The DNA Myth: What the Peer-Reviewed Literature Actually Shows About Forensic and Paternity Testing

An Essay

Preface

This essay owes a significant debt to Jamie Andrews and his December 2025 article “Who’s The Daddy?” published through The Virology Controls Studies Project. Andrews’ work brought together threads of evidence that had been scattered across academic journals and expert testimony, weaving them into a coherent challenge to one of modern science’s most sacred cows: the reliability of DNA testing.

Who's The Daddy?

I came to this topic through a recent conversation with a colleague. We were discussing genetics more broadly — the claims made about hereditary disease, genetic predisposition, the whole edifice of molecular biology as it’s been presented to the public. I offered my view that much of what we’ve been sold about genetics is questionable at best and serves as a convenient cover story for environmental and industrial causes of disease. My colleague, to his credit, didn’t dismiss this outright. He was open to the thesis but asked me to clarify: what did I think about DNA testing specifically? Did I believe that DNA identification — the forensic and paternity applications — was accurate, even if the disease-related genetics had been weaponised or overstated?

I thought about it for a moment. At the time, I said yes — that seemed fair, given what I knew. DNA testing felt like the solid ground beneath the shakier claims. The courtroom stuff. The paternity results. Surely that, at least, was reliable?

Then I encountered Andrews’ work, and I went looking for the primary sources he cited. What I found has fundamentally changed my answer. The peer-reviewed literature tells a story that the public has never heard — a story of unblinded testing, wildly inconsistent expert interpretations, and error rates that would scandalise anyone who’s been told DNA evidence is “99.99% accurate.”

Part 1: The Unquestioned Consensus

If you’ve watched a crime drama in the past thirty years, you know the scene. The detective hands a swab to the lab technician. Hours or days later, a computer spits out a match. Case closed. The DNA doesn’t lie.

This cultural script has been reinforced so thoroughly that DNA evidence now occupies a unique position in the public imagination. It’s not just evidence — it’s proof. When a prosecutor tells a jury that DNA places the defendant at the scene, the implied message is that science itself has rendered a verdict. The human judgment of twelve peers becomes almost ceremonial. How can you argue with molecules?

The same mystique surrounds paternity testing. A man receives an envelope in the post. Inside, a laboratory has determined with “99.99% probability” that he is — or is not — the biological father of a child. Lives pivot on that number. Marriages end. Child support payments begin. Inheritances are transferred. The test has spoken.

Behind these applications lies an extraordinary claim: that DNA profiling can distinguish any individual human being from all the billions of others on Earth, with near-perfect accuracy. The statistics cited are staggering — “one in a billion,” “one in a trillion,” “99.99% certainty.” These numbers have become so familiar that questioning them feels almost absurd, like disputing that the Earth orbits the Sun.

But here’s the question that guided this investigation: what happens when you actually look at the validation studies? Not the press releases. Not the courtroom testimony. Not the claims on laboratory websites. The actual peer-reviewed research into how accurate these tests are when subjected to proper scientific controls.

The answer turns out to be deeply uncomfortable.

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Part 2: A Five-Second Discovery

To understand how we got here, we need to go back to a laboratory in Leicester, England, in September 1984.

Sir Alec Jeffreys — then just Dr. Jeffreys — was working with DNA samples from a technician’s family: the technician herself, her mother, and her father. He was studying genetic variation, not forensics. But when he looked at the X-ray film from a Southern blot analysis, he saw something that excited him. The banding patterns were highly variable between the parents, but the daughter’s pattern appeared to be a combination of both.

Jeffreys later described the moment of insight as taking “about five seconds.” In that brief window, he concluded two things: that every person has a unique DNA “barcode,” and that these patterns are inherited predictably. He would come to call this “DNA fingerprinting.”

Within months, the technique was being applied to real cases. An immigration dispute in 1985 was resolved using Jeffreys’ method. In 1986, DNA evidence featured in a criminal investigation — the Enderby murders — for the first time. By 1987, DNA profiling was being used in American courts. By 1990, the Human Genome Project was launched, and the technique had spread to laboratories worldwide.

The speed of adoption was breathtaking. But pause for a moment and consider what was — and wasn’t — established in those early years.

Jeffreys had observed patterns in a single family. From that observation, he extrapolated a universal principle: that DNA banding patterns could uniquely identify any human being. This is an enormous inferential leap. One family in Leicester is not the global population. A pattern observed under specific laboratory conditions is not proof that the pattern will hold under all conditions, with all samples, interpreted by all analysts.

