Anti-PD-1/PD-L1 agents have transformed the treatment landscape of advanced non-small cell lung cancer (NSCLC). To expand our understanding of the molecular features underlying response to checkpoint inhibitors in NSCLC, we describe here the first joint analysis of the Stand Up To Cancer-Mark Foundation cohort, a resource of whole exome and/or RNA sequencing from 393 patients with NSCLC treated with anti-PD-(L)1 therapy, along with matched clinical response annotation. We identify a number of associations between molecular features and outcome, including (1) favorable (for example, ATM altered) and unfavorable (for example, TERT amplified) genomic subgroups, (2) a prominent association between expression of inducible components of the immunoproteasome and response and (3) a dedifferentiated tumor-intrinsic subtype with enhanced response to checkpoint blockade. Taken together, results from this cohort demonstrate the complexity of biological determinants underlying immunotherapy outcomes and reinforce the discovery potential of integrative analysis within large, well-curated, cancer-specific cohorts.

Gastric cancer is among the most common malignancies worldwide, characterized by geographical, epidemiological and histological heterogeneity. Here, we report an extensive, multiancestral landscape of driver events in gastric cancer, involving 1,335 cases. Seventy-seven significantly mutated genes (SMGs) were identified, including ARHGAP5 and TRIM49C. We also identified subtype-specific drivers, including PIGR and SOX9, which were enriched in the diffuse subtype of the disease. SMGs also varied according to Epstein-Barr virus infection status and ancestry. Non-protein-truncating CDH1 mutations, which are characterized by in-frame splicing alterations, targeted localized extracellular domains and uniquely occurred in sporadic diffuse-type cases. In patients with gastric cancer with East Asian ancestry, our data suggested a link between alcohol consumption or metabolism and the development of RHOA mutations. Moreover, mutations with potential roles in immune evasion were identified. Overall, these data provide comprehensive insights into the molecular landscape of gastric cancer across various subtypes and ancestries.

In the context of climate change, drought is one of the most limiting factors that influence crop production. Maize, as a major crop, is highly vulnerable to water deficit, which causes significant yield loss. Thus, identification and utilization of drought-resistant germplasm are crucial for the genetic improvement of the trait. Here we report on a high-quality genome assembly of a prominent drought-resistant genotype, CIMBL55. Genomic and genetic variation analyses revealed that 65 favorable alleles of 108 previously identified drought-resistant candidate genes were found in CIMBL55, which may constitute the genetic basis for its excellent drought resistance. Notably, ZmRtn16, encoding a reticulon-like protein, was found to contribute to drought resistance by facilitating the vacuole H+-ATPase activity, which highlights the role of vacuole proton pumps in maize drought resistance. The assembled CIMBL55 genome provided a basis for genetic dissection and improvement of plant drought resistance, in support of global food security.

After severe heart injury, fibroblasts are activated and proliferate excessively to form scarring, leading to decreased cardiac function and eventually heart failure. It is unknown, however, whether cardiac fibroblasts are heterogeneous with respect to their degree of activation, proliferation and function during cardiac fibrosis. Here, using dual recombinase-mediated genetic lineage tracing, we find that endocardium-derived fibroblasts preferentially proliferate and expand in response to pressure overload. Fibroblast-specific proliferation tracing revealed highly regional expansion of activated fibroblasts after injury, whose pattern mirrors that of endocardium-derived fibroblast distribution in the heart. Specific ablation of endocardium-derived fibroblasts alleviates cardiac fibrosis and reduces the decline of heart function after pressure overload injury. Mechanistically, Wnt signaling promotes activation and expansion of endocardium-derived fibroblasts during cardiac remodeling. Our study identifies endocardium-derived fibroblasts as a key fibroblast subpopulation accounting for severe cardiac fibrosis after pressure overload injury and as a potential therapeutic target against cardiac fibrosis.

Zygotic genome activation (ZGA) is a critical postfertilization step that promotes totipotency and allows different cell fates to emerge in the developing embryo. MERVL (murine endogenous retrovirus-L) is transiently upregulated at the two-cell stage during ZGA. Although MERVL expression is widely used as a marker of totipotency, the role of this retrotransposon in mouse embryogenesis remains elusive. Here, we show that full-length MERVL transcripts, but not encoded retroviral proteins, are essential for accurate regulation of the host transcriptome and chromatin state during preimplantation development. Both knockdown and CRISPRi-based repression of MERVL result in embryonic lethality due to defects in differentiation and genomic stability. Furthermore, transcriptome and epigenome analysis revealed that loss of MERVL transcripts led to retention of an accessible chromatin state at, and aberrant expression of, a subset of two-cell-specific genes. Taken together, our results suggest a model in which an endogenous retrovirus plays a key role in regulating host cell fate potential.

IgA nephropathy (IgAN) is a progressive form of kidney disease defined by glomerular deposition of IgA. Here we performed a genome-wide association study of 10,146 kidney-biopsy-diagnosed IgAN cases and 28,751 controls across 17 international cohorts. We defined 30 genome-wide significant risk loci explaining 11% of disease risk. A total of 16 loci were new, including TNFSF4/TNFSF18, REL, CD28, PF4V1, LY86, LYN, ANXA3, TNFSF8/TNFSF15, REEP3, ZMIZ1, OVOL1/RELA, ETS1, IGH, IRF8, TNFRSF13B and FCAR. The risk loci were enriched in gene orthologs causing abnormal IgA levels when genetically manipulated in mice. We also observed a positive genetic correlation between IgAN and serum IgA levels. High polygenic score for IgAN was associated with earlier onset of kidney failure. In a comprehensive functional annotation analysis of candidate causal genes, we observed convergence of biological candidates on a common set of inflammatory signaling pathways and cytokine ligand-receptor pairs, prioritizing potential new drug targets.

H3K4me1 methyltransferases MLL3 (KMT2C) and MLL4 (KMT2D) are critical for enhancer activation, cell differentiation and development. However, roles of MLL3/4 enzymatic activities and MLL3/4-mediated enhancer H3K4me1 in these processes remain unclear. Here we report that constitutive elimination of both MLL3 and MLL4 enzymatic activities prevents initiation of gastrulation and leads to early embryonic lethality in mice. However, selective elimination of MLL3/4 enzymatic activities in embryonic, but not extraembryonic, lineages leaves gastrulation largely intact. Consistent with this, embryonic stem cells (ESCs) lacking MLL3/4 enzymatic activities can differentiate toward the three embryonic germ layers but show aberrant differentiation to extraembryonic endoderm (ExEn) and trophectoderm. The failure in ExEn differentiation can be attributed to markedly reduced enhancer-binding of the lineage-determining transcription factor GATA6. Furthermore, we show that MLL3/4-catalyzed H3K4me1 is largely dispensable for enhancer activation during ESC differentiation. Together, our findings suggest a lineage-selective, but enhancer activation-independent, role of MLL3/4 methyltransferase activities in early embryonic development and ESC differentiation.

In cancer, evolutionary forces select for clones that evade the immune system. Here we analyzed >10,000 primary tumors and 356 immune-checkpoint-treated metastases using immune dN/dS, the ratio of nonsynonymous to synonymous mutations in the immunopeptidome, to measure immune selection in cohorts and individuals. We classified tumors as immune edited when antigenic mutations were removed by negative selection and immune escaped when antigenicity was covered up by aberrant immune modulation. Only in immune-edited tumors was immune predation linked to CD8 T cell infiltration. Immune-escaped metastases experienced the best response to immunotherapy, whereas immune-edited patients did not benefit, suggesting a preexisting resistance mechanism. Similarly, in a longitudinal cohort, nivolumab treatment removes neoantigens exclusively in the immunopeptidome of nonimmune-edited patients, the group with the best overall survival response. Our work uses dN/dS to differentiate between immune-edited and immune-escaped tumors, measuring potential antigenicity and ultimately helping predict response to treatment.

Myocardial interstitial fibrosis is associated with cardiovascular disease and adverse prognosis. Here, to investigate the biological pathways that underlie fibrosis in the human heart, we developed a machine learning model to measure native myocardial T1 time, a marker of myocardial fibrosis, in 41,505 UK Biobank participants who underwent cardiac magnetic resonance imaging. Greater T1 time was associated with diabetes mellitus, renal disease, aortic stenosis, cardiomyopathy, heart failure, atrial fibrillation, conduction disease and rheumatoid arthritis. Genome-wide association analysis identified 11 independent loci associated with T1 time. The identified loci implicated genes involved in glucose transport (SLC2A12), iron homeostasis (HFE, TMPRSS6), tissue repair (ADAMTSL1, VEGFC), oxidative stress (SOD2), cardiac hypertrophy (MYH7B) and calcium signaling (CAMK2D). Using a transforming growth factor β1-mediated cardiac fibroblast activation assay, we found that 9 of the 11 loci consisted of genes that exhibited temporal changes in expression or open chromatin conformation supporting their biological relevance to myofibroblast cell state acquisition. By harnessing machine learning to perform large-scale quantification of myocardial interstitial fibrosis using cardiac imaging, we validate associations between cardiac fibrosis and disease, and identify new biologically relevant pathways underlying fibrosis.

