Researchers analyzed blood samples collected from January 2019 through December 2024 by the Hospital Universitario “Dr. José Eleuterio González” and collaborating hospitals and laboratories across northeastern Mexico. Participants had been referred for possible solid-organ or hematopoietic progenitor cell transplantation, and inclusion required complete high-resolution results for HLA-A, HLA-B, HLA-C and HLA-DRB1. The researchers therefore classified the sample as a clinically referred cohort rather than a population-based cohort.

High-resolution typing was performed with sequencing-based Sanger methods for 468 samples, or 77.2%, collected from January 2019 through January 2024. The remaining 138 samples, or 22.8%, collected from February through December 2024, were typed by next-generation sequencing. Allele assignments were standardized to two-field resolution. Frequencies were calculated by direct counting, while four-locus haplotypes were inferred using an expectation–maximization algorithm with bootstrap resampling.

The most frequent alleles at HLA-A were HLA-A*02:01 at 23.76%, HLA-A*24:02 at 14.44% and HLA-A*03:01 at 6.52%. At HLA-B, the leading alleles were HLA-B*35:01 at 7.43%, HLA-B*40:02 at 6.44% and HLA-B*51:01 at 6.11%. The most frequent HLA-C alleles were HLA-C*04:01 at 17.16%, HLA-C*07:02 at 14.77% and HLA-C*07:01 at 8.25%. At HLA-DRB1, HLA-DRB1*08:02 occurred at 11.88%, HLA-DRB1*04:07 at 9.41% and HLA-DRB1*07:01 at 8.33%.

The two most frequent inferred haplotypes each accounted for 1.24%: HLA-A*03:01–HLA-B*07:02–HLA-C*07:02–HLA-DRB1*15:01 and HLA-A*68:03–HLA-B*39:05–HLA-C*07:02–HLA-DRB1*04:07. Two additional haplotypes exceeded 1%: HLA-A*24:02–HLA-B*39:06–HLA-C*07:02–HLA-DRB1*14:06 at 1.16% and HLA-A*29:02–HLA-B*44:03–HLA-C*16:01–HLA-DRB1*07:01 at 1.07%. All remaining inferred haplotypes occurred below 1%.

HLA-A, HLA-B and HLA-DRB1 did not depart significantly from Hardy–Weinberg equilibrium, with p values of 0.40519, 0.64448 and 0.27685, respectively. HLA-C showed a modest, statistically significant departure associated with a slight heterozygote excess: observed heterozygosity was 0.92409, expected heterozygosity was 0.91903 and p was 0.03804. The heterozygosity-weighted global inbreeding coefficient, FIS, was 0.00886, while locus-specific values were close to zero. The study was not designed to test selection, and the researchers said the HLA-C result could not be attributed to selection from these data alone.

Linkage disequilibrium was significant for all six pairs of loci in likelihood-ratio permutation tests with 10,100 permutations, producing empirical p values below 0.0001. The strongest disequilibrium occurred between HLA-B and HLA-C; the other locus pairs showed less pronounced but still significant associations. D′ values approached positive or negative 1 for many allele combinations, whereas r² values varied more widely. The authors noted that this distinction means limited historical recombination does not necessarily mean that one allele strongly predicts another.

Pairwise fixation-index comparisons with 22 population datasets from the Americas showed the lowest differentiation from admixed populations. The Brazilian, Central American Mestizo, Colombian cord blood and San Diego cohorts each had FST values below 0.003. Greater differentiation was observed for Indigenous and Amerindian reference populations, with FST values ranging from 0.0045 to 0.0494. The largest difference was from Colombia’s North Chimila Amerindian population at FST = 0.0494, followed by the Colombian North Wiwa population at FST = 0.0246. All pairwise comparisons with the northeastern Mexican cohort were statistically significant at p below 0.05.

The dataset offers regional allele and haplotype frequencies for interpreting histocompatibility and donor-search research, but referral-related sampling effects cannot be excluded and it is not a definitive description of northeastern Mexico’s population. Some rare alleles and haplotypes were represented by small counts, the analysis covered only four loci, and the study did not evaluate transplantation outcomes or the effects of specific HLA mismatches. The authors said population-based studies with broader geographic sampling, additional HLA loci and registry information would be needed to define the region’s diversity more comprehensively.

