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Human evolution is intricately linked with culture, which permeates almost all facets of human life from health and reproduction, to the environments in which we live. Nevertheless, our understanding of the ways in which stably transmitted, evolutionarily relevant human cultural traits might interact with the human genome is incomplete, and methods to detect such interactions are limited. Here, we describe some rules of cultural transmission which could pertain to both humans and cultural nonhuman animals that could lead to the formation and maintenance of stable associations between cultural and genetic traits. Next, we show that, in the presence of such associations, a process analogous to genetic hitchhiking is possible in gene-culture systems. These could leave signatures in the human genome similar to, and perhaps indistinguishable from, those left by selection on genetic traits. Finally, we model selective interference between cultural and genetic traits. We show that selective interference between a cultural trait under selection and a genetic trait under selection can reduce the efficacy of natural selection in the human genome, both in terms of the probability of fixation of beneficial alleles and the dynamics of selective sweeps. We then show that the efficiency of selection at genetic loci can, however, be increased in the presence of strong cultural transmission biases. This implies that the signatures of gene-culture interactions in genetic data may be complex and wide-ranging in gene-culture coevolutionary systems., Competing Interests: Competing interests statement:The authors declare no competing interest.

Background: The Italic Iron Age is characterized by the presence of various ethnic groups partially examined from a genomic perspective. To explore the evolution of Iron Age Italic populations and the genetic impact of Romanization, we focus on the Picenes, one of the most fascinating pre-Roman civilizations, who flourished on the Middle Adriatic side of Central Italy between the 9 th and the 3 rd  century BCE, until the Roman colonization., Results: More than 50 samples are reported, spanning more than 1000 years of history from the Iron Age to Late Antiquity. Despite cultural diversity, our analysis reveals no major differences between the Picenes and other coeval populations, suggesting a shared genetic history of the Central Italian Iron Age ethnic groups. Nevertheless, a slight genetic differentiation between populations along the Adriatic and Tyrrhenian coasts can be observed, possibly due to different population dynamics in the two sides of Italy and/or genetic contacts across the Adriatic Sea. Additionally, we identify several individuals with ancestries deviating from their general population. Lastly, in our Late Antiquity site, we observe a drastic change in the genetic landscape of the Middle Adriatic region, indicating a relevant influx from the Near East, possibly as a consequence of Romanization., Conclusions: Our findings, consistently with archeological hypotheses, suggest genetic interactions across the Adriatic Sea during the Bronze/Iron Age and a high level of individual mobility typical of cosmopolitan societies. Finally, we highlight the role of the Roman Empire in shaping genetic and phenotypic changes that greatly impact the Italian peninsula., Competing Interests: Declarations. Ethics approval and consent to participate: Not applicable. Consent for publication: Not applicable. Competing interests: The authors declare that they have no competing interests., (© 2024. The Author(s).)

Background: Ancient Chang'an in the Tang Dynasty (618-907 AD) was one of the world's largest and most populated cities and acted as the eastern end of the world-famous Silk Road. However, little is known about the genetics of Chang'an people and whether the Western Regions-related gene flows have been prevalent in this cosmopolitan city., Results: Here, we present seven genomes from Xingfulindai (XFLD) sites dating to the Tang Dynasty in Chang'an. We observed that four of seven XFLD individuals (XFLD_1) were genetically homogenous with the Late Neolithic Wadian, Pingliangtai, and Haojiatai populations from the middle reaches of the Yellow River Basin (YR_LN), with no genetic influence from the Western Eurasian or other non-Yellow River-related lineages. The remaining three XFLD individuals were a mixture of YR_LN-related ancestry and ~ 3-15% Western Eurasian-related ancestry. Mixtures of XFLD_1 and Western Eurasian-related ancestry drove the main gradient of genetic variation in northern and central Shaanxi Province today., Conclusions: Our study underlined the widespread distribution of the YR_LN-related ancestry alongside the Silk Road within the territory of China during the historical era and provided direct evidence of trans-Eurasian communication in Chang'an from a genetic perspective., Competing Interests: Declarations. Ethics approval and consent to participate: Not applicable. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests., (© 2024. The Author(s).)

Long-read sequencing technologies have improved the contiguity and, as a result, the quality of genome assemblies by generating reads long enough to span and resolve complex or repetitive regions of the genome. Several groups have shown the power of long reads in detecting thousands of genomic and epigenomic features that were previously missed by short-read sequencing approaches. While these studies demonstrate how long reads can help resolve repetitive and complex regions of the genome, they also highlight the throughput and coverage requirements needed to accurately resolve variant alleles across large populations using these platforms. At the time of this review, whole-genome long-read sequencing is more expensive than short-read sequencing on the highest throughput short-read instruments; thus, achieving sufficient coverage to detect low-frequency variants (such as somatic variation) in heterogenous samples remains challenging. Targeted sequencing, on the other hand, provides the depth necessary to detect these low-frequency variants in heterogeneous populations. Here, we review currently used and recently developed targeted sequencing strategies that leverage existing long-read technologies to increase the resolution with which we can look at nucleic acids in a variety of biological contexts., (© 2024 Iyer et al.; Published by Cold Spring Harbor Laboratory Press.)

The combination of ultra-long (UL) Oxford Nanopore Technologies (ONT) sequencing reads with long, accurate Pacific Bioscience (PacBio) High Fidelity (HiFi) reads has enabled the completion of a human genome and spurred similar efforts to complete the genomes of many other species. However, this approach for complete, "telomere-to-telomere" genome assembly relies on multiple sequencing platforms, limiting its accessibility. ONT "Duplex" sequencing reads, where both strands of the DNA are read to improve quality, promise high per-base accuracy. To evaluate this new data type, we generated ONT Duplex data for three widely studied genomes: human HG002, Solanum lycopersicum Heinz 1706 (tomato), and Zea mays B73 (maize). For the diploid, heterozygous HG002 genome, we also used "Pore-C" chromatin contact mapping to completely phase the haplotypes. We found the accuracy of Duplex data to be similar to HiFi sequencing, but with read lengths tens of kilobases longer, and the Pore-C data to be compatible with existing diploid assembly algorithms. This combination of read length and accuracy enables the construction of a high-quality initial assembly, which can then be further resolved using the UL reads, and finally phased into chromosome-scale haplotypes with Pore-C. The resulting assemblies have a base accuracy exceeding 99.999% (Q50) and near-perfect continuity, with most chromosomes assembled as single contigs. We conclude that ONT sequencing is a viable alternative to HiFi sequencing for de novo genome assembly, and provides a multirun single-instrument solution for the reconstruction of complete genomes., (© 2024 Koren et al.; Published by Cold Spring Harbor Laboratory Press.)

Chromosomal inversions (INVs) are particularly challenging to detect due to their copy-number neutral state and association with repetitive regions. Inversions represent about 1/20 of all balanced structural chromosome aberrations and can lead to disease by gene disruption or altering regulatory regions of dosage-sensitive genes in cis Short-read genome sequencing (srGS) can only resolve ∼70% of cytogenetically visible inversions referred to clinical diagnostic laboratories, likely due to breakpoints in repetitive regions. Here, we study 12 inversions by long-read genome sequencing (lrGS) ( n = 9) or srGS ( n = 3) and resolve nine of them. In four cases, the inversion breakpoint region was missing from at least one of the human reference genomes (GRCh37, GRCh38, T2T-CHM13) and a reference agnostic analysis was needed. One of these cases, an INV9 mappable only in de novo assembled lrGS data using T2T-CHM13 disrupts EHMT1 consistent with a Mendelian diagnosis (Kleefstra syndrome 1; MIM#610253). Next, by pairwise comparison between T2T-CHM13, GRCh37, and GRCh38, as well as the chimpanzee and bonobo, we show that hundreds of megabases of sequence are missing from at least one human reference, highlighting that primate genomes contribute to genomic diversity. Aligning population genomic data to these regions indicated that these regions are variable between individuals. Our analysis emphasizes that T2T-CHM13 is necessary to maximize the value of lrGS for optimal inversion detection in clinical diagnostics. These results highlight the importance of leveraging diverse and comprehensive reference genomes to resolve unsolved molecular cases in rare diseases., (© 2024 Bilgrav Saether et al.; Published by Cold Spring Harbor Laboratory Press.)

