United States of America v. Hunter Ryan Anderson

U.S. COURT OF APPEALS
FOR THE THIRD CIRCUIT
No. 25-1223
UNITED STATES OF AMERICA
v.
HUNTER RYAN ANDERSON,
Appellant
_____________________________
Appeal from the U.S. District Court, M.D. Pa.
Chief Judge Matthew W. Brann, No. 4:21-cr-00204-001
Before: BIBAS, PORTER, and BOVE, Circuit Judges
Submitted Jan. 29, 2026; Decided Mar. 26, 2026
_____________________________
OPINION OF THE COURT
BOVE, Circuit Judge. If you dislike jargon, buckle up.
The focus of this appeal is the reliability of probabilistic
genotyping software in forensic DNA identification under
Daubert v. Merrell Dow Pharmaceuticals, Inc., 509 U.S. 579
(1993) and Rule 702 of the Federal Rules of Evidence.1
1 Unless otherwise indicated, case quotations omit all internal
citations, quotation marks, footnotes, alterations, and
subsequent history. Unless otherwise indicated, references to
a “Rule” are to the Federal Rules of Evidence.

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The underlying criminal case involved a charge of
unlawful possession of a firearm. At the request of law
enforcement, a private company used software—called
TrueAllele—to compare DNA profiles swabbed from the gun
and Defendant Hunter Anderson. TrueAllele calculated a
likelihood ratio of 11.5 trillion. We explain what that means
below, but it was not good for Defendant. He challenged the
TrueAllele evidence under Daubert. Following a battle of
experts, the District Court ruled that the government had
cleared the threshold reliability requirement for admissibility
of expert evidence under Rule 702. We agree. TrueAllele may
not be perfect, but most science is not. TrueAllele’s
probabilistic genotyping methodology has adequate scientific
foundations to be used in federal trials. It is reliable enough.
Cross-examination at trial is the appropriate time to address
any alleged flaws in TrueAllele’s methodology or results.
Defendant’s other appellate arguments are without
merit. Accordingly, we will affirm.
I.
The relevant facts are straightforward even if the related
science is not. While executing a search warrant, Pennsylvania
State Police seized a gun from a bag that also contained
Defendant’s ID and two loaded magazines. Defendant was in
the same bedroom as the bag at the time of the search. He was
on parole for a state-law offense at the time.
Police swabbed DNA evidence from the gun. They also
collected a DNA sample from Defendant based on a separate
search warrant. The Pennsylvania State Police Crime
Laboratory found multiple sources of DNA in the gun swab

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and could not state “within a degree of scientific certainty”
whether there was a match with Defendant’s DNA. A501.
Law enforcement sent the DNA evidence and
Defendant’s sample to Pittsburgh-based Cybergenetics, Corp.,
which used TrueAllele to do a comparison. TrueAllele
concluded that the multi-source mixture of DNA from the gun
was 11.5 trillion times more likely to have been created if
Defendant contributed to that mixture than if another
Caucasian contributed to the mixture. In plain English,
TrueAllele’s likelihood ratio was strong evidence that
Defendant had left DNA on the gun, in a case that turned on
whether Defendant possessed that gun.
Defendant moved to exclude the TrueAllele evidence
under Daubert. After a two day-hearing at which the
government offered testimony from the founder of
Cybergenetics and Defendant relied on testimony from two
experts of his own, the District Court denied the motion in a
thorough opinion. Defendant also moved to dismiss the
Indictment based on facial and as-applied Second Amendment
challenges to § 922(g)(1). The District Court denied that
motion as well.
Defendant later pleaded guilty to the § 922(g)(1)
charge. His plea agreement preserved his ability to challenge
the District Court’s rulings relating to the DNA evidence and
his Second Amendment motion. At sentencing, the District
Court imposed a prison term of 78 months of imprisonment,
which the court ordered to run consecutive to Defendant’s
anticipated state-law sentence for the parole violation.
Defendant timely appealed.