Subject of Research: High-resolution HLA-A, HLA-B, HLA-C and HLA-DRB1 diversity in a clinically referred northeastern Mexican cohort

Article Title: High-resolution HLA genetic diversity in a Northeastern Mexican clinical cohort

Article References: Ayala-Verdugo, J. A., Jaime-Pérez, J. C., Mendoza-Ibarra, S. I., Cancela-Murrieta, C. O., García-Valdez, A. J., Solis-Estrada, J., Salazar-Riojas, R., Gómez-De León, A., Colunga-Pedraza, P. R., Cantú-Rodríguez, O., Gómez-Almaguer, D., & Moncada-Saucedo, N. K. (2026). High-resolution HLA genetic diversity in a Northeastern Mexican clinical cohort. Frontiers in Genetics, 17, Article 1960786. https://doi.org/10.3389/fgene.2026.1960786

Image Credits: AI Generated

DOI: 10.3389/fgene.2026.1960786

Keywords: HLA diversity, northeastern Mexico, two-field typing, allele frequencies, HLA haplotypes, Hardy–Weinberg equilibrium, linkage disequilibrium, population genetics, histocompatibility, next-generation sequencing, High-resolution, genetic

The original study had compared 150 patients with COVID-19 against 300 healthy controls, genotyping two well-known variants: Factor V Leiden, the G1691A mutation in the F5 gene that causes activated protein C resistance and raises thrombosis risk, and the A1298C variant of the MTHFR gene, which encodes methylenetetrahydrofolate reductase, a key enzyme in folate and homocysteine metabolism. Both variants have long attracted attention in COVID-19 research because severe disease is strongly associated with abnormal coagulation, and the pandemic prompted a wave of candidate-gene association studies searching for inherited predictors of susceptibility and severity.

The first concern raised by the commenter involved Hardy–Weinberg equilibrium, a foundational quality check in population genetics. Under HWE, genotype frequencies in a randomly mating, stable population should follow predictable proportions determined by the allele frequencies themselves. When the control group of a case–control study deviates significantly from these expected proportions, it can signal population stratification, selection bias, sampling artifacts, or genotyping errors, any of which can distort the apparent association between a variant and disease status. Because the control group is meant to represent the baseline population, HWE testing in controls is considered an essential safeguard before any case–control comparison is interpreted.

When the authors re-ran the analysis, the results vindicated part of the critique. For Factor V Leiden, the control group showed 261 GG homozygotes, 39 GA heterozygotes, and no AA homozygotes, a distribution consistent with Hardy–Weinberg expectations: the Pearson chi-squared test yielded a statistic of 1.45 with a p value of 0.229, and an exact test was similarly non-significant at p = 0.623. For MTHFR A1298C, however, the picture was different. The controls comprised 184 AA and 116 AC individuals with no CC homozygotes, and this distribution departed sharply from equilibrium, with a chi-squared statistic of 17.23 (p = 3.31 × 10⁻⁵) and an exact test p value of 1.26 × 10⁻⁶. The authors acknowledged that this deviation should have been reported and discussed in the original publication and now flag it as an important limitation.

Importantly, the authors did not attempt to explain away the anomaly. They note that HWE deviations can arise from population structure, selection effects, sampling characteristics, or genotyping-related factors, and they concede that the available data do not allow them to determine which explanation applies. The original methods described PCR amplification followed by high-resolution agarose gel analysis, with genotype calls confirmed by DNA sequencing, but the published report did not document duplicate samples or negative controls, and the authors explicitly declined to retrospectively claim quality-control procedures that were never written down. This refusal to overstate what the record supports is itself a methodological lesson in scientific accountability.

