There is a wealth of literature on the observed association between childhood trauma and psychotic illness. However, the relationship between childhood trauma and psychosis is complex and could be explained, in part, by gene–environment correlation.The association between schizophrenia polygenic scores (PGS) and experiencing childhood trauma was investigated using data from the Avon Longitudinal Study of Parents and Children (ALSPAC) and the Norwegian Mother, Father and Child Cohort Study (MoBa). Schizophrenia PGS were derived in each cohort for children, mothers, and fathers where genetic data were available. Measures of trauma exposure were derived based on data collected throughout childhood and adolescence  and at age 8 years (MoBa).Within ALSPAC, we found a positive association between schizophrenia PGS and exposure to trauma across childhood and adolescence; effect sizes were consistent for both child or maternal PGS. We found evidence of an association between the schizophrenia PGS and the majority of trauma subtypes investigated, with the exception of bullying. These results were comparable with those of MoBa. Within ALSPAC, genetic liability to a range of additional psychiatric traits was also associated with a greater trauma exposure.Results from two international birth cohorts indicate that genetic liability for a range of psychiatric traits is associated with experiencing childhood trauma. Genome-wide association study of psychiatric phenotypes may also reflect risk factors for these phenotypes. Our findings also suggest that youth at higher genetic risk might require greater resources/support to ensure they grow-up in a healthy environment. There is a wealth of literature from observational studies showing an association between childhood trauma and psychotic illness. Recent studies have suggested that exposure to interpersonal violence or neglect in childhood can increase the risk of psychotic symptoms by two–three times  and trauma could occur as a result of gene–environment correlation, meaning without trauma being causal. However, it is also possible that gene–environment correlation can be part of a causal chain, a fact utilised by Mendelian randomisation, which relies on such correlations to infer causality  alongside trauma at several intervals . Details of genotyping and quality control measures are available in online Supplementary Materials.Genotyped data were available for 7977 children and 8196 mothers in the ALSPAC study. Data were also available on a subset of fathers within the cohort , with a greater number of SNPs included as the p-value thresholds became less conservative.PGS for schizophrenia liability were derived for mothers, fathers and children with genetic data available and were standardised prior to analyses. Schizophrenia PGS were derived as described in Jones et al. . These sTo maximise sample size and reduce selection bias due to attrition, multiple imputation was used to impute missing values in the trauma data. The imputation model included a broad range of variables related to trauma or variables known to be associated with sample missingness, in addition to the schizophrenia PGS and all variables included in any of the analysis models. A full description of the imputation strategy can be found in the online Supplementary Materials.p < 0.05 (PGS0.05) as this explains the most variability in genetic liability for schizophrenia in other samples  and then at specific intervals during this period . The associations between schizophrenia PGSp < 0.5 to p < 5 × 10−8 and exposure to trauma measured between 0 and 4.9 years of age. By restricting our analyses to this younger age group, it was expected that attrition would be minimal, and effects observed within this subset would be less prone to selection bias. Analyses were also performed using the complete-case data, to check consistency of results with the imputed dataset.We performed three sensitivity analyses. First, we examined the association between all PGS thresholds from 0.05 on trauma exposure, restricting the analysis to children with data available on mother and father genotype. We repeated this model, adjusting for mother and father genotype to estimate the association between child PGS and trauma, independent of parental genotype.Second, we performed unadjusted logistic regression of child PGS0.05 for several additional psychiatric phenotypes, described below, and conducted unadjusted logistic regressions to investigate the association between these scores with exposure to ‘any trauma’ at age 0–4.9 years. These additional phenotypes were attention-deficit hyperactivity disorder (ADHD) . MoBa is a prospective population-based pregnancy cohort study conducted by the Norwegian Institute of Public Health . Childhood trauma was measured in MoBa at age 8 years using maternal responses to questions selected to reflect experiences that would be highly upsetting to anyone encountering them , emotional neglect as not usually letting the child know when he/she is doing a good job at something  and physical abuse as being subjected to beating, kicking or other violence by adults in the past year . In addition, the three questions were used to derive a composite measure of ‘any trauma’ . Details of the questions and prevalence in the full MoBa cohort are available in online Supplementary Table S3.Details of genotyping and quality control measures in MoBa are available in online Supplementary Materials. PGSing them . There w0.05 and trauma exposure was assessed in complete cases using logistic regression adjusting for chip, batch and 10 principle components.Within MoBa, the association between PGSn = 9946), with 70.8% of participants experiencing trauma compared to 78.5% of those without genetic data (n = 3649)  . The preIn MoBa, 42 236 participants had information on trauma measured at 8 years; of these individuals, 25.3% reported experiencing any trauma, with ‘bullying’ being the most frequently endorsed exposure (online Supplementary Table S3).0.05 and increased exposure to childhood trauma at ages 0–4.9 years, 5–10.9 years and 11–17 years, as well as across childhood and adolescence [0–17 years: odds ratio (ORChild) 1.14, 95% confidence interval (CI) 1.08–1.20, p = 8.4 × 10−6; ORMother 1.13, 95% CI 1.06–1.20, p = 8.5 × 10−5; 0.05 available, the effect estimates were compatible with those observed using the mother and child PGS0.05, although there was no strong evidence of an association .When using both parental and child PGS generated using SNPs associated with schizophrenia at a range of 0.05 and trauma across the life course using complete-case data, effect estimates were largest at the youngest ages where attrition was minimal (online Supplementary Table S4), and these were in a consistent direction, although slightly attenuated, compared to the effects estimated when using the imputed data. The pattern of effects remained consistent at each age group, with evidence of an association between the schizophrenia PGS0.05 and increased exposure to childhood trauma (online Supplementary Table S5).Analyses were repeated using complete case data. The rates of trauma were comparable across each domain, and particularly at the youngest age group (0–4.9 years) where attrition was lowest (online Supplementary Table S2). Similarly, when looking at the association between PGS0.05 and trauma, with the observed effect sizes larger than those in the analyses containing the full child sample . For both cross-disorder and MDD, these associations were stronger when using the child PGS0.05 than the maternal PGS0.05. There was also some evidence of an association between childhood trauma and ADHD and bipolar disorder PGS0.05, but this was weaker. Evidence for these findings was consistent when using either maternal or child PGS0.05.We also examined the association of alternative psychiatric PGS0.05 and trauma exposures at age 8 years (Within MoBa, there was some evidence of association between the schizophrenia PGS 8 years . The eff 8 years .Table 5The main finding of this study is that PGS for schizophrenia liability were associated with increased exposure to childhood trauma within two independent and international birth cohorts. This finding was consistent when using both parental and child PGS, suggesting that it is unlikely to be solely driven by direct genetic effect from either parents or children. We also found that genetic liability to a range of additional psychiatric traits was associated with a greater trauma exposure. This highlights that individuals at higher genetic risk for poor mental health may benefit from supports that decrease their risk of trauma. It may also suggest that the original GWASs of these psychiatric traits are not only identifying variants associated with the trait of interest, but are also reflecting the effects of risk factors for these traits.CHRNA5-A3-B4 (Amos et al., As GWASs become larger and thus statistically more powerful, they can identify variants of increasingly small effect that are robustly associated with the disorder being studied. For example, prior to 2014, GWAS had identified around 30 loci associated with schizophrenia (Ripke et al., In addition to investigating the association with a single composite measure of trauma, we also examined several subdomains of trauma to further disentangle the relationship. These subdomains were: bullying, domestic violence, sexual abuse, emotional neglect, emotional cruelty and physical cruelty. When using both maternal and child PGS, we observed strong evidence of an association between increased schizophrenia PGS and all domains of childhood trauma across the life course, with the exception of bullying. Paternal PGS again generally showed a consistent direction of effect but, due to the smaller sample size, analyses were underpowered to detect effects. With the exception of bullying, each of our trauma subdomains is likely to occur within the home environment. Bullying, which often takes place at school and in the neighbourhood, showed little evidence of an effect in the ALSPAC cohort. Within ALSPAC, the bullying domain predominantly captured peer bullying, with only a single item on sibling bullying. In contrast, we did observe some association within MoBa, however, the bullying measure in MoBa includes exposure to violence from siblings. This suggests that there may be something specific to the home environment that is influencing this PGS–trauma exposure association, indicative of gene–environment correlation. If evocative gene–environment correlation was at play, we may expect to observe an association with bullying in addition to the other types of trauma. Equally, if active gene–environment correlation was occurring, we may expect to see a stronger association with the child PGS compared to the parental PGS. Given that we do not see either, and instead find that the association appears specific to more likely home-based exposures, it is possible that some form of passive gene–environment correlation is occurring, and that trauma could be a marker of genetic liability for later psychosis as well as a causal risk factor for psychosis. If the association that we observe in the home environment is due to parental traits, then establishing which of these traits play a role in shaping the greater ‘traumagenic’ environment could enable us to target modifiable factors to reduce their occurrence and impact. Although sufficient data are not currently available, analyses investigating between-sibling effects could go some way to disentangling these associations.After adjusting analyses for parental PGS to investigate the effect of child genotype independently of parental genetic effects, we found that at younger ages the child genotype effect estimates attenuated, while during adolescence effect estimates remained consistent. Since trauma is predominantly parent-reported at younger ages with an increasing number of self-reported measures in adolescence, one explanation is that this could be capturing reporting bias. An alternative argument is that child genotype becomes more influential for later trauma as children age; hence, a transition occurs from a passive gene–environment correlation to a more active gene–environment correlation model. Previous findings suggest that those exposed to trauma in early life are at greater risk of revictimisation in adolescence, which may also contribute to this observation (Fisher et al., We also investigated the association between childhood trauma with child and maternal PGS for a number of alternative psychiatric phenotypes, using the latest available data (Pardiñas et al., The observed association between genetic liability for poor mental health and exposure to childhood trauma does not suggest that the majority of individuals with a mental health disorder will expose their children to traumatic events. The measure of trauma used in our study included a range of different types of trauma exposure and were experienced by a large proportion of participants, including those with a low genetic liability. The mechanisms through which the association between genetic liability and childhood trauma acts could be such that individuals with a higher genetic liability for poor mental health outcomes may end up in more deprived neighbourhoods where exposure to trauma may be more common (Solmi, Lewis, Zammit, & Kirkbride, Another potential implication of our findings is that the observational association between childhood trauma and subsequent psychotic events may be partly due to genetic confounding, meaning that any causal effect of trauma on psychosis is somewhat weaker than the observed association between childhood trauma and subsequent psychosis would indicate. In a previous study using the ALSPAC cohort, we showed that the association between trauma and psychotic experiences was not attenuated by adjusting for either child or mother genetic risk for schizophrenia (Croft et al., There are several limitations in our study that should be considered. First, there was substantial missing data in the trauma variable which could have resulted in selection bias. However, we used multiple imputation to impute our sample and maximise sample size, and the effect estimates in the complete cases at the youngest age bracket were comparable with those from the imputed dataset. In ALSPAC, attrition has been shown to be patterned by genetic risk factors (Taylor et al., Analyses across two international birth cohorts indicate that genetic liability for schizophrenia, as well as other psychiatric phenotypes, is associated with childhood trauma. This could suggest that GWAS for these psychiatric traits are not only identifying variants associated with these traits, but may also reflect risk factors associated with them. We also found evidence to suggest the association between genetic liability for schizophrenia and exposure to trauma has some specificity to the home environment and suggests that youth at higher genetic risk might require greater resources/support to ensure they grow up in a healthy environment.


