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As demonstrated by the SARS-CoV-2 pandemic, the emergence of novel viral strains with increased transmission rates poses a serious threat to global health. Statistical models of genome sequence evolution may provide a critical tool for early detection of these strains. Using a novel stochastic model that links transmission rates to the entire viral genome sequence, we study the utility of phylogenetic methods that use a phylogenetic tree relating viral samples versus count-based methods that use case counts of variants over time exclusively to detect increased transmission rates and identify candidate causative mutations. We find that phylogenies in particular can detect novel transmission-enhancing variants very soon after their origin and may facilitate the development of early detection systems for outbreak surveillance.

Papua New Guinea (PNG) hosts distinct environments mainly represented by the ecoregions of the Highlands and Lowlands that display increased altitude and a predominance of pathogens, respectively. Since its initial peopling approximately 50,000 years ago, inhabitants of these ecoregions might have differentially adapted to the environmental pressures exerted by each of them. However, the genetic basis of adaptation in populations from these areas remains understudied. Here, we investigated signals of positive selection in 62 highlanders and 43 lowlanders across 14 locations in the main island of PNG using whole-genome genotype data from the Oceanian Genome Variation Project (OGVP) and searched for signals of positive selection through population differentiation and haplotype-based selection scans. Additionally, we performed archaic ancestry estimation to detect selection signals in highlanders within introgressed regions of the genome. Among highland populations we identified candidate genes representing known biomarkers for mountain sickness (SAA4, SAA1, PRDX1, LDHA) as well as candidate genes of the Notch signaling pathway (PSEN1, NUMB, RBPJ, MAML3), a novel proposed pathway for high altitude adaptation in multiple organisms. We also identified candidate genes involved in oxidative stress, inflammation, and angiogenesis, processes inducible by hypoxia, as well as in components of the eye lens and the immune response. In contrast, candidate genes in the lowlands are mainly related to the immune response (HLA-DQB1, HLA-DQA2, TAAR6, TAAR9, TAAR8, RNASE4, RNASE6, ANG). Moreover, we find two candidate regions to be also enriched with archaic introgressed segments, suggesting that archaic admixture has played a role in the local adaptation of PNG populations.

MOTIVATION: Somatic mosaicism has been implicated in several developmental disorders, cancers, and other diseases. Short tandem repeats (STRs) consist of repeated sequences of 1-6 bp and comprise >1 million loci in the human genome. Somatic mosaicism at STRs is known to play a key role in the pathogenicity of loci implicated in repeat expansion disorders and is highly prevalent in cancers exhibiting microsatellite instability. While a variety of tools have been developed to genotype germline variation at STRs, a method for systematically identifying mosaic STRs is lacking. RESULTS: We introduce prancSTR, a novel method for detecting mosaic STRs from individual high-throughput sequencing datasets. prancSTR is designed to detect loci characterized by a single high-frequency mosaic allele, but can also detect loci with multiple mosaic alleles. Unlike many existing mosaicism detection methods for other variant types, prancSTR does not require a matched control sample as input. We show that prancSTR accurately identifies mosaic STRs in simulated data, demonstrate its feasibility by identifying candidate mosaic STRs in Illumina whole genome sequencing data derived from lymphoblastoid cell lines for individuals sequenced by the 1000 Genomes Project, and evaluate the use of prancSTR on Element and PacBio data. In addition to prancSTR, we present simTR, a novel simulation framework which simulates raw sequencing reads with realistic error profiles at STRs. AVAILABILITY AND IMPLEMENTATION: prancSTR and simTR are freely available at https://github.com/gymrek-lab/trtools. Detailed documentation is available at https://trtools.readthedocs.io/.

The impact of variations in the three-dimensional structure of the genome has been recognized, but solid cancer tissue studies are limited. Here, we performed integrated deep Hi-C sequencing with matched whole-genome sequencing, whole-genome bisulfite sequencing, 5-hydroxymethylcytosine (5hmC) sequencing and RNA sequencing across a cohort of 80 biopsy samples from patients with metastatic castration-resistant prostate cancer. Dramatic differences were present in gene expression, 5-methylcytosine/5hmC methylation and in structural variation versus mutation rate between A and B (open and closed) chromatin compartments. A subset of tumors exhibited depleted regional chromatin contacts at the AR locus, linked to extrachromosomal circular DNA (ecDNA) and worse response to AR signaling inhibitors. We also identified topological subtypes associated with stark differences in methylation structure, gene expression and prognosis. Our data suggested that DNA interactions may predispose to structural variant formation, exemplified by the recurrent TMPRSS2-ERG fusion. This comprehensive integrated sequencing effort represents a unique clinical tumor resource.

With over 16 million submitted genomic sequences, the SARS-CoV-2 (SC2) virus, the cause of the most recent worldwide COVID-19 pandemic, has become the most sequenced genome of all known viruses, revealing, for example, a vast number of expanding viral lineages. Since the pandemic phase appears to be over, we performed a retrospective re-examination of the demographic grouping pattern and their genomic characteristics during the entire pandemic period up to the peak of the last pandemic wave. For our study, we extracted from the NCBI only unique viral sequences and converted each sequence data to a relational vector, indicating the presence/absence of each variational event compared to a reference sequence. Our study revealed several genomic features that are unexpected or different from those of previous studies. For example, approximately 44,000 variants with unique sequences emerged during the pandemic period; they group into only four major viral-genomic groups and each has a set of mostly unique highly-conserved variant-genotypes (HCVGs); and a small set from the first (ancestral) group was inherited by the three (descendant) groups, suggesting that HCVGs in the next group may be predictable from the current group(s). Such a concept may be potentially important in designing panvalent vaccines against the current and future waves of viral infections.

Patterns of transcriptional activity are encoded in our genome through regulatory elements such as promoters or enhancers that, paradoxically, contain similar assortments of sequence-specific transcription factor (TF) binding sites1-3. Knowledge of how these sequence motifs encode multiple, often overlapping, gene expression programs is central to understanding gene regulation and how mutations in non-coding DNA manifest in disease4,5. Here, by studying gene regulation from the perspective of individual transcription start sites (TSSs), using natural genetic variation, perturbation of endogenous TF protein levels and massively parallel analysis of natural and synthetic regulatory elements, we show that the effect of TF binding on transcription initiation is position dependent. Analysing TF-binding-site occurrences relative to the TSS, we identified several motifs with highly preferential positioning. We show that these patterns are a combination of a TF's distinct functional profiles-many TFs, including canonical activators such as NRF1, NFY and Sp1, activate or repress transcription initiation depending on their precise position relative to the TSS. As such, TFs and their spacing collectively guide the site and frequency of transcription initiation. More broadly, these findings reveal how similar assortments of TF binding sites can generate distinct gene regulatory outcomes depending on their spatial configuration and how DNA sequence polymorphisms may contribute to transcription variation and disease and underscore a critical role for TSS data in decoding the regulatory information of our genome.

