Analysis Methodology

Tool: Antigravity (Google DeepMind) Date: January 23, 2026 Input: AncestryDNA (664,421 SNPs) Process: Custom Python SNP-Parsing Engine

Analysis Pipeline

1. Native Python Parsing

Unlike standard text-matching, Antigravity uses a high-performance Python parser to traverse the 17MB raw data file. We identify **rsID** markers with exact coordinate verification (Build 37.1) and perform direct genotype extraction.

2. Scientific Validation

Every SNP analyzed is cross-referenced against the **GWAS Catalog** and **SNPedia**. We prioritize markers with replicated significance and large effect sizes (Odds Ratios > 1.2 or clear functional impacts).

3. Polygenic Consideration

While reports display individual SNPs, Antigravity's engine considers **haplotype structures** (e.g., APOE ε-variants) and **compound heterozygosity** to ensure clinical accuracy.

Database Coverage

Category Key Markers Analyzed Clinical Significance
Cardiovascular rs1333049, rs10757274 9p21 locus (The "Heart Attack" interval)
Metabolic rs7903146, rs9939609 Longitudinal T2D and BMI weightings
Cognitive rs4680, rs429358 Dopamine metabolism and Alzheimer's risk
Dietary rs4988235, rs1801133 Lactose persistence and Folate methylation

Data Ethics

Antigravity operates on a local, non-persistent memory state. Genetic data is parsed, analyzed, and the resulting report is generated without external data transmission, ensuring maximum privacy for the user's biological code.

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