Cybersecurity (CS) is a contemporary field for research and applied study of a range of aspects from across multiple disciplines. A cybersecurity expert has an in-depth knowledge of technology but is often also recognized for the ability to view technology in a non-standard way. This paper explores how CS specialists are both a combination of professional computing-based skills and genetically encoded traits. Almost every human behavioral trait is a result of many genome variants in action altogether with environmental factors. The review focuses on contextualizing the behavior genetics aspects in the application of cybersecurity. It reconsiders methods that help to identify aspects of human behavior from the genetic information. And stress is an illustrative factor to start the discussion within the community on what methodology should be used in an ethical way to approach those questions. CS positions are considered stressful due to the complexity of the domain and the social impact it can have in cases of failure. An individual risk profile could be created combining known genome variants linked to a trait of particular behavior using a special biostatistical approach such as a polygenic score. These revised advancements bring challenging possibilities in the applications of human behavior genetics and CS. Cybersecurity (CS) is a contemporary field for research and applied study of a range of aspects from across multiple disciplines. The guidelines for CS curricula distinguish a knowledge area of Human Security (HS) and Organizational Security (OS). OS relates to laws, regulations, standards to support risk management, planning, governance, and risk assessment concerning insider threats that come from authorized access to sensitive data and systems. These tasks are accompanied by the challenges of coping with stress, fatigue, and the need for effective teamwork. HS covers topics such as CS awareness, social engineering attacks, and abilities related to human misbehavior, e.g., the ability to implement measures to detect and mitigate social engineering attacks and discuss the importance of risk perception in the context of mental models of CS and privacy.CS positions are considered stressful due to the complexity of the domain and the social impact it can have in cases of failure. The defense must be timely, as errors might cause severe effects. Attack models and vectors are becoming more advanced due to the development of technologies. Many soft skills lead to complex human behavior. One of the soft social skills shaping factors might be genetic factors. Almost every human behavioral trait is a result of many genome variants in action altogether with environmental factors. The Human Genome Project  promptedThe possible underlying genetic background of physical and psychological traits, fundamental for human performance under stressful conditions, might also be the same mechanisms responsible for substance abuse, cardiovascular diseases, bipolar disorder, as well as soft skills such as the ability to communicate, cope with fatigue, pressure, stress, remain attentive, assertive, consistent, maintain sharp cognition and incident response. An individual risk profile could be created combining known genome variants linked to a trait of particular behavior using a special biostatistical approach such as a polygenic score. With this knowledge, a CS specialist could become more aware of personal characteristics and environmental conditions and learn to mitigate potential threats. Therefore, genetic information can become an excellent tool for self-knowledge, which can ensure better CS performance.This paper explores how CS specialists are both a combination of professional computing-based skills and genetically encoded traits. We review methods that help to identify aspects of human behavior from genetic information. Behavior genetics addresses the interdisciplinary effort to establish causal links between genomic loci and human behavioral traits and neural mechanisms. A CS specialist who understands the risks of his behavior can better adapt to adverse environmental conditions and cope with risk factors through well-rehearsed techniques.According to recent evolutionary-inspired theories , humans differ substantially in their sensitivity to contextual factors, with some more susceptible to environmental influences than others. Importantly, these theories suggest that heightened sensitivity predicts both the reactivity to adverse contexts as well as the propensity to benefit from supportive features of positive environments . The intThe primary idea of this research was to aid the CS field as it became essential in all parts of life and increasingly problematic year on year. Our main goal was to spark the scientific discussion of whether the genetic component could advance the CS topic in any way. We believe this discussion could inspire further research studies exploring interdisciplinary approaches in CS.The article addresses the CS field as a complex discipline with multiple layers. We deconstruct the CS specialist as a material  and non-material  entity. Then, we map this entity to CS competences required to conduct everyday tasks with stress as a psychological factor. All the structural prerequisites for the development and functioning of the psyche are genetically coded and controlled. This could be extrapolated universally in other research areas related to human behavior as well.In this paper, we discuss the complex matter which was, and still is, the main limiting factor of behavior genetics applications—the complexity of behavioral traits, methodologies, and technologies dedicated to the research of genetic architecture of a trait, ethical aspects, and competence frameworks.The genetic architecture of a phenotype refers to an entire complement of underlying genetic factors, including their number, variant frequencies, and effect sizes of contributing variants. The variation spectrum underlying complex phenotypes includes at least three major classes of DNA variants: common single-nucleotide variants , rare single-nucleotide variants , structural variants, including copy number variants (CNVs), insertions/deletions, and balanced translocations. In addition to inherited variation, rare variants can occur de novo, arising in a parental gamete, a fertilized embryo, or the developing fetus .Until recently, the success of the prediction of genetic effects was limited due to the small number of variants that could be assayed. Today, technology allows for the genotyping of individuals to extract sets of genomic variation and is no longer an obstacle. Usually, contemporary genotyping assays include hundreds of thousands of common genetic variants. However, common DNA variation adds only a small effect to the phenotype, and many variants are contributing. Thus, there is a need to identify lots of variants before testing them, and large sample sizes are needed to do that. However, even if we would have sequences of genomes from every person on Earth and perform a genome-wide association study (GWAS), traits could not be predicted with 100% accuracy. Statistically, there is no linear endpoint. With such a complete database, science could only tell how much of the variability in the world was due to genetics . To get Besides the intricacy of complex trait genetics itself and statistical challenges, the critical factor limiting the application of behavior genetic findings in practice is ethical issues. The memory of the improper application of genetic knowledge, known as eugenics, still casts a shadow on current attempts to apply behavior genetics. The discrimination regarding genetic information in various socially important aspects of life, such as employment or health insurance, is also a considerable concern. There are also common worries such as privacy and safety of personal information. Nevertheless, genetics paves the way in medicine, forensics, recreation (related to direct to consumer (DTC) tests). All we need is to work hard to integrate different disciplines, and in our case—improve computational genetic methods, advance human performance in CS based on the comprehensive knowledge we extract from different sources.Probably most of us usually do not even think about how the response to scalding with boiling water is generated. The instant retreat from the boiling water is the result of genetically determined behavioral patterns. Every trait on its basis is a genetic working, and the level of trait expression depends on the environment, which shapes the genetic program. As Dr. Francis Collins , leader of the Human Genome Project) once said: “The gene proposes, the environment disposes. Genes load the gun, but the environment pulls the trigger”. Only a few human traits are purely monogenic , most of them are multifactorial, thus as implied in the name—complex. Behavior is one of these. There are several genetic methods used to evaluate the level of genetic components