Bacteroides fragilis is a Gram-negative commensal bacterium commonly found in the human colon, which differentiates into two genomospecies termed divisions I and II. Through a comprehensive collection of 694 B. fragilis whole genome sequences, we identify novel features distinguishing these divisions. Our study reveals a distinct geographic distribution with division I strains predominantly found in North America and division II strains in Asia. Additionally, division II strains are more frequently associated with bloodstream infections, suggesting a distinct pathogenic potential. We report differences between the two divisions in gene abundance related to metabolism, virulence, stress response, and colonization strategies. Notably, division II strains harbor more antimicrobial resistance (AMR) genes than division I strains. These findings offer new insights into the functional roles of division I and II strains, indicating specialized niches within the intestine and potential pathogenic roles in extraintestinal sites.ImportanceUnderstanding the distinct functions of microbial species in the gut microbiome is crucial for deciphering their impact on human health. Classifying division II strains as Bacteroides fragilis can lead to erroneous associations, as researchers may mistakenly attribute characteristics observed in division II strains to the more extensively studied division I B. fragilis. Our findings underscore the necessity of recognizing these divisions as separate species with distinct functions. We unveil new findings of differential gene prevalence between division I and II strains in genes associated with intestinal colonization and survival strategies, potentially influencing their role as gut commensals and their pathogenicity in extraintestinal sites. Despite the significant niche overlap and colonization patterns between these groups, our study highlights the complex dynamics that govern strain distribution and behavior, emphasizing the need for a nuanced understanding of these microorganisms.

Tandem repeats are frequent across the human genome, and variation in repeat length has been linked to a variety of traits. Recent improvements in long read sequencing technologies have the potential to greatly improve tandem repeat analysis, especially for long or complex repeats. Here, we introduce LongTR, which accurately genotypes tandem repeats from high-fidelity long reads available from both PacBio and Oxford Nanopore Technologies. LongTR is freely available at https://github.com/gymrek-lab/longtr and https://zenodo.org/doi/10.5281/zenodo.11403979 .

Variants in cis-regulatory elements link the noncoding genome to human pathology; however, detailed analytic tools for understanding the association between cell-level brain pathology and noncoding variants are lacking. CWAS-Plus, adapted from a Python package for category-wide association testing (CWAS), enhances noncoding variant analysis by integrating both whole-genome sequencing (WGS) and user-provided functional data. With simplified parameter settings and an efficient multiple testing correction method, CWAS-Plus conducts the CWAS workflow 50 times faster than CWAS, making it more accessible and user-friendly for researchers. Here, we used a single-nuclei assay for transposase-accessible chromatin with sequencing to facilitate CWAS-guided noncoding variant analysis at cell-type-specific enhancers and promoters. Examining autism spectrum disorder WGS data (n = 7280), CWAS-Plus identified noncoding de novo variant associations in transcription factor binding sites within conserved loci. Independently, in Alzheimers disease WGS data (n = 1087), CWAS-Plus detected rare noncoding variant associations in microglia-specific regulatory elements. These findings highlight CWAS-Pluss utility in genomic disorders and scalability for processing large-scale WGS data and in multiple-testing corrections. CWAS-Plus and its user manual are available at https://github.com/joonan-lab/cwas/ and https://cwas-plus.readthedocs.io/en/latest/, respectively.

BackgroundInfection prevention (IP) measures are designed to mitigate the transmission of pathogens in healthcare. Using large-scale viral genomic and social network analyses, we determined if IP measures used during the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic were adequate in protecting healthcare workers (HCWs) and patients from acquiring SARS-CoV-2.MethodsWe performed retrospective cross-sectional analyses of viral genomics from all available SARS-CoV-2 viral samples collected at UC San Diego Health and social network analysis using the electronic medical record to derive temporospatial overlap of infections among related viromes and supplemented with contact tracing data. The outcome measure was any instance of healthcare transmission, defined as cases with closely related viral genomes and epidemiological connection within the healthcare setting during the infection window. Between November 2020 through January 2022, 12 933 viral genomes were obtained from 35 666 patients and HCWs.ResultsAmong 5112 SARS-CoV-2 viral samples sequenced from the second and third waves of SARS-CoV-2 (pre-Omicron), 291 pairs were derived from persons with a plausible healthcare overlap. Of these, 34 pairs (12%) were phylogenetically linked: 19 attributable to household and 14 to healthcare transmission. During the Omicron wave, 2106 contact pairs among 7821 sequences resulted in 120 (6%) related pairs among 32 clusters, of which 10 were consistent with healthcare transmission. Transmission was more likely to occur in shared spaces in the older hospital compared with the newer hospital (2.54 vs 0.63 transmission events per 1000 admissions, P < .001).ConclusionsIP strategies were effective at identifying and preventing healthcare SARS-CoV-2 transmission.

Health equity is the state in which everyone has fair and just opportunities to attain their highest level of health. The field of human genomics has fallen short in increasing health equity, largely because the diversity of the human population has been inadequately reflected among participants of genomics research. This lack of diversity leads to disparities that can have scientific and clinical consequences. Achieving health equity related to genomics will require greater effort in addressing inequities within the field. As part of the commitment of the National Human Genome Research Institute (NHGRI) to advancing health equity, it convened experts in genomics and health equity research to make recommendations and performed a review of current literature to identify the landscape of gaps and opportunities at the interface between human genomics and health equity research. This Perspective describes these findings and examines health equity within the context of human genomics and genomic medicine.

Phenotypic variation among species is a product of evolutionary changes to developmental programs1,2. However, how these changes generate novel morphological traits remains largely unclear. Here we studied the genomic and developmental basis of the mammalian gliding membrane, or patagium-an adaptative trait that has repeatedly evolved in different lineages, including in closely related marsupial species. Through comparative genomic analysis of 15 marsupial genomes, both from gliding and non-gliding species, we find that the Emx2 locus experienced lineage-specific patterns of accelerated cis-regulatory evolution in gliding species. By combining epigenomics, transcriptomics and in-pouch marsupial transgenics, we show that Emx2 is a critical upstream regulator of patagium development. Moreover, we identify different cis-regulatory elements that may be responsible for driving increased Emx2 expression levels in gliding species. Lastly, using mouse functional experiments, we find evidence that Emx2 expression patterns in gliders may have been modified from a pre-existing program found in all mammals. Together, our results suggest that patagia repeatedly originated through a process of convergent genomic evolution, whereby regulation of Emx2 was altered by distinct cis-regulatory elements in independently evolved species. Thus, different regulatory elements targeting the same key developmental gene may constitute an effective strategy by which natural selection has harnessed regulatory evolution in marsupial genomes to generate phenotypic novelty.