Wolbachia bacteria, or genome engineering-based mosquito control strategies including gene drives have been developed to address these problems, both requiring the release of modified mosquitoes into the environment. Here, we review the latest developments, notable similarities, and critical distinctions between these promising technologies and discuss their future applications for mosquito-borne disease control.Mosquito-borne diseases, such as dengue and malaria, pose significant global health burdens. Unfortunately, current control methods based on insecticides and environmental maintenance have fallen short of eliminating the disease burden. Scalable, deployable, genetic-based solutions are sought to reduce the transmission risk of these diseases. Pathogen-blocking  Mosquito-borne diseases pose significant global health burdens. In this review, the authors explore Wolbachia and genome engineering approaches to mosquito-borne disease population control. Roughly half of the world’s population is at risk of mosquito-borne diseases, with the highest-burden for socioeconomically disadvantaged populations. Urbanization, globalization, climate change, and land-use shifts have each contributed to the re-emergence and expansion of mosquito-borne diseases. For example, dengue incidence has increased >30-fold in the past 50 years and outbreaks of chikungunya, yellow fever, and malaria have increased in size and frequency since 2014. The 2015–2016 Zika virus (ZIKV) epidemic in Latin America and the Caribbean also resulted in hundreds of thousands of infections, resulting in large-scale socioeconomic disruptions. Supply-chain disruptions due to the coronavirus disease 2019 pandemic are expected to increase the number of malaria-related deaths in sub-Saharan Africa in 2020–2021 as well.There is a critical need for safe, sustainable approaches to reduce the burden of mosquito-borne pathogens. Common mosquito control strategies with chemical insecticides and environmental management are only moderately effective, in part due to resistance arising from physiological  and behavioral changes . Chemical interventions also have unintended effects on non-target insects, including pollinators.Wolbachia . Wolbachia-infected females transmit Wolbachia to their offspring via egg cytoplasm. If mated to Wolbachia uninfected males the incompatibility between the sperm and egg  results in non-viable offspring. In the presence of two Wolbachia strains, pairings between Wolbachia-infected females and uninfected males or males infected with a different Wolbachia strain  leads to the death of offspring. Viable offspring are generated from Wolbachia-infected females when mated with uninfected males or males infected with the same Wolbachia strain, but Wolbachia-infected males only produce viable offspring when mated with females infected with the same Wolbachia strain.Box 1 Wolbachia is an intracellular reproductive parasite of arthropods and nematodes, naturally found in many insect species. Transmitted vertically from mother to offspring, Wolbachia maximize their transmission by manipulating host reproduction through feminization, parthenogenesis, male-killing, cytoplasmic incompatibility (CI), and by increasing host fitness with or without reproductive manipulation1. Through CI, Wolbachia-infected females produce viable Wolbachia-infected offspring when mated with uninfected males, or males infected with the same Wolbachia strain. However, Wolbachia-infected males only produce viable offspring when mated with females infected with the same Wolbachia strain  and should ensure similar mating competitiveness between Wolbachia-infected and wild males. Several Wolbachia strains that satisfy these conditions have been successfully transfected into Aedes, Anopheles, and Culex mosquitoes.ion Fig. . Ideal sWolbachia-infected females into the population can compromise IIT as the Wolbachia-infected females are fertile with both infected and uninfected males 2. Notably, this study employed the most advanced sex sorting technologies available today3, yet still fell short of the sex sorting efficiencies necessary to avoid Wolbachia establishment in the population2. These results suggest that Wolbachia IIT technologies alone will unlikely be fruitful for long-term, sustained population suppression, particularly in smaller populations where the unintentional release of fewer Wolbachia infected mosquitoes can lead to more rapid IIT establishment in the population. If IIT strains do become established in populations, however, their pathogen blocking properties may still reduce disease transmission and therefore remain beneficial for disease control programs (see “Wolbachia-based population modification”).Despite advances, several drawbacks of IIT limit its long-term sustainability. IIT requires multiple, frequent releases of large numbers of perfectly sex-sorted male mosquitoes. The effort and resources required to implement IIT on a scale needed to disrupt disease transmission may not be sustainable for many areas. Accidental, unintended releases of ales Box  and therWolbachia IIT strain, the Singapore trial also used a combined IIT and low-dose radiation sterilization approach to achieve sustained population suppression2. This combined IIT-SIT approach was also previously implemented in a small field trial with Aedes albopictus  and even the malaria parasite, Plasmodium8. Studies have suggested that Wolbachia may block pathogens by competing for fatty acids, regulating host microRNAs, or upregulating innate immune response pathways9, or may interact directly with viral RNA to limit pathogen infection, dissemination, and transmission10. The precise mechanism for the anti-pathogen transmission activity of Wolbachia remains unclear but is hypothesized to be due to either Wolbachia-associated activation of host immunity or competition between the virus and Wolbachia for cellular resources11. Both of these mechanisms could interfere with virus replication, but these interactions are complicated, varying by the host, Wolbachia strain, and pathogen.Population modification approaches to disease control aim to modify vector populations to harbor heritable factors that reduce or block pathogen transmission. Remarkably, Wolbachia-based population modification in Australia demonstrated that Wolbachia infection rates reached up to 90% at 11 weeks following an initial release of Wolbachia-infected female mosquitoes12 and successfully reduced the dengue transmission in Cairns and Townsville cities13. High Wolbachia-infection rates were sustained for 6 months following release in dengue virus (DENV)-endemic Yogyakarta, Indonesia, where Wolbachia-infected adults or eggs were released over 20 or 24 weeks, respectively14 and, importantly, resulted in a significant reduction in dengue incidence in the treatment area16. High Wolbachia-infection rates (>80%) were also sustained for more than 2 years after release in DENV-endemic Kuala Lumpur, Malaysia18 and recently in Brazil, where significant reductions in dengue, Zika, and chikungunya incidence were also observed in Wolbachia intervention areas19. In general, Wolbachia-based population modification strategies appear to require fewer releases than IIT strategies and allow the release of both sexes of Wolbachia-infected mosquitoes, enabling long-term persistence in the environment , also “selfish”, but a genetic-based system that can spread through populations by biasing inheritance in its favor21  but developed from scratch, allowing them to be better understood and tailored for specific pathogens/vectors. There are several GD types with different characteristics including homing-based gene drives (HGDs)27 and sex-linked meiotic drives29, which have been demonstrated in mosquitoes, other GD types include Medea and various under dominance systems32. In CRISPR HGDs33, CRISPR-associated protein 9 (Cas9) is guided by a programmable guide RNA (gRNA) to generate a double stranded break (DSB) in a precise location. This DNA break is then repaired using the cell’s homology-directed repair (HDR) machinery. When this process occurs in the germline of heterozygous individuals, wild-type alleles get converted into drive alleles, thereby enabling super-Mendelian inheritance of the drive allele.Scientists are developing synthetic GDs, which are often mechanistically inspired by natural GDs , and sometimes also to limit their temporal persistence. For example, scientists have proposed the use of homing-based gene drive (split HGD) as an alternative to autonomous HGD37  cannot reach high frequencies, because the non-driving component is limited and declines from a low initial frequency due to negative selection. In this way, split HGDs enable spatiotemporal confinement of HGD elements25 and may provide an optimal platform for a safe phased release program of a gene drive. Split HGDs have reduced spread relative to equivalent autonomous drives, especially at low prevalence; however, this issue can be substantially restored by linking several elements together if desired38. Recent advances enabled the development of a split HGD in an Aedes mosquito27. Li et al. examined drive dynamics of a GD element comprising a gRNA targeting a phenotypic gene, white, together with an unlinked source of germline Cas9. Researchers achieved inheritance rates of up to 94%. This proof-of-principle study paves the way for further development of linked effectors for population modification strategies or transgenes useful for population suppression in Ae. aegypti.Despite aspirations that HGDs can solve world health issues, there are safety concerns due to the predicted ability of HGDs to persist indefinitely and invade non-target populationssee Fig. . In an aAnopheles mosquitoes43. An HGD was designed to target female fertility genes of An. gambiae, to decrease both female reproductive output and mosquito population size39. In another example, an HGD targeting doublesex (dsx) eliminated laboratory cage populations of An. gambiae42  at the target site45. These sites can then become resistant to drive cleavage, and as these indels accumulate over time due to increased imperfect repair events and/or positive selection of the resistance allele, they will lead to extinction of the GD from the population46. To attempt to further incrrease the stability of the drive, an alternative GD design was engineered by incorporating a previously characterized X-chromosome shredding nuclease, I-PpoI, into the CRISPR-based dsx GD system43  and RIDL . The pgSIT study characterized three dual-gRNA strains, all targeting the beta-2-tubulin gene and one of three sex determination genes: sex lethal, transformer, or the female isoform of dsx. When these dual gRNA strains were crossed to three Cas9 strains, male progeny were sterile, and females were either dead or converted to sterile intersex. Importantly, pgSIT males were able to compete with wild-type males for females. This approach provides an exciting opportunity for application in mosquitoes, because genes like beta-2-tubulin, and dsx, are conserved in mosquitoes  into the field. Given that sterile pgSIT males can be generated and scaled in a factory then released, neither naturally existing polymorphisms in the gRNA target sites, nor the evolution of resistance in the wild, are expected to impact this approach, as they would for gene drives, and therefore this technology may prove to be more evolutionarily stable and effective.Non-GD, genetic-based technologies have demonstrated population and disease suppression in smaller-scale field trials. The release of Insects carrying a Dominant Lethal gene (RIDL) has been successfully used for many years for the control of insect pests, including mosquitoes48 Fig. . Despiteoes Fig. . RecentlAnopheles stephensi52, achieving super-Mendelian inheritance of the effector gene, however, both fitness costs and drive resistant alleles immediately appeared limiting the utility of this HGD. Recently, a recoded HGD rescue system in Anopheles stephensi was developed to relieve the fitness costs associated with a nonfunctional target site, and prevent the formation of resistance alleles, and this system showed promising performance in multi-generational laboratory population cages53  while providing a barrier to prevent such reinvasion, but there are nuances to carefully consider for even this intervention. For example, population modification requires the spread of an evolutionary stable gene drive linked to an anti-pathogen effector with demonstrated stability and effectiveness against wild/evolving circulating pathogens. In 2015, a population modification approach was developed using a HGD linked to an antimalarial effector in s53 Fig. . This sys26 Fig. . Proof-oClvR)57 and “TARE” drives58, which can enhance their