The second set of concerns concerned effect-size reporting. P values alone, the commenter argued, convey little about the magnitude or precision of a genetic association; odds ratios with 95 percent confidence intervals and allele frequencies are far more informative. The authors agreed and recomputed the full set of estimates from the original individual-level data. For Factor V Leiden, the GA heterozygous genotype appeared in 34 of 150 patients (22.7 percent) versus 39 of 300 controls (13.0 percent). Relative to GG homozygotes, carrying one A allele was associated with higher odds of being in the COVID-19 group, with an odds ratio of 1.96 (95 percent CI 1.18–3.26; Fisher’s exact p = 0.010). At the allele level, the A-allele frequency was 11.3 percent in patients versus 6.5 percent in controls, giving an allele-based odds ratio of 1.84 (95 percent CI 1.13–2.98; p = 0.014).

The MTHFR analysis pointed in the opposite direction. The AC genotype was present in 35 of 150 patients (23.3 percent) but 116 of 300 controls (38.7 percent), so relative to AA homozygotes the heterozygous genotype was associated with lower odds of belonging to the COVID-19 group (OR = 0.48, 95 percent CI 0.31–0.75; p = 0.0014). The C-allele frequency was 11.7 percent in patients versus 19.3 percent in controls, an allele-based odds ratio of 0.55 (95 percent CI 0.37–0.83; p = 0.0034). On purely numerical grounds, the pattern suggests that the C allele might be protective. Yet the authors immediately qualify this: because the MTHFR genotype distribution in controls violates Hardy–Weinberg equilibrium, both the genotype-based and allele-based estimates must be treated with caution, and the team now classifies the MTHFR case–control finding as exploratory, requiring confirmation in an independently recruited population before any protective association can be claimed.

A further structural limitation emerged from the data themselves: no Factor V Leiden AA homozygotes and no MTHFR CC homozygotes were observed in either group. This absence is not surprising, since both variants are relatively uncommon, but it means that homozygote-specific or recessive genetic models could not be meaningfully evaluated at all. In genetic epidemiology, the inability to test a full range of genotypes constrains the biological interpretations available, because dose-dependent effects of an allele can only be assessed when all genotype classes are represented. The reanalysis table therefore reports heterozygotes against reference homozygotes, with odds ratios calculated from 2 × 2 contingency tables using the log-odds (Wald) method and two-sided Fisher’s exact tests for significance.

The authors are careful to delineate the scope of the damage. The Hardy–Weinberg deviation was detected in the healthy control group, which primarily undermines the between-group MTHFR comparison, that is, the claim about who develops COVID-19. It does not, by itself, invalidate the within-cohort analyses that examined relationships between genotype and clinical outcomes among the 150 patients themselves, since those comparisons do not depend on the control group’s genotype distribution. This distinction matters for readers trying to judge which parts of the original study survive scrutiny and which do not: the Factor V Leiden association, supported by a control group in equilibrium and by confidence intervals that exclude the null value, retains its direction, while the MTHFR association is now explicitly downgraded to preliminary.

Beyond the specific variants, the exchange highlights what rigorous genetic association research demands. The authors list the lessons directly: prospectively defined genotyping quality-control procedures, explicit Hardy–Weinberg equilibrium assessment, complete reporting of genotype and allele frequencies, and effect estimates with confidence intervals, ideally in larger and independently recruited populations. The COVID-19 pandemic produced hundreds of candidate-gene studies conducted under intense time pressure, many with modest sample sizes and inconsistent reporting standards, and this reply demonstrates how post-publication critique can improve the scientific record without hostility. The authors close by thanking Dr. Panda for the opportunity to clarify their findings, a courteous ending to a dispute whose real value lies in modeling how statistical scrutiny, honest reanalysis, and transparent limitation-setting should work together to keep genetic medicine on solid ground.

Subject of Research: Hardy–Weinberg equilibrium reassessment of Factor V Leiden and MTHFR polymorphism associations with COVID-19 outcomes

Article Title: Reply to “Important Methodological and Statistical Concerns Regarding the Association of Factor V Leiden and MTHFR Polymorphisms With COVID‐19 Outcomes”

Article References: Çıralıoğlu, H., Adalı, Y., Özen, M., Oskay, A., Yılmaz, A., Seyit, M., Köseler, A., & Türkçüer, İ. (2026). Reply to “Important Methodological and Statistical Concerns Regarding the Association of Factor V Leiden and MTHFR Polymorphisms With COVID‐19 Outcomes”. Immunity, Inflammation and Disease, 14(10), Article e70556. https://doi.org/10.1002/iid3.70556