that shape human behavior  studies are the two main types of methodologies to investigate behavioral traits. To determine whether the trait is inherited, it must run in families, and when the significant familial recurrence ratio is achieved; it can be concluded that the disorder is familial. To evaluate tability , i.e., wtability ,9. Furthtability . HeritabOnce the heritability of a trait is determined, molecular genetic studies can be undertaken to map and identify the genetic factors at the level of DNA variation. For complex traits to identify genetic regions (loci), association studies are a first-choice method and more powerful than linkage studies. GWAS became a dominant strategy for many traits, disorders, and conditions. Association studies typically utilize a case-control design to determine whether specific genetic variants  are more common among the group expressing the trait (cases) than among the individuals without a trait (controls) . In geneAfter we determine underlying genetic factors, the next step would be to analyze the possible gene x gene interactions  and also gene x environment (GxE) interactions. These studies examine whether the effect of a genetic variant is modified by environmental exposure.Establishing that a genome variant, gene, or gene set is associated with a disorder, or a trait of interest is only the first step in answering the question of how specific genes contribute to the disorder or the particular trait . A wide range of molecular, cellular, and clinical research studies may be needed to characterize the pathogenic or alternative mechanisms involved. These include studies of gene expression, animal and cellular models in which genes may be experimentally altered to study functional effects, and clinical neuroscience studies  examining the effect of genetic variation on brain structure and function .As a consequence of the heterogeneity found in nature, most of the multifactorial traits, such as behavioral traits are difficult to identify in humans. Thousands of variants can have a small effect size on the trait, and this can differ dramatically in different subgroups of individuals. Studies of distinct populations and families with extensive pedigrees added great value to gene mapping and the genetic architecture of traits. Dogs can provide an illustration of this. For example, it has been shown that the dog is a valuable resource to study the genetic architecture of behavior . The strOnce the genetic factors of a trait are mapped, then we can proceed with the models for trait prognosis, or if it is a disease/condition, estimate the risk. There are several established methods on how to predict traits, and this field is quickly evolving . CurrentModern behavior genetics studies face ethical concerns relating to the medicalization of behavior traits, mistreatment, and abuse of information for insurance or employment, social aspects of information misuse such as public discrimination, impact on law and judgment, and the risk of modern eugenics. Eugenics was rather a misunderstanding of inheritance, thinking that a single gene can account for a complex behavioral trait. Eugenicists sought to improve the human population and its gene pool by encouraging “fit” individuals to procreate (positive eugenics) and discouraging or preventing the reproduction of the “unfit” (negative eugenics).One of the ethical issues for behavior genetics is medicalization. Sometimes medicalization of human traits is not always necessary as traits previously thought to be normal can later be presented as a deviation from the normal human population ,24,25,26It is of great concern that knowledge of behavior-related variation in the genome could be misused in social and political aspects such as voting. A study by Hatemi et al. (2009) showed that there is genome variation between liberal and conservative voters in the US and that it is related to the cognitive processing of fear .There has recently been concern over the use of genome profiling that is performed by DTC genetic testing companies  as the results they provide are conflicting. “My Gene Profile” company declared that it was able to provide a very wide behavior profile—intelligence, emotion, artistic ability, addiction traits, and also physical performance. This company was shut down , and thiScience in recent years revealed a high amount of data related to behavior genetics that can benefit the military, and this knowledge has already been in use. A good example of behavior genetics application for military purposes is the Defense Advanced Research Projects Agency (DARPA) research project of the drug called modafinil, which boosted cognitive and physical performance in soldiers ,34.Taking into account ethical issues, the major risks for CS field specialists might be personal genetic data leak or another mistreatment  and information abuse for a discriminative purpose . These hazards should be carefully articulated and tackled before introducing genetic data into the practice.Human factors play an essential role in CS, and in most well-known cases, a human is either the possible source, vehicle, or destination  of cyberTypically, CS professionals are viewed as smart, technically skilled individuals , and it Most CS study programs follow the ACM/IEEE Cybersecurity Curricula  that incThe NIST NICE Cybersecurity Workforce Framework  is one oEsparza et al. (2020) consider the habituation factor in knowledge, attitude, and behavior model and support individuals’ likelihood of action by background, beliefs, and prior experiences to assess cyber hygiene-related skills . AlohaliPrevention against the attacks is the best strategy in CS. Furthermore, to understand criminal behavior, it is not enough to focus on technology . MoreoveRecent research has shown that genetic heritability of personality is calculated to be 0.40, but this varies depending on which personality inventory is used and which personality factors are measured . For exaOther psychological factors, such as cognitive styles, may also have genetic underpinnings that are relevant for CS specialist profiles. Cognitive styles, biases, and appraisals have been shown to have correlations with genetic underpinnings, while personality factors show less heritability as we age. For example, the heredity of intellect increases significantly as a person ages, whereas the heritability of personality declines slightly, and although increasing cognitive stability with age is largely mediated by genetic variables, rising personality stability with age shows mediation by environmental factors .This discrepancy between personality and cognitive aspects could be due to developmental processes in childhood and adolescence. For example, personality has strong correlations to infantile temperament , while cA recent review has shown that there are discrepancies in genetic explanations for personality factors and that new techniques, i.e., next-generation sequencing, can help better understand the genetic contribution to personality factors .Recent research on personality and cognitive factors in CS specialists is in its early stages, but findings on both personality and cognitive profiles are becoming more available ,55.We present an example to identify the potential of human factor identification based on the genetic data. We chose the stress factor (SF) as a representative human factor understandable globally and frequently used in an everyday setting.Stress is defined as a state of threatened homeostasis that evokes a multiplicity of somatic and adaptive reactions ,57. As w6. How do we identify those individuals who are more susceptible to stress? This knowledge is important for an individual when choosing a profession or place of work, as well as for the institution hiring a specialist. For the more susceptible ones, stressful working conditions such as flight control or CS might be too difficult to cope with. Studies are showing that individuals might differ substantially according to how they respond to similar experiences. It has been demonstrated that environmental sensitivity depends equally on genetic factors as well as on environmental factors and that there are overlaps between personality traits of neuroticism and extraversion [The role of stressful environments and the physiology of stress response systems have been most closely linked to depression, anxiety, and traumatic stress disordersaversion . Also, aaversion .It is already well known that cortisol, which is produced in the adrenal gland, is the main stress hormone. After a person experiences a stressful event, the level of glucocorticoid rises in the blood. The prefrontal cortex reacts to stress by making things look less scary, the amygdala identifies danger from the environment. Then, the hypothalamus activates the pituitary gland, which synthesizes hormones and induces the adrenal glands to produce cortisol. Released into the bloodstream, cortisol reaches cells, binds to the