transmission by creating conditions in which progeny lacking these systems perish. These function using CRISPR to disrupt the function of an endogenous essential gene, and encode a linked cleavage-resistant copy of the target gene that can provide rescue to inheriting individuals. These nonhoming toxin-antidote GD designs are able to spread and overcome resistance-related challenges akin to homing-based drive architectures. Although ClvR has only been demonstrated as a proof-of-principle in fruit flies, this GD has the potential to become a promising strategy for future mosquito modification approaches.Additional nonhoming-based designs for modification include toxin-antidote CRISPR GD systems, such as “cleave and rescue” elements , or to express synthetic effectors, such as single-chain antibodies, antiviral hammerhead ribozymes, and small RNAs that target mosquito-borne viruses via the host RNA interference (RNAi) pathwayi61 Fig. , a dual- 62 Fig. , and an  63 Fig. . Strategens Fig. . NotwitsWolbachia and transgenic approaches have distinct similarities and differences . Gene editing tools also enable precise genetic changes that permit creative control approaches51. These tools can facilitate the development of GDs with varying degrees of spatiotemporal spread, persistence, and novel traits, although they are yet to be rigorously tested outside the lab65. In contrast, Wolbachia engineering has not been successful, thus Wolbachia-based methods depend on the inherent properties of the Wolbachia strains that are found in nature.While at the forefront of innovation for mosquito-borne disease control, es Table . ParticuWolbachia strains are capable of blocking many arboviruses66, but they can do so only through mechanisms naturally dictated by the bacterium, which are not well understood, nor manipulatable. Transgenic approaches can be tailored to specific pathogens through various mechanisms . This mechanistic aspect is an important design consideration, as it influences the ability to target pathogens at different developmental stages or multiple sites, which can be crucial for preventing or mitigating the emergence of pathogen resistance61. To date, there is no evidence of pathogen escape, or evasion, in Wolbachia-infected wild populations, but there have been numerous examples in laboratory studies67 with different pathogens and different arthropod hosts underscoring the importance of continued monitoring for such an escape. For example, an elegant artificial selection and gene association study on Wolbachia-infected mosquitoes collected from the WMP program release site in Cairns, Australia, was able to quickly select pathogen blocking phenotypes and identified 61 genes associated with modulation of pathogen blocking, suggesting that breakdown of pathogen blocking could occur easily under some circumstances68. If pathogen evasion did arise in wild populations, it may be difficult to modify this technology to address it or to remove the system from the environment.Optimization capabilities of pathogen resistance also vary by approach. Wolbachia-infected mosquitoes have repeatedly demonstrated successful Wolbachia spread and effective pathogen blocking into populations on a scale ranging from 6 months to >2 years69. High temperatures may reduce CI and transmissibility of some Wolbachia strains though, which may impact their establishment and persistence in some field locations70. Theoretical modeling suggests that some HGD systems can be established in a population within a year of the initial release71. There is no current field data on HGDs, however, to validate these models.Both approaches are likely to become established at a high frequency in relatively short time scales. Field releases of Wolbachia for population modification will require less production and scaling costs than for Wolbachia IIT. For gene drive, non-confinable HGDs for either population modification, or suppression, would have less production and scaling costs than confineable split-HGDs which would require repeated releases to control a local population.The production and scaling cost considerations for these technologies may also differ. In general, the scaling costs are going to be lower for technologies that require lower release frequencies Table . For theWolbachia strains can block pathogen transmission, enabling release without sex-sorting, with the intent of Wolbachia-based population modification . Notwithstanding, under certain laboratory conditions, Wolbachia strains have also been observed to enhance infections74, but it is not yet clear whether this phenomenon is applicable for stable Wolbachia infections released into the wild. In field demonstrations, however, Wolbachia was able to quickly invade wild populations and reduce pathogen transmission19. Given that evolution of either the virus, mosquito, or Wolbachia itself may result in alterations to the Wolbachia transmission blocking abilities—future efforts to characterize these long-term effects in wild populations where Wolbachia has been deployed should be of very high priority. Genetic control systems are intended to be transmitted only from parents to offspring . The possibility of horizontal transmission to non-target species, followed by spread within that species, has been widely discussed but seems highly implausible.Some Wolbachia are clearly modern biotechnological products, they have not encountered the same regulatory hurdles as transgenic approaches and go through different regulatory pathways75 . This lack of regulatory clarity is an issue for genetically and non-genetically modified methods in many jurisdictions. Early engagement with communities, stakeholders, and the public has led to fewer public-relation barriers for Wolbachia-based approaches76. Both Wolbachia population suppression (IIT) and modification approaches have been successfully trialed in several countries. The same is true for transgenic suppression approaches; the RIDL approach successfully obtained regulatory and community approval in several countries, despite the more complex environment for genetically engineered organisms. Multiple trials in Brazil, Panama, and the Cayman Islands showed strong suppression of target Aedes mosquito populations. Some RIDL trials, however, have received strong public opposition, notably the trials in Key Haven, FL, which delayed trials in this area until recently77 while Wolbachia-based approaches have received essentially no opposition78. GD technologies have received significant opposition, but the lessons learned from GE have served GD programs, such as Target Malaria, and they have developed a meticulous, cautious step-wise phased approach towards a potential approval for transgenic GD releases. As transgene-based GD field releases have yet to become a reality, however, time will tell whether this seemingly more judicious approach to field trial approval meets expectations.Regulatory hurdles and public perception differ substantially between the two methods. Although mosquitoes transfected with 60; or for Wolbachia loss of CI79, or reduction/loss of pathogen refractoriness which is vulnerable to environmental factors, temperature, and host diet80. Moreover, remediation may be necessary if these approaches are affected by an unintended consequence, shift in public opinion, or end of a trial period. Resistance alleles can limit transgene spread for GDs. However, innovative GD designs, such as reversal GDs that recall a problematic GD from the population82, have been proposed to address this problem84. Anti-CRISPR proteins can theoretically be applied as “natural brakes” to CRISPR-based HGDs85. Ongoing field studies indicate that Wolbachia can remain at high infection frequency with strong pathogen blocking and CI abilities for up to 8 years from initial invasion12. However, there is no simple way to remove Wolbachia-infected mosquitoes after release and spread. One could perhaps release mosquitoes infected with a different Wolbachia strain and exploit bidirectional incompatibilities between the two strains, replacing the old strain with the new one86. Alternatively, one could super-infect the old mosquito strain with an additional Wolbachia strain to generate a new strain that can spread into the already-invaded population. However, this scenario can lead to superinfection, where mosquitoes with multiple Wolbachia strains can have an incomplete maternal transmission or incompatible CI87. The release of wild-type mosquitoes to dilute the Wolbachia strain to sub-threshold levels is another potential remediation strategy; however, such wild-type releases would include large numbers of wild-type female mosquitoes capable of disease transmission and are therefore not ideal. Finally, IIT-SIT could be used to reduce the population below the threshold needed for persistence. If the populations are susceptible to insecticides, insecticide-based tools could also be used for remediation for some GD and Wolbachia technologies.Finally, a remediation plan to recall these technologies is also essential. Both approaches have the possibility of losing function, for example, loss of transgene expression or pathogen evolving resistance to the linked effectorWolbachia and transgene-based tools are both innovative approaches that may revolutionize mosquito-borne disease control. Immense progress has been made in genetically modified and Wolbachia-infected mosquitoes, leading to field trials around the world. Despite appreciable progress, knowledge gaps remain regarding Wolbachia-mosquito and Wolbachia-pathogen interactions. For example, not much is known about environment-host interactions, or how the host microbiome affects Wolbachia efficiency in mosquitoes88. Additional work is also needed to optimize CI and pathogen-blocking capabilities. Screening for temperature-insensitive Wolbachia strains is also crucial to avoid CI loss79. Identification and characterization of CI-inducing genes can pave the way for alternative control strategies. Likewise, additional work on transgene-based strategies is required, including reducing the cost of transgene fitness, finding ideal target sites for GD insertion, and eliminating resistance-allele formation, determining the effectiveness of anti-pathogen effectors using pathogens present in the field. For sterile male-based suppression approaches, male mosquitoes must be released multiple times. Improvements in mass mosquito production, precise sex separation, and release technologies are crucial to make these approaches more sustainable and cost-efficient3.89, informed consent, regulation90, possilbe use of global registries, and associated risks, should be carefully considered before any implementation93. Discussions regarding who should regulate and assess the risks of GD technology are in process and may take years to reach a consensus. The concept of a phased GD release approach is likely the best option to enable safe testing of the various components beginning with confinable approaches first. For example, to safely test GD components  one could begin with a self-limiting type approach  to suppress the local population (Phase 1). This could be followed by a confinable split-GD approach which would enable the safe testing of linked effectors (Phase 2). Then if even necessary, this could be followed by a non-confinable GD approach—which would use the same components  that were tested in the previous phases (Phase 3).Ethical and regulatory issues over GD use, including the role of public participation in GD developmentWolbachia-based population control technologies do not result in the genome modification of the target species, they do not have the notable stigma associated with genetic-based technologies. To date, Wolbachia-based control efforts have therefore been an easier technology for the public to accept. Several countries, including Australia, Malaysia, Indonesia, Vietnam, Colombia, and Brazil have already released Wolbachia-infected mosquitoes, with some observing reductions in local mosquito-borne disease transmission (Table Wolbachia), scientists should scrutinize all proposed technologies to fully understand their advantages and disadvantages. There is no single silver bullet for mosquito disease control, and different communities may prefer different approaches that suit their local needs. Therefore, the development of multiple approaches is crucial.As on Table . RegardlGoing forward, the prospect of controlling mosquito-borne diseases using innovative technologies is promising and we are in the golden age of the development of population control technologies. With increasing public confidence, time, and progress, we will continue to see these technologies developed and safely used to tackle global health issues and safe human lives.


