In forest genetic improvement programs for non-domesticated species, limited knowledge of kinship can compromise or make the estimation of variance components and genetic parameters of traits of interest unfeasible. We used mixed models and genomics  to evaluate the genetic architecture of 12 traits in juçaizeiro for fruit production. A population of 275 genotypes without genetic relationship knowledge was phenotyped over three years and genotyped by whole genome SNP markers. We have verified superiority in the quality of the fits, the prediction accuracy for unbalanced data, and the possibility of unfolding the genetic effects into their additive and non-additive terms in the genomic models. Estimates of the variance components and genetic parameters obtained by the additive models may be overestimated since, when considering the dominance effect in the model, there are substantial reductions in them. The number of bunches, fresh fruit mass of bunch, rachis length, fresh mass of 25 fruits, and amount of pulp were strongly influenced by the dominance effect, showing that genomic models with such effect should be considered for these traits, which may result in selective improvements by being able to return more accurate genomic breeding values. The present study reveals the additive and non-additive genetic control of the evaluated traits and highlights the importance of genomic information-based approaches for populations without knowledge of kinship and experimental design. Our findings underscore the critical role of genomic data in elucidating the genetic control architecture of quantitative traits, thereby providing crucial insights for driving species' genetic improvement. Euterpe edulis, commonly known in commercial settings as "juçaizeiro," is a medium to tall palm tree that easily exceeds ten meters in height2. The species has shown great potential for use in fruit growing due to its excellent quality fruit and pulp with beneficial health attributes. In addition, this species has high fruit production and represents sustainable crop diversification with the possibility of management in native areas, as well as significant socioeconomic and ecological potential impacts. Another important factor highlighting the juçaizeiro in the fruit industry is the potential to sustain the market chain of açai pulp and sorbet3. Açaí is a product derived from the processing of fruits from species of the genus Euterpe4, and on a production scale, the raw material for this product comes almost exclusively from Euterpe oleracea . Although the processed products from both species are similar, the pulp of the juçaizeiro shows superiority in terms of nutritional, mineralogical, energetic, and flavor aspects6.7. The juçaizeiro, naturally occurring throughout the Atlantic Forest10, shows a high level of phenotypic plasticity due to the great environmental variation resulting from the latitudinal range of this biome. Therefore, the juçaizeiro has the potential for cultivation in climatic zones not recommended for the açaí palm7.The açaí palm, in turn, requires higher temperatures and high water availability, limiting the geographic region for cultivationDespite all these advantages, the species does not have registered varieties available in the seed market, which generates concerns regarding its acceptance in establishing commercial orchards. To that end, genetic improvement programs play a fundamental role in acceptance of the culture. They aim to promote procedures that provide more productive and higher-quality industrial varieties. However, due to the long juvenile period of the species, improvement cycles are long and costly and may require more than 15 years between recombination and the development of a new variety.11, and obtaining precise estimates is of great importance since genetic parameters such as heritability are among the most essential for determining the potential for genetic progress of the program12.The non-domesticated status of the juçaizeiro means that the current technique applied to the culture is phenotypic selection, with a focus on additive terms, given that the species is exclusively propagated by seeds. However, a deeper understanding of the genetic architecture of agronomic traits can help breeders maximize genetic gains in each selective cycle13. However, although effective, the results may be inflated since the genetic information matrix assumes unrealistic conditions, such that individuals from the same family share the same degree of genetic similarity14. As a result, variance components are overestimated, leading to inflated predicted genetic values16. This effect is amplified for analysis of non-domesticated species evaluated in natural populations without prior knowledge of parents, making genealogical information among individuals nonexistent, as is the case for juçaizeiro. This lack of information can result in overestimating additive genetic variance components and inflating genetic values15.To estimate variance components and genetic values of individuals, it is common to use the information on genetic kinship through genealogy, which is represented by the A matrix, using the restricted maximum likelihood (REML) and the best linear unbiased predictor (BLUP) methods17 has enabled the acquisition of genomic information from individuals of interest, allowing breeders to construct the realized relationship matrix (G matrix), which represents the genetic sharing among individuals, and can replace the pedigree-based relationship matrix (A matrix)18. Substitution of the A matrix for the G matrix results in the genomic best linear unbiased predictor (GBLUP) method19, which can provide more accurate predictions of individual genetic values17. The use of genomic markers represents an enormous opportunity for non-domesticated species, as it allows decomposition of the genetic variance into its additive and non-additive terms, which would not be possible under normal conditions with unknown parents, providing fundamental information to breeders on the genetic control of the traits studied. The Diversity Array Technology Sequencing (DArTseq) approach uses high-density molecular markers for genomic analysis with broad genomic coverage, efficiency in detecting genetic polymorphisms, ease of analysis, and low cost22.However, the increased accessibility of high-density genome sequencing techniques26. In addition, the use of molecular markers can represent significant savings for breeding programs, in terms of time and cost, since establishing and developing experimental plots, as well as conducting controlled crosses in species for which there is limited knowledge of their pollination system, can be complex and require greater financial resources to develop a complete pedigree for in-depth evaluation of the genetic control of traits of interest. Thus, genetic markers can be a viable alternative to accelerate the more comprehensive evaluation of genetic control of traits and provide more information to breeders.Although non-additive effects, such as dominance, are not currently considered in selective practices for juçara palm due to its exclusively sexual propagation via seeds, studies on forest tree species have demonstrated that incorporating dominance effects into statistical models improves the accuracy of heritability, predictive abilities, and influences on genetic gainsGiven that stated above, this study aimed to use a whole genomic genotyping of DArTseq-derived SNPs to provide insights into the genetic structure controlling traits of interest to improve fruit production of the juçara palm tree, using BLUP and GBLUP approaches, as well as comparing the efficiency of models with different genetic effect structures.Euterpe edulis) plantation, resulting from the process of natural recomposition and seed dispersal in a pasture area located in the municipality of Rio Novo do Sul, state of Espírito Santo, Brazil  measured throughout the four years of the study, evaluated by counting the number of bunches with fruit.The other variables were evaluated in 2018, 2019, and 2021. Fruits were harvested only when they reached the point of industrial maturation, which was determined every year by the same employee at the juçaizeiro fruit processing plant. After extraction of the fruit bunch, the following traits were evaluated:Fruit weight per bunch (kg) (FWB) by weighing in kilograms (kg) the fruits extracted from the bunch of each genotype.After extracting the fruit, the number of rachillas per bunch (NRB) was measured in these genetic materials by counting the number of rachillas inserted into the center rachis, which was then measured using a tape measure, evaluating the rachis length (cm) (RL) in centimeters (cm).A fruit sample of each genotype was obtained, packed in properly identified plastic bags, and sent to the Plant Biometry Laboratory at the Federal University of Espírito Santo, where morphometric evaluations of fruit and seeds were carried out following a completely randomized design.AP is the amount of pulp and WF is the fresh mass of 25 fruits.The weight of 25 fruits (g) (WF) and the weight of 25 seeds (g) (WS) were evaluated in grams (g) in four replications per genotype. With this information, the difference between the morphometric traits of fruits (WF) and the morphometric traits of seeds (WS) resulted in the amount of pulp in 25 fruits (g) (AP) in grams. The following formula was used to obtain the pulp yield (%) (PY):31 concluded that five measurements are necessary to achieve a 95% coefficient of determination — although they provide additional information on the studies, measurements beyond this number increase cost and evaluation time.To evaluate the equatorial and longitudinal diameters of fruits and seeds  (mm), five fruits were measured in millimeters (mm) using a digital caliper. Marçal et al. (2016)32 with modifications33. After verifying the quantity and quality using a NanodropTM 2000 spectrophotometer (Thermo Scientific), the genomic DNA was sent to Mexico for genotyping high throughput using the DArTseqTM methodology at the Genetic Analysis Service for Agriculture (SAGA).Genomic DNA was obtained from leaf samples of the 275 genotypes following the CTAB method34 and Sansaloni et al. (2020)22. Additional information can be found at 35.The sequences were analyzed using Dartsoft14, an automated analysis program of genomics data, and DArTdb, a laboratory management system, developed and patented by DArT Pvt. Ltd. , generating SNP marker data as described by Killian et al. (2012)36, using a Call Rate of 90% and MAF (Minor Allele Frequency) of 5%, reducing the marker dataset by 81.75%, starting with 44,457 markers and keeping 8112 for use in the analysis.The SNP markers were of the codominant type, and the molecular dataset underwent the quality control process carried out in RThree models were employed to predict breeding values in the context of genetic improvement: BLUP and two genomic models (GBLUP). The first model considered only the additive effects (GBLUP-A), while the second considered the additive-dominant effects (GBLUP-AD).Traditional BLUP the prediction of breeding values using the BLUP model was performed in R36 using the SOMMER package version 3.4. The mixed linear model used was:Z is the random effects incidence matrix considered as genotype, and a is the vector of additive genetic effects considered random with Genomic BLUP (GBLUP) the GBLUP model is similar to BLUP, except for the assumed kinship matrix. So GBLUP replaces the A matrix with the genomic kinship G matrix derived from the marker information. The genomic breeding values (GEBVs) are predicted using the model below:g is the vector of additive genetic effects considered random with 18 as follows:W is the centered marker matrix as presented by Vitezica et al. (2013), BLUP genomic dominant additive (GBLUP-AD)D is the genomic relationship matrix for dominance effects generated from marker information, and Dominance genomic kinship matrices (D) the D matrix was obtained as follows39: ith row and jth column of the S matrix, and jth marker.The methodologies were compared based on the theoretical accuracy of breeding values (r), variance of the prediction error (PEV), maximum likelihood (LL), Akaike information criterion (AIC), and Bayesian information criterion (BIC).PEV is the variance of the prediction error obtained through the diagonal elements of the mixed model matrix40, and The r estimates were estimated using the following expression:41 was calculated as follows:log(L) is the logarithm of the residual likelihood function (LL), and k is the number of estimated parameters.AIC42 can be written as:BICPredictive ability was estimated by the correlation between the predicted breeding values of each BLUP, GBLUP-A, and GBLUP-AD model . When the dominance effect was added to the genomic model (GBLUP-AD), the differences between the h2 and the BLUP model intensified, reducing NBP up to 67.57%. In general, the group of traits related to fruit production in the field , except for NBP, showed the greatest changes when considering the dominance effect on the structure of the model.Comparing the h2 demonstrated low (RL), medium , to high  heritability. We observed the similarity between the broad-sense heritability (H2) estimates by the GBLUP-AD and the narrow-sense heritability (h2) obtained by the BLUP and GBLUP-A methods.Overall, H2 values ranged from 0.04 (PY) to 0.27 (FWB) when excluding the LDS traits. The lower values of 2 for NBP and FWB indicate the strong influence of dominance for these traits; we noticed that the dominance effect surpassed the additive effect since Table 50 Table .Table 3RTable 2  and lower values of The Maximum Likelihood Ratio Test (LRT) between genomic models indicated a significant dominance effect for the traits FWB, NBP, WF, and AP, as seen in Table The lowest estimates of entclass1pt{minimaUnder five-fold cross-validation, a slight difference was observed between the genomic models, with the difference more pronounced for NBP. The dominance effect was more pronounced for NBP, reflecting the better performance of the GBLUP-AD model Table . This isentclass1pt{minimaFurthermore, except for NR, the traits related to fruit production in the field  presented lower entclass1pt{minimaAssessing the productive behavior of juçaizeiro Table  is of grIn the present study, BLUP, GBLUP-A, and GBLUP-AD were evaluated to estimate the variance components, genetic parameters, prediction accuracies, and predictive abilities, with the objective of assessing the efficiency of genomic selection and genetic control of several productive traits of the juçaizeiro. This species is in the early stages of improvement, and our study aimed to provide information on its genetic control of several traits of interest to fruit production.46. Additionally, only additive effects are incorporated into the genetic value prediction models, which are unaware of the kinship between individuals. In contrast, we utilized not only morphometric aspects but also data related to fruit yield and incorporated dominance effects into additive models through the knowledge of kinship among individuals. Therefore, this is the first study to include dominance in the genetic control study of juçaizeiro traits.As this crop is only found in forest fragments with small commercial orchards, few improvement studies, which address only fruit, seed, and germination traits, have been carried out with it46, since the dominance effects will not be transmitted to the selected materials due to the propagation method used.To better understand the importance of knowledge about the genetic control structure of productive traits in the juçaizeiro, some specific aspects related to species breeding must be presented. The species is currently propagated exclusively by seeds, and breeding programs primarily focus on understanding the additive genetic control for estimating and selecting the best genotypes based solely on these effectsAlthough dominance is not considered in seed recommendations for commercial plantations, it plays a crucial role in the species' breeding program. Thus, a deeper understanding of the genetic control structure and the magnitude of these effects under the traits of interest is essential for decision-making by genetics breeders. By quantifying the effects that explain the observed phenotypic variation, breeders can optimize selective gains and use more effective methods.47, as crosses of the best genotypes based solely on additive effects may not provide the best progenies49. Thus, knowledge of dominance associated with genetic divergence among individuals becomes essential for promoting the capitalization of the heterotic effect through controlled crosses, seeking better results in the population of the second selection cycle.Moreover, assessing genetic control and the magnitude of the effects of genetic dominance is necessary for making informed decisions about genetically complementary breeding. This understanding enables breeders to identify the most effective crosses to achieve desirable traits50. However, in classical breeding programs, estimating these variance components becomes unfeasible due to the requirement to conduct a large number of controlled crosses and experimental areas. Moreover, crop development time can delay the species improvement program, taking at least 10 to 15 years to estimate genetic effects such as dominance, considering that the tested materials originate from controlled crosses. Thus, genomic techniques have proven effective for the juçaizeiro by providing rapid, in-depth information about the genetic control of its traits and improving estimates of additive genetic effects.In addition to being a crucial aspect for developing offing hybrids, understanding the dominance effect is fundamental to detecting disturbing effects in selecting superior genetic materials51, long periods for plant development, and large experimental areas.In this sense, the genomic methods applied in the present work already surpass the traditional BLUP method since it does not allow estimation of dominance effects due to the lack of pedigree information. Nevertheless, these more sophisticated genomic techniques offer advantages as they generate increased selective accuracy and allow more extensive studies of the genetic control of traits in a more immediate way, while traditional methods would require the performance of controlled crosses (prone to errors and contamination by pollen)To compare the methodologies, the quality measures of AIC and BIC adjustments were used. Through them, we were able to conclude that the inclusion of dominance effects improves model fit for all evaluated traits, as evidenced by the smaller AIC and BIC values Table . Convers}r Table . IncludiAs previously mentioned, the lower 2 did not have high magnitudes. This same behavior was observed in the morphological traits of pine52. However, in contrast to these observations, studies conducted on Eucalyptus found more pronounced changes between the estimates15. Nevertheless, in the cited studies, the drastic changes that can be achieved using marker-based models compared to pedigree information are evident; this was also observed in the present study when the dominance effect was added to the model, which can correct for overestimations in additive genetic variance, as shown by the reduction in the h2 estimates  obtained by GBLUP-AD and narrow-sense heritability (h2) obtained by GBLUP-A may be associated with the fact that the type of regularization used in the marker matrix for estimating additive effects using the VanRaden (2008) method18 makes the additive effects of markers correspond to the allelic substitution effect, whose estimation terms contain both the additive effect of the homozygote and the dominant effect of the heterozygote 2 and H2 estimates between the methods.The similarity observed between broad-sense heritability estimates (H2), which shows how much of the phenotype variation is explained by the genotype, can be classified as low (RL) (< 0.30), medium  (0.30–0.60), and high  (> 0.60)55. The results shown in Table 2 of 0.68 classified as high.In addition, the use of molecular markers shows great relevance for a more in-depth investigation of the genetic control of traits and allows more realistic gains estimates. The observed broad-sense heritability H, which s2 can also be observed in simulated data from pine populations49. Such improvements are fundamental for the juçaizeiro. Although not propagated clonally, removing the dominance effects from estimates of additive effects can increase selective accuracy and promote greater future gains, highlighting the relevance of genomic techniques in the studied population.Adding dominance effects to the predictive models can increase the predictive ability of the genomic model, with the improvement determined by the proportion in which this effect can explain the total variation. This behavior can be observed mainly for the traits FWB Table  and NBP 2 for most traits.Therefore, the presented results contribute to improving the understanding of the genetic structure that governs part of the phenotypic variation of quantitative traits of productive and industrial interest. Moreover, the importance of including the dominance effect to obtain a more realistic partitioning of genetic variance in its additive and non-additive terms is evident by the reduction of h2. This is further evidence of the superiority of genomic methods over the traditional method.Predictive ability is a striking measure of comparison among the models tested in this study. Since conventional BLUP does not provide any pedigree information, the genetic values predicted by this method for untested materials correspond to the average; consequently, the predictive ability becomes zero. For the models based on genomic information, estimates of 56. We found estimates of In general, two factors affect the predictive ability of marker-based models, including heritability, which is dependent on the genetic control of the trait, and phenotyping quality, associated with field evaluation processes57, with or without covariates, may be an alternative to maximize predictive abilities. This method not only uses molecular marker information but also benefits from the correlated effects among the traits of individuals whose genetic value is being predicted to improve the predictions of a key trait.In this sense, it is possible that including covariates associated with plant age, like height and circumference, could improve the performance of predictions and covariates associated with the plot conditions where a group of plants is located. Additionally, the trait-assisted method of genomic prediction60. In this sense, Azevedo et al. (2015)60 concluded that other parameters should be considered for model comparison. The authors determined that estimates of heritability, bias, and the relationship between variation due to dominant and additive effects are more relevant factors for such a comparison. Therefore, in the present study, as the GBLUP-AD model presented the best fitting parameters (AIC and BIC) and demonstrated that GBLUP-A may have confounded its additive term with dominance effect, overestimating h2, we conclude that the model that included the dominance term improved the selection of superior genetic materials, with GBLUP-AD superior to GBLUP- A.In general, the values of BP Table . Such siOur findings on model adjustment parameters and predictive capabilities show that genomic methods are superior to conventional BLUP for the juçaizeiro. The superiority of genomic prediction is further confirmed by the fact that it allows a more in-depth study of the genetic control of traits, which is not possible by BLUP due to the complete absence of knowledge of kinship between individuals. To generate such information, this method would require great effort and financial resources, in addition to the time that would easily exceed 15 years of research.For all traits but LDS, which presented variance due to dominance equal to zero, the models that considered only the additive effects presented estimates of the variance components and genetic parameters greater than the GBLUP-AD model, indicating that including the dominance effect corrects overestimations, returning more real estimates. Thus, genetic gains can be estimated with greater accuracy from the results generated by the GBLUP-AD model.Among the methods and models tested , GBLUP-AD had the best results, demonstrating the importance of estimating the dominance effect for productive traits in juçaizeiro.


