Human Genomic Data Research Articles

Implementation of entropically secure encryption: Securing personal health data

AbstractEntropically secure encryption (ESE) offers unconditional security with shorter keys compared to the One‐Time Pad. Here, the first implementation of ESE for bulk encryption is presented. The main computational bottleneck for bulk ESE is a multiplication in a very large finite field. This involves multiplication of polynomials followed by modular reduction. A polynomial multiplication is implemented based on the gf2x library, with modifications that avoid inputs of vastly different length, thus improving speed. Additionally, a recently proposed efficient reduction algorithm that works for any polynomial degree is implemented. Two use cases are investigated: x‐ray images of patients and human genome data. Entropy estimation is conducted using compression methods whose results determine the key lengths required for ESE. The running times for all steps of the encryption are reported. The potential of ESE to be used in conjunction with quantum key distribution (QKD), in order to achieve full information‐theoretic security of QKD‐protected links for these use cases is discussed.

CascAGS: Comparative Analysis of SNP Calling Methods for Human Genome Data in the Absence of Gold Standard.

The development of third-generation sequencing has accelerated the boom of single nucleotide polymorphism (SNP) calling methods, but evaluating accuracy remains challenging owing to the absence of the SNP gold standard. The definitions for without-gold-standard and performance metrics and their estimation are urgently needed. Additionally, the possible correlations between different SNP loci should also be further explored. To address these challenges, we first introduced the concept of a gold standard and imperfect gold standard under the consistency framework and gave the corresponding definitions of sensitivity and specificity. A latent class model (LCM) was established to estimate the sensitivity and specificity of callers. Furthermore, we incorporated different dependency structures into LCM to investigate their impact on sensitivity and specificity. The performance of LCM was illustrated by comparing the accuracy of BCFtools, DeepVariant, FreeBayes, and GATK on various datasets. Through estimations across multiple datasets, the results indicate that LCM is well-suitable for evaluating callers without the SNP gold standard, and accurate inclusion of the dependency between variations is crucial for better performance ranking. DeepVariant has a higher sum of sensitivity and specificity than other callers, followed by GATK and BCFtools. FreeBayes has low sensitivity but high specificity. Notably, appropriate sequencing coverage is another important factor for precise callers' evaluation. Most importantly, a web interface for assessing and comparing different callers was developed to simplify the evaluation process.

Comprehensive evaluation and guidance of structural variation detection tools in chicken whole genome sequence data

BackgroundStructural variations (SVs) are widespread across genome and have a great impact on evolution, disease, and phenotypic diversity. Despite the development of numerous bioinformatic tools, commonly referred to as SV callers, tailored for detecting SVs using whole genome sequence (WGS) data and employing diverse algorithms, their performance necessitates rigorous evaluation with real data and validated SVs. Moreover, a considerable proportion of these tools have been primarily designed and optimized using human genome data. Consequently, their applicability and performance in Avian species, characterized by smaller genomes and distinct genomic architectures, remain inadequately assessed.ResultsWe performed a comprehensive assessment of the performance of ten widely used SV callers using population-level real genomic data with the validated five common types of SVs. The performance of SV callers varies with the types and sizes of SVs. As compared with other tools, GRIDSS, Lumpy, Wham, and Manta present better detection accuracy. Pindel can detect more small SVs than others. CNVnator and CNVkit can detect more medium and large copy number variations. Given the poor consistency among different SV callers, the combination calling strategy is not recommended. All tools show poor ability in the detection of insertions (especially with size > 150 bp). At least 50× read depth is required to detect more than 80% of the SVs for most tools.ConclusionsThis study highlights the importance and necessity of using real sequencing data, rather than simulated data only, with validated SVs for SV caller evaluation. Some practical guidance and suggestions are provided for SV detection in future researches.

Disease coverage of human genome-wide association studies and pharmaceutical research and development

BackgroundDespite the growing interest in the use of human genomic data for drug target identification and validation, the extent to which the spectrum of human disease has been addressed by genome-wide association studies (GWAS), or by drug development, and the degree to which these efforts overlap remain unclear.MethodsIn this study we harmonize and integrate different data sources to create a sample space of all the human drug targets and diseases and identify points of convergence or divergence of GWAS and drug development efforts.ResultsWe show that only 612 of 11,158 diseases listed in Human Disease Ontology have an approved drug treatment in at least one region of the world. Of the 1414 diseases that are the subject of preclinical or clinical phase drug development, only 666 have been investigated in GWAS. Conversely, of the 1914 human diseases that have been the subject of GWAS, 1121 have yet to be investigated in drug development.ConclusionsWe produce target-disease indication lists to help the pharmaceutical industry to prioritize future drug development efforts based on genetic evidence, academia to prioritize future GWAS for diseases without effective treatments, and both sectors to harness genetic evidence to expand the indications for licensed drugs or to identify repurposing opportunities for clinical candidates that failed in their originally intended indication.

