Varroa destructor. We therefore describe two new trait evaluation protocols, ‘Recapping’ and ‘Solidness’, conceived to enable an easy evaluation of two putative colony resistance traits, recapping  and solidness  of worker brood, respectively. The hypothesis of this study is that higher levels of ‘Recapping’ and ‘Solidness’ could provide resistance to V. destructor. Repeatability and heritability of the two traits, as well as their phenotypic correlations with other colony traits were calculated, in order to investigate their potential for resistance selection. Both traits showed low repeatability between different measurements within each year. ‘Recapping’ had a low heritability and a negative correlation to hygienic behavior evaluated by the pin-test method. The heritability of ‘Solidness’ was moderate. The two traits did not show an association with V. destructor infestation levels. Further research is needed to confirm these results, as only a small number of colonies could be evaluated.Selection of honey bees requires traits which can be easily measured in the field by beekeepers. This is also the case for traits linked to honey bee resistance against the parasitic mite Varroa destructor and to allow for their straightforward implementation in honey bee selection. These traits are new field estimates of already-described colony traits: brood recapping rate (‘Recapping’) and solidness (‘Solidness’). ‘Recapping’ refers to a specific worker characteristic wherein they reseal a capped and partly opened cell containing a pupa, whilst ‘Solidness’ assesses the percentage of capped brood in a predefined area. According to the literature and beekeepers’ experiences, a higher recapping rate and higher solidness could be related to resistance to V. destructor. During a four-year field trial in Switzerland, the two resistance traits were assessed in a total of 121 colonies of Apis mellifera mellifera. We estimated the repeatability and the heritability of the two traits and determined their phenotypic correlations with commonly applied selection traits, including other putative resistance traits. Both traits showed low repeatability between different measurements within each year. ‘Recapping’ had a low heritability  and a negative phenotypic correlation to non-removal of pin-killed brood (r = −0.23). The heritability of ‘Solidness’ was moderate (h2 = 0.24 to 0.25) and did not significantly correlate with resistance traits. The two traits did not show an association with V. destructor infestation levels. Further research is needed to confirm the results, as only a small number of colonies was evaluated.For the development of novel selection traits in honey bees, applicability under field conditions is crucial. We thus evaluated two novel traits intended to provide resistance against the ectoparasitic mite  Varroa destructor is still the main pest threatening Apis mellifera in many countries [V. destructor within the colony, is a strategy to decrease infestation levels and ultimately improve colony survival [ountries ,2. Curreountries ,4,5,6,7,survival ,9.V. destructor infestation levels is still under discussion [To select for resistance, chosen traits have to be closely and stably linked to resistance, they must be heritable, and for practical application, they should be easily assessable to beekeepers under field conditions in order to facilitate broad-scale selection success. For instance, hygienic behaviour towards dead brood is assessed with a pin test, which is an affordable test routinely performed by many European beekeepers. However, the correlation of the results of this test with scussion ,10,11. Oscussion : for insscussion , which cV. destructor, we derived two novel traits from existing phenotypes, namely recapping (‘Recapping’) and solidness (‘Solidness’). Recapping refers to a specific worker characteristic wherein a capped and partly opened cell containing a pupa is re-sealed. This mechanism has been suggested to disturb mite reproduction [Recapping’) which allows for a cost- and time-effective evaluation of this trait. Brood solidness assesses the percentage of capped brood in a predefined area. Beekeepers in Europe often state that a solid brood pattern might be associated with a healthy brood and a low infestation rate, whereas others tend to attribute a lower solidness to a high level of Varroa Sensitive Hygiene in the colony. To the best of our knowledge no results have been published on the association between solidness and colony resistance. Hence, we evaluated solidness to verify beekeeper’s assumptions. To date, brood solidness has been assessed independently from resistance to V. destructor [Solidness’) to better fit with the requirements of our study.To support the selection against oduction  and in soduction . A recapoduction ,15, suggoduction ,16. To istructor ; this prA. m. mellifera to calculate heritability estimates. Simultaneously, the repeatability between different measurements was calculated for each year. Further, we investigated the association between the currently applied traits and the two novel selection traits. For this, we calculated phenotypic correlations between all traits. Routinely evaluated traits not related to V. destructor were also included in the calculation of phenotypic correlations, in order to evaluate the relevance of the two new traits with regard to the current selection