Image Credits: AI Generated

DOI: 10.1002/iid3.70556

Keywords: COVID-19, Factor V Leiden, MTHFR A1298C, Hardy-Weinberg equilibrium, genetic association study, odds ratio, case-control study, genotyping quality control, population genetics, thrombosis, scientific reproducibility, allele frequency

The research, led by Elika Garg, Jaffa Romain, Lei Sun and Andrew D. Paterson, turned to the 1000 Genomes Project, one of the most widely used reference resources in human genetics. Rather than relying on the standard reference genome that has underpinned most sequencing studies for two decades, the team realigned high-coverage whole genome sequencing data from 2,490 individuals to the telomere-to-telomere T2T-v2 assembly. This assembly, completed by the Telomere-to-Telomere Consortium, closes gaps that plagued earlier versions of the human reference sequence, particularly in repetitive and structurally complex regions where short-read sequencing has historically struggled. The choice matters because misalignment in these difficult regions can produce spurious genotype calls, and spurious genotypes can masquerade as violations of Hardy-Weinberg expectations.

The motivation for the study came from two converging lines of evidence. First, previous work had suggested that sex-based selection operates at numerous autosomal loci in cohorts with active recruitment, meaning that the genetic composition of men and women in a study sample may differ in ways that standard analyses never detect. Second, sequences derived from the sex chromosomes can interfere with the mapping of reads to autosomes, contaminating genotype calls in ways that differ between males and females. Because Hardy-Weinberg testing is usually implemented on a homogeneous population without stratifying by sex, both of these effects could distort the results of a routine quality-control step without anyone noticing. The researchers set out to determine how much of the apparent disequilibrium in a large, diverse dataset could be explained once sex and population structure were properly modelled.

To do this, the team imposed strict filters before any testing began. Only bi-allelic single nucleotide polymorphisms, or SNPs, with non-missing genotypes and a minor allele frequency of at least 5 percent in both sexes of each of the five super-populations represented in the 1000 Genomes Project were retained. This ensured that the statistical tests had adequate power in every stratum and that rare, poorly characterised variants would not drive the conclusions. The five super-populations, spanning African, European, East Asian, South Asian and American ancestry groups, allowed the researchers to ask whether any deviation from equilibrium was a universal feature of a genomic region or something specific to a particular ancestral background.

Methodologically, the study departed from the conventional approach of simply rejecting or accepting equilibrium at each SNP. Instead, the researchers applied an allele-based framework that quantifies both the magnitude and the direction of Hardy-Weinberg disequilibrium, distinguishing an excess of heterozygotes from a deficit. This distinction is informative: an excess of heterozygotes often points to technical artefacts such as collapsed duplications or mapping errors, whereas a deficit can suggest inbreeding, copy-number variation or genuine selection. On top of the per-SNP estimates, the team performed a second-order omnibus meta-analysis that combined disequilibrium results across populations and across sexes, allowing them to separate signals that were consistent everywhere from those that varied by ancestry or by sex.

At a genome-wide significance threshold of p less than 5 times 10 to the minus 8, the same stringent standard used in genome-wide association studies, only 0.9 percent of autosomal SNPs showed significant deviations from Hardy-Weinberg equilibrium. That figure might sound reassuringly small, but with millions of SNPs tested it still represents thousands of individual markers. The crucial finding was that the majority of these deviations were associated with genomic features indicative of poor sequence quality, such as segmental duplications and other regions where read mapping is unreliable. In other words, most of the apparent departures from equilibrium were not biological surprises but artefacts of the measurement process, exactly the kind of noise that quality control is designed to catch.

When the analysis was restricted to reliable genomic regions, the number of signals dropped dramatically, leaving 255 autosomal SNPs and a single non-pseudoautosomal chromosome X SNP that remained significantly out of equilibrium. This refinement is one of the study’s most practical contributions: it demonstrates that aligning sequencing reads to the gapless T2T assembly, combined with careful masking of problematic regions, can strip away a large fraction of false positives that would otherwise clutter downstream association analyses. Among the surviving signals, 140 autosomal SNPs displayed significant heterogeneity across populations but not across sexes, suggesting that ancestry-specific factors, whether demographic history or local genomic architecture, drive much of the residual disequilibrium rather than any systematic difference between males and females.

