Human milk contains all of the essential nutrients required by the infant within a complex matrix that enhances the bioavailability of many of those nutrients. In addition, human milk is a source of bioactive components, living cells and microbes that facilitate the transition to life outside the womb. Our ability to fully appreciate the importance of this matrix relies on the recognition of short- and long-term health benefits and, as highlighted in previous sections of this supplement, its ecology . Designing and interpreting studies to address this complexity depends on the availability of new tools and technologies that account for such complexity. Past efforts have often compared human milk to infant formula, which has provided some insight into the bioactivity of human milk, as a whole, or of individual milk components supplemented with formula. However, this experimental approach cannot capture the contributions of the individual components to the human milk ecology, the interaction between these components within the human milk matrix, or the significance of the matrix itself to enhance human milk bioactivity on outcomes of interest. This paper presents approaches to explore human milk as a biological system and the functional implications of that system and its components. Specifically, we discuss study design and data collection considerations and how emerging analytical technologies, bioinformatics, and systems biology approaches could be applied to advance our understanding of this critical aspect of human biology. The matrix of human milk refers to the nutrient and nonnutrient components of foods and their molecular relationships to each other (USDA).Text Box 1•In the context of this paper, “ecology” is defined as a complex biological system and its interactions with its environment. In this case, the complex system is human milk composition and its inherent biology, and the environment consists of parental and infant inputs and the influence of their respective internal and external environments.•With due recognition of the need to be observant of issues of gender identity/neutrality, and to improve precision, to the extent possible, for the purposes of the papers described herein, we will use gender neutral terminology where appropriate , to reflect the reality that not all who lactate identify as female. The term “lactating parent” respects and recognizes those who may have been born female but do not identify as such as well as other gender-relevant contingencies. In situations where reporting primary data , we will refer to the population as specified . Moreover, rather than using terms such as “maternal” or “maternal milk,” we will use the terms such as “birthing parent” throughout the report as appropriate as they accurately reflect the biological nature of the birthing parent–infant dyad.•1) breastmilk produced by a parent for their infant and fed directly to infants via the breast or expressed by the lactating parent and then fed to the infant; and 2) donor/banked human milk produced by lactating persons that is either donated to human milk banks or fed to infants other than their own child.“Human milk” refers to milk produced by lactating parents and includes both: Alt-text: Text Box 1The “Breastmilk Ecology: Genesis of Infant Nutrition (BEGIN)” Project was designed to: 1) parental factors affecting human milk production and composition; 2) the components of human milk and the interactions of those components within this complex biological system; 3) infant factors affecting the matrix, emphasizing the bidirectional relationships associated with the breastfeeding dyad; 4) the application of existing and new technologies and methodologies to study human milk as a complex biological system; and 5) approaches to translation and implementation of new knowledge to support safe and efficacious infant feeding practices. This paper represents the results of the deliberations of WG 4.The overarching conceptual framework and description of the Project is presented in the BEGIN Executive Summary, the first of 6 manuscripts of this supplement. The subsequent manuscripts in this supplement present the findings of the individual thematic BEGIN Working Groups (WGs) as a continuum of thought that reflects a larger conceptual view of how we can move this important research and public health agenda forward . SpecifiThe key concept underlying the BEGIN Project is that human milk exists as a biological system that reflects both internal (lactating parent) and external  influences ,6. To adMany challenges exist in delineating the functional implications of human milk as a biological system, including the inherent complexity of human milk, the limited availability of noninvasive techniques suitable for use in infants, and the historical lack of application of systems biology approaches to human milk and lactation research. Studies of human milk bioactivity have largely focused on breastfeeding practices , single human milk components, or a limited number of outcomes. These studies fail to account for the full biological potential of the human milk matrix, as viewed within the context of the “nourish, protect, and communicate” paradigm outlined by WG 2 [Text Box 2Section 1Human milk and infant feeding on outcomes of term infantsSection 2Human milk components, alone or in combination on outcomes of term infantsSection 3Experimental framework and analytical approaches to study human milk as a complex biological system in term infantsAlt-text: Text Box 2The task of BEGIN WG 4 was to identify approaches to integrate the interactions of the lactating parent–human milk–infant triad. WG4 explored how emerging analytical technologies, bioinformatics and systems biology approaches could be used to advance our understanding of this critical aspect of human biology .Text BoxText Box 3•Physical growth and body composition•Endocrine development•Neurocognitive development•Intestinal development and microbiome composition•Immune system development and tolerance•MetabolomeAlt-text: Text Box 3In this section, we summarize key findings and limitations of available evidence linking human milk composition and feeding practices to infant outcomes, which were selected based on the potential for long-term programming by dietary intake during early postnatal life see . A commowithin the matrix of human milk composition impacts infant outcomes. First, evaluating dietary interventions to the lactating parent to modify one or more components within human milk. Second, studying triads experiencing environmental conditions that could modify human milk composition. Lastly, investigating genetic differences in the lactating parent or offspring, for example secretor status and human milk oligosaccharide (HMO) content and composition.Furthermore, the forms and concentrations of nutrients and the matrix in which they exist differ markedly between human milk and infant formula. Three main experimental approaches have been used to study how variability of one or more components In Section 1, we summarize the evidence regarding human milk feeding and its impact on key functional outcomes . Growth Text Box 4•Heterogeneity in outcomes and definitions of infant feeding practices •Inconsistent reporting of nutrient composition and volume of human milk consumed•z-scores, weight velocity), andInconsistent growth outcomes reported  birth cohort study revealed an inverse association between breastfeeding and weight gain velocity/BMI in a dose-dependent manner, which was diminished when human milk was fed from a bottle and when human milk was combined with formula supplementation ,100. ThiBody composition outcomes with breastfeeding are less well studied , and onlA meta-analysis of data from low- and middle-income countries reported that supplementation with multiple micronutrients during lactation did not improve infant length, stunting, or head circumference . SimilarThe most prominent example of the examination of genetic variations on human milk composition have focused on variation in genes for encoding for secretor and Lewis phenotypes influencing HMO content and composition . AssociaIn summary, future prospective cohort studies should incorporate robust study design, with standardized and frequent measurements of growth and body composition, measurement of human milk composition and intake, and documentation of pertinent parental and infant co-variates to uncover underlying mechanisms whereby human milk and/or breastfeeding influence infant growth. The extant data is limited in assessing the impact of exclusivity or duration of human milk feeding, parental supplementation or that of genetic differences on the human milk matrix.Neurodevelopment is exquisitely sensitive to early life dietary and environmental exposures and has been an active area of research . Some 11,112, buThe effect of LC-PUFA supplementation in the lactating parent, either as fish oil or as purified forms of DHA, EPA, and/or AA on infant neurocognitive development has been investigated. Meta-analyses of randomized control trials have not shown associations between the consumption of fish oil or DHA/EPA supplements in breastfeeding females and the cognitive performance of their children ,14. A syFew studies have investigated how human milk feeding affects endocrine development, other than for risk of type 1 and type 2 diabetes. Updated systematic reviews and meta-analyses suggest that breastfeeding reduces the odds for the development of type 2 diabetes by 33% . For typThe intestinal tract is functionally immature and immunologically naïve at birth  and undeThe immune system is also immature at birth as evidenced by incomplete physical and chemical barriers, poor innate effector cell function, limited and delayed secretory immunoglobulin A (IgA) production, underdeveloped complement cascade function, and insufficient anti-inflammatory mechanisms of the intestinal and respiratory tracts . As reviThe impact of human milk consumption on clinical immune outcomes  is summarized in Most of the available evidence describing relationships between shorter or longer periods of exposure to human milk, or naturally occurring low versus high levels of human milk components, and outcomes such as IgE development, humoral immunity, and food allergy is limited to observational studies in humans, which are suitable only for suggesting association, not for determining causation. In addition, the evidence is difficult to interpret due to application of varying definitions of breastfeeding and allergic disease, potential for reverse causality, insufficient power for or lack of reporting of specific atopic disease outcomes, and the potential confounding effect of gene-environment interactions.Metabolomics has emerged as an important tool to investigate how infant diet (human milk vs. infant formula with or without added bioactive components) impacts serum, fecal and urinary metabolites . Human mThere are multiple microbial ecologies that influence human health and development. While less is known about several of these microbiomes , emerging evidence indicates a significant role of infant feeding in the development and health of the gut microbiome. The gut microbiome is established over the first 2 to 3 y of life through sequential phases that are influenced by numerous factors, most notably route of delivery and form of nutrition ,130. MicLarge longitudinal cohort studies show that breastfeeding duration and exclusivity influence infant gut microbiome composition and function, with exclusive human milk feeding establishing a less diverse and different microbiota