process of the beekeepers.Over a time period of four years (2017–2020), the two resistance traits were evaluated in a total of 121 colonies of A. m. mellifera colonies was established in Canton Bern, Switzerland. The queens heading these colonies were reared by the association mellifera.ch in the same year and were clipped after introduction. In 2018, the experimental apiary was re-located to Canton Fribourg, Switzerland, and in 2020, a second apiary was established in Canton Bern. All colonies were hosted in 12-frame Dadant-Blatt hives, with undrawn frames and supers added according to colony development. Swarming control was performed twice per week as long as swarming cells were found in the colonies. Colonies were re-queened in autumn, with daughter queens produced in summer. These queens were reared from tested colonies and, each year, all mated at official mating stations with A. m. mellifera drones reared by the association mellifera.ch. In 2017 and 2019, drones descended from one single paternal origin, i.e., queens of the drone-producing colonies were sisters, whereas in 2018 a pool of drone producing colonies of two different origins were used for mating.In the summer of 2016, an experimental apiary with V. destructor infestation and colony size in summer due to the disturbance caused by swarming. Before the start of the study and after each year, V. destructor infestation levels were standardized to a negligible rate (<50 mites) by applying acaricides to the colonies in summer and autumn  and later during winter .From 2017 to 2020, a total of 121 colonies completed a performance testing protocol between spring (time of willow blooming) and summer (last honey harvest). The following numbers indicate the successful evaluated colonies per year: 6 in 2017, 29 in 2018, 39 in 2019, and 47 in 2020. The minimum, median and maximum number of tested daughters/dam queen were 2, 9, and 13, respectively. The two novel traits (described in detail below), as well as commonly applied selection traits routinely measured by beekeepers [Recapping’  were counted under a stereomicroscope (x4).Solidness’ , which took into account the year/apiary combination (5 cases) as a fixed effect, as all colonies were located in a given apiary, equally managed and evaluated the same day. A joint estimation of worker and queen effects did not generate results. This was because the restricted size of the preliminary dataset did not enable convergence of the restricted maximum likelihood algorithm. Therefore, the variance for worker effect in the worker model to some extent includes effects of the queen as the pedigrees for workers and queen are partly the same, as is the residual variance. This in reverse is the case for the queen model. To facilitate interpretation of the results, observation data were not transformed prior to the analysis. Pairwise phenotypic correlations were calculated between all traits, as defined in N − 2 being the number of degrees of freedom associated with N colonies having observations for both traits  model ,25, whiccolonies . Brieflycolonies . Varianc used by ). The si used by , with a Recapping’ and ‘Solidness’ is presented in Solidness’, with values up to 0.80, whilst measurements on the diagonal were not different from those off the diagonal.The repeatability for ‘Gentleness’ and ‘Colbroodgrowth’. Therefore, these two traits were removed from the downstream analyses. The other traits had estimated heritabilities ranging from 0.01 to 0.72, many of them being theoretically compatible with selection. For ‘Recapping’, heritabilities (±SE) were estimated at 0.05 (0.24) and 0.04 (0.26) using models for queen and worker effects, respectively, whilst the same models estimated the heritability for ‘Solidness’ at 0.25 (0.29) and 0.24 (0.26), respectively. The overall highest heritabilities were obtained for the traits ‘Calmness’, ’Varroasummer’, ‘Hygfull’ and ‘Colbeessummer’. Heritability estimates under the two different models (worker and queen) were generally comparable.The heritability estimates and phenotypic correlation results of all traits are summarised in Recapping’, a negative correlation (r = −0.23) was found with ‘Hygfull’, whilst ‘Solidness’ was negatively correlated with the quantity of brood produced in spring as well as with the adult worker populations in both spring and summer (−0.24 to −0.28). Otherwise, positive correlations were identified between honey production and colony size traits, between V. destructor infestation rates and colony size and between ‘Hygfull’ and ‘Colbeessummer’. A positive correlation was found between the number of untouched pinned cells (‘Hygfull’) and V. destructor infestation in summer (‘Varroasummer’) (0.32 (0.10)). A negative correlation was symmetrically obtained between the rate of cleared cells (‘Hygempty’) and ‘Varroasummer’ (−0.26 (0.11)).Some of the correlations between phenotypes corrected for fixed effects showed moderate-to-high values (up to 0.65). For ‘Recapping’ and ‘Solidness’, in an experimental A. m. mellifera population. Beekeepers can easily measure both traits as part of routine colony testing with minimal additional costs.In this study, we evaluated two novel traits, ‘Solidness’ was improved compared to 2018. However, based on the applied sampling strategy and the low number of evaluated colonies, it is not possible to draw conclusions on the consistency