Evaluation of genotype imputation using Glimpse tools on low coverage ancient DNA.

Ancient DNA provides a unique frame for directly studying human population genetics in time and space. Still, since most of the ancient genomic data is low coverage, analysis is confronted with a low number of SNPs, genotype uncertainties, and reference-bias. Here, we for the first time benchmark the two distinct versions of Glimpse tools on 120 ancient human genomes from Eurasia including those largely from previously under-evaluated regions and compare the performance of genotype imputation with de facto analysis approaches for low coverage genomic data analysis. We further investigate the impact of two distinct reference panels on imputation accuracy for low coverage genomic data. We compute accuracy statistics and perform PCA and f4-statistics to explore the behaviour of genotype imputation on low coverage data regarding (i)two versions of Glimpse, (ii)two reference panels, (iii)four post-imputation filters and coverages, as well as (iv)data type and geographical origin of the samples on the analyses. Our results reveal that even for 0.1X coverage ancient human genomes, genotype imputation using Glimpse-v2 is suitable. Additionally, using the 1000 Genomes merged with Human Genome Diversity Panel improves the accuracy of imputation for the rare variants with low MAF, which might be important not only for ancient genomics but also for modern human genomic studies based on low coverage data and for haplotype-based analysis. Most importantly, we reveal that genotype imputation of low coverage ancient human genomes reduces the genetic affinity of the samples towards human reference genome. Through solving one of the most challenging biases in data analysis, so-called reference bias, genotype imputation using Glimpse v2 is promising for low coverage ancient human genomic data analysis and for rare-variant-based and haplotype-based analysis.

Evolutionary genomic analyses of canine E. coli infections identify a relic capsular locus associated with resistance to multiple classes of antimicrobials.

Much of the human genomic epidemiology data available for E. coli mechanism discovery studies has been heavily biased toward shiga-toxin producing strains from humans and livestock. E. coli occupies many niches and produces a wide variety of other significant pathotypes, including some implicated in chronic disease. We hypothesized that since dogs tend to share similar strains with their owners and are treated with similar antibiotics, their pathogenic isolates will harbor unexplored AMR mechanisms of importance to humans as well as animals. By comparing over 1,000 genomes with in vitro antimicrobial susceptibility data from sick dogs across the US and Canada, we identified a strong multidrug resistance association with an operon that appears to have once conferred a type 1 capsule production system.

Evaluation of heritability partitioning approaches in livestock populations

BackgroundHeritability partitioning approaches estimate the contribution of different functional classes, such as coding or regulatory variants, to the genetic variance. This information allows a better understanding of the genetic architecture of complex traits, including complex diseases, but can also help improve the accuracy of genomic selection in livestock species. However, methods have mainly been tested on human genomic data, whereas livestock populations have specific characteristics, such as high levels of relatedness, small effective population size or long-range levels of linkage disequilibrium.ResultsHere, we used data from 14,762 cows, imputed at the whole-genome sequence level for 11,537,240 variants, to simulate traits in a typical livestock population and evaluate the accuracy of two state-of-the-art heritability partitioning methods, GREML and a Bayesian mixture model. In simulations where a single functional class had increased contribution to heritability, we observed that the estimators were unbiased but had low precision. When causal variants were enriched in variants with low (< 0.05) or high (> 0.20) minor allele frequency or low (below 1st quartile) or high (above 3rd quartile) linkage disequilibrium scores, it was necessary to partition the genetic variance into multiple classes defined on the basis of allele frequencies or LD scores to obtain unbiased results. When multiple functional classes had variable contributions to heritability, estimators showed higher levels of variation and confounding between certain categories was observed. In addition, estimators from small categories were particularly imprecise. However, the estimates and their ranking were still informative about the contribution of the classes. We also demonstrated that using methods that estimate the contribution of a single category at a time, a commonly used approach, results in an overestimation. Finally, we applied the methods to phenotypes for muscular development and height and estimated that, on average, variants in open chromatin regions had a higher contribution to the genetic variance (> 45%), while variants in coding regions had the strongest individual effects (> 25-fold enrichment on average). Conversely, variants in intergenic or intronic regions showed lower levels of enrichment (0.2 and 0.6-fold on average, respectively).ConclusionsHeritability partitioning approaches should be used cautiously in livestock populations, in particular for small categories. Two-component approaches that fit only one functional category at a time lead to biased estimators and should not be used.

