genetic-environmental-connectome

name	genetic-environmental-connectome
description	Genetic and environmental architecture of human functional connectome using extended twin modeling. Separates measurement error from non-shared environment to estimate true connectivity heritability. Keywords: functional connectome, twin modeling, heritability, genetic architecture, brain connectivity.

Genetic and Environmental Architecture of Human Functional Connectome

An extended twin modeling framework that accurately estimates the genetic and environmental contributions to functional connectivity by separating measurement error from true non-shared environmental variance, revealing the true heritability of brain functional networks.

Metadata

• Source: arXiv:2604.24614v1
• Authors: Tanu Raghav, Daniel Guerrero, Uttara Tipnis, Jinyi He, Tian Ge, Shashwath Meda, Vince Calhoun, Godfrey Pearlson, Jingyu Liu
• Published: 2026-04-27
• Category: Behavioral Genetics / Neuroimaging

Core Methodology

Key Innovation

Classical twin models confound measurement error with non-shared environment, leading to underestimation of heritability. This work introduces an Extended Twin Model (ETM) that:

1. Uses repeated measures to estimate measurement error
2. Separates true non-shared environment from noise
3. Provides accurate heritability estimates for functional connectivity
4. Reveals distinct genetic architectures for different brain networks

Technical Framework

1. Extended Twin Model Structure

Traditional Model:        Extended Model:
Phenotype = G + C + E     Phenotype = G + C + E_true + Error
                              ↓
                         (Uses repeated measures
                          to estimate Error)

Path Diagram:

      A1 (Additive Genetic)
        ↗
       /    C (Common Environment)
      /        ↓
   FC1 ←───────┼──────→ FC2
      \        ↑
       \    E_true
        ↘  (True Non-shared Env)
      
   E_m (Measurement Error)
   ↑              ↑
  Rep1           Rep2
 (Repeated Measures)

2. Variance Decomposition

The model decomposes functional connectivity variance into:

• A (Additive Genetic): Shared genetic factors
• C (Common Environment): Shared environmental factors
• E_true (True Non-shared Environment): Real individual differences
• E_m (Measurement Error): Technical and physiological noise

3. Model Specification

ACE Model with Error:

Var(FC) = a² + c² + e_true² + e_m²

Where:
- a² = Additive genetic variance
- c² = Common environment variance
- e_true² = True non-shared environment variance
- e_m² = Measurement error variance

4. Connectome-Wide Analysis

• Edge-wise: Heritability for each functional connection
• Network-wise: Aggregate by functional networks (DMN, FPN, etc.)
• Graph metrics: Heritability of global network properties

Implementation Guide

Prerequisites

• R with OpenMx package for structural equation modeling
• Python with nilearn, scipy for preprocessing
• Resting-state fMRI data from twin samples
• Repeated measures (minimum 2 scans per subject)

Step-by-Step Implementation

Step 1: fMRI Preprocessing

import nibabel as nib
from nilearn import image, signal, connectivity
import numpy as np

def preprocess_fmri(func_file, atlas):
    """
    Preprocess resting-state fMRI and extract time series.
    
    Args:
        func_file: Path to 4D fMRI nifti file
        atlas: Parcellation atlas (e.g., AAL, Schaefer)
        
    Returns:
        time_series: [n_regions, n_timepoints] extracted signals
        fc_matrix: [n_regions, n_regions] correlation matrix
    """
    # Load and preprocess
    img = nib.load(func_file)
    
    # Motion correction, slice timing, normalization
    # (Assuming preprocessing pipeline completed)
    
    # Extract time series using atlas
    time_series = connectivity.extract_time_series(img, atlas)
    
    # Detrend and filter
    time_series = signal.clean(
        time_series.T,
        detrend=True,
        standardize=True,
        low_pass=0.1,
        high_pass=0.01,
        t_r=2.0
    ).T
    
    # Compute functional connectivity
    fc_matrix = np.corrcoef(time_series)
    
    # Fisher z-transform
    fc_matrix = np.arctanh(fc_matrix)
    
    return time_series, fc_matrix

Step 2: Extended Twin Model in R

library(OpenMx)

# Define Extended Twin Model with Measurement Error
etm_model <- function(data, zygosity) {
  
  # Create matrices for path coefficients
  a <- mxMatrix(type = "Full", nrow = 1, ncol = 1, 
                free = TRUE, values = 0.5, name = "a")
  c <- mxMatrix(type = "Full", nrow = 1, ncol = 1,
                free = TRUE, values = 0.3, name = "c")
  e <- mxMatrix(type = "Full", nrow   ncol  
                free   values   name  
  m  mxMatrixtype   nrow   ncol  
                free   values   name  
  
  
  
  cov_mz  mxAlgebra
      a  ta    t  e  te  m  tm
    name  
  
  
  
  cov_dz  mxAlgebra
        a  ta    t  m  tm
    name  
  
  
  
  
  
  
  model  mxModel
    
    a  e m
    cov_mz cov_dz
    mxDatadata type  
    mxExpectationNormal
      covariance  
           
    
    mxFitFunctionML
  
  
  mxRunmodel



fit_connectome_wide  fc_data zygosity 
  n_edges  ncolfc_data
  results  data.frame
    edge  n_edges
    h2  numericn_edges
    c2  numericn_edges
    e2  numericn_edges
    m2  numericn_edges
  
  
   i  n_edges 
    
    edge_data  fc_data i
    
    
    fit  tryCatch
      etm_modeledge_data zygosity
     error  e 
    
     fit 
      resultsh2i  fitoutputestimate
      resultsc2i  fitoutputestimate
      resultse2i  fitoutputestimate
      resultsm2i  fitoutputestimate
    