that can be disrupted by formula feeding, particularly in the early postpartum period ,75. In aExisting clinical and epidemiological evidence support improved health and developmental outcomes for infants fed human milk. To date, there is limited research that has explored the full ecology of human milk including critical aspects of the triadic relationships highlighted by WG 1-3 , and theIn addition to the types of studies reviewed in Section 1 that have explored the impact of human milk on specific functional outcomes, considerable effort has gone into the study of specific constituents of human milk on infant health and development. While these studies have rarely explored the intersection of the human milk ecology and the triad, they have provided some potential avenues for pursuit of a deeper understanding of that intersection. The following brief review explores some examples of milk constituents and the most common approaches that have been used to evaluate their impact on infant health and development.A common approach to the exploration of the impact of milk constituents has been the use of analogs of human milk bioactives isolated from bovine milk or synthesized to study their physiological functions in preclinical models and human infants. Among the most studied are HMO, milk fat globule membrane (MFGM), osteopontin (OPN) and LC-PUFA . Summariin vivo, none of these studies recapitulate the composition or complexity of oligosaccharide structures present in human milk.Select HMOs tested in RCTs were well-tolerated and supported age-appropriate growth in term infants . Two RCTBifidobacterium, as well as lower succinate, butyrate, propionate, and 5-aminovalerate, and Escherichia. By univariate analysis, infants experiencing no bronchitis or LRTI showed higher acetate and B. longum subsp. infantis [Machine learning–based classification tools have been used to investigate mechanistic links between fecal microbiome, metabolome, and gut health markers of infants in the 2′FL + LNnT trial  who expeinfantis . Of releLactoferrin (LF), a multifunctional, iron-binding protein, comprises approximately 15% to 20% of total protein in human milk , and is Fats in human milk are packaged within a 3-layered membrane, or MFGM, which contains anti-infective proteins, carbohydrates , and lipids (gangliosides and cholesterol), the latter of which are purported to be involved in neurodevelopment . ReadersMoraxella catarrhalis, a microorganism commonly linked to otitis media, providing a potential mechanism of action [Formula contains very little or no MFGM as defatted bovine milk is used as the starting material. RCT have investigated supplementation of formula with bovine milk MFGM, which is not identical to human milk MFGM, but contains similar components and at similar levels ,159 Tab. The addf action . There wf action . At 4 anf action  and seruf action ,69 and sf action  differedf action . Furtherf action .As with the previous components discussed in this section, while there is evidence to suggest a functional impact of the MFGM on infant related outcomes, no studies were found that explored the nature of the triadic relationships on MFGM content or functional impact. Furthermore, a need exists to apply the ecological systems approach advocated throughout the BEGIN WG reports to advance our understanding of the biology of the MFGM not just as a component of human milk, but as a unique biological system in and of itself.OPN is abundant in human milk, but is low in cow’s milk and, consequently, infant formula ,165. OPNAn RCT studied 1- to 6-month-old infants who were fed standard formula, formula with bovine OPN added at half the human milk level (65 mg/L) or formula with the same level as in human milk (130 mg/L) and compared these groups with a BF group . InfantsThe evidence of the important immunomodulatory role of OPN and the potential impact of the response of both the parent and infant to infection reveals both an intimate relationship between the triad and OPN content, as well as the potential importance of the compound in our response to current and emerging infectious diseases.LC-PUFAs are the most studied functional human milk components, with AA, DHA, and EPA being the most commonly studied n-6 LC-PUFAs and n-3 LC-PUFAs, respectively. Over 40 y ago, dietary LC-PUFA intake by the lactating parent was shown to affect human milk. LC-PUFA concentration  and humaThe DHA intake and measurement of neural development (DIAMOND) study is the only dose-response RCT of DHA and AA that investigated the addition of AA  and 4 doses of DHA  to infant formula, which was fed for the first year of life . In thisHowever, 2 recent systematic reviews found no benefit of LC-PUFA supplementation on longer-term cognitive outcomes ,184. In Differences in growth and body composition in response to LC-PUFA supplementation have been reported in preterm and term infants. Preterm infants fed formula with DHA and AA had increased lean mass and reduced fat mass compared with infants fed formula without LC-PUFA . A folloLastly, the influence of LC-PUFA supplementation on infant immune development has received considerable attention. In some studies, infants fed formulas with both DHA and AA, exhibited less skin and respiratory allergic diseases in childhood compared with those fed formulas without LC-PUFA . An analThe value of systems approach with specific regard to these compounds is that questions remain with regard to need for and value of LC-PUFA at specific times in infant development, e.g., the value of supplementation to preterm vs term infants. A systems approach to the triad would shed light on not only the biology but the metabolism and chronobiology of these compounds to determine the role of parental supply and critical periods of need for the infant.within the infant as development of one system in the infant  can impact infant immune, gut, or brain development , which creates and perpetuates imbalance for field sites. Shifting this imbalance must be a priority in the design of future human milk studies. Specifically greater attention, understanding, and funding need to be allocated for utilities such as electricity to facilitate long-term sample storage at LMIC field sites, clinics, and hospitals, as well as infrastructure to support data capture and development of local bioinformatics cores. Intellectual property must be shared with LMIC collaborators in addition to equal acknowledgment for their contributions. Compensation for researchers and staff in LMIC environs is generally budgeted lower than that of HIC counterparts; this perpetuates the misconception that there is a gap in research capacity. Highly skilled researchers are present in LMIC settings, it is a matter of HIC collaborators recognizing that the capacity exists and budgeting appropriately to retain this expertise. Travel for field site visits and conference presentations is often budgeted for HIC research teams; however, LMIC counterparts rarely receive funding in these areas. Trainee exchange programs must be encouraged, as well as financial support for field site investigators and research teams to present findings in their own right rather than the HIC PI presenting on their behalf.The challenge of studying the complexity of human milk and its ecology will require cross-disciplinary teams of investigators representing multiple disciplines  working together in an integrated approach . While sIn addition to the collection of human milk itself , was a preservative added? Alt-text: Text Box 6In addition, how the human milk sample was collected is a crucial design component as numerous factors can influence accuracy of milk composition. These factors are described in detail elsewhere ,207,208.In addition to the coverage of human milk components offered by WG 2, readers are referred to the recent book summarizing milk sampling and nutrient and macromolecule analysis . It is iThe previous sections illustrate the myriad of internal and external ecologies that may affect human milk composition and its impact on the lactating parent–infant dyad. Due to this complexity, outcome-based studies on a population level often only cover very specific aspects of human milk ecology. At the same time, recent advances in high-throughput technologies have provided access to multidomain and multi-omic data, enabling unprecedented insights into complex biological systems from multiple viewpoints such as the microbiome, proteome or metabolome, as well as a wide range of other omics . Such teText Box 7•Large numbers of measurements across several omics technologies per sample, while the number of samples is small (commonly machine learning models are trained and optimized on datasets with many more samples than measurements)•Different omics techniques are highly heterogeneous in individual characteristics and number of features . In particular, sparse data with many undefined values require different processing and model imperatives than data with dense input, and•Omics data of high dimensionality, but with low information content, may preclude information from smaller, more dense omics to be included in a model. Stack generalization has previously been utilized successfully to address this challenge•Omics data from various study sites may be impacted by a combination of genetic, environmental, and technical factors. Careful use of machine learning algorithms can enable the development of generalizable models despite these variationsAlt-text: Text Box 7In contrast to population level approaches, multi-omic studies typically exhibit different data characteristics and challenges . High-thWhile integrating multiple omic datasets is challenging, it has been demonstrated to improve the capacities of models for biological processes in the context of the lactating parent–infant dyad, particularly as they relate to pregnancy outcomes. For example, several studies illustrate that combining the information from a wide variety of omics—including transcriptome, or proteome, and more—can be used to model gestational age , as wellApplying and adjusting these methods to studying human milk holds great potential to uncover the complex processes involved in the triad from a systems biology viewpoint. A particular challenge in this context is to integrate population level studies with multi-omics approaches due to the demanding collection process of synchronized omic measurements as well as the technical aspects of integrating omics with nonomics records, such as clinical meta data ,228. HowHuman milk and feeding within the breastfeeding dyad influence infant development as well as various clinical/functional outcomes. These effects are highly interrelated and may point toward intertwined underlying processes. This interrelatedness can be studied and can be exploited by using novel concepts from machine learning in the field of multitask learning . MultitaIn summary, recent advances in machine learning, including multimodal learning, multi-view representation learning ,236 and Prevotellaceae, Veillonellaceae, and Micrococcaceae cluster. Development of atopic disease in early childhood was more common in urban lifestyle and associated with lower levels of total IgA and IgA2 to dust mite, as well as of thymic stromal lymphopoietin. Thus, external ecologies  were shown to be strong regulators of immune and metabolic factors derived from the lactating parent, which may have downstream implications for postnatal developmental programming of infant’s gut microbiome and immune system [Recently, hierarchial cluster analyses have been used to assess correlations