of observed repeatabilities over the years, as only 29 to 47 colonies were evaluated for each trait during these three years. In order to get more reliable values, repeatability should be calculated across multiple years based on data recorded at constant dates annually. This would facilitate the identification of suitable trait evaluation periods during which the measurements are more repeatable. Low repeatability was also reported for other resistance traits, e.g., hygienic behaviour towards dead brood [Recapping’ and ‘Solidness’.The repeatability of the two traits was relatively low in 2018–2020, and some consecutive measurements were even negatively correlated. This result reveals that it was not possible to obtain standardised measurements, due to high observed variations of the traits across the season. In 2020, the repeatability of ‘ad brood ,29. Despad brood ,31,32. Aad brood ,34, thisRecapping’ was as low as the previous reported estimates for recapping measured by the cell-by-cell inspection protocol [Solidness’ was compatible with selection. However, for both traits, more data is needed to obtain more precise estimates.Heritability of ‘protocol , whilst V. destructor. An association between ‘Recapping’ and hygienic behaviour is suggested from our data; this is comparable with the published correlation between hygienic behavior and recapping data measured by the previously developed protocol [V. destructor infestation levels, which we did not observe in our analysis. It can be hypothesised that both traits either have a distinct genetic background or that the best periods to measure them do not overlap. In this study, it was not possible to evaluate the mite infestation rate and the number of mite offspring in recapped cells. The commonly applied protocol to evaluate recapping is more appropriate for this, as it enables cell-by-cell content analysis. ‘Solidness’ is associated with colony size, as colonies rearing more compact brood have more brood and so more workers. However, even within large colonies, which can host more mites, direct associations between ‘Solidness’ and V. destructor infestation levels were not found. It therefore seems that ‘Solidness’ is not directly associated with V. destructor resistance but could be useful for selecting honey bees that will build up larger colonies.The two new traits could not be linked to better colony resistance against protocol . This asprotocol ,35, descV. destructor infestation levels showed moderate heritability estimates in our experimental population, whilst the estimates were very low or equalled zero using the observations from beekeepers. Infestation level in summer had moderate heritability values between 0.5 and 0.7, which seems promising for selection. However, the infestation growth rate correlates better with the infestation level in spring (−0.64) than with the infestation level in summer (0.38). Furthermore, these associations demonstrate that the assessment of V. destructor infestation in spring is not practical for later identifying colonies with lower mite development [Hygfull’) also had more mites at this time of the year (r = 0.32). As the association between hygienic behaviour and infestation level is still being discussed in the literature [Compared to previous results based on performance testing carried out by beekeepers , some trelopment . This maummer 0.3. Furtherterature ,10,11, mEstimating genetic parameters in small populations can be challenging, as obtained estimates often have high standard errors ,36. HoweA. m. mellifera do their best to assess different traits, but the selected population is itself of limited size, and the association faces difficulties in recruiting motivated members for colony evaluation. Therefore, an increased dataset will require the allocation of testing apiaries in different environments and a dedicated workforce able to test several hundred colonies each year based on scientific protocols, an undertaking that requires a financial investment far beyond either the framework of ordinary research projects or the capacity of one beekeeping association. Such problems may be encountered by other countries where honey bee populations are also of small size or where beekeepers are not yet coordinated around shared selection programmes.The validation of the heritabilities and assertions between traits found in our study require a larger testing capacity in the field, with an increased number of different environmental conditions and more precise trait assessment. This is necessary for beekeepers to evaluate the relevance of given traits under field conditions, as beekeepers’ management may differ from beekeeping performed by scientists. Swiss beekeepers currently involved in selection of