Author information: (1)Department of Zoology, Genetics and Physical Anthropology, Universidade de Santiago de Compostela, Santiago de Compostela, Spain. (2)Biomedical Research Centre (CINBIO), University of Vigo, Vigo, Spain. (3)Centre for Research in Molecular Medicine and Chronic Diseases (CIMUS), Universidade de Santiago de Compostela, Santiago de Compostela, Spain. (4)Transmissible Cancer Group, Department of Veterinary Medicine, University of Cambridge, Cambridge, UK. (5)The Biomedical Research Centre (CINBIO), Universidade de Vigo, Vigo, Spain. (6)Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK. (7)Genome Data Science, Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology (BIST), Barcelona, Spain. (8)Institucio Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain. (9)The Francis Crick Institute, London, UK. (10)Department of Human Genetics, University of Leuven, Leuven, Belgium. (11)Genomes and Disease, Centre for Research in Molecular Medicine and Chronic Diseases (CIMUS), Universidade de Santiago de Compostela, Santiago de Compostela, Spain. (12)Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea. (13)Cancer Ageing and Somatic Mutation Programme, Wellcome Sanger Institute, Cambridge, UK. (14)Department of Biochemistry, Genetics and Immunology, University of Vigo, Vigo, Spain. (15)Galicia Sur Health Research Institute, Vigo, Spain. (16)Faculty of Science and Technology, University of Vic-Central University of Catalonia (UVic-UCC), Vic, Spain. (17)Barcelona Supercomputing Center (BSC), Barcelona, Spain. (18)Experimental Cancer Genetics, Wellcome Sanger Institute, Cambridge, UK. (19)Oxford Big Data Institute, University of Oxford, Oxford, UK. (20)DNA Repair and Genome Integrity, Centre for Research in Molecular Medicine and Chronic Diseases (CIMUS), Universidade de Santiago de Compostela, Santiago de Compostela, Spain. (21)Department of Biochemistry and Molecular Biology, Universidade de Santiago de Compostela, Santiago de Compostela, Spain. (22)Medical Research Council (MRC) Cancer Unit, University of Cambridge, Cambridge, UK. (23)Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Baltimore, MD, USA. (24)University of Cambridge, Cambridge, UK. (25)Li Ka Shing Centre, Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK. (26)European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Heidelberg, Germany. (27)Finsen Laboratory and Biotech Research & Innovation Centre (BRIC), University of Copenhagen, Copenhagen, Denmark. (28)Department of Urology, Charité Universitätsmedizin Berlin, Berlin, Germany. (29)Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. (30)Department of Genetics and Informatics Institute, University of Alabama at Birmingham (UAB) School of Medicine, Birmingham, AL, USA. (31)University of Texas MD Anderson Cancer Center, Houston, TX, USA. (32)Division of Genetics and Genomics, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA. (33)The Broad Institute of Harvard and MIT, Cambridge, MA, USA. (34)Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA. (35)Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA. (36)Harvard Medical School, Boston, MA, USA. (37)Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA. (38)Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA. (39)Department of Computer Science, Yale University, New Haven, CT, USA. (40)Department of Human Genetics, McGill University, Montreal, Québec, Canada. (41)Department of Oncology and Onco-Hematology, University of Milan, Milan, Italy. (42)Department of Medical Oncology and Hematology, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy. (43)Canadian Center for Computational Genomics, McGill University, Montreal, Quebec, Canada. (44)Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA. (45)Ludwig Center at Harvard, Boston, MA, USA. (46)Oxford NIHR Biomedical Research Centre, Oxford, UK. (47)European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK. (48)Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, Baltimore, MD, USA. (49)McKusick-Nathans Institute of Genetic Medicine, Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins University School of Medicine, Baltimore, MD, USA. (50)Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK. pc8@sanger.ac.uk. (51)Department of Haematology, University of Cambridge, Cambridge, UK. pc8@sanger.ac.uk. (52)Department of Zoology, Genetics and Physical Anthropology, Universidade de Santiago de Compostela, Santiago de Compostela, Spain. jose.mc.tubio@usc.es. (53)Biomedical Research Centre (CINBIO), University of Vigo, Vigo, Spain. jose.mc.tubio@usc.es. (54)Centre for Research in Molecular Medicine and Chronic Diseases (CIMUS), Universidade de Santiago de Compostela, Santiago de Compostela, Spain. jose.mc.tubio@usc.es. (55)Cancer Ageing and Somatic Mutation Programme, Wellcome Sanger Institute, Cambridge, UK. jose.mc.tubio@usc.es. (#)Contributed equally

Malignant pleural mesothelioma (MPM) is an aggressive cancer with rising incidence and challenging clinical management. Through a large series of whole-genome sequencing data, integrated with transcriptomic and epigenomic data using multiomics factor analysis, we demonstrate that the current World Health Organization classification only accounts for up to 10% of interpatient molecular differences. Instead, the MESOMICS project paves the way for a morphomolecular classification of MPM based on four dimensions: ploidy, tumor cell morphology, adaptive immune response and CpG island methylator profile. We show that these four dimensions are complementary, capture major interpatient molecular differences and are delimited by extreme phenotypes that-in the case of the interdependent tumor cell morphology and adapted immune response-reflect tumor specialization. These findings unearth the interplay between MPM functional biology and its genomic history, and provide insights into the variations observed in the clinical behavior of patients with MPM.

Aberrant splicing is a major cause of genetic disorders but its direct detection in transcriptomes is limited to clinically accessible tissues such as skin or body fluids. While DNA-based machine learning models can prioritize rare variants for affecting splicing, their performance in predicting tissue-specific aberrant splicing remains unassessed. Here we generated an aberrant splicing benchmark dataset, spanning over 8.8 million rare variants in 49 human tissues from the Genotype-Tissue Expression (GTEx) dataset. At 20% recall, state-of-the-art DNA-based models achieve maximum 12% precision. By mapping and quantifying tissue-specific splice site usage transcriptome-wide and modeling isoform competition, we increased precision by threefold at the same recall. Integrating RNA-sequencing data of clinically accessible tissues into our model, AbSplice, brought precision to 60%. These results, replicated in two independent cohorts, substantially contribute to noncoding loss-of-function variant identification and to genetic diagnostics design and analytics.

In BCR-ABL1 lymphoblastic leukemia, treatment heterogeneity to tyrosine kinase inhibitors (TKIs), especially in the absence of kinase domain mutations in BCR-ABL1, is poorly understood. Through deep molecular profiling, we uncovered three transcriptomic subtypes of BCR-ABL1 lymphoblastic leukemia, each representing a maturation arrest at a stage of B-cell progenitor differentiation. An earlier arrest was associated with lineage promiscuity, treatment refractoriness and poor patient outcomes. A later arrest was associated with lineage fidelity, durable leukemia remissions and improved patient outcomes. Each maturation arrest was marked by specific genomic events that control different transition points in B-cell development. Interestingly, these events were absent in BCR-ABL1+ preleukemic stem cells isolated from patients regardless of subtype, which supports that transcriptomic phenotypes are determined downstream of the leukemia-initialing event. Overall, our data indicate that treatment response and TKI efficacy are unexpected outcomes of the differentiation stage at which this leukemia transforms.

Patients with high-risk neuroblastoma generally present with widely metastatic disease and often relapse despite intensive therapy. As most studies to date focused on diagnosis-relapse pairs, our understanding of the genetic and clonal dynamics of metastatic spread and disease progression remain limited. Here, using genomic profiling of 470 sequential and spatially separated samples from 283 patients, we characterize subtype-specific genetic evolutionary trajectories from diagnosis through progression and end-stage metastatic disease. Clonal tracing timed disease initiation to embryogenesis. Continuous acquisition of structural variants at disease-defining loci (MYCN, TERT, MDM2-CDK4) followed by convergent evolution of mutations targeting shared pathways emerged as the predominant feature of progression. At diagnosis metastatic clones were already established at distant sites where they could stay dormant, only to cause relapses years later and spread via metastasis-to-metastasis and polyclonal seeding after therapy.

Obesity-associated morbidity is exacerbated by abdominal obesity, which can be measured as the waist-to-hip ratio adjusted for the body mass index (WHRadjBMI). Here we identify genes associated with obesity and WHRadjBMI and characterize allele-sensitive enhancers that are predicted to regulate WHRadjBMI genes in women. We found that several waist-to-hip ratio-associated variants map within primate-specific Alu retrotransposons harboring a DNA motif associated with adipocyte differentiation. This suggests that a genetic component of adipose distribution in humans may involve co-option of retrotransposons as adipose enhancers. We evaluated the role of the strongest female WHRadjBMI-associated gene, SNX10, in adipose biology. We determined that it is required for human adipocyte differentiation and function and participates in diet-induced adipose expansion in female mice, but not males. Our data identify genes and regulatory mechanisms that underlie female-specific adipose distribution and mediate metabolic dysfunction in women.

A complete telomere-to-telomere (T2T) finished genome has been the long pursuit of genomic research. Through generating deep coverage ultralong Oxford Nanopore Technology (ONT) and PacBio HiFi reads, we report here a complete genome assembly of maize with each chromosome entirely traversed in a single contig. The 2,178.6 Mb T2T Mo17 genome with a base accuracy of over 99.99% unveiled the structural features of all repetitive regions of the genome. There were several super-long simple-sequence-repeat arrays having consecutive thymine-adenine-guanine (TAG) tri-nucleotide repeats up to 235 kb. The assembly of the entire nucleolar organizer region of the 26.8 Mb array with 2,974 45S rDNA copies revealed the enormously complex patterns of rDNA duplications and transposon insertions. Additionally, complete assemblies of all ten centromeres enabled us to precisely dissect the repeat compositions of both CentC-rich and CentC-poor centromeres. The complete Mo17 genome represents a major step forward in understanding the complexity of the highly recalcitrant repetitive regions of higher plant genomes.

Epigenetic reprogramming in the germline contributes to the erasure of epigenetic inheritance across generations in mammals but remains poorly characterized in plants. Here we profiled histone modifications throughout Arabidopsis male germline development. We find that the sperm cell has widespread apparent chromatin bivalency, which is established by the acquisition of H3K27me3 or H3K4me3 at pre-existing H3K4me3 or H3K27me3 regions, respectively. These bivalent domains are associated with a distinct transcriptional status. Somatic H3K27me3 is generally reduced in sperm, while dramatic loss of H3K27me3 is observed at only ~700 developmental genes. The incorporation of the histone variant H3.10 facilitates the establishment of sperm chromatin identity without a strong impact on resetting of somatic H3K27me3. Vegetative nuclei harbor thousands of specific H3K27me3 domains at repressed genes, while pollination-related genes are highly expressed and marked by gene body H3K4me3. Our work highlights putative chromatin bivalency and restricted resetting of H3K27me3 at developmental regulators as key features in plant pluripotent sperm.

