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Glycemic traits are used to diagnose and monitor type 2 diabetes and cardiometabolic health. To date, most genetic studies of glycemic traits have focused on individuals of European ancestry. Here we aggregated genome-wide association studies comprising up to 281,416 individuals without diabetes (30% non-European ancestry) for whom fasting glucose, 2-h glucose after an oral glucose challenge, glycated hemoglobin and fasting insulin data were available. Trans-ancestry and single-ancestry meta-analyses identified 242 loci (99 novel; P < 5 × 10 ), 80% of which had no significant evidence of between-ancestry heterogeneity. Analyses restricted to individuals of European ancestry with equivalent sample size would have led to 24 fewer new loci. Compared with single-ancestry analyses, equivalent-sized trans-ancestry fine-mapping reduced the number of estimated variants in 99% credible sets by a median of 37.5%. Genomic-feature, gene-expression and gene-set analyses revealed distinct biological signatures for each trait, highlighting different underlying biological pathways. Our results increase our understanding of diabetes pathophysiology by using trans-ancestry studies for improved power and resolution.

The role of epistasis in the genetic architecture of quantitative traits is controversial, despite the biological plausibility that nonlinear molecular interactions underpin the genotype-phenotype map. This controversy arises because most genetic variation for quantitative traits is additive. However, additive variance is consistent with pervasive epistasis. In this Review, I discuss experimental designs to detect the contribution of epistasis to quantitative trait phenotypes in model organisms. These studies indicate that epistasis is common, and that additivity can be an emergent property of underlying genetic interaction networks. Epistasis causes hidden quantitative genetic variation in natural populations and could be responsible for the small additive effects, missing heritability and the lack of replication that are typically observed for human complex traits.

Characterizing genetic influences on DNA methylation (DNAm) provides an opportunity to understand mechanisms underpinning gene regulation and disease. In the present study, we describe results of DNAm quantitative trait locus (mQTL) analyses on 32,851 participants, identifying genetic variants associated with DNAm at 420,509 DNAm sites in blood. We present a database of >270,000 independent mQTLs, of which 8.5% comprise long-range (trans) associations. Identified mQTL associations explain 15-17% of the additive genetic variance of DNAm. We show that the genetic architecture of DNAm levels is highly polygenic. Using shared genetic control between distal DNAm sites, we constructed networks, identifying 405 discrete genomic communities enriched for genomic annotations and complex traits. Shared genetic variants are associated with both DNAm levels and complex diseases, but only in a minority of cases do these associations reflect causal relationships from DNAm to trait or vice versa, indicating a more complex genotype-phenotype map than previously anticipated.

Modern tomatoes have narrow genetic diversity limiting their improvement potential. We present a tomato pan-genome constructed using genome sequences of 725 phylogenetically and geographically representative accessions, revealing 4,873 genes absent from the reference genome. Presence/absence variation analyses reveal substantial gene loss and intense negative selection of genes and promoters during tomato domestication and improvement. Lost or negatively selected genes are enriched for important traits, especially disease resistance. We identify a rare allele in the TomLoxC promoter selected against during domestication. Quantitative trait locus mapping and analysis of transgenic plants reveal a role for TomLoxC in apocarotenoid production, which contributes to desirable tomato flavor. In orange-stage fruit, accessions harboring both the rare and common TomLoxC alleles (heterozygotes) have higher TomLoxC expression than those homozygous for either and are resurgent in modern tomatoes. The tomato pan-genome adds depth and completeness to the reference genome, and is useful for future biological discovery and breeding.

Many gene expression quantitative trait locus (eQTL) studies have published their summary statistics, which can be used to gain insight into complex human traits by downstream analyses, such as fine mapping and co-localization. However, technical differences between these datasets are a barrier to their widespread use. Consequently, target genes for most genome-wide association study (GWAS) signals have still not been identified. In the present study, we present the eQTL Catalogue ( https://www.ebi.ac.uk/eqtl ), a resource of quality-controlled, uniformly re-computed gene expression and splicing QTLs from 21 studies. We find that, for matching cell types and tissues, the eQTL effect sizes are highly reproducible between studies. Although most QTLs were shared between most bulk tissues, we identified a greater diversity of cell-type-specific QTLs from purified cell types, a subset of which also manifested as new disease co-localizations. Our summary statistics are freely available to enable the systematic interpretation of human GWAS associations across many cell types and tissues.

