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The microbiome community consists of microbes living in or on an organism and has been implicated in both host health and function. Environmental and host-related drivers of the microbiome have been studied in many fish species, but the role of the host genetic architecture across populations and among-families within a population is not well characterized. Here, Chinook salmon (Oncorhynchus tshawytscha) were used to determine inter-population differences and additive genetic variation within populations for gut microbiome diversity and composition. Specifically, hybrid stocks of Chinook salmon were created by crossing males from eight populations with eggs from an inbred line of self-fertilized hermaphrodite salmon. Based on high-throughput sequencing of the 16S rRNA gene, significant gut microbiome community diversity and composition differences were found among the hybrid stocks. These differences likely reflect divergent selection shaping the gut microbiome and its co-evolution with the host. Furthermore, additive genetic variance components varied among hybrid stocks, indicative of population-specific heritability patterns, suggesting the potential to select for specific gut microbiome composition for aquaculture purposes. Determining the role of host genetics in shaping their gut microbiome has important implications for predicting population responses to environmental changes and will thus impact conservation efforts for declining populations of Chinook salmon.

BackgroundIsolated growth hormone deficiency (IGHD) is caused by a severe shortage or absence of growth hormone (GH), which results in aberrant growth and development. Patients with IGHD type IV (IGHD4) have a short stature, reduced serum GH levels, and delayed bone age.ObjectivesTo identify the causative mutation of IGHD in a consanguineous family comprising four affected patients with IGHD4 (MIM#618157) and explore its functional impact in silico.MethodsClinical and radiological studies were performed to determine the phenotypic spectrum and hormonal profile of the disease, while whole-exome sequencing (WES) and Sanger sequencing were performed to identify the disease-causing mutation. In-silico studies involved protein structural modeling and docking, and molecular dynamic simulation analyses using computational tools. Finally, data from the Qatar Genome Program (QGP) were screened for the presence of the founder variant in the Qatari population.ResultsAll affected individuals presented with a short stature without gross skeletal anomalies and significantly reduced serum GH levels. Genetic mapping revealed a homozygous nonsense mutation [NM_000823:c.G214T:p.(Glu72*)] in the third exon of the growth-hormone-releasing hormone receptor gene GHRHR (MIM#139191) that was segregated in all patients. The substituted amber codon (UAG) seems to truncate the protein by deleting the C-terminus GPCR domain, thus markedly disturbing the GHRHR receptor and its interaction with the growth hormone-releasing hormone.ConclusionThese data support that a p.Glu72* founder mutation in GHRHR perturbs growth hormone signaling and causes IGHD type IV. In-silico and biochemical analyses support the pathogenic effect of this nonsense mutation, while our comprehensive phenotype and hormonal profiling has established the genotype–phenotype correlation. Based on the current study, early detection of GHRHR may help in better therapeutic intervention.

Background: Type 2 Diabetes (T2D) is a pervasive chronic disease influenced by a complex interplay of environmental and genetic factors. To enhance T2D risk prediction, leveraging genetic information is essential, with polygenic risk scores (PRS) offering a promising tool for assessing individual genetic risk. Our study focuses on the comparison between multi-trait and single-trait PRS models and demonstrates how the incorporation of multi-trait PRS into risk prediction models can significantly augment T2D risk assessment accuracy and effectiveness. Methods: We conducted genome-wide association studies (GWAS) on 12 distinct T2D-related traits within a cohort of 14,278 individuals, all sequenced under the Qatar Genome Programme (QGP). This in-depth genetic analysis yielded several novel genetic variants associated with T2D, which served as the foundation for constructing multiple weighted PRS models. To assess the cumulative risk from these predictors, we applied machine learning (ML) techniques, which allowed for a thorough risk assessment. Results: Our research identified genetic variations tied to T2D risk and facilitated the construction of ML models integrating PRS predictors for an exhaustive risk evaluation. The top-performing ML model demonstrated a robust performance with an accuracy of 0.8549, AUC of 0.92, AUC-PR of 0.8522, and an F1 score of 0.757, reflecting its strong capacity to differentiate cases from controls. We are currently working on acquiring independent T2D cohorts to validate the efficacy of our final model. Conclusion: Our research underscores the potential of PRS models in identifying individuals within the population who are at elevated risk of developing T2D and its associated complications. The use of multi-trait PRS and ML models for risk prediction could inform early interventions, potentially identifying T2D patients who stand to benefit most based on their individual genetic risk profile. This combined approach signifies a stride forward in the field of precision medicine, potentially enhancing T2D risk prediction, prevention, and management.