intracellular glucocorticoid receptors, and the receptors change, then translocate to the nucleus where interact with the DNA . Figure Cortisol is a stress response system that increases heart rate and breathing, provides energy to the muscles, allows clear thinking, and may boost memory. Cortisol shifts the balance of neurotransmitters. It lowers dopamine that reduces the activity of the pleasure pathway, lowers norepinephrine which reduces motivation, and lowers serotonin which reduces the mood.GR, NR3C1, GABRA6, OPRM1, ACE, FKBP5, GCCR, CNR1, DRD2, ANKK1, NPY, DBH [COMT gene, whose main function is to regulate the production of dopamine and affect how decisions are being made under pressure, control stress, and resiliency [COMT gene has been proved not only in human but also animal model studies such as those on rats or primates [COMT gene impact to stress by studying Japanese macaques, which have a high level of aggressive interaction and have a different response to stressful events [BDNF, which serves as a neurotransmitter modulator, participates in neuronal plasticity, and stabilizes synapses [BDNF have better stress resilience. The final gene worth mentioning related to stress is SLC6A4, which is the serotonin transporter gene. Serotonin is known as the mood neurotransmitter and has been well-studied for depression, suicide, and stress [COMT gene, rs6265 variant on BDNF gene, and rs25531 on SLC6A4 gene [COMT), rs6265 (BDNF) have an impact on this [Twins studies revealed 69–72% genetic impact on cortisol level ,61. GeneNPY, DBH ,62. Howesiliency . Other asiliency ,65. For primates . For exal events . Anothersynapses . This gesynapses ,68,69. Pd stress ,71. It i6A4 gene ,72. For The specific genome variants allow using this knowledge for practical purposes without extensive, expensive, and lengthy analysis studies. Furthermore, these variants are already being used by private companies for their customers to learn more about their genome variation. Knowing stress-related genes may be used for sports, education, or finding a more suitable occupation. Vogel and Schwabe (2016) studied the learning process under the stress condition and found that there was a different effect on different people, but the administration of 20 mg of cortisol increased memory and learning process rate . The “waTwin studies on the heritability of different neurotransmitters have uncovered differentiating aspects of genetic involvement. For example, genetic influences on cortisol have been shown to have high heritability at younger ages for cortisol production, and this decreases during development . They shstress mentioned at all. The document of ACM/IEEE Cybersecurity Curricula  does notstress is not found in the NIST NICE framework [time (or timely) is used to define stressful conditions that require urgent decisions and sharing to authorities (or administration) at the right time. The word ramework  either. Language analysts manipulate time-sensitive information to inform authorities (T0854). Threat/Warning analysts and all-source analysts should provide timely notice about hostile activities, as delay would mean higher cost (T0800). Cyber defense analysts should detect and alert on time about anomalous activities (T0258).time is related to the analysis of timelines, checking states of data/objects, real-time monitoring (or controls), network analysis, project management, time management in social media, and agile decision making. Nevertheless, time pressure—the stress factor, is missing. In the context of higher education, the critical aspect is learning what and how to look for tools and develop abilities to combine data sources, while professionals have to deliver tasks on time with an impact of the decision in mind.In ACM/IEEE Cybersecurity Curricula, concept The ACM/IEEE Cybersecurity Curricula and the NIST NICE Framework do not reflect the stress factor that is very common in CS work roles. Oltsik (2019) reports that 13% and 33% of CS specialists strongly agree or disagree, respectively, that they feel an unhealthy level of stress as part of the job . Other sThe standard CS workforce preparation provides the basic skills required to enter positions as junior specialists , but thecommunication with collaborators that are busy with an incident response (their primary focus is not reporting);systemic thinking (IoCs must be correct and context-related);self-control (the report must be submitted in time).As an example, we can deconstruct the following real CS situation. Objectives “Identify indicators of compromise (IoCs) using threat detection tools” and “Negotiate IoCs with collaborators” could be associated with a competence “Management and sharing of threat information” because the competence requires the collection of IoCs from the tools, a collaboration between team members during IoC retrieval process, and defining IoCs in the reports. The competence can be related to the task shortly defined as “Timely information management”. The person should be stress-resistant as the task requires:Therefore, it would be beneficial to identify the stress-related risk level of a particular person. The genomic analysis could be a complementary part of the assessment as it encompasses the information on the natural traits of the person. The personal risk makes an impact on the performance indicators of the person and the team itself.This paper describes how human factors could be identified from the genetic data and used for personalized risk assessment taking stress as a case. The current competence frameworks do not include personalized approaches. Integrating human behavior factors identified from genomic data into risk assessment strategies and professional training outside the standard IT-oriented training schema is a thrilling challenge but with great additional value. We chose the sufficiently genetically reasoned stress factor to emphasize the impact of genetic information on performance in the CS field. Stress creates situations when humans behave not as trained but naturally, based on genetic traits. Therefore, various human features that can be defined by genetic information could be important risk factors, e.g., addiction, introversion, aggressive behavior, depression, post-traumatic stress disorder. Integration of genomic data into CS provides new opportunities to support an individualized approach. For a particular person learning his/her genome variants related to human behavior can help to expose and accept his/her strengths and weaknesses to use and/or overcome them later. The provided example of stress-related genome variants could guide the person on how to affect one’s reaction to the situations and circumstances caused by stress, demonstrate the level of learning process under stress conditions, and show resistance to stress.Next to new approaches to CS, a number of challenges arise with their application. Firstly, human genome data are considered highly sensitive data. Data administration should follow procedures to ensure restricted access and high-level security, including both raw, derived, and aggregated data associated with a person or any incident response team. Secondly, interpretation of data and personalized risk assessment based on genomic data should be performed carefully and ethically with many additional factors in mind. The risk assessment cannot be based solely on genetic data to assign a person for a work role. Any employee should be protected by legal documents against discrimination based on findings from genetic data. Finally, to integrate genomic data into the CS exercises requires that challenges in human and technological resources are overcome. Multicomplexity/multifactoriality of the discussed research field—the behavioral genetics of the aspects of the cybersecurity specialists’ activities is the main challenge in identifying the wide combination of most possible factors. The ever-increasing number of GWAS hits harness the power of molecular genetics to identify specific genes responsible for genetic influence on reacting to environmental triggers, such as stress. Future research findings will be utilized for the analytics of the prediction scores. As well the delineation of specific combinations of genomic factors and environmental factors (or construct of gene-gene and gene-environment landscape plot/map) will suggest the best calculation methods and attempt for ranging those prediction scores. The work can be extended in several directions. Pilot studies should be executed to evaluate exercise implementation and collect quantitative data for further research. Behavioral Neural Networks should also be addressed to identify the risks detection process and the needs for behavioral references. Risk assessment strategies with more precise parameter settings should be built with additional risks in mind. Evaluation of ethically concerning aspects and determination of measures have to be taken in order to tackle ethics-related risk management.