Integrative analyses of genome-wide association studies and gene expression data have implicated many disease-critical tissues. However, co-regulation of genetic effects on gene expression across tissues impedes distinguishing biologically causal tissues from tagging tissues. In the present study, we introduce tissue co-regulation score regression (TCSC), which disentangles causal tissues from tagging tissues by regressing gene-disease association statistics (from transcriptome-wide association studies) on tissue co-regulation scores, reflecting correlations of predicted gene expression across genes and tissues. We applied TCSC to 78 diseases/traits (average n = 302,000) and gene expression prediction models for 48 GTEx tissues. TCSC identified 21 causal tissue-trait pairs at a 5% false discovery rate (FDR), including well-established findings, biologically plausible new findings (for example, aorta artery and glaucoma) and increased specificity of known tissue-trait associations (for example, subcutaneous adipose, but not visceral adipose, and high-density lipoprotein). TCSC also identified 17 causal tissue-trait covariance pairs at 5% FDR. In conclusion, TCSC is a precise method for distinguishing causal tissues from tagging tissues.

The release of 150,119 UK Biobank sequences represents an unprecedented opportunity as a reference panel to impute low-coverage whole-genome sequencing data with high accuracy but current methods cannot cope with the size of the data. Here we introduce GLIMPSE2, a low-coverage whole-genome sequencing imputation method that scales sublinearly in both the number of samples and markers, achieving efficient whole-genome imputation from the UK Biobank reference panel while retaining high accuracy for ancient and modern genomes, particularly at rare variants and for very low-coverage samples.

Inducing fetal hemoglobin (HbF) in red blood cells can alleviate β-thalassemia and sickle cell disease. We compared five strategies in CD34+ hematopoietic stem and progenitor cells, using either Cas9 nuclease or adenine base editors. The most potent modification was adenine base editor generation of γ-globin -175A>G. Homozygous -175A>G edited erythroid colonies expressed 81 ± 7% HbF versus 17 ± 11% in unedited controls, whereas HbF levels were lower and more variable for two Cas9 strategies targeting a BCL11A binding motif in the γ-globin promoter or a BCL11A erythroid enhancer. The -175A>G base edit also induced HbF more potently than a Cas9 approach in red blood cells generated after transplantation of CD34+ hematopoietic stem and progenitor cells into mice. Our data suggest a strategy for potent, uniform induction of HbF and provide insights into γ-globin gene regulation. More generally, we demonstrate that diverse indels generated by Cas9 can cause unexpected phenotypic variation that can be circumvented by base editing.

Author information: (1)Division of Molecular Genetics, German Cancer Research Center (DKFZ), Heidelberg, Germany. (2)Informatics and Bio-computing Program, Ontario Institute for Cancer Research, Toronto, Ontario, Canada. (3)Norwich Medical School, University of East Anglia, Norwich, UK. (4)Earlham Institute, Norwich, UK. (5)Heinrich-Pette-Institute, Leibniz Institute for Experimental Virology, Hamburg, Germany. (6)German Center for Infection Research (DZIF), Partner Site Hamburg-Borstel-Lübeck-Riems, Hamburg, Germany. (7)Bioinformatics Core, University Medical Center Hamburg-Eppendorf, Hamburg, Germany. (8)Bioinformatics Group, Department of Computer Science, University College London, London, UK. (9)Biomedical Data Science Laboratory, Francis Crick Institute, London, UK. (10)National Center for Tumor Diseases (NCT) Heidelberg, Heidelberg, Germany. (11)Division of Cancer Genome Research, German Cancer Research Center (DKFZ), Heidelberg, Germany. (12)German Cancer Consortium (DKTK), Heidelberg, Germany. (13)Department of Pathology and Molecular Pathology, University and University Hospital Zürich, Zurich, Switzerland. (14)Institute of Cancer Research, London, UK. (15)University of East Anglia, Norwich, UK. (16)Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany. (17)Department of Bioinformatics and Functional Genomics, Institute of Pharmacy and Molecular Biotechnology, Heidelberg University and BioQuant Center, Heidelberg, Germany. (18)Center for Digital Health, Berlin Institute of Health and Charité Universitätsmedizin Berlin, Berlin, Germany. (19)Ontario Institute for Cancer Research, MaRS Centre, Toronto, Ontario, Canada. (20)Department of Biochemistry and Molecular Medicine, University of Montreal, Montreal, Québec, Canada. (21)Division of Molecular Genetics, German Cancer Research Center (DKFZ), Heidelberg, Germany. peter.lichter@dkfz-heidelberg.de. (22)German Cancer Consortium (DKTK), Heidelberg, Germany. peter.lichter@dkfz-heidelberg.de. (#)Contributed equally

DNA sequencing-based studies of neurodevelopmental disorders (NDDs) have identified a wide range of genetic determinants. However, a comprehensive analysis of these data, in aggregate, has not to date been performed. Here, we find that genes encoding the mammalian SWI/SNF (mSWI/SNF or BAF) family of ATP-dependent chromatin remodeling protein complexes harbor the greatest number of de novo missense and protein-truncating variants among nuclear protein complexes. Non-truncating NDD-associated protein variants predominantly disrupt the cBAF subcomplex and cluster in four key structural regions associated with high disease severity, including mSWI/SNF-nucleosome interfaces, the ATPase-core ARID-armadillo repeat (ARM) module insertion site, the Arp module and DNA-binding domains. Although over 70% of the residues perturbed in NDDs overlap with those mutated in cancer, ~60% of amino acid changes are NDD-specific. These findings provide a foundation to functionally group variants and link complex aberrancies to phenotypic severity, serving as a resource for the chromatin, clinical genetics and neurodevelopment communities.

Common genetic variants across individuals modulate the cellular response to pathogens and are implicated in diverse immune pathologies, yet how they dynamically alter the response upon infection is not well understood. Here, we triggered antiviral responses in human fibroblasts from 68 healthy donors, and profiled tens of thousands of cells using single-cell RNA-sequencing. We developed GASPACHO (GAuSsian Processes for Association mapping leveraging Cell HeterOgeneity), a statistical approach designed to identify nonlinear dynamic genetic effects across transcriptional trajectories of cells. This approach identified 1,275 expression quantitative trait loci (local false discovery rate 10%) that manifested during the responses, many of which were colocalized with susceptibility loci identified by genome-wide association studies of infectious and autoimmune diseases, including the OAS1 splicing quantitative trait locus in a COVID-19 susceptibility locus. In summary, our analytical approach provides a unique framework for delineation of the genetic variants that shape a wide spectrum of transcriptional responses at single-cell resolution.

Detecting mutations from single DNA molecules is crucial in many fields but challenging. Next-generation sequencing (NGS) affords tremendous throughput but cannot directly sequence double-stranded DNA molecules ('single duplexes') to discern the true mutations on both strands. Here we present Concatenating Original Duplex for Error Correction (CODEC), which confers single duplex resolution to NGS. CODEC affords 1,000-fold higher accuracy than NGS, using up to 100-fold fewer reads than duplex sequencing. CODEC revealed mutation frequencies of 2.72 × 10-8 in sperm of a 39-year-old individual, and somatic mutations acquired with age in blood cells. CODEC detected genome-wide, clonal hematopoiesis mutations from single DNA molecules, single mutated duplexes from tumor genomes and liquid biopsies, microsatellite instability with 10-fold greater sensitivity and mutational signatures, and specific tumor mutations with up to 100-fold fewer reads. CODEC enables more precise genetic testing and reveals biologically significant mutations, which are commonly obscured by NGS errors.

Cohesin loss-of-function mutations are frequently observed in tumors, but the mechanism underlying its role in tumorigenesis is unclear. Here, we found that depletion of RAD21, a core subunit of cohesin, leads to massive genome-wide DNA breaks and 147 translocation hotspot genes, co-mutated with cohesin in multiple cancers. Increased DNA damages are independent of RAD21-loss-induced transcription alteration and loop anchor disruption. However, damage-induced chromosomal translocations coincide with the asymmetrically distributed Okazaki fragments of DNA replication, suggesting that RAD21 depletion causes replication stresses evidenced by the slower replication speed and increased stalled forks. Mechanistically, approximately 30% of the human genome exhibits an earlier replication timing after RAD21 depletion, caused by the early initiation of >900 extra dormant origins. Correspondingly, most translocation hotspot genes lie in timing-altered regions. Therefore, we conclude that cohesin dysfunction causes replication stresses induced by excessive DNA replication initiation, resulting in gross DNA damages that may promote tumorigenesis.

Spatiotemporal orchestration of gene expression is required for proper embryonic development. The use of single-cell technologies has begun to provide improved resolution of early regulatory dynamics, including detailed molecular definitions of most cell states during mouse embryogenesis. Here we used Slide-seq to build spatial transcriptomic maps of complete embryonic day (E) 8.5 and E9.0, and partial E9.5 embryos. To support their utility, we developed sc3D, a tool for reconstructing and exploring three-dimensional 'virtual embryos', which enables the quantitative investigation of regionalized gene expression patterns. Our measurements along the main embryonic axes of the developing neural tube revealed several previously unannotated genes with distinct spatial patterns. We also characterized the conflicting transcriptional identity of 'ectopic' neural tubes that emerge in Tbx6 mutant embryos. Taken together, we present an experimental and computational framework for the spatiotemporal investigation of whole embryonic structures and mutant phenotypes.

Extrachromosomal DNAs (ecDNAs) are common in cancer, but many questions about their origin, structural dynamics and impact on intratumor heterogeneity are still unresolved. Here we describe single-cell extrachromosomal circular DNA and transcriptome sequencing (scEC&T-seq), a method for parallel sequencing of circular DNAs and full-length mRNA from single cells. By applying scEC&T-seq to cancer cells, we describe intercellular differences in ecDNA content while investigating their structural heterogeneity and transcriptional impact. Oncogene-containing ecDNAs were clonally present in cancer cells and drove intercellular oncogene expression differences. In contrast, other small circular DNAs were exclusive to individual cells, indicating differences in their selection and propagation. Intercellular differences in ecDNA structure pointed to circular recombination as a mechanism of ecDNA evolution. These results demonstrate scEC&T-seq as an approach to systematically characterize both small and large circular DNA in cancer cells, which will facilitate the analysis of these DNA elements in cancer and beyond.

