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.

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.

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.