 linked disease spectrum primarily drives kidney-specific pathways.

The relationship between commonly occurring genetic variants (G1 and G2) in the APOL1 gene in African Americans and different disease traits, such as kidney disease, cardiovascular disease, and pre-eclampsia, remains the subject of controversy. Here we took a genotype-first approach, a phenome-wide association study, to define the spectrum of phenotypes associated with APOL1 high-risk variants in 1,837 African American participants of Penn Medicine Biobank and 4,742 African American participants of Vanderbilt BioVU. In the Penn Medicine Biobank, outpatient creatinine measurement-based estimated glomerular filtration rate and multivariable regression models were used to evaluate the association between high-risk APOL1 status and renal outcomes. In meta-analysis of both cohorts, the strongest phenome-wide association study associations were for the high-risk APOL1 variants and diagnoses codes were highly significant for "kidney dialysis" (odds ratio 3.75) and "end stage kidney disease" (odds ratio 3.42). A number of phenotypes were associated with APOL1 high-risk genotypes in an analysis adjusted only for demographic variables. However, no associations were detected with non-renal phenotypes after controlling for chronic/end stage kidney disease status. Using calculated estimated glomerular filtration rate -based phenotype analysis in the Penn Medicine Biobank, APOL1 high-risk status was associated with prevalent chronic/end stage kidney disease /kidney transplant (odds ratio 2.27, 95% confidence interval 1.67-3.08). In high-risk participants, the estimated glomerular filtration rate was 15.4 mL/min/1.73m2; significantly lower than in low-risk participants. Thus, although APOL1 high-risk variants are associated with a range of phenotypes, the risks for other associated phenotypes appear much lower and in our dataset are driven by a primary effect on renal disease.