The mixed linear model (MLM) has been widely used in genome-wide association study (GWAS) to dissect quantitative traits in human, animal, and plant genetics. Most methodologies consider all single nucleotide polymorphism (SNP) effects as random effects under the MLM framework, which fail to detect the joint minor effect of multiple genetic markers on a trait. Therefore, polygenes with minor effects remain largely unexplored in today’s big data era. In this study, we developed a new algorithm under the MLM framework, which is called the fast multi-locus ridge regression (FastRR) algorithm. The FastRR algorithm first whitens the covariance matrix of the polygenic matrix K and environmental noise, then selects potentially related SNPs among large scale markers, which have a high correlation with the target trait, and finally analyzes the subset variables using a multi-locus deshrinking ridge regression for true quantitative trait nucleotide (QTN) detection. Results from the analyses of both simulated and real data show that the FastRR algorithm is more powerful for both large and small QTN detection, more accurate in QTN effect estimation, and has more stable results under various polygenic backgrounds. Moreover, compared with existing methods, the FastRR algorithm has the advantage of high computing speed. In conclusion, the FastRR algorithm provides an alternative algorithm for multi-locus GWAS in high dimensional genomic datasets. Genome-wide association study (GWAS) has been widely used in the genetic dissection of quantitative traits in human, animal, and plant genetics. GWAS typically searches for the correlations between genetic variants and hundreds or thousands of individuals. However, a complete characterization of the biological mechanism for most quantitative traits remains elusive  and a nuSince the introduction of the Q + K (Q represents the population structure and K represents the kinship matrix) mixed linear model (MLM) approach  to the cp) is many times larger than the number of individuals (n), the approaches will fail to analyze this oversaturated model. Under this circumstance, a natural response is to consider reducing the number of SNP effects in the multi-locus genetic model. p, small n” problem. Currently, two-stage methodologies  variables/SNPs. In our model, the fast multi-locus ridge regression algorithm (FastRR), all SNP effects are considered as random effects. The FastRR algorithm first whitens the covariance matrix of the polygenic matrix K and environmental noise. Subsequently, the FastRR algorithm reduces the number of SNPs according to correlation, the variables of which significantly correlate with the response are retained for the next stage. In the final stage, deshrinking ridge regression (DRR) is applied to implement parametric estimation and significance tests of variables. In this study, a series of simulated and real dataset analyses are used to validate this new method. For comparison, five established methods – lasso, adaptive lasso, smoothly clipped absolute deviation (SCAD), EMMA, and decontaminated efficient mixed model association (DEMMA) are used for analysis.yi be the phenotypic value of the i-th individual in a sample of size n from a natural population, and the genetic model can be described as:Let y = T; α is a c × 1 vector of the fixed effects, such as the intercept, population structure effect and so on, W is the corresponding designed matrix for α; Z is an n × 1 vector of marker genotypes, and n × 1 random vector of polygenic effects, K is a known n × n relatedness matrix; ε is an n × 1 vector of residual errors with an assumed MVN distribution; σ2 is the variance of residual error; and In is a n × n identity matrix. MVN denotes multivariate normal distribution.where y in the model (1) is:As γ is treated as being a random effect, the variance of where The FastRR algorithm is a multi-stage flexible approach for GWAS, which simultaneously implements estimation and testing to detect associated variables/SNPs. We describe it with the following stages:λγ and λg, which cause expensive computational burden. It is noted that polygenic variance is always larger than zero, while variance components for most SNPs are zero because these markers are not associated with the interested trait, which is λγ = 0 for most SNPs. Therefore, in the first step, we estimate Zγ with only polygenic background, and replace λg in (2) by the λg for each single marker scanning. Thus,The key point of solving the model (1) is to estimate two ratios of variance components,  the λ^g , 2020, aAn eigen  decomposition of the positive definite matrix Q is orthogonal and Λ is a diagonal matrix with positive eigenvalues. Let where yc = Cy, Wc = CW, Zc = CZ, εc = Cu+Cε∼MVN  , 2020.A number of studies have illustrated that most quantitative traits are controlled by a small portion of genes, including a few genes with large effects and polygenes with minor effects . It is cZc (variables after polygenic background correction) and yc (phenotype after polygenic background correction) under model (5), R function cor.test returns the p-value of the correlation test. The critical value for significance was set at p-value < 0.01 ; α is a vector of fixed effects, γ is a q × 1 random effect vector of the selected q markers from the above stage, and γk ∼ N , k = 1, …, q; W and Z are the corresponding design matrices for α and γ. Here, polygenic background correction is not considered in model (6), because the above two steps under the polygenic background model had already selected all potential associated QTNs. All the parameters in model (6) are estimated by DRR proposed by where Before introducing the DRR, let us briefly recall the ordinary ridge regression (ORR). According to the best linear unbiased prediction (BLUP) of the marker effects and the prediction error variances using the conditional expectation and conditional variance, the estimates of ORR are as follows,where Ordinary ridge regression is inflexible and inaccurate for GWAS . Therefok-th element of ϕ2and k, respectively.Wk, follows a Chi-square distribution with one degree of freedom under the null model, H0:γk = 0. The DRR method deshrinks both the estimated effects of markers and their estimated variances from the ORR, resulting in deshrunk Wald test statistics.The test statistic of DRR, L1 regularization and selects the most correlated with response variables. It is a popular method for simultaneous estimation and variable selection. The method was implemented by the R software package lars1.Lasso regression  is a typL1 penalty. Adaptive lasso shows more consistence for variable selection than lasso in data analysis. The method was implemented by the R software package glmnet2.Similar to the lasso, the adaptive lasso  is a maincvreg3.SCAD  as the v4.Efficient mixed-model association  is an es5.The polygenic effect  is treated as a random effect in EMMA. On the other side, EMMA already included the marker effect as the fixed effect. Thus, there are two effects for each marker, which lead to a reduced power for testing. n = 2000 individuals with p = 10,000 genetic variants, which were generated with MLM. The total average was set at 10.0 and residual variance was set at 10.0. We considered three scenarios for each simulation, including two times polygenic background, five times polygenic background, and ten times polygenic background.Three Monte Carlo simulation experiments were conducted to evaluate the performances of the FastRR algorithm and other methods. We generated genotypes according to the minor allele frequency (MAF) in the interval  under Hardy–Weinberg equilibrium. The simulation datasets contained p, where p was the number of markers in the genetic model. The false positive rate was calculated as the ratio of the number of false positive effects to the total number of zero effects.Only one QTN with a fixed position  was simu6To validate the FastRR algorithm, the rice data that was used in this study for GWAS demonstration consists of 524 inbred varieties, which were collected from China and southeast Asia . A totalArabidopsis published by 7. A total of 199 Arabidopsis lines and 216,130 SNPs were used for analysis. Among all traits, we analyzed three traits related to flowering time: (1) LD: days to flowering under long days; (2) SD: days to flowering under short days; and (3) SDV: days to flowering under short days with vernalization.To further evaluate the performance of FastRR, we reanalyzed the genetic data sets of r2 = 2%) under different polygenic backgrounds. One hundred random QTNs are simulated in the third experiment and the total heritabilities are 50%. As the genetic background increases, the power of the FastRR algorithm is getting increasingly high  are used to measure the accuracy of an estimated QTN effect. We evaluated the accuracies for the  estimates using all six methods , 2A–C. AThe false positive rate is a crucial index in GWAS. All the false positive rate results of simulation experiment 1 and 2 are listed in We compare the computing time of 100 repeated simulated analyses by using six approaches. In each of the three simulation experiments, computing times are recorded and are shown in GW5 . The results are illustrated in Arabidopsis Information Resource8. The FastRR algorithm detects the confirmed genes AGL17 and CDKG1, which are detected by SCAD and DEMMA as well. From the analysis results, lasso shows several false positive loci in the detection of SD and SDV, meanwhile the adaptive lasso and SCAD methods are inflexible in dissecting the SNPs associated with the target traits. The statistical tests of EMMA are under the Bonferroni corrected threshold. The FastRR algorithm shows a similar pattern as the DEMMA method for all results of three traits, the statistics of part SNPs using the DEMMA method are slightly more significant than the FastRR algorithm, which is similar to the results of the rice datasets.To further validate the FastRR algorithm, this new algorithm FastRR along with lasso, adaptive lasso, SCAD, EMMA, and DEMMA methods are used to reanalyze the In terms of the computing speed for all three traits, lasso is computationally much faster than the other methods. The computing times of FastRR, SCAD, and adaptive lasso are on the same order of magnitude, which require less than 200 s. The DEMMA and EMMA methods have much more computational burden than the other methods, both of which require over ten times the computing time required by the FastRR algorithm. Overall, the FastRR algorithm is recommended from the perspective of detection and computing speed across all experiments.The FastRR algorithm is a multi-stage flexible approach for QTNs dissection in GWAS, and displays high power for detecting QTN of large and minor effects, even under the ten times polygenic background. We aimed to understand the performance of regression analysis methods, thus the following three regression analysis methods, ORR, DRR, and FastRR, are used to analyze simulation experiment 1 and 2. As the results show , 2A–C, OCurrently, the two-stage methodologies  are moreϕ2 and σ2) requires DRR to estimate. The results from simulation and real data analysis indicate that the estimation under this simple assumption has achieved better performance for QTN detection and fast computational speed. Lastly, multithreaded marginal correlation is implemented in the FastRR.Mixed linear model methodologies are mainstream in GWAS; most of them treat QTN effects as fixed effects. In this study, the QTN effects are viewed as random, and it is more consistent with genetic mechanisms . In ordeEfficient mixed model association and DEMMA as popular single-locus genome scan approaches have been successfully used in GWAS to dissect quantitative traits. However, single-locus approaches ignore the potential information of neighboring markers and fail to consider the joint minor effect of multiple genetic markers on a trait. The FastRR algorithm overcomes this shortcoming. From the results of the simulation, FastRR is more powerful in the detection of QTNs , 3. AlthThe analysis of large-scale genetic data in GWAS is a hot topic at present. In this study, the correlation coefficients are employed to reduce the dimension of potentially related variables, which are then included in the subsequent multi-locus analysis. The threshold of the correlation coefficient test is set to 0.01 , and evehttps://doi.org/10.1093/bioinformatics/btaa345; The Arabidopsis data used for the analysis described in this manuscript was obtained from http://www.arabidopsis.usc.edu/.The rice data used for the analysis described in this manuscript was obtained from JZ and JC conceived and supervised the study. JZ, MC, YW, and YL performed all experiments and analyzed the data and revised the manuscript. YZ, MC, SW, and JC made all figures and forms. JZ, YW, and JC also wrote and revised the manuscript. All authors read and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