Author information: (1)Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. (2)Cancer Genome Project, Wellcome Trust Sanger Institute, Hinxton, UK. (3)Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea. (4)Department of Health Science and Technology, Samsung Advanced Institute for Health Science and Technology, Sungkyunkwan University School of Medicine, Seoul, Korea. (5)Samsung Genome Institute, Samsung Medical Center, Seoul, Korea. (6)Quantitative and Computational Biosciences Graduate Program, Baylor College of Medicine, Houston, TX, USA. (7)Division of Biostatistics, The University of Texas Health Science Center at Houston School of Public Health, Houston, TX, USA. (8)Department of Medicine and Dan L. Duncan Cancer Center Division of Biostatistics, Baylor College of Medicine, Houston, TX, USA. (9)Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. (10)Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD, USA. (11)Department of Biochemistry and Molecular Biology, The University of Texas Health Science Center at Houston McGovern Medical School, Houston, TX, USA. (12)Department of Biochemistry, Ewha Womans University School of Medicine, Seoul, Korea. (13)Laboratory for Cancer Genomics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan. (14)Division of Hematology/Oncology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea. kpark@skku.edu. (15)Samsung Advanced Institute for Health Sciences and Technology, Sungkyunkwan University School of Medicine, Seoul, South Korea. kpark@skku.edu. (16)Cancer Genome Project, Wellcome Trust Sanger Institute, Hinxton, UK. pc8@sanger.ac.uk. (17)Department of Haematology, University of Cambridge, Cambridge, UK. pc8@sanger.ac.uk. (18)Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. hliang1@mdanderson.org. (19)Quantitative and Computational Biosciences Graduate Program, Baylor College of Medicine, Houston, TX, USA. hliang1@mdanderson.org. (20)Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. hliang1@mdanderson.org. (#)Contributed equally

Author information: (1)Department of Psychology, University of Edinburgh, Edinburgh, UK. (2)23andMe, Inc., Sunnyvale, CA, USA. (3)Language and Genetics Department, Max Planck Institute for Psycholinguistics, Nijmegen, the Netherlands. (4)Genetic Epidemiology Laboratory, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia. (5)School of Psychology, Shaanxi Normal University and Shaanxi Key Research Center of Child Mental and Behavioral Health, Xi'an, China. (6)Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands. (7)MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK. (8)Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK. (9)School of Medicine, University of St Andrews, St Andrews, UK. (10)Institute of Health and Neurodevelopment, Aston University, Birmingham, UK. (11)Office of the President, Tufts University, Medford, MA, USA. (12)Department of Physiology, Anatomy and Genetics, Oxford University, Oxford, UK. (13)Departments of Pediatrics and Genetics, Yale Medical School, New Haven, CT, USA. (14)Department of Psychology and Neuroscience, University of Colorado, Boulder, CO, USA. (15)Institute for Behavioral Genetics, University of Colorado, Boulder, CO, USA. (16)Department of Psychology, University of Denver, Denver, CO, USA. (17)Department of Neurological Sciences, College of Medicine, University of Nebraska Medical Center, Omaha, NE, USA. (18)Queensland Brain Institute, University of Queensland, Brisbane, Queensland, Australia. (19)Department of Psychology, University of Edinburgh, Edinburgh, UK. michelle.luciano@ed.ac.uk.

Fine-mapping is commonly used to identify putative causal variants at genome-wide significant loci. Here we propose a Bayesian model for fine-mapping that has several advantages over existing methods, including flexible specification of the prior distribution of effect sizes, joint modeling of summary statistics and functional annotations and accounting for discrepancies between summary statistics and external linkage disequilibrium in meta-analyses. Using simulations, we compare performance with commonly used fine-mapping methods and show that the proposed model has higher power and lower false discovery rate (FDR) when including functional annotations, and higher power, lower FDR and higher coverage for credible sets in meta-analyses. We further illustrate our approach by applying it to a meta-analysis of Alzheimer's disease genome-wide association studies where we prioritize putatively causal variants and genes.

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.

Setaria italica (foxtail millet), a founder crop of East Asian agriculture, is a model plant for C4 photosynthesis and developing approaches to adaptive breeding across multiple climates. Here we established the Setaria pan-genome by assembling 110 representative genomes from a worldwide collection. The pan-genome is composed of 73,528 gene families, of which 23.8%, 42.9%, 29.4% and 3.9% are core, soft core, dispensable and private genes, respectively; 202,884 nonredundant structural variants were also detected. The characterization of pan-genomic variants suggests their importance during foxtail millet domestication and improvement, as exemplified by the identification of the yield gene SiGW3, where a 366-bp presence/absence promoter variant accompanies gene expression variation. We developed a graph-based genome and performed large-scale genetic studies for 68 traits across 13 environments, identifying potential genes for millet improvement at different geographic sites. These can be used in marker-assisted breeding, genomic selection and genome editing to accelerate crop improvement under different climatic conditions.

Transcriptional regulation exhibits extensive robustness, but human genetics indicates sensitivity to transcription factor (TF) dosage. Reconciling such observations requires quantitative studies of TF dosage effects at trait-relevant ranges, largely lacking so far. TFs play central roles in both normal-range and disease-associated variation in craniofacial morphology; we therefore developed an approach to precisely modulate TF levels in human facial progenitor cells and applied it to SOX9, a TF associated with craniofacial variation and disease (Pierre Robin sequence (PRS)). Most SOX9-dependent regulatory elements (REs) are buffered against small decreases in SOX9 dosage, but REs directly and primarily regulated by SOX9 show heightened sensitivity to SOX9 dosage; these RE responses partially predict gene expression responses. Sensitive REs and genes preferentially affect functional chondrogenesis and PRS-like craniofacial shape variation. We propose that such REs and genes underlie the sensitivity of specific phenotypes to TF dosage, while buffering of other genes leads to robust, nonlinear dosage-to-phenotype relationships.

Pangenomes provide access to an accurate representation of the genetic diversity of species, both in terms of sequence polymorphisms and structural variants (SVs). Here we generated the Saccharomyces cerevisiae Reference Assembly Panel (ScRAP) comprising reference-quality genomes for 142 strains representing the species' phylogenetic and ecological diversity. The ScRAP includes phased haplotype assemblies for several heterozygous diploid and polyploid isolates. We identified circa (ca.) 4,800 nonredundant SVs that provide a broad view of the genomic diversity, including the dynamics of telomere length and transposable elements. We uncovered frequent cases of complex aneuploidies where large chromosomes underwent large deletions and translocations. We found that SVs can impact gene expression near the breakpoints and substantially contribute to gene repertoire evolution. We also discovered that horizontally acquired regions insert at chromosome ends and can generate new telomeres. Overall, the ScRAP demonstrates the benefit of a pangenome in understanding genome evolution at population scale.

With the emergence of large-scale sequencing data, methods for improving power in rare variant association tests are needed. Here we show that adjusting for common variant polygenic scores improves yield in gene-based rare variant association tests across 65 quantitative traits in the UK Biobank (up to 20% increase at α = 2.6 × 10-6), without marked increases in false-positive rates or genomic inflation. Benefits were seen for various models, with the largest improvements seen for efficient sparse mixed-effects models. Our results illustrate how polygenic score adjustment can efficiently improve power in rare variant association discovery.

Although enhancers are central regulators of mammalian gene expression, the mechanisms underlying enhancer-promoter (E-P) interactions remain unclear. Chromosome conformation capture (3C) methods effectively capture large-scale three-dimensional (3D) genome structure but struggle to achieve the depth necessary to resolve fine-scale E-P interactions. Here, we develop Region Capture Micro-C (RCMC) by combining micrococcal nuclease (MNase)-based 3C with a tiling region-capture approach and generate the deepest 3D genome maps reported with only modest sequencing. By applying RCMC in mouse embryonic stem cells and reaching the genome-wide equivalent of ~317 billion unique contacts, RCMC reveals previously unresolvable patterns of highly nested and focal 3D interactions, which we term microcompartments. Microcompartments frequently connect enhancers and promoters, and although loss of loop extrusion and inhibition of transcription disrupts some microcompartments, most are largely unaffected. We therefore propose that many E-P interactions form through a compartmentalization mechanism, which may partially explain why acute cohesin depletion only modestly affects global gene expression.

Genome-wide genealogies compactly represent the evolutionary history of a set of genomes and inferring them from genetic data has the potential to facilitate a wide range of analyses. We introduce a method, ARG-Needle, for accurately inferring biobank-scale genealogies from sequencing or genotyping array data, as well as strategies to utilize genealogies to perform association and other complex trait analyses. We use these methods to build genome-wide genealogies using genotyping data for 337,464 UK Biobank individuals and test for association across seven complex traits. Genealogy-based association detects more rare and ultra-rare signals (N = 134, frequency range 0.0007-0.1%) than genotype imputation using ~65,000 sequenced haplotypes (N = 64). In a subset of 138,039 exome sequencing samples, these associations strongly tag (average r = 0.72) underlying sequencing variants enriched (4.8×) for loss-of-function variation. These results demonstrate that inferred genome-wide genealogies may be leveraged in the analysis of complex traits, complementing approaches that require the availability of large, population-specific sequencing panels.

Pearl millet is an important cereal crop worldwide and shows superior heat tolerance. Here, we developed a graph-based pan-genome by assembling ten chromosomal genomes with one existing assembly adapted to different climates worldwide and captured 424,085 genomic structural variations (SVs). Comparative genomics and transcriptomics analyses revealed the expansion of the RWP-RK transcription factor family and the involvement of endoplasmic reticulum (ER)-related genes in heat tolerance. The overexpression of one RWP-RK gene led to enhanced plant heat tolerance and transactivated ER-related genes quickly, supporting the important roles of RWP-RK transcription factors and ER system in heat tolerance. Furthermore, we found that some SVs affected the gene expression associated with heat tolerance and SVs surrounding ER-related genes shaped adaptation to heat tolerance during domestication in the population. Our study provides a comprehensive genomic resource revealing insights into heat tolerance and laying a foundation for generating more robust crops under the changing climate.