One cluster of results stood out. Eight SNPs within a remarkably tight 15-base-pair window on chromosome 14q31.3 showed an excess of heterozygosity in both sexes of the African super-population. The consistency of the signal across sexes within a single ancestry group, and its confinement to such a small genomic interval, makes it a compelling candidate for closer inspection, whether the underlying cause turns out to be a mapping artefact specific to African haplotypes, a structural variant not yet fully resolved even by the T2T assembly, or a genuine biological phenomenon. The finding illustrates the value of the study’s population-stratified design, since an analysis that pooled all super-populations together would likely have diluted or obscured this ancestry-specific pattern.

Beyond cataloguing individual deviations, the researchers distilled their findings into a practical tool: a multivariate predictor of Hardy-Weinberg disequilibrium built from sequence features. By learning which characteristics of a genomic locus, such as its repetitiveness, GC content and mapping properties, are associated with spurious disequilibrium, the predictor can flag problematic SNPs before they contaminate an analysis. Because it is multivariate, it integrates many weak indicators into a single score, offering a more nuanced assessment than the binary pass-or-fail verdict of a traditional equilibrium test. The authors position this predictor as something that can be slotted directly into existing quality-control pipelines for whole genome sequencing studies, which is where its impact is likely to be felt first.

The broader lesson of the study is that even the most routine statistical checks deserve periodic re-examination as the underlying technology changes. Hardy-Weinberg testing was developed for an era of gene frequencies in defined populations, yet it now serves as a first line of defence against sequencing artefacts in datasets of thousands of genomes. The transition to telomere-to-telomere reference assemblies, the growing recognition that sex must be modelled explicitly, and the increasing diversity of cohorts all change what the test is telling us. By quantifying how much apparent disequilibrium dissolves once these factors are handled properly, and by providing a predictor that separates technical noise from genuine signal, the work offers the genomics community a clearer view of where its measurements are trustworthy and where they still need scrutiny, ensuring that one of genetics’ oldest equations remains a sharp instrument in the telomere-to-telomere era.

Subject of Research: Sex-aware assessment of Hardy-Weinberg equilibrium deviations in telomere-to-telomere-aligned 1000 Genomes Project whole genome sequencing data

Article Title: Assessing Hardy-Weinberg equilibrium in T2T-aligned 1000 genomes project

Article References: Garg, E., Romain, J., Sun, L., & Paterson, A. D. (2026). Assessing Hardy-Weinberg equilibrium in T2T-aligned 1000 genomes project. PLOS Genetics, 22(9), e1012280. https://doi.org/10.1371/journal.pgen.1012280

Image Credits: AI Generated

DOI: 10.1371/journal.pgen.1012280

Keywords: Hardy-Weinberg equilibrium, 1000 Genomes Project, telomere-to-telomere assembly, T2T-v2, whole genome sequencing, quality control, Hardy-Weinberg disequilibrium, single nucleotide polymorphisms, population stratification, sex-specific analysis, minor allele frequency, meta-analysis

The research, published in the journal Immunity, Inflammation and Disease, was conceived as a direct replication attempt of an earlier Iranian study that had reported a worrying association. That study, led by Kolsari and colleagues, found that carriers of the TT genotype at a specific variant in the SFTPD gene, known as rs721917, were more likely to be hospitalized and to suffer severe symptoms after SARS-CoV-2 infection. The TT genotype appeared more frequently among patients admitted to intensive care and cardiac care units than the CC genotype, and it was linked to higher overall hospitalization rates. If correct, the finding would have identified a common genetic variant, present in roughly a third of many populations, as a risk factor for severe disease.

To test this, a team at the Institute for Clinical and Experimental Medicine in Prague, led by Jaroslav A. Hubacek, analyzed the same polymorphism in a much larger Czech cohort. The variant rs721917 is a single-letter change in the DNA sequence, a C-to-T substitution that alters the amino acid at position 31 of the SP-D protein, replacing methionine with threonine. This region of the protein matters functionally, because SP-D is a collectin, a molecule that binds both pathogens and immune cells, helping to clear viruses and bacteria from the lungs while modulating inflammation. Genetic variation in this gene is therefore a biologically plausible candidate for influencing the course of a respiratory infection such as COVID-19.