N = 360,628). Phenotypic factor scores for two participant-selected nonresponse answers, ‘Prefer not to answer’ (PNA) and ‘I don’t know’ (IDK), each predicted participant nonresponse in follow-up surveys , even when controlling for education and self-reported health . After performing genome-wide association studies of our factors, PNA and IDK were highly genetically correlated with one another (rg = 0.73 (s.e. = 0.03)) and with education ; rg,IDK = −0.38 (s.e. = 0.02)), health ; rg,IDK = 0.49 (s.e. = 0.02)) and income ; rg,IDK = −0.46 (s.e. = 0.02)), with additional unique genetic associations observed for both PNA and IDK (P < 5 × 10−8). We discuss how these associations may bias studies of traits correlated with item nonresponse and demonstrate how this bias may substantially affect genome-wide association studies. While the UK Biobank data are deidentified, we further protected participant privacy by avoiding exploring non-response behaviour to single questions, assuring that no information can be used to associate results with any particular respondents.Response to survey questionnaires is vital for social and behavioural research, and most analyses assume full and accurate response by participants. However, nonresponse is common and impedes proper interpretation and generalizability of results. We examined item nonresponse behaviour across 109 questionnaire items in the UK Biobank ( Mignogna et al. analysed item nonresponse behaviour across 109 questionnaire items from 360,628 individuals in the UK Biobank using phenotypic and genetic data. This can inform our understanding of how item nonresponse might lead to bias in genetic studies more generally. Ethics, participant privacy and interpretation of behavioural genetics research are primary concerns for this research team and this study takes particular precautions in these areas. We urge readers to carefully read Box 1. Nonresponse is interesting both as a behavioural choice by a survey participant and as a statistical concern due to missing data. Much social and behavioural research relies on surveys, and data analysis of survey data usually assumes full and accurate response by survey participants, or at least that any nonresponse is independent of the outcomes that a researcher is interested in. In reality, nonresponse is common and the independence assumption often unjustified, impeding proper interpretation and generalizability of results.Item nonresponse occurs when no substantive answer is recorded for a study participant on a given questionnaire item, such as when the participant does not provide an answer or responds ‘I do not know’2. As an observable behaviour, nonresponse represents a complex interplay between survey design for questionnaires and a respondent’s cognitive processes, that is, in understanding a question and choosing a response3. Nonresponse at the item level may be thought of as an intermediate behaviour on the spectrum between providing complete data and complete nonparticipation—that is, unit nonresponse4—and nonresponse is predictive of future study dropout5. Further, nonresponse is unlikely to be captured by a single construct since individuals may differ in their likelihood to select different nonresponse choices in a questionnaire, for example ‘I don’t know’, I’m not sure’ or ‘I don’t want to answer’, both overall and when responding to questions in certain categories6.Understanding the causes of nonresponse and nonparticipation has long been a concern for survey-based research7. As a general framework, data may be either missing completely at random (MCAR), missing randomly conditional on the remaining observed data (missing at random or MAR) or missing dependent on the unobserved data (missing not at random or MNAR)8. Thus, if there are unobserved individual phenotypic differences influencing the likelihood of item nonresponse, then the resulting missingness is considered MNAR. Nonrandom missingness is of particular concern because common statistical methods such as full information maximum likelihood estimation or multiple imputation are only sufficient to address MAR data. MNAR requires more direct modelling that includes assumptions about the type of missingness11.One primary motivation to understand this nonresponse behaviour is that it not only reduces the effective sample size of scientific studies but can also introduce bias15. For instance epidemiological comparisons with UK Biobank show strong evidence of sampling bias18, and correlation of this selection with geography has the potential to increase effects of population stratification20. Recent research has demonstrated that the collider bias induced by nonrandom participation can substantially bias associations with genetic variants, creating spurious associations for variants associated with sampling probabilities and reducing generalizability of results outside the studied population23. These concerns have prompted several suggestions for analytic methods to reduce selection bias in genetic studies of large biobanks25, but no consensus solution has emerged thus far.In recent years, these concerns about the consequences of voluntary participation in research cohorts have seen renewed interest for large-scale biorepositories such as the UK Biobank (UKB). Several researchers have raised concerns that biobank cohorts may not be representative of the population22). Broadly, since analyses of observed data such as the UKB implicitly condition on nonresponse, associations may be biased if this conditioning induces collider bias on the joint distribution of genetic data, covariates and phenotypes. In particular, consider the case of a phenotype that is positively associated with nonresponse or other selection, either directly  or through some mediator. Single nucleotide polymorphisms (SNPs) that increase this phenotype  will then also be associated with the nonresponse and the observed correlation of the SNP with the phenotype will probably be reduced due to restriction of range. Conversely, SNPs that increase nonresponse without being mediated by the phenotype  will become negatively associated with the phenotype among observed samples since increased nonresponse will favour lower expected values of the nonresponse-associated phenotype. By extension, when a pair of traits have this same pattern of confounding , GWAS of the observed data could yield spurious genetic correlation even if the genetic associations on the two traits  are orthogonal. Similar concerns arise for paths mediated through other variables , depending on their relationship with the phenotype of interest .To understand these concerns about selection bias in genome-wide association studies (GWAS), it is instructive to diagram the potential links between nonresponse, genetics and target outcomes that could induce biased associations  or ‘I don’t know’ (IDK). We then perform GWAS of these two factors, identifying significantly associated loci and modelling genetic correlations with other heritable traits. We validate these genetic findings through out-of-sample polygenic prediction of non-response behaviour, and we demonstrate that by modelling the missingness mechanism using auxiliary variables in Heckman correction we can reduce bias from nonresponse that does not depend directly on the missing value. Throughout this investigation, we avoid any analysis that would violate any participant’s stated desire to avoid answering a question Box . We antiBox 1 Ethical considerationsWe acknowledge that a dark history of pseudoscientific discrimination and genetic essentialism underlies the study of behavioural outcomes in genetics. Along with this history comes a responsibility for genetics researchers to carefully consider and develop robust ethical protocols when conducting their research, especially in the area of behavioural genetics and sociogenomics. However, we also recognize the extraordinary benefits of using genetics as a powerfully emergent tool to better understand behaviour and its relation to health.This paper is one such attempt to uniquely evaluate a source of bias that is present in nearly all questionnaire-based research: item nonresponse. We believe the analyses and conclusions of our study not only underscore the importance of how item nonresponse relates to different domains of health and health behaviour, but also push researchers to be highly sceptical that selection biases can be tackled without considering the specific genetic and behavioural sources of selection of the specific data and system under study. In short, because so much research into the genetics of human health and wellbeing is based on surveys, it is important to understand how nonresponse could be impacting the generalizability of genetics results.Participant consent is critical for the ethical conduct of research. Nonresponse, including at the item level, in some instances will reflect a participant exercising their (entirely justified) right to voluntarily not participate in some aspect of a study. This is especially true in the case of item nonresponse in the form of actively responding ‘Prefer not to answer’. As a result, it requires careful ethical consideration to evaluate how to study nonresponse without breaching the participant’s consent as reflected in both the item-level nonresponse and the study-level informed consent and participation. There can be ethical harms to ignoring the source of missing data in research. Consideration of missingness is necessary to identify the ways in which a study or a particular analysis may not be representative of the population, otherwise researchers risk the myriad impacts of uncritically producing biased or ungeneralizable results. Reifying incorrect results risks group harm from incorrect conclusions that may reinforce misperceptions of disadvantaged groups. Decades of social science research on mechanisms of missing data and their influence on research results reflect recognition of the imperative to wrestle with this challenge.https://www.nature.com/articles/s41562-022-01443-2; https://www.nature.com/articles/s41562-022-01472-x), we have carefully planned and implemented our ethical approach by: (1) seeking specific approval from the datasets we use to conduct this study; (2) following closely the requirements and evaluations of our Institutional Review Boards (IRBs); and (3) creating and documenting our approach in our Box In line with the new guidance on ethical standards announced by Nature Human Behaviour  may implicitly estimate associations of missingness with expected values of the target phenotype, but these interim statistical quantities are neither designed to draw conclusions about any individual nor are they the scientific goal. For this reason, we do not report effect sizes for the stage 1 model in Heckman correction to similarly avoid inference from missingness of individual items ) outside of necessary calculation of intermediate quantities.We stress that these ethical considerations apply not only within this study, but also in future applications and extensions of this work.41 . The PNA option was available for all 109 analysed questions with non-response options, of which 83 questions allowed the IDK option, administered to the study population of 360,628 unrelated participants of European genetic ancestry with available genetic data. Participants selected PNA less frequently (8.82% at least once) than IDK (67.02% at least once), possibly reflecting the effects of the IDK option being presented first among the response options or effects of social desirability bias or satisficing to avoid attributing a nonresponse to personal preference18 (Table P < 5 × 10−5), while males were more likely to