between immune factors in human milk showing that those factors  that were correlated with infant cow’s milk allergy as an outcome were actually independent of maternal atopy . A folloe system .In the BEGIN Project Executive Summary , lactatiStreptococcus spp. and Veillonella dispar) were observed within mother–infant dyads, which was reduced when infants received pumped human milk in a bottle [2adj). While this is a low percentage, it was greater than that explained by breastfeeding exclusivity (0.76%), birth mode (0.87%), intrapartum antibiotics (0.72%), or older siblings (0.58%) [In the CHILD cohort, co-occurrence of specific bacteria in human milk and infant feces (including a bottle . The com (0.58%) . Multiva (0.58%) . As migh (0.58%) . Further (0.58%) . Another (0.58%) . Taken tWhile there are notable challenges in the study of human milk as a complex biological system, emerging scientific and analytical advances are uncovering unprecedented opportunities to study the biology of human milk and uncover the internal and external ecologies that drive outcomes observed in the breastfeeding triad over time and across a myriad of conditions. Considerations for the design of human studies of these complex relationships, including the integration of new approaches in multi-omic data collection, computational systems biology and approaches to the use of large complex datasets, will be important to advance the field of human milk and lactation research. Unprecedented opportunities await the field to uncover new mechanistic interactions between the triad of the human milk biological system. The challenges remain great, but the return will be greater.There was no committed funding for the writing of this manuscript for any author. However, during the period of manuscript preparation authors were funded from the following sources: AA ; NA ; MBA  Humans and the Microbiome Program. Research funding from the Canadian Institutes of Health Research, Research Manitoba, the Canada Foundation for Innovation, the Bill and Melinda Gates Foundation, the Manitoba Children's Hospital Foundation, Prolacta Biosciences, Mitacs, CIFAR, the GarfieldWeston Foundation, Health Data Research UK, and Canadian COVID Immunity Task Force); MB ; SEC ; SMD ; KJ ; WL ; BL (ByHeart); CS .Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) of the NIH in partnership with the Bill & Melinda Gates Foundation and the Academy of Nutrition and Dietetics. The publication of this supplement was made possible by the NICHD, and support for assistance  with editing, proofing, and submitting the manuscript was also provided by the NICHD.The BEGIN Project was initiated by the Pediatric Growth and Nutrition Branch of the The authors acknowledge contributions by Natalie Rodriguez MBA, ACC, CCIP, Director of Operations, Diversity & Inclusion - THRiVE Discovery Lab at Children’s Hospital Research Institute of Manitoba for her contributions on how diversity, equity and inclusion can be applied to human milk research. We also acknowledge the role of BioCentric, Inc.  and its staff  in editing and formatting the manuscript in accordance with the journal style, and assisting with the manuscript submission process.The contributions of the authors were as follows—DJR conceptualized the project; all authors contributed to writing the manuscript: Introduction led by DJR and SMD, Section 1 led by AA, SEC, SMD, KMJ, WL, and CMS; Section 2 led by SEC, SMD, BL, and CMS; Section 3 led by NA, MBA, MB and SMD. SMD, DJR and AS prepared the final draft of the manuscript, which was read and approved by all authors.All authors have declared no conflicts of interest related to existing financial arrangements with any company or organization sponsoring this manuscript. (AS is an employee of the Academy of Nutrition and Dietetics).


In the growing aquaculture sector, more concerns have arisen regarding health management in farmed fish. Geographic information systems (GISs) dedicated to storing, collecting, retrieving, transforming, and displaying large sets of spatial data may help enhance disease control and surveillance, especially during outbreaks. To shed light on the actual implementation of GISs for health management in farmed fish in marine and freshwater environments, this scoping review analysed 54 relevant retained records from a base of over 1000 papers in the scientific literature published between 2010 and 2022. What stands out from this literature analysis is that these remote technologies are most successfully adopted in the highly valued salmonid farming industry of northern Europe and Chile. Furthermore, implementing such remote technologies and related disease mapping seemed particularly effective in controlling viral outbreaks. This review highlighted the gap between the potential of these smart GIS tools and the actual benefits accrued for disease control and surveillance when applied to farmed aquatic animals.GIS) are used in farmed fish health management and to unveil the main GIS technologies, databases, and functions used to update the spatiotemporal data underpinning risk and predictive models in aquatic surveillance programmes. After filtering for eligibility criteria, the literature search provided 54 records, highlighting the limited use of GIS technologies for disease prevention and control, as well as the prevalence of GIS application in marine salmonid farming, especially for viruses and parasitic diseases typically associated with these species. The text mining generated five main research areas, underlining a limited range of investigated species, rearing environments, and diseases, as well as highlighting the lack of GIS-based methodologies at the core of such publications. This scoping review provides a source of information for future more detailed literature analyses and outcomes to support the development of geospatial disease spread models and expand in-field GIS technologies for the prevention and mitigation of fish disease epidemics.Finfish aquaculture, one of the fastest growing intensive sectors worldwide, is threatened by numerous transmissible diseases that may have devastating impacts on its economic sustainability. This review (2010–2022) used a PRISMA extension for scoping reviews and a text mining approach to explore the extent to which geographical information systems ( Aquaculture is one of the fastest growing food production sectors in the world , and itsSurveillance is paramount for controlling animal diseases transmitted via water, animals, animal products, biological materials, and other vectors, such as wild populations, where diseases that do not affect wild hosts can be problematic if transferred to an aquaculture setting . To ensuIn this context, many veterinary institutions and national fisheries have been suggesting a set of primary strategies to prevent, control, and reduce infectious outbreaks. This is especially true in salmonid farming, where great emphasis is on preventative measures, disease surveillance, control, and research to reduce the incidence of animal diseases and minimise the impact of their outbreaks . As showGIS) technologies have proved to be reliable in modelling the spatiotemporal risk of exposure to disease, assessing the probability of disease transmission, and controlling the spread of outbreaks [To support the development of risk-based monitoring and surveillance systems to ensure optimal animal health management in the veterinary domain, geographic information system  extension for scoping reviews (PRISMA-ScR), including only primary research studies published between 2010 and 2022, available in full text, and written in English [Aquatic environments ;WOAH) [Diseases  , i.e., dEpidemiological issues ;GIS issues .The protocol used for this scoping review was drafted using the preferred reporting items for systematic reviews and meta-analysis  OR  OR (cartograph*) OR (map*) OR (geographic*)) AND (aquaculture) AND ((fish AND disease*)) AND ((surveillance) OR (epidemiolog*) OR (monitoring) OR (disease AND response))) AND (((gis) OR  OR (cartograph*) OR (map*) OR (geographic*)) AND ((wild AND fish) AND (aquaculture))).Does the title/abstract make reference to GIS technologies/applications, spatial analysis, or mapping procedures?Does the title/abstract make reference to the type of aquaculture farms or wild fish populations of interest for this review?The screening and final selection of eligible references were performed over two subsequent phases. The first screening phase was conducted by two authors to verify whether the titles and abstracts fell within the topics of this scoping review. This first screening phase was based on the following questions:Does the study describe an application of GIS technology within the aquaculture sector alone, in aquaculture sector interactions with the wild fish population, or in environmental issues that can affect farmed fish health?Does the study describe the support activities of veterinary services or farmed fish health services?Does the study describe surveillance activities or epidemiological investigation/analysis in the aquaculture sector alone or in aquaculture sector interactions with the wild fish population?For submission to the second screening phase, both questions had to be answered positively by both screening authors, and disagreements were resolved by a third author. Then, the second screening phase was conducted by the first two authors, who analysed the full-text articles to evaluate the objectives and methods of the studies. In this second screening phase, the questions to be answered were as follows:At least one question had to be answered positively by both screening authors to match the objectives and methods of this scoping review and, hence, to be submitted for final evaluation. During this phase, articles with unclear purposes or with experimental methods not comprehensive enough to answer the above-mentioned questions were rejected. Disagreements between the two screening authors were resolved by the same third author.Bibliographic ;Country/location (where the studies were conducted or to which they referred);Purpose of the study ;Demographic ;Epidemiological and surveillance ;GIS application: purposes of GIS use ;GIS methodology: operations and functions to elaborate data ;Type of analysis: visualisation , geostatistics estimation , clusterisation ;0, logistic regression);Modelling: statistical or mathematical models .The data charting process was carried out using a data-charting form (Excel spreadsheet) previously developed for the extraction of data from the selected studies. Subsequently, the following details, information, and data were extracted from all the selected references:Based on the extracted data, a descriptive analysis of the selected references was performed, and the main study characteristics were described.TF-IDF) technique was applied to weight the number of times a word stem appeared across titles, abstracts, and keywords of the selected records. A research area modelling analysis was carried out using latent Dirichlet allocation (LDA) to generate research theme representations according to the frequency distribution of word stems within titles, abstracts, and keywords via an iterative probabilistic process using a Gibbs sampling option of the research area models package in R. The five generated research themes based on the ten most probable word stems were presented as an unstructured set of word stems using histograms, where every bar representing each word stem is proportional to the probability  of finding it in a theme [Text mining and research area modelling were