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II.
The District Court had jurisdiction under 18 U.S.C.
§ 3231. We have jurisdiction under 28 U.S.C. § 1291.
In Part III, we review the District Court’s application of
Daubert for abuse of discretion. Cohen v. Cohen, 125 F.4th
454, 459 n.2 (3d Cir. 2025). In Part IV, we review the District
Court’s Second Amendment analysis de novo. United States v.
Harris, 144 F.4th 154, 157 (3d Cir. 2025). In Part V, we review
the District Court’s sentence for abuse of discretion. United
States v. Jumper, 74 F.4th 107, 111 (3d Cir. 2023).
III.
The government established at the Daubert hearing that
TrueAllele’s probabilistic genotyping methodology was
sufficiently reliable to be admissible pursuant to Rule 702. Our
holding is a natural extension of existing precedent based on
the record before the District Court and the scientific
advancements that the record reflects.
We previously held that the government had established
the reliability of a DNA-testing methodology that relied on
software and statistics but was not as complex as TrueAllele.
See United States v. Trala, 386 F.3d 536, 541-42 (3d Cir. 2004).
The Sixth Circuit has held that a different probabilistic
genotyping software, STRmix, is reliable under Daubert. See
United States v. Gissantaner, 990 F.3d 457, 467 (6th Cir. 2021).
We agree with substantially all of Chief Judge Sutton’s points
in Gissantaner, as well as the District Court’s thoughtful
analysis of the issues in this case. We write here to address
Defendant’s arguments on appeal and underscore some of the

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District Court’s points regarding TrueAllele’s reliability under
the relevant standard.
A.
The purpose of a Daubert hearing is to permit a trial
court to address preliminary questions relating to the
admissibility of expert evidence under Rule 702. See In re
Paoli R.R. Yard PCB Litig., 35 F.3d 717, 743-44 (3d Cir. 1994).
In this context, the proponent of the evidence must establish
admissibility by a preponderance under Rule 104(a). See id. at
744 n.11. This “rigorous gatekeeping function” usually
requires trial courts to develop an evidentiary record, through
a hearing or otherwise, in support of their findings. Cohen, 125
F.4th at 460.
Admissibility under Rule 702 is governed by three
issues: expert qualifications, reliability of the methodology,
and relevance of the evidence. See Elcock v. Kmart Corp., 233
F.3d 734, 741 (3d Cir. 2000). Reliability is the issue here. The
expert does not have to be right. “[T]he evidentiary
requirement of reliability is lower than the merits standard of
correctness.” In re TMI Litig., 193 F.3d 613, 665 (3d Cir.
1999). Some of the factors bearing on reliability are testability,
peer review, error rates, existence of standards, and general
acceptance of the method. See United States v. Mitchell, 365
F.3d 215, 235 (3d Cir. 2004). These are just guideposts. The
list is not exhaustive. Determining reliability is not a check-
the-box exercise, and we give trial courts significant autonomy
to do the necessary work.

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B.
“DNA testing has an unparalleled ability both to
exonerate the wrongly convicted and to identify the guilty.”
Dist. Att’y’s Off. for Third Jud. Dist. v. Osborne, 557 U.S. 52,
55 (2009). “The advent of DNA technology is one of the most
significant scientific advancements of our era,” and “the utility
of DNA identification in the criminal justice system is already
undisputed.” Maryland v. King, 569 U.S. 435, 442 (2013).
Caution and analytic precision are required, however, because
forensic DNA testing “often fails to provide absolute proof of
anything.” Osborne, 557 U.S. at 80-81 (Alito, J., concurring).
Some context is necessary to understand why.
DNA is a molecule shaped like a long, twisted ladder.
A gene is a strand from a DNA ladder. The rungs of the ladder
are called base pairs. DNA base pairs are organized on
chromosomes. A person’s genotype—that is, his genetic
makeup—is governed by the chromosomes and all of the base-
pair information those chromosomes contain. Only identical
twins have the same genotype.
Zooming in, forensic DNA identification involves
analyzing particular locations on chromosomes. Each location
is sensibly called a locus. The loci at issue are mostly
standardized. A locus usually has two alleles, which are the
DNA information from each parent. The focus at a locus is on
the content of the DNA base pairs and the number of times the
pairs repeat in a sequence. This is one of the distinctive
features of each person’s DNA. The unit of measure is called
a short tandem repeat. An allele has a specific number of short
tandem repeats that can be measured and compared to alleles
at the same locus from other DNA.

