Connecting climate action with other sustainable development goals

The international community has committed to combat climate change and achieve 17 Sustainable Development Goals (SDGs). Here we explore (dis)connections in evidence and governance between these commitments. Our structured evidence review suggests that climate change can undermine 16 SDGs, while combatting climate change can reinforce all 17 SDGs but undermine efforts to achieve 12. Understanding these relationships requires wider and deeper interdisciplinary collaboration. Climate change and sustainable development governance should be better connected to maximize the effectiveness of action in both domains. The emergence around the world of new coordinating institutions and sustainable development planning represents promising progress

Impacts of past abrupt land change on local biodiversity globally

Abrupt land change, such as deforestation or agricultural intensification, is a key driver of biodiversity change. Following abrupt land change, local biodiversity often continues to be influenced through biotic lag effects. However, current understanding of how terrestrial biodiversity is impacted by past abrupt land changes is incomplete. Here we show that abrupt land change in the past continues to influence present species assemblages globally. We combine geographically and taxonomically broad data on local biodiversity with quantitative estimates of abrupt land change detected within time series of satellite imagery from 1982 to 2015. Species richness and abundance were 4.2% and 2% lower, respectively, and assemblage composition was altered at sites with an abrupt land change compared to unchanged sites, although impacts differed among taxonomic groups. Biodiversity recovered to levels comparable to unchanged sites after >10 years. Ignoring delayed impacts of abrupt land changes likely results in incomplete assessments of biodiversity change

Refining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetes

We aggregated coding variant data for 81,412 type 2 diabetes cases and 370,832 controls of diverse ancestry, identifying 40 coding variant association signals (P &lt; 2.2 × 10-7); of these, 16 map outside known risk-associated loci. We make two important observations. First, only five of these signals are driven by low-frequency variants: even for these, effect sizes are modest (odds ratio ≤1.29). Second, when we used large-scale genome-wide association data to fine-map the associated variants in their regional context, accounting for the global enrichment of complex trait associations in coding sequence, compelling evidence for coding variant causality was obtained for only 16 signals. At 13 others, the associated coding variants clearly represent 'false leads' with potential to generate erroneous mechanistic inference. Coding variant associations offer a direct route to biological insight for complex diseases and identification of validated therapeutic targets; however, appropriate mechanistic inference requires careful specification of their causal contribution to disease predisposition.</p

The trans-ancestral genomic architecture of glycemic traits

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 × 10−8), 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

Implicating genes, pleiotropy, and sexual dimorphism at blood lipid loci through multi-ancestry meta-analysis.

BACKGROUND: Genetic variants within nearly 1000 loci are known to contribute to modulation of blood lipid levels. However, the biological pathways underlying these associations are frequently unknown, limiting understanding of these findings and hindering downstream translational efforts such as drug target discovery. RESULTS: To expand our understanding of the underlying biological pathways and mechanisms controlling blood lipid levels, we leverage a large multi-ancestry meta-analysis (N = 1,654,960) of blood lipids to prioritize putative causal genes for 2286 lipid associations using six gene prediction approaches. Using phenome-wide association (PheWAS) scans, we identify relationships of genetically predicted lipid levels to other diseases and conditions. We confirm known pleiotropic associations with cardiovascular phenotypes and determine novel associations, notably with cholelithiasis risk. We perform sex-stratified GWAS meta-analysis of lipid levels and show that 3-5% of autosomal lipid-associated loci demonstrate sex-biased effects. Finally, we report 21 novel lipid loci identified on the X chromosome. Many of the sex-biased autosomal and X chromosome lipid loci show pleiotropic associations with sex hormones, emphasizing the role of hormone regulation in lipid metabolism. CONCLUSIONS: Taken together, our findings provide insights into the biological mechanisms through which associated variants lead to altered lipid levels and potentially cardiovascular disease risk

A saturated map of common genetic variants associated with human height

Common single-nucleotide polymorphisms (SNPs) are predicted to collectively explain 40-50% of phenotypic variation in human height, but identifying the specific variants and associated regions requires huge sample sizes(1). Here, using data from a genome-wide association study of 5.4 million individuals of diverse ancestries, we show that 12,111 independent SNPs that are significantly associated with height account for nearly all of the common SNP-based heritability. These SNPs are clustered within 7,209 non-overlapping genomic segments with a mean size of around 90 kb, covering about 21% of the genome. The density of independent associations varies across the genome and the regions of increased density are enriched for biologically relevant genes. In out-of-sample estimation and prediction, the 12,111 SNPs (or all SNPs in the HapMap 3 panel(2)) account for 40% (45%) of phenotypic variance in populations of European ancestry but only around 10-20% (14-24%) in populations of other ancestries. Effect sizes, associated regions and gene prioritization are similar across ancestries, indicating that reduced prediction accuracy is likely to be explained by linkage disequilibrium and differences in allele frequency within associated regions. Finally, we show that the relevant biological pathways are detectable with smaller sample sizes than are needed to implicate causal genes and variants. Overall, this study provides a comprehensive map of specific genomic regions that contain the vast majority of common height-associated variants. Although this map is saturated for populations of European ancestry, further research is needed to achieve equivalent saturation in other ancestries.A large genome-wide association study of more than 5 million individuals reveals that 12,111 single-nucleotide polymorphisms account for nearly all the heritability of height attributable to common genetic variants

A saturated map of common genetic variants associated with human height.