The Czech researchers used a PCR-based restriction fragment length polymorphism assay to genotype 436 adults with confirmed SARS-CoV-2 infection, of whom 164 had remained completely asymptomatic and 272 had developed symptoms. They compared these patients with 467 adult population controls drawn from the Czech post-MONICA study, a long-running cardiovascular risk survey that provides a representative sample of the national population. Infection status was confirmed by PCR screening, and follow-up extended until December 2024, ensuring that participants’ infection histories were reliably documented. Statistical analyses were performed with SPSS Statistics 27.

The first reassuring observation was that genotype distributions in both patients and controls conformed to the Hardy-Weinberg equilibrium, the genetic baseline that indicates a population is mating randomly and that genotyping is technically sound. The equilibrium probabilities were p equals 0.80 for controls and p equals 0.24 for patients. When the researchers compared the entire group of SARS-CoV-2-positive individuals with the controls, they found no significant difference in genotype frequencies, with a p-value of 0.59. In other words, carrying any particular version of rs721917 did not appear to influence whether a person became infected in the first place.

The more interesting results emerged when the patients were stratified by disease course. Genotype frequencies differed significantly between asymptomatic subjects and population controls, with a p-value of 0.02, and between asymptomatic subjects and symptomatic patients, with a p-value of 0.01. The driving force behind these differences was a higher proportion of heterozygotes, individuals carrying one C and one T allele, among the asymptomatic group, where they made up 60.3 percent of subjects compared with 47.8 percent of controls and 45.6 percent of symptomatic patients. The researchers caution that such an excess of heterozygotes raises the possibility of an overdominant effect, in which having two different versions of the gene confers an advantage over having two identical copies.

A second pattern pointed in the same direction. The frequency of the CC genotype rose in a borderline, nonsignificant trend, with p equal to 0.08, from the asymptomatic group through the population controls to the symptomatic subjects. Consistent with this gradient, carriers of at least one T allele were slightly less likely to appear in the symptomatic COVID-19 group than CC homozygotes, with an odds ratio of 0.59 and a 95 percent confidence interval of 0.34 to 1.05, at a nominal p-value of 0.07. This is the opposite direction of association to that reported in the Iranian study, which had flagged the T-bearing TT genotype as a risk factor rather than a protective one.

The authors offer several explanations for the discrepancy. The original Iranian study was small, with only between 17 and 42 subjects in each severity category, spanning mild, moderate, severe, and critical courses of COVID-19. Samples of that size are notoriously prone to false positive results, particularly in genetic association studies where many variants are tested simultaneously. More troubling still, the genotype frequencies in the Iranian cohort deviated dramatically from the Hardy-Weinberg expectation, with a p-value below 0.0000001. Such a marked deviation complicates interpretation and may reflect ascertainment bias, hidden population structure, or genotyping inaccuracies, any of which could produce a spurious association. Interethnic differences in genetic susceptibility to SARS-CoV-2 infection are also well documented, providing a further possible reason why results from an Iranian population might not replicate in Central Europeans.

Crucially, the Czech findings align with the largest genetic evidence base available. A major meta-analysis of genome-wide association studies, published in Nature by Pairo-Castineira and colleagues, identified 49 genetic variants associated with critical COVID-19, and rs721917 was among them. In that analysis, the T allele was associated with approximately a 7 percent lower risk of critical disease, a result that supports the Czech case-control finding, where the T allele showed a nominal 4 percent decrease in risk of symptomatic illness. The convergence of an independent large-scale GWAS meta-analysis with a targeted replication study strengthens the conclusion that the T allele is not a risk factor, and may instead confer modest protection against severe or symptomatic COVID-19.