What would proper validation have looked like? Large-scale blinded studies. Thousands of samples, from diverse populations, analysed by independent laboratories who didn’t know which samples came from whom. Rigorous assessment of error rates — both false positives (saying two samples match when they don’t) and false negatives (saying they don’t match when they do). Examination of how factors like sample degradation, contamination, and mixed-source samples affect accuracy.

This validation did not happen. Not before the technique was deployed in courts. Not before men were sentenced to death on the basis of DNA evidence. The legal and forensic establishments embraced DNA profiling with an enthusiasm that outpaced the science.

As we’ll see, when blinded validation studies were eventually conducted — often decades later — the results were not what the public had been led to expect.

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Part 3: The Blinding Problem

In any rigorous scientific test, blinding is fundamental. The principle is simple: the person conducting the analysis shouldn’t know what result is expected. A drug trial is blinded so the doctor doesn’t unconsciously favour the experimental treatment. An exam is blinded so the marker can’t give higher grades to students they like. The concept is so basic that its absence in any high-stakes testing regime should raise immediate red flags.

Dr. Dan Krane is a geneticist and forensic expert who has provided testimony in over 100 court cases. He’s spent years trying to understand why forensic DNA laboratories resist blinding — and why this resistance should concern anyone who cares about justice.

In a TEDx talk that Andrews’ article highlights, Krane offered a devastating analogy. When he asks crime lab employees and prosecutors about blind testing, he said, their response follows a predictable pattern:

They argue that their work is critically important. Lives are on the line. They need to get the right answer. And to get the right answer, they need access to all available information — including the suspect’s DNA profile.

Krane’s response: “It seems to me it’s very similar to a student telling a teacher, ‘I really want to do well on the test you’re about to give me, and I’m confident I’ll do much better if you give me the answer key before you give me the test.’”

The audience laughed. But the comparison is precise. A test where you already know the expected answer isn’t a test at all. It’s a confirmation exercise. And yet this is how forensic DNA analysis routinely operates.

In 2008, Krane and several colleagues — including other eminent forensic scientists — published a letter in the Journal of Forensic Sciences proposing a solution. They called it “sequential unmasking.” The protocol was straightforward: analysts would first interpret the evidentiary samples (the crime scene DNA) without knowing the suspect’s profile. Only after documenting their interpretation would they be given the reference sample to compare.

This isn’t a radical demand. It’s the minimum standard that any legitimate scientific endeavour should meet. The proposal was politely worded, carefully argued, and backed by experts in the field.

It was ignored.

The forensic DNA establishment continued operating exactly as before. Analysts receive case files. They know who the suspect is. They know what the prosecution hopes to prove. And then they interpret ambiguous molecular data in that context.

The question is: why would laboratories resist such an obviously sensible reform?

Andrews suggests an uncomfortable answer: because they know what would happen if they actually ran blind tests. The results wouldn’t look like 99.99% accuracy. They’d look like guesswork.

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Part 4: The Forensic Evidence — Dror & Hampikian

In 2011, a study was published in the journal Science & Justice that should have made headlines. It didn’t — the public remained largely unaware — but within forensic science circles, it was explosive.

The researchers were Itiel Dror, a cognitive neuroscientist who studies bias and decision-making, and Greg Hampikian, a geneticist and innocence project advocate. They had obtained DNA evidence from a real criminal case — a gang rape prosecution in Georgia — through a Freedom of Information Act request.

The case had high stakes. One of the assailants had testified against the others in exchange for a reduced sentence. But under Georgia law, his testimony alone wasn’t sufficient for conviction — there needed to be corroborating evidence. The DNA analysis provided that corroboration. The original laboratory had concluded that one of the accused men “could not be excluded” as a contributor to the DNA mixture found on the victim. This conclusion was essential to the prosecution.

Dror and Hampikian took the same DNA evidence — the same electropherograms that the original analysts had examined — and sent it to 17 independent DNA examiners working in accredited governmental laboratories across North America. These weren’t amateurs. They were qualified, experienced professionals, with an average of nearly nine years of experience in DNA analysis.

The critical difference: the 17 independent examiners received only the DNA data itself. They were given the relevant technical information — concentration levels, amplification conditions, injection times — but not the case context. They didn’t know about the rape accusation, the cooperating witness, or the prosecution’s theory. They were asked simply to examine the DNA mixture and determine, for each suspect, whether he could be excluded, could not be excluded, or whether the result was inconclusive.