The fishing cat, Prionailurus viverrinus, faces a population decline, increasing the importance of maintaining healthy zoo populations. Unfortunately, zoo-managed individuals currently face a high prevalence of transitional cell carcinoma (TCC), a form of bladder cancer. To investigate the genetics of inherited diseases among captive fishing cats, we present a chromosome-scale assembly, generate the pedigree of the zoo-managed population, reaffirm the close genetic relationship with the Asian leopard cat (Prionailurus bengalensis), and identify 7.4 million single nucleotide variants (SNVs) and 23,432 structural variants (SVs) from whole genome sequencing (WGS) data of healthy and TCC cats. Only BRCA2 was found to have a high recurrent number of missense mutations in fishing cats diagnosed with TCC when compared to inherited human cancer risk variants. These new fishing cat genomic resources will aid conservation efforts to improve their genetic fitness and enhance the comparative study of feline genomes.

The Genome Aggregation Database (gnomAD), widely recognized as the gold-standard reference map of human genetic variation, has largely overlooked tandem repeat (TR) expansions, despite the fact that TRs constitute ∼6% of our genome and are linked to over 50 human diseases. Here, we introduce the TR-gnomAD (https://wlcb.oit.uci.edu/TRgnomAD), a biobank-scale reference of 0.86 million TRs derived from 338,963 whole-genome sequencing (WGS) samples of diverse ancestries (39.5% non-European samples). TR-gnomAD offers critical insights into ancestry-specific disease prevalence using disparities in TR unit number frequencies among ancestries. Moreover, TR-gnomAD is able to differentiate between common, presumably benign TR expansions, which are prevalent in TR-gnomAD, from those potentially pathogenic TR expansions, which are found more frequently in disease groups than within TR-gnomAD. Together, TR-gnomAD is an invaluable resource for researchers and physicians to interpret TR expansions in individuals with genetic diseases.

In the human genome, heterozygous sites refer to genomic positions with a different allele or nucleotide variant on the maternal and paternal chromosomes. Resolving these allelic differences by chromosomal copy, also known as phasing, is achievable on a short-read sequencer when using a library preparation method that captures long-range genomic information. TELL-Seq is a library preparation that captures long-range genomic information with the aid of molecular identifiers (barcodes). The same barcode is used to tag the reads derived from the same long DNA fragment within a range of up to 200 kilobases (kb), generating linked-reads. This strategy can be used to phase an entire genome. Here, we introduce a TELL-Seq protocol developed for targeted applications, enabling the phasing of enriched loci of varying sizes, purity levels, and heterozygosity. To validate this protocol, we phased 2-200 kb loci enriched with different methods: CRISPR/Cas9-mediated excision coupled with pulse-field electrophoresis for the longest fragments, CRISPR/Cas9-mediated protection from exonuclease digestion for mid-size fragments, and long PCR for the shortest fragments. All selected loci have known clinical relevance: BRCA1, BRCA2, MLH1, MSH2, MSH6, APC, PMS2, SCN5A-SCN10A, and PKI3CA. Collectively, the analyses show that TELL-Seq can accurately phase 2-200 kb targets using a short-read sequencer.

Pangenome references address biases of reference genomes by storing a representative set of diverse haplotypes and their alignment, usually as a graph. Alternate alleles determined by variant callers can be used to construct pangenome graphs, but advances in long-read sequencing are leading to widely available, high-quality phased assemblies. Constructing a pangenome graph directly from assemblies, as opposed to variant calls, leverages the graph's ability to represent variation at different scales. Here we present the Minigraph-Cactus pangenome pipeline, which creates pangenomes directly from whole-genome alignments, and demonstrate its ability to scale to 90 human haplotypes from the Human Pangenome Reference Consortium. The method builds graphs containing all forms of genetic variation while allowing use of current mapping and genotyping tools. We measure the effect of the quality and completeness of reference genomes used for analysis within the pangenomes and show that using the CHM13 reference from the Telomere-to-Telomere Consortium improves the accuracy of our methods. We also demonstrate construction of a Drosophila melanogaster pangenome.

Genomic evidence supports an important role for selection in shaping patterns of introgression along the genome, but frameworks for understanding the evolutionary dynamics within hybrid populations that underlie these patterns have been lacking. Due to the clock-like effect of recombination in hybrids breaking up parental haplotypes, drift and selection produce predictable patterns of ancestry variation at varying spatial genomic scales through time. Here, we develop methods based on the Discrete Wavelet Transform to study the genomic scale of local ancestry variation and its association with recombination rates and show that these methods capture temporal dynamics of drift and genome-wide selection after hybridization. We apply these methods to published datasets from hybrid populations of swordtail fish (Xiphophorus) and baboons (Papio) and to inferred Neanderthal introgression in modern humans. Across systems, upward of 20% of variation in local ancestry at the broadest genomic scales can be attributed to systematic selection against introgressed alleles, consistent with strong selection acting on early-generation hybrids. Signatures of selection at fine genomic scales suggest selection over longer time scales; however, we suggest that our ability to confidently infer selection at fine scales is likely limited by inherent biases in current methods for estimating local ancestry from contiguous segments of genomic similarity. Wavelet approaches will become widely applicable as genomic data from systems with introgression become increasingly available and can help shed light on generalities of the genomic consequences of interspecific hybridization.

Ancient environmental DNA (aeDNA) from lake sediments has yielded remarkable insights for the reconstruction of past ecosystems, including suggestions of late survival of extinct species. However, translocation and lateral inflow of DNA in sediments can potentially distort the stratigraphic signal of the DNA. Using three different approaches on two short lake sediment cores of the Yamal peninsula, West Siberia, with ages spanning only the past hundreds of years, we detect DNA and identified mitochondrial genomes of multiple mammoth and woolly rhinoceros individuals-both species that have been extinct for thousands of years on the mainland. The occurrence of clearly identifiable aeDNA of extinct Pleistocene megafauna (e.g. >400 K reads in one core) throughout these two short subsurface cores, along with specificities of sedimentology and dating, confirm that processes acting on regional scales, such as extensive permafrost thawing, can influence the aeDNA record and should be accounted for in aeDNA paleoecology.

