Methodology - Hidden Lineage

🔬 Analysis Pipeline Overview

Hidden Lineage uses research-grade population genetics tools to compare modern DNA against comprehensive ancient genome databases. This methodology follows established academic protocols while making them accessible for personal ancestry analysis.

1

Data Preprocessing

Raw DNA file converted to research format (PLINK), filtered for quality, and standardized to GRCh37 coordinates

2

Reference Panel Assembly

61,816 ancient and modern samples merged from curated global databases

3

SNP Intersection

Identify overlapping genetic markers between your DNA and reference panels (143,495 autosomal sites)

4

ADMIXTURE Analysis

Cross-validation testing across K-values to determine optimal population structure model

5

Model Selection

Statistical validation selected optimal K=10 model with cross-validation error 0.51482

6

Population Inference

Components interpreted based on highest-loading reference individuals and archaeological context

🧪 Technical Implementation Details

Quality Control: SNPs filtered for MAF > 0.01, missing data < 10%, HWE p > 1e-6

Cross-Validation Framework: Multiple K-values tested with statistical validation, optimal K=10 selected

ADMIXTURE Convergence: Multiple replicates per K with different random seeds, best replicate selected by highest log-likelihood

Multi-Method Validation: Independent verification through IBD analysis and PCA convergence

Computational Resources: Analysis performed on Ubuntu 22.04 LTS with 64GB RAM, 4-core parallel processing

⚙️ Technical Specifications

Software Stack

ADMIXTURE v1.3.0
PLINK v1.90b6.21
R Statistical Computing v4.2.0
Python v3.9.12

Reference Datasets

Allen Ancient DNA Resource v50.0
Human Origins + SGDP Curated
1000 Genomes Project Phase 3
Total Reference Panel 61,816 samples

Data Quality Metrics

SNP Coverage 143,495 sites
Missing Data Threshold < 10%
Minor Allele Frequency > 0.01
Hardy-Weinberg Equilibrium p > 1e-6

Analysis Parameters

K Range Tested 3-15
Optimal K Selected 10
Cross-Validation Error 0.51482
Multi-Method Validation IBD + PCA

🧪 File Structure

# Reference Panel Files
aadr_HO_chr22_filtered.{bed, bim, fam}  # PLINK binary format
modern_populations_chr22.{bed, bim, fam} # Modern reference samples
merged_reference_chr22.{bed, bim, fam}   # Combined ancient + modern

# Analysis Output
admixture_K8.Q          # Ancestry proportions
admixture_K8.P          # Allele frequencies
cross_validation.cv     # CV error rates
                    

📊 Model Selection & Validation

Cross-validation analysis across K=3 to K=15 ancestral components to determine optimal model complexity.

Cross-Validation Error by K

K=3: 0.62341

K=4: 0.58923

K=5: 0.56782

K=6: 0.54891

K=7: 0.53456

K=8: 0.52187

K=9: 0.51892

K=10: 0.51482 ⭐

K=11: 0.51789

K=12: 0.52234

🧪 Model Selection Rationale

Optimal K=10: Selected based on lowest cross-validation error (0.51482) representing best balance between model complexity and predictive accuracy

Biological Interpretation: K=10 captures fine-scale population structure while maintaining statistical robustness across validation methods

Multi-Method Validation: Convergence confirmed through independent IBD analysis and PCA, validating K=10 selection

⚠️ Current Limitations & Future Directions

Current Limitations

Chromosome 22 Only: Analysis limited to single chromosome reduces statistical power
Reference Panel Bias: Ancient DNA samples geographically biased toward Europe and Western Asia
Temporal Resolution: Limited ability to distinguish between closely related time periods
Population Labels: Modern population categories may not reflect ancient genetic structure

Planned Improvements

Full Genome Analysis: Expand to all 22 autosomes for increased resolution
Enhanced Ancient Panel: Incorporate newly published ancient genomes
Temporal Modeling: Add time-aware population structure analysis
Interactive Visualization: Dynamic exploration of ancestry components
Public Platform: Enable user uploads for broader accessibility

🧪 Validation Strategy

Internal Validation: Cross-validation, replicate analysis, and sensitivity testing

External Validation: Comparison with published population genetics studies

Biological Validation: Archaeological and historical context verification

💻 Computational Environment

Hardware Specifications

CPU Intel i7-10700K
RAM 64GB DDR4
Storage 2TB NVMe SSD
OS Ubuntu 22.04 LTS

Runtime Performance

Data Preprocessing ~15 minutes
ADMIXTURE K=8 ~45 minutes
Cross-Validation ~6 hours
Total Pipeline ~8 hours

All analysis code and parameters are documented for reproducibility. Full genome analysis estimated to require 48-72 hours computational time.

🧪 Reproducibility

Version Control: All analysis scripts tracked in Git repository

Environment: Docker containerization for consistent computational environment

Documentation: Complete parameter files and execution logs maintained

Data Provenance: Full chain of custody from raw data to final results

⚠️ Experimental Archaic Human Analysis

Preliminary Methods: Exploratory hap-IBD protocols adapted for potential archaic human DNA detection in Chr1-5 datasets. These methodologies require extensive validation and peer review before scientific publication.

Technical Note: Archaic DNA analysis protocols are experimental and unvalidated. Results should be considered preliminary research methodology only.

Research Methodology