Computational Genetic Genealogy

From Theory to Practice

While previous labs have focused on the theoretical foundations and algorithmic approaches of computational genetic genealogy, this lab addresses the "reality gap"—the set of challenges that emerge when applying these methods to real-world data:

Key Real-World Challenges

• Data Quality and Completeness: Missing, sparse, or variable-quality genetic data
• Population Diversity: Different genetic patterns across diverse human populations
• Testing Platform Differences: Variations in SNP coverage, phasing quality, and analysis methods
• Privacy and Ethical Constraints: Limitations on data access and usage
• Scale Challenges: Handling large datasets with thousands or millions of individuals

Bonsai v3 incorporates numerous adaptations to address these real-world challenges, enabling more robust performance across diverse scenarios and datasets.

Handling Imperfect Data

Real-world genetic genealogy datasets rarely have the completeness and quality assumed by theoretical models. Bonsai v3 implements several strategies to handle these imperfections:

Common Data Quality Issues

# Pseudocode for coverage-aware IBD normalization
def normalize_ibd_for_coverage(observed_ibd, coverage_fraction):
    """
    Adjust observed IBD sharing to account for incomplete coverage.
    
    Args:
        observed_ibd: Observed IBD sharing in cM
        coverage_fraction: Fraction of the genome with adequate coverage
        
    Returns:
        Normalized IBD estimate
    """
    if coverage_fraction < MIN_COVERAGE_THRESHOLD:
        # Too little coverage for reliable normalization
        return observed_ibd, "low_confidence"
    
    # Simple linear normalization
    normalized_ibd = observed_ibd / coverage_fraction
    
    # Apply confidence rating based on coverage
    if coverage_fraction > 0.9:
        confidence = "high"
    elif coverage_fraction > 0.7:
        confidence = "medium"
    else:
        confidence = "low"
    
    return normalized_ibd, confidence

Partial Data Strategies

Bonsai implements several approaches for working with partial or incomplete data:

1. Graceful Degradation: Algorithms that continue to function with reduced accuracy as data quality decreases
2. Confidence Calibration: Adjusting confidence scores based on data completeness
3. Imputation Techniques: Filling in missing data using population references where appropriate
4. Feature Weighting: Giving more weight to high-quality data regions in relationship inference

Adapting to Human Genetic Diversity

Human populations have different genetic histories and characteristics that affect genetic genealogy analysis. Bonsai v3 accounts for these population-specific patterns:

Key Population Considerations

• Recombination Rate Variation: Different populations show different patterns of genetic recombination
• Runs of Homozygosity (ROH): Endogamous populations have more and longer ROH regions
• Demographic History: Population bottlenecks and expansions affect genetic diversity
• Admixture Patterns: Mixed ancestry creates complex IBD patterns
• Reference Bias: Most genetic references are skewed toward European populations

Implementation Approaches

Several implementation strategies enable effective handling of population diversity:

1. Population Inference: Automatically detecting population background from genetic data
2. Adaptive Parameters: Adjusting algorithm parameters based on detected population
3. Multi-Reference Models: Using multiple population references for improved accuracy
4. Admixture-Aware Analysis: Handling segments from different ancestral populations appropriately

Integrating Data from Multiple Sources

Real-world genetic genealogy often involves integrating data from multiple testing platforms, each with different characteristics. Bonsai v3 addresses these platform differences:

Major Testing Platform Variations

• SNP Coverage: Different tests analyze different subsets of SNPs
• Chip Versions: Testing companies frequently update their genotyping arrays
• Analysis Algorithms: Different companies use different algorithms for IBD detection
• Reporting Formats: Data formats and segment reporting criteria vary
• Reference Populations: Different platforms use different reference populations

Cross-Platform Integration Approaches

Bonsai implements several strategies for effective cross-platform integration:

1. Common SNP Analysis: Focusing analysis on SNPs common to all platforms
2. Platform-Specific Calibration: Adjusting expectations based on known platform characteristics
3. Format Normalization: Converting data from different sources to a consistent internal format
4. Confidence Adjustment: Modifying confidence scores based on platform compatibility

Responsible Genetic Genealogy

Working with real-world genetic data involves navigating important privacy and ethical considerations. Bonsai v3 incorporates several features to support responsible use:

Key Privacy and Ethical Challenges

• Sensitive Relationship Discovery: Uncovering previously unknown family connections
• Data Access Controls: Managing who can access genetic relationship information
• Informed Consent: Ensuring participants understand how their data will be used
• Secondary Discoveries: Handling incidental findings like health-related information
• Re-identification Risk: Protecting against identification of individuals from anonymized data

Privacy-Preserving Features

Bonsai implements several privacy-preserving features:

1. Data Minimization: Using only the data necessary for relationship inference
2. Access Controls: Supporting granular permissions for relationship visibility
3. Anonymization Options: Allowing identity redaction while preserving relationship structure
4. Consent-Based Processing: Respecting user preferences for data usage

Handling Large-Scale Analyses

Real-world genetic genealogy applications often involve large datasets with thousands or millions of individuals. Bonsai v3 includes specialized optimizations for large-scale analyses:

Key Scale Challenges

• Computational Complexity: Many key algorithms have quadratic or worse complexity
• Memory Constraints: Large datasets can exceed available memory
• Pairwise Comparison Explosion: The number of potential relationships grows quadratically
• Visualization Complexity: Large pedigrees become difficult to comprehend
• Consistency Maintenance: Ensuring biological consistency becomes harder at scale

Bonsai's Scale Optimizations

Several key optimizations enable Bonsai to handle large-scale analyses:

1. Hierarchical Processing: Multi-level approaches that progressively refine analyses
2. Filtering Strategies: Intelligent pre-filtering to focus on likely relationships
3. Chunking and Batching: Processing data in manageable chunks
4. Parallelization: Distributing workloads across multiple processors
5. Incremental Updates: Efficiently incorporating new data without full recomputation

# Pseudocode for hierarchical IBD analysis
def hierarchical_ibd_analysis(individuals, max_group_size=1000):
    """
    Process large datasets using a hierarchical approach.
    
    Args:
        individuals: List of all individuals to analyze
        max_group_size: Maximum size for direct comparison groups
        
    Returns:
        Complete IBD relationship graph
    """
    # Phase 1: Cluster individuals into manageable groups
    groups = cluster_by_genetic_similarity(individuals, max_group_size)
    
    # Phase 2: Perform detailed IBD analysis within each group
    within_group_results = {}
    for group in groups:
        within_group_results[group] = analyze_group_ibd(group)
    
    # Phase 3: Perform selected cross-group comparisons
    cross_group_results = analyze_cross_group_connections(groups)
    
    # Phase 4: Merge results into a unified relationship graph
    complete_graph = merge_ibd_results(within_group_results, cross_group_results)
    
    return complete_graph

Performance Metrics

Bonsai's performance optimizations yield significant improvements in processing time and memory usage:

• Filtering: Typically reduces computation by 90-99% with minimal accuracy impact
• Chunking: Reduces peak memory usage by 70-80% for large datasets
• Parallelization: Near-linear speedup with processor count for many operations
• Incremental Updates: Can be 10-100x faster than full recomputation

Learning from Application to Diverse Datasets

Bonsai's development has been informed by application to diverse real-world datasets, each presenting unique challenges and learning opportunities:

Key Dataset Categories

• Founder Populations: Groups with documented founder effects and endogamy
• Admixed Populations: Groups with complex ancestral mixing patterns
• Multi-Generation Pedigrees: Deep family trees with genetic data for multiple generations
• Sparse Coverage Datasets: Pedigrees with genetic data for only a subset of individuals
• Cross-Platform Collections: Data integrated from multiple testing platforms

Key Lessons from Real-World Application

1. Accuracy vs. Coverage Tradeoff: Sometimes less data with higher quality yields better results than more data with quality issues
2. Contextual Calibration: Parameters should be adjusted based on dataset context (population, platform, etc.)
3. Complementary Evidence: Combining genetic evidence with demographic and documentary evidence improves results
4. Appropriate Confidence: Confidence reporting should reflect all sources of uncertainty, not just statistical uncertainty

Working with real-world genetic genealogy datasets presents numerous challenges that go beyond theoretical models and ideal conditions. Bonsai v3 addresses these challenges through a combination of robust algorithms, adaptive parameters, population-specific calibration, and careful handling of data quality issues.

By understanding and accounting for data quality issues, population-specific patterns, testing platform differences, privacy considerations, and scale challenges, Bonsai creates more reliable and accurate pedigree reconstructions from real-world data.

In the next lab, we'll explore performance tuning techniques for Bonsai v3, focusing on how to optimize the system for specific application scenarios and computational environments.