The harsh climate of Arabia has posed challenges in generating ancient DNA from the region, hindering the direct examination of ancient genomes for understanding the demographic processes that shaped Arabian populations. In this study, we report whole-genome sequence data obtained from four Tylos-period individuals from Bahrain. Their genetic ancestry can be modeled as a mixture of sources from ancient Anatolia, Levant, and Iran/Caucasus, with variation between individuals suggesting population heterogeneity in Bahrain before the onset of Islam. We identify the G6PD Mediterranean mutation associated with malaria resistance in three out of four ancient Bahraini samples and estimate that it rose in frequency in Eastern Arabia from 5 to 6 kya onward, around the time agriculture appeared in the region. Our study characterizes the genetic composition of ancient Arabians, shedding light on the population history of Bahrain and demonstrating the feasibility of studies of ancient DNA in the region.

The UCSC Genome Browser (https://genome.ucsc.edu) is a web-based genomic visualization and analysis tool that serves data to over 7,000 distinct users per day worldwide. It provides annotation data on thousands of genome assemblies, ranging from human to SARS-CoV2. This year, we have introduced new data from the Human Pangenome Reference Consortium and on viral genomes including SARS-CoV2. We have added 1,200 new genomes to our GenArk genome system, increasing the overall diversity of our genomic representation. We have added support for nine new user-contributed track hubs to our public hub system. Additionally, we have released 29 new tracks on the human genome and 11 new tracks on the mouse genome. Collectively, these new features expand both the breadth and depth of the genomic knowledge that we share publicly with users worldwide.

MOTIVATION: The genomic surveillance of viral pathogens such as SARS-CoV-2 and HIV-1 has been critical to modern epidemiology and public health, but the use of sequence analysis pipelines requires computational expertise, and web-based platforms require sending potentially sensitive raw sequence data to remote servers. RESULTS: We introduce ViralWasm, a user-friendly graphical web application suite for viral genomics. All ViralWasm tools utilize WebAssembly to execute the original command line tools client-side directly in the web browser without any user setup, with a cost of just 2-3x slowdown with respect to their command line counterparts. AVAILABILITY AND IMPLEMENTATION: The ViralWasm tool suite can be accessed at: https://niema-lab.github.io/ViralWasm.

A mechanistic understanding of host-microbe interactions in the gut microbiome is hindered by poorly annotated bacterial genomes. While functional genomics can generate large gene-to-phenotype datasets to accelerate functional discovery, their applications to study gut anaerobes have been limited. For instance, most gain-of-function screens of gut-derived genes have been performed in Escherichia coli and assayed in a small number of conditions. To address these challenges, we develop Barcoded Overexpression BActerial shotgun library sequencing (Boba-seq). We demonstrate the power of this approach by assaying genes from diverse gut Bacteroidales overexpressed in Bacteroides thetaiotaomicron. From hundreds of experiments, we identify new functions and phenotypes for 29 genes important for carbohydrate metabolism or tolerance to antibiotics or bile salts. Highlights include the discovery of a D-glucosamine kinase, a raffinose transporter, and several routes that increase tolerance to ceftriaxone and bile salts through lipid biosynthesis. This approach can be readily applied to develop screens in other strains and additional phenotypic assays.

Methicillin-resistant Staphylococcus aureus (MRSA) is an important zoonotic pathogen associated with a wide range of infections in humans and animals. Thus, the emergence of MRSA clones poses an important threat to human and animal health. This study is aimed at elucidating the genomics insights of a strong biofilm-producing and multidrug-resistant (MDR) S. aureus MTR_BAU_H1 strain through whole-genome sequencing (WGS). The S. aureus MTR_BAU_H1 strain was isolated from food handlers hand swabs in Bangladesh and phenotypically assessed for antimicrobial susceptibility and biofilm production assays. The isolate was further undergone to high throughput WGS and analysed using different bioinformatics tools to elucidate the genetic diversity, molecular epidemiology, sequence type (ST), antimicrobial resistance, and virulence gene distribution. Phenotypic analyses revealed that the S. aureus MTR_BAU_H1 strain is a strong biofilm-former and carries both antimicrobial resistance (e.g., methicillin resistance; mecA, beta-lactam resistance; blaZ and tetracycline resistance; tetC) and virulence (e.g., sea, tsst, and PVL) genes. The genome of the S. aureus MTR_BAU_H1 belonged to ST1930 that possessed three plasmid replicons (e.g., rep16, rep7c, and rep19), seven prophages, and two clustered regularly interspaced short palindromic repeat (CRISPR) arrays of varying sizes. Phylogenetic analysis showed a close evolutionary relationship between the MTR_BAU_H1 genome and other MRSA clones of diverse hosts and demographics. The MTR_BAU_H1 genome harbours 42 antimicrobial resistance genes (ARGs), 128 virulence genes, and 273 SEED subsystems coding for the metabolism of amino acids, carbohydrates, proteins, cofactors, vitamins, minerals, and lipids. This is the first-ever WGS-based study of a strong biofilm-producing and MDR S. aureus strain isolated from human hand swabs in Bangladesh that unveils new information on the resistomes (ARGs and correlated mechanisms) and virulence potentials that might be linked to staphylococcal pathogenesis in both humans and animals.

In vitro evolution and whole genome analysis has proven to be a powerful method for studying the mechanism of action of small molecules in many haploid microbes but has generally not been applied to human cell lines in part because their diploid state complicates the identification of variants that confer drug resistance. To determine if haploid human cells could be used in MOA studies, we evolved resistance to five different anticancer drugs (doxorubicin, gemcitabine, etoposide, topotecan, and paclitaxel) using a near-haploid cell line (HAP1) and then analyzed the genomes of the drug resistant clones, developing a bioinformatic pipeline that involved filtering for high frequency alleles predicted to change protein sequence, or alleles which appeared in the same gene for multiple independent selections with the same compound. Applying the filter to sequences from 28 drug resistant clones identified a set of 21 genes which was strongly enriched for known resistance genes or known drug targets (TOP1, TOP2A, DCK, WDR33, SLCO3A1). In addition, some lines carried structural variants that encompassed additional known resistance genes (ABCB1, WWOX and RRM1). Gene expression knockdown and knockout experiments of 10 validation targets showed a high degree of specificity and accuracy in our calls and demonstrates that the same drug resistance mechanisms found in diverse clinical samples can be evolved, discovered and studied in an isogenic background.