Glycine max) data set that consisted of 25,158 records collected at 52 environments. ECGC illustrated what meteorological factors shaped the G It has not been fully understood in real fields what environment stimuli cause the genotype-by-environment (G  Genotype-by-environment (G  decades . Howeverenotypes . These sGlycine max). The traits were days to flowering (DTF), days to maturity (DTM), stem length , protein content of seeds , yield  and seed weight .Here, we propose a novel method termed Environmental Covariate Search Affecting Genetic Correlations (ECGC) to reveal quantitative environmental stimuli  and genetic architecture underpinning the G ractions . This prAs described above, ECGC searches environmental covariates whose similarity matrices between environments are correlated with genetic correlation matrices between environments. Here it will be illustrated how the similarity of environmental covariates is associated with genetic correlation between environments.jth environment, the phenotype adjusted for variations due to years and management conditions, I is the identity matrix. After scaling with the genetic standard deviation  represents the proportion of the variance of (1). Calculation of environmental covariates ((2). Calculation of similarities of environmental covariates between environments ((3). Estimation of genetic correlation between environments ((4). Scanning environmental covariates associated with genetic correlation based on (5). Estimation of slopes  as promising lines. In addition, from the ∼2,000 varieties, 149 varieties that were registered as major varieties by the Ministry of Agriculture, Forestry and Fisheries and 187 varieties included in the soybean core collection . The seeds were sown in pots and grown until the first trifoliate leaves emerged in greenhouses. Total genomic DNA was extracted from the first trifoliate leaves of one plant using a method based on guanidine hydrochloride and proteinase K , with mollection  were alsllection . The avellection .The varieties that had SNP genotypes from the Axiom custom array were subjected to subsequent analyses. Because the names of varieties usually change with the advancement of generations, the variety names were integrated using the names at the last generations. Among the 440 fields included in the historical data, 41 fields were selected for analyses because of the number of records and balanced geological locations across Japan . For eacThe following six traits were selected for analyses: DTF (days), DTM (days), SL (cm), PR (%), YI (kg/a), and SW (g/100 seeds). These traits were selected because of their importance for breeding and the number of phenotyped records. In Japanese soybean breeding, to unify the measuring methods of traits, regulations on trait measurement were established in 1954 by a committee where the Ministry of Agriculture and Forestry at the time took the leading role. Although these regulations seem to have undergone several minor updates to date, trait definitions were largely common across the study period. Briefly, DTF was defined as the date when 40–50% of the buds of the strains reached flowering. DTM was the date when 80–90% of the pods of the strains showed variety-unique colours at maturity. SL was the length of the main culm between the ground surface and the growth point. PR was mainly measured using near-infrared spectroscopy, but there seemed to be some variations in the measuring methods and used machines. YI and SW were defined as the weight with 15% moisture, although there also seemed to be some variations in the moisture percentage among stations and years.Records that had at least one observation for these six traits were extracted for subsequent analyses. As a result, the extracted records consisted of 25,158 records of 624 varieties evaluated at 41 fields . The sum2) and potential evapotranspiration (mm), were used. Among these factors, wind speed and maximum wind speed were adjusted as observed at 2.5 m of altitude by the developer of the database from the original values of the JMA. Solar radiation was estimated by the database developer from the observations of the JMA regarding hours of sunshine. Potential evapotranspiration was also estimated by the developer based on mean temperature, vapour pressure, wind speed, solar radiation and air pressure. The values of the other factors were according to the observations of the JMA. In addition to these factors, photoperiod (h) was calculated for each environment. For simplicity, daily length was also considered as a meteorological factor. The values of these meteorological factors are included in Meteorological information was taken from MeteoCropDB ver. 1.0 . The datSingle-trait mixed models were fitted to the records at each environment. This process had two purposes; the first was to eliminate variations attributable to years and management conditions from phenotypic values, that is, to create The year effects were added as fixed effects in the mixed models. The conditions of three management methods  were modelled in various ways. In nine fields , multiple management conditions were consistently conducted in multiple years. For these fields, different conditions were defined as different environments resulting in a total of 52 environments .In most fields, however, the management conditions often varied across years. When the effects of management conditions were indistinguishable from those of year effects, the effects were absorbed to the year effects and not modelled. Otherwise, the effects were added in the mixed models. The modelling schemes varied according to the environments and management methods. Although the conditions of plant density, fertilizer, and sowing date are intrinsically continuous variables, when the number of conditions at an environment was few, these conditions were regarded as categorical variables and the effects were modelled as fixed effects. Otherwise, the conditions were regarded as continuous and the effects were model using basis functions  and genotypes. Thus, a meteorological event  on a calendar day can have different effects on plants with different growth stages. To consider the difference in growth stage, the growth period between the sowing dates and days of flowering was divided into 10 equal-sized stages (on average 5.5 ± 1.2 days per stage), and the period between the days of flowering and days of maturity was divided into 20 equal-sized stages (3.8 ± 0.5 days per stage). For each variety at each environment and year, the daily meteorological values within stages were averaged .Meteorological values representing each environment were then obtained by averaging the above-mentioned meteorological values of all plants included in the environment, yielding 30 values for the 30 growth stages for each environment . In addiThree issues were notable in this procedure. First, the number of stages will depend on the prior knowledge or assumptions on growth stages. For soybean, typically five and eight growth stages are assumed for the vegetative and reproductive phases . The numjth and kth environments were calculated as xjxk where xj and xk indicate the deviations  of the environmental covariate values at environments j and k, respectively. As a result, a 52 × 52 similarity matrix was obtained for each environmental covariate. Total 465 growth stages × 14 meteorological factors = 6,510 similarity matrices were obtained for each trait.For each environmental covariate , 52 representative values corresponding to 52 environments were used to calculate the similarity between environments . The simGenetic correlations between environments were estimated bivariate mixed models in a pairwise manner. The model can be written as87 by 52 . Note th87 by 52 .Subsequently, for each trait, genetic correlations between environments were estimated in a pairwise manner. First, the bivariate mixed model  was fittapproach . The resapproach .It is notable that genetic correlations between environment can be estimated using methods other than the pairwise estimation described here . But the pairwise estimation will be one of the easiest methods to conduct in particular when the number of environments is great.p values of the coefficients were also calculated using the function. The significance of the coefficients was judged after Bonferroni correction of the p values. Considering the number of environmental covariates  and traits (6), the threshold of significance was set at 0.05/ = 1.28e–6.Now we have the 6,510  similarity matrices of environmental covariates and six (number of traits) genetic correlation matrices. The sizes of both kind matrices were 52 (number of environments) × 52 for the traits except for PR where the sizes were 30 × 30. The Pearson’s product moment correlation coefficients between the upper (or lower) triangle off-diagonals of the similarity matrices and genetic correlation matrices were then calculated  using thr2 was selected, resulting in 555 significant environmental covariate/trait combinations  varieties were randomly selected from the 624 varieties and subjected to GWA mapping. Random sampling was adopted because the aim of this subsampling is to perturbate the genetic structure which might produce false positives. This procedure was repeated 10 times. Then replications where the regions detected using the full data also showed significant associations were counted . Gene ontology of these orthologs were surveyed with overrepresentation analyses provided by PANTHER ver.16.0 (http://pantherdb.org/) using Arabidopsis genes as a reference.Orthologs of r2) and the number of environments denoted as M. The detection power was expected to increase as r2 and M increase. For the sake of simplification, j, and r2 and M from grids 0.01, 0.018, 0.025, 0.035, 0.053, 0.075, 0.1, and 0.15, and 5, 10, 15, 20, 30, 40, 50, and 60, respectively.