The Genetic Architecture of Multimodal Human Brain Age

Wen, Junhao; Zhao, Bingxin; Yang, Zhijian; Erus, Güray; Skampardoni, Ioanna; Mamourian, Elizabeth; Cui, Yuhan; Hwang, Gyujoon; bao, jingxuan; Boquet-Pujadas, Aleix; zhou, zhen; Veturi, Yogasudha; Ritchie, Marylyn D.; Thompson, Paul M.; Shou, Haochang; Shen, Li; Toga, Arthur W.; davatzikos, christos O

doi:10.1101/2023.04.13.536818

Cited by 8 publications

(24 citation statements)

References 133 publications

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“…These findings provide an invaluable foundation to validate genes or regulatory elements, molecular pathways, and biological processes related to the clinical traits and diseases of interest in the current study and future GWAS analyses. Previous GWAS mainly focused on the BAG in one organ system, such as the brain BAG 54–57 from imaging-derived phenotypes. These investigations have largely overlooked the inherent interconnectedness of human organ systems, which are intricately intertwined with distinct axes.…”

Section: Discussionmentioning

confidence: 99%

“…Furthermore, whether chronic diseases and lifestyle factors causally impact the divergence between predicted age and chronological age in these organ systems remains to be established, manifesting as either a younger or older biological age. Our previous genome-wide association study (GWAS) uncovered the genetic heterogeneity of the multimodal brain BAGs using magnetic resonance imaging (MRI) data 14 . Expanding on prior research, the current study sought to comprehensively depict the genetic architecture underlying biological aging across nine human organ systems, including the brain, cardiovascular, eye, hepatic, immune, metabolic, musculoskeletal, pulmonary, and renal BAGs.…”

Section: Mainmentioning

confidence: 99%

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The Genetic Architecture of Biological Age in Nine Human Organ Systems

Wen,

Tian,

Skampardoni

et al. 2023

Preprint

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Understanding the genetic basis of biological aging in multi-organ systems is vital for elucidating age-related disease mechanisms and identifying therapeutic interventions. This study characterized the genetic architecture of the biological age gap (BAG) across nine human organ systems in 377,028 individuals of European ancestry from the UK Biobank. We discovered 393 genomic loci, including 143 novel loci, associated with the BAG of the brain, eye, cardiovascular, hepatic, immune, metabolic, musculoskeletal, pulmonary, and renal systems. We also observed BAG-organ specificity and inter-organ crosstalk. Genetic variants associated with the nine BAGs are predominantly specific to the respective organ system while exerting pleiotropic effects on traits linked to multiple organ systems. A gene-drug-disease network confirmed the involvement of the metabolic BAG-associated genes in drugs targeting various metabolic disorders. Genetic correlation analyses supported Cheverud's Conjecture1. The genetic correlation between BAGs mirrors their phenotypic correlation. A causal network revealed potential causal effects linking chronic diseases (e.g., Alzheimer's disease), body weight, and sleep duration to the BAG of multiple organ systems. Our findings shed light on promising therapeutic interventions to enhance human organ health within a complex multi-organ network, including lifestyle modifications and potential drug repositioning strategies for treating chronic diseases.

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Section: Discussionmentioning

confidence: 99%

Section: Mainmentioning

confidence: 99%

The Genetic Architecture of Biological Age in Nine Human Organ Systems

Wen,

Tian,

Skampardoni

et al. 2023

Preprint

Self Cite

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“…A logical continuation of this work is to develop organ-specific PRS based on genomic data to assess genetic predisposition to either significantly rejuvenated or minimally aged organs as a measure of aging resilience. Efforts in this direction are already in progress, notably in the context of brain age PRS [28].…”

Section: Discussionmentioning

confidence: 99%

AgeML: Age modelling with Machine Learning

Garcia Condado,

Tellaetxe,

Cortes

et al. 2024

Preprint

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A successful approach to age modeling involves the supervised prediction of age using machine learning from subject features. Used for exploring the relationship between healthy and pathological ageing in brain and multiple body systems, as well as the interactions between them, we lack a standard for prediction of age from any generic system. In this work we developed AgeML, an OpenSource software for age-prediction following well-established and tested methodologies from any type of tabular clinical data. The objective is to set standards for reproducibility and standardization of reporting in supervised age modelling tasks. AgeML allows for modelling age and calculating age deltas, the difference between predicted and chronological age, measuring correlations between age deltas and factors, visualising differences in age deltas of different clinical populations and classifying clinical populations based on age deltas. Using the software AgeML, we're demoing its capabilities on a hybrid dataset, reproduce published work, and unveil novel relationships between body organs and polygenetic risk scores. AgeML made easy for standardization and reproducibility.

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“…To adjust for population stratification 68 , we derived the first 40 genetic principle components (PC) using the FlashPCA software 69 . Details of the genetic quality check protocol are described elsewhere 70,71 .…”

Section: Discussionmentioning

confidence: 99%

Five dominant dimensions of brain aging are identified via deep learning: associations with clinical, lifestyle, and genetic measures

Yang,

Wen,

Erus

et al. 2023

Preprint

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Brain aging is a complex process influenced by various lifestyle, environmental, and genetic factors, as well as by age-related and often co-existing pathologies. MRI and, more recently, AI methods have been instrumental in understanding the neuroanatomical changes that occur during aging in large and diverse populations. However, the multiplicity and mutual overlap of both pathologic processes and affected brain regions make it difficult to precisely characterize the underlying neurodegenerative profile of an individual from an MRI scan. Herein, we leverage a state-of-the art deep representation learning method, Surreal-GAN, and present both methodological advances and extensive experimental results that allow us to elucidate the heterogeneity of brain aging in a large and diverse cohort of 49,482 individuals from 11 studies. Five dominant patterns of neurodegeneration were identified and quantified for each individual by their respective (herein referred to as) R-indices. Significant associations between R-indices and distinct biomedical, lifestyle, and genetic factors provide insights into the etiology of observed variances. Furthermore, baseline R-indices showed predictive value for disease progression and mortality. These five R-indices contribute to MRI-based precision diagnostics, prognostication, and may inform stratification into clinical trials.

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The Genetic Architecture of Multimodal Human Brain Age

Cited by 8 publications

References 133 publications

The Genetic Architecture of Biological Age in Nine Human Organ Systems

The Genetic Architecture of Biological Age in Nine Human Organ Systems

AgeML: Age modelling with Machine Learning

Five dominant dimensions of brain aging are identified via deep learning: associations with clinical, lifestyle, and genetic measures

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