Linkage disequilibrium (LD) is the correlation among nearby genetic variants. In genetic association studies, LD is often modeled using large correlation matrices, but this approach is inefficient, especially in ancestrally diverse studies. In the present study, we introduce LD graphical models (LDGMs), which are an extremely sparse and efficient representation of LD. LDGMs are derived from genome-wide genealogies; statistical relationships among alleles in the LDGM correspond to genealogical relationships among haplotypes. We published LDGMs and ancestry-specific LDGM precision matrices for 18 million common variants (minor allele frequency >1%) in five ancestry groups, validated their accuracy and demonstrated order-of-magnitude improvements in runtime for commonly used LD matrix computations. We implemented an extremely fast multiancestry polygenic prediction method, BLUPx-ldgm, which performs better than a similar method based on the reference LD correlation matrix. LDGMs will enable sophisticated methods that scale to ancestrally diverse genetic association data across millions of variants and individuals.

Author information: (1)Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA. (2)Ludwig Center at Harvard, Boston, MA, USA. (3)Centre for Molecular Science Informatics, Department of Chemistry, University of Cambridge, Cambridge, UK. (4)European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK. (5)School of Mathematical Sciences and Center for Statistical Science, Peking University, Beijing, China. (6)Ben May Department for Cancer Research, University of Chicago, Chicago, IL, USA. (7)Department of Human Genetics, The University of Chicago, Chicago, IL, USA. (8)Genome Integrity and Structural Biology Laboratory, National Institute of Environmental Health Sciences, US National Institutes of Health, Durham, NC, USA. (9)Integrative Bioinformatics Group, National Institute of Environmental Health Sciences, US National Institutes of Health, Durham, NC, USA. (10)Broad Institute of MIT and Harvard, Cambridge, MA, USA. (11)Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA. (12)Department of Pediatric Oncology, Dana-Farber Cancer Institute, Boston, MA, USA. (13)Department of Cell Biology, Harvard Medical School, Blavatnik Institute, Boston, MA, USA. (14)Howard Hughes Medical Institute, Boston, MA, USA. (15)Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA. peter_park@hms.harvard.edu. (16)Ludwig Center at Harvard, Boston, MA, USA. peter_park@hms.harvard.edu.

Author information: (1)Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA. (2)Ludwig Center at Harvard, Boston, MA, USA. (3)Centre for Molecular Science Informatics, Department of Chemistry, University of Cambridge, Cambridge, UK. (4)European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK. (5)School of Mathematical Sciences and Center for Statistical Science, Peking University, Beijing, China. (6)Ben May Department for Cancer Research, University of Chicago, Chicago, IL, USA. (7)Department of Human Genetics, The University of Chicago, Chicago, IL, USA. (8)Genome Integrity and Structural Biology Laboratory, National Institute of Environmental Health Sciences, US National Institutes of Health, Durham, NC, USA. (9)Integrative Bioinformatics Group, National Institute of Environmental Health Sciences, US National Institutes of Health, Durham, NC, USA. (10)Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, USA. (11)Department of Pediatric Oncology, Dana-Farber Cancer Institute, Boston, MA, USA. (12)Department of Cell Biology, Harvard Medical School, Blavatnik Institute, Boston, MA, USA. (13)Howard Hughes Medical Institute, Boston, MA, USA. (14)Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA. peter_park@hms.harvard.edu. (15)Ludwig Center at Harvard, Boston, MA, USA. peter_park@hms.harvard.edu.

Cellular differentiation requires extensive alterations in chromatin structure and function, which is elicited by the coordinated action of chromatin and transcription factors. By contrast with transcription factors, the roles of chromatin factors in differentiation have not been systematically characterized. Here, we combine bulk ex vivo and single-cell in vivo CRISPR screens to characterize the role of chromatin factor families in hematopoiesis. We uncover marked lineage specificities for 142 chromatin factors, revealing functional diversity among related chromatin factors (i.e. barrier-to-autointegration factor subcomplexes) as well as shared roles for unrelated repressive complexes that restrain excessive myeloid differentiation. Using epigenetic profiling, we identify functional interactions between lineage-determining transcription factors and several chromatin factors that explain their lineage dependencies. Studying chromatin factor functions in leukemia, we show that leukemia cells engage homeostatic chromatin factor functions to block differentiation, generating specific chromatin factor-transcription factor interactions that might be therapeutically targeted. Together, our work elucidates the lineage-determining properties of chromatin factors across normal and malignant hematopoiesis.

Understanding the genetic and nongenetic determinants of tumor protein 53 (TP53)-mutation-driven clonal evolution and subsequent transformation is a crucial step toward the design of rational therapeutic strategies. Here we carry out allelic resolution single-cell multi-omic analysis of hematopoietic stem/progenitor cells (HSPCs) from patients with a myeloproliferative neoplasm who transform to TP53-mutant secondary acute myeloid leukemia (sAML). All patients showed dominant TP53 'multihit' HSPC clones at transformation, with a leukemia stem cell transcriptional signature strongly predictive of adverse outcomes in independent cohorts, across both TP53-mutant and wild-type (WT) AML. Through analysis of serial samples, antecedent TP53-heterozygous clones and in vivo perturbations, we demonstrate a hitherto unrecognized effect of chronic inflammation, which suppressed TP53 WT HSPCs while enhancing the fitness advantage of TP53-mutant cells and promoted genetic evolution. Our findings will facilitate the development of risk-stratification, early detection and treatment strategies for TP53-mutant leukemia, and are of broad relevance to other cancer types.

Effective utilization of wild relatives is key to overcoming challenges in genetic improvement of cultivated tomato, which has a narrow genetic basis; however, current efforts to decipher high-quality genomes for tomato wild species are insufficient. Here, we report chromosome-scale tomato genomes from nine wild species and two cultivated accessions, representative of Solanum section Lycopersicon, the tomato clade. Together with two previously released genomes, we elucidate the phylogeny of Lycopersicon and construct a section-wide gene repertoire. We reveal the landscape of structural variants and provide entry to the genomic diversity among tomato wild relatives, enabling the discovery of a wild tomato gene with the potential to increase yields of modern cultivated tomatoes. Construction of a graph-based genome enables structural-variant-based genome-wide association studies, identifying numerous signals associated with tomato flavor-related traits and fruit metabolites. The tomato super-pangenome resources will expedite biological studies and breeding of this globally important crop.

During development, Hox genes are temporally activated according to their relative positions on their clusters, contributing to the proper identities of structures along the rostrocaudal axis. To understand the mechanism underlying this Hox timer, we used mouse embryonic stem cell-derived stembryos. Following Wnt signaling, the process involves transcriptional initiation at the anterior part of the cluster and a concomitant loading of cohesin complexes enriched on the transcribed DNA segments, that is, with an asymmetric distribution favoring the anterior part of the cluster. Chromatin extrusion then occurs with successively more posterior CTCF sites acting as transient insulators, thus generating a progressive time delay in the activation of more posterior-located genes due to long-range contacts with a flanking topologically associating domain. Mutant stembryos support this model and reveal that the presence of evolutionary conserved and regularly spaced intergenic CTCF sites controls the precision and the pace of this temporal mechanism.

In this study, we leveraged the combined evidence of rare coding variants and common alleles to identify therapeutic targets for osteoporosis. We undertook a large-scale multiancestry exome-wide association study for estimated bone mineral density, which showed that the burden of rare coding alleles in 19 genes was associated with estimated bone mineral density (P < 3.6 × 10-7). These genes were highly enriched for a set of known causal genes for osteoporosis (65-fold; P = 2.5 × 10-5). Exome-wide significant genes had 96-fold increased odds of being the top ranked effector gene at a given GWAS locus (P = 1.8 × 10-10). By integrating proteomics Mendelian randomization evidence, we prioritized CD109 (cluster of differentiation 109) as a gene for which heterozygous loss of function is associated with higher bone density. CRISPR-Cas9 editing of CD109 in SaOS-2 osteoblast-like cell lines showed that partial CD109 knockdown led to increased mineralization. This study demonstrates that the convergence of common and rare variants, proteomics and CRISPR can highlight new bone biology to guide therapeutic development.

Metazoan promoters are enriched in secondary DNA structure-forming motifs, such as G-quadruplexes (G4s). Here we describe 'G4access', an approach to isolate and sequence G4s associated with open chromatin via nuclease digestion. G4access is antibody- and crosslinking-independent and enriches for computationally predicted G4s (pG4s), most of which are confirmed in vitro. Using G4access in human and mouse cells, we identify cell-type-specific G4 enrichment correlated with nucleosome exclusion and promoter transcription. G4access allows measurement of variations in G4 repertoire usage following G4 ligand treatment, HDAC and G4 helicases inhibitors. Applying G4access to cells from reciprocal hybrid mouse crosses suggests a role for G4s in the control of active imprinting regions. Consistently, we also observed that G4access peaks are unmethylated, while methylation at pG4s correlates with nucleosome repositioning on DNA. Overall, our study provides a new tool for studying G4s in cellular dynamics and highlights their association with open chromatin, transcription and their antagonism to DNA methylation.

Interacting proteins tend to have similar functions, influencing the same organismal traits. Interaction networks can be used to expand the list of candidate trait-associated genes from genome-wide association studies. Here, we performed network-based expansion of trait-associated genes for 1,002 human traits showing that this recovers known disease genes or drug targets. The similarity of network expansion scores identifies groups of traits likely to share an underlying genetic and biological process. We identified 73 pleiotropic gene modules linked to multiple traits, enriched in genes involved in processes such as protein ubiquitination and RNA processing. In contrast to gene deletion studies, pleiotropy as defined here captures specifically multicellular-related processes. We show examples of modules linked to human diseases enriched in genes with known pathogenic variants that can be used to map targets of approved drugs for repurposing. Finally, we illustrate the use of network expansion scores to study genes at inflammatory bowel disease genome-wide association study loci, and implicate inflammatory bowel disease-relevant genes with strong functional and genetic support.

