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Luong, J.

Publications and source records attributed to Luong, J..

2 recordsLinked to original sources

Sensory-sleep dysfunction is a shared phenotype across genetically distinct neurodevelopmental disorder models

Sensory abnormalities and sleep disruption frequently co-occur in neurodevelopmental disorders (NDDs), but how sensory input interacts with sleep regulation in NDDs remains poorly understood. Here, we examined vibration-induced sleep (VIS), in which prolonged gentle vibration promotes sleep in Drosophila, in three genetically distinct NDD models: dNf1, dNrx1, and dFmr1. Despite markedly different baseline sleep phenotypes, all three mutants exhibited impaired VIS, identifying disrupted sensory-sleep integration as a shared phenotype across these NDD models. Behavioral responses to vibration, activity-associated CRTC signaling in Nanchung-positive (Nan+) mechanosensory neurons, and thermogenetic activation of Nan+ neurons revealed distinct underlying abnormalities across the three models, indicating that disruption at different points along the sensory-sleep axis can converge on the same behavioral phenotype. The effect of mechanosensory stimulation was also strongly shaped by homeostatic sleep drive: following sleep deprivation, vibration promoted sleep in dNf1 mutants, remained ineffective in dNrx1 mutants, and opposed recovery sleep in dFmr1 mutants. Together, these findings identify impaired sensory regulation of sleep as a point of convergence across genetically distinct NDD models and demonstrate that the expression of this shared phenotype depends on internal state.

neuroscience

Estimating indirect parental genetic effects on offspring phenotypes using virtual parental genotypes derived from sibling and half sibling pairs

Indirect parental genetic effects may be defined as the influence of parental genotypes on offspring phenotypes over and above that which results from the transmission of genes from parents to children. However, given the relative paucity of large-scale family-based cohorts around the world, it is difficult to demonstrate parental genetic effects on human traits, particularly at individual loci. In this manuscript, we illustrate how parental genetic effects on offspring phenotypes, including late onset diseases, can be estimated at individual loci in principle using large-scale genome-wide association study (GWAS) data, even in the absence of parental genotypes. Our strategy involves creating "virtual" mothers and fathers by estimating the genotypic dosages of parental genotypes using physically genotyped data from relative pairs. We then utilize the expected dosages of the parents, and the actual genotypes of the offspring relative pairs, to perform conditional genetic association analyses to obtain asymptotically unbiased estimates of maternal, paternal and offspring genetic effects. We develop a freely available web application that quantifies the power of our approach using closed form asymptotic solutions. We implement our methods in a user-friendly software package IMPISH (IMputing Parental genotypes In Siblings and Half-Siblings) which allows users to quickly and efficiently impute parental genotypes across the genome in large genome-wide datasets, and then use these estimated dosages in downstream linear mixed model association analyses. We conclude that imputing parental genotypes from relative pairs may provide a useful adjunct to existing large-scale genetic studies of parents and their offspring.

genetics