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One of the most basic situations for DNA comparison
involves an evidentiary sample that contains DNA from only
one person. Think of skin cells or blood. In a lab, the DNA is
extracted from the biological material. Particular loci are
replicated to facilitate the analysis. The loci are then examined
and labeled, and the short tandem repeats that help make up
alleles at the loci are measured. These measurements can be
depicted as a DNA profile in several ways. One is an
electropherogram that plots the short tandem repeats and
related characteristics.
Sticking with our base case of a single-source DNA
sample, scientists would anticipate no more than two alleles at
each locus. That is because we expect to see one allele from
each parent. One or both of the alleles may not have made it
into the sample, however, so lab personnel would not be
shocked to see zero or one allele at a locus. But the short
tandem repeats in the alleles extracted from the evidence can
be compared to the short tandem repeats in alleles at the same
locus in another DNA profile to assess the likelihood of a
match. Sometimes the comparison can be performed manually
when forensic scientists are dealing with a single-source
sample.
Things get more complicated when the sample has more
than two alleles at a locus. This suggests DNA from multiple
people is present. More than one person can leave DNA on a
piece of physical evidence by touching it, among other things.
It is hardly uncommon. Then we have what is called a multi-
source mixture. For example, three alleles at a locus could
result from one person contributing two alleles to the mixture
and a second person leaving only one allele behind. Or three
people could each have contributed one allele. The more
alleles at the locus, the more challenging it becomes to figure

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out which alleles came from each contributor and how many
contributors there were to the sample. There are also other
variables that add to the complexity, such as the quantity of
DNA from each contributor in the evidentiary sample.
These are the types of complications that left the
Pennsylvania State Police Crime Laboratory uncomfortable
making comparative findings between Defendant’s DNA and
the evidentiary DNA mixture from the gun in this case. That
is where probabilistic genotyping came in. Law enforcement
sent Cybergenetics the DNA profiles that the lab had processed
from the gun and Defendant. The company did not handle
biological material for the government in this case.
Cybergenetics deployed the TrueAllele software to compare
the DNA profiles using an algorithm and calculate a likelihood
ratio.
A likelihood ratio is a DNA match statistic that
compares (1) the probability of a DNA mixture that includes a
target’s DNA to (2) the probability of the same DNA mixture
including DNA from a random person. A positive likelihood
ratio supports the first hypothesis involving the presence of the
target’s DNA. Here, the likelihood ratio was 11.5 trillion. The
government’s expert explained that this likelihood ratio meant
that a match between some of the DNA from the gun and
Defendant’s DNA is “11.5 trillion times more probable than a
coincidental match” between the evidentiary sample and a
random person’s DNA. A72. This tells us that, “in the abstract
and without considering any other evidence in this case, it
would be unusual if this DNA contained no DNA contributed
from [Defendant].” Gissantaner, 990 F.3d at 462.

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C.
Because the defense challenged the TrueAllele
evidence, the government had to prove by a preponderance that
TrueAllele’s probabilistic genotyping methodology was
reliable. To resolve the dispute, the District Court played the
traditional role of Daubert gatekeeper during a two-day
hearing. The government established during that process that
TrueAllele’s probabilistic genotyping is (1) capable of being
tested; (2) susceptible to error-rate calculations; (3) governed
by standards that can be applied to reduce errors; (4) a product
of peer-review scrutiny; and last, but not least, (5) generally
accepted in the relevant scientific field of DNA evidence
interpretation. These considerations were sufficient to support
the District Court’s conclusion that the TrueAllele evidence
would have been admissible at trial under Rule 702 had
Defendant not pleaded guilty.
1.
The government established that TrueAllele’s testability
supported a finding of reliability.
Daubert does not require “directed, specific actual
testing.” Mitchell, 365 F.3d at 238. Where a methodology is
capable of being tested using scientific methods, that is
indicative of an adequately rigorous and reliable design. The
fact that TrueAllele’s likelihood ratios are probabilistic rather
than absolute is not an insurmountable barrier. See id. at 236-
37; see also United States v. Mornan, 413 F.3d 372, 381 (3d
Cir. 2005) (“Handwriting experts often give their opinions in
terms of probabilities rather than certainties.”). The issue is
whether the expert’s methodology can be “challenged in some
objective sense, or whether it is instead simply a subjective,

