ABSTRACT
PURPOSE: The genetic underpinning of neurodevelopmental disorders (NDDs) in diverse ethnic populations, especially those with high rates of consanguinity, remains largely unexplored. Here, we aim to elucidate genomic insight from 576 well phenotyped and highly consanguineous (16%) NDD cohort. METHODS: We employed chromosomal microarray (CMA; N:247), exome sequencing (ES; N:127), combined CMA and ES (N:202) and long-read genome sequencing to identify genetic etiology. Deep clinical multi-variate data was coupled with genomic variants for stratification analysis. RESULTS: Genetic diagnosis rates were 17% with CMA, 29.92% with ES, and 37.13% with combined CMA and ES. Notably, children of consanguineous parents showed a significantly higher diagnostic yield (p<0.01) compared to those from non-consanguineous parents. Among the ES-identified pathogenic variants, 36.19% (38/105) were novel, implicating 35 unique genes. Long-read sequencing of seizure participants unresolved by combined test identified expanded FMR1 trinucleotide repeats. Additionally, we identified two recurrent X-linked variants in the G6PD in 3.65% (12/329) of NDD participants. These variants were absent in large population control cohorts and cohort comprising neurodevelopmental and neuropsychiatric populations of European descendants, indicating a possible associated risk factor potentially resulting from ancient genetic drift. CONCLUSION: This study unveils unique clinical and genomic insights from a consanguinity rich Bangladeshi NDD cohort, highlighting a strong association of G6PD with NDD in this population.
ABSTRACT
The recent development of Automated Traffic Signal Performance Measures (ATSPMs), has provided new opportunities and insights into traffic signal operations. As agencies begin to make decisions regarding investment in infrastructure and operation systems, it is imperative to understand the impacts these systems may have on safety. Past research has thoroughly investigated the impact of geometry and signal timing parameters on the safety of intersections, but little is understood on the relationship between improved signal performance and safety. This study uses vehicle trajectory data to create performance metrics for 121 signalized intersections on ten corridors near Columbus, Ohio. These metrics are used to understand the relationship between signal performance and safety. Two performance metrics, percent arrivals on green (POG) and level of travel time reliability (LOTTR), were used along with other volume and geometric data to model the total crash frequency on signalized mainline approaches. The crash data were modeled using a random parameters negative binomial approach. In consideration of potential unobserved heterogeneity between intersections, a correlated random parameters specification was tested alongside the traditional uncorrelated random parameters and fixed parameters model. Based on goodness of fit measures, the correlated random parameter model was chosen to interpret results because this model explains the complex cross-correlation among the estimates of random parameters. The elasticity values revealed a one percent increase in percent arrivals on green is associated with a reduction in total crashes by 1.12 %. The results of this study show the investment in signal operations and optimization result in an improvement in safety at signalized intersections. Further research should be explored to expand this study to additional intersections over a larger time period.