As an anatomical extension of the brain, the retina of the eye is synaptically connected to the visual cortex, establishing physiological connections between the eye and the brain. Despite the unique opportunity retinal structures offer for assessing brain disorders, less is known about their relationship to brain structure and function. Here we present a systematic cross-organ genetic architecture analysis of eye-brain connections using retina and brain imaging endophenotypes. Novel phenotypic and genetic links were identified between retinal imaging biomarkers and brain structure and function measures derived from multimodal magnetic resonance imaging (MRI), many of which were involved in the visual pathways, including the primary visual cortex. In 65 genomic regions, retinal imaging biomarkers shared genetic influences with brain diseases and complex traits, 18 showing more genetic overlaps with brain MRI traits. Mendelian randomization suggests that retinal structures have bidirectional genetic causal links with neurological and neuropsychiatric disorders, such as Alzheimer's disease. Overall, cross-organ imaging genetics reveals a genetic basis for eye-brain connections, suggesting that the retinal images can elucidate genetic risk factors for brain disorders and disease-related changes in intracranial structure and function.
Sleep is essential for the health of the brain and heart. Although sleep has been identified as a factor in a few specific clinical outcomes, a systematic analysis of the relationship between sleep and brain/heart and their genetic underpinnings is lacking. Medical images can provide useful clinical endophenotypes for organ structures and functions. Here we present a systematic genetic investigation of sleep-brain/heart connections using multi-modal brain and cardiac images from over 40,000 subjects in the UK Biobank. We identified novel phenotypic and genetic links between sleep and a wide range of imaging traits, such as brain structures, white matter integrity, brain activities, as well as cardiac structures and functions. We prioritized a number of imaging modalities and traits for specific sleep conditions, such as the resting brain function measures in the somatomotor network with narcolepsy. Sleep and imaging had overlapping genetic influences in 39 genomic loci, some of which showed evidence of shared causal genetic variants. In conclusion, large-scale imaging genetic data illuminate the implications of sleep on brain and cardiac health and their genetic links. An interactive web browser (www.ig4sleep.org) has been developed to facilitate exploring our results.
Functional magnetic resonance imaging (fMRI) has been widely used to identify brain regions linked to critical functions, such as language and vision, and to detect tumors, strokes, brain injuries, and diseases. It is now known that large sample sizes are necessary for fMRI studies to detect small effect sizes and produce reproducible results. Here we report a systematic association analysis of 647 traits with imaging features extracted from resting-state and task-evoked fMRI data of more than 40,000 UK Biobank participants. We used a parcellation-based approach to generate 64,620 functional connectivity measures to reveal fine-grained details about cerebral cortex functional architectures. The difference between functional organizations at rest and during task has been quantified, and we have prioritized important brain regions and networks associated with a variety of human traits and clinical outcomes. For example, depression was most strongly associated with decreased connectivity in the somatomotor network. We have made our results publicly available and developed a browser framework to facilitate exploration of brain function-trait association results (http://165.227.92.206/).
Functional and morphological architectures of major human organs have been well characterized using imaging biomarkers. Nevertheless, deciphering the causal relationships between imaging biomarkers and major clinical outcomes, as well as understanding the causal interplay across multiple organs, remains a formidable challenge. Mendelian randomization (MR) presents a framework for inferring causality by using genetic variants as instrumental variables. Here we report a systematic multi-organ MR analysis between 402 imaging biomarkers and 88 clinical outcomes. We identified 488 genetic causal links for 62 diseases and 130 imaging biomarkers from 9 organs, tissue, or systems, including the brain, heart, liver, kidney, lung, pancreas, spleen, adipose tissue, and skeleton system. We prioritized crucial intra-organ causal connections, such as the bidirectional genetic links between Alzheimer's disease and brain function, as well as inter-organ causal effects, such as the adverse impact of heart diseases on brain health. Our findings uncover the genetic causal links spanning multiple organs, offering a more profound understanding of the intricate relationships between organ imaging biomarkers and clinical outcomes.
As large-scale biobanks provide increasing access to deep phenotyping and genomic data, genome-wide association studies (GWAS) are rapidly uncovering the genetic architecture behind various complex traits and diseases. GWAS publications typically make their summary-level data (GWAS summary statistics) publicly available, enabling further exploration of genetic overlaps between phenotypes gathered from different studies and cohorts. However, systematically analyzing high-dimensional GWAS summary statistics for thousands of phenotypes can be both logistically challenging and computationally demanding. In this paper, we introduce BIGA (http://bigagwas.org/), a website that offers unified data analysis pipelines and centralized data resources for cross-trait genetic architecture analyses using GWAS summary statistics. We have developed a framework to implement statistical genetics tools on a cloud computing platform, combined with extensive curated GWAS data resources. Through BIGA, users can upload data, submit jobs, and share results, providing the research community with a convenient tool for consolidating GWAS data and generating new insights.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.