Biology subjects

Geraghty, D. E.

Publications and source records attributed to Geraghty, D. E..

6 recordsLinked to original sources

bioRxiv2026

High-resolution HLA and MICA/MICB typing of the 1000 Genomes panel: a resource for precision medicine

High-resolution characterization of human leukocyte antigen (HLA) and MICA/MICB variation is important for transplantation, immunogenetics, and the interpretation of disease associations within the major histocompatibility complex (MHC). However, publicly available population reference datasets remain limited in their resolution and coverage of these loci. We performed high-resolution typing of the 1000 Genomes Phase 3 panel across 13 HLA and MIC loci, generating HLA genotypes for 2,486 individuals, extended HLA-A, -B, and -C allele assignments, and MICA/MICB genotypes for 2,393 individuals representing 25 populations and five continental super-populations. Concordance with previously reported HLA genotypes was 96.2-97.4% at two-field resolution, and independent retyping confirmed the present assignments for samples exhibiting reciprocal genotype discrepancies with an earlier dataset. Analysis of extended class I variation within defined assay boundaries revealed that most of the 78 common three-field HLA lineages were dominated by a single extended sequence group. However, eleven lineages exhibited substantial heterogeneity, with the predominant group accounting for less than 80% of observed copies. The distinguishing sequence differences were noncoding, involving intronic and 3' untranslated regions, and their distributions varied among ancestral populations. These findings identify recurrent, population-relevant variants for which functional consequences remain to be established. Population analyses demonstrated extensive allelic diversity at classical class I loci and DRB1, whereas the greatest between-population differentiation occurred at selected class II and DRB-linked loci. Most common alleles were shared among populations but differed substantially in frequency. Multilocus haplotypes revealed characteristic population-associated combinations of HLA and MIC alleles and provided the basis for predicting MIC genotypes from existing HLA typing. Using two-field HLA-B and HLA-C genotypes, five-fold cross-validation achieved exact genotype prediction accuracies of 77.3% for MICA and 53.6% for MICB, compared with 4.6% and 21.6%, respectively, without HLA information. Together, these results provide an extended-resolution HLA and MIC reference resource, demonstrate the limited routine utility of universal fourth-field typing while identifying specific variants for functional investigation, and establish the potential for recovering MIC variation from existing HLA-typed cohorts. The resource also supports future investigations of MHC variation underlying disease associations and transplantation outcomes.

genetics↗

bioRxiv2026

Technology solutions for immune loci: integrated high-resolution KIR genotyping reveals allelic, structural, and KIR-HLA diversity across 24 human populations

High-resolution genotyping of killer cell immunoglobulin-like receptor (KIR) genes remains challenging because of extensive sequence homology, copy-number variation, and complex haplotype structures. Although KIR allelic variation influences receptor expression and function, routine typing has largely been limited to gene presence or absence. Here, we evaluated an integrated short-read sequencing system combining a four-reaction, two-stage multiplex PCR assay (ScisGo-KIR-v3), centralized cloud-based copy-aware allele analysis, and interactive genotype review. The system was applied to 2,298 individuals representing 24 populations from the 1000 Genomes Project, with matched high-resolution HLA class I genotypes. Independent validation against complete, phased PacBio KIR sequences from 48 samples demonstrated 98.6% automated copy-number concordance and 92.8% combined copy-number and allele concordance across 768 gene-sample comparisons. Following desktop review, all evaluated assignments were concordant with the reference sequences. In a separate comparison of four computational genotyping methods involving 311 shared samples, the automated Scisco pipeline showed the lowest overall mismatch with the four-method consensus (1.62%). Population analyses identified substantial allele diversity and distinct centromeric and telomeric haplotype-motif distributions. Although exact haplotype pairs were frequently ambiguous, structural assignments became increasingly resolvable at the motif level, and motif backgrounds exhibited characteristic allele compositions. Paired KIR and HLA analysis revealed inherited receptor-ligand configurations concealed by gene-content typing. Among 1,401 KIR3DL1/HLA-B Bw4 co-carriers, 57 lacked an allele annotated for ordinary surface expression, while 457 of 1,175 KIR2DS4 co-carriers of six specified HLA-C allotypes carried only truncated receptor forms. Allele-specific KIR2DL1 and KIR2DS5 classifications further distinguished receptor properties and published ligand-binding annotations that varied among populations. Although potential receptor-ligand repertoire composition differed across populations, analyses did not establish preferential KIR-HLA genetic association after accounting for population structure and multiple testing. Together, these findings demonstrate that integrated short-read KIR genotyping can provide scalable, high-resolution allele and copy-number information while explicitly retaining uncertainty in inferred haplotype structures. Compatibility with established HLA laboratory workflows provides a practical foundation for incorporating allele-resolved KIR analysis into immunogenetic research and future clinical investigations.

genetics↗

bioRxiv2026

Microchimerism in the human brain, quantitative assessment and single nuclei profiling establish cell types and diversity