Potato (Solanum tuberosum) and tomato (Solanum lycopersicon) crops suffer severe losses to late blight caused by the oomycete pathogen Phytophthora infestans. Solanum americanum, a relative of potato and tomato, is globally distributed and most accessions are highly blight resistant. We generated high-quality reference genomes of four S. americanum accessions, resequenced 52 accessions, and defined a pan-NLRome of S. americanum immune receptor genes. We further screened for variation in recognition of 315P. infestans RXLR effectors in 52 S. americanum accessions. Using these genomic and phenotypic data, we cloned three NLR-encoding genes, Rpi-amr4, R02860 and R04373, that recognize cognate P. infestans RXLR effectors PITG_22825 (AVRamr4), PITG_02860 and PITG_04373. These genomic resources and methodologies will support efforts to engineer potatoes with durable late blight resistance and can be applied to diseases of other crops.

Author information: (1)Department of Genomic Medicine, University of Texas MD Anderson Cancer Center, Houston, TX, USA. (2)Salk Institute for Biological Studies, La Jolla, CA, USA. (3)Wellcome Trust Sanger Institute, Cambridge, UK. (4)Division of Computational Biology, The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA. (5)Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA. (6)Department of Cancer Biology, Dana-Farber Cancer Institute, Cambridge, MA, USA. (7)Broad Institute of MIT and Harvard, Cambridge, MA, USA. (8)Harvard Medical School, Boston, MA, USA. (9)Department of Haematology, University of Cambridge, Cambridge, UK. (10)Institute for Health Transformation University of Texas, Houston, TX, USA. (11)Department of Genomic Medicine, University of Texas MD Anderson Cancer Center, Houston, TX, USA. afutreal@mdanderson.org.

Modified parental histones are segregated symmetrically to daughter DNA strands during replication and can be inherited through mitosis. How this may sustain the epigenome and cell identity remains unknown. Here we show that transmission of histone-based information during DNA replication maintains epigenome fidelity and embryonic stem cell plasticity. Asymmetric segregation of parental histones H3-H4 in MCM2-2A mutants compromised mitotic inheritance of histone modifications and globally altered the epigenome. This included widespread spurious deposition of repressive modifications, suggesting elevated epigenetic noise. Moreover, H3K9me3 loss at repeats caused derepression and H3K27me3 redistribution across bivalent promoters correlated with misexpression of developmental genes. MCM2-2A mutation challenged dynamic transitions in cellular states across the cell cycle, enhancing naïve pluripotency and reducing lineage priming in G1. Furthermore, developmental competence was diminished, correlating with impaired exit from pluripotency. Collectively, this argues that epigenetic inheritance of histone modifications maintains a correctly balanced and dynamic chromatin landscape able to support mammalian cell differentiation.

Copy number variants (CNVs) are major contributors to genetic diversity and disease. While standardized methods, such as the genome analysis toolkit (GATK), exist for detecting short variants, technical challenges have confounded uniform large-scale CNV analyses from whole-exome sequencing (WES) data. Given the profound impact of rare and de novo coding CNVs on genome organization and human disease, we developed GATK-gCNV, a flexible algorithm to discover rare CNVs from sequencing read-depth information, complete with open-source distribution via GATK. We benchmarked GATK-gCNV in 7,962 exomes from individuals in quartet families with matched genome sequencing and microarray data, finding up to 95% recall of rare coding CNVs at a resolution of more than two exons. We used GATK-gCNV to generate a reference catalog of rare coding CNVs in WES data from 197,306 individuals in the UK Biobank, and observed strong correlations between per-gene CNV rates and measures of mutational constraint, as well as rare CNV associations with multiple traits. In summary, GATK-gCNV is a tunable approach for sensitive and specific CNV discovery in WES data, with broad applications.

Genome-wide association studies (GWASs) are a valuable tool for understanding the biology of complex human traits and diseases, but associated variants rarely point directly to causal genes. In the present study, we introduce a new method, polygenic priority score (PoPS), that learns trait-relevant gene features, such as cell-type-specific expression, to prioritize genes at GWAS loci. Using a large evaluation set of genes with fine-mapped coding variants, we show that PoPS and the closest gene individually outperform other gene prioritization methods, but observe the best overall performance by combining PoPS with orthogonal methods. Using this combined approach, we prioritize 10,642 unique gene-trait pairs across 113 complex traits and diseases with high precision, finding not only well-established gene-trait relationships but nominating new genes at unresolved loci, such as LGR4 for estimated glomerular filtration rate and CCR7 for deep vein thrombosis. Overall, we demonstrate that PoPS provides a powerful addition to the gene prioritization toolbox.

Phasing involves distinguishing the two parentally inherited copies of each chromosome into haplotypes. Here, we introduce SHAPEIT5, a new phasing method that quickly and accurately processes large sequencing datasets and applied it to UK Biobank (UKB) whole-genome and whole-exome sequencing data. We demonstrate that SHAPEIT5 phases rare variants with low switch error rates of below 5% for variants present in just 1 sample out of 100,000. Furthermore, we outline a method for phasing singletons, which, although less precise, constitutes an important step towards future developments. We then demonstrate that the use of UKB as a reference panel improves the accuracy of genotype imputation, which is even more pronounced when phased with SHAPEIT5 compared with other methods. Finally, we screen the UKB data for loss-of-function compound heterozygous events and identify 549 genes where both gene copies are knocked out. These genes complement current knowledge of gene essentiality in the human genome.

Aldosterone-producing adenomas (APAs) are the commonest curable cause of hypertension. Most have gain-of-function somatic mutations of ion channels or transporters. Herein we report the discovery, replication and phenotype of mutations in the neuronal cell adhesion gene CADM1. Independent whole exome sequencing of 40 and 81 APAs found intramembranous p.Val380Asp or p.Gly379Asp variants in two patients whose hypertension and periodic primary aldosteronism were cured by adrenalectomy. Replication identified two more APAs with each variant (total, n = 6). The most upregulated gene (10- to 25-fold) in human adrenocortical H295R cells transduced with the mutations (compared to wildtype) was CYP11B2 (aldosterone synthase), and biological rhythms were the most differentially expressed process. CADM1 knockdown or mutation inhibited gap junction (GJ)-permeable dye transfer. GJ blockade by Gap27 increased CYP11B2 similarly to CADM1 mutation. Human adrenal zona glomerulosa (ZG) expression of GJA1 (the main GJ protein) was patchy, and annular GJs (sequelae of GJ communication) were less prominent in CYP11B2-positive micronodules than adjacent ZG. Somatic mutations of CADM1 cause reversible hypertension and reveal a role for GJ communication in suppressing physiological aldosterone production.

The current understanding of the genetic determinants of thoracic aortic aneurysms and dissections (TAAD) has largely been informed through studies of rare, Mendelian forms of disease. Here, we conducted a genome-wide association study (GWAS) of TAAD, testing ~25 million DNA sequence variants in 8,626 participants with and 453,043 participants without TAAD in the Million Veteran Program, with replication in an independent sample of 4,459 individuals with and 512,463 without TAAD from six cohorts. We identified 21 TAAD risk loci, 17 of which have not been previously reported. We leverage multiple downstream analytic methods to identify causal TAAD risk genes and cell types and provide human genetic evidence that TAAD is a non-atherosclerotic aortic disorder distinct from other forms of vascular disease. Our results demonstrate that the genetic architecture of TAAD mirrors that of other complex traits and that it is not solely inherited through protein-altering variants of large effect size.

Glaucoma, a leading cause of irreversible blindness, is a highly heritable human disease. Previous genome-wide association studies have identified over 100 loci for the most common form, primary open-angle glaucoma. Two key glaucoma-associated traits also show high heritability: intraocular pressure and optic nerve head excavation damage quantified as the vertical cup-to-disc ratio. Here, since much of glaucoma heritability remains unexplained, we conducted a large-scale multitrait genome-wide association study in participants of European ancestry combining primary open-angle glaucoma and its two associated traits (total sample size over 600,000) to substantially improve genetic discovery power (263 loci). We further increased our power by then employing a multiancestry approach, which increased the number of independent risk loci to 312, with the vast majority replicating in a large independent cohort from 23andMe, Inc. (total sample size over 2.8 million; 296 loci replicated at P < 0.05, 240 after Bonferroni correction). Leveraging multiomics datasets, we identified many potential druggable genes, including neuro-protection targets likely to act via the optic nerve, a key advance for glaucoma because all existing drugs only target intraocular pressure. We further used Mendelian randomization and genetic correlation-based approaches to identify novel links to other complex traits, including immune-related diseases such as multiple sclerosis and systemic lupus erythematosus.

