Evidence map›Paper›PMID 39586186›Full record

ArticleForensic science international. Genetics2025

Benchmarking for genotyping and imputation using degraded DNA for forensic applications across diverse populations.

Elena I Zavala, Rori V Rohlfs, Priya Moorjani

Abstract read
In one paragraph

Article in Forensic science international. Genetics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Review
  3. On forensic likelihood ratios from low-coverage sequencing.Forensic science international. Genetics · 2025
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Elena I ZavalaDepartment of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA, United States. Electronic address: ezavala9@berkeley.edu.
Rori V RohlfsDepartment of Data Science, Institute of Ecology and Evolution, University of Oregon, Eugene, OR, United States. Electronic address: rori@uoregon.edu.
Priya MoorjaniDepartment of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA, United States; Center for Computational Biology, University of California, Berkeley, Berkeley, CA, United States. Electronic address: moorjani@berkeley.edu.

Funding

Genomic Insights into Human Population Mixture and its Role in Adaptation and DiseaseR35GM142978 · NIGMS · UNIVERSITY OF CALIFORNIA BERKELEY · PI Priya Moorjani · 2021 to 2026
$2.5M
NIGMS NIH HHS R35 GM142978
6 · The paper itself

Abstract

Advancements in sequencing and laboratory technologies have enabled forensic genetic analysis on increasingly low quality and degraded DNA samples. However, existing computational methods applied to genotyping and imputation for generating DNA profiles from degraded DNA have not been tested for forensic applications. Here we simulated sequencing data of varying qualities-coverage, fragment lengths, and deamination patterns-from forty individuals of diverse genetic ancestries. We used this dataset to test the performance of commonly used genotype and imputation methods (SAMtools, GATK, ATLAS, Beagle, and GLIMPSE) on five different SNP panels (MPS-plex, FORCE, two extended kinship panels, and the Human Origins array) that are used for forensic and population genetics applications. For genome mapping and variant calling with degraded DNA, we find use of parameters and methods (such as ATLAS) developed for ancient DNA analysis provides a marked improvement over conventional standards used for next generation sequencing analysis. We find that ATLAS outperforms GATK and SAMtools, achieving over 90 % genotyping accuracy for the four largest SNP panels with coverages greater than 10X. For lower coverages, decreased concordance rates are correlated with increased rates of heterozygosity. Genotype refinement and imputation improve the accuracy at lower coverages by leveraging population reference data. For all five SNP panels, we find that using a population reference panel representative of worldwide populations (e.g., the 1000 Genomes Project) results in increased genotype accuracies across genetic ancestries, compared to ancestry-matched population reference panels. Importantly, we find that the low SNP density of commonly used forensics SNP panels can impact the reliability and performance of genotype refinement and imputation. This highlights a critical trade-off between enhancing privacy by using panels with fewer SNPs and maintaining the effectiveness of genomic tools. We provide benchmarks and recommendations for analyzing degraded DNA from diverse populations with widely used genomic methods in forensic casework.

Indexed as

BenchmarkingDNA Degradation, NecroticGenotypeGenotyping TechniquesHigh-Throughput Nucleotide SequencingPolymorphism, Single NucleotideDNADNA FingerprintingForensic GeneticsGenetics, PopulationHumansSequence Analysis, DNASoftwareDNADegraded DNAForensic geneticsGenotypingImputationPopulation geneticsSNPs

Identifiers

PMID39586186
PMCPMC12056732

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LicenceCC BY-NC-ND
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.