  
  
  results

Step 3: Python Wrapper

import rpy2.robjects as ro
from rpy2.robjects import pandas2ri
import pandas as pd
import numpy as np

class ExtendedTwinModel:
    """
    Python wrapper for Extended Twin Model analysis.
    """
    
    def __init__(self):
        pandas2ri.activate()
        ro.r('library(OpenMx)')
        
    def fit(self, fc_data, zygosity, n_repeats=2):
        """
        Fit extended twin model to functional connectivity data.
        
        Args:
            fc_data: [n_subjects, n_edges] functional connectivity
            zygosity: [n_subjects] 1=MZ, 2=DZ twin pairs
            n_repeats: Number of repeated measurements
            
        Returns:
            results: DataFrame with heritability estimates
        """
        # Convert to R dataframe
        r_data = pandas2ri.py2rpy(pd.DataFrame(fc_data))
        r_zyg = ro.IntVector(zygosity)
        
        # Source R model
        ro.r('''
            source('etm_model.R')
            fit_result <- fit_connectome_wide(fc_data, zygosity)
        ''')
        
        # Extract results
        results = pandas2ri.rpy2py(ro.r('fit_result'))
        return results
    
    def calculate_heritability(self, results):
        """
        Calculate proportion of variance explained.
        
        Args:
            results: Model output DataFrame
            
        Returns:
            heritability_summary: Summary statistics
        """
        total_var = results['h2'] + results['c2'] + results['e2'] + results['m2']
        
        summary = {
            : np.mean(results[] / total_var),
            : np.median(results[] / total_var),
            : np.(results[] / total_var > ),
            : np.mean(results[] / total_var)
        }
        
         summary


etm = ExtendedTwinModel()


fc_data = np.load()  
zygosity = np.load()     


results = etm.fit(fc_data, zygosity)


heritability_summary = etm.calculate_heritability(results)
()

Step 4: Network-Level Analysis

import seaborn as sns
import matplotlib.pyplot as plt
from nilearn import plotting

def analyze_network_heritability(results, network_mapping):
    """
    Aggregate heritability by functional network.
    
    Args:
        results: Edge-wise heritability estimates
        network_mapping: Dict mapping regions to networks
        
    Returns:
        network_h2: Heritability by network
    """
    networks = {}
    
    for edge_idx, h2 in enumerate(results['h2']):
        # Determine which networks this edge connects
        region1, region2 = get_edge_regions(edge_idx)
        net1 = network_mapping[region1]
        net2 = network_mapping[region2]
        
        # Within-network vs between-network
        if net1 == net2:
            key = f"within_{net1}"
        else:
            key = f"between_{net1}_{net2}"
        
        if key not in networks:
            networks[key] = []
        networks[key].append(h2)
    
    # Calculate mean heritability per network
    network_h2 = {k: np.mean(v) for k, v in networks.items()}
    
    return network_h2

# Visualization
def plot_heritability_map(results, atlas):
    """Plot heritability on brain surface."""
    fig = plotting.plot_connectome(
        edge_weights=results['h2'],
        node_coords=atlas.coordinates,
        node_color='auto',
        title='Functional Connectivity Heritability'
    )
     fig

Key Findings

Heritability Estimates

Network	Traditional ACE	Extended ETM	Error Variance
Default Mode Network	0.42	0.58	0.22
Frontoparietal Network	0.38	0.52	0.19
Salience Network	0.45	0.61	0.18
Sensorimotor Network	0.35	0.48	0.25
Visual Network	0.40	0.55	0.20

Key Insight: Accounting for measurement error increases heritability estimates by ~40% on average.

Genetic Architecture Patterns

1. Heteromodal Networks (DMN, FPN): Highest heritability
2. Sensorimotor Networks: Lower heritability, higher environmental influence
3. Between-Network Connections: Generally less heritable than within-network

Applications

• Precision Medicine: Identify genetically-informed biomarkers
• Psychiatric Genetics: Understand disorder-related connectivity patterns
• Developmental Studies: Track genetic influences across lifespan
• Neurodegeneration: Distinguish genetic risk from environmental factors

Pitfalls

1. Sample Size: Requires large twin samples (>200 pairs) for stable estimates
2. Assumptions: Equal environments assumption (EEA) may be violated
3. Generalizability: Results specific to resting-state; task FC may differ
4. Scanner Effects: Different scanners can inflate measurement error

Related Skills

• functional-connectome-fingerprint
• brain-graph-neural
• dcho-higher-order-brain-connectivity
• dgcl-brain-network-construction

References

@article{raghav2026genetic,
  title={The Genetic and Environmental Architecture of the Human Functional Connectome},
  author={Raghav, Tanu and Guerrero, Daniel and Tipnis, Uttara and He, Jinyi and Ge, Tian and Meda, Shashwath and Calhoun, Vince and Pearlson, Godfrey and Liu, Jingyu},
  journal={arXiv preprint arXiv:2604.24614},
  year={2026}
}