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SNP profile completeness varied, but DNA metrics only partly predicted it

A scientist wearing protective blue gown, headgear, and gloves works at a biosafety cabinet in a laboratory, using a pipette to handle samples from green and red sample racks arranged on the work surface.
Research area:Biochemistry, Genetics and Molecular BiologyMolecular Biology Techniques and ApplicationsForensic Anthropology and Bioarchaeology Studies

What the study found

Common pre-sequencing DNA metrics were associated with SNP profile completeness in unidentified human remains, but they did not predict it reliably. The strongest relationships were with measures reflecting the ratio of endogenous human DNA to total DNA, which includes exogenous background DNA.

Why the authors say this matters

The authors conclude that MPS-based SNP profiling can generate useful genetic data from unidentified human remains, and they support forensic genetic genealogy as the preferred approach for generating actionable identification data. They also state that common pre-sequencing metrics can help with some workflow decisions, but are not enough to predict the full range of samples encountered.

What the researchers tested

The researchers analyzed 500 anonymized skeletal samples submitted for forensic genome sequencing. They measured human-specific DNA using short and long autosomal quantitative PCR targets, measured total DNA fluorometrically, and used SNP call rate as a measure of profile completeness.

What worked and what didn't

Of the 500 samples, 399 met the minimum human DNA threshold and were sequenced. Among sequenced samples, SNP call rates ranged from 8% to 91%, and 95.7% had call rates above 50%; call rate was most strongly associated with the total:short DNA ratio and estimated human DNA input into library preparation, while the degradation index showed only a modest association. Bone type affected whether samples advanced to sequencing, but among sequenced samples, call rate distributions were similar across major bone type categories; machine-learning models reached only moderate predictive performance, with the best validation R² at 0.47.

What to keep in mind

The abstract does not describe detailed limitations beyond noting that bone samples vary in DNA quality and quantity. The findings are limited to the sample set and metrics described here, and the authors state that the common pre-sequencing measures were insufficient predictors for the range of unidentified human remains encountered.

Key points

  • In 500 anonymized skeletal samples, 399 met the minimum human DNA threshold and were sequenced.
  • SNP call rates among sequenced samples ranged from 8% to 91%, and 95.7% were above 50%.
  • The strongest associations with call rate were the total:short DNA ratio and estimated human DNA input into library preparation.
  • The degradation index had only a modest association with SNP call rate.
  • Machine-learning models showed moderate predictive performance, with the best validation R² at 0.47.
  • Bone type influenced whether samples progressed to sequencing, but call rates were similar across major bone type categories among sequenced samples.

Disclosure

Research title:
SNP profile completeness varied, but DNA metrics only partly predicted it
Publication date:
2026-02-25
OpenAlex record:
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