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conclusory approach that cannot reasonably be assessed for
reliability.” Fed. R. Evid. 702 advisory committee’s note to
2000 amendment. Testability based on objective metrics also
“assures the opponent of proffered evidence the possibility of
meaningful cross-examination (should he or someone else
undertake the testing).” Mitchell, 365 F.3d at 238.
TrueAllele can be tested. This can be accomplished by
creating multi-source DNA mixtures in lab settings for the
software to analyze. See Gissantaner, 990 F.3d at 463-64. In
such a test, a false negative would occur if TrueAllele returned
a likelihood ratio suggesting that one of the samples that was
actually used in the experiment was not present in the mixture.
See id. at 464. A false positive would occur if TrueAllele
returned a likelihood ratio suggesting that some other DNA,
which was not included in the experiment, was a part of the
experimental mixture. See id. The feasibility of these types of
tests was enough to resolve the testability factor in favor of the
government.
The defense also had an opportunity to conduct tests.
Defense counsel had access to the algorithm that guides
TrueAllele’s calculations. The government gave defense
counsel an opportunity to use a computer running TrueAllele,
the DNA profiles from the gun and Defendant, and about
27,000 other DNA profiles from a university research lab. The
government also produced to the defense materials relating to
the testing of TrueAllele’s server, which runs the math to
separate multi-source DNA mixtures into individual profiles,
and TrueAllele’s Visual User Interface (VUIer), which
calculates and displays likelihood ratios. These were
additional objective materials relevant to the operation of
TrueAllele that put the defense in a position to seek to falsify
the hypotheses underlying the software’s methodology. The

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possibility of challenging the hypotheses underlying a
methodology is the core issue for testability.
In addition, TrueAllele “has been” tested. Daubert, 509
U.S. at 593. Cybergenetics used five unaffiliated probabilistic
genotyping programs to compare the DNA profiles from the
gun and Defendant. Each of the other programs produced a
likelihood ratio that was similar to the ratio calculated by
TrueAllele. The government disclosed those results, as well as
TrueAllele’s calculations, to the defense. At the Daubert
hearing, the government also identified 42 validation studies
relating to testing of TrueAllele.
Despite these disclosures, Defendant argues on appeal
that he should have been granted access to TrueAllele’s source
code so that he could test that too. The District Court rejected
Defendant’s demand, and some state courts have issued similar
rulings when applying their own expert-admissibility
standards. See Anderson, 673 F. Supp. 3d at 681-82; see also
People v. Wakefield, 195 N.E.3d 19, 29-30 (N.Y. 2022); State
v. Simmer, 935 N.W.2d 167, 180-81 (Neb. 2019);
Commonwealth v. Foley, 38 A.3d 882, 890 (Pa. Super. Ct.
2012). We agree with the thrust of those decisions and are not
persuaded by Defendant’s non-binding contrary authorities.
Because the issue was whether TrueAllele is capable of
being tested based on objective criteria, Cybergenetics was not
required to let the defense under TrueAllele’s hood by
disclosing the source code. Keep in mind that the source code
is just a compilation of words and programmer syntax
implementing TrueAllele’s algorithm. As noted, the defense
already had access to that algorithm. Source code is not
presumed to be flawless, but the government also produced a
log of changes to TrueAllele’s source code, including updates