answer IDK than females . Nonresponders had markedly lower educational attainment .The demographic trends of individuals with at least one nonresponse were broadly consistent with previous non-response literatureThe items with the highest rates of nonresponse were consistent with differential use of the two nonresponse options. PNA was the more common response among questions capturing potential illegal behaviour or social stigma   = 0.17) for PNA and mean r2 = 0.28 (IQR = 0.13) for IDK), indicating that individuals who responded to questions with PNA tended to do so more consistently across questions than individuals who responded to questions with IDK. Indeed, we identified a small number of individuals who responded to all survey questions with PNA (N = 11).To measure the degree to which item nonresponse behaviour is shared across survey questions, we began by inspecting phenotypic (tetrachoric) correlations Fig. . PNA ansr2 = 0.85 (IQR = 0.08) and mean r2 = 0.76 (IQR = 0.12), respectively) was higher than the correlations between food intake and mental health questions (mean r2 = 0.14 (IQR = 0.05)).Item non-response behaviour was also more similar among survey questions from similar phenotype domains. In other words, item non-response behaviour was not independent of answering patterns across questions. For example, the average correlation of PNA answers among questions within the food intake and the mental health domains  factors for overall IDK and PNA item non-response behaviour, conditional on the correlated substructure. To do so, for each response type, we first assessed the survey substructure by performing factor analysis (FA) with the full set of questions and examining cluster analyses of the residual correlations from a single-factor model  that was accounted for by additional domain-specific factors rather than a general factor , 25.3% responded IDK at least once and 10.1% responded PNA at least once across the 58 questions available for analysis. This is a substantially lower rate of ever responding IDK than the baseline (67%), probably reflecting the differential content of the survey and the added opportunities for nonparticipation in the follow-up . PNA and IDK factor scores computed at baseline jointly predicted whether an individual selected a non-response option for at least one of the 58 questions included in the analysis , beyond basic sample covariates . Within this model, each non-response factor contributed significant variance (βPNA = 0.336 (s.e. = 0.02), P = 3.60 × 10−83; βIDK = 0.823 (s.e. = 0.01), P < 1 × 10−308). These results were only minimally altered by the inclusion of college completion and self-reported health as additional covariates , βIDK = 0.800 (s.e. = 0.01)), indicating that our estimated non-response factors capture information about item nonresponse that is not fully explained by health and education as previously established correlates of nonresponse26. The generalizability of baseline item-level non-response behaviour to robustly predict future item nonresponse, albeit with modest accuracy, suggests that item nonresponse is at least partially durable as an individual trait, consistent with potential genetic associations with this behaviour.Among individuals in our sample who completed the mental health follow-up questionnaire  or in all the 4 waves of the invitation . We quantified the improved prediction after controlling for basic covariates, as well as education and self-rated health status since these have been shown to be proxies for survey non-response behaviours24. PNA and IDK factors slightly improved prediction of participation for all four waves of survey invitation, on top of education and self-rated health status, both when each factor was considered independently  and when the two factors were combined . Combining PNA and IDK resulted in a better prediction of not responding to all four waves of follow-up survey invitations compared with predicting just one wave   loci for PNA and 35 loci for IDK , psychiatry , personality , cognition  and socioeconomic status . The lead SNP of the top locus for IDK, rs9401593 in an intronic region of chromosome 6, has been previously associated with socioeconomic status (SES) traits such as household income47 and years of education48, as well as with broader participation-related phenotypes such as providing valid information necessary for recontact and accepting an invitation to complete a mental health follow-up questionnaire38. Beyond the top locus, several additional IDK loci were also associated with participation phenotypes in previous studies . The top locus for PNA, a locus shared with IDK, maps to a highly pleiotropic missense SNP in SLC39A8 . Given the wide range of phenotypes associated with this variant—from schizophrenia49 to Crohn’s disease50 to scoliosis51—it is perhaps unsurprising that this locus would be associated with both forms of non-response patterns in this study. Such overlap with GWAS of other traits may reflect some instances of shared aetiology, for example through the many traits associated with health and socioeconomic status, but may also be evidence of genetic variants associated with nonresponse leading to selection bias in some previous GWAS studies.Variants within these loci have been previously associated with traits in the domains of health  than for PNA . We also observed a significant heritability enrichment for central nervous system cell types 52, consistent with the expectation that nonresponse relies on cognitive processes3. Within the brain53, both PNA and IDK were significantly associated with regions differentially expressed in the cerebellum . This is consistent with an expectation that the factor analysis provides improved power, reducing measurement error across items and clarifying the signal in the context of correlated residual structure.The general factors for PNA and IDK were both significantly heritable, with higher estimated SNP heritability for IDK (rg = 0.40(0.03), P = 2.13 × 10−34) and we identified 4 genome-wide significant loci for this GWAS. None of these 4 loci were genome-wide significant in the GWAS of either the PNA or IDK factors. Instead, the top genetic results appear to correlate with the number of sunburn occasions. For example, the IDK-increasing allele for rs35407 allele, a 3′ UTR variant in SLC45A2, is associated with increased risk for melanoma54 and cutaneous squamous cell carcinoma55. We hypothesize that this result reflects that it is harder to recall the number of childhood sunburns when that number is large compared with having few or no sunburns, thus increased predisposition to sunburns will increase both IDK responses and skin cancer risk. This result suggests that our factor score GWAS successfully highlights shared components affecting item nonresponse generally (rg > 0) while avoiding capturing more question-specific non-response behaviour that is less related to overall nonresponse .One concern in our analysis is that the GWAS results for the item non-response phenotypes may be driven by questions with the highest number of PNA and IDK responses  (Supplementary Table rg = −0.47 (0.05), P = 6.75 × 10−19; IDK rg = −0.29 (0.04), P = 1.00 × 10−15), indicating that associations with item-level nonresponse partially overlap with those of response to follow-up. The PNA and IDK factors also show high genetic correlation with opting to skip the sexual history section in the UKB survey (PNA rg = 0.58(0.04), P = 1.61 × 10−46; IDK rg = 0.50(0.03), P = 1.05 × 10−52). Importantly neither of these questions were used for deriving PNA and IDK factors, providing additional evidence that the derived phenotypes are indeed capturing consistent item non-response behaviour in the UK Biobank.Consistent with our phenotypic findings reported above, the PNA and IDK factors are strongly genetically correlated with other survey participation in UKB. Specifically, we observed a strong genetic correlation between our factors and agreeing to participate in the online follow-up diet questionnaire (PNA rg_PNA = 0.51(0.03) and rg_IDK = 0.49(0.02) with overall health rating), psychiatric disorders  and rg_IDK = 0.30(0.03) with attention-deficit hyperactivity disorder), personality measures  and rg_IDK = 0.24(0.02) with neuroticism score) and cognition  and rg_IDK = −0.27(0.03)) with fluid intelligence score  and rg_IDK = −0.38(0.02)) and total household income before tax (rg_PNA = −0.57(0.04) and rg_IDK = −0.46(0.02)). These results are consistent with previous work33 suggesting that non-response behaviours are strongly linked with SES.More broadly, we observed strong genetic correlations between the PNA and IDK factors and a wide range of phenotypes including health indicators . First, we estimated that total household income47, region-based social deprivation58 and education46—items traditionally considered major indicators of SES59—jointly explain 34% of the SNP heritability in PNA ) and 22% of the heritability in IDK ) . The IDK factor in particular remained significantly associated with a number of health-related items, such as ‘Number of self-reported non-cancer illnesses’  and ‘Long-standing illness, disability or infirmity’ , while PNA retained specific associations to psychiatric items such as schizophrenia  and feeling ‘tense’ or ‘high-strung’ . These results suggest that genetic association of nonresponse with poor overall physical and mental health is not fully accounted for by the genetics of socioeconomic factors. Overall, these results highlight traits that may be genetically associated with nonresponse and are at highest risk of being affected by bias from nonrandom missingness, beyond what we can learn from using phenotypic information alone.Given the strong previous evidence for the association of nonresponse with SES, we next evaluated the degree to which the overall pattern of genetic correlations could be explained by SES using genomic structural equation modellingrg = 0.73(0.03), P = 3.92 × 10−125), reflecting partial but not complete  genetic overlap between these two factors. Notably, genetic correlation facilitates this comparison between forms of item nonresponse by using genetics to overcome the limitation that UK Biobank participants could only respond with one of PNA or IDK on a given item, not both. The substantial genetic correlation may in part reflect shared aetiology or overlapping use of the nonresponse options beyond their literal meaning, such as reporting IDK to avoid invoking personal preference41 or reporting PNA when an item’s answer is not immediately obvious.In addition to the genetic correlation of the PNA and IDK factors with other traits, we observe substantial genetic correlation between these two factors (60rg_PNA = 0.21(0.03) vs rg_IDK = −0.006(0.02), Pdiff = 3.72 × 10−12), cognitive  vs rg_IDK = −0.27(0.02), Pdiff = 3.33 × 10−12) and sociodemographic variables  vs rg_IDK = −0.38(0.02), Pdiff = 2.05 × 10−8). IDK showed more substantial correlation with reported activities  vs rg_PNA = 0.05(0.02), Pdiff = 1.41 × 10−6) and nutrition  vs rg_PNA = −0.02(0.04), Pdiff = 5.83 × 10−7).To help understand which genetic components are unique to PNA and IDK, we considered traits whose correlation with the two non-response types differed  and total household income , while the association with self-rated general health  was attenuated. Conversely, ‘IDK-adjusted’ PNA maintained significant associations with educational attainment , income  and general health . Corresponding analyses of conditional genetic correlations for psychiatric disorders yielded a cross-over effect, with ‘PNA-adjusted’ IDK correlations with schizophrenia  and bipolar disorder  moving towards the opposite