performed using the R package . A document-term matrix was calculated. A term frequency-inverse document frequency ( a theme .The data items described in The flow of the selection of the references via the screening phases is described in n = 18), epidemiological examinations (n = 9), descriptive investigations (n = 10), disease spread (n = 7), environmental issues (n = 6), and surveillance and biosecurity plans (n = 4).The yearly and geographical distribution of the included and analysed references are displayed in panels (a) and (b) of n = 19), buffering (n = 16), Euclidean distance calculation (n = 10), nearest neighbour search (n = 5), point in polygon (n = 4), map algebra (n = 5), and centroid calculation (n = 4). Less frequently used methodologies included the following: overlay analysis, area calculation, Haversine distance, spatial filtering, classification, basic cartography, and generalisation. Regarding the algorithm component, it was possible to identify a modelling approach in most (n = 42) papers. Image representations and spatial analysis were performed using the R-software (n = 7) or ArcGIS (n = 7) alone or in combination with other software, such as QGIS, GIMP, GoogleEarth, WinBugs, ERDAS, Satscan, and STATA. Less frequently used software packages were as follows: Acolite, CrimeStat, Python, Ecosim, Gedis, SAS, ARC CALC, and OpenGeoDa. However, 14 references did not explicitly indicate the software used to represent the data or to perform the analysis. The most frequently studied fish species was Atlantic salmon ; a few other references concerned a single species, such as rainbow trout (n = 2), sea trout (n = 2), Atlantic cod (n = 1), European sea bream (n = 1), and topmouth gudgeon (n = 1). The remaining 15 references included more species or referred to fish generically. The most frequent fish diseases or issues were sea lice, infectious salmon anaemia virus (ISAV), and virus-induced pancreas disease . An additional 19 publications referred to other, less common, specific viral, bacterial, or parasitic diseases or a generic risk of disease. Meanwhile, 11 studies focused on the health risks from interactions between farmed and wild populations within a restricted intensive farming area in both marine and freshwater environments. Most (n = 39) of the studies were performed exclusively on marine environments, while fewer papers were based on freshwater environments (n = 9) or a mix of marine and/or freshwater and/or transitional environments (n = 6). Most papers used coastline background information in the maps (n = 34), along with other data, such as bathymetry, sea regions, sea depth, ports, and harbours. Other background mapping information included administrative division, type of seabed, hydrography, rivers, inland facilities, sea currents, riparian zones, and coral reefs , sav (0.99), isav (0.97), sea (0.91), coral (0.66), salmon (0.63), reef (0.62), chile (0.61), cage (0.60), and temperature (0.55). As reported in This present scoping review aimed to investigate the current implementation of GIS technologies in original research papers on farmed and wild fish when interactions between reared stock and surrounding free-living aquatic populations occurred. Across the analysed references, it was possible to verify the actual use of GIS, mainly coupled with spatial and network-based risk models and assessments, aiming to enhance insights into disease spread dynamics and the magnitude and duration of the related epidemiological phenomena. Furthermore, scenario modelling of spatiotemporal data within a GIS was seen as a useful approach for the development of risk-based monitoring and surveillance systems to support the implementation of preventative measures for veterinary and health services in aquatic animal health management.The restricted number of included papers  confirmsThe meta-analysis of the selected references indicated that the current use of GIS technologies is clustered in terms of country, species, disease, and type of environment of application. The vast majority of the studies were conducted on the European and American continents, with Norwegian and Chilean research , and assThe powerful capabilities of GIS have been demonstrated in coastal and marine resource management  . HoweverConsidering the associations between results on farming locations and environments, as well as diseases investigated, marine sites dedicated to host cages for the production of salmonids seemed to be an ideal setting for the application of GIS tools ,44. IndeIt should also be noted that, through this meta-analysis of the literature, we noted that innovative raster techniques  seemed to be used to a limited extent in fish health management, while rasterised spatial data and their integration with remotely sensed data can be exploited to fulfil the vector-based solutions and develop a comprehensive approach in spatial data analysis. This is especially suitable in aquaculture, where the physical medium is a spatial environment in which natural phenomena change continuously. Consideration of the reasons behind the reduced application of innovative GIS technologies in current aquatic veterinary medicine, especially when raster-based approaches are preferred over vector-based approaches, is beyond the scope of this review. Nonetheless, we should recognise that improvements in the quality and resolution of satellite raster data and advances in computing processing performance are leading to rapid progress in these applications.IDW) as interpolation methods to construct risk maps for bacteriosis, trichodiniasis, and girodactylosis in tilapia and rainbow trout farms over three years, highlighting that IDW was more adequate as a prediction method [HSMI), including many risk factors, such as cohort indexes, infection pressure, and a polynomial regression line of the geo-index position of a group of marine farms located along the Norwegian coastline [AIS), which provide detailed positioning data in real time, were used to develop a model to simulate pancreatic disease spread in marine farms arising from vessel movements and associated farm seaway distance [The use of GIS as a primary operational tool for the surveillance, monitoring, and early detection of changes in the incidence of infectious diseases is explored and described in a very limited number of papers, e.g., Mardones et al., who applied such tools to assess the time–space clustering of disease outbreaks  . Based on method . A logisoastline . A furthoastline . Similardistance . Anotherdistance .Despite the numerous potential implementations of GIS for farmed fish health management, this literature analysis also showcased that the stakeholders operating in the sector lack both practical and theoretical knowledge and familiarisation with the available GIS-based tools and their applications. GIS training and access to standardised sources of information should be boosted, together with the consistent and timely collection of useful surveillance data through adequate monitoring infrastructures . As an example, management and prevention of disease introduction via water or other transboundary vectors rely on surveillance systems for data collection, recording, and control, especially to ensure the sustainability and social acceptance of salmon aquaculture .The synergistic contribution given by integrating text mining within a scoping review proved to be effective in grasping the current multidimensional usage of GIS technologies applied for disease surveillance and response in farmed fish health management. As shown in This scoping review unveiled unrealised opportunities for the use of GIS technologies in supporting fish health management. There are only few studies whose output is the investigation of large-scale spatial distributions and long-term temporal  evolution of fish diseases to perform models for outbreak simulation in real time to assess the probability of disease transmission. Such investigations would be essential for developing fish health management strategies based on robust surveillance plans and applications dedicated to integrating farm biosecurity. Moreover, this may lead to the promotion of both sustainable aquaculture operations and other regional businesses that share the aquatic environments .This scoping review analysed and described a literature text corpus surrounding the actual implementation of GIS technologies over the last decade to support farmed fish health management, including suggestions for risk assessment and predictive modelling techniques that could enhance epidemiological and surveillance programmes. The limited number of records included in the text corpus is due to the intentionally strict selection of keywords and eligibility criteria applied during the literature search, as well as the exclusion of grey technical literature that may report applications of GIS technologies not considered in the bibliographical repositories used. The main conclusion is that intensive aquaculture systems and highly valued food products are the discriminant factors justifying the use of GISs for health monitoring and enhancing the effectiveness of epidemiological models, especially for transmissible diseases that can undermine production efficiency in aquaculture. The consistently low publication rate confirms that the high investment and advanced knowledge required for applying GIS technologies puts them beyond reach for use in other low-input aquaculture systems. Both the PRISMA approach and the text mining analysis highlighted that GIS technologies are mostly employed in research activities for the visual delineation of disease areas, as well as tracking interactions between farmed and wild fish populations or farmed fish and environmental biota, rather than for implementing risk factor analysis and modelling disease in actual surveillance programmes. A gap appeared to emerge in operative research promoting GIS implementation to interlink farmed fish health with the surrounding ecosystem, making the most of up-to-date environmental, ecological, and epidemiological inputs  to map, prevent, and control outbreaks and the spread of disease.According to this literature analysis, the application of GIS to support surveillance and disease response in aquaculture still seems focused on mapping study areas, outbreak distributions, and plotting outputs of statistical or mathematical modelling. Despite the potential of GIS-based tools to develop analytical methods, data models, and cartographic and visualisation solutions, the aquaculture stakeholders appear to need training and familiarisation with these digital technologies and statistical modelling. Hence, the identification of barriers limiting GIS exploitation in surveillance and disease response can help establish theoretical knowledge and technical expertise that should be developed and disseminated for fish rearing. Therefore, future research design should employ GIS tools and techniques to facilitate the update and implementation of large operational and randomised geospatial datasets and to enhance the understanding of connectivity and spread of infectious agents during fish disease epidemics. This challenge could be achieved by enhancing network-based and geostatistical disease spread models with associated environmental data to investigate transmission dynamics and the magnitude and duration of epidemics more realistically. This would enable scenario modelling to underpin the development of risk-based monitoring and robust surveillance systems for the timely notification of disease outbreaks in support of farmed fish health management.


