De novo mutations across 1,465 diverse genomes reveal mutational insights and reductions in the Amish founder population.

De novo mutations (DNMs), or mutations that appear in an individual despite not being seen in their parents, are an important source of genetic variation whose impact is relevant to studies of human evolution, genetics, and disease. Utilizing high-coverage whole-genome sequencing data as part of the Trans-Omics for Precision Medicine (TOPMed) Program, we called 93,325 single-nucleotide DNMs across 1,465 trios from an array of diverse human populations, and used them to directly estimate and analyze DNM counts, rates, and spectra. We find a significant positive correlation between local recombination rate and local DNM rate, and that DNM rate explains a substantial portion (8.98 to 34.92%, depending on the model) of the genome-wide variation in population-level genetic variation from 41K unrelated TOPMed samples. Genome-wide heterozygosity does correlate with DNM rate, but only explains <1% of variation. While we are underpowered to see small differences, we do not find significant differences in DNM rate between individuals of European, African, and Latino ancestry, nor across ancestrally distinct segments within admixed individuals. However, we did find significantly fewer DNMs in Amish individuals, even when compared with other Europeans, and even after accounting for parental age and sequencing center. Specifically, we found significant reductions in the number of C→A and T→C mutations in the Amish, which seem to underpin their overall reduction in DNMs. Finally, we calculated near-zero estimates of narrow sense heritability (h2), which suggest that variation in DNM rate is significantly shaped by nonadditive genetic effects and the environment.

Age is a predictor of a small decrease in lung function in children with sickle cell anemia.

The longitudinal pattern of lung function in children with sickle cell anemia (SCA) has shown a decrease in FEV1 % predicted, a risk factor for death in adults with SCA, but predictors for this decline are poorly characterized. In a prospective longitudinal multi-center cohort of children with SCA, we tested the hypotheses that: (1) FEV1 % predicted declines over time; and (2) SCA-specific characteristics and therapy predict this decline. At three clinical centers, children with SCA (HbSS or HbSß0 thalassemia), unselected for respiratory disease, were enrolled in the Sleep and Asthma Cohort (SAC) study. Study-certified pulmonary function technicians performed spirometry and lung volumes. Each assessment was reviewed centrally. Predicted values were determined for TLC, FEV1 , FVC, and FEV1 /FVC ratio. A total of 197 participants, mean age 11.0 years at first testing (range 4-19.3 years), had a minimum of three spirometry measurements, over an average of 4.4 years (range 1.1-6.5 years) from baseline to endpoint. In a multivariable model, FEV1 % predicted declines by 0.3% for every additional year of age (95% CI -0.56 to -0.05, P = .020). Sex, asthma history, hemoglobin, reticulocyte count, white blood cell count, incidence rate of severe acute pain and acute chest syndrome episodes, and hydroxyurea therapy were not associated with a decline in FEV1 % predicted. In a large, rigorously evaluated, prospective cohort of an unselected group of children with SCA, FEV1 % predicted declines minimally over an average of 4 years, and none of the examined disease features predict the decline.

Obstructive Sleep Apnea and Subclinical Interstitial Lung Disease in the Multi-Ethnic Study of Atherosclerosis (MESA).

RATIONALE: Obstructive sleep apnea (OSA) has been postulated to contribute to idiopathic pulmonary fibrosis by promoting alveolar epithelial injury via tractional forces and intermittent hypoxia. OBJECTIVES: To determine whether OSA is associated with subclinical interstitial lung disease (ILD) and with biomarkers of alveolar epithelial injury and remodeling. METHODS: We performed cross-sectional analyses of 1,690 community-dwelling adults who underwent 15-channel in-home polysomnography and thoracic computed tomographic imaging in the Multi-Ethnic Study of Atherosclerosis. We measured the obstructive apnea-hypopnea index (oAHI) by polysomnography and high-attenuation areas (HAAs) and interstitial lung abnormalities (ILAs) by computed tomography. Serum matrix metalloproteinase-7 (MMP-7) and surfactant protein-A (SP-A) were measured by ELISA in 99 participants. We used generalized linear models to adjust for potential confounders. RESULTS: The mean age was 68 years, and the mean forced vital capacity was 97% predicted. The median oAHI was 8.4 events/h, and 32% had an oAHI greater than 15. After adjusting for demographics, smoking, and center, an oAHI greater than 15 was associated with a 4.0% HAA increment (95% confidence interval [CI], 1.4-6.8%; P = 0.003) and 35% increased odds of ILA (95% CI, 13-61%; P = 0.001). However, there was evidence that these associations varied by body mass index (BMI) (P for interaction = 0.08 and 0.04, respectively). Among those with a BMI less than 25 kg/m2, an oAHI greater than 15 was associated with a 6.1% HAA increment (95% CI, 0.5-12%; P = 0.03) and 2.3-fold increased odds of ILA (95% CI, 1.3-4.1; P = 0.005). Among those with a BMI greater than 30 kg/m2, an oAHI greater than 15 was associated with 1.8-fold greater odds of ILA (95% CI, 1.1-2.9; P = 0.01) but was not associated with HAA. There were no meaningful associations detected among those with a BMI of 25-30 kg/m2. Greater oAHI was associated higher serum SP-A and MMP-7 levels, particularly among those with a BMI less than 25 kg/m2. CONCLUSIONS: Moderate to severe OSA is associated with subclinical ILD and with evidence of alveolar epithelial injury and extracellular matrix remodeling in community-dwelling adults, an association that is strongest among normal-weight individuals. These findings support the hypothesis that OSA might contribute to early ILD.

MiDas: automatic extraction of a common domain of discourse in sleep medicine for multi-center data integration.

Clinical studies often use data dictionaries with controlled sets of terms to facilitate data collection, limited interoperability and sharing at a local site. Multi-center retrospective clinical studies require that these data dictionaries, originating from individual participating centers, be harmonized in preparation for the integration of the corresponding clinical research data. Domain ontologies are often used to facilitate multi-center data integration by modeling terms from data dictionaries in a logic-based language, but interoperability among domain ontologies (using automated techniques) is an unresolved issue. Although many upper-level reference ontologies have been proposed to address this challenge, our experience in integrating multi-center sleep medicine data highlights the need for an upper level ontology that models a common set of terms at multiple-levels of abstraction, which is not covered by the existing upper-level ontologies. We introduce a methodology underpinned by a Minimal Domain of Discourse (MiDas) algorithm to automatically extract a minimal common domain of discourse (upper-domain ontology) from an existing domain ontology. Using the Multi-Modality, Multi-Resource Environment for Physiological and Clinical Research (Physio-MIMI) multi-center project in sleep medicine as a use case, we demonstrate the use of MiDas in extracting a minimal domain of discourse for sleep medicine, from Physio-MIMI's Sleep Domain Ontology (SDO). We then extend the resulting domain of discourse with terms from the data dictionary of the Sleep Heart and Health Study (SHHS) to validate MiDas. To illustrate the wider applicability of MiDas, we automatically extract the respective domains of discourse from 6 sample domain ontologies from the National Center for Biomedical Ontologies (NCBO) and the OBO Foundry.