The results were remarkable.

For the suspect who had been deemed “could not be excluded” by the original laboratory — the finding that had helped send him to prison — only 1 out of 17 independent analysts agreed.

Four analysts found the result “inconclusive.”

Twelve analysts concluded “exclude.”

Let that sink in. The same DNA evidence. Qualified experts. Working independently. And 12 out of 17 reached the opposite conclusion from the one presented in court.

The study revealed two devastating findings. First, even without any contextual bias, DNA mixture interpretation is remarkably subjective. The 17 analysts working “context-free” still disagreed among themselves — evidence that the interpretation of complex DNA profiles involves significant human judgment, not objective readout.

Second, comparing the original analysts (who had the biasing case context) to the independent analysts (who didn’t), the influence of contextual information was stark. The original conclusion aligned with what the prosecution needed. The blinded analysts, freed from that context, mostly reached a different verdict.

In 2016, the President’s Council of Advisors on Science and Technology (PCAST) released a major report on forensic science. Reviewing the evidence on DNA mixture interpretation, PCAST acknowledged what researchers like Dror had been demonstrating: that subjective interpretation plays a significant role, and that the field had not adequately established error rates through proper validation studies.

The man in the Georgia case remained in prison. The broader forensic establishment continued much as before.

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Part 5: Who’s The Daddy? — The Paternity Evidence

Forensic DNA analysis — crime scene samples, sexual assault evidence — often involves mixtures and degraded material, which everyone acknowledges introduces complexity. But surely paternity testing is different? A cheek swab from the child, a cheek swab from the alleged father, clean samples, straightforward comparison. This, at least, must be reliable.

A 2006 study published in Forensic Science International tested that assumption directly. The research team, led by Micaela Poetsch at Ernst-Moritz-Arndt University in Germany, designed what should have been a routine validation exercise.

They collected samples from 336 children and 348 men who were involved in paternity investigations between 2001 and 2004. The men were known to be unrelated to the children — they were comparison subjects, not actual fathers. The researchers then applied standard paternity testing methods (STR analysis — the same technology used worldwide) to see how often the tests would correctly exclude these unrelated men.

There was a wrinkle: they excluded the mother’s DNA profile from the analysis. This created a “motherless” test scenario — which happens often in real-world cases where the mother is unavailable, uncooperative, or deceased.

The expected result, based on the claimed accuracy of paternity testing, was that virtually all unrelated men would be excluded. If DNA profiling is as discriminating as advertised, a random man should have essentially zero chance of matching a random child.

The actual result: for 322 of the 336 children — that’s 95.8% — at least one unrelated man could not be excluded from fatherhood according to standard German testing guidelines.

Ninety-five point eight percent.

In other words, nearly every child in the study matched with at least one man who could not possibly be the father.

The distribution was even more alarming. Many children matched multiple unrelated men. The chart in the original paper shows children matching with 2, 3, 4, 5, and upward. One child in the study matched with 32 different unrelated men.

But wait, you might think — this was a motherless test. Surely including the mother’s profile would fix the problem?

The researchers tested this too. Even when the mother’s DNA was included, 26 child-unrelated man pairings still could not be excluded. That’s an 8% failure rate even with complete information.

The study’s authors acknowledged that the false inclusion rate seemed “rather high” and warned that “caution must be taken.” They attributed some of the problem to the limited geographic origin of their samples (Northern Germany) and the occurrence of common alleles in that population.

But consider what this means for real-world applications. Paternity tests are used to assign legal fatherhood, determine child support obligations, resolve inheritance disputes, and establish immigration eligibility. The life-altering consequences of these tests are premised on accuracy rates that, according to this study, are nowhere near what the public believes.

If 95.8% of children can match with at least one unrelated man under standard conditions, then what is a paternity test actually measuring? How many men are currently paying child support for children who aren’t theirs — or being denied relationships with children who are — based on laboratory interpretations that wouldn’t survive proper scientific scrutiny?

These questions don’t have comfortable answers.

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Part 6: But What About...?

At this point, reasonable readers will have objections. The claims being made here contradict deeply held assumptions. Let’s address the most common counterarguments directly.

“But DNA evidence has exonerated innocent people.”

