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BACKGROUND A current challenge in osteoporosis is identifying patients at risk of bone fracture. PURPOSE To identify the machine learning classifiers that predict best osteoporotic bone fractures and, from the data, to highlight the imaging features and the anatomical regions that contribute most to prediction performance. STUDY TYPE Prospective (cross-sectional) case-control study. POPULATION Thirty-two women with prior fragility bone fractures, of mean age = 61.6 and body mass index (BMI) = 22.7 kg/m2 , and 60 women without fractures, of mean age = 62.3 and BMI = 21.4 kg/m2 . Field Strength/ Sequence: 3D FLASH at 3T. ASSESSMENT Quantitative MRI outcomes by software algorithms. Mechanical and topological microstructural parameters of the trabecular bone were calculated for five femoral regions, and added to the vector of features together with bone mineral density measurement, fracture risk assessment tool (FRAX) score, and personal characteristics such as age, weight, and height. We fitted 15 classifiers using 200 randomized cross-validation datasets. Statistical Tests: Data: Kolmogorov-Smirnov test for normality. Model Performance: sensitivity, specificity, precision, accuracy, F1-test, receiver operating characteristic curve (ROC). Two-sided t-test, with P < 0.05 for statistical significance. RESULTS The top three performing classifiers are RUS-boosted trees (in particular, performing best with head data, F1 = 0.64 ± 0.03), the logistic regression and the linear discriminant (both best with trochanteric datasets, F1 = 0.65 ± 0.03 and F1 = 0.67 ± 0.03, respectively). A permutation of these classifiers comprised the best three performers for four out of five anatomical datasets. After averaging across all the anatomical datasets, the score for the best performer, the boosted trees, was F1 = 0.63 ± 0.03 for All-features dataset, F1 = 0.52 ± 0.05 for the no-MRI dataset, and F1 = 0.48 ± 0.06 for the no-FRAX dataset. Data Conclusion: Of many classifiers, the RUS-boosted trees, the logistic regression, and the linear discriminant are best for predicting osteoporotic fracture. Both MRI and FRAX independently add value in identifying osteoporotic fractures. The femoral head, greater trochanter, and inter-trochanter anatomical regions within the proximal femur yielded better F1-scores for the best three classifiers. LEVEL OF EVIDENCE 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:1029-1038.

Hair provides a direct measure of long-term exposure of atazanavir (ATV). We report the results of the first genome-wide association study (GWAS) of ATV exposure measured in hair in an observational cohort representative of US women living with HIV; the Women's Interagency HIV Study. Approximately 14.1 million single nucleotide polymorphisms (SNPs) were analyzed in linear regression-based GWAS, with replication, adjusted for nongenetic predictors collected under conditions of actual use of ATV in 398 participants. Lastly, the PharmGKB database was used to identify pharmacogene associations with ATV exposure. The rs73208473, within intron 1 of SORCS2, resulted in a 0.46-fold decrease in ATV exposure, with the strongest association (P=1.71×10-8) in GWAS. A priori pharmacogene screening did not identify additional variants statistically significantly associated with ATV exposure, including those previously published in ATV plasma candidate pharmacogene studies. The findings demonstrate the potential value of pharmacogenomic GWAS in ethnically diverse populations under conditions of actual use.

BackgroundIrritable bowel syndrome (IBS) shows genetic predisposition, however, large-scale, powered gene mapping studies are lacking. We sought to exploit existing genetic (genotype) and epidemiological (questionnaire) data from a series of population-based cohorts for IBS genome-wide association studies (GWAS) and their meta-analysis.MethodsBased on questionnaire data compatible with Rome III Criteria, we identified a total of 1335 IBS cases and 9768 asymptomatic individuals from 5 independent European genotyped cohorts. Individual GWAS were carried out with sex-adjusted logistic regression under an additive model, followed by meta-analysis using the inverse variance method. Functional annotation of significant results was obtained via a computational pipeline exploiting ontology and interaction networks, and tissue-specific and gene set enrichment analyses.Key ResultsSuggestive GWAS signals (P5.0x10(-6)) were detected for 7 genomic regions, harboring 64 gene candidates to affect IBS risk via functional or expression changes. Functional annotation of this gene set convincingly (best FDR-corrected P=3.1x10(-10)) highlighted regulation of ion channel activity as the most plausible pathway affecting IBS risk.Conclusion & InferencesOur results confirm the feasibility of population-based studies for gene-discovery efforts in IBS, identify risk genes and loci to be prioritized in independent follow-ups, and pinpoint ion channels as important players and potential therapeutic targets warranting further investigation.

Background Gastroesophageal reflux disease (GERD), the regurgitation of gastric acids often accompanied by heartburn, affects up to 20% of the general population. Genetic predisposition is suspected from twin and family studies but gene-hunting efforts have so far been scarce and no conclusive genome-wide study has been reported. We exploited data available from general population samples, and studied self-reported reflux symptoms in relation to genome-wide single nucleotide polymorphism (SNP) genotypes. Methods We performed a GWAS meta-analysis of three independent population-based cohorts from Sweden, Finland, and UK. GERD cases (n=2247) and asymptomatic controls (n=4503) were identified using questionnaire-derived symptom data. Upon stringent quality controls, genotype data for more than 2.5M markers were used for association testing. Bioinformatic characterization of genomic regions associated with GERD included gene-set enrichment analysis (GSEA), in silico prediction of genetic risk effects on gene expression, and computational analysis of drug-induced gene expression signatures using Connectivity Map (cMap). Key results We identified 30 GERD suggestive risk loci (P≤5×10−5), with concordant risk effects in all cohorts, and predicted functional effects on gene expression in relevant tissues. GSEA revealed involvement of GERD risk genes in biological processes associated with the regulation of ion channel and cell adhesion. From cMap analysis, omeprazole had significant effects on GERD risk gene expression, while antituberculosis and anti-inflammatory drugs scored highest among the repurposed compounds. Conclusions We report a large-scale genetic study of GERD, and highlight genes and pathways that contribute to further our understanding of its pathogenesis and therapeutic opportunities.