Bi-directional maternal-fetal exchange during pregnancy creates a long-term microchimerism (Mc) legacy in both individuals, but its presence and cellular fate in human brain are largely unknown. We studied surgically resected epilepsy brain specimens with targetable maternal polymorphisms using polymorphism-specific quantitative PCR. Maternal Mc was prevalent, detectable in 70% of patients, and often at striking quantities spanning temporal, frontal, parietal, and hippocampal regions. Next, we employed single nucleus RNA profiling using cellector, a genetic demultiplexing tool designed to detect rare allogeneic cells. We identified Mc across major neural and glial populations. Finally, analysis of publicly available snRNA-seq datasets from neurotypical brains from gestation to late adulthood further revealed widespread Mc, persisting into advanced age, and preferentially adopting L2/3 intratelencephalic neuronal or microglial/macrophage-like fates. These data show that naturally acquired Mc is prevalent, diverse, and persistent in human brain, inviting reconsideration of what constitutes "self," with broad implications for health and disease.

neuroscience↗

bioRxiv2023

Natural malaria infection elicits rare but potent neutralizing antibodies to the blood-stage antigen RH5

Plasmodium falciparum RH5 is the most advanced blood-stage malaria vaccine candidate and is under evaluation for efficacy in endemic regions, emphasizing the need to study the underlying antibody response to RH5 during natural infection. Here, we found that RH5-reactive B cells were rare in malaria-exposed individuals despite repeated infections over multiple years. RH5-specific monoclonal antibodies isolated from these individuals were extensively mutated but mostly targeted non-neutralizing epitopes, in contrast to antibodies from RH5-vaccinated, malaria-naive individuals. However, infection-derived MAD8-151 and MAD8-502 were among the most potent neutralizers out of 186 antibodies isolated from both cohorts and target the same epitopes as the most effective vaccine-induced antibodies. Binding to basigin receptor-proximal epitopes was the primary factor governing the potency of RH5-specific antibodies from both natural infection and vaccination, followed by the strength of binding. These results indicate a clear strategy for the development of next-generation RH5 vaccines for use in malaria-endemic regions.

immunology↗

bioRxiv2021

Tracking SARS-CoV-2 Spike Protein Mutations in the United States (2020/01 - 2021/03) Using a Statistical Learning Strategy

The emergence and establishment of SARS-CoV-2 variants of interest (VOI) and variants of concern (VOC) highlight the importance of genomic surveillance. We propose a statistical learning strategy (SLS) for identifying and spatiotemporally tracking potentially relevant Spike protein mutations. We analyzed 167,893 Spike protein sequences from US COVID-19 cases (excluding 21,391 sequences from VOI/VOC strains) deposited at GISAID from January 19, 2020 to March 15, 2021. Alignment against the reference Spike protein sequence led to the identification of viral residue variants (VRVs), i.e., residues harboring a substitution compared to the reference strain. Next, generalized additive models were applied to model VRV temporal dynamics, to identify VRVs with significant and substantial dynamics (false discovery rate q-value <0.01; maximum VRV proportion > 10% on at least one day). Unsupervised learning was then applied to hierarchically organize VRVs by spatiotemporal patterns and identify VRV-haplotypes. Finally, homology modelling was performed to gain insight into potential impact of VRVs on Spike protein structure. We identified 90 VRVs, 71 of which have not previously been observed in a VOI/VOC, and 35 of which have emerged recently and are durably present. Our analysis identifies 17 VRVs [~]91 days earlier than their first corresponding VOI/VOC publication. Unsupervised learning revealed eight VRV-haplotypes of 4 VRVs or more, suggesting two emerging strains (B1.1.222 and B.1.234). Structural modeling supported potential functional impact of the D1118H and L452R mutations. The SLS approach equally monitors all Spike residues over time, independently of existing phylogenic classifications, and is complementary to existing genomic surveillance methods.

genomics↗

bioRxiv2020

A Detailed View of KIR Haplotype Structures and Gene Families as Provided by a New Motif-based Multiple Sequence Alignment

Human chromosome 19q13.4 contains genes encoding killer-cell immunoglobulin-like receptors (KIR). Reported haplotype lengths range from 67 to 269 kilobases and contain 4 to 18 genes. The region has certain properties such as single nucleotide variation, structural variation, homology, and repetitive elements that make it hard to align accurately beyond single gene alleles. To the best of our knowledge, a multiple sequence alignment of KIR haplotypes has never been published or presented. Such an alignment would be useful to precisely define KIR haplotypes and loci, provide context for assigning alleles (especially fusion alleles) to genes, infer evolutionary history, impute alleles, interpret and predict co-expression, and generate markers. In order to extend the framework of KIR haplotype sequences in the human genome reference, 27 new sequences were generated including 24 haplotypes from 12 individuals of African American ancestry that were selected for genotypic diversity and novelty to the reference, to bring the total to 68 full length genomic KIR haplotype sequences. We leveraged these data and tools from our long-read KIR haplotype assembly algorithm to define and align KIR haplotypes at <5 kb resolution on average. We then used a standard alignment algorithm to refine that alignment down to single base resolution. This processing demonstrated that the high-level alignment recapitulates human-curated annotation of the human haplotypes as well as a chimpanzee haplotype. Further, assignments and alignments of gene alleles were consistent with their human curation in haplotype and allele databases. These results define KIR haplotypes as 14 loci containing 9 genes. The multiple sequence alignments have been applied in two software packages as probes to capture and annotate KIR haplotypes and as markers to genotype KIR from WGS.

bioinformatics↗