Iron is essential for many biological processes, but iron levels must be tightly regulated to avoid harmful effects of both iron deficiency and overload. Here, we perform genome-wide association studies on four iron-related biomarkers (serum iron, serum ferritin, transferrin saturation, total iron-binding capacity) in the Trøndelag Health Study (HUNT), the Michigan Genomics Initiative (MGI), and the SardiNIA study, followed by their meta-analysis with publicly available summary statistics, analyzing up to 257,953 individuals. We identify 123 genetic loci associated with iron traits. Among 19 novel protein-altering variants, we observe a rare missense variant (rs367731784) in HUNT, which suggests a role forDNAJC13in transferrin recycling. We further validate recently published results using genetic risk scores for each biomarker in HUNT (6% variance in serum iron explained) and present linear and non-linear Mendelian randomization analyses of the traits on all-cause mortality. We find evidence of a harmful effect of increased serum iron and transferrin saturation in linear analyses that estimate population-averaged effects. However, there was weak evidence of a protective effect of increasing serum iron at the very low end of its distribution. Our findings contribute to our understanding of the genes affecting iron status and its consequences on human health.

SUMMARY: Identifying individuals at high risk of heart failure during precursor stages could allow for earlier initiation of treatments to modify disease progression. We performed a GWAS meta- analysis to generate a heart failure (HF) polygenic risk score (PRS) then tested the association with phenotypic subtypes (reduced ejection fraction [HFrEF] and preserved ejection fraction [HFpEF]) to evaluate the value of polygenic risk prediction. Results from the European-ancestry analysis showed that an ancestry-matched PRS, calculated from GBMI meta-analysis outperformed the previous HF GWAS (HERMES), yielding an adjusted odds ratio (aOR) of 2.27 (95% CI: 2.05-2.51; p: 1.76×10 ⁻⁵⁶ ) from GBMI compared to 1.30 (95% CI: 1.18-1.44; p: 1.42×10 ^{− 7} ) from HERMES, and 1.49 (95% CI: 1.33-1.66; p: 8.38×10 ⁻¹³ ) compared to 1.17 (95% CI: 1.05- 1.31; p: 0.004) for HFrEF and HFpEF, respectively. Next, we evaluated the performance differences between ancestry-matched and multi-ancestry PRS in the African American cohort. The GBMI multi-ancestry GWAS-based PRS had a significant aOR of 1.49 (p: 0.006). Findings suggest that a PRS for heart failure derived from the GBMI multi-ancestry study is useful in predicting HFrEF, but less powerful in predicting HFpEF in an independent cohort. The difficulty in predicting HFpEF could result from the GBMI HF phenotype, preferencing HFrEF over HFpEF, and/or greater genetic heterogeneity in the HFpEF phenotype.

Glioblastoma multiforme (GBM) has been recognized as the most lethal type of malignant brain tumor. Despite efforts of the medical and research community, patients’ survival remains extremely low. Multi-omic profiles (including DNA sequence, methylation and gene expression) provide rich information about the tumor. These profiles are likely to reveal processes that may be predictive of patient survival. However, the integration of multi-omic profiles, which are high dimensional and heterogeneous in nature, poses great challenges. The goal of this work was to develop models for prediction of survival of GBM patients that can integrate clinical information and multi-omic profiles, using multi-layered Bayesian regressions. We apply the methodology to data from GBM patients from The Cancer Genome Atlas (TCGA, n = 501) to evaluate whether integrating multi-omic profiles (SNP-genotypes, methylation, copy number variants and gene expression) with clinical information (demographics as well as treatments) leads to an improved ability to predict patient survival. The proposed Bayesian models were used to estimate the proportion of variance explained by clinical covariates and omics and to evaluate prediction accuracy in cross validation (using the area under the Receiver Operating Characteristic curve, AUC). Among clinical and demographic covariates, age (AUC = 0.664) and the use of temozolomide (AUC = 0.606) were the most predictive of survival. Among omics, methylation (AUC = 0.623) and gene expression (AUC = 0.593) were more predictive than either SNP (AUC = 0.539) or CNV (AUC = 0.547). While there was a clear association between age and methylation, the integration of age, the use of temozolomide, and either gene expression or methylation led to a substantial increase in AUC in cross-validaton (AUC = 0.718). Finally, among the genes whose methylation was higher in aging brains, we observed a higher enrichment of these genes being also differentially methylated in cancer.