Historically, genomics and forensic science have progressed along distinct trajectories. Genomics, driven by large-scale initiatives like the Human Genome Project, focused on genotyping or sequencing genomes to study genetic variation, disease associations, and population genetics. In contrast, forensic science took a targeted approach, relying on typing small panels of microsatellites or short tandem repeats (STRs). This method was developed before the genomics revolution and is highly effective for direct identity testing. However, its utility is limited to applications in which a direct reference STR profile is available for comparison or in kinship cases mostly with first degree relationships. Fortunately, the marriage of massively parallel sequencing (MPS) and forensic genetic genealogy (FGG) has brought forensic genetics into the genomics realm. The overarching benefit is that victims of crime do not have to go without answers or justice, and human remains no longer need to be nameless.
Dense SNP testing is a force multiplier in forensic science
As part of the integration of genomics-based tools into forensic science, dense single nucleotide polymorphism (SNP) testing has become a force multiplier for assisting in solving cases that remained unsolved for years or even decades [1]. Unlike STR profiling, which relies on a relatively small number of preselected genetic markers, SNP testing provides a vastly richer dataset of hundreds of thousands of markers, which expands capabilities to analyze forensic biological evidence to provide investigative leads far beyond those of STR typing. The power of SNPs lies in their stability, genome-wide distribution, and ability to be detected in smaller DNA fragments, making them particularly useful for analyzing degraded forensic samples. This latter feature allows for the recovery of genetic information from evidence that would otherwise yield incomplete or no STR data.
STR-based familial searches are limited typically to parent–child or full-sibling comparisons due to analysis of a small number of loci and the relatively high mutation rate of STR loci. Because of the access to a very large number of SNPs, kinship associations can be inferred well beyond first-degree relationships [2]. FGG leverages this near and distant kinship association capability particularly in cases that involve unknown suspects or unidentified human remains. By leveraging SNPs in the human genome, investigators can establish familial connections across multiple generations, which in turn generate investigative leads to determine the source of crime scene evidence through pedigree development and location of most likely common ancestors. There are many cases in which STR typing does not provide any lead value because a database search, such as is done in the Combined DNA Index System (CODIS), requires that the actual donor of crime scene evidence is in the database. FGG is a genomic solution to the limits of STR typing.
Beyond kinship analysis, SNP testing enables biogeographical ancestry inference, which is additional context about an unknown individual. Unlike STR profiles, which only offer identity information, SNP-based ancestry analysis can estimate an individual’s genetic origins at high resolution, which can help narrow or focus investigative efforts. Again, in cases where traditional investigative methods yield no viable suspects, ancestry inference can help guide outreach efforts to communities from which unidentified person(s) or their relatives may derive. Additionally, genomics-based ancestry analysis complements traditional anthropological techniques, such as skeletal and cranial morphology assessments, by providing a genetic perspective to enhance the accuracy and precision of population affinity assignments [3].
SNP testing also supports forensic DNA phenotyping, which allows for prediction of physical traits such as eye color, hair color, skin pigmentation, freckling, male pattern baldness, and even facial morphology [4]. While still an evolving field, forensic DNA phenotyping has the potential to generate investigative leads in cases where no other identifying information is available, further expanding the utility of SNP-based forensic methods beyond traditional STR profiling.
Forensic genetic genealogy: the catalyst for MPS adoption
While MPS has broad applications, its integration into forensic science has been propelled significantly by FGG. FGG combines SNP-based DNA profiling with genealogical databases to identify unknown individuals and sources of forensic evidence. This approach has led to a surge in resolutions involving unsolved violent crimes and unidentified human remains cases. The growing number of cases solved with the assistance of FGG underscores the increasing adoption of genomics tools in forensic investigations.
This trend is illustrated in Fig. 1, which tracks the cumulative rise in case solve announcements in recent years, reported by one of the largest United States providers of FGG services, Othram. However, this figure likely underestimates the true growth and adoption rate of FGG. There are other organizations and agencies, aside from Othram, also making progress in the field. Additionally, many cases are not reported until after adjudication or because they were resolved without public disclosure [5].
Cumulative number of cases publicly associated with Othram (y-axis) by year (x-axis) that have been announced as solved. These numbers represent a subset of the total cases solved using dense SNP testing, as other organizations also have leveraged this technology in forensic investigations. Additionally, many more forensic cases remain unannounced due to ongoing investigations or pending adjudication. Othram-associated cases were used in this figure because the authors are affiliated with Othram and the corresponding case information is publicly accessible and verifiable. The general trend observed in this figure is consistent with the findings reported by Dowdeswell [5]
Technical overlaps between genomics and forensic science
One of the most pressing issues in forensic science is working with degraded or low-input DNA. This challenge has been addressed in ancient DNA (aDNA) research. Sophisticated techniques to extract and analyze highly fragmented genetic material have been developed, allowing for recovery of DNA from samples that are thousands of years old. Indeed, Svante Pääbo was awarded the Nobel Prize in 2022 for his aDNA work sequencing the Neanderthal genome [6]. These same methods are now being applied directly to forensic samples, where biological evidence is often compromised due to environmental insults. The foundations of aDNA research have contributed to much of the success of FGG analyses [7, 8].
