Exploitation of crop heterosis is crucial for increasing global agriculture production. However, the quantitative genomic analysis of heterosis was lacking, and there is currently no effective prediction tool to optimize cross-combinations. Here 2,839 rice hybrid cultivars and 9,839 segregation individuals were resequenced and phenotyped. Our findings demonstrated that indica-indica hybrid-improving breeding was a process that broadened genetic resources, pyramided breeding-favorable alleles through combinatorial selection and collaboratively improved both parents by eliminating the inferior alleles at negative dominant loci. Furthermore, we revealed that widespread genetic complementarity contributed to indica-japonica intersubspecific heterosis in yield traits, with dominance effect loci making a greater contribution to phenotypic variance than overdominance effect loci. On the basis of the comprehensive dataset, a genomic model applicable to diverse rice varieties was developed and optimized to predict the performance of hybrid combinations. Our data offer a valuable resource for advancing the understanding and facilitating the utilization of heterosis in rice.

Mosaic chromosomal alterations (mCAs) are common in cancers and can arise decades before diagnosis. A quantitative understanding of the rate at which these events occur, and their functional consequences, could improve cancer risk prediction and our understanding of somatic evolution. Using mCA clone size estimates from the blood of approximately 500,000 UK Biobank participants, we estimate mutation rates and fitness consequences of acquired gain, loss and copy-neutral loss of heterozygosity events. Most mCAs have moderate to high fitness effects but occur at a low rate, being more than tenfold less common than equivalently fit single-nucleotide variants. Notable exceptions are mosaic loss of X and Y, which we estimate have roughly 1,000-fold higher mutation rates than autosomal mCAs. Although the way in which most mCAs increase in prevalence with age is consistent with constant growth rates, some mCAs exhibit different behavior, suggesting that their fitness may depend on inherited variants, extrinsic factors or distributions of fitness effects.

We report a genome-wide association study of venous thromboembolism (VTE) incorporating 81,190 cases and 1,419,671 controls sampled from six cohorts. We identify 93 risk loci, of which 62 are previously unreported. Many of the identified risk loci are at genes encoding proteins with functions converging on the coagulation cascade or platelet function. A VTE polygenic risk score (PRS) enabled effective identification of both high- and low-risk individuals. Individuals within the top 0.1% of PRS distribution had a VTE risk similar to homozygous or compound heterozygous carriers of the variants G20210A (c.*97 G > A) in F2 and p.R534Q in F5. We also document that F2 and F5 mutation carriers in the bottom 10% of the PRS distribution had a risk similar to that of the general population. We further show that PRS improved individual risk prediction beyond that of genetic and clinical risk factors. We investigated the extent to which venous and arterial thrombosis share clinical risk factors using Mendelian randomization, finding that some risk factors for arterial thrombosis were directionally concordant with VTE risk (for example, body mass index and smoking) whereas others were discordant (for example, systolic blood pressure and triglyceride levels).

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.