Our genomes influence nearly every aspect of human biology from molecular and cellular functions to phenotypes in health and disease. Human genetics studies have now associated hundreds of thousands of differences in our DNA sequence (“genomic variation”) with disease risk and other phenotypes, many of which could reveal novel mechanisms of human biology and uncover the basis of genetic predispositions to diseases, thereby guiding the development of new diagnostics and therapeutics. Yet, understanding how genomic variation alters genome function to influence phenotype has proven challenging. To unlock these insights, we need a systematic and comprehensive catalog of genome function and the molecular and cellular effects of genomic variants. Toward this goal, the Impact of Genomic Variation on Function (IGVF) Consortium will combine approaches in single-cell mapping, genomic perturbations, and predictive modeling to investigate the relationships among genomic variation, genome function, and phenotypes. Through systematic comparisons and benchmarking of experimental and computational methods, we aim to create maps across hundreds of cell types and states describing how coding variants alter protein activity, how noncoding variants change the regulation of gene expression, and how both coding and noncoding variants may connect through gene regulatory and protein interaction networks. These experimental data, computational predictions, and accompanying standards and pipelines will be integrated into an open resource that will catalyze community efforts to explore genome function and the impact of genetic variation on human biology and disease across populations. Exome and genome sequencing studies have already observed hundreds of millions of genomic variants, including single-nucleotide variants (SNVs), small insertions and deletions (indels), and larger structural variants  to influence organismal phenotype . At a mo16; mapping associations between genomic variants and effects on gene or protein expression across dozens of human tissues18; profiling hundreds of cell types and states through single-cell measurements of gene expression20; applying saturation mutagenesis to analyze coding variants in selected disease genes23; and characterizing how genes and proteins interact genetically or physically in molecular networks26. These and other studies have also demonstrated how mapping the impacts of genomic variation on genome function can reveal molecular mechanisms in human biology and disease, guide genetic diagnosis and clinical management, and facilitate the development of novel therapeutics (28). In instances when disease mechanisms have been revealed, there are often accompanying advances in understanding basic biology with far-reaching benefits beyond the specific disease of study.Previous and ongoing efforts have produced breakthroughs in mapping various aspects of genome function, including locating and annotating millions of noncoding regulatory elements in the human genome29, and genetic diagnosis for rare diseases continues to be hindered by the preponderance of variants of uncertain significance (VUS)30. New coordinated research activities will be needed to address the scale and scope of these challenges and thereby unlock the vast unrealized potential for understanding human biology and for improving human health32.Yet, connecting genomic variants to functions and phenotypes continues to prove challenging, and numerous obstacles have blocked rapid progress. The sheer number of genomic variants, both those we have observed already and those we might observe in the future, is immense, and we lack any perturbation-based data for most variants. Due to linkage disequilibrium, most genetic associations with common diseases contain many candidate variants, and the variant(s) that causally affect disease risk are unknown. The vast space of possible molecular and cellular effects has been challenging to study systematically: for example, coding variants might affect protein function via effects on stability, localization, or interactions with proteins; noncoding variants might affect gene expression through effects on transcription factor binding, chromatin state, and regulatory interactions; and genes and proteins might impact cellular processes through diverse mechanisms involving gene regulatory networks, signaling pathways, protein complexes, and other interactions. Genomic variants, elements, and networks can also have highly cell-type or context-dependent activities, yielding additional complexity given the large number of cell types in the human body. Finally, while previous efforts have largely focused on individual layers of genome function, such as studying coding variants or annotating noncoding regulatory elements, understanding the impact of genomic variation on phenotypes and disease may require a more holistic, integrative understanding of genome function that connects molecular to cellular to physiological processes. Due to these and other challenges, the molecular mechanisms underlying many genetic associations for common diseases remain to be establishedWith these challenges in mind, the National Human Genome Research Institute (NHGRI) launched the IGVF Consortium in 2021, with the goal of developing a systematic understanding of the effects of genomic variation on genome function and how these effects shape phenotypes. The Consortium consists of >120 laboratories collaborating on five key activities: (i) Mapping Centers, to analyze regulatory element and gene activity at single-cell resolution across hundreds of cell types; (ii) Functional Characterization Centers, to systematically characterize the molecular and cellular effects of introducing variants or perturbing elements and genes; (iii) Predictive Modeling Projects, to develop and apply computational approaches to comprehensively model the impact of genomic variation on genome function and guide experimental design; (iv) Regulatory Network Projects, to advance network-level understanding of the influence of genetic variation and genome function on cellular and organismal phenotypes; and (v) Data and Administrative Coordinating Centers, to lead development of resources and infrastructure to share IGVF data, standards, and pipelines with the scientific community. IGVF membership and activities are expanding further via Affiliate Membership, a process by which any researcher or research project can apply to join IGVF to drive its vision and execution. Through these activities, the IGVF Consortium aims to generate an extensive resource of experimental data, standardized protocols, and computational tools integrated into a catalog that can be broadly deployed for exploring genome function and the impact of genetic variation on human biology and diseases in diverse populations. Below we describe the goals, strategies, and anticipated deliverables of IGVF .To create a comprehensive catalog of the effects of genomic variation, IGVF has developed a strategy that integrates three complementary components . One com20, and computational analysis of these datasets can help to locate candidate regulatory elements, correlate element and gene activities, identify transcription factor (TF) binding regions and footprints, and reveal molecular pathways35. We will collect single-cell data across hundreds of cell types and states . We will apply primarily single-nucleus (sn)ATAC-seq and snRNA-seq, including in multiomic formats, and explore new single-cell approaches for TF binding, histone modifications, chromatin interactions, and clonal tracing. These data will provide a foundation for interpreting the effects of functional characterization experiments and building cell-type-specific maps of variant effects.Identifying noncoding regulatory elements and genes and mapping their activities across cell types and states is foundational for understanding where and when genomic variation might impact genome function. Due to technological limitations, many previous efforts have lacked this level of resolution. Recent advances in single-cell technologies now enable the generation of comprehensive maps of chromatin state and gene expression in nearly any cell type in the body43; massively parallel reporter assays (MPRAs) to quantify enhancer and promoter activities of noncoding sequences and their variants50; and studies of naturally occurring genetic variation to identify and fine-map different types of quantitative trait loci (QTLs)52. IGVF plans to conduct millions of experimental perturbations, including to directly study the effects of naturally occurring or designed DNA variants, and to perturb regulatory elements and genes to build maps of genome function. We will characterize the effects of these perturbations using diverse assays including measurements of chromatin accessibility53, gene expression56, protein expression and activity60, and molecular and cellular phenotypes61. These data will enable directly characterizing variants of interest, such as those associated with disease, and provide data to train or evaluate predictive models of variant effects.Perturbation experiments will be crucial for understanding the causal relationships among variants, regulatory elements, genes, and phenotypes, but until recently have been challenging to apply at sufficient scale. New enabling technologies include high-throughput genetic screens using CRISPR genetic or epigenetic perturbations and over-expression strategies65; identify TF footprints65; connect regulatory elements to their target genes67; or identify causal genes and cell types enriched for heritability for complex diseases or traits73. Equally importantly, successes by CASP74, ENCODE15, and others75 have illustrated how developing uniform standards, gold-standard datasets, and benchmarking pipelines can catalyze advances throughout the global scientific community by enabling rigorous evaluation of accuracy and direct comparison of alternative strategies. We will leverage new advances in machine learning and large-scale perturbation datasets across cell types and contexts to tackle key prediction problems — including mapping aspects of genome function, interpreting the impact of genomic variation, and guiding the design of future experimental assays such that the data produced will be maximally informative for subsequent predictive modeling. To systematically evaluate and calibrate such models, we will build benchmarking pipelines that