(1). Assign values for j ((2). Generate r2 and the variance of (3). Determine the variance of (4). Generate r. 2.13) . Paramet(5). Generate a symmetric matrix by adding (6). Convert the symmetric matrix of (5) to the correlation matrix.Simulation analyses were conducted to assess the performance to ECGC to detect environment covariates. As illustrated in r2 and M, the genetic correlation matrix was simulated 2000 times. For each simulated matrix, 99 additional environment covariates that were not associated with the correlation matrix were also simulated as true negatives. Then the Pearson correlation between For each combination of  1.0-11) .The multi-environment data set used here consisted of 25,158 records of 624 varieties that were evaluated from 1961 to 2015 at 41 fields. The included varieties consisted of cultivars and breeding lines developed in Japan for the sake of food production, such as tofu . Among tThe steps of ECGC are shown in r) between the off-diagonal elements of the matrices. p values of r calculated for 10 meteorological factors , values were extracted from the 52 environments, and the linear kernel  between environments was calculated . In the  than SW . In the  than SW . Strongep values. The first region was detected around the 2nd to 22nd growth stages . This stage corresponds to a period from sowing to, on average, 16.5 days after sowing. Soybeans are vulnerable to waterlogging, and, in particular, waterlogging around germination has a severe impact on YI . During this period, these meteorological factors also showed a high −log10 p value for SW; the highest value for SW around this period was 11.101 for hours of sunshine at the 29th to 30th stage (r2 = 0.035).For YI, three regions in elopment , this peelopment . Becauseelopment , it is lct on YI . In addict on YI . Thus, op value for SW was observed in relation to potential evapotranspiration at the 8th to 10th stages , maximum temperature (r2 = 0.049), minimum temperature (r2 = 0.056), vapor pressure , solar radiation (r2 = 0.079) and potential evapotranspiration (r2 = 0.079). The 21st to 27th stages occurred on average 38–64.6 days after flowering, when the accumulation of protein is past its rapidest growth period (20–40 days after flowering), but is still ongoing (The  ongoing . The metr2 and the number of environment M. The receiver operating characteristic (ROC) curves obtained under different combinations of r2 and M are shown in r2 and M increased. In r2 values for each trait are shown for comparison with r2 values of the detected environmental covariates were greater than 0.053 for PR (M = 30) and greater than 0.018 for the other traits (M = 52)  suggest that ECGC detected the environmental covariates with reasonable accuracy under these r2 and M values.To assess the validity of these detected environmental covariates, the detective power of ECGC was verified with simulations. As illustrated in the Methods, the performance of ECGC is affected by (M = 52) . The ROCE2)  . On the E2) . These rdetected . For DTFGlyma.12G142900 (18.503–18.505 Mbp) in Arabidopsis thaliana (AT4G27280 and AT5G54490) are involved in cell response to hypoxia. In addition, the gene (Glyma.12G142900) was reported to show higher expression on root tissues , suggesting the gene is a plausible candidate.GWA mapping of ECGC could narrow down a genomic region that was suggested to be responsible for the G d stages . These rd stages , which iE2 (45.29–45.32 Mbp), which is an analogous gene of GIGANTEA of Arabidopsis and a major gene for flowering time in soybean (Glyma.10G180600 and Glyma.10G209600) of Arabidopsis flowering genes . Known flowering genes of soybean responsible for photosensitivity, such as E1 (E3 (E4 (E1 (E3 and E4 were probably not involved in the G E2 gene effect was the most variable across environments (Strong associations were observed on multiple adjacent regions on chromosome 10 spanning 44.46–46.61 Mbp for various meteorological factors, including temperature-related covariates and photoperiod . These rractions . These r soybean . These rch as E1 , E3 (Wats E1 (E3 , and E4  (E3 (E4 , were no (E4 (E1  were str (E4 (E1 , the effg sommer  showed tronments .p < 0.05 after Bonferroni correction, The slopes provide insights on how the G I and SW  and ofteThe base model of ECGC is a conventional model for reaction norm where the genotypic value is divided into a component representing sensitivity to environmental covariates and a component free from them . The novrelation . Lastly,relation  and paraOwing to these advantages, our proposed ECGC was applicable to the large-scale multi-environment data set of soybean, and able to depict comprehensive landscapes on how environment stimuli are involved in the G


Adolescent Idiopathic Scoliosis (AIS) is a prevalent and important spine disorder in the pediatric age group. An increased family tendency was observed for a long time, but the underlying genetic mechanism was uncertain. In 1999, Dr. Yves Cotrel founded the Cotrel Foundation in the Institut de France, which supported collaboration of international researchers to work together to better understand the etiology of AIS. This new concept of AIS as a complex trait evolved in this setting among researchers who joined the annual Cotrel meetings. It is now over a decade since the first proposal of the complex trait genetic model for AIS. Here, we review in detail the vast information about the genetic and environmental factors in AIS pathogenesis gathered to date. More importantly, new insights into AIS etiology were brought to us through new research data under the perspective of a complex trait. Hopefully, future research directions may lead to better management of AIS, which has a tremendous impact on affected adolescents in terms of both physical growth and psychological development. In 2005, Tang and Miller et al. received grant support from the Fondation Yves Cotrel in the Institut de France to investigate the genetic etiology of adolescent Idiopathic Scoliosis (AIS) . During Adolescent Idiopathic Scoliosis (AIS) is the most common form of spinal deformity . It affeAlthough the tendency of AIS to run in families was described for a long time, the mechanism and mode of inheritance were unknown. Linkage analyses were carried out by different research groups to locate the causative genetic loci. Both parametric and nonparametric linkage analysis were applied to study familial forms of AIS and resulted in different modes of inheritance, including autosomal dominant and X-linked while multiple loci in chromosomes 9, 16, 17, 19, and X and others were implicated as the loci of major AIS disease genes ,9. AlthoGenetic susceptibility for AIS is confusing and its inheritance mechanism is uncertain, however, it is now widely accepted that the complex trait  fits well with AIS. When we put forward this bold new theory in 2007 , we borrUnlike other common diseases, there was a concern that a specific value of heritability was not yet determined for AIS ,12,13. Wp < 0.0001) supporting a strong familial tendency of AIS. The average sibling recurrent risk ratio was 13-fold higher than that of the general population. Overall, heritability for AIS was estimated to be 87.5%, which was comparable to that of body height. Tang et al. examined the sibling recurrent risk and heritability of AIS in first-degree relatives of 415 Chinese patients who were first diagnosed by a community screening program [These results support the prevailing impression of a strong genetic liability for development of AIS . Due to Therefore, a high heritability of AIS liability is (1) universal across populations and (2) applied to both mild and severe disease . HoweverHeterogeneity both in terms of phenotype and genotype are enormous in AIS. It was widely reported that discordant profiles of spine curvature are present among siblings in a family ,19 and eTo reconcile these observations, we proposed to understand AIS in two phases. Firstly, an initiation (onset) phase when the spine curvature starts to form, and a second progression phase, which determines the primary direction, severity, and the outcome of the curvature. With this 2-phase concept, the contradictory observations mentioned above could be reasoned if different genetic and environmental factors operate at different phases. Firstly, some genetic and environmental risk factors trigger the initiation of curvature. Then, it is followed by a separate battery of factors , which determine how far the curve would advance during the progression phase. In 2007, Cheng et al. elaborated this concept . Figure The implication of the 2-phase model is far-reaching. In the past, the traditional research approach lumped the 2 phases together and results were poorly reproducible due to risk factors of different phases that were confounding each other. Recent