Rare or de novo structural variation, primarily in the form of copy number variants, is detected in 5%-10% of autism spectrum disorder (ASD) families. While complex structural variants involving duplications can generally be detected using microarray or short-read genome sequencing (GS), these methods frequently fail to characterize breakpoints at nucleotide resolution, requiring additional molecular methods for validation and fine-mapping. Here, we use Oxford Nanopore Technologies PromethION long-read GS to characterize complex genomic rearrangements (CGRs) involving large duplications that segregate with ASD in five families. In total, we investigated 13 CGR carriers and were able to resolve all breakpoint junctions at nucleotide resolution. While all breakpoints were identified, the precise genomic architecture of one rearrangement remained unresolved with three different potential structures. The findings in two families include potential fusion genes formed through duplication rearrangements, involving IL1RAPL1-DMD and SUPT16H-CHD8 In two of the families originating from the same geographical region, an identical rearrangement involving ANK2 was identified, which likely represents a founder variant. In addition, we analyze methylation status directly from the long-read data, allowing us to assess the activity of rearranged genes and regulatory regions. Investigation of methylation across the CGRs reveals aberrant methylation status in carriers across a rearrangement affecting the CREBBP locus. In aggregate, our results demonstrate the utility of nanopore sequencing to pinpoint CGRs associated with ASD in five unrelated families, and highlight the importance of a gene-centric description of disease-associated complex chromosomal rearrangements., (© 2024 Eisfeldt et al.; Published by Cold Spring Harbor Laboratory Press.)

Automated telomere-to-telomere (T2T) de novo assembly of diploid and polyploid genomes remains a formidable task. A string graph is a commonly used assembly graph representation in the assembly algorithms. The string graph formulation employs graph simplification heuristics, which drastically reduce the count of vertices and edges. One of these heuristics involves removing the reads contained in longer reads. In practice, this heuristic occasionally introduces gaps in the assembly by removing all reads that cover one or more genome intervals. The factors contributing to such gaps remain poorly understood. In this work, we mathematically derived the frequency of observing a gap near a germline and a somatic heterozygous variant locus. Our analysis shows that (1) an assembly gap due to contained read deletion is an order of magnitude more frequent in Oxford Nanopore Technologies (ONT) reads than Pacific Biosciences high-fidelity (PacBio HiFi) reads due to differences in their read-length distributions, and (2) this frequency decreases with an increase in the sequencing depth. Drawing cues from these observations, we addressed the weakness of the string graph formulation by developing the repeat-aware fragmenting tool (RAFT) assembly algorithm. RAFT addresses the issue of contained reads by fragmenting reads and producing a more uniform read-length distribution. The algorithm retains spanned repeats in the reads during the fragmentation. We empirically demonstrate that RAFT significantly reduces the number of gaps using simulated data sets. Using real ONT and PacBio HiFi data sets of the HG002 human genome, we achieved a twofold increase in the contig NG50 and the number of haplotype-resolved T2T contigs compared to hifiasm., (© 2024 Kamath et al.; Published by Cold Spring Harbor Laboratory Press.)

Fewer than half of individuals with a suspected Mendelian or monogenic condition receive a precise molecular diagnosis after comprehensive clinical genetic testing. Improvements in data quality and costs have heightened interest in using long-read sequencing (LRS) to streamline clinical genomic testing, but the absence of control data sets for variant filtering and prioritization has made tertiary analysis of LRS data challenging. To address this, the 1000 Genomes Project (1KGP) Oxford Nanopore Technologies Sequencing Consortium aims to generate LRS data from at least 800 of the 1KGP samples. Our goal is to use LRS to identify a broader spectrum of variation so we may improve our understanding of normal patterns of human variation. Here, we present data from analysis of the first 100 samples, representing all 5 superpopulations and 19 subpopulations. These samples, sequenced to an average depth of coverage of 37× and sequence read N50 of 54 kbp, have high concordance with previous studies for identifying single nucleotide and indel variants outside of homopolymer regions. Using multiple structural variant (SV) callers, we identify an average of 24,543 high-confidence SVs per genome, including shared and private SVs likely to disrupt gene function as well as pathogenic expansions within disease-associated repeats that were not detected using short reads. Evaluation of methylation signatures revealed expected patterns at known imprinted loci, samples with skewed X-inactivation patterns, and novel differentially methylated regions. All raw sequencing data, processed data, and summary statistics are publicly available, providing a valuable resource for the clinical genetics community to discover pathogenic SVs., (© 2024 Gustafson et al.; Published by Cold Spring Harbor Laboratory Press.)

Variant detection from long-read genome sequencing (lrGS) has proven to be more accurate and comprehensive than variant detection from short-read genome sequencing (srGS). However, the rate at which lrGS can increase molecular diagnostic yield for rare disease is not yet precisely characterized. We performed lrGS using Pacific Biosciences "HiFi" technology on 96 short-read-negative probands with rare diseases that were suspected to be genetic. We generated hg38-aligned variants and de novo phased genome assemblies, and subsequently annotated, filtered, and curated variants using clinical standards. New disease-relevant or potentially relevant genetic findings were identified in 16/96 (16.7%) probands, nine of which (8/96, ∼9.4%) harbored pathogenic or likely pathogenic variants. Nine probands (∼9.4%) had variants that were accurately called in both srGS and lrGS and represent changes to clinical interpretation, mostly from recently published gene-disease associations. Seven cases included variants that were only correctly interpreted in lrGS, including copy-number variants (CNVs), an inversion, a mobile element insertion, two low-complexity repeat expansions, and a 1 bp deletion. While evidence for each of these variants is, in retrospect, visible in srGS, they were either not called within srGS data, were represented by calls with incorrect sizes or structures, or failed quality control and filtration. Thus, while reanalysis of older srGS data clearly increases diagnostic yield, we find that lrGS allows for substantial additional yield (7/96, 7.3%) beyond srGS. We anticipate that as lrGS analysis improves, and as lrGS data sets grow allowing for better variant-frequency annotation, the additional lrGS-only rare disease yield will grow over time., (© 2024 Hiatt et al.; Published by Cold Spring Harbor Laboratory Press.)

The human leukocyte antigen (HLA) genes and the killer cell immunoglobulin-like receptor (KIR) genes are critical to immune responses and are associated with many immune-related diseases. Located in highly polymorphic regions, it is difficult to study them with traditional short-read alignment-based methods. Although modern long-read assemblers can often assemble these genes, using existing tools to annotate HLA and KIR genes in these assemblies remains a nontrivial task. Here, we describe Immuannot, a new computation tool to annotate the gene structures of HLA and KIR genes and to type the allele of each gene. Applying Immuannot to 56 regional and 212 whole-genome assemblies from previous studies, we annotate 9931 HLA and KIR genes and found that almost half of these genes, 4068, have novel sequences compared with the current Immuno Polymorphism Database (IPD). These novel gene sequences are represented by 2664 distinct alleles, some of which contained nonsynonymous variations, resulting in 92 novel protein sequences. We demonstrate the complex haplotype structures at the two loci and report the linkage between HLA/KIR haplotypes and gene alleles. We anticipate that Immuannot will speed up the discovery of new HLA/KIR alleles and enable the association of HLA/KIR haplotype structures with clinical outcomes in the future., (© 2024 Zhou et al.; Published by Cold Spring Harbor Laboratory Press.)