sign of the observed genetic correlations with PNA. Taken together, these results highlight that although PNA and IDK have substantial genetic overlap, they remain partially distinct. The genetic association of IDK with education and other SES-related variables may be largely explained by genetic factors shared with PNA, while the association of PNA with psychiatric phenotypes appears to involve more genetically distinct elements.Given the strong genetic correlation between IDK and PNA, these differences create the possibility that the genetic correlation between one of the non-response types and an outside trait may be fully explained by correlations with the other non-response type. To test this possibility, we estimated the conditional genetic correlation between PNA and other heritable phenotypes controlling for the genetic associations of IDK and vice versa for correlations with IDK conditional on PNA, using genomic SEMR2 = 0.1%). We also estimated that a one-standard-deviation increase in the IDK polygenic score is associated with a 2% increase in the probability of an individual ever answering with ‘I don’t know’ in the Wave 4 Add Health data . Taken together, these results suggest that our findings in the UK Biobank replicate in an external US-based study of younger individuals.To test the generalizability of our genetic findings, we constructed polygenic scores for the PNA and IDK factors in Wave 4 of the Add Health data. Item nonresponse in Add Health was identified on the basis of 163 questions with a possible response of ‘I don’t know’ and 217 questions with a possible response of ‘refused to answer’  could improve prediction of our two non-response outcomes (Supplementary Table R2 = 0.08%) and was not significantly associated with the ‘I don’t know’ outcome, suggesting that polygenic scores based on the GWAS of IDK and PNA should currently be preferred for prediction of overall nonresponse.We also considered whether better polygenic prediction can currently be achieved in Add Health using a recently developed polygenic score for educational attainment (continuous years of completed education)61. Briefly, the Heckman two-step estimator first builds a selection model for each individual’s likelihood of missingness and then uses this prediction of missingness as a covariate to adjust the regression model of interest , since it has substantial item- and survey-level missingness and strong genetic correlations with both PNA (rg = −0.39(0.04)) and IDK (rg = -0.26(0.03)). We find that the selection model modestly predicts missingness in FI (pseudo-R2 = 0.045) with significant effects of the factor scores for PNA (P = 1.46 × 10−69) and IDK (P = 1.93 × 10−43). The predicted missingness is then strongly associated with FI in the response model (Heckman λ coefficient = 2.886(0.042), P < 1 × 10−308), suggesting that the observed missingness is informative and correction may reduce bias.Finally, we evaluate the potential impact of adjustment for missingness from nonresponse in GWAS using Heckman correctionP = 8.86 × 10−11 for test of slope = 1; Fig. rg = 0.967(0.002), P = 2.14 × 10−39 for test of rg = 1). Encouragingly, the Heckman correction reduces genetic correlation with PNA (rg = −0.28(0.04)) and IDK (rg = −0.11(0.03)), with the remaining correlation possibly reflecting incomplete correction for nonresponse or true underlying correlation with non-response behaviour. More substantively, meaningful differences in genetic correlation with GWAS of other traits are observed, such as a much weaker correlation with coronary artery disease after Heckman correction (uncorrected GWAS rg = −0.14(0.03), corrected GWAS rg = −0.04(0.03); Supplementary Table 63. Taken together, these results broadly demonstrate the potential impact of correcting for nonresponse on substantive GWAS findings.In this example application, we find substantial differences between GWAS results for FI with and without using Heckman correction. Of 11 loci significantly associated with FI in the uncorrected GWAS, only 3 of the lead SNPs remain genome-wide significant after Heckman correction ). Notably genetics enables this comparison despite the forced choice between responding either IDK or PNA to any given item in UKB. Among genome-wide significant loci, two of the four loci associated with PNA are also associated with IDK, and three of IDK-associated loci have recently been associated with at least one aspect of study participation . These results are consistent with the idea that item nonresponse, unit nonresponse and study nonparticipation may be associated with a shared spectrum of factors, and this shared spectrum may have a genetic component, with remaining residual genetic factors more specific to particular forms of nonresponse.The current results for IDK and PNA suggest substantial similarity in the use of these two forms of nonresponse, but with some response-specific features. Phenotypically, both IDK and PNA exhibit substantial correlation across questions, both broadly and within clusters that reflect the survey’s content and structure. Strong genetic correlation is observed between the GWAS of estimated general factors for PNA and IDK across items, but the correlation is significantly less than 1  can be largely explained by its correlation with PNA. This may indicate that the genetic overlap between IDK and PNA includes the genetic correlates of SES, with additional SES-related effects potentially unique to PNA responses. Third, IDK and PNA continue to show significant genetic and phenotypic associations conditional on SES and health variables. This is important for two reasons: (1) it suggests that non-response behaviour is not fully mediated through, for example education, and thus associations beyond these variables need to be identified to fully characterize nonresponse and (2) the current estimates for IDK and PNA behaviour in this paper are already sufficient to improve prediction of nonresponse both phenotypically  and genetically , including generalization of the polygenic score to other samples. As discussed below, accurate modelling of non-response behaviour is likely to be a key component of successfully correcting GWAS and other analysis for selection bias.Given that previous literature suggests a strong relationship of nonresponse with socioeconomic status and healthα × θ in Fig. rg,IDK = 0.183(0.025), rg,PNA = 0.339(0.041)), while some IDK responses may reflect strategic avoidance of disclosing some health-related behaviours  rg,IDK = −0.213(0.031), rg,PNA = −0.021). The latter hypothesis would be consistent with the possibility that the higher phenotypic rate of IDK responses is associated with social desirability bias or satisficing41. On the other hand, presupposing that the observed genetic correlation reflects collider bias  not observed in IDK, while IDK shows some signs of unique correlation with health-related diet and lifestyle behaviours. If we presuppose that these correlations reflect true shared genetic variants associated with nonresponse . Identifying genetic associations with missingness, whether from item nonresponse or other selection effects, has the advantage of aiding this modelling of missingness solely from genetic data without advanced knowledge of what phenotypic measures explain the missingness.Finally, this investigation of the genetics of nonresponse in UKB is motivated primarily by the desire to understand and address selection bias in other GWAS. The current results demonstrate that simple correction  of GWAS using the current estimates of general IDK and PNA behaviour has the potential to substantially impact observed genetic results. Our Heckman correction however has a key limitation: the effectiveness of this correction will depend on having the ability to correctly specify an accurate model for the missing data mechanisms affecting the sample70 and/or comparison to expected population descriptive statistics from national registries16 or by linkage to individual data71, since those analyses will still provide valuable information about participation and nonresponse, and highlight what individuals may be underrepresented in the study or particular analysis. While we were limited in our correction exercise here, we anticipate that ideal correction of selection bias in GWAS will require incorporating the associations with item nonresponse identified here with findings on unit nonresponse38, study participation66 and other ascertainment effects39, as well as other confounds such as stratification and longitudinal change or misreporting72.Where possible, however, these genetic elements should be supplemented by considering phenotypic correlates of nonresponse within the sampleIn conclusion, we use phenotypic and genetic data to provide an investigation of overall item-level nonresponse across items in the UK Biobank. These results should be considered when analysing the UK Biobank, among other biobank-scale survey efforts, and when developing novel methods aimed at correcting and leveraging nonresponse in genetic analyses. We also encourage readers to carefully consider the ethical considerations and interpretations of our work, which are highlighted in Box Use of the UKB data was approved under application 31063. Because of the sensitive nature of this study, we also explicitly sought permission for the specific scope of this paper  and to examine whether there are any genetic factors that correlate with these response phenotypes’), which the UK Biobank granted under the same application.Analysis of the UKB data was reviewed by the Partners HealthCare IRB (Partners Human Research), which determined in expedited review that the project met the US federal criteria definition of ‘not human subjects research’ .Analysis of the Add Health data was reviewed by the Office of Research Subject Protection (OSRP) at the Broad Institute of MIT and Harvard, which determined that the project met US federal criteria for exemption from IRB review .73. It consists of a prospective cohort of 502,620 men and women aged 40–69 recruited in the years 2006–2010 throughout the United Kingdom. The touchscreen questionnaire is a collection of self-reported information regarding general health, dietary habits, physical activity, psychological and cognitive states, sociodemographic factors and so on. We began with 361,194 unrelated individuals of European genetic ancestry who passed quality control measures . We excluded individuals who were enrolled only in the UKB pilot study (N = 335). Participants who decided to terminate the touchscreen questionnaire were asked to select PNA to all subsequent questions and they were kept in our analyses. Conversely, individuals who withdrew from the study without filling out the touchscreen survey were excluded from the analysis (N = 231). As a result, a total of N = 360,628 participants took part in the survey and answered every question of interest in the study; this is the final analytic sample size.The UKB is a health resource which has the purpose of improving the prevention, diagnosis and treatment of human diseaseN questions = 109 and 83 for PNA and IDK, respectively). This included one instance of a derived item that retained nonresponse information (UKB FieldID 20116). Items excluded from our analyses thus included those with incomplete data due to, for example, being asked only to a subset of participants, or being added to the touchscreen questionnaire later in the recruitment process.We considered only the touchscreen questionnaire phenotypes with the response options ‘Prefer not to answer’  or ‘I don’t know’ . Items were included in our analyses only if valid responses existed for all participants in our sample  