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The Pharma Proteomics Project is a precompetitive biopharmaceutical consortium characterizing the plasma proteomic profiles of 54,219 UK Biobank participants. Here we provide a detailed summary of this initiative, including technical and biological validations, insights into proteomic disease signatures, and prediction modelling for various demographic and health indicators. We present comprehensive protein quantitative trait locus (pQTL) mapping of 2,923 proteins that identifies 14,287 primary genetic associations, of which 81% are previously undescribed, alongside ancestry-specific pQTL mapping in non-European individuals. The study provides an updated characterization of the genetic architecture of the plasma proteome, contextualized with projected pQTL discovery rates as sample sizes and proteomic assay coverages increase over time. We offer extensive insights into trans pQTLs across multiple biological domains, highlight genetic influences on ligand–receptor interactions and pathway perturbations across a diverse collection of cytokines and complement networks, and illustrate long-range epistatic effects of ABO blood group and FUT2 secretor status on proteins with gastrointestinal tissue-enriched expression. We demonstrate the utility of these data for drug discovery by extending the genetic proxied effects of protein targets, such as PCSK9, on additional endpoints, and disentangle specific genes and proteins perturbed at loci associated with COVID-19 susceptibility. This public–private partnership provides the scientific community with an open-access proteomics resource of considerable breadth and depth to help to elucidate the biological mechanisms underlying proteo-genomic discoveries and accelerate the development of biomarkers, predictive models and therapeutics 1 . The Pharma Proteomics Project generates the largest open-access plasma proteomics dataset to date, offering insights into trans protein quantitative trait loci across multiple biological domains, and highlighting genetic influences on ligand–receptor interactions and pathway perturbations across a diverse collection of cytokines and complement networks.

Regulation of transcript structure generates transcript diversity and plays an important role in human disease 1 – 7 . The advent of long-read sequencing technologies offers the opportunity to study the role of genetic variation in transcript structure 8 – 16 . In this Article, we present a large human long-read RNA-seq dataset using the Oxford Nanopore Technologies platform from 88 samples from Genotype-Tissue Expression (GTEx) tissues and cell lines, complementing the GTEx resource. We identified just over 70,000 novel transcripts for annotated genes, and validated the protein expression of 10% of novel transcripts. We developed a new computational package, LORALS, to analyse the genetic effects of rare and common variants on the transcriptome by allele-specific analysis of long reads. We characterized allele-specific expression and transcript structure events, providing new insights into the specific transcript alterations caused by common and rare genetic variants and highlighting the resolution gained from long-read data. We were able to perturb the transcript structure upon knockdown of PTBP1, an RNA binding protein that mediates splicing, thereby finding genetic regulatory effects that are modified by the cellular environment. Finally, we used this dataset to enhance variant interpretation and study rare variants leading to aberrant splicing patterns. To understand the contribution of variants to transcript expression regulation, long-read transcriptome data are generated from the GTEx resource, and a new software package to perform allele-specific analysis is developed.

Proteomic analysis of cells, tissues and body fluids has generated valuable insights into the complex processes influencing human biology. Proteins represent intermediate phenotypes for disease and provide insight into how genetic and non-genetic risk factors are mechanistically linked to clinical outcomes. Associations between protein levels and DNA sequence variants that colocalize with risk alleles for common diseases can expose disease-associated pathways, revealing novel drug targets and translational biomarkers. However, genome-wide, population-scale analyses of proteomic data are only now emerging. Here, we review current findings from studies of the plasma proteome and discuss their potential for advancing biomedical translation through the interpretation of genome-wide association analyses. We highlight the challenges faced by currently available technologies and provide perspectives relevant to their future application in large-scale biobank studies.In this Review, Suhre, McCarthy and Schwenk describe how combining genetics with plasma proteomics is providing notable insights into human disease. As changes in the circulating proteome are often an intermediate molecular readout between a genetic variant and its organismal effect, proteomics can enable a deeper understanding of disease mechanisms, clinical biomarkers and therapeutic opportunities.