Background: Rare copy number variants (CNVs) significantly influence the human genome and may contribute to disease susceptibility. High-throughput SNP genotyping platforms provide data that can be used for CNV detection, but it requires the complex pipelining of bioinformatic tools. Here, we propose a flexible bioinformatic pipeline for rare CNV analysis from human SNP array data., Results: The pipeline consists of two major sub-pipelines: (1) Calling and quality control (QC) analysis, and (2) Rare CNV analysis. It is implemented in Snakemake following a rule-based structure that enables automation and scalability while maintaining flexibility., Conclusions: Our pipeline automates the detection and analysis of rare CNVs. It implements a rigorous CNV quality control, assesses the frequencies of these rare CNVs in patients versus controls, and evaluates the impact of CNVs on specific genes or pathways. We hence aim to provide an efficient yet flexible bioinformatic framework to investigate rare CNVs in biomedical research., Competing Interests: Declarations Competing interests The authors declare that they have no competing interests., (© 2024. The Author(s).)

Transition of cytosine to thymine in CpG dinucleotides is the most frequent type of mutation in cancer. This increased mutability is commonly attributed to the spontaneous deamination of 5-methylcytosine (5mC), which is normally repaired by the base-excision repair (BER) pathway. However, the contribution of 5mC deamination in the increasing diversity of cancer mutational signatures remains poorly explored. We integrate mutational signatures analysis in a large series of tumor whole genomes with lineage-specific epigenomic data to draw a detailed view of 5mC deamination in cancer. We uncover tumor type-specific patterns of 5mC deamination signatures in CpG and non-CpG contexts. We demonstrate that the BER glycosylase MBD4 preferentially binds to active chromatin and early replicating DNA, which correlates with lower mutational burden in these domains. We validate our findings by modeling BER deficiencies in isogenic cell models. Here, we establish MBD4 as the main actor responsible for 5mC deamination repair in humans., Competing Interests: Competing interests D. Rieke reports advisory agreement with BeiGene and Bayer, honoraria from Bristol Myers Squibb, Bayer and Roche, research support from Seagen, and personal fees from Bayer and Johnson & Johnson, all outside the submitted work. A. Picca reports personal fees from AstraZeneca and Servier, all outside the submitted work. F. Bielle reports funding of research from Abbvie, service agreement for research contracted between his institution and Treefrog Therapeutics as well as Owkin, personal fees from Bristol Myers Squibb and a next-of-kin employed by Bristol Myers Squibb, all outside the submitted work. M.L. Yaspo is COO/CSO and shareholder of Alacris Theranostics without conflict of interest with the submitted work. M. Rodrigues reports non-financial support from AstraZeneca and Merck Sharp and Dohme, grants from Daiichi Sankyo, personal fees from AstraZeneca, Immunocore, Merck Sharp and Dohme and GlaxoSmithKline, all outside the submitted work. M.-H. Stern reports grants from Immunocore and Bionano, and royalties from Myriad Genetics, all outside the submitted work. The remaining authors have no conflict of interest to declare., (© 2024. The Author(s).)

Complex structural variations (cxSVs) are often overlooked in genome analyses due to detection challenges. We developed ARC-SV, a probabilistic and machine-learning-based method that enables accurate detection and reconstruction of cxSVs from standard datasets. By applying ARC-SV across 4,262 genomes representing all continental populations, we identified cxSVs as a significant source of natural human genetic variation. Rare cxSVs have a propensity to occur in neural genes and loci that underwent rapid human-specific evolution, including those regulating corticogenesis. By performing single-nucleus multiomics in postmortem brains, we discovered cxSVs associated with differential gene expression and chromatin accessibility across various brain regions and cell types. Additionally, cxSVs detected in brains of psychiatric cases are enriched for linkage with psychiatric GWAS risk alleles detected in the same brains. Furthermore, our analysis revealed significantly decreased brain-region- and cell-type-specific expression of cxSV genes, specifically for psychiatric cases, implicating cxSVs in the molecular etiology of major neuropsychiatric disorders., Competing Interests: Declaration of interests M.P.S. is a co-founder and on the advisory boards of Personalis, Qbio, January AI, SensOmics, Filtricine, Protos, Mirvie, Onza, Marble Therapeutics, Iollo, and NextThought AI. He is also on the advisory boards of Jupiter, Applied Cognition, Neuvivo, Mitrix, and Enovone. W.J.G. is a consultant for 10× Genomics, Guardant Health, Quantapore, and Ultima Genomics, a co-founder of Protillion Biosciences, and is named on ATAC-seq patents. A.K. is a consulting fellow with Illumina; a member of the SABs of OpenTargets (GSK), PatchBio, and SerImmune; and a co-founder of RavelBio. S.B.M. is an advisor for BioMarin, MyOme, and Tenaya Therapeutics., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Enhancers and promoters are classically considered to be bound by a small set of transcription factors (TFs) in a sequence-specific manner. This assumption has come under increasing skepticism as the datasets of ChIP-seq assays of TFs have expanded. In particular, high-occupancy target (HOT) loci attract hundreds of TFs with often no detectable correlation between ChIP-seq peaks and DNA-binding motif presence. Here, we used a set of 1003 TF ChIP-seq datasets (HepG2, K562, H1) to analyze the patterns of ChIP-seq peak co-occurrence in combination with functional genomics datasets. We identified 43,891 HOT loci forming at the promoter (53%) and enhancer (47%) regions. HOT promoters regulate housekeeping genes, whereas HOT enhancers are involved in tissue-specific process regulation. HOT loci form the foundation of human super-enhancers and evolve under strong negative selection, with some of these loci being located in ultraconserved regions. Sequence-based classification analysis of HOT loci suggested that their formation is driven by the sequence features, and the density of mapped ChIP-seq peaks across TF-bound loci correlates with sequence features and the expression level of flanking genes. Based on the affinities to bind to promoters and enhancers we detected five distinct clusters of TFs that form the core of the HOT loci. We report an abundance of HOT loci in the human genome and a commitment of 51% of all TF ChIP-seq binding events to HOT locus formation thus challenging the classical model of enhancer activity and propose a model of HOT locus formation based on the existence of large transcriptional condensates., Competing Interests: SH, IO No competing interests declared

Non-homologous chromosomal contacts (NHCCs) between different chromosomes participate considerably in gene and genome regulation. Due to analytical challenges, NHCCs are currently considered as singular, stochastic events, and their extent and fundamental principles across cell types remain controversial. We develop a supervised and unsupervised learning algorithm, termed Signature, to call NHCCs in Hi-C datasets to advance our understanding of genome topology. Signature reveals 40,282 NHCCs and their properties across 62 Hi-C datasets of 53 diploid human cell types. Genomic regions of NHCCs are gene-dense, highly expressed, and harbor genes for cell-specific and sex-specific functions. Extensive inter-telomeric and inter-centromeric clustering occurs across cell types [Rabl's configuration] and 61 NHCCs are consistently found at the nuclear speckles. These constitutive 'anchor loci' facilitate an axis of genome activity whilst cell-type-specific NHCCs act in discrete hubs. Our results suggest that non-random chromosome positioning is supported by constitutive NHCCs that shape genome topology along an off-centered spatial gradient of genome activity., Competing Interests: Competing interests The authors declare no competing interests. Inclusion, diversity, and ethics statement We support inclusive, diverse, ethical and equitable conduct of research., (© 2024. The Author(s).)