SeqCAT: Sequence Conversion and Analysis Toolbox.

Dealing with sequence coordinates in different formats and reference genomes is challenging in genetic research. This complexity arises from the need to convert and harmonize datasets of different sources using alternating nomenclatures. Since manual processing is time-consuming and requires specialized knowledge, the Sequence Conversion and Analysis Toolbox (SeqCAT) was developed for daily work with genetic datasets. Our tool provides a range of functions designed to standardize and convert gene variant coordinates based on various sequence types. Its user-friendly web interface provides easy access to all functionalities, while the Application Programming Interface (API) enables automation within pipelines. SeqCAT provides access to human genomic, protein and transcript data, utilizing various data resources and packages and extending them with its own unique features. The platform covers a wide range of genetic research needs with its 14 different applications and 3 info points, including search for transcript and gene information, transition between reference genomes, variant mapping, and genetic event review. Notable examples are 'Convert Protein to DNA Position' for translation of amino acid changes into genomic single nucleotide variants, or 'Fusion Check' for frameshift determination in gene fusions. SeqCAT is an excellent resource for converting sequence coordinate data into the required formats and is available at: https://mtb.bioinf.med.uni-goettingen.de/SeqCAT/.

Geographical migration and fitness dynamics of Streptococcus pneumoniae

Streptococcus pneumoniae is a leading cause of pneumonia and meningitis worldwide. Many different serotypes co-circulate endemically in any one location1,2. The extent and mechanisms of spread and vaccine-driven changes in fitness and antimicrobial resistance remain largely unquantified. Here using geolocated genome sequences from South Africa (n = 6,910, collected from 2000 to 2014), we developed models to reconstruct spread, pairing detailed human mobility data and genomic data. Separately, we estimated the population-level changes in fitness of strains that are included (vaccine type (VT)) and not included (non-vaccine type (NVT)) in pneumococcal conjugate vaccines, first implemented in South Africa in 2009. Differences in strain fitness between those that are and are not resistant to penicillin were also evaluated. We found that pneumococci only become homogenously mixed across South Africa after 50 years of transmission, with the slow spread driven by the focal nature of human mobility. Furthermore, in the years following vaccine implementation, the relative fitness of NVT compared with VT strains increased (relative risk of 1.68; 95% confidence interval of 1.59–1.77), with an increasing proportion of these NVT strains becoming resistant to penicillin. Our findings point to highly entrenched, slow transmission and indicate that initial vaccine-linked decreases in antimicrobial resistance may be transient.

Simultaneous assessment of human genome and methylome data in a single experiment using limited deamination of methylated cytosine.

Multiomics require concerted recording of independent information, ideally from a single experiment. In this study, we introduce RIMS-seq2, a high-throughput technique to simultaneously sequence genomes and overlay methylation information while requiring only a small modification of the experimental protocol for high-throughput DNA sequencing to include a controlled deamination step. Importantly, the rate of deamination of 5-methylcytosine is negligible and thus does not interfere with standard DNA sequencing and data processing. Thus, RIMS-seq2 libraries from whole- or targeted-genome sequencing show the same germline variation calling accuracy and sensitivity compared with standard DNA-seq. Additionally, regional methylation levels provide an accurate map of the human methylome.

Regulatory barriers to US-China collaboration for generative AI development in genomic research

Here, we examine the challenges posed by laws in the United States and China for generative-AI-assisted genomic research collaboration. We recommend renewing the Agreement on Cooperation in Science and Technology to promote responsible principles for sharing human genomic data and to enhance transparency in research.

Defining predictors of responsiveness to advanced therapies in Crohn’s disease and ulcerative colitis: protocol for the IBD-RESPONSE and nested CD-metaRESPONSE prospective, multicentre, observational cohort study in precision medicine

IntroductionCharacterised by chronic inflammation of the gastrointestinal tract, inflammatory bowel disease (IBD) symptoms including diarrhoea, abdominal pain and fatigue can significantly impact patient’s quality of life. Therapeutic developments in the...