Author information: (1)Department of Obstetrics and Gynaecology, Sahlgrenska Academy, Institute of Clinical Science, University of Gothenburg, Gothenburg, Sweden. pol.sole.navais@gu.se. (2)Department of Obstetrics and Gynaecology, Sahlgrenska Academy, Institute of Clinical Science, University of Gothenburg, Gothenburg, Sweden. (3)deCODE genetics/Amgen, Reykjavik, Iceland. (4)Center for Diabetes Research, Department of Clinical Science, University of Bergen, Bergen, Norway. (5)Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA. (6)Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA. (7)Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia. (8)Department of Biological Sciences, Vanderbilt University, Nashville, TN, USA. (9)Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark. (10)Department of Obstetrics and Gynaecology, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark. (11)Methods and Analysis, Statistics Denmark, Copenhagen, Denmark. (12)K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Norwegian University of Science and Technology, Trondheim, Norway. (13)Department of Epidemiology Research, Statens Serum Institut, Copenhagen, Denmark. (14)MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK. (15)Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK. (16)Department of Genetics and Bioinformatics, Health Data and Digitalization, Norwegian Institute of Public Health, Oslo, Norway. (17)Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK. (18)Genentech, South San Francisco, CA, USA. (19)Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, UK. (20)Department of Dermatology, Medical University of Vienna, Vienna, Austria. (21)Northwestern University Feinberg School of Medicine, Chicago, IL, USA. (22)Department of Population Medicine, Harvard Medical School, Harvard Pilgrim Health Care Institute, Boston, MA, USA. (23)School of Medicine and Public Health, College of Health, Medicine and Wellbeing, The University of Newcastle, Callaghan, New South Wales, Australia. (24)Hunter Medical Research Institute, New Lambton Heights, New South Wales, Australia. (25)Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway. (26)University of Queensland Diamantina Institute, University of Queensland, Woolloongabba, Australia. (27)Institute of Biomedical and Clinical Science, College of Medicine and Health, University of Exeter, Exeter, UK. (28)Quantinuum Research LLC, Wayne, PA, USA. (29)Children's Hospital of Philadelphia, Philadelphia, PA, USA. (30)Department of Clinical Immunology, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark. (31)Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark. (32)Research Unit of Gynecology and Obstetrics, Institute of Clinical Research, University of Southern Denmark, Odense, Denmark. (33)Australian Centre for Precision Health, Uni Clinical & Health Sciences, University of South Australia, Adelaide, Australia. (34)South Australian Health and Medical Research Institute, Adelaide, Australia. (35)Division of Human Genetics, Center for the Prevention of Preterm Birth, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA. (36)Centre de recherche du Centre hospitalier universitaire de Sherbrooke (CHUS), Sherbrooke, Québec, Canada. (37)Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, USA. (38)Department of Obstetrics and Gynaecology, University of Texas Medical Branch, Galveston, TX, USA. (39)Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway. (40)Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA. (41)Department of Biochemistry and Functional Genomics, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, Québec, Canada. (42)Clinical Department of Laboratory Medicine, Centre intégré universitaire de santé et de services sociaux (CIUSSS) du Saguenay-Lac-St-Jean - Hôpital Universitaire de Chicoutimi, Saguenay, Québec, Canada. (43)Department of Clinical Immunology, Zealand University Hospital, Køge, Denmark. (44)School of Engineering and Natural Sciences, University of Iceland, Reykjavik, Iceland. (45)Department of Clinical Immunology, Aarhus University Hospital, Aarhus, Denmark. (46)Department of Clinical Medicine, Faculty of Health, University of Aarhus, Aarhus, Denmark. (47)Department of Global Public Health and Primary Care, University of Bergen, Bergen, Norway. (48)Department of Obstetrics and Gynecology, Helsinki University Central Hospital, Helsinki, Finland. (49)University of Helsinki, Helsinki, Finland. (50)PEDEGO Research Unit and Medical Research Center Oulu, University of Oulu, Oulu, Finland. (51)Department of Children and Adolescents, Oulu University Hospital, Oulu, Finland. (52)The Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, USA. (53)Department of Pediatrics, The Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. (54)Division of Human Genetics, Children's Hospital of Philadelphia, Philadelphia, PA, USA. (55)Division of Pulmonary Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA. (56)State Serum Institute, Copenhagen, Denmark. (57)Department of Pediatric and Adolescents Medicine, Akershus University Hospital, Lørenskog, Norway. (58)Maternal Newborn Health Innovations, PBC, Geneva, Switzerland. (59)Faculty of Medicine, University of Iceland, Reykjavik, Iceland. (60)Department of Obstetrics and Gynecology, Landspitali - The National University Hospital of Iceland, Reykjavik, Iceland. (61)Population, Policy, Practice. Great Ormond Street Institute of Child Health, University College London, London, UK. (62)Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford, UK. (63)Department of Health Science and Technology, Aalborg University, Aalborg, Denmark. (64)Bakar Computational Health Sciences Institute and Department of Epidemiology and Statistics, University of California San Francisco, San Francisco, CA, USA. (65)NORMENT Centre, University of Oslo, Oslo, Norway. (66)Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway. (67)Center for Spatial and Functional Genomics Children's Hospital of Philadelphia, Philadelphia, PA, USA. (68)Divisions of Human Genetics and Endocrinology and Diabetes, Children's Hospital of Philadelphia, Philadelphia, PA, USA. (69)Department of Genetics, The Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. (70)Department of Endocrinology, Morbid Obesity and Preventive Medicine, Oslo University Hospital, Oslo, Norway. (71)Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA. (72)Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland. (73)Biocenter of Oulu, University of Oulu, Linnanmaa, Oulu, Finland. (74)Department of Life Sciences, College of Health and Life Sciences, Brunel University London, Uxbridge, UK. (75)NIHR Bristol Biomedical Research Centre, Bristol, UK. (76)The Recurrent Pregnancy Loss Unit, The Capital Region, Copenhagen University Hospitals Rigshospitalet & Hvidovre Hospital, Hvidovre, Denmark. (77)Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN, USA. (78)Vanderbilt Genetics Institute, Vanderbilt University, Nashville, TN, USA. (79)HUNT Research Centre, Department of Public Health and Nursing, Norwegian University of Science and Technology, Levanger, Norway. (80)Department of Medicine, Levanger Hospital, Nord-Trøndelag Hospital Trust, Levanger, Norway. (81)Children and Youth Clinic, Haukeland University Hospital, Bergen, Norway. (82)Department of Medical Genetics, Haukeland University Hospital, Bergen, Norway. (83)Department of Obstetrics and Gynaecology, Sahlgrenska Academy, Institute of Clinical Science, University of Gothenburg, Gothenburg, Sweden. bo.jacobsson@obgyn.gu.se. (84)Department of Genetics and Bioinformatics, Health Data and Digitalization, Norwegian Institute of Public Health, Oslo, Norway. bo.jacobsson@obgyn.gu.se. (#)Contributed equally

SF3B1 hotspot mutations are associated with a poor prognosis in several tumor types and lead to global disruption of canonical splicing. Through synthetic lethal drug screens, we identify that SF3B1 mutant (SF3B1MUT) cells are selectively sensitive to poly (ADP-ribose) polymerase inhibitors (PARPi), independent of hotspot mutation and tumor site. SF3B1MUT cells display a defective response to PARPi-induced replication stress that occurs via downregulation of the cyclin-dependent kinase 2 interacting protein (CINP), leading to increased replication fork origin firing and loss of phosphorylated CHK1 (pCHK1; S317) induction. This results in subsequent failure to resolve DNA replication intermediates and G2/M cell cycle arrest. These defects are rescued through CINP overexpression, or further targeted by a combination of ataxia-telangiectasia mutated and PARP inhibition. In vivo, PARPi produce profound antitumor effects in multiple SF3B1MUT cancer models and eliminate distant metastases. These data provide the rationale for testing the clinical efficacy of PARPi in a biomarker-driven, homologous recombination proficient, patient population.

We explored ancestry-related differences in the genetic architecture of whole-blood gene expression using whole-genome and RNA sequencing data from 2,733 African Americans, Puerto Ricans and Mexican Americans. We found that heritability of gene expression significantly increased with greater proportions of African genetic ancestry and decreased with higher proportions of Indigenous American ancestry, reflecting the relationship between heterozygosity and genetic variance. Among heritable protein-coding genes, the prevalence of ancestry-specific expression quantitative trait loci (anc-eQTLs) was 30% in African ancestry and 8% for Indigenous American ancestry segments. Most anc-eQTLs (89%) were driven by population differences in allele frequency. Transcriptome-wide association analyses of multi-ancestry summary statistics for 28 traits identified 79% more gene-trait associations using transcriptome prediction models trained in our admixed population than models trained using data from the Genotype-Tissue Expression project. Our study highlights the importance of measuring gene expression across large and ancestrally diverse populations for enabling new discoveries and reducing disparities.

Identification of therapeutic targets from genome-wide association studies (GWAS) requires insights into downstream functional consequences. We harmonized 8,613 RNA-sequencing samples from 14 brain datasets to create the MetaBrain resource and performed cis- and trans-expression quantitative trait locus (eQTL) meta-analyses in multiple brain region- and ancestry-specific datasets (n ≤ 2,759). Many of the 16,169 cortex cis-eQTLs were tissue-dependent when compared with blood cis-eQTLs. We inferred brain cell types for 3,549 cis-eQTLs by interaction analysis. We prioritized 186 cis-eQTLs for 31 brain-related traits using Mendelian randomization and co-localization including 40 cis-eQTLs with an inferred cell type, such as a neuron-specific cis-eQTL (CYP24A1) for multiple sclerosis. We further describe 737 trans-eQTLs for 526 unique variants and 108 unique genes. We used brain-specific gene-co-regulation networks to link GWAS loci and prioritize additional genes for five central nervous system diseases. This study represents a valuable resource for post-GWAS research on central nervous system diseases.

Our understanding of the genetics of the human cerebral cortex is limited both in terms of the diversity and the anatomical granularity of brain structural phenotypes. Here we conducted a genome-wide association meta-analysis of 13 structural and diffusion magnetic resonance imaging-derived cortical phenotypes, measured globally and at 180 bilaterally averaged regions in 36,663 individuals and identified 4,349 experiment-wide significant loci. These phenotypes include cortical thickness, surface area, gray matter volume, measures of folding, neurite density and water diffusion. We identified four genetic latent structures and causal relationships between surface area and some measures of cortical folding. These latent structures partly relate to different underlying gene expression trajectories during development and are enriched for different cell types. We also identified differential enrichment for neurodevelopmental and constrained genes and demonstrate that common genetic variants associated with cortical expansion are associated with cephalic disorders. Finally, we identified complex interphenotype and inter-regional genetic relationships among the 13 phenotypes, reflecting the developmental differences among them. Together, these analyses identify distinct genetic organizational principles of the cortex and their correlates with neurodevelopment.

Binge eating disorder (BED) is the most common eating disorder, yet its genetic architecture remains largely unknown. Studying BED is challenging because it is often comorbid with obesity, a common and highly polygenic trait, and it is underdiagnosed in biobank data sets. To address this limitation, we apply a supervised machine-learning approach (using 822 cases of individuals diagnosed with BED) to estimate the probability of each individual having BED based on electronic medical records from the Million Veteran Program. We perform a genome-wide association study of individuals of African (n = 77,574) and European (n = 285,138) ancestry while controlling for body mass index to identify three independent loci near the HFE, MCHR2 and LRP11 genes and suggest APOE as a risk gene for BED. We identify shared heritability between BED and several neuropsychiatric traits, and implicate iron metabolism in the pathophysiology of BED. Overall, our findings provide insights into the genetics underlying BED and suggest directions for future translational research.