The tripartite ancestral structure is a recently proposed model for the genetic origin of modern Japanese, comprising indigenous Jomon hunter-gatherers and two additional continental ancestors from Northeast Asia and East Asia. To investigate the impact of the tripartite structure on genetic and phenotypic variation today, we conducted biobank-scale analyses by merging Biobank Japan (BBJ; n = 171,287) with ancient Japanese and Eurasian genomes (n = 22). We demonstrate the applicability of the tripartite model to Japanese populations throughout the archipelago, with an extremely strong correlation between Jomon ancestry and genomic variation among individuals. We also find that the genetic legacy of Jomon ancestry underlies an elevated body mass index (BMI). Genome-wide association analysis with rigorous adjustments for geographical and ancestral substructures identifies 132 variants that are informative for predicting individual Jomon ancestry. This prediction model is validated using independent Japanese cohorts (Nagahama cohort, n = 2993; the second cohort of BBJ, n = 72,695). We further confirm the phenotypic association between Jomon ancestry and BMI using East Asian individuals from UK Biobank (n = 2286). Our extensive analysis of ancient and modern genomes, involving over 250,000 participants, provides valuable insights into the genetic legacy of ancient hunter-gatherers in contemporary populations., Competing Interests: Competing interests The authors declare no competing interests., (© 2024. The Author(s).)

Clinical biomedical applications of genomic technologies are extensive and provide possibilities to enhance healthcare covering the span of medical talents. Genome disorder prediction is an important issue in biomedical research. Genome disorders cause multivariate diseases such as cancer, dementia, diabetes, Leigh syndrome, etc. Existing machine and deep learning-based methods were introduced to forecast genome disorders. However, the genome prediction outcomes were not sufficient. To address this issue, propose a new method called Quadratic Feynman Polynomial Interpolated and Vector Nearest Centroid-based (QFPI-VNC) for acutely predicting the genome disorder with improved sensitivity and specificity. First, we utilized medical data about children from a public genomes dataset and applied it to Linear Quadratic and Feynman Kac Genome filtering to obtain computationally efficient filtered results. Next, the results are fed to the Concordance Correlated Polynomial Interpolation with the purpose of extracting genome wide data in an accurate manner. Finally, the features extracted are fused and fed to the Support Vector and Nearest Centroid model for genome disorder prediction. Experimental investigations of the proposed method employing the genome dataset confirm that the performance of the proposed method is prospective and in the scope of acceptance with relative to state-of-the-art methods in terms of convergence speed, recognition rate, sensitivity, and specificity. Results suggest that the QFPI-VNC method produces the best performance with a higher genome disease detection rate by 14%, accuracy by 11%, sensitivity by 14% specificity by 12%, and lesser convergence speed by 29% than compared to state-of-the-art methods., Competing Interests: Declarations Competing interests The authors declare no competing interests., (© 2024. The Author(s).)

Humans are exposed to DNA alkylating agents through endogenous metabolism, environmental exposure and cancer chemotherapy. The resulting alkylated DNA adducts may elicit genome instability by perturbing DNA replication and transcription. R-loops regulate various cellular processes, including transcription, DNA repair, and telomere maintenance. However, unscheduled R-loops are also recognized as potential sources of DNA damage and genome instability. In this study, by employing fluorescence microscopy and R-loop sequencing approaches, we uncovered, for the first time, that minor-groove N2-alkyl-dG lesions elicit elevated R-loop accumulation in chromatin and in plasmid DNA in cells. We also demonstrated that the N2-alkyl-dG-induced R-loops impede transcription elongation and compromise genome integrity. Moreover, genetic depletion of DDX23, a R-loop helicase, renders cells more sensitive toward benzo[a]pyrene diolepoxide, a carcinogen that induces mainly the minor-groove N2-dG adduct. Together, our work unveiled that unrepaired minor-groove N2-alkyl-dG lesions may perturb genome integrity through augmenting R-loop levels in chromatin. Our findings suggest a potential therapeutic strategy involving the combination of R-loop helicase inhibitors with DNA alkylating drugs., (© The Author(s) 2024. Published by Oxford University Press on behalf of Nucleic Acids Research.)

A recent publication describing the assembly of the Y chromosomes of 43 males was remarkable not only for its ambitious technical scope but also for the startling suggestion that the boundary of the pseudoautosomal region 1 (PAR1), where the human X and Y chromosomes engage in crossing-over during male meiosis, lies 500 kb distal to its previously reported location. Where is the boundary of the human PAR1? We first review the evidence that mapped the PAR boundary, or PAB, before the human genome draft sequence was produced, then examine post-genomic datasets for evidence of crossing-over between the X and Y, and lastly re-examine contiguous sequence assemblies of the PAR-NPY boundary to see whether they support a more distal PAB. We find ample evidence of X-Y crossovers throughout the 500 kb in question, some as close as 246 bp to the previously reported PAB. Our new analyses, combined with previous studies over the past 40 years, provide overwhelming evidence to support the original position and narrow the probable location of the PAB to a 201-bp window., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Genomic structural variants (SVs) greatly impact human health, but much is unknown about the mechanisms that generate the largest class of nonrecurrent alterations. Common fragile sites (CFSs) are unstable loci that provide a model for SV formation, especially large deletions, under replication stress. We study SV junction formation as it occurs in human cell lines by applying error-minimized capture sequencing to CFS DNA harvested after low-dose aphidicolin treatment. SV junctions form throughout CFS genes at a 5-fold higher rate after cells pass from G2 into M-phase. Neither SV formation nor CFS expression depend on mitotic DNA synthesis (MiDAS), an error-prone form of replication active at CFSs. Instead, analysis of tens of thousands of de novo SV junctions combined with DNA repair pathway inhibition reveal a primary role for DNA polymerase theta (POLQ)-mediated end-joining (TMEJ). We propose an important role for mitotic TMEJ in nonrecurrent SV formation genome wide., (© 2024. The Author(s).)

Patterson's f-statistics are among the most heavily utilized tools for analyzing genome-wide allele frequency data for demographic inference. Beyond studying admixture, f3- and f4-statistics are also used for clustering populations to identify groups with similar histories. However, previous studies have noted an unexpected behavior of f-statistics: multiple populations from a certain region systematically show higher genetic affinity to a more distant population than to their neighbors, a pattern that is mismatched with alternative measures of genetic similarity. We call this counter-intuitive pattern "sister repulsion". We first present a novel instance of sister repulsion, where genomes from Bronze Age East Anatolian sites show higher affinity toward Bronze Age Greece rather than each other. This is observed both using f3- and f4-statistics, contrasts with archaeological/historical expectation, and also contradicts genetic affinity patterns captured using principal components analysis or multidimensional scaling on genetic distances. We then propose a simple demographic model to explain this pattern, where sister populations receive gene flow from a genetically distant source. We calculate f3- and f4-statistics using simulated genetic data with varying population genetic parameters, confirming that low-level gene flow from an external source into populations from 1 region can create sister repulsion in f-statistics. Unidirectional gene flow between the studied regions (without an external source) can likewise create repulsion. Meanwhile, similar to our empirical observations, multidimensional scaling analyses of genetic distances still cluster sister populations together. Overall, our results highlight the impact of low-level admixture events when inferring demographic history using f-statistics., Competing Interests: Conflicts of interest The author(s) declare no conflicts of interest., (© The Author(s) 2024. Published by Oxford University Press on behalf of The Genetics Society of America.)

Significant efforts have been made to identify and validate oncoproteins and ncRNAs as therapeutic targets for cancer therapy; however, emerging observations suggest that noncoding cis-regulatory elements, which orchestrate the 3D organization of the genome and thus the transcriptional landscape, are potential therapeutic targets as well. In this commentary, we envisage that further efforts to decipher the noncoding cis-regulatory code and performing systematic surveys of functional noncoding cis-regulatory elements and recurrent 3D genome alterations in both cancerous and nonmalignant cells within tumor tissues will pave the way to the development of novel therapeutic strategies., (©2024 American Association for Cancer Research.)