research design incorporates the 2-phase model and leads to ground-breaking discoveries of disease predisposition genes in initiation of AIS (to be discussed in the next section).While genetic studies are very informative, it is important to tease out false-positive associations. That is why replication of studies and confirmation of association signals in other sample sets, or even better in other population samples, are fundamental principles of high-quality genetic studies. While predisposition genes leading to the initiation of spinal curvature were found and validated by different studies, genetic findings were poorly reproducible for prediction of the extent of curve progression . A biotechnology startup company developed a single nucleotide polymorphism (SNP) based prognostic test to predict curve progression among AIS patients . It is cCollectively, data are suggestive that the progression phase is potentially under secondary influences including both environmental influence and genetic effects together with potential epigenetic influence. This hypothesis was further supported by the lack of correlation between curve severity and family history of scoliosis . TherefoIn view of the complex trait nature of AIS, genetic association studies would be the most appropriate methodology in finding the susceptibility genes. Linkage analysis typically used a few families  and few The issue of genetic heterogeneity in AIS is also evident in linkage study results, as no single locus found by linkage analysis was replicated by another research center. This lack of overlap in the reported linkage loci may be a result of several reasons. Firstly, they might be false-positive linkage loci. Secondly, it is also plausible that every family has their own private locus, which implies that many scoliosis causative genes are present in the genome. Furthermore, a genealogical study of IS patients from the Intermountain West  also sugIn the 2007 annual Cotrel foundation research meeting, the genetic association study methodology was proposed. The complex trait paper was written and published before the era of genome-wide association study. The objectives of the earlier research supported by the Cotrel Foundation were to perform genetic association studies and to fine map a previously linkage-supported locus and other candidate genes. On the other hand, genetic association analysis of a dichotomous (discrete) trait may be more suitable to identify genetic etiology of the multifactorial complex trait that could account for a great proportion of AIS patients .While genome-wide association study (GWAS) would provide an unbiased representation of association signals across the whole genome, large funding support was needed. Immediately after the first GWAS WTCCC paper , the Cotp-values, it does not guarantee all GWAS hits are true positives. Replication provides the ultimate validation of any association [The research field understands that false-positive findings are common in candidate gene associations due to spurious associations. GWAS is also susceptible to spurious association as it performs so many statistical tests in one study. Although stringent Bonferroni correction is applied to define the cutoff ociation ,45. LBX1ociation ,41. Howeociation ,47,48. Lociation . In contLBX1-AS1. The predisposition allele of rs11190870 was the major  allele in various populations with an allele frequency ranging from 0.49 to 0.58) [Genetic associations were detected near to but outside the protein-coding sequence of LBX1 gene , together with rs678741 in a nearby antisense transcript called Rs3904778 located in an intron of BNC2 was first associated with AIS in a GWAS of Japanese patients . The higBoth LBX1 and BNC2 are functionally related to early muscle development. These findings provide potential new insight into the initiation (onset) of scoliosis. AIS was traditionally viewed as a bone disease involving the various levels of vertebrae and their growth. With these highly replicated genetic predisposition loci identified, which do not have an obvious function in bone, the traditional understanding of AIS as a primary bone disease needs to be reconsidered. Instead, a primary functional alteration of muscle with subsequent interplay between muscle and bone tissues may be occurring during the initiation of scoliosis.Patients with Marfan syndrome and Ehlers–Danlos syndrome often develop scoliosis among other inherited types of connective tissue diseases. They are autosomal dominant diseases with mutations in fibrillin (FBN1) or one of the collagen genes ,55. RareA major hurdle in the study of AIS is the development of a reliable animal model that is representative of the disease phenotype. Previous attempts of inducing scoliosis with surgical intervention, such as using pinealectomy chicks and bipedal mice, were largely unsatisfactory. A spontaneous scoliosis animal model with spinal curvature developing during a rapid growth phase  was long sought. Zygotic ptk7 (Protein tyrosine kinase-7) mutant zebrafish provide important clues to the etiology of scoliosis and represent a breakthrough in animal model research of AIS ,59. Ptk7In zebrafish, due to the small CSF space, cilia may be essential for a normal CSF flow. On the other hand, Chu et al. measured the CSF flow in patients with neural axis anomalies but failed to demonstrate any observable impairment . A studyWith these scoliosis of spontaneous-onset fish models, various treatment strategies can now be examined. Traditional scoliosis treatments target primarily mechanical correction by using bracing or surgery. Few attempts of medical treatment other than melatonin were tried. Melatonin treatment was based on the pinealectomy induced scoliosis model in chicken, however, the efficiency in patients was limited . The zygA key difference between single-gene disease and the polygenic complex trait is that the role played by environmental factors should have a prominent contribution in the latter. Cheng et al and other researchers showed that systemic low bone mineral density (BMD) was a significant and independent prognostic factor for curve progression. Furthermore, over 30% of AIS had low BMD as compared to 16% in that of the general population . Low BMDThis is a question frequently raised by colleagues: how can genetic studies benefit patients? It is an excellent question, and it deserves an answer. We hope that after reading this review, many colleagues will appreciate that the knowledge and understanding of AIS were much enhanced. More than 20 years ago, Dr. Cotrel initiated the visions as shown in The next question is whether such understanding could be obtained without utilizing genetic studies. We believe that the advances in the past decade are largely related to genetic research. Genetic studies have a unique advantage that all genetic alleles, mutation, or genetic risk factors must be the primary event in any causative analysis. Identifying a causative chain of events, and determining which are the primary and secondary adaptive events in pathogenesis is always difficult. Furthermore, it was a century-long argument about which tissue  early curve control/correction, and (2) screening for early curve.Remodeling of soft tissue is more feasible than remodeling hard tissue. For example, soft tissues can be remodeled by various means, e.g., exercise, stretching, bracing, etc. Inspired by the soft tissue remodeling idea, Bracing provides regional control of the spine curvature and was used for a long time. However, there is no biological ground to determine when bracing should be started. The implication that being a soft tissue disease in some AIS patients from the outset would suggest that we need to start bracing early in the course of disease before secondary adaptive bone changes occur which perpetuate a vicious cycle of maladaptation.For an early intervention to become available and effective, patients need to be identified early. As a matter of fact, early diagnosis is also required to evaluate the effectiveness of any early intervention. Hong Kong is among the few regions in the World that practice universal scoliosis screening of school children. Screening enables detection of early patients with milder curves, whereas hospital cases are usually biased toward severe cases so that a comprehensive, full-spectrum understanding of the epidemiology of AIS is possible. In addition, patients with mild curves could be studied so that risk factors for curve progression can be analyzed. The recent advance in low-dose radiation imaging instruments allows accurate and close monitoring of curve progression which was not possible before. These new technologies will very likely bring us new understandings of the biomechanics of early stage of disease when targeted intervention could be possible. All such new concepts, of course, need further evidence and clinical trial to support.In conclusion, genetic studies of AIS yielded breakthroughs in the understanding of its etiology. Genetic predisposition in genes related to muscle development and extracellular matrix components indicates that initiation of AIS in some individuals is potentially due to soft tissue pathology. The new insights clarify the dated arguments in pathogenesis and provide new directions for future research and potential treatment of the disease.


