Testing of two SNP array-based genealogy algorithms using extended Han Chinese pedigrees and recommendations for...

Area of Science:

• Forensic Genetics
• Population Genetics
• Bioinformatics

Background:

• Genetic genealogy utilizes identity-by-state (IBS) and identity-by-descent (IBD) shared segments to link distant relatives to forensic samples.
• Accurate estimation of kinship is crucial for forensic investigations, requiring robust algorithms and parameter optimization.

Purpose of the Study:

• To evaluate and optimize genetic genealogy estimation methods for forensic applications.
• To assess the performance of the KING and GERMLINE+ERSA algorithms in inferring kinship relationships.

Main Methods:

• A family-based genetic genealogy analysis was conducted on 262 Han Chinese individuals from 11 families.
• The KING algorithm (calculating IBS and IBD statistics) and the GERMLINE+ERSA algorithm (analyzing IBD segments) were employed.
• Simulated low call rate data was used to test algorithm tolerance to marker decrease.

Main Results:

• The KING algorithm reliably identified first-degree relationships and maintained high accuracy up to the fifth degree (false positive rate <1.8%).
• The GERMLINE+ERSA algorithm provided reliable inference up to the eighth degree, with zero false positives but up to 27.4% false negatives.
• Genetically undetectable relationships began at the sixth degree; KING demonstrated better tolerance to low marker call rates than GERMLINE+ERSA.

Conclusions:

• KING and GERMLINE+ERSA algorithms offer complementary strengths, ensuring accurate inference from first to eighth-degree relatives.
• These methods are valuable for forensic investigations, particularly when dealing with distant genetic relatives.
• Algorithm performance, especially tolerance to data quality issues, is critical for practical forensic application.