Summary: Admixture is a fundamental process that has shaped levels and patterns of genetic variation in human populations. RFMIX version 2 (RFMIX2) utilizes a robust modeling approach to identify the genetic ancestries in admixed populations. However, this software does not have a built-in method to visually summarize the results of analyses. Here, we introduce the AncestryGrapher toolkit, which converts the numerical output of RFMIX2 into graphical representations of global and local ancestry (i.e. the per-individual ancestry components and the genetic ancestry along chromosomes, respectively)., Results: To demonstrate the utility of our methods, we applied the AncestryGrapher toolkit to visualize the global and local ancestry of individuals in the North African Mozabite Berber population from the Human Genome Diversity Panel. Our results showed that the Mozabite Berbers derived their ancestry from the Middle East, Europe, and sub-Saharan Africa (global ancestry). We also found that the population origin of ancestry varied considerably along chromosomes (local ancestry). For example, we observed variance in local ancestry in the genomic region on Chromosome 2 containing the regulatory sequence in the MCM6 gene associated with lactase persistence, a human trait tied to the cultural development of adult milk consumption. Overall, the AncestryGrapher toolkit facilitates the exploration, interpretation, and reporting of ancestry patterns in human populations., Availability and Implementation: The AncestryGrapher toolkit is free and open source on https://github.com/alisi1989/RFmix2-Pipeline-to-plot., (© The Author(s) 2024. Published by Oxford University Press.)

Motivation: Pangenome graphs offer a comprehensive way of capturing genomic variability across multiple genomes. However, current construction methods often introduce biases, excluding complex sequences or relying on references. The PanGenome Graph Builder (PGGB) addresses these issues. To date, though, there is no state-of-the-art pipeline allowing for easy deployment, efficient and dynamic use of available resources, and scalable usage at the same time., Results: To overcome these limitations, we present nf-core/pangenome, a reference-unbiased approach implemented in Nextflow following nf-core's best practices. Leveraging biocontainers ensures portability and seamless deployment in High-Performance Computing (HPC) environments. Unlike PGGB, nf-core/pangenome distributes alignments across cluster nodes, enabling scalability. Demonstrating its efficiency, we constructed pangenome graphs for 1000 human chromosome 19 haplotypes and 2146 Escherichia coli sequences, achieving a two to threefold speedup compared to PGGB without increasing greenhouse gas emissions., Availability and Implementation: nf-core/pangenome is released under the MIT open-source license, available on GitHub and Zenodo, with documentation accessible at https://nf-co.re/pangenome/docs/usage., (© The Author(s) 2024. Published by Oxford University Press.)

The 3D genome prediction in cancer is crucial for uncovering the impact of structural variations (SVs) on tumorigenesis, especially when they are present in noncoding regions. We present InfoHiC, a systemic framework for predicting the 3D cancer genome directly from whole-genome sequencing (WGS). InfoHiC utilizes contig-specific copy number encoding on the SV contig assembly, and performs a contig-to-total Hi-C conversion for the cancer Hi-C prediction from multiple SV contigs. We showed that InfoHiC can predict 3D genome folding from all types of SVs using breast cancer cell line data. We applied it to WGS data of patients with breast cancer and pediatric patients with medulloblastoma, and identified neo topologically associating domains. For breast cancer, we discovered super-enhancer hijacking events associated with oncogenic overexpression and poor survival outcomes. For medulloblastoma, we found SVs in noncoding regions that caused super-enhancer hijacking events of medulloblastoma driver genes (GFI1, GFI1B, and PRDM6). In addition, we provide trained models for cancer Hi-C prediction from WGS at https://github.com/dmcb-gist/InfoHiC , uncovering the impacts of SVs in cancer patients and revealing novel therapeutic targets., (© 2024. The Author(s).)

Whole-genome sequencing (WGS), whole-exome sequencing (WES) and array genotyping with imputation (IMP) are common strategies for assessing genetic variation and its association with medically relevant phenotypes. To date, there has been no systematic empirical assessment of the yield of these approaches when applied to hundreds of thousands of samples to enable the discovery of complex trait genetic signals. Using data for 100 complex traits from 149,195 individuals in the UK Biobank, we systematically compare the relative yield of these strategies in genetic association studies. We find that WGS and WES combined with arrays and imputation (WES + IMP) have the largest association yield. Although WGS results in an approximately fivefold increase in the total number of assayed variants over WES + IMP, the number of detected signals differed by only 1% for both single-variant and gene-based association analyses. Given that WES + IMP typically results in savings of lab and computational time and resources expended per sample, we evaluate the potential benefits of applying WES + IMP to larger samples. When we extend our WES + IMP analyses to 468,169 UK Biobank individuals, we observe an approximately fourfold increase in association signals with the threefold increase in sample size. We conclude that prioritizing WES + IMP and large sample sizes rather than contemporary short-read WGS alternatives will maximize the number of discoveries in genetic association studies., (© 2024. The Author(s).)

G-protein-coupled receptors (GPCRs) are the largest superfamily in the human genome and the major targets for the market drugs. Recent massive genomics studies revealed numerous natural variations in the general population. 54KJPN is the most extensive Japanese population genomics study, curating the whole genome sequences from about 54,000 individuals. Here, by analyzing 390 non-olfactory GPCR genes in the 54KJPN dataset, we annotated 25,443 missense single-nucleotide variations. Among them, we found 120 major variations that appear with an allele frequency greater than 0.5, including variations that occurred on posttranslational modification sites. Structural alignment of GPCRs using the generic numbering system in the GPCRdb reveals enrichment of alterations in the conserved arginine residue within the DRY motif, which contributes to downstream G-protein signaling. A comparison with the worldwide 1000 Genomes Project (1KGP) dataset found 23 variations that were present exclusively in the 54KJPN dataset. This study will be the basis for future pharmacogenomics studies for the Japanese population., (© 2024 Molecular Biology Society of Japan and John Wiley & Sons Australia, Ltd.)

Motivation: Detection of germline variants in next-generation sequencing data is an essential component of modern genomics analysis. Variant detection tools typically rely on statistical algorithms such as de Bruijn graphs or Hidden Markov models, and are often coupled with heuristic techniques and thresholds to maximize accuracy. Despite significant progress in recent years, current methods still generate thousands of false-positive detections in a typical human whole genome, creating a significant manual review burden., Results: We introduce a new approach that replaces the handcrafted statistical techniques of previous methods with a single deep generative model. Using a standard transformer-based encoder and double-decoder architecture, our model learns to construct diploid germline haplotypes in a generative fashion identical to modern large language models. We train our model on 37 whole genome sequences from Genome-in-a-Bottle samples, and demonstrate that our method learns to produce accurate haplotypes with correct phase and genotype for all classes of small variants. We compare our method, called Jenever, to FreeBayes, GATK HaplotypeCaller, Clair3, and DeepVariant, and demonstrate that our method has superior overall accuracy compared to other methods. At F1-maximizing quality thresholds, our model delivers the highest sensitivity, precision, and the fewest genotyping errors for insertion and deletion variants. For single nucleotide variants, our model demonstrates the highest sensitivity but at somewhat lower precision, and achieves the highest overall F1 score among all callers we tested., Availability and Implementation: Jenever is implemented as a python-based command line tool. Source code is available at https://github.com/ARUP-NGS/jenever/., (© The Author(s) 2024. Published by Oxford University Press.)

Pangenomes reduce reference bias by representing genetic diversity better than a single reference sequence. Yet when comparing a sample to a pangenome, variants in the pangenome that are not part of the sample can be misleading, for example, causing false read mappings. These irrelevant variants are generally rarer in terms of allele frequency, and have previously been dealt with by filtering rare variants. However, this blunt heuristic both fails to remove some irrelevant variants and removes many relevant variants. We propose a new approach that imputes a personalized pangenome subgraph by sampling local haplotypes according to k-mer counts in the reads. We implement the approach in the vg toolkit ( https://github.com/vgteam/vg ) for the Giraffe short-read aligner and compare its accuracy to state-of-the-art methods using human pangenome graphs from the Human Pangenome Reference Consortium. This reduces small variant genotyping errors by four times relative to the Genome Analysis Toolkit and makes short-read structural variant genotyping of known variants competitive with long-read variant discovery methods., (© 2024. The Author(s), under exclusive licence to Springer Nature America, Inc.)