This review describes the scientific basis of a research and breeding pathway designed to boost genetic gains in wheat under drought and heat stress. Triticum aestivum and Triticum durum) fall behind those in other staple crops. Current yield gains will not meet 2050 needs, and climate stresses compound this challenge. However, there is good evidence that heat and drought resilience can be boosted through translating promising ideas into novel breeding technologies using powerful new tools in genetics and remote sensing, for example. Such technologies can also be applied to identify climate resilience traits from among the vast and largely untapped reserve of wheat genetic resources in collections worldwide. This review describes multi-pronged research opportunities at the focus of the Heat and Drought Wheat Improvement Consortium (coordinated by CIMMYT), which together create a pipeline to boost heat and drought resilience, specifically: improving crop design targets using big data approaches; developing phenomic tools for field-based screening and research; applying genomic technologies to elucidate the bases of climate resilience traits; and applying these outputs in developing next-generation breeding methods. The global impact of these outputs will be validated through the International Wheat Improvement Network, a global germplasm development and testing system that contributes key productivity traits to approximately half of the global wheat-growing area.Despite being the world’s most widely grown crop, research investments in wheat ( Triticum aestivum and Triticum durum) is a key pillar of food security, being the most widely grown crop worldwide and constituting 20% of all human calories and protein  and the establishment of the IWIN has been particularly important in promoting the sharing and evaluation of germplasm. Impacts of the IWIN are apparent from the fact that >50% of all cultivars released, globally, contain IWIN germplasm in their pedigree, while many advanced lines are released directly as cultivars . The types of research synergies generated by such research platforms has already been demonstrated under the International Wheat Yield Partnership .Linking these translational research activities to the IWIN can generate considerable scale-out, aided by access to cutting-edge germplasm, extensive databases relevant to target environments, and to the actual field environments currently used to achieve genetic gains worldwide through the IWIN. Crowd-sourcing of novel ideas and technologies can help facilitate engagement with a range of international collaborators, including those of the Heat and Drought Wheat Improvement Consortium  due to the phenomenon of genotype by environment interaction (GEI). The main causes of GEI, assuming pests and diseases are controlled genetically or through husbandry, are related to the climate, the soil, and crop management. While the latter is controllable by farmers, the effects of the climate and soil are much less so. In order to improve the efficacy of breeding strategies, the principal causes of GEI need to be better understood, as outlined in this section.While it is recognized that different heat and drought profiles exist across the range of wheat cropping systems  . Combined with the extensive pedigree relationship information recorded for IWIN accessions, the capacity to cheaply genotype those accessions, and the availability of the reference genome for wheat , which underpin the reproductive biology and determination of yield under different heat and drought profiles   . RecentlA multicrop model and multiglobal climate model ensemble in a gridded format across wheat-growing regions can be used to: (i) collate high-quality drought and heat years data to calibrate and validate model simulation response to heat and drought, with particular emphasis on any yield reductions associated with the impact of high temperature on heading date and length of the grain-filling period, and the impact of drought affecting crop assimilation and expansive growth processes; (ii) run historical simulations (1980–2010) with the best combination of traits for heat and drought environments to maximize average grain yield including optimized phenological length, drought escape   . The largr.org/) . DiversiCharacterization of unadapted, wild germplasm for traits important to modern agriculture has its challenges, especially for traits controlled by many genes. For example, phenology of wild germplasm may be out of sync with locally adapted checks, making the timing of stress treatments and direct comparison with adapted germplasm problematic. There is also the possibility that constitutive stress-adaptive traits could negatively impact yield in more favourable cycles, for example high transpiration efficiency was associated with reduced stomatal conductance .Aegilops tauschii (genome DD) (The usefulness of wild species for climate resilience gene sources has been demonstrated most in diploid wild grass nome DD)  and drounome DD) , althougThinopyrum 7E translocation, which showed a 13% increase in yield potential across genetic backgrounds . In addition, as a century-old technology, now accelerated by marker technology .Phenotyping is a cornerstone of plant breeding, and effective use of genomic technologies is rooted in the quality and the relevance of phenotyping. To accelerate genetic gain, especially with respect to designing crosses more deterministically and making use of untested genetic resources, genetic and physiological understanding must be underpinned by rigorous phenotyping  provides additional information about evapotranspiration  which is sensitive to water fluxes in the canopy (Both the plant genotype and the environment have strong effects on the composition and diversity of the soil microbiome (Plant growth-promoting rhizobacteria (PGPRs) have been shown to enhance plant resilience to stress  from multiple experiments including unbalanced phenotypes used in a single meta-analysis have been shown to result in higher power and fewer false positives  panels, nested association mapping panels, genetic diversity panels, and trait panels. Known and novel genomic regions related to heat and drought adaptation can be described and summarized in a comprehensive quantitative trait locus (QTL) catalogue. To accomplish this, genomic regions identified across different mapping studies are aligned to available wheat reference genomes or pan-genomes, allowing comparisons across studies at a physical position level. Through review of these comparisons, one can then identify clusters or hot-spot segments harbouring stress-related marker associations, which suggests that they play an important and consistent role across different genetic backgrounds and environments. Results can include or be compared with meta-analyses studies . Meta-anhttp://www.wheatgenome.org/) and the ‘pan genome’ from the 10+ Wheat Genomes Project  contributing to 15.6% of them , fructan (a polymer of fructosyl residues), and leaf starch] can be screened. In addition, although the whole-plant metabolome can be measured , thus reducing phenotyping costs and accelerating breeding cycles. Simulations have shown that SpeedGS in combination with phenomics can accelerate genetic gain per year and facilitate a more rapid incorporation of novel genetic variation into breeding programmes, compared with pedigree-based breeding (b). In essence, phenomics, for example measurements of yield secondary traits on selection candidates, can be incorporated into multivariate GS models together with field-measured grain yield of a training population with the aim of increasing the prediction ability above that of a univariate genomic selection approach , which most probably impairs genetic analysis such as the prediction of GEBVs. Genome catalogues or multigenome reference graphs can capture a wider range of genetic variation and can therefore improve the mapping sensitivity of short sequence reads  for strategic crossing can be implemented by first carefully choosing the progenitors to use in the mainstream breeding population. Regarding selection and advancement in the breeding programme, cost-effective and breeder-accessible selection tools are fundamental to identify the best progeny derived from the strategic crossing , can be validated through the IWIN, the largest public–private network of wheat collaborators in the world, which routinely tests new bread and durum wheat and triticale lines . CIMMYT To ensure that the translational research and breeding pipelines capitalize on cutting-edge developments in science, promising new ideas need to be tested in a realistic breeding context, opening up bottlenecks between discovery research and wheat breeding. One way to achieve this is through calls aimed at the international plant research community, with the view to accelerate the transfer of technologies from the laboratory to field breeding. Such ideas may encompass: testing novel traits likely to boost climate resilience; methods for accessing a wider range of genetic resources; improving the practical understanding of trait and allelic interactions in successful progeny; and validating new phenomic and genomic selection models. While the immediate benefit would be to wheat, it is expected that useful breakthroughs can be scaled out to other crops. Additional benefits include more efficient use of research resources and opportunities for closer interactions between disciplinary scientists and practising breeders. Testing of crowd-sourced ideas can be supported through links to the IWIN by provision of exotic germplasm, panels of elite breeding materials, and managed stress profiles in actual breeding environments.https://wheat.org/download/wheat-phase-ii-full-proposal/), and include a variety of outputs, such as advanced lines and experimental germplasm, research results and other new knowledge, and curated data and metadata sets. Capacity building has also been an important output of the IWIN, sharing knowledge through publications of research results and methodologies; and training activities from short-term practical courses to mentoring of PhD candidates and early career scientists. In addition, and through linkages with the HeDWIC and other initiatives, expertise and research infrastructure can be shared and developed through collaborative agreements among laboratories worldwide, resulting in significant scale-out of capacity, including to other major staple crops and possibly laying the foundation of climate resilience research for a range of other ‘orphan’ crops.New breeding technologies developed by CIMMYT and collaborators are delivered through the IWIN and other partnerships (see The explosion in fundamental plant science in recent decades has uncovered the physiological and genetic bases of many traits, as well as genetic markers and assays to select for them. This has resulted in a massive pileup of ideas that have yet to be tested and translated into applied breeding programmes . Since t