This is true, and it’s important. Organizations like the Innocence Project have used DNA testing to free hundreds of wrongfully convicted individuals, some of whom spent decades in prison for crimes they didn’t commit. This is unambiguous good.

But notice what this argument actually demonstrates: DNA evidence has the power to exclude — to show that a person’s DNA does not match the crime scene sample. This exclusionary power is less controversial.

The problem lies with inclusion — the claim that someone’s DNA does match. The studies reviewed above show that inclusion conclusions are far more subjective and error-prone than the public realizes. If DNA evidence can be misinterpreted to falsely include someone, then the same technology that has freed innocent people may also have helped convict them.

It’s worth noting that even mainstream forensic education acknowledges this limitation. The miniPCR bio laboratory guide used in universities states plainly: “Remember though, that even if the statistical analysis very strongly suggests a DNA match, that still doesn’t prove a suspect’s guilt. It only supports that the suspect’s DNA was present.”

This is exactly right — and exactly what juries are rarely told. A DNA match establishes presence, not guilt. Someone’s DNA at a crime scene might mean they committed the crime. It might also mean they were there earlier for innocent reasons, that their DNA was transferred inadvertently, or that the sample was contaminated. The leap from “DNA was present” to “defendant is guilty” requires assumptions that go far beyond what the molecular evidence can establish.

The exonerations prove the stakes are high. They don’t prove the system is working correctly.

“Laboratories have accreditation and quality controls.”

They do. Forensic laboratories undergo accreditation processes, participate in proficiency testing, and follow documented procedures.

But accreditation doesn’t address cognitive bias. A laboratory can follow every protocol perfectly and still produce biased results if the analyst knows what answer is expected. Quality control catches contamination and procedural errors; it doesn’t catch the subtle influence of context on subjective interpretation.

The sequential unmasking proposal — rejected by the field — was designed precisely to address this gap. Until laboratories operate blind to case context, accreditation provides false assurance.

“Modern STR methods are more accurate than early techniques.”

It’s true that forensic DNA technology has evolved since Jeffreys’ original Southern blot method. Short Tandem Repeat (STR) analysis examines more genetic markers and uses more sophisticated statistical models.

But both the Poetsch paternity study and the Dror forensic study used standard STR methods. These weren’t critiques of outdated techniques — they were examinations of current practice. The problems identified aren’t technological artifacts from the 1980s. They’re features of the system as it operates today.

“Not all DNA cases involve mixtures or motherless tests.”

This is true. A single-source DNA sample from a crime scene, compared to a single suspect, with high-quality material and unambiguous results, presents fewer interpretive challenges than a degraded mixture.

But mixture cases and complex scenarios are not rare edge cases. They’re common, especially in serious crimes. Sexual assaults typically produce mixed samples. Crime scenes often contain DNA from multiple individuals. And motherless paternity tests happen frequently in the real world.

More importantly, these difficult cases are precisely where the stakes are highest. A murder conviction. A rape prosecution. A custody battle. When someone’s freedom or family hangs in the balance, saying “well, the easy cases are more reliable” is cold comfort.

“Scientists have peer-reviewed this stuff. Surely they’d have caught major problems.”

The studies cited throughout this essay are peer-reviewed. The Dror and Hampikian study appeared in Science & Justice. The Poetsch study appeared in Forensic Science International. The PCAST report was produced by the President’s science advisors.

The problem isn’t that scientists haven’t identified these issues. The problem is that the findings haven’t penetrated public consciousness or changed institutional practice. The forensic establishment has proven remarkably resistant to reform. Courts continue admitting DNA evidence under standards established before these problems were documented. The gap between what researchers know and what juries believe remains vast.

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Part 7: The Statistics Game

One of the most powerful rhetorical weapons in the DNA evidence arsenal is statistics. Juries hear that the probability of a random match is “one in a billion” or that paternity is established with “99.99% certainty.” These numbers sound unassailable. What could be more objective than mathematics?

But these statistics don’t mean what most people think they mean. Understanding their limitations is essential to evaluating DNA evidence critically.

First, the headline numbers refer to the probability of a coincidental match between two unrelated individuals in a population — assuming the laboratory interpretation is correct. They don’t account for the probability that the laboratory made an error. A test might have a random match probability of one in a billion but an interpretation error rate of one in a hundred. The second number, not the first, determines the practical reliability of the result.

