RHE Model

The RHE class implements the Randomized Haseman-Elston regression model for heritability estimation. It inherits from the Base class and provides efficient estimation of additive genetic variance components.

Variance Components

The RHE model estimates:

  • Additive genetic variance components (σ²g) for each bin

  • Environmental variance component (σ²e)

  • Total heritability (h²)

  • Enrichment scores

Class Inheritance

class RHE(Base):
    """Randomized Haseman-Elston regression model."""

Key Methods

get_num_estimates()

Returns the number of variance components to estimate:

  • Returns num_bin for additive genetic effects

get_M_last_row()

Specifies the last row of the M matrix:

  • Returns self.len_bin for additive effects

  • self.len_bin is provided by the base class, which is the number of SNPs in each bin.

pre_compute_jackknife_bin(j, all_gen)

Pre-computes statistics for jackknife estimation:

Parameters:

  • j (int) – Jackknife sample index

  • all_gen (list) – List of genotype matrices for each bin

def pre_compute_jackknife_bin(j, all_gen):
    for k, X_kj in enumerate(all_gen): # Loop through the partitioned genotype matrix
         # 1. Process genotype data
         X_kj = self.standardize_geno(X_kj)

         # 2. Update M matrix
         self.M[j][k] = self.M[self.num_jack][k] - X_kj.shape[1]

         # 3. Compute statistics
         for b in range(self.num_random_vec):
             self.XXz[k, j, b, :] = self._compute_XXz(b, X_kj)
             if self.use_cov:
                 self.UXXz[k, j, b, :] = self._compute_UXXz(self.XXz[k][j][b])
                 self.XXUz[k, j, b, :] = self._compute_XXUz(b, X_kj)

         # 4. Compute phenotype-related statistics
         self.yXXy[k][j] = self._compute_yXXy(X_kj, y=self.pheno)
b_trace_calculation(k, j, b_idx)

Calculates trace terms for variance estimation:

  • Returns num_indv for standardized genotypes

run(method)

Runs the complete RHE analysis:

Parameters:

method (str) – Estimation method (“lstsq” or “QR”)

Returns:

Dictionary containing: - sigma_ests_total: Estimated variance components - sig_errs: Standard errors of variance components - h2_total: Heritability estimates - h2_errs: Standard errors of heritability - enrichment_total: Enrichment scores - enrichment_errs: Standard errors of enrichment - h2_jackknife_overlap: Jackknife heritability estimates computed based on overlapping setting - h2_errs_overlap: Standard errors of jackknife heritability computed based on overlapping setting - h2_total_overlap: Overlapping heritability estimates computed based on overlapping setting - h2_errs_total_overlap: Standard errors of overlapping heritability computed based on overlapping setting

Usage Example

from pyrhe.models import RHE

# Initialize model
rhe_model = RHE(
    geno_file="path/to/genotype",
    annot_file="path/to/annotation",
    pheno_file="path/to/phenotype",
    cov_file="path/to/covariate",
    num_bins=10,
    num_jack=100,
    num_random_vec=10,
    num_workers=5,
    ...
)

# Run analysis
results = rhe_model()

# Access results
# The outputs are automatically logged in the output file.
# In addition, you can also access the results:
print(results)
print(results['sigma_ests_total'])
# The results are stored in a dictionary. The keys are:
# - sigma_ests_total: Estimated variance components
# - sig_errs: Standard errors of variance components
# - h2_total: Heritability estimates
# - h2_errs: Standard errors of heritability
# - enrichment_total: Enrichment scores
# - enrichment_errs: Standard errors of enrichment
# - h2_jackknife_overlap: Jackknife heritability estimates computed based on overlapping setting
# - h2_errs_overlap: Standard errors of jackknife heritability computed based on overlapping setting
# - h2_total_overlap: Overlapping heritability estimates computed based on overlapping setting
# - h2_errs_total_overlap: Standard errors of overlapping heritability computed based on overlapping setting