We identified male sterile lines with good combining ability as well as F1 hybrids with potential further use in product development. For yield per plant, accuracies of genomic predictions of hybrid genetic values ranged from 0.490 to 0.822 in cross-validation if neither parent or up to both parents were included in the training set, and both general and specific combining abilities were modeled. The accuracy of phenotypic selection for hybrid yield per plant was 0.682. The accuracy of genomic predictions of male GCA for yield per plant was 0.241, while the accuracy of phenotypic selection was 0.562. At the observed accuracies, genomic prediction of hybrid genetic value could allow improved identification of high-performing single crosses. In a reciprocal recurrent genomic selection program with an accelerated breeding cycle, observed male GCA genomic prediction accuracies would lead to similar rates of genetic gain as phenotypic selection. It is likely that prediction accuracies of male GCA could be improved further by targeted expansion of the training set. Additionally, we tested the correlation of parental genetic distance with mid-parent heterosis in the phenotyped hybrids. We found the average mid-parent heterosis for yield per plant to be consistent with existing literature values at 32.0%. In the overall population of study, parental genetic distance was significantly negatively correlated with mid-parent heterosis for yield per plant (r = −0.131) and potential yield (r = −0.092), but within female families the correlations were non-significant and near zero. As such, positive parental genetic distance was not reliably associated with positive mid-parent heterosis.Hybrid rice varieties can outyield the best inbred varieties by 15 – 30% with appropriate management. However, hybrid rice requires more inputs and management than inbred rice to realize a yield advantage in high-yielding environments. The development of stress-tolerant hybrid rice with lowered input requirements could increase hybrid rice yield relative to production costs. We used genomic prediction to evaluate the combining abilities of 564 stress-tolerant lines used to develop Green Super Rice with 13 male sterile lines of the International Rice Research Institute for yield-related traits. We also evaluated the performance of their F Oryza sativa L.) is a self-pollinated crop that has traditionally been cultivated as an inbred, but the introduction of male sterility into cultivated germplasm in the 1970s enabled hybrid breeding  of the existing male sterile lines of the International Rice Research Institute (IRRI) with stress-tolerant germplasm used in the development of Green Super Rice varieties, as well as the performance, or genetic value, of their F hybrids . The fou hybrids . We sougWe also tested whether parental genetic distance was correlated with mid-parent heterosis using a large sample of hybrids and genome-wide molecular markers. Mid-parent heterosis due to dominance is expected to be positively correlated with parental squared difference in allele frequency (SDAF) by quantitative genetic theory . It has 1 hybrids  lines of the five CMS female parents were used to estimate mid-parent heterosis. Six inbred lines were used as commercial checks to estimate commercial relative performance: five of the donor parents, Y 134 (DP 6), Khazar (DP 8), OM 997 (DP 10), M 401 (DP17), and X 21 (DP19), and the recurrent parent, WTR-1.1 hybrids, their inbred parents, the female maintainer lines, and the commercial checks were phenotyped in an unbalanced randomized complete block design (RCBD) in two environments, irrigated lowland and irrigated upland, at the IRRI farm  in the dry season of 2018. After establishment in seedbeds on January 8, 2018, seedlings were transplanted at the three-leaf stage. Transplanting occurred on January 31, 2018, at the irrigated lowland site and on February 8, 2018, at the irrigated upland site. The plants were harvested the week of May 14, 2018. Basal NPK fertilizer was applied at a rate of 30 kg/ha, and zinc was applied at a rate of 5 kg/ha. N fertilizer was also applied at 28–30 days after transplanting and at the panicle initiation stage (42 days after transplanting) at a rate of 35 kg/ha. Rat fences were installed at both locations; bird pressure was controlled by farmworkers in the lowland environment, and by a bird net at the upland environment. Both environments were hand-weeded. Both environments were irrigated, but the lowland environment was continuously flooded to a depth of ∼10 cm, whereas water depth was allowed to vary in the upland field. Insect pressure was controlled by application of Regent® pesticide (fipronil). Temperatures were sufficient throughout the growing season to ensure seed set in the TGMS female line A07.The FThe field layout was designed in PBTools 1.4, which depends on the R package agricolae . There wThe following traits or trait derivatives were phenotyped: plant height, number of tillers, panicle dry weight, panicle length, proportion of spikelets filled, yield per plant, and yield potential per plant . For all® (tGBS) by data2Bio and its subsidiary, Freedom Markers, in Ames, Iowa  relationship matrix FMG was the Kronecker product of the male and female additive relationship matrices j was the effect of the kth block nested within the jth environment with ijkl was the random error associated with each replicate with where The genetic variance for proportion of spikelets filled and potential yield per plant was estimated using the following model in (3), without the environment term and its associated interactions, because the traits were only phenotyped in the irrigated lowland environment:Yijk was the random phenotypic response of the ith hybrid genotype in the jth block from the kth replicate,μ was the grand mean, Hi was the random effect of the ith hybrid genotype with Bj was the random effect of the jth block with ijk was the random error associated with each replicate with The entry-mean heritability was estimated for each of height, tiller number, panicle length, and yield per plant by (4) following the method of hj was the harmonic mean of the number of total observations of each hybrid genotype within an environment, and ht was the harmonic mean of the total number of observations per hybrid genotype.where For proportion of spikelets filled and potential yield, which were phenotyped in a single environment, entry-mean heritability was estimated by (5) also following H was assumed to have a multivariate normal (MVN) distribution with GH was the additive genomic relationship matrix of the F1 hybrids:Additive genetic variances were estimated from models of the form in (6) for plant height, tiller number, panicle length, and yield per plant. The terms of (6) are the same as in (2), but in (6) the random effect H was assumed to have a multivariate normal (MVN) distribution with HG was the additive genomic relationship matrix of the F1 hybrids:Additive genetic variances for proportion of spikelets filled and potential yield were estimated from model of the form in (7), with the same terms as (3), but the random effect Narrow-sense heritability, or the proportion of additive genetic variance out of total phenotypic variance, was estimated on a single-plant basis for all traits. Variance components were estimated from the models of the form in (6) for plant height, tiller number, panicle length, and yield per plant, and narrow-sense heritability was estimated with (8):For proportion of spikelets filled and potential yield, narrow-sense heritability was estimated using (9) with variances estimated from the models of the form in (7):For each trait, genomic best linear unbiased predictions (GBLUPs) of hybrid genetic value, male GCA, and female GCA were each estimated using two separate models: the genomic GCA model and the genomic GCA + SCA model. Model fits were compared with the Akaike information criterion (AIC) and Bayesian information criterion (BIC). The genomic GCA model was:Yijklm was the random phenotypic response of a single-cross hybrid of the ith female line and the jth male line observed in the kth environment, lth block, and mth replicate, μ was the grand mean, Fi was the random GCA effect of the ith female parent with GF was the additive genomic relationship matrix among females, Mj was the random GCA effect of the jth male parent with GM was the additive genomic relationship matrix among males, Ek was the effect of the kth environment with B(t)k was the effect of the lth block nested in the kth environment with FEik was the random interaction of the ith female and the kth environment with MEjk was the random interaction of the jth male and the kth environment with ijklm was the random error of each observation with where The genomic GCA + SCA model was:FMij was the additional random SCA interaction effect of the ith female and the jth male, with GFM was the Kronecker product of GF and GM  of hybrid genetic value and male and female GCAs were also estimated without genomic information to 1) estimate the predictive ability and prediction accuracy of the genomic prediction models, and 2) estimate the accuracy of phenotypic selection. For the GCA model, all terms remained the same as in (10), but the distribution of the random effects of F, M and FM were assumed to be Similarly, for the GCA + SCA model, all terms remained the same as in (11), but the distribution of the random effects There are multiple methods to estimate genomic prediction accuracy . Here, pPredictive abilities and prediction accuracies were also estimated for hybrid genetic values for each phenotypic response following PEV) of the appropriate BLUPs  were obtained in sommer by inverting the coefficient matrix of the relevant model. For the BLUPs of male GCA for a given model and trait, the reliability was the average of:The accuracies of phenotypic selection for each trait were estimated for male GCA, female GCA, and hybrid genetic value as the square root of the reliabilities of their respective BLUPs . ReliabiPEVj was the prediction error variance of the jth BLUP of male GCA and where PEVi was the prediction error variance of the ith BLUP of female GCA, and where PEVi was