Second, the statistics depend on population databases and genetic models. Different populations have different allele frequencies. If the statistical model assumes one population structure but the actual individuals come from a different population, the numbers can be dramatically wrong. The National Research Council addressed this issue in their 1996 report The Evaluation of Forensic DNA Evidence, noting that match probabilities are estimates contingent on modeling assumptions — not absolute truths.

Third, the way statistics are presented to juries often conflates different questions. The probability that a randomly selected person would match the crime scene DNA is not the same as the probability that the defendant is innocent given the match. This distinction — known as the prosecutor’s fallacy — has been extensively documented in legal and statistical literature, yet it continues to influence courtroom outcomes.

Here’s what makes this particularly striking: the prosecutor’s fallacy isn’t some obscure academic concern. It’s explicitly taught in undergraduate forensic science courses. A 2021 laboratory guide from miniPCR bio — a mainstream educational resource used in universities — warns students directly about this error:

“Unfortunately, random match probabilities have been misinterpreted in many legal settings. For example, given a random match probability of 1 in 5 trillion, a prosecutor might claim that there is only ‘a 1 in 5 trillion chance that the defendant is innocent.’ This error in reasoning is referred to as the prosecutor’s fallacy.”

Students learning forensic DNA analysis are taught to recognise this fallacy. And yet juries — the people actually deciding guilt or innocence — rarely hear any such caveat. The gap between what forensic educators acknowledge in the classroom and what prosecutors present in the courtroom is remarkable.

Fourth, mixture interpretation introduces additional statistical complexity. When a DNA sample contains material from multiple contributors, analysts must make judgment calls about which peaks in the electropherogram represent true alleles and which represent noise. These calls affect the statistical calculations. As the Dror study demonstrated, different analysts make different calls — meaning the resulting statistics are contingent on subjective interpretations.

Fifth — and this is rarely discussed in courtroom settings — the impressive-sounding statistics collapse rapidly when sample quality degrades. The same miniPCR educational guide includes an exercise that illustrates this vividly. Students are asked to calculate random match probabilities using different numbers of STR locations. With seven or more locations, the numbers look astronomically small — one in billions or trillions. But if DNA evidence has degraded and only two STR locations can be analysed (which happens in real cases involving old or environmentally exposed samples), the random match probability can jump to levels where millions of people in a single country would match the profile.

The exercise asks students: “How confident would you be in using this DNA sample to convict someone of a crime?”

The honest answer, which students are meant to discover through calculation, is: not very confident at all. And yet degraded samples are routinely used in criminal prosecutions, often without juries understanding how dramatically the statistical certainty has eroded.

A 2020 paper by Ge and colleagues in Forensic Science International: Genetics examined false positive and false negative rates in familial relationship testing. Their analysis showed that even well-conducted tests carry non-zero error rates, and that the confidence intervals around paternity probabilities are wider than commonly appreciated.

The statistician’s view of DNA evidence is considerably more humble than the courtroom presentation. Probabilities are estimates. Models have assumptions. Error rates exist. When a forensic expert tells a jury that there’s a “one in a trillion” chance of a coincidental match, they’re speaking a language that sounds like certainty but rests on foundations far less solid than the words imply.

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Part 8: The Classroom-Courtroom Gap

Perhaps the most revealing aspect of the miniPCR educational materials is what they tell us about the gap between forensic science education and courtroom practice.

In the classroom, students are taught that DNA evidence has limitations. They learn about the prosecutor’s fallacy. They calculate how quickly statistical certainty evaporates with degraded samples. They’re reminded that a match doesn’t prove guilt. They work through exercises designed to instil appropriate humility about what DNA evidence can and cannot establish.

In the courtroom, juries hear none of this.

Instead, they hear experts present DNA evidence as if it were infallible. They hear statistics that sound like mathematical proof. They’re shown confident conclusions without the caveats that forensic science students are taught to apply. The nuance that educators consider essential for understanding DNA evidence is stripped away precisely when the stakes are highest.

This isn’t a minor inconsistency. It represents a systematic failure to communicate the limitations of forensic DNA analysis to the people making life-altering decisions based on that analysis. Students in laboratory courses are trusted with the truth about uncertainty and error rates. Juries — making decisions about imprisonment and sometimes death — are given a sanitised version that overstates certainty.

The question this raises is uncomfortable: if forensic educators believe these caveats are important enough to teach, why aren’t they important enough to share with juries? And if the legal system believes juries can’t handle statistical nuance, what does that say about the foundations on which convictions rest?