Giant viruses (Nucleocytoviricota) are significant lethality agents of various eukaryotic hosts. Although metagenomics indicates their ubiquitous distribution, available giant virus isolates are restricted to a very small number of protist and algal hosts. Here we report on the first viral isolate that replicates in the amoeboflagellate Naegleria. This genus comprises the notorious human pathogen Naegleria fowleri, the causative agent of the rare but fatal primary amoebic meningoencephalitis. We have elucidated the structure and infection cycle of this giant virus, Catovirus naegleriensis (a.k.a. Naegleriavirus, NiV), and show its unique adaptations to its Naegleria host using fluorescence in situ hybridization, electron microscopy, genomics, and proteomics. Naegleriavirus is only the fourth isolate of the highly diverse subfamily Klosneuvirinae, and like its relatives the NiV genome contains a large number of translation genes, but lacks transfer RNAs (tRNAs). NiV has acquired genes from its Naegleria host, which code for heat shock proteins and apoptosis inhibiting factors, presumably for host interactions. Notably, NiV infection was lethal to all Naegleria species tested, including the human pathogen N. fowleri. This study expands our experimental framework for investigating giant viruses and may help to better understand the basic biology of the human pathogen N. fowleri.

Evidence is accumulating that perturbed postnatal development of the gut microbiome contributes to childhood malnutrition1-4. Here we analyse biospecimens from a randomized, controlled trial of a microbiome-directed complementary food (MDCF-2) that produced superior rates of weight gain compared with a calorically more dense conventional ready-to-use supplementary food in 12-18-month-old Bangladeshi children with moderate acute malnutrition4. We reconstructed 1,000 bacterial genomes (metagenome-assembled genomes (MAGs)) from the faecal microbiomes of trial participants, identified 75 MAGs of which the abundances were positively associated with ponderal growth (change in weight-for-length Z score (WLZ)), characterized changes in MAG gene expression as a function of treatment type and WLZ response, and quantified carbohydrate structures in MDCF-2 and faeces. The results reveal that two Prevotella copri MAGs that are positively associated with WLZ are the principal contributors to MDCF-2-induced expression of metabolic pathways involved in utilizing the component glycans of MDCF-2. The predicted specificities of carbohydrate-active enzymes expressed by their polysaccharide-utilization loci are correlated with (1) the in vitro growth of Bangladeshi P. copri strains, possessing varying degrees of polysaccharide-utilization loci and genomic conservation with these MAGs, in defined medium containing different purified glycans representative of those in MDCF-2, and (2) the levels of faecal carbohydrate structures in the trial participants. These associations suggest that identifying bioactive glycan structures in MDCFs metabolized by growth-associated bacterial taxa will help to guide recommendations about their use in children with acute malnutrition and enable the development of additional formulations.

Perennial grasses are important forage crops and emerging biomass crops and have the potential to be more sustainable grain crops. However, most perennial grass crops are difficult experimental subjects due to their large size, difficult genetics, and/or their recalcitrance to transformation. Thus, a tractable model perennial grass could be used to rapidly make discoveries that can be translated to perennial grass crops. Brachypodium sylvaticum has the potential to serve as such a model because of its small size, rapid generation time, simple genetics, and transformability. Here, we provide a high-quality genome assembly and annotation for B. sylvaticum, an essential resource for a modern model system. In addition, we conducted transcriptomic studies under 4 abiotic stresses (water, heat, salt, and freezing). Our results indicate that crowns are more responsive to freezing than leaves which may help them overwinter. We observed extensive transcriptional responses with varying temporal dynamics to all abiotic stresses, including classic heat-responsive genes. These results can be used to form testable hypotheses about how perennial grasses respond to these stresses. Taken together, these results will allow B. sylvaticum to serve as a truly tractable perennial model system.

Helicobacter pylori, a dominant member of the gastric microbiota, shares co-evolutionary history with humans. This has led to the development of genetically distinct H. pylori subpopulations associated with the geographic origin of the host and with differential gastric disease risk. Here, we provide insights into H. pylori population structure as a part of the Helicobacter pylori Genome Project (HpGP), a multi-disciplinary initiative aimed at elucidating H. pylori pathogenesis and identifying new therapeutic targets. We collected 1011 well-characterized clinical strains from 50 countries and generated high-quality genome sequences. We analysed core genome diversity and population structure of the HpGP dataset and 255 worldwide reference genomes to outline the ancestral contribution to Eurasian, African, and American populations. We found evidence of substantial contribution of population hpNorthAsia and subpopulation hspUral in Northern European H. pylori. The genomes of H. pylori isolated from northern and southern Indigenous Americans differed in that bacteria isolated in northern Indigenous communities were more similar to North Asian H. pylori while the southern had higher relatedness to hpEastAsia. Notably, we also found a highly clonal yet geographically dispersed North American subpopulation, which is negative for the cag pathogenicity island, and present in 7% of sequenced US genomes. We expect the HpGP dataset and the corresponding strains to become a major asset for H. pylori genomics.

Topological associating domains (TADs) are self-interacting genomic units crucial for shaping gene regulation patterns. Despite their importance, the extent of their evolutionary conservation and its functional implications remain largely unknown. In this study, we generate Hi-C and ChIP-seq data and compare TAD organization across four primate and four rodent species and characterize the genetic and epigenetic properties of TAD boundaries in correspondence to their evolutionary conservation. We find 14% of all human TAD boundaries to be shared among all eight species (ultraconserved), while 15% are human-specific. Ultraconserved TAD boundaries have stronger insulation strength, CTCF binding, and enrichment of older retrotransposons compared to species-specific boundaries. CRISPR-Cas9 knockouts of an ultraconserved boundary in a mouse model lead to tissue-specific gene expression changes and morphological phenotypes. Deletion of a human-specific boundary near the autism-related AUTS2 gene results in the upregulation of this gene in neurons. Overall, our study provides pertinent TAD boundary evolutionary conservation annotations and showcases the functional importance of TAD evolution.

The order Sordariales is taxonomically diverse, and harbours many species with different lifestyles and large economic importance. Despite its importance, a robust genome-scale phylogeny, and associated comparative genomic analysis of the order is lacking. In this study, we examined whole-genome data from 99 Sordariales, including 52 newly sequenced genomes, and seven outgroup taxa. We inferred a comprehensive phylogeny that resolved several contentious relationships amongst families in the order, and cleared-up intrafamily relationships within the Podosporaceae. Extensive comparative genomics showed that genomes from the three largest families in the dataset (Chaetomiaceae, Podosporaceae and Sordariaceae) differ greatly in GC content, genome size, gene number, repeat percentage, evolutionary rate, and genome content affected by repeat-induced point mutations (RIP). All genomic traits showed phylogenetic signal, and ancestral state reconstruction revealed that the variation of the properties stems primarily from within-family evolution. Together, the results provide a thorough framework for understanding genome evolution in this important group of fungi.