The picture is rounded out by studies of the protein itself. Two investigations measured circulating concentrations of SP-D and reached similar conclusions. No difference in plasma SP-D levels was observed between 141 convalescent COVID-19 patients examined ten weeks after infection and 98 uninfected subjects, and in a separate pediatric cohort of 325 children, SP-D levels were not associated with COVID-19 severity. In children, the SFTPD rs721917 polymorphism likewise showed no effect on disease severity. The authors also note, somewhat surprisingly, that despite the strong biological plausibility of SFTPD as a susceptibility gene, no further adult studies outside GWAS analyses have been published on this topic, underscoring how much remains to be learned. Taken together, the Czech results and the wider literature suggest that carriers of the T allele at rs721917 are potentially protected, rather than placed at increased risk, against symptomatic or severe forms of COVID-19, though the authors emphasize that further studies are needed to detect potential ethnic-specific effects of this variant across different populations.

Subject of Research: Association between the SFTPD rs721917 polymorphism and susceptibility to symptomatic and severe COVID-19 in the Czech population

Article Title: Surfactant‐Associated Protein D Polymorphism and Protection Against COVID‐19 in the Czech Population

Article References: Hubacek, J. A., Philipp, T., Ondrackova, M., Adamkova, V., & Dusek, L. (2026). Surfactant‐Associated Protein D Polymorphism and Protection Against COVID‐19 in the Czech Population. Immunity, Inflammation and Disease, 14(10), Article e70541. https://doi.org/10.1002/iid3.70541

Image Credits: AI Generated

DOI: 10.1002/iid3.70541

Keywords: SFTPD, surfactant protein D, rs721917, COVID-19, SARS-CoV-2, genetic polymorphism, host genetics, case-control study, Hardy-Weinberg equilibrium, GWAS meta-analysis, Czech population, innate immunity

Short tandem repeats, or STRs, are stretches of DNA in which a short sequence of two to six base pairs repeats over and over. The number of repeats at any given locus varies from person to person, which is precisely what makes STRs the backbone of modern forensic DNA profiling. Because these repeat counts are inherited, one copy from each parent, they allow investigators to compare a crime scene sample with a suspect’s profile, or to establish biological relationships in paternity cases. But the statistics underpinning those comparisons are only valid if laboratories know how common each repeat variant, or allele, is in the relevant population. A profile match that looks overwhelming in one population may carry different weight in another, which is why population-specific frequency databases are a cornerstone of ethical and accurate forensic practice.

The research team, led by Brajesh Kumar and Amitabh Biswas of Galgotias University’s School of Forensic Sciences, together with colleagues from the Central Forensic Science Laboratory in Chandigarh, Ingenomics India, and the Regional Forensic Science Laboratory in Jabalpur, used the Ingenomics AutoProfiler STR Kit to amplify all 24 markers simultaneously. This six-dye multiplex system, which was independently validated for forensic applications, covers the expanded set of loci now favored internationally, including the highly variable SE33 and Penta E markers alongside familiar names such as FGA, vWA, and TH01. Genotyping was carried out by capillary electrophoresis, and the resulting allele calls were subjected to statistical analysis using established population genetics software, including Arlequin, GenAlEx, and PowerStats.

The headline numbers are striking. Across the 24 loci, the probability of match, which expresses the chance that two randomly selected unrelated individuals share the same genotype, ranged from 0.012 to 0.186. Inverted into its more familiar form, the power of discrimination ranged from 0.814 to 0.988, meaning the best markers could distinguish between any two individuals in the population with near certainty. SE33 topped the list with a discrimination power of 0.988, followed closely by Penta E at 0.983 and FGA at 0.962. At the other end of the spectrum, CSF1PO at 0.814 and D2S441 at 0.844 were comparatively modest performers, a pattern consistent with their lower allelic diversity in populations worldwide.

Polymorphism information content, a measure of how informative a marker is for linkage and identity analysis, ranged from 0.601 to 0.933, with the majority of loci exceeding 0.7, a threshold conventionally regarded as indicating high polymorphism. Gene diversity values, which capture the expected heterozygosity at each locus, spanned 0.666 to 0.939, confirming that the Santhal sample carries substantial genetic variability. Observed and expected heterozygosities were calculated for every locus, and tests against Hardy-Weinberg equilibrium were performed to verify that allele frequencies behave as expected under random mating, a critical quality check before any forensic statistic can be considered reliable.

