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Tinnitus is a common sensorineural complication that can occur de novo or after cancer treatments involving cisplatin or radiotherapy. Considering the heterogeneous etiology and pathophysiology of tinnitus, the extent to which shared genetic risk factors contribute to de novo tinnitus and cancer treatment-induced tinnitus is not clear. Here we report a GWAS for de novo tinnitus using the UK Biobank cohort with nine loci showing significantly associated variants (p < 5 × 10–8). To our knowledge, significant associations in four of these loci are novel, represented by rs7336872, rs115125870, rs1532898 and rs2537, with UBAC2, NUDT9, TGM4 and MPP2 as their nearest protein coding genes, respectively. Through quantitative comparison of results from GWAS of de novo tinnitus with GWAS of radiation-induced tinnitus, two intronic variants (rs7023227 and rs3780395) from a locus within immunoregulatory gene PD-L1 (CD274) reached the replication threshold using comparison thresholds of 10–5 and 10–4, with no other shared genetic risk factors identified. We did not observe shared genetic risk factors between de novo and cisplatin-induced tinnitus. Our results suggest that genetic risk factors are mainly distinct based on etiology of tinnitus and future efforts to study, prevent or treat tinnitus are expected to benefit from strategies that allow for distinction of cases based on the primary environmental risk factor. [ABSTRACT FROM AUTHOR]

Frailty, a prevalent clinical syndrome in aging adults, is characterized by poor health outcomes, represented via a standardized frailty-phenotype (FP), and Frailty Index (FI). While the relevance of the syndrome is gaining awareness, much remains unclear about its underlying biology. Further elucidation of the genetic determinants and possible underlying mechanisms may help improve patients' outcomes allowing healthy aging. Genotype, clinical and demographic data of subjects (aged 60–73 years) from UK Biobank were utilized. FP was defined on Fried's criteria. FI was calculated using electronic-health-records. Genome-wide-association-studies (GWAS) were conducted and polygenic-risk-scores (PRS) were calculated for both FP and FI. Functional analysis provided interpretations of underlying biology. Finally, machine-learning (ML) models were trained using clinical, demographic and PRS towards identifying frail from non-frail individuals. Thirty-one loci were significantly associated with FI accounting for 12% heritability. Seventeen of those were known associations for body-mass-index, coronary diseases, cholesterol-levels, and longevity, while the rest were novel. Significant genes CDKN2B and APOE, previously implicated in aging, were reported to be enriched in lipoprotein-particle-remodeling. Linkage-disequilibrium-regression identified specific regulation in limbic-system, associated with long-term memory and cognitive-function. XGboost was established as the best performing ML model with area-under-curve as 85%, sensitivity and specificity as 0.75 and 0.8, respectively. This study provides novel insights into increased vulnerability and risk stratification of frailty syndrome via a multi-modal approach. The findings suggest frailty as a highly polygenic-trait, enriched in cholesterol-remodeling and metabolism and to be genetically associated with cognitive abilities. ML models utilizing FP and FI + PRS were established that identified frailty-syndrome patients with high accuracy. [ABSTRACT FROM AUTHOR]