Two independent temporal-spatial clusters of hospital-onset Rhizopus infections were evaluated using whole-genome sequencing (WGS). Phylogenetic analysis confirmed that isolates within each cluster were unrelated despite epidemiological suspicion of outbreaks. The ITS1 region alone was insufficient for accurate analysis. WGS has utility for rapid rule-out of suspected nosocomial Rhizopus outbreaks.

Genomic deep learning models can predict genome-wide epigenetic features and gene expression levels directly from DNA sequence. While current models perform well at predicting gene expression levels across genes in different cell types from the reference genome, their ability to explain expression variation between individuals due to cis-regulatory genetic variants remains largely unexplored. Here, we evaluate four state-of-the-art models on paired personal genome and transcriptome data and find limited performance when explaining variation in expression across individuals. In addition, models often fail to predict the correct direction of effect of cis-regulatory genetic variation on expression.

Short polypeptides encoded by small open reading frames (smORFs) are ubiquitously found in eukaryotic genomes and are important regulators of physiology, development, and mitochondrial processes. Here, we focus on a subset of 298 smORFs that are evolutionarily conserved between Drosophila melanogaster and humans. Many of these smORFs are conserved broadly in the bilaterian lineage, and ∼182 are conserved in plants. We observe remarkably heterogeneous spatial and temporal expression patterns of smORF transcripts-indicating wide-spread tissue-specific and stage-specific mitochondrial architectures. In addition, an analysis of annotated functional domains reveals a predicted enrichment of smORF polypeptides localizing to mitochondria. We conduct an embryonic ribosome profiling experiment and find support for translation of 137 of these smORFs during embryogenesis. We further embark on functional characterization using CRISPR knockout/activation, RNAi knockdown, and cDNA overexpression, revealing diverse phenotypes. This study underscores the importance of identifying smORF function in disease and phenotypic diversity.

Coral reefs are both exceptionally biodiverse and threatened by climate change and other human activities. Here, we review population genomic processes in coral reef taxa and their importance for understanding responses to global change. Many taxa on coral reefs are characterized by weak genetic drift, extensive gene flow, and strong selection from complex biotic and abiotic environments, which together present a fascinating test of microevolutionary theory. Selection, gene flow, and hybridization have played and will continue to play an important role in the adaptation or extinction of coral reef taxa in the face of rapid environmental change, but research remains exceptionally limited compared to the urgent needs. Critical areas for future investigation include understanding evolutionary potential and the mechanisms of local adaptation, developing historical baselines, and building greater research capacity in the countries where most reef diversity is concentrated.

Somatic mosaicism is defined as an occurrence of two or more populations of cells having genomic sequences differing at given loci in an individual who is derived from a single zygote. It is a characteristic of multicellular organisms that plays a crucial role in normal development and disease. To study the nature and extent of somatic mosaicism in autism spectrum disorder, bipolar disorder, focal cortical dysplasia, schizophrenia, and Tourette syndrome, a multi-institutional consortium called the Brain Somatic Mosaicism Network (BSMN) was formed through the National Institute of Mental Health (NIMH). In addition to genomic data of affected and neurotypical brains, the BSMN also developed and validated a best practices somatic single nucleotide variant calling workflow through the analysis of reference brain tissue. These resources, which include >400 terabytes of data from 1087 subjects, are now available to the research community via the NIMH Data Archive (NDA) and are described here.

Advances in long-read sequencing and assembly now mean that individual labs can generate phased genomes that are more accurate and more contiguous than the original human reference genome. With declining costs and increasing democratization of technology, we suggest that complete genome assemblies, where both parental haplotypes are phased telomere to telomere, will become standard in human genetics. Soon, even in clinical settings where rigorous sample-handling standards must be met, affected individuals could have reference-grade genomes fully sequenced and assembled in just a few hours given advances in technology, computational processing, and annotation. Complete genetic variant discovery will transform how we map, catalog, and associate variation with human disease and fundamentally change our understanding of the genetic diversity of all humans.

Trimethylation of histone H3 lysine 9 (H3K9me3) is crucial for the regulation of gene repression and heterochromatin formation, cell-fate determination and organismal development1. H3K9me3 also provides an essential mechanism for silencing transposable elements1-4. However, previous studies have shown that canonical H3K9me3 readers (for example, HP1 (refs. 5-9) and MPP8 (refs. 10-12)) have limited roles in silencing endogenous retroviruses (ERVs), one of the main transposable element classes in the mammalian genome13. Here we report that trinucleotide-repeat-containing 18 (TNRC18), a poorly understood chromatin regulator, recognizes H3K9me3 to mediate the silencing of ERV class I (ERV1) elements such as LTR12 (ref. 14). Biochemical, biophysical and structural studies identified the carboxy-terminal bromo-adjacent homology (BAH) domain of TNRC18 (TNRC18(BAH)) as an H3K9me3-specific reader. Moreover, the amino-terminal segment of TNRC18 is a platform for the direct recruitment of co-repressors such as HDAC-Sin3-NCoR complexes, thus enforcing optimal repression of the H3K9me3-demarcated ERVs. Point mutagenesis that disrupts the TNRC18(BAH)-mediated H3K9me3 engagement caused neonatal death in mice and, in multiple mammalian cell models, led to derepressed expression of ERVs, which affected the landscape of cis-regulatory elements and, therefore, gene-expression programmes. Collectively, we describe a new H3K9me3-sensing and regulatory pathway that operates to epigenetically silence evolutionarily young ERVs and exert substantial effects on host genome integrity, transcriptomic regulation, immunity and development.

Megabase-scale mosaic chromosomal alterations (mCAs) in blood are prognostic markers for a host of human diseases. Here, to gain a better understanding of mCA rates in genetically diverse populations, we analyzed whole-genome sequencing data from 67,390 individuals from the National Heart, Lung, and Blood Institute Trans-Omics for Precision Medicine program. We observed higher sensitivity with whole-genome sequencing data, compared with array-based data, in uncovering mCAs at low mutant cell fractions and found that individuals of European ancestry have the highest rates of autosomal mCAs and the lowest rates of chromosome X mCAs, compared with individuals of African or Hispanic ancestry. Although further studies in diverse populations will be needed to replicate our findings, we report three loci associated with loss of chromosome X, associations between autosomal mCAs and rare variants in DCPS, ADM17, PPP1R16B and TET2 and ancestry-specific variants in ATM and MPL with mCAs in cis.