Supplemental Digital Content is available in the text. Observational epidemiological studies have reported that higher maternal blood pressure (BP) during pregnancy is associated with increased future risk of offspring cardiometabolic disease. However, it is unclear whether this association represents a causal relationship through intrauterine mechanisms. We used a Mendelian randomization (MR) framework to examine the relationship between unweighted maternal genetic scores for systolic BP and diastolic BP and a range of cardiometabolic risk factors in the offspring of up to 29 708 genotyped mother-offspring pairs from the UKB study (UK Biobank) and the HUNT study . We conducted similar analyses in up to 21 423 father-offspring pairs from the same cohorts. We confirmed that the BP-associated genetic variants from the general population sample also had similar effects on maternal BP during pregnancy in independent cohorts. We did not detect any association between maternal  unweighted genetic scores and cardiometabolic offspring outcomes in the meta-analysis of UKB and HUNT after adjusting for offspring genotypes at the same loci. We find little evidence to support the notion that maternal BP is a major causal risk factor for adverse offspring cardiometabolic outcomes in later life. Understanding this relationship will help determine whether intervening on maternal BP during pregnancy will combat the rising incidence of offspring cardiometabolic diseases in adulthood.Observational epidemiological studies using multivariable regression have shown that gestational hypertensive disorders are associated with increased risk of offspring cardiometabolic diseases in later life, including cardiovascular diseases and type 2 diabetes.12 Mendel’s Laws of Inheritance  mean that genetic variants are often less susceptible to confounding and reverse causality than the variables used in traditional observational epidemiological studies.13 We have previously developed a MR framework to investigate the potential maternal exposures on offspring’s health and disease in later life14 .Mendelian randomization (MR) is an epidemiological method used to estimate the causal relationship between a modifiable environmental exposure of interest and a medically relevant trait or disease.18 This has partly been due to the paucity of cohorts world-wide with genotyped mother-offspring pairs and offspring of advanced age, hindering the estimation of maternal genetic effects on offspring who have developed cardiometabolic disease. In the current study, we addressed these issues by performing a genetic association study in up to 29 708 genotyped mother-offspring pairs and up to 21 423 father-offspring pairs from the UKB study (UK Biobank)19 and HUNT study .20 Specifically, we regressed offspring cardiometabolic risk factors on maternal genetic risk scores (GRSs) for BP while simultaneously conditioning on offspring genotypes at the same loci, thereby accounting for the potential contaminating influences of genetic pleiotropy through the offspring genome.14 Associations between maternal GRSs and offspring outcomes would be consistent with a causal effect of maternal BP on the offspring outcomes.Most previous MR studies investigating the relationship between early life environmental exposures and later-life cardiometabolic traits and diseases have not distinguished between maternal and offspring genetic effects, which has complicated interpretation of the results of such investigations.http://www.ukbiobank.ac.uk/). Phenotype and genotype data from the ALSPAC  and HUNT studies are archived centrally with the corresponding cohort studies. Individual-level data can be made available to researchers upon application to the resources. Requirements for data access to the UKB, ALSPAC,23 and the HUNT studies are described at https://www.ukbiobank.ac.uk/, http://www.bristol.ac.uk/alspac/, and www.ntnu.edu/hunt/, respectively.Human genotype and phenotype data from the UKB on which the results of this study were based were accessed with accession ID 12703 and 53641. The genotype and phenotype data are available upon application to the UKB (24), recruited from across the United Kingdom at age 40 to 69 years between 2006 and 2010, with a broad range of health-related information and genome-wide genetic data25 . Only individuals of European ancestry were included in the present study .The UKB study is a study of over 500 000 volunteers . After cleaning, there were 4119 mother-offspring pairs and 1829 father-offspring pairs available for analysis .Parent-offspring relationships were inferred by the KING software using genotyping data29 . Similar to the UKB, parent-offspring pairs were identified using the KING software,28 reported sex, and date of birth.32 Only individuals of European ancestry were included in the study . After cleaning, there were 26 057 mother-offspring pairs and 19 792 father-offspring pairs available for analysis (Table S1).The HUNT is a large population-based study of ≈240 000 participants (with >50% response rate of those invited) with a broad range of health-related information and genome-wide genetic dataSupplemental Material41 and Table S1.The offspring cardiometabolic risk factors included in our analysis were systolic BP, diastolic BP, body mass index, lipid profile  [lipoprotein A], HDL-C [high-density lipoprotein cholesterol], and triglycerides), glycemic biomarkers , and other relevant cardiometabolic traits . Further details of the collection and availability of UKB and HUNT variables are given in the 44 The genome-wide association studies of BP used for the selection of instruments did not include participants from the UKB or HUNT studies in the discovery stages, which avoids potential sample overlap with mothers/fathers that were included in the current analysis. Unweighted genetic scores were constructed by summing BP-raising alleles .The BP-associated SNPs were identified from external genome-wide association studies performed by the International Blood Pressure Consortium.Supplemental Material47).We conducted 3 analyses to confirm that the BP-associated SNPs from the general population sample also had similar effects on BP during pregnancy .32 The details of the regression analyses and secondary analyses in mother-offspring pairs are given in the Supplemental Material49 and Table S3.48Maternal BP during pregnancy was not physically measured in the UKB or HUNT cohorts; instead, we instrumented this exposure using maternal GRSs for BP. Thus, we directly tested the association between maternal unweighted genetic scores and offspring outcomes in up to 29 708 mother-offspring pairs from UKB and HUNT, adjusting for the offspring’s genetic score calculated from the same BP-associated SNPs .If the effect of maternal BP is operating on offspring via the intrauterine environment, then we would expect no causal relationship in father-offspring pairs. Therefore, we also conducted sensitivity analyses in up to 21 423 father-offspring pairs from the UKB and the HUNT studies to explore the possibility of postnatal effects . In the case of all analyses, we present P values that have not been corrected for multiple testing.We meta-analyzed the results of the primary analyses from the UKB and HUNT studies for each offspring variable using Stouffer Z score which weights each study’s contribution by the square root of the sample size; this facilitated meta-analysis of variables that were scaled differently in UKB versus HUNT.https://evansgroup.di.uq.edu.au/MGPC/)52 .We calculated the statistical power to detect maternal genetic effects on offspring cardiometabolic risk factors conditional on offspring genotype using the Maternal and Offspring Genetic Effects Power Calculator .We found strong evidence that our selected BP-associated SNPs from the general population sample have relatively consistent direction of effects on BP during pregnancy and gestational hypertensive disorders in independent cohorts . Similarly, we did not detect any association in the father-offspring pairs in the meta-analysis, consistent with the absence of a postnatal effect operating (Table S6). The results of the main analyses in individual cohorts (UKB and HUNT) are presented in the Table, and the results of sensitivity analyses are given in Tables S7 through S14.The results from the analyses assessing the association between unweighted maternal genetic scores for systolic BP- or diastolic BP-associated SNPs and offspring cardiometabolic traits, after adjusting for offspring genetic scores, in the UKB and HUNT studies are presented in the Table, along with the meta-analysis Figures S3 and S4, Table S15, and Supplemental Material).Power calculations indicated that we had ≥80% power to detect a maternal genetic effect that explained as little as 0.035% of the variance in the offspring cardiometabolic trait with 29 708 mother-offspring pairs (2-tailed α=0.05). For the traits that were available in the UKB only, with 3756 mother-offspring pairs, we were underpowered (19%) to detect an effect size as low as 0.04%; however, we had >80% power to detect a large effect size of 0.28% of the variance in the offspring cardiometabolic outcome  pairs in the UKB and HUNT studies to examine a possible causal relationship between these variables using MR. Importantly, all offspring from the UKB and the majority of offspring from the HUNT study are middle-aged and elderly adults who are old enough to manifest elevated levels of risk factors for cardiometabolic disease. Our results in general, however, did not support a strong association between genetically predicted maternal BP and offspring cardiometabolic risk factors. The implication is that modest increases in maternal BP during pregnancy are unlikely to drive large increases in offspring cardiometabolic risk in later life. This implication is consistent with a previous study of siblings in HUNT.We did not find any strong indications of effects of maternal BP on offspring outcomes, however, in the smaller and underpowered analysis of UKB alone, we did identify 2 nominal associations between maternal systolic BP risk score and ApoB. We were unable to meta-analyze/replicate this finding in the HUNT study as ApoB was not available for analysis. It is