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Conclusion: What I’d Tell My Colleague Now

If my colleague asked me today what I think about DNA testing, my answer would be different from the one I gave a few months ago.

I would tell him that the peer-reviewed literature reveals a field that has oversold its reliability for decades. That forensic DNA laboratories routinely resist basic scientific controls like blinding. That when independent analysts examine the same evidence without contextual bias, they frequently reach different conclusions. That paternity tests under real-world conditions produce false inclusions at rates that would shock anyone who’s been told these results are definitive.

I would tell him that none of this is fringe or conspiratorial. The studies come from accredited researchers publishing in mainstream journals. The concerns have been raised by the President’s science advisors. The proposals for reform have come from within the forensic science community itself — and been rejected.

I would tell him that this doesn’t mean DNA evidence is worthless. The exclusionary power of DNA — showing that someone’s profile doesn’t match — remains valuable. The problem lies with inclusion, interpretation, and the statistical claims wrapped around them.

And I would tell him that this matters beyond the specific domain of forensic genetics. What we’ve uncovered here is a case study in how institutional science can calcify around claims that haven’t been adequately validated. How professional consensus can substitute for rigorous testing. How “99.99% accurate” can become a mantra that nobody questions because questioning it seems to put you outside the bounds of respectable discourse.

The lesson isn’t that science is worthless — it’s that science is a method, not an institution. The method demands blinding, replication, falsification, and humility about uncertainty. When those demands are subordinated to professional convenience, legal expediency, or the desire to project certainty, what remains may wear the costume of science without embodying its spirit.

DNA testing has been called the gold standard of forensic evidence. The evidence suggests the gold is considerably less pure than advertised. And if this is true here — in a domain where the technology is relatively mature and the applications relatively straightforward — what does it suggest about scientific claims in domains where the phenomena are more complex, the stakes more politically charged, and the validation even less rigorous?

I’m grateful to researchers like Jamie Andrews who ask these uncomfortable questions. The answers don’t always make us feel secure. But security built on illusion isn’t security at all.

Stay curious. Look at primary sources. And when someone tells you the science is settled, ask them: settled by whom? And how would we know if they were wrong?

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References

Andrews, J. (2025). Who’s The Daddy? The Virology Controls Studies Project.

Dror, I. E., & Hampikian, G. (2011). Subjectivity and bias in forensic DNA mixture interpretation. Science & Justice, 51(4), 204–208. https://doi.org/10.1016/j.scijus.2011.08.004

Ge, J., Eisenberg, A. J., Budowle, B., & Chakraborty, R. (2020). How many familial relationship testing results could be wrong? Forensic Science International: Genetics, 46, 102257. https://doi.org/10.1016/j.fsigen.2020.102257

Jeffreys, A. J., Wilson, V., & Thein, S. L. (1985). Hypervariable “minisatellite” regions in human DNA. Nature, 314, 67–73. https://doi.org/10.1038/314067a0

Krane, D. E., Ford, S., Gilder, J. R., Inman, K., Jamieson, A., Koppl, R., … Thompson, W. C. (2008). Sequential unmasking: A means of minimizing observer effects in forensic DNA interpretation. Journal of Forensic Sciences, 53(4), 1006–1007. https://doi.org/10.1111/j.1556-4029.2008.00787.x

miniPCR bio. (2021). Electrophoresis Forensics Lab: Wrongfully Convicted? Instructor’s and Student’s Guide (Version 1.0). miniPCR bio.

National Research Council. (1996). The evaluation of forensic DNA evidence. National Academies Press. https://www.ncbi.nlm.nih.gov/books/NBK232615/

Poetsch, M., Lüdcke, C., Repenning, A., Fischer, L., Mályusz, V., Simeoni, E., Lignitz, E., Oehmichen, M., & von Wurmb-Schwark, N. (2006). The problem of single parent/child paternity analysis—Practical results involving 336 children and 348 unrelated men. Forensic Science International, 159(2–3), 98–103. https://doi.org/10.1016/j.forsciint.2005.07.003

President’s Council of Advisors on Science and Technology. (2016). Forensic science in criminal courts: Ensuring scientific validity of feature-comparison methods. Executive Office of the President. https://obamawhitehouse.archives.gov/sites/default/files/microsites/ostp/PCAST/pcast_forensic_science_report_final.pdf

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