compare predictions to perturbation data, including both from IGVF functional characterization experiments and external sources such as QTL, GWAS, and genome sequencing studies. In areas where data collection is already advanced, we will engage the external community by designing prediction challenges with held-out validation datasets produced by IGVF.Genome function is complex, and we cannot expect to experimentally map the effects of all possible variants on all possible activities in all possible cellular contexts. To address this, recent studies have highlighted the possibility of developing powerful predictive models that can make predictions that generalize across contexts — for example, to link genetic variants to effects on TF binding and chromatin accessibilityin vivo to inform how cell-cell interactions and environment alter genome function  impact transcription factor binding, regulatory element activity, and target gene expression in particular cell contexts, for noncoding variants; (ii) impact protein function, for coding variants; and (iii) connect to other genes/proteins via gene regulatory networks and/or protein-interaction networks, for both coding and noncoding variants? We will integrate this map of genome function, along with external data, into a multi-relational knowledge graphFor each of these aspects of genome function, existing computational models have been shown to have some utility in understanding the impact of genomic variation on diseases and traits, but much work is needed to improve the accuracy of these models and to conduct systematic evaluations using more precise and comprehensive gold-standard datasets. To address this, we will establish a pipeline to benchmark all predictions against functional characterization datasets and external human genetics datasets — allowing us to rigorously evaluate and guide interpretation of the draft map. We anticipate that providing genome-wide predictions from the best models, together with a reproducible benchmarking framework, will help launch an iterative and ongoing effort extending beyond IGVF to improve the accuracy of this map over time .81. We will seek to build genome-wide annotations of key components of this cis-regulatory code: Which single-nucleotide variants affect transcription factor binding sites, regulatory element activity, and gene expression in cis, in which cell types or states, with what magnitude and direction of effect?In the 99% of our genome that does not encode for proteins, noncoding variants can impact genome function by altering gene expression or regulation. While previous studies have mapped regulatory elements, gene expression patterns, transcription factor binding, and the effects of variants on gene expression in tissues or cells, we still lack models that can make accurate causal inferences about how genomic variation affects gene regulation33, and predict enhancer-gene relationships67; (ii) test >1 million noncoding variants in enhancer activity reporter assays82; (iii) test >100,000 noncoding variants for effects on expression through fine-mapping of eQTLs or direct CRISPR-based genome editing51; (iv) measure >100,000 putative regulatory interactions between candidate regulatory elements and nearby genes, for example using dCas9-based epigenome editing paired with single-cell readouts of RNA expression86; (v) and perturb transcription factors to read out effects on gene expression using Perturb-seq56. The variants and elements studied will include both naturally occurring and designed sequences, which will be critical for building accurate models of the gene regulatory code87. Each of these experiments will be conducted in multiple cellular models, so that the data can be used to refine and develop predictive models that can construct maps of noncoding variant effects across many cell types.To do so, IGVF plans to (i) generate multiomic snRNA+ATAC-seq data to a depth needed to comprehensively identify candidate cis-regulatory elements, detect transcription factor footprintsFor protein-coding sequences, our ability to interpret the functions of genomic variation is based on our knowledge of the genetic code for protein synthesis — which has enabled identifying open reading frames encoding novel proteins, identifying null, frameshift or nonsense variants, and predicting damaging missense variants. However, missense variants and inframe indels remain difficult to interpret, and we still lack a comprehensive understanding of how changes in protein sequence might affect different aspects of protein structure, expression, dynamics, and activity, including the impact on stability, subcellular localization, or interactions with other proteins.60 to experimentally characterize the impacts of >200,000 missense variants on various properties of proteins and their phenotypic impacts in cellular models, including protein stability, subcellular localization, cell viability, cell morphology, and protein-protein interactions. These experiments will focus on clinically relevant genes, such as those associated with Mendelian diseases, to provide direct look-up tables for certain genes, and provide data to refine or develop new predictive models to predict the likely impact of any coding variant across the genome.We will improve annotations of how protein-coding missense variants impact protein stability and activity by applying high-throughput technologiescis, we will seek to annotate the sets of other genes and proteins linked to the variant in trans through molecular networks in a given cell type or state. To a more limited extent, we will explore links to downstream cellular phenotypes. Genes and proteins can work together in many different ways, and it has been challenging to map or infer these sets of functionally related genes and corresponding cellular phenotypes in a comprehensive and cell-type specific fashion.Upon linking a variant to effects on gene expression or protein activity in To construct molecular networks, we will focus on defining (i) gene expression programs, described by sets of genes whose expression levels are correlated across single cells; (ii) gene regulatory networks that infer which transcription factors directly regulate which target genes via particular regulatory sequences; (iii) sets of interacting proteins or protein complexes; and (iv) dynamic changes to these programs/networks across cell state transitions.91; study how genes and proteins interact in molecular networks, including by mapping protein-protein interactions24 and conducting large-scale Perturb-seq56; and assessing how CRISPR-based perturbations or natural genetic variation across individuals affects cellular phenotypes including differentiation, gene expression programs, and cellular states. We will establish benchmarks to evaluate how best to use these data to construct cell-type and state-specific molecular networks and assess the impact of genomic variation on cellular phenotypes.To build these maps, we will collect longitudinal multiomic RNA and ATAC-seq data across dynamic cellular processes including differentiation and reprogramming92.We anticipate that many aspects of this map of genome function and variant effects will be cell-type specific, with annotations for each of the hundreds of cell types, states, and contexts studied by IGVF. This could be accomplished by developing predictive models that use multiomic snRNA-seq and ATAC-seq as their only cell-type specific input dataThe research infrastructure IGVF develops to build these maps will set in motion community efforts to expand on this framework by collecting additional datasets, training improved models, generating more accurate maps, and expanding the approach to additional cell types and aspects of genome function. This draft map will also offer immediate opportunities to address questions about the impact of genomic variation and genome function on phenotypes (see next section).The map-perturb-predict framework and the resulting variant-effect maps will provide new resources for the community to study the impact of genomic variation on human diseases and phenotypes, but this goal presents additional challenges.103 — including for many diseases presumed to follow Mendelian inheritance patterns, where penetrance and expressivity may include a component of polygenic risk104. Molecular networks are highly interconnected — a single variant may influence multiple genes, multiple gene networks, in diverse cell types — making it difficult to determine which of those genes, networks, and cell types are important for disease70. Disease susceptibility can involve many different cell types, possibly at specific timepoints, with effects accumulating over decades or in specific environmental contexts105. The impact of genomic variation on genome function can also differ across age, sex, populations, and ancestry: expanding human genetic studies across diverse populations has revealed examples where additional disease associations are discovered due to differences in allele frequencies106, and some cases in which variants with comparable allele frequencies appear to have different effect sizes on a disease111.For many diseases, an individual’s risk is likely to be determined by a combination of thousands of independently acting variants114, and these exploratory efforts will seek to integrate with other efforts in the field.Toward addressing some of these challenges, we will focus on assessing the impacts of variants in molecular networks and diverse cell contexts and then explore how best to apply this framework to: (i) inform clinical variant interpretation, particularly for rare diseases; (ii) learn about molecular and cellular mechanisms underlying risk for common and rare diseases; and (iii) ensure that lessons about the impact of genomic variation on genome function are applicable across diverse populations. Notably, each of these questions represents a major research area involving many strategies beyond those pursued in IGVFBRCA1, 70% in TP53, 74% in MSH2, and 90% in DDX3X into more definitive pathogenic or benign classifications116. In some cases, updated genetic test results were provided to individuals that had received VUS results when tested for cancer risk, and diagnostic odysseys were ended for families with DDX3X-associated neurodevelopmental disease.One key use case for variant-effect maps generated by IGVF, particularly for coding variation, will be to inform the clinical interpretation