the prediction error variance of the ith BLUP of hybrid genetic value, and where 1 hybrid was estimated using BLUPs of genetic values from the following model:To assess correlation of SDAF with mid-parent heterosis, mid-parent heterosis of each FYijklm was the random phenotypic response and μ was the grand mean. In the vein of Fi was the fixed effect of an indicator of whether the genotype was an inbred or F1 hybrid to account for the possibility of differing inbred and hybrid group means in the presence of heterosis. Gj was the random effect of the jth inbred parent, inbred commercial check, or F1 hybrid genotype with Ek was the random effect of the kth environment with GEjk was the random interaction effect of the jth genotype and the kth environment with B(l) was the random effect of the lth block nested within the kth environment with ijklmwas the random error associated with each observation with 1 hybrid was obtained as:Mid-parent heterosis of each FMPH was mid-heterosis, 1 genotype value from (17), and ith genotype value was the sum of μ, t test of significance was conducted at α = 0.05 for each correlation, with the null hypothesis that a given correlation did not significantly differ from zero and the alternate hypothesis that the given correlation significantly differed from zero.where e values . The BLU1 hybrid with phenotypic observations against each check as:Commercial relative performance  was estimated for each FCRP was commercial relative performance, 1 hybrid genotype value from (17), and ith genotype value was the sum of μ, where Mean phenotypic values for height, tiller number, panicle length, proportion of spikelets filled, yield per plant, and potential yield were respectively 85 cm, 14 tillers, 226 mm, 0.757, 36 g per plant, and 54 g per plant ; Table 1For all traits, model fit was superior for the genomic GCA + SCA model compared to the genomic GCA model as assessed by either AIC or BIC , 3. HoweThe accuracy of phenotypic selection for hybrid genetic value ranged from 0.566 to 0.952 among traits . For the1 hybrid yield was 43.352 grams per plant, which scaled to 8.670 tons per hectare  ranged from −43.9% to 70.0% for yield per plant  to identify parental lines with high GCA for use in future reciprocal recurrent genomic selection programs, and (3) to evaluate genomic prediction and phenotypic selection accuracies in our hybrid breeding population, with the end goal of developing stress-tolerant hybrid rice varieties. Compared to inbred commercial checks , phenotyped F1 hybrids showed genetic yield advantages of up to 80% and warrant further testing  to identify high-yielding F testing ; Table 8’ fields . Relativ in rice . However in rice . BecauseRf) alleles  despite that the males all shared a recurrent parent and as such a large proportion of their genomes (75% ± Mendelian sampling and selection; Phenotypic accuracies were similar to or lower than genomic prediction accuracies for hybrid performance in the T2 case . Notably1F5 male lines, the correlation of parental SDAF and mid-parent heterosis was not observed within female families of hybrids (Considering all hybrids in the study, we observed parental SDAF to be negatively correlated with mid-parent heterosis and hybrid genetic value for all traits surveyed except tiller number, for which SDAF was positively correlated with mid-parent heterosis . In many hybrids . This suBased on the study findings, we caution against the conventional wisdom that increased genetic distance between parents alone will always confer improved hybrid performance or positive heterosis. Increased genetic distance in the potential founders of heterotic pools of rice screened was not reliably associated with desired positive heterosis for yield, even though the pools could be genetically distinguished. In this population, and probably in rice more generally, empirical selection for GCA is preferable to selection based on genetic distance to breed high-performing hybrid rice.For hybrid performance, genomic prediction accuracies were similar to or higher than phenotypic accuracies. Therefore, genomic prediction could be useful for product development in the population of study. Most notably, inclusion of genomic information increased prediction accuracy for hybrid yield per plant by approximately 13.8% compared to phenotype alone , 5. ObseIn contrast, genomic prediction accuracies for male GCA were substantially lower than phenotypic accuracies. For yield, genomic prediction accuracies were approximately three times less than phenotypic selection accuracies. However, reciprocal recurrent genomic selection for GCA can reduce cycle length by two-thirds compared to reciprocal recurrent phenotypic selection, because parents can be immediately recycled using genomic predictions of their GCA, leading to a cycle length of one . In convPRJNA479931, excluding the female genotype data. The female genotype data for this article are not publicly available as they are the property of the International Rice Research Institute. Requests to access the female genotype data should be directed to JA at j.ali@irri.org.The phenotypic datasets generated and analyzed for this study can be found in ML planned the study, executed the field trial and analysis, and wrote the manuscript. JA, MA, EA, MP, and MS developed plant materials used. EA, MP, and MS directed field operations. AL supervised the statistical analysis. AS supervised the marker analysis. JR supervised the quantitative genetic analysis. JA conceived of the study, directed the study, and provided breeding insights. All authors edited and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Genomic studies increasingly integrate expression quantitative trait loci (eQTL) information into their analysis pipelines, but few tools exist for the visualization of colocalization between eQTL and GWAS results. Those tools that do exist are limited in their analysis options, and do not integrate eQTL and GWAS information into a single figure panel, making the visualization of colocalization difficult.p-values, 3) enrichment of eQTLs among trait-significant variants, 4) the LD landscape of the locus in question, and 5) the relationship between the direction of effect of eQTL signals and the direction of effect of colocalizing GWAS peaks. These clear and comprehensive plots provide a unique view of eQTL-GWAS colocalization, allowing for a more complete understanding of the interaction between gene expression and trait associations.To address this issue, we developed the intuitive and user-friendly R package eQTpLot. eQTpLot takes as input standard GWAS and cis-eQTL summary statistics, and optional pairwise LD information, to generate a series of plots visualizing colocalization, correlation, and enrichment between eQTL and GWAS signals for a given gene-trait pair. With eQTpLot, investigators can easily generate a series of customizable plots clearly illustrating, for a given gene-trait pair: 1) colocalization between GWAS and eQTL signals, 2) correlation between GWAS and eQTL eQTpLot provides a unique, user-friendly, and intuitive means of visualizing eQTL and GWAS signal colocalization, incorporating novel features not found in other eQTL visualization software. We believe eQTpLot will prove a useful tool for investigators seeking a convenient and customizable visualization of eQTL and GWAS data colocalization.https://github.com/RitchieLab/eQTpLotthe eQTpLot R package and tutorial are available at  Non-protein-coding genetic variants make up the majority of statistically significant associations identified by genome wide association studies (GWAS). As these variants typically do not have obvious consequences for gene function, it can be difficult to map their effects to specific genes. To address this issue, genomic studies have increasingly begun to integrate expression quantitative trait loci (eQTL) information into their analysis pipelines, with the thought that non-coding variants might be exerting their effects on patient phenotypes through the modulation of expression levels of nearby genes. Through this approach, indirect evidence for causality can be obtained if a genetic locus significantly associated with candidate gene expression levels is found to colocalize with a genetic locus significantly associated with the phenotype of interest.A number of excellent tools have been developed to discover and analyze colocalization between eQTL and GWAS association signals –8, but fp-values, 3) enrichment of eQTLs among trait-significant variants, 4) the LD landscape of the locus in question, and 5) the relationship between the directions of effect of eQTL signals and colocalizing GWAS peaks. These clear and comprehensive plots provide a unique view of eQTL-GWAS colocalization, allowing for a more complete understanding of the interaction between gene expression and trait associations. We believe eQTpLot will prove a useful tool for investigators seeking a convenient and robust visualization of genomic data colocalization.For these reasons, we developed eQTpLot, an R package for the intuitive visualization of colocalization between eQTL and GWAS signals. In its most basic implementation, eQTpLot takes standard GWAS summary data, formatted as one might obtain from a GWAS analysis in PLINK , and cishttps://github.com/RitchieLab/eQTpLot) and can be downloaded for use at the command line, or in any R-based integrated development environment, such as RStudio. Example data and a complete tutorial on the use of eQTpLot and its various features have also been made available on GitHub.eQTpLot was developed in R version 4.0.0 and depends on a number of packages for various aspects of its implementation  –21. The gtexportal.org [At a minimum, eQTpLot requires two input files, imported into R as data frames: one of GWAS summary statistics (as might be obtained from a standard associations study as completed in PLINK ) and onertal.org ). Table In its simplest implementation, eQTplot takes as input two data frames, one of GWAS summary data and the other of eQTL summary data, with the user specifying the name of the gene to be analyzed, the GWAS trait to be analyzed ), and the tissue type to use for the eQTL analysis. Using these inputs, eQTpLot generates a series of plots intuitively illustrating the colocalization of GWAS and eQTL signals in chromosomal space, and the enrichment of and correlation between the candidate gene eQTLs and trait-significant variants. Additional parameters and data can be supplied, such as pairwise variant LD information, allowing for an even more comprehensive visualization of the interaction between eQTL and GWAS data within a given genomic locus.congruence is set to TRUE, all variants are divided into two groups: congruous, or those with the same direction of effect on gene expression and the GWAS trait , and incongruous, or those with opposite directions of effect on gene expression and the GWAS trait . The division between congruous and incongruous variants provides a more nuanced view of the relationship between gene expression level and GWAS associations – a variant associated with increased expression of a candidate gene and an increase in a given GWAS trait would seem to be operating through different mechanisms than a variant that is similarly associated with increased expression of the same candidate gene, but a decrease in the same GWAS trait. eQTpLot intuitively