MotivationDouble-stranded RNAs (dsRNAs) are potent triggers of innate immune responses upon recognition by cytosolic dsRNA sensor proteins. Identification of endogenous dsRNAs helps to better understand the dsRNAome and its relevance to innate immunity related to human diseases.ResultsHere, we report dsRID (double-stranded RNA identifier), a machine-learning-based method to predict dsRNA regions in silico, leveraging the power of long-read RNA-sequencing (RNA-seq) and molecular traits of dsRNAs. Using models trained with PacBio long-read RNA-seq data derived from Alzheimer's disease (AD) brain, we show that our approach is highly accurate in predicting dsRNA regions in multiple datasets. Applied to an AD cohort sequenced by the ENCODE consortium, we characterize the global dsRNA profile with potentially distinct expression patterns between AD and controls. Together, we show that dsRID provides an effective approach to capture global dsRNA profiles using long-read RNA-seq data.Availability and implementationSoftware implementation of dsRID, and genomic coordinates of regions predicted by dsRID in all samples are available at the GitHub repository: https://github.com/gxiaolab/dsRID.

Recessive alleles have been shown to directly affect both human Mendelian disease phenotypes and complex traits. Pedigree studies also suggest that consanguinity results in increased childhood mortality and adverse health phenotypes, presumably through penetrance of recessive mutations. Here, we test whether the accumulation of homozygous, recessive alleles decreases reproductive success in a human population. We address this question among the Namibian Himba, an endogamous agro-pastoralist population, who until very recently practiced natural fertility. Using a sample of 681 individuals, we show that Himba exhibit elevated levels of "inbreeding," calculated as the fraction of the genome in runs of homozygosity (FROH). Many individuals contain multiple long segments of ROH in their genomes, indicating that their parents had high kinship coefficients. However, we do not find evidence that this is explained by first-cousin consanguinity, despite a reported social preference for cross-cousin marriages. Rather, we show that elevated haplotype sharing in the Himba is due to a bottleneck, likely in the past 60 generations. We test whether increased recessive mutation load results in observed fitness consequences by assessing the effect of FROH on completed fertility in a cohort of postreproductive women (n = 69). We find that higher FROH is significantly associated with lower fertility. Our data suggest a multilocus genetic effect on fitness driven by the expression of deleterious recessive alleles, especially those in long ROH. However, these effects are not the result of consanguinity but rather elevated background identity by descent.

Invasive plant pathogenic fungi have a global impact, with devastating economic and environmental effects on crops and forests. Biosurveillance, a critical component of threat mitigation, requires risk prediction based on fungal lifestyles and traits. Recent studies have revealed distinct genomic patterns associated with specific groups of plant pathogenic fungi. We sought to establish whether these phytopathogenic genomic patterns hold across diverse taxonomic and ecological groups from the Ascomycota and Basidiomycota, and furthermore, if those patterns can be used in a predictive capacity for biosurveillance. Using a supervised machine learning approach that integrates phylogenetic and genomic data, we analyzed 387 fungal genomes to test a proof-of-concept for the use of genomic signatures in predicting fungal phytopathogenic lifestyles and traits during biosurveillance activities. Our machine learning feature sets were derived from genome annotation data of carbohydrate-active enzymes (CAZymes), peptidases, secondary metabolite clusters (SMCs), transporters, and transcription factors. We found that machine learning could successfully predict fungal lifestyles and traits across taxonomic groups, with the best predictive performance coming from feature sets comprising CAZyme, peptidase, and SMC data. While phylogeny was an important component in most predictions, the inclusion of genomic data improved prediction performance for every lifestyle and trait tested. Plant pathogenicity was one of the best-predicted traits, showing the promise of predictive genomics for biosurveillance applications. Furthermore, our machine learning approach revealed expansions in the number of genes from specific CAZyme and peptidase families in the genomes of plant pathogens compared to non-phytopathogenic genomes (saprotrophs, endo- and ectomycorrhizal fungi). Such genomic feature profiles give insight into the evolution of fungal phytopathogenicity and could be useful to predict the risks of unknown fungi in future biosurveillance activities.

Long-read sequencing technologies substantially overcome the limitations of short-reads but have not been considered as a feasible replacement for population-scale projects, being a combination of too expensive, not scalable enough or too error-prone. Here we develop an efficient and scalable wet lab and computational protocol, Napu, for Oxford Nanopore Technologies long-read sequencing that seeks to address those limitations. We applied our protocol to cell lines and brain tissue samples as part of a pilot project for the National Institutes of Health Center for Alzheimer's and Related Dementias. Using a single PromethION flow cell, we can detect single nucleotide polymorphisms with F1-score comparable to Illumina short-read sequencing. Small indel calling remains difficult within homopolymers and tandem repeats, but achieves good concordance to Illumina indel calls elsewhere. Further, we can discover structural variants with F1-score on par with state-of-the-art de novo assembly methods. Our protocol phases small and structural variants at megabase scales and produces highly accurate, haplotype-specific methylation calls.

Predicting the effects of coding variants is a major challenge. While recent deep-learning models have improved variant effect prediction accuracy, they cannot analyze all coding variants due to dependency on close homologs or software limitations. Here we developed a workflow using ESM1b, a 650-million-parameter protein language model, to predict all ~450 million possible missense variant effects in the human genome, and made all predictions available on a web portal. ESM1b outperformed existing methods in classifying ~150,000 ClinVar/HGMD missense variants as pathogenic or benign and predicting measurements across 28 deep mutational scan datasets. We further annotated ~2 million variants as damaging only in specific protein isoforms, demonstrating the importance of considering all isoforms when predicting variant effects. Our approach also generalizes to more complex coding variants such as in-frame indels and stop-gains. Together, these results establish protein language models as an effective, accurate and general approach to predicting variant effects.

Large-effect loci-those statistically significant loci discovered by genome-wide association studies or linkage mapping-associated with key traits segregate amidst a background of minor, often undetectable, genetic effects in wild and domesticated plants and animals. Accurately attributing mean differences and variance explained to the correct components in the linear mixed model analysis is vital for selecting superior progeny and parents in plant and animal breeding, gene therapy, and medical genetics in humans. Marker-assisted prediction and its successor, genomic prediction, have many advantages for selecting superior individuals and understanding disease risk. However, these two approaches are less often integrated to study complex traits with different genetic architectures. This simulation study demonstrates that the average semivariance can be applied to models incorporating Mendelian, oligogenic, and polygenic terms simultaneously and yields accurate estimates of the variance explained for all relevant variables. Our previous research focused on large-effect loci and polygenic variance separately. This work aims to synthesize and expand the average semivariance framework to various genetic architectures and the corresponding mixed models. This framework independently accounts for the effects of large-effect loci and the polygenic genetic background and is universally applicable to genetics studies in humans, plants, animals, and microbes.