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and fixes for software bugs. And the defense could have
compared those materials to the readily accessible source code
for the other probabilistic genotyping programs that
Cybergenetics used to test TrueAllele’s likelihood ratio in this
case. No further disclosures were required under Daubert or
Rule 702 in order to establish testability based on the evidence
at the hearing.
Finally, we take seriously Defendant’s invocation of
potential fairness concerns arising from his lack of access to
TrueAllele’s source code. But Daubert is not a criminal
discovery device. Courts have long relied upon other
mechanisms to ensure fairness in prosecutions. For example,
in addition to the general discovery requirements of Rule 16 of
the Federal Rules of Criminal Procedure, which apply to
information in the possession of the prosecution team, the
government has additional, particular discovery obligations
with respect to expert witnesses. See Fed. R. Crim. P.
16(a)(1)(G); see also 18 U.S.C. § 3500; Fed. R. Crim. P. 26.2.
The Constitution requires prosecutors to disclose evidence
within the possession of the prosecution team that is favorable
to the defense, including information that undercuts material
factual, expert, and legal theories of the case. See Dennis v.
Sec’y, Pa. Dep’t of Corr., 834 F.3d 263, 284 (3d Cir. 2016).
This obligation mandates disclosure of evidence bearing on the
credibility of government witnesses. See United States v.
Scarfo, 41 F.4th 136, 227 (3d Cir. 2022). District Courts also
have tools at their disposal to remedy “stonewalling” by
experts in response to cross-examination, including the
discretion to exclude the testimony in its entirety. Mitchell, 365
F.3d at 246.
In light of these overlapping discovery obligations, and
in the context of a record that reveals robust disclosures by the

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government, we will not authorize a fishing expedition through
TrueAllele’s source code under the auspices of Daubert. For
purposes of testability, what matters is that the challengers of
the methodology could run scientific tests to try to show that
TrueAllele does not function in the manner that the
government’s expert described. The defense in this case was
as well-positioned to do so as Daubert and our related caselaw
requires. We therefore agree with the District Court that the
government established that TrueAllele is capable of being
tested and has in fact been tested, and that additional
disclosures were not necessary.
2.
The government demonstrated that it is possible to
calculate error rates relating to TrueAllele’s performance, and
that the software’s error rates are low. These considerations
also favored admissibility.
We focus on false positives when we consider error
rates under Daubert. See Mitchell, 365 F.3d at 240. On this
issue, the government’s expert relied in part on a 2014 study
that he conducted with the Virginia Department of Forensic
Science. The study identified a false-positive rate for
TrueAllele of 0.005%.2 At the Daubert hearing, the
government’s expert estimated that the error rate for manual-
review comparative DNA analysis by humans was between 2%
and 6%. The expert also described how the Virginia study
demonstrated that TrueAllele’s rate of false positives decreased
2 See Mark W. Perlin, et al., TrueAllele Casework on Virginia
DNA Mixture Evidence: Computer and Manual Interpretation
in 72 Reported Criminal Cases, 9 PLOS ONE 3: e92837 1, 12
(Mar. 25, 2014), https://perma.cc/4T76-EBUM.

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as the strength of the likelihood ratio increased. When
TrueAllele calculated a likelihood ratio expressed in trillions,
the rate of false positives was infinitesimally small. In this
case, based on the strength of the likelihood ratio, the
government’s expert estimated an error rate of approximately
one in 146 trillion.
Defendant argues that these error rates are inaccurate
because, in his view, they fail to account for potential errors by
software operators and problems lurking in TrueAllele’s source
code. Neither argument is compelling. There was no dispute
that analysts running TrueAllele have some discretion. They
input hypotheses about the number of contributors to an
evidentiary DNA mixture and set parameters relating to
degradation of the DNA, and they also make decisions about
how many times to run the software when doing the
comparison. Discretion is not a dealbreaker. In Mitchell, we
held that a fingerprint identification methodology was reliable
under Daubert notwithstanding the fact that the process
involved “an unspecified, subjective, sliding-scale” and human
judgment calls relating to the quality and level of detail in a
fingerprint. 365 F.3d at 236. The discretion exercised by
TrueAllele analysts is more limited, and the testing process
described above would permit the defense to identify outcome-
determinative errors whether they result from human or
technological problems.
Defendant’s second argument regarding the error rate is
based on testimony from a defense expert about an average
error rate of “six errors per thousand lines of code.” A381.
Defense counsel described this as the “average rate in the field
of software engineering.” A157-58. Despite having access to
a change log for core aspects of TrueAllele’s code, and aided
by experts who had been permitted to view TrueAllele’s source

