Genetic association studies using disease liabilities from deep neural networks.

The case-control study is a widely used method for investigating the genetic landscape of binary traits. However, the health-related outcome or disease status of participants in long-term, prospective cohort studies such as the UK Biobank are subject to change. Here, we develop an approach for the genetic association study leveraging disease liabilities computed from a deep patient phenotyping framework (AI-based liability). Analyzing 44 common traits in 261,807 participants from the UK Biobank, we identified novel loci compared to the conventional case-control (CC) association studies. Our results showed that combining liability scores with CC status was more powerful than the CC-GWAS in detecting independent genetic loci across different diseases. This boost in statistical power was further reflected in increased SNP-based heritability estimates. Moreover, polygenic risk scores calculated from AI-based liabilities better identified newly diagnosed cases in the 2022 release of the UK Biobank that served as controls in the 2019 version (6.2% percentile rank increase on average). These findings demonstrate the utility of deep neural networks that are able to model disease liabilities from high-dimensional phenotypic data in large-scale population cohorts. Our pipeline of genome-wide association studies with disease liabilities can be applied to other biobanks with rich phenotype and genotype data.

Estimating the causal effect of liability to disease on healthcare costs using Mendelian Randomization.

Accurate measurement of the effects of disease status on healthcare costs is important in the pragmatic evaluation of interventions but is complicated by endogeneity bias. Mendelian Randomization, the use of random perturbations in germline genetic variation as instrumental variables, can avoid these limitations. We used a novel Mendelian Randomization analysis to model the causal impact on inpatient hospital costs of liability to six prevalent diseases and health conditions: asthma, eczema, migraine, coronary heart disease, Type 2 diabetes, and depression. We identified genetic variants from replicated genome-wide associations studies and estimated their association with inpatient hospital costs on over 300,000 individuals. There was concordance of findings across varieties of sensitivity analyses, including stratification by sex and methods robust to violations of the exclusion restriction. Results overall were imprecise and we could not rule out large effects of liability to disease on healthcare costs. In particular, genetic liability to coronary heart disease had substantial impacts on costs.

Molecular Diagnosis and Genetic Counseling of Cystic Fibrosis and Related Disorders: New Challenges.

Identification of the cystic fibrosis transmembrane conductance regulator (CFTR) gene and its numerous variants opened the way to fantastic breakthroughs in diagnosis, research and treatment of cystic fibrosis (CF). The current and future challenges of molecular diagnosis of CF and CFTR-related disorders and of genetic counseling are here reviewed. Technological advances have enabled to make a diagnosis of CF with a sensitivity of 99% by using next generation sequencing in a single step. The detection of heretofore unidentified variants and ethnic-specific variants remains challenging, especially for newborn screening (NBS), CF carrier testing and genotype-guided therapy. Among the criteria for assessing the impact of variants, population genetics data are insufficiently taken into account and the penetrance of CF associated with CFTR variants remains poorly known. The huge diversity of diagnostic and genetic counseling indications for CFTR studies makes assessment of variant disease-liability critical. This is especially discussed in the perspective of wide genome analyses for NBS and CF carrier screening in the general population, as future challenges.

What can the CF registry tell us about rare CFTR-mutations? A Belgian study.

BACKGROUND: CFTR2 provides clinical and functional information of the most common CFTR-mutations. Rare mutations (RMs) occur in only a few patients with limited reported clinical data. Their role in CF-disease liability is hardly documented. METHODS: Belgian CF-Registry 2013 data were analyzed to identify CF with at least 1 RM (CF+RM). Clinical data and sweat chloride of CF+RM were compared to CF-controls, carrying 2 class 1 to 3 mutations (CFclassic). Disease severity was compared between both groups. To avoid bias in the comparison, transplanted patients were excluded from each group. RESULTS: Seventy-seven CF+RM were identified (77/1183 = 6.5%). Sixty-four different RM were detected, of which 21 had not been previously reported. All RMs, corresponding to HGVS (Human Genome Variation Society) nomenclature, were listed in supplementary data. Seven transplanted CF+RM were excluded for further analysis. CF+RM had higher age at diagnosis [median (IQR)] [3.7 y (0.3-18.3) vs. 0.3y (0.1-2,0) (p < 0.0001)], lower sweat chloride [96 mmol/L (64-107) vs. 104 mmol/L (97-115) (p < 0.0001)], higher FEV1%pred [77%pred (58-96) vs. 68%pred (48-86) (p = 0.017)], were less frequently pancreatic insufficient [56% vs. 98% (p < 0.0001)], Pseudomonas aeruginosa colonized [24% vs. 44% (p = 0.0093)] and needed fewer IV antibiotics [36% vs. 51% (p = 0.041)] than CFclassic. However, a wide spectrum of disease severity was seen amongst CF+RM. CONCLUSIONS: CF-patients with a RM cover 6.5% of the Belgian CF-population. Rare mutations can be found in severely ill patients, but more often in late diagnosed, pancreatic sufficient patients.