Trait-based approaches have led to significant advances in plant ecology, but are currently biased toward above-ground traits. It is becoming clear that a stronger emphasis on below-ground traits is needed to better predict future changes in plant biodiversity and their consequences for ecosystem functioning. Here I propose six ‘below-ground frontiers’ in traitbased plant ecology, with an emphasis on traits governing soil nutrient acquisition: redefining fine roots; quantifying root trait dimensionality; integrating mycorrhizas; broadening the suite of root traits; determining linkages between root traits and abiotic and biotic factors; and understanding ecosystem-level consequences of root traits. Focusing research efforts along these frontiers should help to fulfil the promise of trait-based ecology: enhanced predictive capacity across ecological scales.

During the past decade, genome-wide association studies (GWAS) have been used to successfully identify tens of thousands of genetic variants associated with complex traits and diseases. These studies have produced extensive repositories of genetic variation and trait measurements across large numbers of individuals, providing tremendous opportunities for further analyses. However, privacy concerns and other logistical considerations often limit access to individual-level genetic data, motivating the development of methods that analyse summary association statistics. Here, we review recent progress on statistical methods that leverage summary association data to gain insights into the genetic basis of complex traits and diseases.

Fine roots acquire essential soil resources and mediate biogeochemical cycling in terrestrial ecosystems. Estimates of carbon and nutrient allocation to build and maintain these structures remain uncertain because of the challenges of consistently measuring and interpreting fine-root systems. Traditionally, fine roots have been defined as all roots ≤ 2 mm in diameter, yet it is now recognized that this approach fails to capture the diversity of form and function observed among fine-root orders. Here, we demonstrate how order-based and functional classification frameworks improve our understanding of dynamic root processes in ecosystems dominated by perennial plants. In these frameworks, fine roots are either separated into individual root orders or functionally defined into a shorter-lived absorptive pool and a longer-lived transport fine-root pool. Using these frameworks, we estimate that fine-root production and turnover represent 22% of terrestrial net primary production globally – a c. 30% reduction from previous estimates assuming a single fine-root pool. Future work developing tools to rapidly differentiate functional fine-root classes, explicit incorporation of mycorrhizal fungi into fine-root studies, and wider adoption of a two-pool approach to model fine roots provide opportunities to better understand below-ground processes in the terrestrial biosphere.

Nonrandom collecting practices may bias conclusions drawn from analyses of herbarium records. Recent efforts to fully digitize and mobilize regional floras online offer a timely opportunity to assess commonalities and differences in herbarium sampling biases. We determined spatial, temporal, trait, phylogenetic, and collector biases in c. 5 million herbarium records, representing three of the most complete digitized floras of the world: Australia (AU), South Africa (SA), and New England, USA (NE). We identified numerous shared and unique biases among these regions. Shared biases included specimens collected close to roads and herbaria; specimens collected more frequently during biological spring and summer; specimens of threatened species collected less frequently; and specimens of close relatives collected in similar numbers. Regional differences included overrepresentation of graminoids in SA and AU and of annuals in AU; and peak collection during the 1910s in NE, 1980s in SA, and 1990s in AU. Finally, in all regions, a disproportionately large percentage of specimens were collected by very few individuals. We hypothesize that these mega-collectors, with their associated preferences and idiosyncrasies, shaped patterns of collection bias via ‘founder effects’. Studies using herbarium collections should account for sampling biases, and future collecting efforts should avoid compounding these biases to the extent possible.

The search for a root economics spectrum (RES) has been sparked by recent interest in trait-based plant ecology. By analogy with the one-dimensional leaf economics spectrum (LES), fine-root traits are hypothesised to match leaf traits which are coordinated along one axis from resource acquisitive to conservative traits. However, our literature review and meta-level analysis reveal no consistent evidence of an RES mirroring an LES. Instead the RES appears to be multidimensional. We discuss three fundamental differences contributing to the discrepancy between these spectra. First, root traits are simultaneously constrained by various environmental drivers not necessarily related to resource uptake. Second, above- and belowground traits cannot be considered analogues, because they function differently and might not be related to resource uptake in a similar manner. Third, mycorrhizal interactions may offset selection for an RES. Understanding and explaining the belowground mechanisms and trade-offs that drive variation in root traits, resource acquisition and plant performance across species, thus requires a fundamentally different approach than applied aboveground. We therefore call for studies that can functionally incorporate the root traits involved in resource uptake, the complex soil environment and the various soil resource uptake mechanisms – particularly the mycorrhizal pathway – in a multidimensional root trait framework.