Background and Objectives: Genome-scale metabolic networks (GEMs) represent a valuable modeling and computational tool in the broad field of systems biology. Their ability to integrate constraints and high-throughput biological data enables the study of intricate metabolic aspects and processes of different cell types and conditions. The past decade has witnessed an increasing number and variety of applications of GEMs for the study of human diseases, along with a huge effort aimed at the reconstruction, integration and analysis of a high number of organisms. This paper presents a systematic review of the scientific literature, to pursue several important questions about the application of constraint-based modeling in the investigation of human diseases. Hopefully, this paper will provide a useful reference for researchers interested in the application of modeling and computational tools for the investigation of metabolic-related human diseases., Methods: This systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Elsevier Scopus®, National Library of Medicine PubMed® and Clarivate Web of Science™ databases were enquired, resulting in 566 scientific articles. After applying exclusion and eligibility criteria, a total of 169 papers were selected and individually examined., Results: The reviewed papers offer a thorough and up-to-date picture of the latest modeling and computational approaches, based on genome-scale metabolic models, that can be leveraged for the investigation of a large variety of human diseases. The numerous studies have been categorized according to the clinical research area involved in the examined disease. Furthermore, the paper discusses the most typical approaches employed to derive clinically-relevant information using the computational models., Conclusions: The number of scientific papers, utilizing GEM-based approaches for the investigation of human diseases, suggests an increasing interest in these types of approaches; hopefully, the present review will represent a useful reference for scientists interested in applying computational modeling approaches to investigate the aetiopathology of human diseases; we also hope that this work will foster the development of novel applications and methods for the discovery of clinically-relevant insights on metabolic-related diseases., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

The development of sequencing technology has promoted discovery of variants in the human genome. Identifying functions of these variants is important for us to link genotype to phenotype, and to diagnose diseases. However, it usually requires researchers to visit multiple databases. Here, we presented a one-stop webserver for variant function annotation tools (VCAT, https://biomed.nscc-gz.cn/zhaolab/VCAT/ ) that is the first one connecting variant to functions via the epigenome, protein, drug and RNA. VCAT is also the first one to make all annotations visualized in interactive charts or molecular structures. VCAT allows users to upload data in VCF format, and download results via a URL. Moreover, VCAT has annotated a huge number (1,262,041,068) of variants collected from dbSNP, 1000 Genomes projects, gnomAD, ICGC, TCGA, and HPRC Pangenome project. For these variants, users are able to searcher their functions, related diseases and drugs from VCAT. In summary, VCAT provides a one-stop webserver to explore the potential functions of human genomic variants including their relationship with diseases and drugs., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

The North Caucasus played a key role during the ancient colonization of Eurasia and the formation of its cultural and genetic ancestry. Previous archeogenetic studies described a relative genetic and cultural continuity of ancient Caucasus societies, since the Eneolithic period. The Koban culture, which formed in the Late Bronze Age on the North Caucasian highlands, is considered as a cultural "bridge" between the ancient and modern autochthonous peoples of the Caucasus. Here, we discuss the place of this archeological culture and its representatives in the genetic orbit of Caucasian cultures using genome-wide SNP data from five individuals of the Koban culture and one individual of the early Alanic culture as well as previously published genomic data of ancient and modern North Caucasus individuals. Ancient DNA analysis shows that an ancient individual from Klin-Yar III, who was previously described as male, was in fact a female. Additional studies on well-preserved ancient human specimens are necessary to determine the level of local mobility and kinship between individuals in ancient societies of North Caucasus. Further studies with a larger sample size will allow us gain a deeper understanding of this topic., Competing Interests: Competing interests The authors declare no competing interests. Ethical approval This research was carried out under the ethics guidelines for DNA research on human remains described previously: Alpaslan-Roodenberg S, Anthony D, Babiker H, Bánffy E, Booth T, Capone P et al. Ethics of DNA research on human remains: five globally applicable guidelines. Nature 2021; 599: 41–46., (© 2023. The Author(s), under exclusive licence to European Society of Human Genetics.)

Arabic populations are underrepresented in large genome projects; therefore, the frequency of clinically actionable variants among Arabs is largely unknown. Here, we investigated genetic variation in 14,392 whole genomes from the Qatar Genome Program (QGP) across the list of 78 actionable genes (v3.1) determined by the American College of Medical Genetics and Genomics (ACMG). Variants were categorized into one of the following groups: (1) Pathogenic (P), (2) Likely pathogenic (LP), and (3) Rare variants of uncertain significance with evidence of pathogenicity. For the classification, we used variant databases, effect predictors, and the disease-relevant phenotypes available for the cohort. Data on cardiovascular disease, cancer, and hypercholesterolemia allowed us to assess the disease-relevant phenotype association of rare missense variants. We identified 248 distinct variants in 50 ACMG genes that fulfilled our criteria to be included in one of the three groups affecting 1036 genotype-positive participants of the QGP cohort. The most frequent variants were in TTN, followed by RYR1 and ATP7B. The prevalence of reportable secondary findings was 3.5%. A further 46 heterozygous variants in six genes with an autosomal recessive mode of inheritance were detected in 200 individuals, accounting for an additional 1.4%. Altogether, they affect 5% of the population. Due to the high consanguinity rate in the QGP cohort (28% in spouses and 60% in parents), P and LP variants both in genes with dominant and recessive inheritance are important for developing better treatment options and preventive strategies in Qatar and the Arabic population of the Middle East., Competing Interests: Competing interests The authors declare no competing interests. Ethics approval and consent to participate Ethical approval was provided by the institutional review board of the Qatar Biobank (QBB) Doha, Qatar (Protocol no. QF-QGP-RES-PUB-002). A signed consent was obtained from all participants by the QBB, and WGS data used in the current study was obtained from the QGP data., (© 2024. The Author(s).)

Background: Like the coronavirus disease 2019, the hepatitis B virus is also wreaking havoc worldwide, which has infected over 2 billion people globally. Using an experimental animal model, our previous research observed that the hepatitis B virus genes integrated into human spermatozoa can replicate and express after being transmitted to embryos. However, as of now, this phenomenon has not been confirmed in clinical data from patients., Objectives: To explore the integration of the hepatitis B virus into patients' sperm genome and its potential clinical risks., Materials and Methods: Forty-eight patients with chronic hepatitis B virus infection were categorized into two groups: Test Group-1 comprised 23 patients without integration of hepatitis B virus DNA within the sperm genome. Test Group-2 comprised 25 patients with integration of hepatitis B virus DNA within the sperm genome. Forty-eight healthy male donors were included as control. The standard semen parameter analysis, real-time polymerase chain reaction, quantitative real-time polymerase chain reaction, sperm chromatin structure assay, fluorescence in situ hybridization, and immunofluorescence assays were utilized., Results: The difference in the median copy number of hepatitis B virus DNA per mL of sera between Test Group-1 and Group-2 was not statistically significant. In Test Group-2, the integration rate of hepatitis B virus DNA was 0.109%, which showed a significant correlation with the median copy number of hepatitis B virus DNA in motile spermatozoa (1.18 × 10 3 /mL). Abnormal semen parameters were found in almost all these 25 patients. The integrated hepatitis B virus S, C, X, and P genes were detected to be introduced into sperm-derived embryos through fertilization and retained their function in replication, transcription, and translation., Conclusion: Our findings suggest that hepatitis B virus infection can lead to sperm quality deterioration and reduced fertilization capacity. Furthermore, viral integration causes instability in the sperm genome, increasing the potential risk of termination, miscarriage, and stillbirth. This study identified an unconventional mode of hepatitis B virus transmission through genes rather than virions. The presence of viral sequences in the embryonic genome poses a risk of liver inflammation and cancer., (© 2024 The Authors. Andrology published by Wiley Periodicals LLC on behalf of American Society of Andrology and European Academy of Andrology.)