The computational analysis of degraded DNA is another area where genomics and forensics share common ground. Just as genomic scientists use bioinformatics to reconstruct genetic information from fragmented, damaged, or incomplete DNA sequences, forensic scientists must employ computational techniques to improve the interpretability of degraded and damaged DNA samples. However, to fully enable the development of genome analysis pipelines purpose-built for forensic applications, there is also a critical need for standards, reference materials, and performance testing tools. These needs parallel existing efforts in clinical genomics, where benchmarking datasets have been established to promote accuracy across sequencing methods and variant-calling algorithms [9].
Opportunities for genomics to advance forensic science
The convergence of genomics and forensic science presents several opportunities to enhance forensic methodologies, such as the above-mentioned application of aDNA methods to enable and improve the analysis of degraded forensic samples. Pipelines and analysis frameworks developed in genomics should be leveraged to enhance forensic DNA analysis, and the genomics community’s experience with ethical challenges can help guide the responsible use of genetic information in forensic investigations.
When new technology enters the forensic field, a common question is how its cost compares to that of established methods. On a per-sample reagent basis, STR typing is less expensive than whole genome sequencing (WGS), although sequencing costs continue to decline [1]. However, this direct cost comparison is misleading. The more relevant metric is cost-effectiveness, i.e., what benefit(s) are derived relative to cost.
Approximately half of the forensic DNA profiles in CODIS have not been associated with any known individual. Thus, many cases involving victims of violent crime remain unresolved, prolonging trauma for victims and their families. Moreover, this figure underrepresents the true number of unsolved cases: many DNA samples never make it into CODIS because they fail to meet the stringent quality and quantity thresholds for STR typing.
WGS and FGG overcome many of these limitations. They enable the generation of usable DNA profiles from degraded samples and, crucially, allow investigative leads to be developed even when the person of interest is not present in any existing database. While STR typing and WGS can and should be used in parallel, WGS is particularly valuable for the most challenging cases, such as decades-old cold cases or evidence that has failed to yield results by previous STR-based testing.
It is important to recognize that many profiles in CODIS are from repeat offenders, and many more eligible recidivists’ profiles have yet to be added. Identifying serial perpetrators, especially in cases of sexual assault and violent crime, early in their offending history has immense social and economic value. The tangible and intangible savings from such early interventions have been estimated in the billions of dollars in the United States alone. Budowle et al. have previously detailed the economic rationale for FGG, which need not be repeated here [10].
Expanding the use of FGG will require new investments. But can we afford not to invest, just as was done to build CODIS over the past three decades? Like earlier forensic milestones, such as the introduction of DNA typing in the 1980 s, capillary electrophoresis in the 1990 s, and CODIS in the early 2000 s, FGG presents initial challenges, but none are insurmountable. In fact, today we have far better tools to support this transition: scalable robotic and sequencing platforms, reduced sequencing costs, advanced bioinformatics, and robust quality assurance frameworks. The path to full implementation of FGG is not a technical one—it is one of will and commitment.
Enhancing automation, scale, and scientific rigor in forensic analysis
Even with funding for this work, is it realistic to be able to scale the investigation of hundreds of thousands of unsolved violent crimes and tens of thousands of unidentified human remains? First, high-throughput genomic sequencing already is highly amenable to automation, especially compared with traditional forensic workflows. Once sequencing is completed, the downstream analysis of forensic SNP markers can be automated to a far greater extent than the analysis of STR marker profiles generated via capillary electrophoresis. This capability opens the door to scalable, software-driven pipelines. Second, not every case demands the same level of effort. Some cases are resolved quickly through kinship testing when there are known or suspected references. Other cases can be solved rapidly through close DNA matches in a genetic genealogical database, even without a reference sample. Third, automation is accelerating genealogical analysis. Newer tools will combine graph-based models of genealogical records and DNA match data, allowing for AI-assisted family tree construction and analysis. Many of these tools can automatically surface relationships, generate hypotheses, and identify likely connections with a level of speed and scale that manual methods cannot achieve. This shift enables investigators to explore complex relationships quickly and with minimal manual effort.
Importantly, the reward for automation also is its drive towards objectivity (or at least consistency) and transparency. Automated analyses can reduce reliance on subjective expert interpretation and create reproducible, auditable outputs that can be scrutinized by peer review and in judicial proceedings. Thus, the evidentiary foundation of forensic work is strengthened, and conclusions can be independently verified.
Ultimately, the goal is not to solve every case simultaneously, but to establish a sustainable, high-throughput pipeline that uses better tools, smarter prioritization, and continuous learning to improve over time. This situation is not unlike the way complex fields like cybersecurity or epidemiology manage massive data challenges: through intelligent systems that get faster and more accurate with each new case.
Beyond technological advancements, the impact of applying genomics to forensic science is profound. Biomedical research and clinical genomics are improving health outcomes and enhancing lives. Similarly, forensic genomics offers a parallel opportunity—one that delivers immediate and long-term benefits. Each solved case touches countless lives: survivors, victims, their loved ones, first responders, and even those individuals who may have been wrongfully accused of committing a crime. The impact is immeasurable. Yet, there remain hundreds of thousands of unsolved violent crimes and tens of thousands of unidentified human remains and these cases are just those reported in the United States. It is time to harness these genomics advancements, apply them at scale, and achieve the goal that no case is left unresolved, which in turn translates into reducing trauma and uncertainty to all those impacted by crime.