and the mean age at the time of assessment (Wave 4) is 29.0 years (s.d. = 1.8). All phenotypes included in this study came from Wave 4, the latest wave of Add Health data collection (2007–2009).Add Health originated as an in-school survey of a nationally representative sample of US adolescents enroled in grades 7 through 12 during the 1994–1995 school year46 (completed years of education).To investigate item non-response bias phenotypes in Add Health, we considered two possible answer choices across hundreds of questions from the Wave 4 Add Health in-home interview questionnaire: ‘refused to answer’ and ‘I don’t know’. The final study population included 3,414 unrelated participants of European genetic ancestry with available genetic data. The ‘refused to answer’ option was available for 217 questions, while only 163 questions allowed the ‘I don’t know’ option. Our final outcomes were whether respondents ever answered at least once with ‘refused to answer’ or ‘I don’t know’, respectively. We also predicted the two non-response outcomes in Add Health using a recently developed polygenic score for educational attainmentExploratory factor analysis (EFA) with a single latent factor was performed separately on tetrachoric correlation matrices between each of the dichotomized PNA and IDK responses and was implemented using the fa function of the psych package in R software (v.3.4.4) with the oblique rotation ‘biquartimin’ and the ‘ordinary least squares’ extraction method.Residuals from the initial EFA revealed a correlation structure indicative of further clustering unaccounted for by the general factor, with both broad correlations across item domains as well as some highly specific pairwise structure at the item level. Given that we were interested in modelling overall non-response behaviour, not behaviour specific to or driven by single item groupings or domains, we sought to reduce this additional structure first by pruning items with highly correlated non-response patterns and then by fitting a bifactor model. This initial pruning step was necessary to flatten the nested correlation structure observed within domains to facilitate fitting of the more interpretable bifactor model. To implement this pruning, we performed agglomerative clustering of the residuals from the single-factor EFA and inspected the resulting dendrogram  and specific factors influencing sets of items with related content. We chose to focus on the results for this general factor since we expect it to be more generalizable to nonresponse on a broad range of items in UKB and other studies, and to be less influenced by the specific set of questions included in our analyses.The bifactor model is a factor analysis model that assumes the observed covariance between items (approximately) reflects the contributions on one shared factor influencing all items and two or more specific factors influencing subsets of itemsN = 288,502) for EFA77 and 20% of participants  for CFA. For EFA, we used the fa function, with ‘biquartimin’ and ‘OLS’ as the rotation and factoring methods, respectively. We implemented CFA using the cfa function from the lavaan package78 in R software (v.3.4.4) and also using the weighted least square mean and variance adjusted estimator. We selected the initial factor structure from the EFA, first fitting models with different numbers of domain factors  and, of those, all had an option to respond PNA, while only 14 had an IDK option. Due to this imbalance and the hypothesis of shared overlap in PNA and IDK behaviours, we chose to collapse PNA and IDK responses across items, identifying 36,517 individuals (30.5%) responding either PNA or IDK at least once across the 58 included items. Logistic regression was performed to associate this binarized outcome with PNA and IDK factor scores computed at baseline, along with chromosomal sex, age, age2, sex × age2 and the first 20 principal components of the variance–covariance matrix of the genetic data as covariates. To determine the significance and magnitude of prediction, we calculated the incremental pseudo-R2  of including PNA and IDK scores over the baseline covariates described above. We also examined the significance of the individual PNA and IDK coefficients. We repeated these analyses using the additional covariates college completion (UKB fieldID 6138) and self-reported overall health (UKB fieldID 2178) to determine whether associations with PNA and IDK remained conditional on these well-known predictors of nonresponse.We ran logistic regression to predict item nonresponse on a follow-up mental health questionnaire using our PNA and IDK factor scores. Of 141 total items on the mental health follow-up questionnaire, only 58 contained valid responses for all participants . Individuals with missing values (NA), reflecting individuals not invited to the dietary follow-up, were removed from the analysis . Similarly, completion of all 4 waves of the dietary questionnaire was coded as 1 if someone completed all 4 waves and 0 if someone did not complete all 4 waves of the questionnaire . Participants coded as missing or who completed some but not all of the dietary questionnaires were not considered in this analysis . We examined the association of our standardized factors with sex, age, age2, sex × age2, the first 20 principal components of the variance–covariance matrix of the genetic data, self-reported health (UKB fieldID 2178) and years of education. Years of education was created by recoding the Qualifications field (UKB fieldID 6138) as follows, according to the International Standard Classification of Education (ISCED) codes 46:College or University degree (ISCED) = 20 yr of educationAdvanced (A) levels/Advanced Subsidiary (AS) levels or equivalent (ISCED 3) = 15 yr of educationOrdinary (O) levels/General Certificate of Secondary Education (GCSE) or equivalent (ISCED 2) = 13 yr of educationCertificate of Secondary Education (CSE) or equivalent (ISCED 2) = 12 yr of educationNational Vocational Qualification (NVQ) or Higher National Diploma (HND) or Higher National Certificate (HNC) or equivalent (ISCED 5) = 19 yr of educationOther professional qualification  (ISCED 4) = 17 yr of educationNone of the above (ISCED 1) = 6 yr of educationWe additionally ran logistic regression to predict completion of an online follow-up 24 h recall dietary questionnaire (UKB fieldID 110001) by using our PNA and IDK factors as predictor variables. To measure the variance explained by the model, we computed the pseudo-Participants who chose PNA or IDK for either years of education or self-reported health were excluded.73. Briefly, UKB participants were genotyped using the Affymetrix UK BiLEVE Axiom array and UKB Axiom array. After extensive quality control, imputation was performed using SHAPEIT380 and a reimplementation of IMPUTE281 with the Haplotype Reference Consortium and merged 1000 Genomes82 + UK10K83 reference panels.Genotyping and imputation procedures for UKB have been detailed previously84. After imputing the genetic data to the Haplotype Reference Consortium85 using the Michigan Imputation Server86, only HapMap3 variants were included, as these are well imputed and provide good coverage of common variation across the genome. Analyses were limited to individuals of European ancestry, and cryptically related individuals and ancestry outliers were dropped from analyses. Finally, only HapMap3 variants with a call rate above 98% and a minor allele frequency >1% were used.Genotyping in Add Health was performed at the Institute for Behavioral Genetics in Boulder, Colorado, using Illumina’s Human Omni1-Quad-BeadChip87, including chromosomal sex, age, age2, sex × age2 and the first 20 principal components of the variance–covariance matrix of the genetic data as covariates. We included autosomal variants with imputation INFO column values > 0.8, minor allele frequency (MAF) > 0.01 and Hardy–Weinberg equilibrium (HWE) P > 1 × 10−10, as well as annotated protein-truncating or missense variants with MAF > 1 × 10−6 (based on Ensembl VEP consequence annotations). Following these filters, 9,367,367 total variants were included in the GWAS.We performed GWAS using linear regression implemented in Hail88 pipeline to identify independent genomic loci. We considered an independent locus as the region including all SNPs in pairwise linkage disequilibrium (r2 > 0.6), with the lead SNPs in a range of 250 kb and independent from other loci at r2 < 0.1. We used the 1000 Genomes Phase3 Northern Europeans LD reference panel82.We used the FUMA89 to estimate the proportion of variation in our PNA and IDK factors that is explained by inherited SNPs, using the Baseline v.1 model with 53 variant annotations for functional categories to better fit variability in effect sizes across the genome. For these analyses, as well as for all analyses involving LDSR, we filtered to SNPs in the HapMap3 reference panel that had MAF > 0.01 and INFO > 0.90 in our sample .We used stratified LD Score regression (S-LDSR)90 was also used to estimate heritability enrichment for certain regions of the genome on the basis of outside annotations conditional on the baseline model. For these analyses, we used three separate annotation sets based on previous papers52: (1) 10 different cell type groups based on unions of 220 individual histone-based annotations94, (2) multitissue expression annotations (N = 205) based on GTEx and Franke lab data96 and (3) annotation of differential gene expression across 13 different brain regions based in GTEx data53. Given the overlap between the annotations of brain tissues and their differential expression, multiple testing correction was only performed within-analysis .S-LDSR97 using reference panel LD estimates from European-ancestry individuals in the 1000 Genomes Project82. We ran genetic correlations for PNA and IDK with a total of 654 traits, 615 which were from UKB and are publicly available98. An additional 39 traits were selected from previous GWAS and spanned the domains of cognition, psychiatry, personality, medical diagnoses, physical characteristics and sociodemographics  obtain PNA and IDK residual heritability conditional on the genetic contributions of SES variables  then includes as a covariate the inverse Mills ratio of the predicted missingness liability 2, sex × age2, the first 20 principal components of the variance–covariance matrix of the genetic data and dummy codes for the baseline assessment centre. The response model included covariates for chromosomal sex, age, age2, sex × age2 and the first 20 principal components of the variance–covariance matrix of the genetic data. GWAS of the response model was implemented in Hail (https://github.com/hail-is/hail).To provide proof of concept for using knowledge of non-response patterns to correct associations for bias from nonresponse, we demonstrated the impact of Heckman correction on GWAS associations. The Heckman two-step estimator (heckit)38.We demonstrated the effect of Heckman correction on associations with FI score in the UKB mental health follow-up survey (UKB fieldID 20191). We anticipated this variable to be a strong proof-of-concept given the strong observed genetic correlation of our non-response factors with fluid intelligence, its measurement after the items contributing to our non-response factor scores and the substantial informative missingness in the mental health follow-upFurther information on research design is available in the Supplementary InformationHeckman Supplementary Note, Frequently Asked Questions and Figs. 1–10.Reporting SummaryPeer Review FileSupplementary Tables 1–14Supplementary Tables 1–14, each with a legend indicated in the first row of the Excel tab.