We systematically analyzed BMP gene family in H. sapiens to elucidate genetic structure, phylogenetic relationships, adaptive evolution and tissue-specific expression pattern. Total of 13 BMPs genes were identified in the H. sapiens genome. Bone morphogenetic proteins (BMPs) are composed of a variable number of exons ranging from 2 to 21. They exhibit a molecular weight ranging from 31,081.81 to 82,899.61 Da. These proteins possess hydrophilic characteristics, display thermostability, and exhibit a pH range from acidic to basic. We identified four segmental and two tandem duplication events in BMP gene family of H. sapiens. All of the vertebrate species that were studied show the presence of BMPs 1, 2, 3, 4, 5, 6, 7, 8A, and 15, however only Homo sapiens demonstrated the presence of BMP9 and BMP11. The pathway and process enrichment analysis of BMPs genes showed that these were considerably enriched in positive regulation of pathway-restricted SMAD protein phosphorylation (92%) and cartilage development (77%) biological processes. These genes exhibited positive selection signals that were shown to be conserved across vertebrate lineages. The results showed that BMP2/3/5/6/8a/15 proteins underwent adaptive selection at many amino acid locations and increased positive selection was detected in TGF-β propeptide and TGF-β super family domains which were involved in dorso-ventral patterning, limb bud development. More over the expression pattern of BMP genes revealed that BMP1 and BMP5; BMP4 and BMP6 exhibited substantially identical expression patterns in all tissues while BMP10, BMP15, and BMP3 showed tissue-specific expression., (© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Homo sapiens and Neanderthals underwent hybridization during the Middle/Upper Paleolithic age, culminating in retention of small amounts of Neanderthal-derived DNA in the modern human genome. In the current study, we address the potential roles Neanderthal single nucleotide polymorphisms (SNP) may be playing in autism susceptibility in samples of black non-Hispanic, white Hispanic, and white non-Hispanic people using data from the Simons Foundation Powering Autism Research (SPARK), Genotype-Tissue Expression (GTEx), and 1000 Genomes (1000G) databases. We have discovered that rare variants are significantly enriched in autistic probands compared to race-matched controls. In addition, we have identified 25 rare and common SNPs that are significantly enriched in autism on different ethnic backgrounds, some of which show significant clinical associations. We have also identified other SNPs that share more specific genotype-phenotype correlations but which are not necessarily enriched in autism and yet may nevertheless play roles in comorbid phenotype expression (e.g., intellectual disability, epilepsy, and language regression). These results strongly suggest Neanderthal-derived DNA is playing a significant role in autism susceptibility across major populations in the United States., (© 2024. The Author(s).)

Background: Cardiomyopathies are clinically important conditions, with a strong genetic component. National genomic initiatives such as 100,000 Genome Project (100KGP) provide opportunity to study these rare conditions at scale beyond conventional research studies., Methods: We present the clinical and molecular characteristics of the 100KGP cohort, comparing paediatric and adult probands with diverse cardiomyopathies. We assessed the diagnostic yield and spectrum of genetic aetiologies across clinical presentations. We re-analysed existing genomic data using an updated analytical strategy (revised gene panels; unbiased analyses of de novo variants; and improved variant prioritisation strategies) to identify new causative variants in genetically unsolved children., Results: We identified 1918 individuals (1563 probands, 355 relatives) with cardiomyopathy (CM) in 100KGP. Probands, comprising 273 children and 1290 adults, were enrolled under > 55 different recruitment categories. Paediatric probands had higher rates of co-existing congenital heart disease (12%) compared to adults (0.9%). Diagnostic yield following 100KGP's initial analysis was significantly higher for children (19%) than for adults (11%) with 11% of diagnoses overall made in genes not on the existing UK paediatric or syndromic CM panel. Our re-analysis of paediatric probands yields a potential diagnosis in 40%, identifying new probable or possible diagnoses in 49 previously unsolved paediatric cases. Structural and intronic variants accounted for 11% of all potential diagnoses in children while de novo variants were identified in 17%., Conclusions: 100KGP demonstrates the benefit of genome sequencing over a standalone panel in CM. Re-analysis of paediatric CM probands allowed a significant uplift in diagnostic yield, emphasising the importance of iterative re-evaluation in genomic studies. Despite these efforts, many children with CM remain without a genetic diagnosis, highlighting the need for better gene-disease relationship curation and ongoing data sharing. The 100KGP CM cohort is likely to be useful for further gene discovery, but heterogeneous ascertainment and key technical limitations must be understood and addressed., (© 2024. The Author(s).)

The human major histocompatibility complex (MHC) is a ∼4 Mb genomic segment on Chromosome 6 that plays a pivotal role in the immune response. Despite its importance in various traits and diseases, its complex nature makes it challenging to accurately characterize on a routine basis. We present a novel approach allowing targeted sequencing and de novo haplotypic assembly of the MHC region in heterozygous samples, using long-read sequencing technologies. Our approach is validated using two reference samples, two family trios, and an African-American sample. We achieved excellent coverage (96.6%-99.9% with at least 30× depth) and high accuracy (99.89%-99.99%) for the different haplotypes. This methodology offers a reliable and cost-effective method for sequencing and fully characterizing the MHC without the need for whole-genome sequencing, facilitating broader studies on this important genomic segment and having significant implications in immunology, genetics, and medicine., (© 2024 Hu et al.; Published by Cold Spring Harbor Laboratory Press.)

The 10q11.22 chromosomal region is a duplication-rich interval of the human genome and one of the last to be fully assembled. It carries copy number-variable genes associated with intellectual disability, bipolar disorder, and obesity. In this study, we characterized the structural diversity at this locus by analyzing 64 haploid assemblies produced by the Human Pangenome Reference Consortium. We identified 11 alternative haplotypes that differ in the copy number and/or orientation of large genomic segments, ranging from hundreds of kilobase pairs (kbp) to over one megabase pair (Mbp). We uncovered a 2.4 Mbp size difference between the shortest and longest haplotypes. Breakpoint analysis revealed that genomic instability results from nonallelic homologous recombination between segmental duplication (SD) pairs with varying similarity (94.4%-99.6%). Nonetheless, these pairs generally recombine at positions where their identity is higher (>99.6%). Recurrent inversions occur with different breakpoints within the same inverted SD pair. Inversion polymorphisms shuffle the entire SD arrangement, creating new predispositions to copy-number variations. The SD architecture is associated with a catarrhine-specific subgroup of the AGAP gene family, which likely triggered the accumulation of SDs at this locus over the past 25 million years of human evolution. Our results reveal extensive structural diversity and genomic instability at the 10q11.22 locus, and expand the general understanding of the mutational mechanisms behind SD-mediated rearrangements., (© 2024 Fornezza et al.; Published by Cold Spring Harbor Laboratory Press.)