Investigating the Roles and Responsibilities of Institutional Signing Officials After Data Sharing Policy Reform for Federally Funded Research in the United States: National Survey.

New federal policies along with rapid growth in data generation, storage, and analysis tools are together driving scientific data sharing in the United States. At the same, triangulating human research data from diverse sources can also create situations where data are used for future research in ways that individuals and communities may consider objectionable. Institutional gatekeepers, namely, signing officials (SOs), are therefore at the helm of compliant management and sharing of human data for research. Of those with data governance responsibilities, SOs most often serve as signatories for investigators who deposit, access, and share research data between institutions. Although SOs play important leadership roles in compliant data sharing, we know surprisingly little about their scope of work, roles, and oversight responsibilities. The purpose of this study was to describe existing institutional policies and practices of US SOs who manage human genomic data access, as well as how these may change in the wake of new Data Management and Sharing requirements for National Institutes of Health-funded research in the United States. We administered an anonymous survey to institutional SOs recruited from biomedical research institutions across the United States. Survey items probed where data generated from extramurally funded research are deposited, how researchers outside the institution access these data, and what happens to these data after extramural funding ends. In total, 56 institutional SOs participated in the survey. We found that SOs frequently approve duplicate data deposits and impose stricter access controls when data use limitations are unclear or unspecified. In addition, 21% (n=12) of SOs knew where data from federally funded projects are deposited after project funding sunsets. As a consequence, most investigators deposit their scientific data into "a National Institutes of Health-funded repository" to meet the Data Management and Sharing requirements but also within the "institution's own repository" or a third-party repository. Our findings inform 5 policy recommendations and best practices for US SOs to improve coordination and develop comprehensive and consistent data governance policies that balance the need for scientific progress with effective human data protections.

Identification of novel homozygous asthenoteratospermia-causing ARMC2 mutations associated with multiple morphological abnormalities of the sperm flagella.

To identify the genetic causes of multiple morphological abnormalities in sperm flagella (MMAF) and male infertility in patients from two unrelated Han Chinese families. Whole-exome sequencing was conducted using blood samples from the two individuals with MMAF and male infertility. Hematoxylin and eosin staining and scanning electron microscopy were performed to evaluate sperm morphology. Ultrastructural and immunostaining analyses of the spermatozoa were performed. The HEK293T cells were used to confirm the pathogenicity of the variants. We identified two novel homozygous missense ARMC2 variants: c.314C > T: p.P105L and c.2227A > G: p.N743D. Both variants are absent or rare in the human population genome data and are predicted to be deleterious. In vitro experiments indicated that both ARMC2 variants caused a slightly increased protein expression. ARMC2-mutant spermatozoa showed multiple morphological abnormalities (bent, short, coiled, absent, and irregular) in the flagella. In addition, the spermatozoa of the patients revealed a frequent absence of the central pair complex and disrupted axonemal ultrastructure. We identified two novel ARMC2 variants that caused male infertility and MMAF in Han Chinese patients. These findings expand the mutational spectrum of ARMC2 and provide insights into the complex causes and pathogenesis of MMAF.

Cancer researchers’ experiences with and perceptions of research data sharing: Results of a cross-sectional survey

ABSTRACT Background: Despite wide recognition of the benefits of sharing research data, public availability rates have not increased substantially in oncology or medicine more broadly over the last decade. Methods: We surveyed 285 cancer researchers to determine their prior experience with sharing data and views on known drivers and inhibitors. Results: We found that 45% of respondents had shared some data from their most recent empirical publication, with respondents who typically studied non-human research participants, or routinely worked with human genomic data, more likely to share than those who did not. A third of respondents added that they had previously shared data privately, with 74% indicating that doing so had also led to authorship opportunities or future collaborations for them. Journal and funder policies were reported to be the biggest general drivers toward sharing, whereas commercial interests, agreements with industrial sponsors and institutional policies were the biggest prohibitors. We show that researchers’ decisions about whether to share data are also likely to be influenced by participants’ desires. Conclusions: Our survey suggests that increased promotion and support by research institutions, alongside greater championing of data sharing by journals and funders, may motivate more researchers in oncology to share their data.