Common single-nucleotide polymorphisms (SNPs) are predicted to collectively explain 40-50% of phenotypic variation in human height, but identifying the specific variants and associated regions requires huge sample sizes1. Here, using data from a genome-wide association study of 5.4 million individuals of diverse ancestries, we show that 12,111 independent SNPs that are significantly associated with height account for nearly all of the common SNP-based heritability. These SNPs are clustered within 7,209 non-overlapping genomic segments with a mean size of around 90 kb, covering about 21% of the genome. The density of independent associations varies across the genome and the regions of increased density are enriched for biologically relevant genes. In out-of-sample estimation and prediction, the 12,111 SNPs (or all SNPs in the HapMap 3 panel2) account for 40% (45%) of phenotypic variance in populations of European ancestry but only around 10-20% (14-24%) in populations of other ancestries. Effect sizes, associated regions and gene prioritization are similar across ancestries, indicating that reduced prediction accuracy is likely to be explained by linkage disequilibrium and differences in allele frequency within associated regions. Finally, we show that the relevant biological pathways are detectable with smaller sample sizes than are needed to implicate causal genes and variants. Overall, this study provides a comprehensive map of specific genomic regions that contain the vast majority of common height-associated variants. Although this map is saturated for populations of European ancestry, further research is needed to achieve equivalent saturation in other ancestries

Collective foresight and intelligence for sustainability

Non-technical summary Charting robust pathways towards more sustainable futures that ‘leave no one behind’ requires that diverse communities engage in collective foresight and intelligence exercises to better understand global systemic challenges, anticipate the emerging risks and opportunities that disruptions present, and share perspectives on how to respond and inform decision-making. We report on the recent use of an international rapid foresight survey to assess expected societal trends over the next 3 years following the COVID-19 crisis. The results illustrate the power of collective foresight approaches to provide timely, nuanced insights for decision-making across sectors and scales, particularly in times of uncertainty. Technical summary We present the findings of a rapid foresight survey launched in spring 2020 to draw on the collective intelligence of the global community on where the world is headed post-COVID-19. Respondents were asked to (i) assess five key societal trends in the coming 3 years, (ii) provide news headlines they both expect and hope to see, and (iii) assess the role of digital technologies during crises. Analysis of over 2000 responses from more than 90 countries revealed important regional differences in expected societal trends related to sustainability. More respondents in the Global South expected shifts towards less inequality while more respondents in the Global North expected shifts towards a smaller ecological footprint. Qualitative analysis of proposed news headlines revealed four broad themes of focus (environment, equity, health, and economy), and yielded insights into perspectives on critical drivers of change. Finally, the survey report found that the vast majority of respondents were not opposed to digital surveillance in crises. In presenting these results, we explore the value of collective foresight and intelligence exercises in providing pluralistic inputs to decision-making and in complementing more prevalent methods of forecasting. Social media summary Collective foresight exercises with diverse communities can help chart robust pathways to more sustainable futures

Agricultural ecosystems and their services: the vanguard of sustainability?

Sustainable Development Goals offer an opportunity to improve human well-being while conserving natural resources. Ecosystem services highlight human well-being benefits ecosystems, including agricultural ecosystems, provides. Whereas agricultural systems produce the majority of our food, they drive significant environmental degradation. This tension between development and environmental conservation objectives is not an immutable outcome as agricultural systems are simultaneously dependents, and providers of ecosystem services. Recognizing this duality allows integration of environmental and development objectives and leverages agricultural ecosystem services for achieving sustainability targets. We propose a framework to operationalize ecosystem services and resilience-based interventions in agricultural landscapes and call for renewed efforts to apply resilience-based approaches to landscape management challenges and for refocusing ecosystem service research on human well-being outcomes

Ecosystem services and integrity trend

Turton, SM ORCiD: 0000-0001-6279-7682Ecosystems are dynamic complexes of plant, animal, and microorganism communities, interacting with the nonliving environment (soils, water, minerals, air) in the form of functional units. These functional units occupy a diverse range of scales in the environment. Ecosystem services may be defined as goods and services from ecosystem structures and functions such as food, fiber, and fuel and climate regulation. These services have also been described as nature’s contributions to people, implying that humans are passive and active recipients of these services but rarely pay for them in any monetary sense. Ecosystem integrity may be defined as the system’s capacity to maintain structure and ecosystem functions using processes and components characteristic for its particular eco-region, i.e., an area where there are similar geographical characteristics, such as geology, vegetation, and climate. Ecosystem services integrity trend refers to changes in ecosystem goods and services, their ecosystem structures and functions, and hence their ability to provide food, fiber, and fuel and regulate climate. Human activities are the main drivers of changes in trends in ecosystem services and hence their integrity trend at different spatial and temporal scales. Social-ecological systems are complex adaptive systems composed of many diverse human and non-human entities that interact; these inherently linked systems adapt to changes in their environment, and their environment changes as a result