Microbiomes play a pivotal role in plant growth and health, but the genetic factors involved in microbiome assembly remain largely elusive. Here, we map the molecular features of the rhizosphere microbiome as quantitative traits of a diverse hybrid population of wild and domesticated tomato. Gene content analysis of prioritized tomato quantitative trait loci suggests a genetic basis for differential recruitment of various rhizobacterial lineages, including a Streptomyces -associated 6.31 Mbp region harboring tomato domestication sweeps and encoding, among others, the iron regulator FIT and the water channel aquaporin SlTIP2.3. Within metagenome-assembled genomes of root-associated Streptomyces and Cellvibrio , we identify bacterial genes involved in metabolism of plant polysaccharides, iron, sulfur, trehalose, and vitamins, whose genetic variation associates with specific tomato QTLs. By integrating ‘microbiomics’ and quantitative plant genetics, we pinpoint putative plant and reciprocal rhizobacterial traits underlying microbiome assembly, thereby providing a first step towards plant-microbiome breeding programs. Genetics factors involved in rhizosphere microbiomes assembly remain largely elusive. Here, the authors integrate microbiomics and quantitative plant genetics to reveal genetic loci associated with specific microbes and rhizobacterial traits underlying microbiome assembly in tomato.

Few studies have explored the impact of rare variants (minor allele frequency < 1%) on highly heritable plasma metabolites identified in metabolomic screens. The Finnish population provides an ideal opportunity for such explorations, given the multiple bottlenecks and expansions that have shaped its history, and the enrichment for many otherwise rare alleles that has resulted. Here, we report genetic associations for 1391 plasma metabolites in 6136 men from the late-settlement region of Finland. We identify 303 novel association signals, more than one third at variants rare or enriched in Finns. Many of these signals identify genes not previously implicated in metabolite genome-wide association studies and suggest mechanisms for diseases and disease-related traits. The Finnish population is enriched for genetic variants which are rare in other populations. Here, the authors find new genetic loci associated with 1391 circulating metabolites in 6136 Finnish men, demonstrating that metabolite genetic associations can help elucidate disease mechanisms.

Non-coding genetic variants may cause disease by modulating gene expression. However, identifying these expression quantitative trait loci (eQTLs) is complicated by differences in gene regulation across fluid functional cell states within cell types. These states—for example, neurotransmitter-driven programs in astrocytes or perivascular fibroblast differentiation—are obscured in eQTL studies that aggregate cells 1 , 2 . Here we modelled eQTLs at single-cell resolution in one complex cell type: memory T cells. Using more than 500,000 unstimulated memory T cells from 259 Peruvian individuals, we show that around one-third of 6,511 cis -eQTLs had effects that were mediated by continuous multimodally defined cell states, such as cytotoxicity and regulatory capacity. In some loci, independent eQTL variants had opposing cell-state relationships. Autoimmune variants were enriched in cell-state-dependent eQTLs, including risk variants for rheumatoid arthritis near ORMDL3 and CTLA4 ; this indicates that cell-state context is crucial to understanding potential eQTL pathogenicity. Moreover, continuous cell states explained more variation in eQTLs than did conventional discrete categories, such as CD4 + versus CD8 + , suggesting that modelling eQTLs and cell states at single-cell resolution can expand insight into gene regulation in functionally heterogeneous cell types. A single-cell Poisson model is used to analyse eQTLs in memory T cells across continuous, dynamic cell states, revealing that the cell context is critical to understanding variation in eQTLs and their association with disease.

Affinity-based proteomics has enabled scalable quantification of thousands of protein targets in blood enhancing biomarker discovery, understanding of disease mechanisms, and genetic evaluation of drug targets in humans through protein quantitative trait loci (pQTLs). Here, we integrate two partly complementary techniques—the aptamer-based SomaScan ® v4 assay and the antibody-based Olink assays—to systematically assess phenotypic consequences of hundreds of pQTLs discovered for 871 protein targets across both platforms. We create a genetically anchored cross-platform proteome-phenome network comprising 547 protein–phenotype connections, 36.3% of which were only seen with one of the two platforms suggesting that both techniques capture distinct aspects of protein biology. We further highlight discordance of genetically predicted effect directions between assays, such as for PILRA and Alzheimer’s disease. Our results showcase the synergistic nature of these technologies to better understand and identify disease mechanisms and provide a benchmark for future cross-platform discoveries. Broad-capture affinity-based proteomic technologies inform how the readout of our genes affects human health. Here, the authors integrate aptamer- and antibody-based profiling to understand the mechanisms underlying gene-protein-disease associations.