For paternity testing, the study’s parameters matter even more directly. The power of exclusion, the probability that a randomly selected man would be excluded as the biological father of a child, ranged from 0.428 to 0.913 across individual loci, with SE33 and Penta E showing the strongest exclusion capacity. The typical paternity index varied from 1.667 to 11.667, again led by the most polymorphic markers, including SE33, Penta E, and D12S391. When the loci were combined, the results became extraordinary: the combined power of exclusion reached 0.999999999991, the combined paternity index stood at 3.51 multiplied by ten to the tenth power, and the combined probability of match fell to 2.41 multiplied by ten to the power of negative 28. The combined power of discrimination, rounded, was effectively 1. In practical terms, the chance that two unrelated Santhal individuals would share an identical 24-locus profile is so vanishingly small that a matching profile constitutes evidence of identity at a level few other forms of forensic evidence can approach.

Why does population-specific data of this kind matter so much? Forensic statistics rest on the product rule: the rarity of a full profile is estimated by multiplying the frequencies of the individual alleles, an operation that assumes statistical independence within the population under consideration. If a population has a history of endogamy, consanguinity, or founder effects, as many isolated or semi-isolated communities do, allele frequencies can drift in ways that inflate or deflate match probabilities. The Santhal, an Austroasiatic-speaking tribal community with a distinct demographic history in eastern India, are precisely the kind of population for which generic national databases may not be adequate. By generating a dedicated allele frequency dataset, the study gives forensic practitioners in Bihar and neighboring states a defensible statistical foundation when the source of a sample may belong to this community.

The work also contributes to the broader scientific picture of Indian genetic diversity. Previous studies have documented STR polymorphism in Chhattisgarh, Himachal Pradesh, northwestern India, and among the Sindhi population, and genomic research on Austroasiatic speakers has highlighted the role of landscape barriers and sex-specific admixture in shaping their genetic structure. Each new population dataset refines the map of how genetic variation is distributed across the subcontinent, and the Santhal data will allow comparisons that illuminate both forensic and anthropological questions. The authors note that the 24 loci are highly informative not only for individual identification but also for kinship analysis, extending their utility to disaster victim identification, missing persons investigations, and complex relationship testing.

The study was approved by the Institutional Ethics Committee of Apex Hospital in Varanasi, and the authors report no conflicts of interest and no external funding. All participants were unrelated volunteers who consented to contribute samples, and the authors acknowledge their role in enabling the research. The sampling strategy, restricted to unrelated individuals, is essential for Hardy-Weinberg testing and for ensuring that the frequency estimates reflect the general population rather than clustered family lineages.

As DNA evidence continues to expand its role in the Indian judicial system, studies like this one quietly underpin every statistic that appears in a courtroom report. A match probability of ten to the negative 28 is only as credible as the allele frequency table behind it, and that table must reflect the population to which a suspect or victim belongs. With the Santhal community now represented in the forensic literature by a rigorously characterized 24-locus dataset, investigators and courts in Bihar gain a resource that makes DNA statistics for this population both more accurate and more defensible. It is a reminder that the power of forensic genetics depends not just on the chemistry of amplification and detection, but on the patient, community-by-community work of measuring human variation itself.

Subject of Research: Forensic genetic profiling of autosomal STR markers in the Santhal tribal population of Bihar, India

Article Title: Allele frequency distribution and forensic efficiency parameters of 24 autosomal STR markers in the santhal tribal community of Bihar, India

Article References: Kumar, B., Biswas, A., Dixit, S., Sharma, S., & Shrivastava, P. (2026). Allele frequency distribution and forensic efficiency parameters of 24 autosomal STR markers in the santhal tribal community of Bihar, India. The Science of Nature, 113(6), Article 125. https://doi.org/10.1007/s00114-026-02176-2

Image Credits: AI Generated

DOI: 10.1007/s00114-026-02176-2

Keywords: forensic genetics, STR markers, Santhal, allele frequency, paternity testing, population genetics, India, DNA profiling, Hardy-Weinberg equilibrium, power of discrimination, tribal population, polymorphism