Bayesian Longitudinal Network Regression With Application to Brain Connectome Genetics
Fuente:
Wiley Open Access
Gespeichert in:
| Hauptverfasser: | , , , , , , |
|---|---|
| Format: | Artículo Open Access |
| Veröffentlicht: |
Wiley
2025
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1867008063860375552 |
|---|---|
| author | Chenxi Li Xinyuan Tian Simiao Gao Selena Wang Gefei Wang Yi Zhao Yize Zhao |
| author_facet | Chenxi Li Xinyuan Tian Simiao Gao Selena Wang Gefei Wang Yi Zhao Yize Zhao Chenxi Li Xinyuan Tian Simiao Gao Selena Wang Gefei Wang Yi Zhao Yize Zhao |
| collection | Wiley Open Access |
| contents | Bayesian Longitudinal Network Regression With Application to Brain Connectome Genetics Chenxi Li Xinyuan Tian Simiao Gao Selena Wang Gefei Wang Yi Zhao Yize Zhao Statistics in Medicine ABSTRACTThe increasing availability of large‐scale brain imaging genetics studies enables more comprehensive exploration of the genetic underpinnings of brain functional organizations. However, fundamental analytical challenges arise when considering the complex network topology of brain functional connectivity, influenced by genetic contributions and sample relatedness, particularly in longitudinal studies. In this paper, we propose a novel method named Bayesian Longitudinal Network‐Variant Regression (BLNR), which models the association between genetic variants and longitudinal brain functional connectivity. BLNR fills the gap in existing longitudinal genome‐wide association studies that primarily focus on univariate or multivariate phenotypes. Our approach jointly models the biological architecture of brain functional connectivity and the associated genetic mixed‐effect components within a Bayesian framework. By employing plausible prior settings and posterior inference, BLNR enables the identification of significant genetic signals and their associated brain sub‐network components, providing robust inference. We demonstrate the superiority of our model through extensive simulations and apply it to the Adolescent Brain Cognitive Development (ABCD) study. This application highlights BLNR's ability to estimate the genetic effects on changes in brain network configurations during neurodevelopment, demonstrating its potential to extend to other similar problems involving sample relatedness and network‐variate outcomes. 10.1002/sim.70069 http://onlinelibrary.wiley.com/termsAndConditions#vor |
| doi_str_mv | 10.1002/sim.70069 |
| format | Artículo Open Access |
| id | wiley_oa_10_1002_sim_70069 |
| institution | Wiley Open Access |
| license_str_mv | http://onlinelibrary.wiley.com/termsAndConditions#vor |
| publishDate | 2025 |
| publisher | Wiley |
| record_format | wiley_oa |
| spellingShingle | Bayesian Longitudinal Network Regression With Application to Brain Connectome Genetics Chenxi Li Xinyuan Tian Simiao Gao Selena Wang Gefei Wang Yi Zhao Yize Zhao Statistics in Medicine Bayesian Longitudinal Network Regression With Application to Brain Connectome Genetics Chenxi Li Xinyuan Tian Simiao Gao Selena Wang Gefei Wang Yi Zhao Yize Zhao Statistics in Medicine ABSTRACTThe increasing availability of large‐scale brain imaging genetics studies enables more comprehensive exploration of the genetic underpinnings of brain functional organizations. However, fundamental analytical challenges arise when considering the complex network topology of brain functional connectivity, influenced by genetic contributions and sample relatedness, particularly in longitudinal studies. In this paper, we propose a novel method named Bayesian Longitudinal Network‐Variant Regression (BLNR), which models the association between genetic variants and longitudinal brain functional connectivity. BLNR fills the gap in existing longitudinal genome‐wide association studies that primarily focus on univariate or multivariate phenotypes. Our approach jointly models the biological architecture of brain functional connectivity and the associated genetic mixed‐effect components within a Bayesian framework. By employing plausible prior settings and posterior inference, BLNR enables the identification of significant genetic signals and their associated brain sub‐network components, providing robust inference. We demonstrate the superiority of our model through extensive simulations and apply it to the Adolescent Brain Cognitive Development (ABCD) study. This application highlights BLNR's ability to estimate the genetic effects on changes in brain network configurations during neurodevelopment, demonstrating its potential to extend to other similar problems involving sample relatedness and network‐variate outcomes. 10.1002/sim.70069 http://onlinelibrary.wiley.com/termsAndConditions#vor |
| title | Bayesian Longitudinal Network Regression With Application to Brain Connectome Genetics |
| topic | Statistics in Medicine |
| url | https://onlinelibrary.wiley.com/doi/10.1002/sim.70069 |