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code in a different matter, counsel did not tie that “average
rate” to the evidence in this case.3 When defense counsel
suggested that such an error could impact a likelihood ratio, the
government’s expert responded that such an outcome was
“hypothetically possible, but extensive testing hasn’t produced
anything like that claim.” A162-63.
We are left, then, with little more than defense
speculation that people sometimes make mistakes, source code
sometimes contains errors, software sometimes malfunctions,
and TrueAllele may suffer from one or more of these issues.
These concerns are not substantial enough to undermine
reliability under Rule 702. “[T]he various methods of
estimating the error rate all suggest that it is very low.”
Mitchell, 365 F.3d at 241. This “strongly” favors reliability
under Daubert. Id.
3 In United States v. Ellis, a District Court permitted defense
experts to review TrueAllele’s source code pursuant to a
protective order. See ECF Nos. 196, 202, No. 19 Cr. 369 (W.D.
Pa. Jan. 2022). The case resulted in a guilty plea, and we did
not have an opportunity to review that discovery ruling.
Defendant retained one of the experts from Ellis, and the expert
testified at the Daubert hearing in this case. While the record
in Ellis and this case both indicate that the defense expert
would have liked even more access to the source code than he
received, the expert did not identify any issues at the Daubert
hearing based on his review in Ellis that specifically tied the
defense arguments about average rates of source-code error to
TrueAllele. Nor did Defendant seek relief from the protective
order in Ellis in order to present any such issues to the District
Court in this case. These circumstances further illustrate the
lack of merit to Defendant’s blanket demand for TrueAllele’s
entire source code.

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3.
TrueAllele’s probabilistic genotyping methodology is
governed by the type of standards that are a hallmark of a
reliable methodology grounded in hard science. We also care
about standards because, if properly applied, they can reduce
error rates. See Mitchell, 365 F.3d at 241. That is true here.
Forensic DNA identification has been governed by
standards for decades. Predecessors of the Scientific Working
Group on DNA Analysis Methods, which is associated with the
FBI, date back to the 1980s. In 2015, this Scientific Working
Group published “Guidelines for the Validation of Probabilistic
Genotyping Systems.” In 2020, the American National
Standards Institute and the Standards Board of the American
Academy of Forensic Sciences jointly issued a “Standard for
Validation of Probabilistic Genotyping Systems.” At the
Daubert hearing, the government offered a written summary
from Cybergenetics describing TrueAllele’s positions
regarding compliance with this Standard. Defendant did not
agree with all of those positions, but that tension was to be
addressed through cross-examination at a trial.
Defendant challenges the government’s standards
evidence by arguing that there was no evidence that the
Pennsylvania State Police Crime Laboratory followed these
standards or was calibrated to use TrueAllele. The problem
with that argument is that the lab did not do any probabilistic
genotyping. Rather, the lab extracted and amplified the DNA
and prepared files depicting short tandem repeats at
standardized loci in Defendant’s sample and the evidentiary
mixture from the gun. Those are separate “wet lab” steps—
extraction, amplification, and quantification of short tandem
repeats—with an even longer history and separate-but-related