Abstract B034: Characterization of MHC-I-mediated immune evasion in triple-negative breast cancer using human transcriptomic datasets and immunocompetent mouse models

Abstract In Triple-Negative Breast Cancer (TNBC) patients, adaptive immune system activity in the tumor microenvironment is associated with improved prognosis. However, tumors can evolve to evade immune detection, likely leading to worse patient outcomes. Multi-omic data of paired primary and metastatic breast tumors demonstrate that TNBC metastases frequently lose expression of genes involved in major histocompatibility complex class I (MHC-I) antigen presentation, a key pathway for adaptive immune activation (PMID: 36585450). Therefore, understanding and addressing MHC-I-mediated immune evasion may have profound clinical benefits for TNBC patients. We hypothesize that breast tumor MHC-I loss results in a unique immune-evasive phenotype, which we are characterizing using human clinical datasets and immunocompetent mouse models of breast cancer. First, using large transcriptomic datasets (i.e., SCAN-B and TCGA), we are characterizing molecular and clinical features of MHC-I-low human breast tumors using expression of the MHC-I gene human leukocyte antigen A (HLA-A) as a continuous variable. We found HLA-A expression varies significantly between PAM50 molecular and clinical subtypes of breast cancer and has differing impacts on patient survival based on clinical subtype. In TNBCs, low HLA-A expression is associated with worse outcomes, while in HR+/HER2- tumors, low HLA-A predicts improved outcomes. We also identified genes highly correlated with HLA-A regardless of molecular subtype. This list was functionally rich, related to antigen processing machinery, adaptive immune-mediated cytotoxicity, and T cell exhaustion features. To complement human genomic data, we are empirically testing the role of MHC-I in syngeneic mouse mammary tumor models by individually knocking out murine MHC-I genes–H2-K, H2-D, and B2m–through CRISPR/Cas9-mediated genome editing. We have generated and validated knockouts (KOs) of H2-K and B2m, as well as empty vector control clones from the “immune-hot” and immune checkpoint inhibitor (ICI) responsive mammary tumor cell lines KPB25L-UV and T11-APOBEC. RNA sequencing of KPB25L-UV H2-K and B2m KO versus wildtype cell lines in vitro identified lists of differentially expressed genes, many of which are related to proteolytic and peptide antigen processing. Next, these CRISPR-modified cell lines were injected into the mammary fat pad of syngeneic immunocompetent mice, and the resulting tumors were harvested for RNA sequencing. Differentially expressed genes in KPB25L-UV H2-K KO versus wildtype tumors were related to increased MHC Class II antigen presentation and dendritic cell activity. Preliminary data suggests that mice bearing KPB25L-UV H2-K KO tumors have shortened survival compared to H2-K wildtype tumors. Experiments are ongoing to further characterize the growth dynamics, metastatic potential, single-cell transcriptomic profiles, and ICI sensitivities of these KO tumors in vivo. Our long-term goal is to identify therapeutic means to overcome this common immune-evasive phenotype to improve TNBC patient outcomes. Citation Format: Constandina E O'Connell, Brooke M Felsheim, Aranzazu Fernandez-Martinez, Kevin R Mott, Charles M Perou. Characterization of MHC-I-mediated immune evasion in triple-negative breast cancer using human transcriptomic datasets and immunocompetent mouse models [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Breast Cancer Research; 2023 Oct 19-22; San Diego, California. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_1):Abstract nr B034.

The wisdom of claiming ownership of human genomic data: A cautionary tale for research institutions.

This article considers the practical question of how research institutions should best structure their legal relationship with the human genomic data that they generate. The analysis, based on South African law, is framed by the legal position that although a research institution that generates human genomic data is not automatically the owner thereof, it is well positioned to claim ownership of newly generated data instances. Given that the research institution exerts effort to generate the data, it can be argued that it has a moral right to claim ownership of such data. Combined with the fact that it has an interest in having comprehensive rights in such data, it appears that the prudent policy for research institutions is to claim ownership of the human genomic data instances that they generate. This policy is tested against two opposing policy positions. The first opposing policy position is that research participants should own the data that relate to them. However, in light of data protection legislation that already provides extensive protections to research participants, bestowing data ownership on research participants would offer little benefit to such individuals, while leading to significant practical problems for research institutions. The second opposing policy position is that the concept of ownership should be abandoned in favour of data custodianship. This opposing position is problematic, as avoiding reference to ownership is a denial of legal reality and hence not a useful policy. Also, avoiding reference to ownership will leave research institutions with limited legal remedies in the event of appropriation of data by third parties. Accordingly, it is concluded that the wisest policy for research institutions is indeed to explicitly claim ownership of the human genomic data instances that they generate.