QTRL.caas-4A.1 and QTRL.caas-4A.2), TRA (QTRA.caas-4A and QTRA.caas-4D), and NRT (QNRT.caas-5B and QNRT.caas-5D) were identified and each explaining 5.94%–9.47%, 6.85%–7.10%, and 5.91%–10.16% phenotypic variances, respectively. Among these, QTRL.caas-4A.1 and QTRA.caas-4A overlapped with previous reports, while QTRL.caas-4A.2, QTRA.caas-4D, QNRT.caas-5B, and QNRT.caas-5D were novel. The favorable alleles of QTRL.caas-4A.1, QTRA.caas-4A, and QTRA.caas-5B were contributed by Doumai, whereas the favorable alleles of QTRL.caas-4A.2, QTRA.caas-4D, and QTRA.caas-5D originated from Shi 4185. Additionally, two competitive allele-specific PCR (KASP) markers, Kasp_4A_RL (QTRA.caas-4A) and Kasp_5D_RT (QNRT.caas-5D), were developed and validated in 165 wheat accessions. This study provides new loci and available KASP markers, accelerating wheat breeding for higher yields.Identifying loci for root system architecture (RSA) traits and developing available markers are crucial for wheat breeding. In this study, RSA-related traits, including total root length (TRL), total root area (TRA), and number of root tips (NRT), were evaluated in the Doumai/Shi4185 recombinant inbred line (RIL) population under hydroponics. In addition, both the RILs and parents were genotyped using the wheat 90K single-nucleotide polymorphism (SNP) array. In total, two quantitative trait loci (QTLs) each for TRL ( Wheat production is influenced seriously by abiotic stresses. Breeding higher-yielding and more stable accessions under abiotic stress is a crucial objective in modern wheat breeding . Root syPrevious breeding programs predominantly focused on aboveground traits such as disease resistance, grain quality, and harvest index, while the application of RSA traits has been limited due to the complexity of phenotypic evaluation . PreviouIn this study, we conducted linkage mapping for RSA traits using the wheat 90K assays in a biparental recombinant inbred line (RIL) population derived from the Doumai/Shi 4185 cross. The primary objective of this study is to uncover the genetic basis of these traits and develop available KASP markers for improving wheat RSA.2:6 RILs derived from the Doumai/Shi 4185 were used for evaluating RSA-related traits. A hydroponic experiment was conducted with three replicates in a greenhouse. There were 20 seeds from each line that were surface sterilized with10% H2O2 for 20 min. Subsequently, the cleaned seeds were placed in Petri dishes with moist filter paper. When the coleoptiles reached about 2 cm in length, wheat seedlings were transferred to plastic trays (53 × 27 cm) containing Hoagland’s nutrient solution. Plastic trays were kept in 25°C with a 16-h light and 8-h darkness cycle. After 3 weeks, roots were evaluated for RSA-related traits .Three RSA-related traits, namely, total root length (TRL), total root area (TRA), and number of root tips (NRT), were assessed using the WinRHIZO root analysis system (LA6400XL). The roots were arranged systematically in a dish and scanned using the Expression 11000XL. Subsequently, the images were imported into WinRHIZO software and analyzed using a fixed threshold parameter of 40. Each accession was scored on five plants for RSA traits, and means of three replicates were obtained. Basic statistical analyses and frequency distributions were conducted using SAS v9.3  by CapitalBio Corporation. SNPs with missing data >20% or minor allele frequency (MAF) <0.5 were filtered for further analysis. The filtered SNPs were then analyzed using the BIN function of IciMapping v4.2  and grouThe inclusive composite interval mapping (ICIM) method using IciMapping v4.1  was applQTRL.caas-4A.1, QTRA.caas-4A, and QNRT.caas-5D) were converted to KASPs (http://www.polymarker.info/) (SNPs flanking QTL with higher PVE (to KASPs  and desir.info/) Table S2r.info/) .https://wheat.pw.usda.gov/GG3/). Genes, excluding hypothetical proteins, transposon proteins, and retrotransposon proteins with SNPs in the coding region, were considered as candidate genes. Quantitative real-time PCR (qRT-PCR) was conducted to test expression differences of the candidate genes between Doumai and Shi 4185. The roots were sampled for RNA extraction after phenotyping. RNA was extracted using the TRIzol method, and the cDNA was synthesized with the HiScript II 1st Strand cDNA Synthesis Kit. Primers were designed using the Primer Premier V5.0. PCR was conducted with a mixture of 20 μl, including 2 μl cDNA, 10 μl ChamQ Universal SYBR qPCR Master Mix, and 0.4 μl of each primer. qRT-PCR was conducted in the ABI StepOnePlus Real-Time PCR System with Tower, and the gene expression level was analyzed by the 2–ΔΔCT method. All assays were performed in two biological replicates and three technical replicates. TaActin1 was used as the internal control to normalize the expression levels of different samples.To identify candidate genes involved in the QTL for RSA-related traits detected in the Doumai/Shi 4185 RIL population, the genes located in the LD block region around the peak SNP  of each QTL were extracted from the wheat IWGSC v1.1 annotation (2 (range: 6.9 cm2–15.2 cm2), and 328.2 root tips (range: 94.7–772.0). The standard deviation and coefficient of variation for TRL, TRA, and NRT were 18.1 cm (24.2%), 1.7 cm2 (16.4%), and 126.4 (38.5%), respectively. A significant correlation was observed between TRL, TRA, and NRT, with the correlation coefficient of 0.603 (P < 0.05) between TRL and TRA, 0.371 (P < 0.05) between TRL and NRT, and 0.312 (P < 0.05) between TRA and NRT.All three RSA-related traits exhibited continuous and significantly wide variation across the 262 RILs (QTRL.caas-4A.1 (BobWhite_c20306_147-Tdurum_contig54973_1510) and QTRL.caas-4A.2 (BS00059454_51-Kukri_c19883_816), respectively. These QTLs explained 5.94% (additive effect: 5.17) and 9.45% (additive effect: −7.12) of the total phenotypic variances  and QTRL.caas-4D (Ex_c6665_1067-wsnp_J-D_rep_c51623_35119179), respectively, explaining 7.10% (additive effect: 0.40) and 6.85% (additive effect: −0.39) of the total phenotypic variance. The favorable allele of QTRA.caas-4A TRA was contributed by Doumai, while the favorable allele of QTRA.caas-4D was originated from Shi4185, respectively. Two QTLs for NRT were detected on chromosomes 5B and 5D and named as QNRT.caas-5B (wsnp_Ex_rep_c67320_65870601-Tdurum_contig10191_996) and QNRT.caas-5D (IAAV6218-Kukri_c46526_103), respectively, explaining 5.91% (additive effect: 26.7) and 10.16% (additive effect: 39.1) of the total phenotypic variances. The favorable alleles of QNRT.caas-5B and QNRT.caas-5D were contributed by Doumai and Shi 4185, respectively  with higher phenotypic effects were used to develop KASP markers. Although attempts were made to develop a KASP marker for QTRA.caas-4A, it was unable to effectively distinguish between the two parental genotypes in the RIL population. Therefore, the marker did not yield conclusive results. Consequently, two KASP markers, Kasp_4A_RL  and Kasp_5D_RT , were successfully developed based on the tightly linked SNP markers  exhibited higher TRL compared to the unfavorable allele  at the P = 0.05 level  showed higher NRT than unfavorable allele  at the P = 0.05 level  encoded an ethylene-regulated nuclear protein (ERT2)-like protein; TraesCS4A01G436700 (QTRL.caas-4A.1) encoded a calcium-binding protein kinase (CDPK)-related kinase; TraesCS4A01G456300 (QTRL.caas-4A.1) encoded a cellulose synthase-like protein; TraesCS4A01G469400 (QTRL.caas-4A.2) encoded an F-box protein; and TraesCS4D01G092000 (QTRA.caas-4D) encoded an ABC transporter, whereas both the TraesCS5D01G378400 and TraesCS5D01G380200 for QNRT.caas-5D encoded E3 ubiquitin-protein ligase. The expressions of the seven candidate genes in Doumai and Shi 4185 were detected using qRT-PCR. Of these, TraesCS4A01G469400, TraesCS4D01G092000, and TraesCS5D01G380200 showed no significant differences between the parents, whereas TraesCS4A01G296500, TraesCS4A01G436700, TraesCS4A01G456300, and TraesCS5D01G378400 showed more than 1.4–3.9-fold higher expression in Doumai compared to Shi4185 , TRA (QTRA.caas-4A and QTRA.caas-4D), and NRT (QTRA.caas-5B and QTRA.caas-5D) TRA. Each of these QTLs accounted for 5.94%–9.47%, 6.85%–7.10%, and 5.91%–10.16% of the phenotypic variance, respectively.Optimization of crop root systems has long been proposed. However, genetic improvement of crop roots has been rarely attempted . A compr9 RIL lines and have identified 19 QTLs mainly distributed on chromosomes 1A, 2B, 2D, 3A, 3B, 3D, 5A, and 5D. Of these, the 5D loci (415.8 Mb–457.6 Mb) overlapped with theQNRT.caas-5D (449.4 Mb–454.1 Mb) identified in this study. IWB21309, 17.01 Mb) and 5B  for RSA traits were also different with the loci identified in this study . Furthermore, MQTL4A.1 (651.78 Mb–705.73 Mb) and MQTL4A.6 (600.04 Mb–691.14 Mb) significantly associated with root length, root surface, and root tips overlapped with QTRL.caas-4A.1 (702.3 Mb–721.3 Mb) and QTRA.caas-4A (594.2 Mb–602.9 Mb). Rht2/Rht10 19.18 Mb, SVP3-4D/BM1-4D 469.46 Mb, Vrn2-4D/ZCCT1-4D 509.43 Mb), 5B , and 5D   are different from the reported wheat genes associated with plant height and vernalization. Therefore, they represent novel genes associated with RSA-related traits. Above all, compared with the previous results and meta-analysis, QTRL.caas-4A.2, QTRL.caas-4D, QNRT.caas-5B, and QNRT.caas-5D were novel.0.00 Mb) . Based oQTRL.caas-4A.1 (702.3 Mb–721.3 Mb) co-located influencing QTL clusters related to thousand kernel weight   co-located with regions affecting QTL clusters related to yield, including SN, KNS, TKW, FLW, and KL   . Additio65.6 Mb) . These rTraesCS4A01G436700 of QTRL.caas-4A.1 encodes the CDPK-related kinase. In plants, CDPKs play a critical role in various signaling pathways and are pivotal in root growth and development -like protein. Ethylene, a simple small molecule, plays a vital role in signal transduction, cell differentiation, division, and elongation (TraesCS5D01G380200, linked to QNRT.caas-5D, encodes an E3 ubiquitin-protein ligase, which plays essential roles in plant development (Four genes involved in the biological metabolism of plant hormones, cellulose, and the ubiquitin pathway were identified as high confidence candidate genes. Among these, elopment . Silencil length . TRA Anotivities . TraesCSongation . Ethylenongation . Additioelopment  and is celopment .Kasp_4A_RL and Kasp_5D_RT were successfully developed based on tightly linked SNP markers, proving to be valuable tools for MAS in breeding programs. Additionally, accessions with more favorable alleles, superior RSA traits and appropriate agronomic traits, such as Yumai 18, Jinhe 9123, Yumai 34, Bainong 3217, Lumai 6, Jinmai 61, Lumai 5, Lumai 23, and Yumai 57 are recommended as parental lines for improvement of RSA traits.Although conventional breeding has contributed to the enhancement of root system, the selection process is time-consuming and less efficient due to the challenges in field measurements of RSA traits . KASP ofIn this study, we identified six QTLs associated with wheat RSA traits and successfully developed two KASP markers that can be utilized in wheat breeding programs aimed at achieving higher and more stable yields. This research serves as a foundational step towards gene cloning and enhancement of wheat root systems.All datasets generated for this study are included in the article or YJ: Data curation, Funding acquisition, Writing – original draft. YW: Data curation, Software, Validation, Writing – original draft, Writing – review & editing. JL: Software, Validation, Writing – review & editing. FW: Formal Analysis, Resources, Software, Investigation, Writing – review & editing. XQ: Investigation, Project administration, Writing – review & editing. PL: Funding acquisition, Supervision, Validation, Writing – original draft.