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standards, which Defendant did not challenge in his Daubert
motion. See, e.g., King, 569 U.S. at 442-43. Speculation about
the state lab’s standards and performance, without a basis in
the record, is insufficient to overcome the government’s
Daubert showing with respect to probabilistic genotyping.
Defendant also argues that TrueAllele should be subject
to additional standards established by the Institute of Electrical
and Electronics Engineers. We take no position on the
technical and scientific merit of that contention, but it is not a
legal basis to exclude evidence. We previously affirmed a
Daubert reliability finding where the expert was subject to the
criticism, by a competing expert, that his field “lacked
measurable standards.” United States v. Velasquez, 64 F.3d
844, 846, 851 (3d Cir. 1995). If a dispute as to the existence of
governing standards is insufficient to preclude expert evidence,
then Defendant’s argument that probabilistic genotyping
software should be subject to additional standards must also
fail. And it does fail, particularly in light of the extent of the
existing standards and the weight of the other factors.
4.
TrueAllele has been subjected to peer review and
publication. These processes are not “necessary conditions of
reliability.” Kannankeril v. Terminix Int’l, Inc., 128 F.3d 802,
809 (3d Cir. 1997). But going through the process is another
“component of good science.” In re TMI Litig., 193 F.3d at
663-64; see also Gissantaner, 990 F.3d at 464-65
(“[P]ublication in a peer-reviewed journal alone typically
satisfies this Daubert inquiry.”).
Of the 42 TrueAllele validation studies presented by the
government at the Daubert hearing, eight of them were subject

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to peer review. One of the peer-reviewed studies addressed
TrueAllele’s efficacy with DNA mixtures involving up to 10
contributors, which are more complex than the mixture
swabbed from the gun in this case. Defendant takes issue with
the fact that the expert helped author seven of the eight studies.
This was another issue to be taken up during cross-examination
at trial, and it is of little consequence under Daubert or Rule
702. See Mitchell, 365 F.3d at 239, 245-46. The important
part, confirmed at the hearing, is that the peer reviews were
conducted by anonymous independent scientists. This factor
provides substantial additional support for TrueAllele’s
reliability.
5.
Probabilistic genotyping is generally accepted, for
purposes of Daubert, in the field of forensic DNA
identification. This is another “important factor in ruling
particular evidence admissible.” Daubert, 509 U.S. at 594.
Courts have previously concluded that probabilistic
genotyping software is generally accepted. See Gissantaner,
990 F.3d at 466-67 (collecting cases). This is true of TrueAllele
in particular, though some of those cases applied expert
standards different than Daubert. See United States v. Lockett,
2023 WL 7181251, at *7 (M.D. La. 2023); Wakefield, 195
N.E.3d at 28; Foley, 38 A.3d at 888. The District Court agreed
with these decisions, and the evidence at the Daubert hearing
substantiated general acceptance for purposes of this case.
The government’s expert testified that TrueAllele had
been used in a variety of settings for more than 25 years,
including by approximately 400 organizations involved in law
enforcement, criminal defense and exoneration work, paternity

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testing and kinship analysis, and identification of remains. See
In re Paoli R.R. Yard PCB Litig., 35 F.3d at 742
(reemphasizing, post-Daubert, relevance of non-judicial uses
of the challenged methodology). More than 250 criminal
defense teams have used the software. The expert also
explained, without contradiction, that the software has been
deemed admissible in 37 cases in the United States.
Collectively, this was enough evidence to establish general
acceptance.
Defendant’s citation to the Ninth Circuit’s non-
precedential discussion of STRmix, a different probabilistic
genotyping software, is not enough to overcome that evidence.
See United States v. Russell, 2024 WL 4054382 (9th Cir. 2024).
In Russell, the trial judge did not conduct a Daubert hearing
and therefore lacked the type of record developed by the
District Court in this case. The Ninth Circuit held that the error
was not harmless based in large part on a draft report from the
National Institute of Standards and Technology (NIST).4 See
id. at *1. The court noted concern in the NIST’s draft about a
supposed lack of “established and accepted criteria” for
complex mixtures involving low quantities of DNA. Id. The
final version of the report deleted that concern. Compare NIST
Draft Report at 82, with NIST Final Report at 73. The NIST
maintained language in the final report regarding a lack of
publicly available data for use in an “external and independent
4 See NIST, DNA Mixture Interpretation: A NIST Scientific
Foundation Review (June 2021), https://perma.cc/J3MU-
6VKL [NIST Draft Report]. The NIST published the final
version of the report in 2024. NIST, DNA Mixture
Interpretation: A NIST Scientific Foundation Review
(December 2024), https://perma.cc/6DQR-3B4W [NIST Final
Report].