A genome-wide association study reveals the relationship between human genetic variation and the nasal microbiome

The nasal cavity harbors diverse microbiota that contributes to human health and respiratory diseases. However, whether and to what extent the host genome shapes the nasal microbiome remains largely unknown. Here, by dissecting the human genome and nasal metagenome data from 1401 healthy individuals, we demonstrated that the top three host genetic principal components strongly correlated with the nasal microbiota diversity and composition. The genetic association analyses identified 63 genome-wide significant loci affecting the nasal microbial taxa and functions, of which 2 loci reached study-wide significance (p < 1.7 × 10-10): rs73268759 within CAMK2A associated with genus Actinomyces and family Actinomycetaceae; and rs35211877 near POM121L12 with Gemella asaccharolytica. In addition to respiratory-related diseases, the associated loci are mainly implicated in cardiometabolic or neuropsychiatric diseases. Functional analysis showed the associated genes were most significantly expressed in the nasal airway epithelium tissue and enriched in the calcium signaling and hippo signaling pathway. Further observational correlation and Mendelian randomization analyses consistently suggested the causal effects of Serratia grimesii and Yokenella regensburgei on cardiometabolic biomarkers (cystine, glutamic acid, and creatine). This study suggested that the host genome plays an important role in shaping the nasal microbiome.

Transformer Neural Networks, Information Biology, and Alzheimer’s Disease

AbstractBackgroundAlzheimer’s disease affects one in ten people older than 65 years, with the prevalence expected to triple by 2030, together with commensurate increases in cost. A comprehensive causal model of Alzheimer’s disease etiology is lacking despite more than three decades of study. Modern computational methods provide a new approach to multiscalar biosystems by modeling neuropathology in its native three‐dimensional character in topological biophysics and quantum computational models.MethodThis work describes how an emerging standard in machine learning, responsible for the AlphaFold (protein folding structure) and Gato generalist agent projects, transformer neural networks, may be applied to the study of Alzheimer’s disease per proven results in bot text chat, image recognition and creation, video generation, software programming, and autonomy in robotics and driving. The self‐attention and self‐learning mechanisms of transformer neural networks are discussed in application to multifaceted Alzheimer’s disease data sets involving neuroimaging scans, whole human genome data, and blood and CSF biomarker data.ResultA machine learning‐based topological biophysics model of Alzheimer’s disease neuropathology hypothesis‐testing (misfolded proteins, gene expression, diabetes type‐3, and peptide immune system stimulation) is described.ConclusionThere is an opportunity to apply state‐of‐the‐art transformer neural network machine learning techniques to the automated analysis of large accruing data stores of Alzheimer’s information (imaging, genomic, proteomic, and self‐reported data) towards the causal understanding of the neuropathology.

Transformer Neural Networks, Information Biology, and Alzheimer’s Disease

AbstractBackgroundAlzheimer’s disease affects one in ten people older than 65 years, with the prevalence expected to triple by 2030, together with commensurate increases in cost. A comprehensive causal model of Alzheimer’s disease etiology is lacking despite more than three decades of study. Modern computational methods provide a new approach to multiscalar biosystems by modeling neuropathology in its native three‐dimensional character in topological biophysics and quantum computational models.MethodThis work describes how an emerging standard in machine learning, responsible for the AlphaFold (protein folding structure) and Gato generalist agent projects, transformer neural networks, may be applied to the study of Alzheimer’s disease per proven results in bot text chat, image recognition and creation, video generation, software programming, and autonomy in robotics and driving. The self‐attention and self‐learning mechanisms of transformer neural networks are discussed in application to multifaceted Alzheimer’s disease data sets involving neuroimaging scans, whole human genome data, and blood and CSF biomarker data.ResultA machine learning‐based topological biophysics model of Alzheimer’s disease neuropathology hypothesis‐testing (misfolded proteins, gene expression, diabetes type‐3, and peptide immune system stimulation) is described.ConclusionThere is an opportunity to apply state‐of‐the‐art transformer neural network machine learning techniques to the automated analysis of large accruing data stores of Alzheimer’s information (imaging, genomic, proteomic, and self‐reported data) towards the causal understanding of the neuropathology.