of single-nucleotide VUS in genes with known and suspected links to Mendelian genetic diseases. Indeed, prior work has shown how applying multiplexed assays of variant effect to study individual genes has been translated into powerful evidence for clinical variant interpretation, for example moving 50% of VUS in 118. We will then assess the extent to which either these experimental data, or computational predictions of variant impact, enrich for variants previously classified as either pathogenic or benign, and determine whether they can be used to calibrate predictors for clinical applications119. These variant-effect maps could ultimately substantially reduce the VUS burden in etiological diagnosis of rare disease114. Integration of maps for both coding and noncoding variants could also aid in the development of the next-generation polygenic risk score methodologies for better risk characterization in complex phenotypes107.To expand this approach to additional diseases, IGVF labs will experimentally measure the effects of thousands of variants in known disease genes, with a particular focus on those where identification of loss-of-function variants is clinically actionableImproved variant-effect maps could be transformative for identifying new biological mechanisms that influence genetic risk for disease. In particular, we will seek to understand how best to combine the map-perturb-predict framework and variant-effect maps with human genetic data to nominate variants, genes, cell types, and cellular programs that influence disease risk.122. By conducting CRISPR screens to identify variants and regulatory elements that affect lipid uptake in cellular models, testing variant effects on enhancer activity in massively parallel reporter assays, and applying state-of-the-art predictive models, we will evaluate which combinations of experiments and/or predictive models provide the strongest enrichment for disease-associated variation and known causal genes. These combined efforts will help to inform mechanisms of genetic risk for selected diseases, and help to develop strategies to identify causal variants, genes, and pathways for any complex disease.We will study specific diseases and traits, including lipid traits, hematological traits, autoimmune diseases such as systemic lupus erythematosus, cardiometabolic diseases such as coronary artery disease, and neurodegenerative diseases such as Alzheimer’s disease. As one example, IGVF investigators are studying variants associated with lipid traits, where GWAS and whole-exome sequencing studies have already identified hundreds of associated noncoding and coding variants, and where certain key genetic pathways involved in lipid handling are already known125.IGVF aims to ensure that insights about the impact of genomic variation and genome function are applicable to and inclusive of people of diverse groups. To do so, we will promote diversity in its functional genomics studies, experimentally study and computationally annotate variants observed in diverse populations, study diseases disproportionately affecting disadvantaged or under-represented populations, and explore the extent to which particular variants might exert the same or different effects due to interactions with genetic background or environment126. Finally, certain predictive models, such as those linking noncoding variants to chromatin state and gene expression65, can make predictions of variant effects based on measurements obtained in a specific individual, allowing systematic annotation and comparison of variant effects across individuals and groups. Altogether, the data and genome-wide variant-effect maps generated by IGVF will offer insights into variant effects across groups and provide a valuable resource for investigating the effects of variants discovered in diverse populations.We will employ a multi-pronged approach encompassing experimental and computational strategies to achieve its goals. In the current design stage, we have incorporated variants, elements, and genes from diverse populations, including those with differential effects on disease. Biological models will include human iPSCs derived from individuals from different ancestries, and genetically diverse mouse lines from the Collaborative Crosshttps://igvf.org).A major goal of IGVF is to catalyze future research to understand the relationships between genome function, genomic variation, and phenotype, including for biomedical researchers across diverse disciplines and with diverse needs. To do so, we will build the IGVF Data Resource to enable researchers to easily access and apply IGVF datasets, predictions, and methods  platform to provide access control in adherence to NIH Policy131.For researchers who want to access raw or processed data generated by IGVF, we will develop the IGVF Data Portal. The Data Portal will provide web-browser and programmatic access to uniformly processed IGVF datasets, analysis products, and rich metadata, which we anticipate will be useful for users who aim to develop new data analysis methods or predictive models, analyze IGVF data in new ways, or compare their data to IGVF standards. The IGVF Data Portal will follow principles of making data Findable, Accessible, Interoperable, and Reusable (FAIR)For researchers who want to apply IGVF methods and strategies to additional systems, the Data Portal will also share documentation on IGVF standards, protocols, and best practices for experimental design, data analysis, and predictive modeling. These resources will include computational methods, data formats, and consensus data processing pipelines for key assays and analysis products, such as for single-nucleus RNA-seq and ATAC-seq, CRISPR-based experiments, massively parallel reporter assays, eQTL studies, and others. All data processing code will be released with open-source licenses to enable others to analyze similar data in an identical fashion, and we will strive to make sure that it can be run on compute resources accessible to researchers throughout the global research community.https://www.youtube.com/@igvf). To teach users how to access data programmatically and use common analytic tools, we will create online notebooks and tutorials demonstrating key uses of the IGVF Portal and the IGVF Catalog. As an additional channel for users to interact with the IGVF Consortium, we will host interactive online seminars and webinars.Finally, for all researchers, we will provide training and support on how to access these IGVF resources. To teach researchers how to find and view IGVF data, we will create instructional streaming videos that we will distribute via the IGVF YouTube channel  consortium, and the Atlas of Variant Effects (AVE) Alliance132. These collaborations will facilitate the open exchange and interoperability of genomic data and resources, for example to use common variant naming schema, genome and transcriptome builds, and experimental and analysis pipelines.IGVF is actively coordinating with other consortia, including ClinGen133; with efforts to catalog disease-associated variation across ancestries, including All of Us134, TOPMed10, and other biobanks; with efforts to map the activities of variants, regulatory elements, and genes at single-cell resolution, such as the Human Cell Atlas20 and HuBMAP19; and with efforts to compare and evaluate strategies for interpreting genetic variation associated with disease, such as the International Common Disease Alliance28. Strong collaborative ties among such efforts and IGVF will propel scientific advances that shape how both basic and clinical research are performed.Similarly, IGVF data and analysis products will benefit from close interactions with efforts to characterize human genomic variation and assemblies, such as the Human Pangenome Reference Consortium (HPRC)With the rapid expansion of human genetics studies linking variation to disease, the interpretation of the impact of genomic variation on function is currently a rate-limiting step for delivering on the promise of precision medicine. The IGVF Consortium will deploy a coordinated strategy for accelerating progress, including generating large-scale data resources, predictive models, and initial variant-effect maps that will reveal new insights into how genomic variation impacts function and phenotype. The tools, data resource, and strategy developed by IGVF — including new experimental assays, design strategies, predictive models, computational methods, data sharing standards, and more — will provide a foundation to facilitate future efforts. We will prioritize open data and resource sharing, inclusion, and outreach so that all members of the research community can participate and benefit.While ambitious, IGVF activities do have limitations in scope, and many challenges lie ahead. Genomic technologies, both experimental and computational, are developing rapidly, and balancing the implementation of the newest scalable tools with continuing standards to ensure data interoperability will require attention. While data generation technologies have increased throughput exponentially over the last 15 years, the amount of data needed to build accurate models of genome function is unknown, and fully realizing the goal of mapping the impact of genomic variation on function will require additional advances in both experimental and computational methods. In particular, the development of computational methods to predict synergistic interactions among variants, environments, and time spans of effects that can occur over decades are open problems. We will initially focus on specific biological systems and cellular models according to its members’ expertise, but full exploration of the many cell types and disease areas relevant to human biology will require community efforts. IGVF aims for systematic analysis of certain aspects of genome function — gene regulation, protein function, and molecular networks. Additional work is required to explore other important layers of genome function, including effects on nuclear organization and chromatin compartmentalization; RNA splicing, transport, and translation; and impacts on cellular phenotypes, cell-cell interactions, and communication. For all of these challenges, the framework developed by the IGVF Consortium to develop and benchmark methods, refine best practices and standards, and share data and methods will drive scientific discoveries in human health and disease for years to come.