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assessment” of probabilistic genotyping, which the Russell
panel had emphasized from the draft. NIST Final Report at 94;
NIST Draft Report at 75. But additional external review of
scientific methodologies has never been dispositive of Daubert
reliability. We look to peer review, which has been conducted
on TrueAllele, and general acceptance, which the government
established. Therefore, the harmless-error analysis in Russell
is not persuasive.
Defendant also attacks the District Court’s conclusion
by pointing to his experts’ opinions that TrueAllele is not
generally accepted in “the community of computer scientists
and software engineers.” Br. 44. The contention fails because
of the mismatch between the methodology and the field
Defendant presses. When evaluating general acceptance, we
have defined the relevant scientific community in a narrower
fashion and with greater emphasis on the salient features of the
challenged methodology. In Mitchell, for example, we defined
the relevant community as “forensic identification” for
purposes of assessing a manual-review latent fingerprint
identification process. 365 F.3d at 220-21, 241. The District
Court decision that we affirmed in Trala, 386 F.3d at 541-42,
which involved use of statistics to analyze short tandem
repeats, defined the relevant community as “forensic
geneticists.” United States v. Trala, 162 F. Supp. 2d 336, 348
(D. Del. 2001). Requiring the government to establish general
acceptance by computer scientists and software engineers
would be in tension with the reasoning in those cases.
It is also difficult to discern a limiting principle from
Defendant’s attempt to bring these additional disciplines into
the fold. Using software in scientific pursuits is hardly unique.
Scientists may ultimately decide that it makes sense to enhance
technical software standards governing probabilistic

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genotyping to align more closely with standards thought wise
by some computer scientists or electrical engineers. We
decline to adopt a rule that weighs in on that debate, and
Defendant’s argument presumes a threshold for reliability
under Rule 702 that does not exist. The government
demonstrated that TrueAllele was generally accepted in the
relevant scientific community, which was all that was
necessary at the Daubert hearing.
IV.
The District Court correctly rejected Defendant’s
Second Amendment challenges to § 922(g)(1) based on the
facts of this case. Under our precedent, because Defendant was
serving a term of parole under state law at the time of his
federal arrest, his as-applied challenge is foreclosed, which
dooms his facial challenge as well. See United States v. Moses,
142 F.4th 126, 135 (3d Cir. 2025).
V.
Defendant’s challenge to his sentence is similarly
without merit. The District Court correctly calculated a
Guidelines range of 77 to 96 months based on an Offense Level
of 21 and Criminal History Category VI. Following a careful
hearing, the District Court sentenced Defendant principally to
a within-Guidelines term of imprisonment of 78 months. The
District Court indicated that the prison term should run
consecutive to any sentence imposed as a result of the state-
law parole violation.
On appeal, Defendant argues that his sentence violated
U.S.S.G. § 5G1.3(d), which authorizes sentencing judges to
impose a consecutive sentence in order to “achieve a

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reasonable punishment for the instant offense.” The District
Court was aware of the related state proceedings, and the
court’s stated concerns about Defendant’s criminal history and
risk of recidivism, among other things, amply supported the
decision to require the federal sentence to run consecutively to
any state-law sentence. Setser v. United States, 566 U.S. 231,
243-45 (2012). We see no abuse of discretion in that decision.
VI.
The government demonstrated that TrueAllele is
reliable enough to be admissible at trial. Defendant’s Second
Amendment arguments are foreclosed by precedents we lack
authority to revisit. His sentencing challenges are meritless.
Accordingly, we will affirm.
Counsel for Appellant
Helen A. Stolinas
Mazza Law Group
Counsel for Appellee
Patrick J. Bannon
Sean A. Camoni
Geoffrey W. MacArthur
Carlo D. Marchioli
Office of United States Attorney
Middle District of Pennsylvania

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