also likely given that the UKB analysis on its own is underpowered, that the finding may be due to type 1 error . Thus, the association needs to be replicated in a larger sample of mother-offspring pairs.Asymptotic power calculations suggested that our study was well powered (≥80%) to detect an effect size as low as 0.035% of the variance explained in the offspring outcome by the unweighted maternal genetic score. However, given that an unweighted genetic score of BP variants explains about 0.8% in maternal BP, the above power calculation translates to a causal effect of maternal BP on offspring cardiometabolic risk which is quite large . This implies that whilst our study is well powered to rule out strong effects of maternal BP on offspring cardiometabolic risk factors, it has less power to investigate small to moderate effects. The corollary is that the nominal associations found in the UKB are likely to reflect false positives (type 1 error) brought about by multiple testing.20 while the UKB study only had a participation rate of 5.45%, tending to enroll healthier people with higher socioeconomic status than the general population.55Differences in results between UKB and HUNT may reflect differences in sample size between the studies, and potentially, contrasting selection biases. For example, over 50% of the inhabitants in the Nord-Trøndelag County participated in the HUNT study,4 have focused on the relationship between gestational hypertension and preeclampsia . We did not specifically investigate gestational hypertension or preeclampsia in the current study due to the lack of genetic variants associated specifically with these diagnoses. A recent genome-wide association study of preeclampsia identified 2 regions of the genome that reached genome-wide significance, both of which have been previously associated with BP in nonpregnant women and men.56 Additionally, that study showed that a GRS for hypertension in a sample of nonpregnant women associated with preeclampsia,56 providing further evidence for the genetic overlap between the 2 diagnoses. It is, therefore, likely that the GRSs used in our study not only increase maternal BP during pregnancy but also increase risk of preeclampsia.Previous observational association studies in humans57 That being said, we note that it is still possible that extreme exposures like gestational hypertension and preeclampsia may causally increase future offspring cardiometabolic risk, but it is difficult to examine these hypotheses via MR until the scientific community discovers genetic instruments that specifically instrument gestational hypertension/preeclampsia.Our analyses used genetic variants that were associated with BP as a quantitative trait in population-based samples of individuals. We, therefore, did not explicitly model the effect of gestational hypertensive disorders (or preterm births/adverse birth outcomes) in our analyses. However, as GRSs which increase maternal BP are also likely to increase the risk of gestational hypertensive disorders, we expect that the presence of mothers with gestational hypertensive disorders in our data set may also contribute to any association between maternal (BP associated) GRS and future cardiometabolic risk in offspring. Nevertheless, it is difficult to assess the relative contribution of each of these sources of variation to our results without detailed clinical information across pregnancy, with the caveat that our study is likely to be better powered to detect the causal effect of quantitative changes in maternal BP during pregnancy particularly within the normal range .14 Second, we have assumed that genetic variants identified in large genome-wide association studies of BP in males and nonpregnant females are also associated with BP (in a similar direction) in pregnant women. Our analyses performed in pregnant mothers in ALSPAC and FinnGen support the assumption that many BP-associated loci operating in the general population also exert similar effects during pregnancy. Third, we assume a linear relationship between and within maternal BP-associated loci and later-life cardiometabolic traits in their offspring, which may not optimally capture the true relationship between the two. Fourth, the blood tests for lipid and glucose traits were performed using nonfasting samples in both UKB and the HUNT studies which may have influenced the estimates for triglycerides and glucose; however, other biomarkers such as glycated hemoglobin, cholesterol, and lipoprotein levels do not change or only differ minimally in fasting versus nonfasting tests.58 Fifth, our model did not completely control for possible pleiotropy through the maternal genome. Although the current model blocks pleiotropic paths through the offspring genome , BP-associated SNPs in the mother could still exert prenatal pleiotropic effects and maternal-specific postnatal effects on offspring cardiometabolic risk through effects other than raising BP. However, this is perhaps less of a concern for the negative results in our study, as any pleiotropic effect would have to have an equal and opposite effect to obscure a true effect of maternal BP on offspring cardiometabolic risk, which is an unlikely scenario. Furthermore, our models do not account for assortative mating, but it seems unlikely that this would cause our observed negative results.59 Sixth, we did not have enough power with the current sample size to conduct analyses stratified by offspring sex, to investigate sexual dimorphism in the maternal genetic effects under study. Seventh, because the analyses were conducted only in participants of European descent, the results need to be replicated in other populations. Finally, only a selection of cardiometabolic traits of interest was available in the HUNT study. Therefore, we could not replicate the association between genetically predicted maternal BP and offspring outcomes, such as ApoB and CRP. These associations will need to be replicated in larger cohorts.There are several limitations to the current study. First, our framework does not formally estimate the size of the causal effect of maternal BP on offspring cardiometabolic traits as is done in most MR analyses , but it nevertheless uses MR principles to provide evidence for or against a causal relationship between these traits.In conclusion, our results suggest that perturbations in maternal BP during pregnancy are unlikely to cause large increases in the risk of offspring cardiometabolic disease in later life. Although previous conventional epidemiological studies have found some evidence for associations between maternal BP and offspring cardiometabolic risk factors, our analyses, which provide a more rigorous assessment of causality, suggest that offspring genetic effects and confounding by environmental factors may be the predominant explanation for such population-level associations. MR studies that specifically examine the long-term effects of extreme exposures like gestational hypertension and preeclampsia on future offspring cardiometabolic risk are needed.We thank the research participants of the UK Biobank, HUNT , and FinnGen studies and are extremely grateful to all the families who took part in the ALSPAC study , the midwives for their help in recruiting them, and the whole ALSPAC team, which includes interviewers, computer and laboratory technicians, clerical workers, research scientists, volunteers, managers, receptionists, and nurses. This research has been conducted using the UK Biobank (Reference 12703 and 53641), ALSPAC (Reference B3544) and HUNT resources. The HUNT study is a collaboration between HUNT Research Center , Trøndelag County Council, Central Norway Regional Health Authority, and the Norwegian Institute of Public Health.http://www.bristol.ac.uk/alspac/external/documents/grant-acknowledgements.pdf); this research was specifically funded by Lifelong Health and Wellbeing (LLHW) via the MRC (G1001357), Wellcome Trust (WT092830/Z/10/Z and WT088806), and the British Heart Foundation (SP/07/008/24066). Genome-wide association studies (GWAS) data were generated by Sample Logistics and Genotyping Facilities at Wellcome Sanger Institute and LabCorp (Laboratory Corporation of America) using support from 23andMe. This publication is the work of the authors and G. Wang, L. Bhatta, N.M. Warrington, D.M. Evans, and B. Brumpton will serve as guarantors for the contents of this article.G. Wang is supported by The University of Queensland Graduate School Scholarship (UQGSS). D.M. Evans is funded by an Australian National Health and Medical Research Council Senior Research Fellowship (APP1137714), and this work was funded by NHMRC project grants (GNT1157714 and GNT1183074). N.M. Warrington is affiliated with a unit that is supported by the UK Medical Research Council (MC_UU_00011/3 and MC_UU_00011/6). J.P. Kemp is funded by a National Health and Medical Research Council  Investigator grant (GNT1177938). G.-H. Moen is supported by the Norwegian Research Council , the Norwegian Diabetes Association, Oslo Diabetes Research Centre, and Nils Normans minnegave. T.A. Bond works in/is affiliated with a unit that is supported by the UK Medical Research Council (MC_UU_00011/6) and is supported by the British Heart Foundation Accelerator Award at the University of Bristol (R100643-101). L. Bhatta, B.O. Åsvold, and B. Brumpton receive support from the K.G. Jebsen Center for Genetic Epidemiology funded by Stiftelsen Kristian Gerhard Jebsen; Faculty of Medicine and Health Sciences, NTNU; The Liaison Committee for education, research and innovation in Central Norway; and the Joint Research Committee between St Olavs Hospital and the Faculty of Medicine and Health Sciences, NTNU. The genotyping in HUNT was financed by the National Institute of Health (NIH); University of Michigan; The Research Council of Norway; The Liaison Committee for education, research and innovation in Central Norway; and the Joint Research Committee between St Olavs Hospital and the Faculty of Medicine and Health Sciences, NTNU. The UK Medical Research Council and Wellcome (Grant ref: 217065/Z/19/Z) and the University of Bristol provide core support for ALSPAC . A comprehensive list of grants funding is available on the ALSPAC website (None.