Using clinical and genetic data to predict pulmonary hypertension in bronchopulmonary dysplasia.

AIM: Pulmonary hypertension significantly increases morbidity and mortality in infants with bronchopulmonary dysplasia. The frequency of single nucleotide polymorphisms in arginase-1 (ARG1 rs2781666) and dimethylarginine dimethylaminohydrolase-1 (DDAH1 rs480414) genes has been found to differ in a cohort of bronchopulmonary dysplasia patients with pulmonary hypertension (cases) and without pulmonary hypertension (controls). Therefore, we tested the hypothesis that combining these genotypes with phenotypic data would better predict pulmonary hypertension in bronchopulmonary dysplasia patients. METHODS: Bronchopulmonary dysplasia patients (n = 79) born at <35 weeks gestation were studied. Pulmonary hypertension was diagnosed by echocardiographic criteria (n = 20). ROC curves to predict pulmonary hypertension in bronchopulmonary dysplasia were generated from genotype and/or clinical data. RESULTS: Cases were born at an earlier gestation and weighed less at birth than did controls. ROC curves for rs2781666 had an AUC of 0.61, while rs480414 had an AUC of 0.66. Together, the AUC was 0.70. When clinical data were added to the genetic model, AUC was 0.73. CONCLUSION: These findings demonstrate that ROC predictive modelling of pulmonary hypertension in bronchopulmonary dysplasia improves with inclusion of both genotypic and phenotypic data. Further refinement of these types of models could facilitate the implementation of precision medicine approaches to pulmonary hypertension in bronchopulmonary dysplasia.

A powerful test of independent assortment that determines genome-wide significance quickly and accurately.

In the analysis of DNA sequences on related individuals, most methods strive to incorporate as much information as possible, with little or no attention paid to the issue of statistical significance. For example, a modern workstation can easily handle the computations needed to perform a large-scale genome-wide inheritance-by-descent (IBD) scan, but accurate assessment of the significance of that scan is often hindered by inaccurate approximations and computationally intensive simulation. To address these issues, we developed gLOD-a test of co-segregation that, for large samples, models chromosome-specific IBD statistics as a collection of stationary Gaussian processes. With this simple model, the parametric bootstrap yields an accurate and rapid assessment of significance-the genome-wide corrected P-value. Furthermore, we show that (i) under the null hypothesis, the limiting distribution of the gLOD is the standard Gumbel distribution; (ii) our parametric bootstrap simulator is approximately 40 000 times faster than gene-dropping methods, and it is more powerful than methods that approximate the adjusted P-value; and, (iii) the gLOD has the same statistical power as the widely used maximum Kong and Cox LOD. Thus, our approach gives researchers the ability to determine quickly and accurately the significance of most large-scale IBD scans, which may contain multiple traits, thousands of families and tens of thousands of DNA sequences.

HLA class I and II alleles are associated with microvascular complications of type 1 diabetes.

Although HLA alleles are associated with type 1 diabetes, association with microvascular complications remains controversial. We tested HLA association with complications in multiplex type 1 diabetes families. Probands from 425 type 1 diabetes families from the Human Biological Data Interchange (HBDI) collection were analyzed. The frequencies of specific HLA alleles in patients with complications were compared with the frequencies in complications-free patients. The complications we examined were: retinopathy, neuropathy, and nephropathy. We used logistic regression models with covariates to estimate odds ratios. We found that the DRB1*03:01 allele is a protective factor for complications (OR=0.58; p=0.03), as is the DQA1*05:01-DQB1*02:01 haplotype found in linkage disequilibrium with DRB1*03:01 (OR=0.59; p=0.031). The DRB1*04:01 allele showed no evidence of association (OR=1.13; p=0.624), although DRB1*04:01 showed suggestive evidence when the carriers of the protective DRB1*03:01 were removed from the analysis. The class II DQA1*03:01-DQB1*03:02 haplotype was not associated with complications, but the class I allele B*39:06 (OR=3.27; p=0.008) suggested a strong positive association with complications. Our results show that in type 1 diabetes patients, specific HLA alleles may be involved in susceptibility to, or protection from, microvascular complications.