Adverse childhood experiences and chronic pain are complex problems affecting millions of people worldwide, and result in significant healthcare utilisation. Our review aimed to determine known associations between adversity in childhood and chronic pain in adulthood.We performed a prospectively registered systematic review (PROSPERO ID: 135625). Six electronic databases  were searched from January 1, 2009 until May 30, 2022. Titles and abstracts were screened, and all original research studies examining associations between adverse childhood experiences and chronic pain in adulthood were considered for inclusion. Full texts were reviewed, and a narrative synthesis was used to identify themes from extracted data. Ten percent of studies were dual reviewed to assess inter-rater reliability. Quality assessment of study methodology was undertaken using recognised tools.Sixty-eight eligible studies describing 196 130 participants were included. Studies covered 15 different types of childhood adversity and 10 different chronic pain diagnoses. Dual reviewed papers had a Cohen's kappa reliability rating of 0.71. Most studies were of retrospective nature and of good quality. There were consistent associations between adverse childhood experiences and chronic pain in adulthood, with a ‘dose’-dependent relationship. Poor mental health was found to mediate the detrimental connection between adverse childhood experiences and chronic pain.A strong association was found between adverse childhood experiences and chronic pain in adulthood. Adverse childhood experiences should be considered in patient assessment, and early intervention to prevent adverse childhood experiences may help reduce the genesis of chronic pain. Further research into assessment and interventions to address adverse childhood experiences is needed. Adverse events in childhood have been shown to be detrimental to adult mental and physical well-being,,,Chronic pain is a major long-term condition, affecting 19% of adults in Europe.,Previous systematic reviews have analysed chronic pain experience and previously self-reported childhood abuse, concluding that there was an increased risk of chronic pain in those who had experienced abuse and neglect.,,Childhood adversity is described and classified in many ways throughout the literature. For the purposes of this review, we defined ‘adverse childhood experiences’ as traumatic experiences endured by children before the age of 18 yr.1.Child OR childhood and child/childhood abuse and/or childhood maltreatment and/or child/childhood adversity and/or childhood trauma and/or childhood neglect;2.Adult survivors of abuse;3.‘Chronic Pain’; ‘Long-term pain’; ‘Physical suffering’; ‘Persistent Pain’4.1 AND 2 AND 3.To optimise study identification, search strategies were developed by the research team with librarian support. Electronic databases  were searched for papers published from 1 January 2009 until 30 May 2022. Further studies were identified by hand searching bibliographies of any relevant systematic reviews and searching tables of contents of relevant journals for articles published in the same time period. The search terms used were:Studies were eligible for inclusion if they included at least one type of adverse childhood experience and examined possible biological, psychological, or social associations between these adversities and the subsequent presence of chronic pain in adulthood (aged 18 yr or older). No limits were applied for country of origin, type of adverse childhood experience, type of chronic pain, or study design. Articles published in languages other than English were excluded owing to lack of access to translation services. Articles for which no full-text version was available were also excluded. Review articles and any papers reporting duplicate data/studies were excluded, although the referenced original source papers were reviewed for inclusion.From the initial longlist, study titles and abstracts were independently screened by two reviewers (KPN and SEEM). Full text review was performed by KPN, and 10% of included studies were double-reviewed by DS to assess inter-rater reliability. Disagreements were resolved through discussion with BHS.The following data were extracted using a standardised form: country of origin, cohort size, demography, type of chronic pain, type of childhood adversity, questionnaire type used to determine the adversity experienced, primary and secondary adverse outcomes reported, association with adversity, conclusions, and key concepts.A narrative synthesis was used to identify common themes reflecting the associations. Two reviewers (KPN and SEEM/DS) independently identified thematic associations between childhood adversity and chronic pain. Emerging themes were combined iteratively into a narrative synthesis to summarise and describe the findings of included studies. Decisions on identification and grouping of themes were agreed by all authors.Each study's risk of bias was independently assessed by two reviewers (KPN and SEEM/DS) using standardised tools selected for each study design. Tools used for quality assessment of included papers were selected from the tools recommended by the Scottish Intercollegiate Guideline Network (SIGN).We identified 2904 records from the search strategy, and 33 records were identified through other sources . Titles,The combination of emotional/physical/sexual abuse was the most commonly studied adverse childhood experience, followed by general ‘adverse childhood experiences’, and ‘physical and sexual abuse’ as shown in Sixty-one of the 68 included studies were assessed as high quality. Seven papers were assessed as having moderate risk of bias. Elements of weakness that were identified in quality analysis included small sample size (11 studies had fewer than 100 participants); limited demographic information; exact age of participants was missing (13 studies); and further details on breakdown of gender was not provided (seven studies).Most papers were consistent in noting recall bias as a potential weakness. The main confounder documented was mental health in the form of anxiety, depression, current stress, and post-traumatic stress disorder (PTSD). In cross-sectional studies, a common weakness was lack of information about non-responders. One paper lacked analysis to determine the statistical significance of associations reported; however, it did not add any factors that had not been identified by other studies included in the review.,,,,,Adverse childhood experiences were shown to increase the risk of subsequent chronic pain as an adult and in one study, 75% of participants with chronic pain had experienced early life adversity.,,,,An insecure environment where children were emotionally deprived was also related to chronic pain development. Those who experienced emotional neglect, affectionless parenting, parental loss, or overprotective parenting in childhood were more likely to experience chronic pain.,,,,Current poor mental health as an adult is thought to mediate the development and severity of chronic pain in the presence of childhood adversity,,,A few studies explored potential biological mechanisms driving the identified associations. The direct physiological changes caused by adverse childhood experiences that influence chronic pain are still debated. Hypotheses include disruption in cortisol regulation or that maternal neglect causes neurobiological changes.. A single type of adversity was rarely found in isolation; where there was one form of adversity, there were usually others.,,Studies in this review consistently documented high levels of adverse childhood experiences in adults with chronic pain. Many studies used a pre-existing formal chronic pain diagnosis as an inclusion criterion, rather than asking specifically about any pain lasting longer than 3 monthsThis is the first comprehensive systematic review to describe known associations between any form of adverse childhood experience and any type of adult chronic pain. Strengths include detailed analysis of a wide range of studies from many different countries covering a wide range of adverse childhood experiences and types of chronic pain. Limitations include exclusion of papers not available in full or in English (for reasons of resource), which may produce a cultural and geographical bias; however, only four papers were excluded for this reason, so the impact of this limitation is likely to have been minimal. Further limitations include terminology for adverse childhood experiences; the heterogeneity of adversity a child can experience and consequent range of descriptive terms, means studies may have been missed. We used a broad search strategy across multiple databases, and extensive hand-searching to supplement the initial search strategy, so we hope to have minimised the risk of missing relevant studies. However, given the heterogeneity of terminology in this field, we cannot be certain that a small number of relevant studies were not missed. The majority of studies were retrospective; therefore, recall bias was also noted. This is a limitation of the methods used to address the research questions. Most of the studies included were of high quality, with only six noted to be of medium quality and two of poor quality. Any studies of poorer methodological quality were included in the final synthesis for completeness, and although their conclusions were given less emphasis in the final synthesis, their findings were consistent with higher quality studies see . The com,,,,,,,,,,,,,,,,The more types of adverse childhood experiences, the greater the detrimental impact on development and experience of pain in adult life. However, much of the published research has a relatively narrow focus on emotional, physical, and sexual abuse. In previous systematic reviews, fibromyalgia has been shown to be associated with prior psychological or physical trauma.vice versa. There was general agreement about the need for more prospective studies to determine the nature and extent of the links between adverse childhood experiences and chronic pain,,,,,,Despite the high prevalence of adverse childhood experiences documented in the included studies, not every person with chronic pain has experienced an adverse childhood experience, or This review identified that a wide range of adverse childhood experiences are associated with the development, severity, and impact of chronic pain in adulthood, and that this association with adverse childhood experiences is consistent across a wide range of chronic pain conditions. The importance of adverse childhood experiences when assessing and managing chronic pain needs to be recognised and integrated into clinical practice. Further research on the underlying mechanisms of these associations is required, along with the assessment and management of childhood adversity in the context of chronic pain.Planning and development of the review: KN, SEEM, LAC, BHS.Initial plan for the review: KN.Conception and design: KN, SEEM, DS.Acquisition of data: KN, SEEM, DS.Analysis and interpretation of data: KN, SEEM, DS.Selection of tools for quality assessment: KN, SEEM.Quality assessment: KN, SEEM, DS.Identification of themes from included papers for narrative synthesis: KN, SEEM, DS.Writing of the first draft of this paper: KN.Overall research supervisors: LAC, BHS.Development, thematic synthesis, interpretation, and overall conclusions: LAC, BHS.Writing of the final version of the paper: KN, SEEM, DS, LAC, BHS.Final approval of the version to be published: KN, SEEM, DS, LAC, BHS.We thank Mr Scott Macgregor, librarian, at The University of Dundee for his assistance.British Journal of Anaesthesia (2010–21), and is currently a member of the editorial board of the BJA and BJA Open. BHS was Scottish Government Lead Clinician for Chronic Pain (2014–2021) and is Speciality Adviser (Chronic Pain) to the Chief Medical Officer of Scotland. The authors have no other competing interests to declare.LAC was past editor of NHS Education for Scotland. KN was academic fellow from 2018 to 2022. SEEM was funded through an academic clinical fellowship from the Scottish Government's Chief Scientist Office (CSO grant number CAF_17_06). DS is a fellow on the Multimorbidity Doctoral Training Programme for Health Professionals, which is supported by the Wellcome Trust [grant number 223499/Z/21/Z].