Type 2 diabetes (T2D) is a very common disease in humans. Here we conduct a meta-analysis of genome-wide association studies (GWAS) with ~16 million genetic variants in 62,892 T2D cases and 596,424 controls of European ancestry. We identify 139 common and 4 rare variants associated with T2D, 42 of which (39 common and 3 rare variants) are independent of the known variants. Integration of the gene expression data from blood ( n  = 14,115 and 2765) with the GWAS results identifies 33 putative functional genes for T2D, 3 of which were targeted by approved drugs. A further integration of DNA methylation ( n  = 1980) and epigenomic annotation data highlight 3 genes ( CAMK1D , TP53INP1 , and ATP5G1 ) with plausible regulatory mechanisms, whereby a genetic variant exerts an effect on T2D through epigenetic regulation of gene expression. Our study uncovers additional loci, proposes putative genetic regulatory mechanisms for T2D, and provides evidence of purifying selection for T2D-associated variants. GWAS have so far identified 129 loci associated with type 2 diabetes (T2D). Here, the authors meta-analyse three large T2D GWA studies which uncovers 42 additional loci, further prioritize 33 functional genes using eQTL and mQTL data and propose regulatory mechanisms for three putative T2D genes.

Plant diseases are responsible for substantial crop losses each year and pose a threat to global food security and agricultural sustainability. Improving crop resistance to pathogens through breeding is an environmentally sound method for managing disease and minimizing these losses. However, it is challenging to breed varieties with resistance that is effective, stable and broad-spectrum. Recent advances in genetic and genomic technologies have contributed to a better understanding of the complexity of host-pathogen interactions and have identified some of the genes and mechanisms that underlie resistance. This new knowledge is benefiting crop improvement through better-informed breeding strategies that utilize diverse forms of resistance at different scales, from the genome of a single plant to the plant varieties deployed across a region.

The authors thank G. Moore and M. Bevan for providing valuable feedback at multiple stages of the project; colleagues for assistance in Watkins field trial and phenotyping work from five experimental stations across China: Z. Zhu, Q. Wang, Y. Song, Y. Zhu and X. Zhang; the John Innes Centre (JIC) NBI Computing Infrastructure for Science group; the JIC Field Trials and Horticultural Services teams for support in field and glasshouse experiments; T. Florio for figure visualization; and the Rothamsted Research farm team and Analytical Chemistry unit for support in field experiments and analytical mineral analyses. This work was supported by the Program for Guangdong \\u201CZhuJiang\\u201D Introducing Innovative and Entrepreneurial Teams (2019ZT08N628), the National Natural Science Foundation of China (32022006), the Agricultural Science and Technology Innovation Program (CAAS-ASTIP-2021-AGIS-ZDRW202101), the Shenzhen Science and Technology Program (AGIS-ZDKY202002) to S. Cheng, and the Guangdong Basic and Applied Basic Research Foundation (2020A1515110677) to L.M. The UK work was possible owing to the long-term investment of the UK Biotechnology and Biological Sciences Research Council (BBSRC) in wheat research through Institute Strategic Programme (ISP) grants and longer larger grants: BBSRC LOLA \\u2018Enhancing diversity in UK wheat through a public sector prebreeding programme\\u2019 (BB/I002545/1); BBSRC ISP \\u2018JIC WISP ISP\\u2014Wheat Institute Strategic Programme\\u2019 (BB/J004596/1); BBSRC ISP \\u2018BBSRC Strategic Programme in Designing Future Wheat (DFW)\\u2019 (BB/P016855/1); BBSRC ISP \\u2018BBSRC Institute Strategic Programme: Delivering Sustainable Wheat (DSW)\\u2019 (BB/X011003/1) and for wheat germplasm conservation and global distribution through the Germplasm Resources BBSRC National Capability award (BBS/E/J/000PR8000). S.G. and C.L. also received support from the UK Department for Environment, Food and Rural Affairs (Defra) as part of WGIN phases 3 and 4 (CH0106 and CH0109). This work was also supported by the European Research Council (ERC-2019-COG-866328), the Sustainable Crop Production Research for International Development (SCPRID) programme (BB/J012017/1), the Mexican Consejo Nacional de Ciencia y Tecnolog\\u00EDa (CONACYT; 2018-000009-01EXTF-00306), the Science, Technology & Innovation Funding Authority (STDF), Egypt-UK Newton-Mosharafa Institutional Links award, project ID 30718 and EG\\u2013US cycle 19\\u2013project ID 42687.