Recent efforts have focused on developing methylation risk scores (MRS), a weighted sum of the individual’s DNA methylation (DNAm) values of pre-selected CpG sites. Most of the current MRS approaches that utilize Epigenome-wide association studies (EWAS) summary statistics only include genome-wide significant CpG sites and do not consider co-methylation. New methods that relax the p-value threshold to include more CpG sites and account for the inter-correlation of DNAm might improve the predictive performance of MRS. We paired informed co-methylation pruning with P-value thresholding to generate pruning and thresholding (P+T) MRS and evaluated its performance among multi-ancestry populations. Through simulation studies and real data analyses, we demonstrated that pruning provides an improvement over simple thresholding methods for prediction of phenotypes. We demonstrated that European-derived summary statistics can be used to develop P+T MRS among other populations such as African populations. However, the prediction accuracy of P+T MRS may differ across multi-ancestry population due to environmental/cultural/social differences. With current advances in and reduction of cost of array-based profiling technologies, increasing numbers of large-scale epigenome-wide association studies (EWAS) have been conducted to study DNAm in association with complex human diseases as well as environmental and social factors ,3. EWAS have thus far been successful in identifying dozens of cytosine guanine dinucleotide sites (CpGs) associated with various diseases and exposures, which could potentially be used for disease diagnosis and prediction, development of drug targets, and monitoring of drug response . However, differential DNAm in individual CpGs often shows a weak prediction capacity and can only explain a small fraction of phenotype variance. Polyepigenetic approaches that aggregate information on differential DNAm from multiple CpGs might produce a more accurate biomarker for clinical usage ) and only including those CpG sites in the final MRS calculation that reached a P-value below those thresholds in the EWAS summary statistics.Next, P-value thresholding step (T) is performed for the pruned set of CpG sites. Specifically, the P-value thresholding step (T) is performed by applying different P-value thresholds  between the phenotype of interest and MRS obtained using each P-value threshold is calculated to represent the prediction accuracy. The P-value threshold that produces MRS with the highest prediction accuracy in the testing data set is selected as the optimal P-value and the corresponding MRS is used for downstream analysis. The pipeline for generating P+T MRS (P+T CoMeBack as well as P+T with the standard pruning approach) is written in an R script, which is available at GitHub (https://github.com/jche453/Pruning-Thresholding-MRS.git).Finally, for each P-value threshold, MRS are calculated as a weighted sum of DNAm In our simulation studies and real data application, we compare the P+T CoMeBack MRS approach to the standard P+T and T approach, which refers to an approach in which the MRS is calculated by only thresholding, not accounting for correlations between included CpGs (no pruning).. Intra-dataset normalization and batch effects correction were performed using ComeBat function in R-package sva , followed by merging of datasets and correction for inter-dataset batch effects using the same function. After removing XY chromosome binding, non-CpG, cross-hybridizing probes and probes that are in close distance (1bp) with common SNPs, there were 386,362 CpGs left for MRS analysis. We randomly selected 1,199 adults  to conduct the simulation studies.To validate the performance of the proposed P+T MRS approach, we conducted simulation studies based on whole blood Illumina Infinium Human Methylation 450K BeadChip data from an ethnically heterogeneous discovery cohort composed of several publicly available datasets  CoMeBack was applied to the DNA methylation j-th subject as follows:The causal CpGs were randomly assigned a ‘true’ effect size from a uniform distribution as i of the j-th subject, and where j for the j-th subject, which was directly affected by ancestry using European ancestry as reference:We also simulated a second phenotype Y*a was set to 0.1 and b to 0.2. In each simulation, both simulated phenotypes Effect of ancestry in our simulations is simulated as the effect of genetic ancestry assuming there were no complex social determinants involved in the causal pathway. Different  in the training dataset. We calculated top 10 principal components (PCs) from DNAm of 386,362 CpGs  and used EpiDISH to estimate cell type proportions of each CpGs . We observed that in our simulation dataset, top 10 PCs are highly correlated with cell type proportions (Supplement 2), statistical power, and type 1 error rate of the P+T MRS. Linear regression analysis was used to access the association between MRS and simulated phenotypes in the test data, and statistical power is defined as the proportion of simulations where MRS were significantly associated with the simulated phenotype with at α level of 0.05. To estimate type 1 error rate, we first obtained a null association between MRS values and simulated phenotype values by permutation of MRS values. Linear regression analysis was used to access the association between permutated MRS and simulated phenotypes, and type 1 error rate is defined as the proportion of simulations where permutated MRS were significantly associated with the simulated phenotype.In each simulation, for fair comparison, 762 Indians were randomly chosen as the training dataset so that there were at least 136 people left for each race group in the testing dataset. Associations between CpGs and each of the two simulated phenotypes were assessed by robust linear regression model using limma R package We evaluated the performance of the MRS not only in scenarios of A) same ancestry in training and test data, but also B) across different ancestry groups  and C) in multi-ancestry populations . For scenario C), we evaluated two analysis strategies: 1. Joint-analysis: MRS analyses in the whole testing dataset where subjects from all racial groups were merged; 2. Standardization: scale MRS to have a standard normal distribution within each racial group before merging all subjects for analyses.. There were 145 Black African infants and 115 Mixed ancestry infants in the DCHS. A detailed description of the enrolment process, inclusion criteria, variables measurement, and ethical approval of the study have been previously published ,34.To evaluate the performance of the P+T CoMeBack approach in a real data setting, we applied the P+T CoMeBack approach to calculate a MRS for maternal smoking status during pregnancy using cord blood DNAm data from newborns in the South African Drakenstein Child Health Study (DCHS), a multi-ancestry longitudinal study investigating determinants of early child development . Cord blood was collected at time of delivery and used to measure DNA methylation by either MethylationEPIC BeadChips  or the Illumina Infinium HumanMethylation450 BeadChips  ,34, followed by quality control and normalization to calculate β values (details have been published elsewhere) .Cotinine levels were measured in urine provided by mothers within four weeks of enrolment and classified as <499 ng/ml (non-smoker), or ≥500 ng/ml (active smoker) . The participants of all cohorts used in the meta-analysis except one were of European ancestry.Summary statistics for the calculation of MRS were obtained from a study that meta-analysed the associations between newborn blood DNA methylation and sustained maternal smoking during pregnancy among 5,648 mother-child pairs as part of the Pregnancy and Childhood Epigenetics (PACE) Consortium  [36]. ThN = 6,685) and children around 6.8 years old  in PACE Consortium respectively. The training population for Reese MRS and Richmond 19 MRS overlapped with the training population for summary statistics used in P+T CoMeBack MRS in our study. Reese et al. used a LASSO regression to select CpGs for Reese MRS, which is a weighted sum of DNAm β values of 28 CpGs with weights estimated from the LASSO regression . Richmond 19 MRS is a weighted sum of DNAm β values of 19 CpGs that were significantly associated with prenatal smoking in an EWAS conducted in peripheral blood from children of averaged 6.8 years age (Richmond 19 MRS) ,39. In the same study, Richmond 568 MRS was proposed based on 568 CpGs that were significantly associated with prenatal smoking in cord blood ,39. We obtained the weights of reported CpGs from the mentioned studies and applied them to DNAm data in the DCHS to generate Reese MRS, Richmond 19 MRS and Richmond 568 MRS.In addition, we compared P+T CoMeBack MRS to three previously published MRS for maternal smoking during pregnancy , cell type proportions and top 5 PCs calculated from genotypes. In order to obtain comparable beta-coefficients and standard errors across different MRS, each MRS was divided by their interquartile range (IQR) before linear regression analysis.We compared the prediction performance of P+T CoMeBack MRS to the T method among 136 Indians in the test data across different simulation scenarios . Figure Next, we assessed the performance of P+T CoMeBack, P+T, and T method across different ancestries and among multi-ancestry populations. All three methods achieved a high power (>95%) and a low type 1 error rate (~5%) within each ancestry in most scenarios for both phenotypes except when the phenotype variance explained by DNA methylation is 10% or 30%  using P-value threshold 5 × 10−9, confirming the benefits of pruning in MRS calculation , which is similar to how the prevalence of outcome affects the predictive ability of PRS .In our real data application, summary statistics for smoking were obtained from a cohort with mainly people of European ancestry To the best of our knowledge, this is the first study to propose using CoMeBack for pruning MRS among multi-ancestry populations. However, there are several potential limitations that warrant mention. First, in the simulation dataset, ancestry information was self-reported and it may not fully represent the genetic ancestry estimated from genetic data. Second, the sample size of the simulation testing dataset and real data analyses was relatively small. Third, lack of different ancestry-specific summary statistics made it impossible to compare the use of external weights from population of different ancestries . Fourth, the prevalence of active maternal smoking was different in different ancestries and has influenced the performance of P+T CoMeBack MRS in the real data application. As a result, real data analysis of smoking MRS could not provide firm evidence about the transferability of MRS between Black African and mixed ancestry infants. Last, we mainly focused on the prediction performance of P+T CoMeBack MRS. Further studies are needed to assess the performance of P+T CoMeBack MRS in mediation analysis.In conclusion, P+T in general and P+T using CoMeBack in particular, provides an improvement for prediction of phenotype of interest, over T method that does not account for co-methylation between CpGs. In contrast to PRS, using existing summary statistics that were derived from European populations can be used to calculate MRS in other ancestries, thus reducing the ancestry/ethnicity disparity in medical research. However, caution is needed in the analyses and interpretation of MRS results across multi-ancestry populations, especially due to environmental/cultural/social differences associated with ancestry. More investigations of MRS are urged to further improve their prediction accuracy and translational values, also in combination with other clinical and non-clinical variables, especially among multi-ancestry population. With the current increase of large consortia-led EWAS for different exposures and health outcomes , we believe the predictive performance of MRS will continue to increase, and the P+T CoMeBack method has the potential to be widely used for risk prediction and association testing.Click here for additional data file.