Epilepsy is a highly heritable disorder affecting over 50 million people worldwide, of which about one-third are resistant to current treatments. Here we report a multi-ancestry genome-wide association study including 29,944 cases, stratified into three broad categories and seven subtypes of epilepsy, and 52,538 controls. We identify 26 genome-wide significant loci, 19 of which are specific to genetic generalized epilepsy (GGE). We implicate 29 likely causal genes underlying these 26 loci. SNP-based heritability analyses show that common variants explain between 39.6% and 90% of genetic risk for GGE and its subtypes. Subtype analysis revealed markedly different genetic architectures between focal and generalized epilepsies. Gene-set analyses of GGE signals implicate synaptic processes in both excitatory and inhibitory neurons in the brain. Prioritized candidate genes overlap with monogenic epilepsy genes and with targets of current antiseizure medications. Finally, we leverage our results to identify alternate drugs with predicted efficacy if repurposed for epilepsy treatment.

Parental histones, the carriers of posttranslational modifications, are deposited evenly onto the replicating DNA of sister chromatids in a process dependent on the Mcm2 subunit of DNA helicase and the Pole3 subunit of leading-strand DNA polymerase. The biological significance of parental histone propagation remains unclear. Here we show that Mcm2-mutated or Pole3-deleted mouse embryonic stem cells (ESCs) display aberrant histone landscapes and impaired neural differentiation. Mutation of the Mcm2 histone-binding domain causes defects in pre-implantation development and embryonic lethality. ESCs with biased parental histone transfer exhibit increased epigenetic heterogeneity, showing altered histone variant H3.3 and H3K27me3 patterning at genomic sites regulating differentiation genes. Our results indicate that the lagging strand pattern of H3.3 leads to the redistribution of H3K27me3 in Mcm2-2A ESCs. We demonstrate that symmetric parental histone deposition to sister chromatids contributes to cellular differentiation and development.

The kidneys operate at the interface of plasma and urine by clearing molecular waste products while retaining valuable solutes. Genetic studies of paired plasma and urine metabolomes may identify underlying processes. We conducted genome-wide studies of 1,916 plasma and urine metabolites and detected 1,299 significant associations. Associations with 40% of implicated metabolites would have been missed by studying plasma alone. We detected urine-specific findings that provide information about metabolite reabsorption in the kidney, such as aquaporin (AQP)-7-mediated glycerol transport, and different metabolomic footprints of kidney-expressed proteins in plasma and urine that are consistent with their localization and function, including the transporters NaDC3 (SLC13A3) and ASBT (SLC10A2). Shared genetic determinants of 7,073 metabolite-disease combinations represent a resource to better understand metabolic diseases and revealed connections of dipeptidase 1 with circulating digestive enzymes and with hypertension. Extending genetic studies of the metabolome beyond plasma yields unique insights into processes at the interface of body compartments.

Hereditary congenital facial paresis type 1 (HCFP1) is an autosomal dominant disorder of absent or limited facial movement that maps to chromosome 3q21-q22 and is hypothesized to result from facial branchial motor neuron (FBMN) maldevelopment. In the present study, we report that HCFP1 results from heterozygous duplications within a neuron-specific GATA2 regulatory region that includes two enhancers and one silencer, and from noncoding single-nucleotide variants (SNVs) within the silencer. Some SNVs impair binding of NR2F1 to the silencer in vitro and in vivo and attenuate in vivo enhancer reporter expression in FBMNs. Gata2 and its effector Gata3 are essential for inner-ear efferent neuron (IEE) but not FBMN development. A humanized HCFP1 mouse model extends Gata2 expression, favors the formation of IEEs over FBMNs and is rescued by conditional loss of Gata3. These findings highlight the importance of temporal gene regulation in development and of noncoding variation in rare mendelian disease.

Author information: (1)Department of Biomedicine - Human Genetics, Aarhus University, Aarhus, Denmark. ditte@biomed.au.dk. (2)The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark. ditte@biomed.au.dk. (3)Center for Genomics and Personalized Medicine, Aarhus, Denmark. ditte@biomed.au.dk. (4)deCODE Genetics/Amgen, Reykjavik, Iceland. (5)Faculty of Medicine, University of Iceland, Reykjavik, Iceland. (6)The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark. (7)Mental Health Centre Sct. Hans, Capital Region of Denmark, Institute of Biological Psychiatry, Copenhagen University Hospital, Copenhagen, Denmark. (8)Department of Evolutionary Biology, Ecology and Environmental Sciences, University of Barcelona, Barcelona, Spain. (9)Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA. (10)Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA. (11)Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA. (12)Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA. (13)Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA. (14)Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA. (15)Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA. (16)Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA. (17)Department of Biomedicine - Human Genetics, Aarhus University, Aarhus, Denmark. (18)Center for Genomics and Personalized Medicine, Aarhus, Denmark. (19)Bioinformatics Research Centre, Aarhus University, Aarhus, Denmark. (20)Center for Neonatal Screening, Department for Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark. (21)Department of Child and Adolescent Psychiatry, National University Hospital, Reykjavik, Iceland. (22)The Centre for Child Development and Behaviour, Capital Area Primary Health Care, Reykjavik, Iceland. (23)National Centre for Register-Based Research, Business and Social Sciences, Aarhus University, Aarhus, Denmark. (24)Centre for Integrated Register-based Research, CIRRAU, Aarhus University, Aarhus, Denmark. (25)Mental Health Services of the Capital Region, Child and Adolescent Psychiatric Center, Glostrup, Denmark. (26)Institute of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark. (27)Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK. (28)Psychiatric Genetics Unit, Group of Psychiatry, Mental Health and Addiction, Vall d'Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain. (29)Department of Psychiatry, Hospital Universitari Vall d'Hebron, Barcelona, Spain. (30)Biomedical Network Research Centre on Mental Health (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain. (31)Department of Genetics, Microbiology, and Statistics, Faculty of Biology, Universitat de Barcelona, Barcelona, Spain. (32)Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands. (33)Amsterdam Public Health Research Institute, Amsterdam, the Netherlands. (34)Department of Human Genetics, Radboud University Medical Center, Nijmegen, the Netherlands. (35)Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands. (36)Psychiatric Genetics, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia. (37)PROMENTA, Department of Psychology, University of Oslo, Oslo, Norway. (38)Mental Health Centre Copenhagen, Capital Region of Denmark, Copenhagen University Hospital, Copenhagen, Denmark. (39)Psychosis Research Unit, Aarhus University Hospital-Psychiatry, Aarhus, Denmark. (40)Department of Medicine, Harvard Medical School, Boston, MA, USA. (41)Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland. (42)Departments of Psychiatry and of Neuroscience and Physiology, SUNY Upstate Medical University, Syracuse, NY, USA. (43)Mental Illness Research Education and Clinical Center, JJ Peters VA Medical Center, Bronx, NY, USA. (44)Department of Psychiatry, Radboud University Medical Center, Nijmegen, the Netherlands. (45)Department of Biomedicine - Human Genetics, Aarhus University, Aarhus, Denmark. anders@biomed.au.dk. (46)The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark. anders@biomed.au.dk. (47)Center for Genomics and Personalized Medicine, Aarhus, Denmark. anders@biomed.au.dk. (#)Contributed equally

Several molecular and phenotypic algorithms exist that establish genotype-phenotype correlations, including facial recognition tools. However, no unified framework that investigates both facial data and other phenotypic data directly from individuals exists. We developed PhenoScore: an open-source, artificial intelligence-based phenomics framework, combining facial recognition technology with Human Phenotype Ontology data analysis to quantify phenotypic similarity. Here we show PhenoScore's ability to recognize distinct phenotypic entities by establishing recognizable phenotypes for 37 of 40 investigated syndromes against clinical features observed in individuals with other neurodevelopmental disorders and show it is an improvement on existing approaches. PhenoScore provides predictions for individuals with variants of unknown significance and enables sophisticated genotype-phenotype studies by testing hypotheses on possible phenotypic (sub)groups. PhenoScore confirmed previously known phenotypic subgroups caused by variants in the same gene for SATB1, SETBP1 and DEAF1 and provides objective clinical evidence for two distinct ADNP-related phenotypes, already established functionally.

Chronic obstructive pulmonary disease (COPD), the third leading cause of death worldwide, is highly heritable. While COPD is clinically defined by applying thresholds to summary measures of lung function, a quantitative liability score has more power to identify genetic signals. Here we train a deep convolutional neural network on noisy self-reported and International Classification of Diseases labels to predict COPD case-control status from high-dimensional raw spirograms and use the model's predictions as a liability score. The machine-learning-based (ML-based) liability score accurately discriminates COPD cases and controls, and predicts COPD-related hospitalization without any domain-specific knowledge. Moreover, the ML-based liability score is associated with overall survival and exacerbation events. A genome-wide association study on the ML-based liability score replicates existing COPD and lung function loci and also identifies 67 new loci. Lastly, our method provides a general framework to use ML methods and medical-record-based labels that does not require domain knowledge or expert curation to improve disease prediction and genomic discovery for drug design.

Transposable elements (TEs) are parasitic DNA sequences accounting for over half of the human genome. Tight control of the repression and activation states of TEs is critical for genome integrity, development, immunity and diseases, including cancer. However, precisely how this regulation is achieved remains unclear. Here we develop a targeted proteomic proximity labeling approach to capture TE-associated proteins in human embryonic stem cells (hESCs). We find that the RNA N6-methyladenosine (m6A) reader, YTHDC2, occupies genomic loci of the primate-specific TE, LTR7/HERV-H, specifically through its interaction with m6A-modified HERV-H RNAs. Unexpectedly, YTHDC2 recruits the DNA 5-methylcytosine (5mC)-demethylase, TET1, to remove 5mC from LTR7/HERV-H and prevent epigenetic silencing. Functionally, the YTHDC2/LTR7 axis inhibits neural differentiation of hESCs. Our results reveal both an underappreciated crosstalk between RNA m6A and DNA 5mC, the most abundant regulatory modifications of RNA and DNA in eukaryotes, and the fact that in hESCs this interplay controls TE activity and cell fate.