It was previously shown that DNA breathing, thermodynamic stability, as well as transcriptional activity and transcription factor (TF) bindings are functionally correlated. To ascertain the precise relationship between TF binding and DNA breathing, we developed the multi-modal deep learning model EPBDxDNABERT-2, which is based on the Extended Peyrard-Bishop-Dauxois (EPBD) nonlinear DNA dynamics model. To train our EPBDxDNABERT-2, we used chromatin immunoprecipitation sequencing (ChIP-Seq) data comprising 690 ChIP-seq experimental results encompassing 161 distinct TFs and 91 human cell types. EPBDxDNABERT-2 significantly improves the prediction of over 660 TF-DNA, with an increase in the area under the receiver operating characteristic (AUROC) metric of up to 9.6% when compared to the baseline model that does not leverage DNA biophysical properties. We expanded our analysis to in vitro high-throughput Systematic Evolution of Ligands by Exponential enrichment (HT-SELEX) dataset of 215 TFs from 27 families, comparing EPBD with established frameworks. The integration of the DNA breathing features with DNABERT-2 foundational model, greatly enhanced TF-binding predictions. Notably, EPBDxDNABERT-2, trained on a large-scale multi-species genomes, with a cross-attention mechanism, improved predictive power shedding light on the mechanisms underlying disease-related non-coding variants discovered in genome-wide association studies., (Published by Oxford University Press on behalf of Nucleic Acids Research 2024.)

Aflatoxin B1 (AFB1), a potent mycotoxin, is one of the environmental risk factors that cause liver cancer. In the liver, the bioactivated AFB1 intercalates into the DNA double helix to form a bulky DNA adduct which will lead to mutation if left unrepaired. Here, we adapted the tXR-seq method to measure the nucleotide excision repair of AFB1-induced DNA adducts at single-nucleotide resolution on a genome-wide scale, and compared it with repair data obtained from conventional UV-damage XR-seq. Our results showed that transcription-coupled repair plays a major role in the damage removal process. We further analyzed the distribution of nucleotide excision repair sites for AFB1-induced DNA adducts within the 3D human genome organization. Our analysis revealed a heterogeneous AFB1-dG repair across four different organization levels, including chromosome territories, A/B compartments, TADs, and chromatin loops. We found that chromosomes positioned closer to the nuclear center and regions within A compartments have higher levels of nucleotide excision repair. Notably, we observed high repair activity around both TAD boundaries and loop anchors. These findings provide insights into the complex interplay between AFB1-induced DNA damage repair, transcription, and 3D genome organization, shedding light on the mechanisms underlying AFB1-induced mutagenesis., (© The Author(s) 2024. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Testicular germ cell tumours (TGCT), which comprise seminoma and non-seminoma subtypes, are the most common cancers in young men. In this study, we present a comprehensive whole genome sequencing analysis of adult TGCTs. Leveraging samples from participants recruited via the UK National Health Service and data from the Genomics England 100,000 Genomes Project, our results provide an extended description of genomic elements underlying TGCT pathogenesis. This catalogue offers a comprehensive, high-resolution map of copy number alterations, structural variation, and key global genome features, including mutational signatures and analysis of extrachromosomal DNA amplification. This study establishes correlations between genomic alterations and histological diversification, revealing divergent evolutionary trajectories among TGCT subtypes. By reconstructing the chronological order of driver events, we identify a subgroup of adult TGCTs undergoing relatively late whole genome duplication. Additionally, we present evidence that human leukocyte antigen loss is a more prevalent mechanism of immune disruption in seminomas. Collectively, our findings provide valuable insights into the developmental and immune modulatory processes implicated in TGCT pathogenesis and progression., (© 2024. The Author(s).)

Genome-scale metabolic models (GEMs) cover the entire list of metabolic genes in an organism and associated reactions, in a tissue/condition non-specific manner. RNA-seq provides crucial information to make the GEMs condition-specific. Integrative Metabolic Analysis Tool (iMAT) and Integrative Network Inference for Tissues (INIT) are the two most popular algorithms to create condition-specific GEMs from human transcriptome data. The normalization method of choice for raw RNA-seq count data affects the model content produced by these algorithms and their predictive accuracy. However, a benchmark of the RNA-seq normalization methods on the performance of iMAT and INIT algorithms is missing in the literature. Another important phenomenon is covariates such as age and gender in a dataset, and they can affect the predictivity of analysis. In this study, we aimed to compare five different RNA-seq data normalization methods (TPM, FPKM, TMM, GeTMM, and RLE) and covariate adjusted versions of the normalized data by mapping them on a human GEM using the iMAT and INIT algorithms to generate personalized metabolic models. We used RNA-seq data for Alzheimer's disease (AD) and lung adenocarcinoma (LUAD) patients. The results demonstrated that RNA-seq data normalized by the RLE, TMM, or GeTMM methods enabled the production of condition-specific metabolic models with considerably low variability in terms of the number of active reactions compared to the within-sample normalization methods (FPKM, TPM). Using these models, we could more accurately capture the disease-associated genes (average accuracy of ~0.80 for AD and ~0.67 for LUAD) for the RLE, TMM, and GeTMM normalization methods. An increase in the accuracies was observed for all the methods when covariate adjustment was applied. We found a similar accuracy trend when we compared the metabolites of perturbed reactions to metabolome data for AD. Together, our benchmark study shows that the between-sample RNA-seq normalization methods reduce false positive predictions at the expense of missing some true positive genes when mapped on GEMs., (© 2024. The Author(s).)

The human genome is highly dynamic across all scales. At the gene level, chromatin is persistently remodeled and rearranged during active processes such as transcription, replication and DNA repair. At the genome level, chromatin moves in micron-scale domains that break up and re-form over seconds, but the origin of these coherent motions is unknown. Here, we investigate the connection between genomic motions and gene-level activity. Simultaneous mapping of single-gene and genome-wide motions shows that the coupling of gene transcriptional activity to flows of the nearby genome is modulated by chromatin compaction. A motion correlation analysis suggests that a single active gene drives larger-scale motions in low-compaction regions, but high-compaction chromatin drives gene motion regardless of its activity state. By revealing unexpected connections among gene activity, spatial heterogeneities of chromatin and its emergent genome-wide motions, these findings uncover aspects of the genome's spatiotemporal organization that directly impact gene regulation and expression., (© 2024. The Author(s).)

Despite the growing variety of sequencing and variant-calling tools, no workflow performs equally well across the entire human genome. Understanding context-dependent performance is critical for enabling researchers, clinicians, and developers to make informed tradeoffs when selecting sequencing hardware and software. Here we describe a set of "stratifications," which are BED files that define distinct contexts throughout the genome. We define these for GRCh37/38 as well as the new T2T-CHM13 reference, adding many new hard-to-sequence regions which are critical for understanding performance as the field progresses. Specifically, we highlight the increase in hard-to-map and GC-rich stratifications in CHM13 relative to the previous references. We then compare the benchmarking performance with each reference and show the performance penalty brought about by these additional difficult regions in CHM13. Additionally, we demonstrate how the stratifications can track context-specific improvements over different platform iterations, using Oxford Nanopore Technologies as an example. The means to generate these stratifications are available as a snakemake pipeline at https://github.com/usnistgov/giab-stratifications . We anticipate this being useful in enabling precise risk-reward calculations when building sequencing pipelines for any of the commonly-used reference genomes., (© 2024. The Author(s).)

Two studies show when-and how-a gene that helps us digest starchy foods proliferated in our genome.

Pluripotent stem cells have remarkable self-renewal capacity: the ability to proliferate indefinitely while maintaining the pluripotent identity essential for their ability to differentiate into almost any cell type in the body. To investigate the interplay between these two aspects of self-renewal, we perform four parallel genome-scale CRISPR-Cas9 loss-of-function screens interrogating stem cell fitness in hPSCs and the dissolution of primed pluripotent identity during early differentiation. These screens distinguish genes with distinct roles in pluripotency regulation, including mitochondrial and metabolism regulators crucial for stem cell fitness, and chromatin regulators that control pluripotent identity during early differentiation. We further identify a core set of genes controlling both stem cell fitness and pluripotent identity, including a network of chromatin factors. Here, unbiased screening and comparative analyses disentangle two interconnected aspects of pluripotency, provide a valuable resource for exploring pluripotent stem cell identity versus cell fitness, and offer a framework for categorizing gene function., (© 2024. The Author(s).)