visualizes these differences as described below. This distinction also serves to illuminate important underlying biologic difference between different gene-trait pairs, discriminating between genes that appear to suppress a particular phenotype and those that appear to promote it.One major implementation feature that sets eQTpLot apart from other eQTL visualization software is the option to divide eQTL/GWAS variants into groups based on their directions of effect. If the argument tissue to a list of tissues contained within eQTL.df ) for a MultiTissue analysis, or by setting the argument tissue to “all” for a PanTissue analysis. In a PanTissue analysis, eQTL data across all tissues contained in eQTL.df will be collapsed, by variant, into a single pan-tissue eQTL; a similar approach is used in a MultiTissue analysis, but in this case eQTL data will be collapsed, by variant, across only the specified tissues. The method by which eQTpLot collapses eQTL data can be specified with the argument CollapseMethod, which accepts as input one of four options – “min,” “median,” “mean,” or “meta.” By setting CollapseMethod to “min” (the default), for each variant the tissue with the smallest eQTL p-value will be selected, such that each variant’s most significant eQTL effect, agnostic of tissue, can be visualized. Setting the parameter to “median” or “mean” will visualize the median or mean p-value and NES value for each SNP across all specified tissues. Lastly, setting CollapseMethod to “meta” will perform a simple sample-size-weighted meta-analysis (i.e. a weighted Z-test) [eQTL.df, which should be supplied in an optional column “N.” If sample size numbers are not readily available , eQTpLot gives the user the option to presume that all eQTL data is derived from identical sample sizes across all tissues – this approach may of course yield inaccurate estimates of a variant’s effect in meta-analysis, but may be useful to the user.Another important feature of eQTpLot that is not found in other eQTL visualization software is the ability to specify a PanTissue or MultiTissue eQTL visualization. In some instances, it may be of interest to visualize a variant’s effect on candidate gene expression across multiple tissue types, or even across all tissues. Such analyses can be accomplished by setting the argument  Z-test) , 23 for https://ritchielab.org/publications/supplementary-data/ajhg-cilium, and the subset of summary data used for our example analyses can be downloaded from the eQTpLot GitHub page such that the reader may experiment with eQTpLot with the pre-supplied data.What follows is a description of the process used to generate each of the plots produced by eQTpLot, along with a series of use examples to both demonstrate the utility of eQTpLot, and to highlight some of the many options that can be combined to generate different outputs. For these examples we have analyzed a subset of data from our recently-published analysis of quantitative laboratory traits in the UK Biobank  – these Genes.df, for the indicated gbuild, for the user-specified gene), along with a range of flanking genome . GWAS summary statistics from GWAS.df are filtered to include only variants that fall within the LOI. The variants are then plotted in chromosomal space along the horizontal axis, with the inverse log of the p-value of association with the specified GWAS trait (PGWAS) plotted along the vertical axis, as one would plot a standard GWAS Manhattan plot. The GWAS significance threshold, sigpvalue_GWAS , is depicted with a red horizontal line.To generate the main eQTL-GWAS Colocalization Plot Figs. A, 3A, 4AeQTL.df  does not meet the specified significance threshold, sigpvalue_eQTL ) are plotted as grey squares. On the other hand, variants that act as eQTLs for the target gene  are plotted as colored triangles, with a color gradient corresponding to the inverse magnitude of PeQTL. As noted above, an analysis can be specified to differentiate between variants with congruous versus incongruous effects on the GWAS trait and candidate gene expression levels – if this is the case, variants with congruous effects will be plotted using a blue color scale, while variants with incongruous effects will be plotted using a red color scale . This dataset is further filtered to include only variants that are in LD (with R2 > R2min) with at least a certain number of other variants . These filtering steps are useful in paring down the number of variants to be plotted in the LDheatmap, keeping the most informative variants and reducing the time needed to generate the eQTpLot. A heatmap illustrating the pairwise linkage disequilibrium of the final filtered variant set is subsequently generated below the main eQTL-GWAS Colocalization Plot, with a fill scale corresponding to R2 for each variant pair. The location of each variant in chromosomal space is indicated at the top of the heatmap, using the same chromosomal coordinates as displayed in panels A and B.A depiction of the genomic positions of all genes within the LOI is generated below the plot using the package Gviz Figs. B, 3B, 4BGWAS less than the specified GWAS significance threshold, sigpvalue_GWAS, the proportion that are also eQTLs for the gene of interest  are calculated and plotted, and the same is done for variants with PGWAS > sigpvalue_GWAS,  and p-value of correlation (p) are computed and displayed on the plot as well , with both the congruous and incongruous lead variants labelled in the main eQTpLot panel A , which contains a number of plausible candidate genes, including GeneList function of eQTpLot, the user supplies both the BBS1 and ACTN3 genes to eQTpLot, along with all required input data, to obtain a crude estimation of which gene’s eQTL data most closely correlates with the GWAS signal observed at this locus. Calling eQTpLot as follows:Using the GWAS and PeQTL for both genes and the LDL trait, using a PanTissue approach (collapsing by method “min” as described above). The output generated is:eQpLot generates Pearson correlation statistics between PBBS1, compared to the gene ACTN3. To visualize these differences using eQTpLot, starting with the gene BBS1, eQTpLot can be called as follows:Demonstrating that there is significantly stronger correlation between the GWAS signal at this locus and eQTLs for the gene BBS1 gene, using the preloaded Genes.df to define the genomic boundaries of BBS1 based on genome build hg19. eQTL data from eQTL.df.example will be filtered to contain only data pertaining to BBS1. Since tissue is set to “all,” eQTpLot will perform a PanTissue analysis, as described above.As written, this command will analyze the GWAS data, as contained within GWAS.df.example, within a default 200 kb range surrounding the BBS1 eQTLs. In Fig. BBS1 expression levels, as indicated by their coloration in bright orange. Figure p = 9.5e-46 by Fisher’s exact test) for BBS1 eQTLs among GWAS-significant variants. Lastly, Fig. GWAS and PeQTL for the analyzed variants, with a Pearson correlation coefficient of 0.823 and a p-value of correlation of 1.62e-127 (as displayed on the plot). Taken together, these analyses provides strong evidence for colocalization between variants associated with LDL cholesterol levels and variants associated with BBS1 expression levels at this genomic locus.The resulting plot Fig.  illustraACTN3 at this locus, the same analysis can be performed, substituting the gene ACTN3 for the gene BBS1, as in the following command:To visualize the possibility that the LDL association signal might also be acting through modulation of the expression of ACTN3 eQTLs and LDL cholesterol-significant variants. Although there is significant enrichment for ACTN3 eQTLs among GWAS-significant variants .Unlike the previous example, the resultant plot Fig.  illustrants Fig. B, there QTL Fig. D, and itBBS1 eQTLs and the GWAS peak for LDL cholesterol on chromosome 11, using a PanTissue analysis approach. The user may next wish to investigate if there are specific tissues in which BBS1 expression is most clearly correlated with the LDL GWAS peak. Using the TissueList function of eQTpLot as follows:The plots generated in Example 1 illustrated colocalization between GWAS and PeQTL for BBS1 and the LDL trait across each tissue contained within eQTL.df. The resultant output, ranked by degree of correlation, is as followseQTpLot generates Pearson correlation statistics between P…BBS1 expression levels in a number of tissues. To further explore these associations, the user can specifically run eQTpLot on data from a single tissue, for example Whole_Blood, while also supplying LD data to eQTpLot using the argument LD.df:This output demonstrates a strong correlation between LDL cholesterol levels and LD.df refers to the LD.df.example data frame containing a list of pairwise LD correlation measurements between all the variants within the LOI, as one might obtain from a PLINK linkage disequilibrium analysis using the --r2 option [R2min is set to 0.25, indicating that LD.df should be filtered to drop variant pairs in LD with R2 less than 0.25. LDmin is set to 100, indicating that only variants in LD with at least 100 other variants should be plotted in the LD heatmap.Here the argument 2 option . AdditioBBS1 cis-eQTL variants in Whole_Blood within the LOI is shown in Fig. BBS1 eQTL variants are in strong LD with each other at this locus. Second, the P-P plot, Fig. BBS1 locus with strong evidence of colocalization between the BBS1 eQTL and LDL GWAS signals.The resultant plot, Fig. BBS1 expression levels. This is accomplished by setting the argument congruence to TRUE:In addition to including LD data in our eQTpLot analysis, we can also include information on the directions of effect of each variant, with respect to the GWAS trait and BBS1 eQTL variants in Whole_Blood into two groups: congruent – those variants associated with either an increase in both, or decrease in both BBS1 expression levels and LDL levels – and incongruent – those variants with opposite directions of effect on BBS1 expression levels and LDL levels. In carrying out such an analysis, it becomes clear that it is specifically variants with congruent directions of effect that are driving the signal colocalization; that is, variants associated with decreases in BBS1 expression strongly colocalize with variants associated with decreases in LDL cholesterol.The resulting plot, Fig. eQTpLot provides a unique, user-friendly, and intuitive means of visualizing cis-eQTL and GWAS signal colocalization in a single figure. As plotted by eQTpLot, colocalization between GWAS and eQTL data for a given gene-trait pair is immediately visually obvious, and can be compared across candidate genes to quickly generate hypotheses about the underlying causal mechanisms driving GWAS association peaks. Additionally, eQTpLot allows for Pan- and MultiTissue eQTL analysis, and for the differentiation between eQTL variants with congruous and incongruous directions of effect on GWAS traits – two features not found in any other visualization software. We believe eQTpLot will prove a useful tool for investigators seeking a convenient and customizable visualization of eQTL and GWAS data colocalization.Project name: eQTpLotProject home page:https://github.com/RitchieLab/eQTpLotOperating system(s): Platform independentProgramming language: ROther requirements: NoneLicense: GNU GPLAny restrictions to use by non-academics: None.