Variations in complex traits are influenced by multiple genetic variants, environmental risk factors, and their interactions. Though substantial progress has been made in identifying single genetic variants associated with complex traits, detecting the gene-gene and gene-environment interactions remains a great challenge. When a large number of genetic variants and environmental risk factors are involved, searching for interactions is limited to pair-wise interactions due to the exponentially increased feature space and computational intensity. Alternatively, recursive partitioning approaches, such as random forests, have gained popularity in high-dimensional genetic association studies. In this article, we propose a U-Statistic-based random forest approach, referred to as Forest U-Test, for genetic association studies with quantitative traits. Through simulation studies, we showed that the Forest U-Test outperformed existing methods. The proposed method was also applied to study Cannabis Dependence CD, using three independent datasets from the Study of Addiction: Genetics and Environment. A significant joint association was detected with an empirical p-value less than 0.001. The finding was also replicated in two independent datasets with p-values of 5.93e-19 and 4.70e-17, respectively.
Deepti Negi, Penelope M. Tsimbouri, Matthew J. Dalby
et al.
Bone is a dynamic tissue with ecological and evolutionary importance, as it can grow and remodel itself in response to mechanical stimuli. In mammals, osteocytes are widely recognised as the central regulators of bone formation and mechanotransduction. However, many advanced teleosts lack these cells yet still exhibit evidence of bone formation and remodelling. This challenges the prevailing view that osteocytes are indispensable for these processes. Notably, these anosteocytic teleosts exhibit clear responses to mechanical loading, suggesting alternative mechanisms at play. African cichlids, known for their remarkable ecological diversification, which occurs in craniofacial bone morphology. However, these differences are based on very few genetic changes, while including interspecific variation in bone remodeling capacities. Thus, cichlid, being anosteocytic, and variable in remodeling abilities based on very few genetic changes, represents an ideal model system for understanding the mechanisms underlying remodeling. This protocol outlines the development of primary cell cultures from cichlid jaw bones that can be applied across species, establishing a foundation for future research aimed at elucidating the cellular and molecular mechanisms underlying bone formation and remodelling in anosteocytic systems.
Epilepsy encompasses a group of heterogeneous brain diseases that affect more than 50 million people worldwide. Epilepsy may have discernible structural, infectious, metabolic, and immune etiologies; however, in most people with epilepsy, no obvious cause is identifiable. Based initially on family studies and later on advances in gene sequencing technologies and computational approaches, as well as the establishment of large collaborative initiatives, we now know that genetics plays a much greater role in epilepsy than was previously appreciated. Here, we review the progress in the field of epilepsy genetics and highlight molecular discoveries in the most important epilepsy groups, including those that have been long considered to have a nongenetic cause. We discuss where the field of epilepsy genetics is moving as it enters a new era in which the genetic architecture of common epilepsies is starting to be unraveled. Expected final online publication date for the Annual Review of Genomics and Human Genetics, Volume 21 is August 31, 2020. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
Youssef Boulaimen, Gabriele Fossi, Leila Outemzabet
et al.
The classification of genetic variants, particularly Variants of Uncertain Significance (VUS), poses a significant challenge in clinical genetics and precision medicine. Large Language Models (LLMs) have emerged as transformative tools in this realm. These models can uncover intricate patterns and predictive insights that traditional methods might miss, thus enhancing the predictive accuracy of genetic variant pathogenicity. This study investigates the integration of state-of-the-art LLMs, including GPN-MSA, ESM1b, and AlphaMissense, which leverage DNA and protein sequence data alongside structural insights to form a comprehensive analytical framework for variant classification. Our approach evaluates these integrated models using the well-annotated ProteinGym and ClinVar datasets, setting new benchmarks in classification performance. The models were rigorously tested on a set of challenging variants, demonstrating substantial improvements over existing state-of-the-art tools, especially in handling ambiguous and clinically uncertain variants. The results of this research underline the efficacy of combining multiple modeling approaches to significantly refine the accuracy and reliability of genetic variant classification systems. These findings support the deployment of these advanced computational models in clinical environments, where they can significantly enhance the diagnostic processes for genetic disorders, ultimately pushing the boundaries of personalized medicine by offering more detailed and actionable genetic insights.