Our knowledge of non-linear genetic effects on complex traits remains limited, in part, due to the modest power to detect such effects. While kernel-based tests offer a versatile approach to test for non-linear relationships between sets of genetic variants and traits, current approaches cannot be applied to Biobank-scale datasets containing hundreds of thousands of individuals. We propose, FastKAST, a kernel-based approach that can test for non-linear effects of a set of variants on a quantitative trait. FastKAST provides calibrated hypothesis tests while enabling analysis of Biobank-scale datasets with hundreds of thousands of unrelated individuals from a homogeneous population. We apply FastKAST to 53 quantitative traits measured across ≈ 300 K unrelated white British individuals in the UK Biobank to detect sets of variants with non-linear effects at genome-wide significance.

Using DNA methylation profiles (n = 15,456) from 348 mammalian species, we constructed phyloepigenetic trees that bear marked similarities to traditional phylogenetic ones. Using unsupervised clustering across all samples, we identified 55 distinct cytosine modules, of which 30 are related to traits such as maximum life span, adult weight, age, sex, and human mortality risk. Maximum life span is associated with methylation levels in HOXL subclass homeobox genes and developmental processes and is potentially regulated by pluripotency transcription factors. The methylation state of some modules responds to perturbations such as caloric restriction, ablation of growth hormone receptors, consumption of high-fat diets, and expression of Yamanaka factors. This study reveals an intertwined evolution of the genome and epigenome that mediates the biological characteristics and traits of different mammalian species.

Cornelia de Lange Syndrome (CdLS) is a rare, dominantly inherited multisystem developmental disorder characterized by highly variable manifestations of growth and developmental delays, upper limb involvement, hypertrichosis, cardiac, gastrointestinal, craniofacial, and other systemic features. Pathogenic variants in genes encoding cohesin complex structural subunits and regulatory proteins (NIPBL, SMC1A, SMC3, HDAC8, and RAD21) are the major pathogenic contributors to CdLS. Heterozygous or hemizygous variants in the genes encoding these five proteins have been found to be contributory to CdLS, with variants in NIPBL accounting for the majority (>60%) of cases, and the only gene identified to date that results in the severe or classic form of CdLS when mutated. Pathogenic variants in cohesin genes other than NIPBL tend to result in a less severe phenotype. Causative variants in additional genes, such as ANKRD11, EP300, AFF4, TAF1, and BRD4, can cause a CdLS-like phenotype. The common role that these genes, and others, play as critical regulators of developmental transcriptional control has led to the conditions they cause being referred to as disorders of transcriptional regulation (or DTRs). Here, we report the results of a comprehensive molecular analysis in a cohort of 716 probands with typical and atypical CdLS in order to delineate the genetic contribution of causative variants in cohesin complex genes as well as novel candidate genes, genotype-phenotype correlations, and the utility of genome sequencing in understanding the mutational landscape in this population.

Structural variants (SVs)-including duplications, deletions, and inversions of DNA-can have significant genomic and functional impacts but are technically difficult to identify and assay compared with single-nucleotide variants. With the aid of new genomic technologies, it has become clear that SVs account for significant differences across and within species. This phenomenon is particularly well-documented for humans and other primates due to the wealth of sequence data available. In great apes, SVs affect a larger number of nucleotides than single-nucleotide variants, with many identified SVs exhibiting population and species specificity. In this review, we highlight the importance of SVs in human evolution by (1) how they have shaped great ape genomes resulting in sensitized regions associated with traits and diseases, (2) their impact on gene functions and regulation, which subsequently has played a role in natural selection, and (3) the role of gene duplications in human brain evolution. We further discuss how to incorporate SVs in research, including the strengths and limitations of various genomic approaches. Finally, we propose future considerations in integrating existing data and biospecimens with the ever-expanding SV compendium propelled by biotechnology advancements.

Populations can adapt to novel selection pressures through dramatic frequency changes in a few genes of large effect or subtle shifts in many genes of small effect. The latter (polygenic adaptation) is expected to be the primary mode of evolution for many life-history traits but tends to be more difficult to detect than changes in genes of large effect. Atlantic cod (Gadus morhua) were subjected to intense fishing pressure over the twentieth century, leading to abundance crashes and a phenotypic shift toward earlier maturation across many populations. Here, we use spatially replicated temporal genomic data to test for a shared polygenic adaptive response to fishing using methods previously applied to evolve-and-resequence experiments. Cod populations on either side of the Atlantic show covariance in allele frequency change across the genome that are characteristic of recent polygenic adaptation. Using simulations, we demonstrate that the degree of covariance in allele frequency change observed in cod is unlikely to be explained by neutral processes or background selection. As human pressures on wild populations continue to increase, understanding and attributing modes of adaptation using methods similar to those demonstrated here will be important in identifying the capacity for adaptive responses and evolutionary rescue. This article is part of the theme issue 'Detecting and attributing the causes of biodiversity change: needs, gaps and solutions'.

Sequencing has revealed hundreds of millions of human genetic variants, and continued efforts will only add to this variant avalanche. Insufficient information exists to interpret the effects of most variants, limiting opportunities for precision medicine and comprehension of genome function. A solution lies in experimental assessment of the functional effect of variants, which can reveal their biological and clinical impact. However, variant effect assays have generally been undertaken reactively for individual variants only after and, in most cases long after, their first observation. Now, multiplexed assays of variant effect can characterise massive numbers of variants simultaneously, yielding variant effect maps that reveal the function of every possible single nucleotide change in a gene or regulatory element. Generating maps for every protein encoding gene and regulatory element in the human genome would create an Atlas of variant effect maps and transform our understanding of genetics and usher in a new era of nucleotide-resolution functional knowledge of the genome. An Atlas would reveal the fundamental biology of the human genome, inform human evolution, empower the development and use of therapeutics and maximize the utility of genomics for diagnosing and treating disease. The Atlas of Variant Effects Alliance is an international collaborative group comprising hundreds of researchers, technologists and clinicians dedicated to realising an Atlas of Variant Effects to help deliver on the promise of genomics.