Biological age (BA), a measure of functional capacity and prognostic of health outcomes that discriminates between individuals of the same chronological age (chronAge), has been estimated using a variety of biomarkers. Previous comparative studies have mainly used epigenetic models (clocks), we use ~1000 participants to compare fifteen omics ageing clocks, with correlations of 0.21-0.97 with chronAge, even with substantial sub-setting of biomarkers. These clocks track common aspects of ageing with 95% of the variance in chronAge being shared among clocks. The difference between BA and chronAge - omics clock age acceleration (OCAA) - often associates with health measures. One year’s OCAA typically has the same effect on risk factors/10-year disease incidence as 0.09/0.25 years of chronAge. Epigenetic and IgG glycomics clocks appeared to track generalised ageing while others capture specific risks. We conclude BA is measurable and prognostic and that future work should prioritise health outcomes over chronAge. Age is a phenotype that we are all familiar with, and is a major risk factor for numerous diseases including the largest causes of mortality . We all Since this concept was proposed, there has been a push to construct models of BA, using a variety of both statistical methods and types of biomarkers; the resultant estimates we shall term omics clock ages (OCAs). The first OCAs were epigenetic clocks that used methylation levels of CpG sites across the genome - DNA methylation (DNAme) - to estimate chronAge using penalised regression , 6. The As we know, second generation clocks such as DNAm PhenoAge and DNAm GrimAge have been shown to outperform previous ageing clocks by more accurately predicting mortality and health outcomes , 22. TheThe deep omic and health outcome annotation of the Scottish population-based Orkney Complex Disease Study  cohort . We found that clock performance in estimating chronAge was independent of penalised regression method used, across all the assays  and so eTo enable comparison with established omics ageing clocks trained on chronAge, we also calculated 4 clocks that have been described previously in the same ~1000 individuals in ORCADES. The four published clocks being: Hannum 2013 , HorvathAges estimated by the model in the test set (i.e. OCAs) were highly correlated with chronAge for the majority of the omics clocks tested , particuWe next used our own clocks trained in ORCADES to estimate age in independent European cohorts to validate if they were more widely applicable beyond the Orkney population. We found that correlations between OCA and chronAge replicated to varying degrees in independent populations . PEA proIf the aim is to create BA clocks that have the potential to be clinically useful, it would be more efficient and cost effective to reduce the numbers of biomarkers that need to be measured in patients. To this end, we investigated the performance of our clocks using a reduced set of biomarkers. For each of our own 11 omics clocks a “core” clock was constructed using only those biomarkers which were selected for model inclusion in >95% of 500 iterations of our clock construction procedure, as done by Enroth et al.  See Met, highligOmics Clock Age Accelerations (OCAAs) showed varying degrees of positive correlation between clocks . UnsurprFor clarity, the correlations between different OCAAs and all further downstream analyses reported were performed using OCAs and OCAAs derived from the standard omics clocks as opposed to the “core” clocks described in the previous section that appear in To determine if our different clocks are explaining the same or different variance in chronAge, we partitioned the variance in chronAge explained among our clocks. We calculated the unique variance in chronAge explained by each OCA as the squared part correlations of chronAge and OCA, while controlling for all other clocks. 94.9% of the variance in chronAge is explained by two or more clocks whilst 3.6% remains unexplained by the 14 ageing clocks tested, with the remaining 1.5% being explained by one clock uniquely . The PEAHaving found that clocks overlap in the information they provide about chronAge, we tested to see if, together, pairs of clocks jointly explained a different proportion of variance in chronAge than would be expected if the clocks were each independently sampling from a latent set of complete predictors of chronAge (ISLSP). This analysis should reveal whether the clocks were tracking complementary dimensions of ageing: situations where the pair of clocks overlapped less than expected if they were independently sampling . Strikingly, excess overlap was found across all pairs of clocks bar MS Fatty Acid Lipidomics and MetaboAge . All othUnsurprisingly, the most overlapping were our own UPLC IgG Glycomics clock trained in ORCADES and GlycanAge  indicating they are capturing entirely overlapping variance in age. These Glycomics-based clocks showed high excess overlap with the PEA Proteomics clock (0.95 and 0.96). The MS Fatty Acids Lipidomics, MS Complex Lipidomics and MS Metabolomics clocks showed high excess overlap with each other (0.97-0.98). Interestingly, the NMR Metabolomics clock, which also contains many lipid features, did not cluster with these three clocks. The four DNAme-based clocks clustered tightly together with DNAme Hannum CpGs and DNAme Horvath CpGs having an excess overlap of 0.91. As these clocks are extremely accurate chronAge predictors, a large amount of overlap in variance explained is inevitable; they are tracking common aspects of ageing. Interestingly, MetaboAge clustered on its own and showed higher excess overlap with DNAme- and PEA Proteomics clocks than those based on metabolite or lipid measures.a priori to associate with age. For risk factors we chose body mass index (BMI), systolic blood pressure (SBP), cortisol, creatinine, C-reactive protein (CRP), forced expiratory volume in 1 second (FEV1), and total cholesterol. For diseases we chose five International Statistical Classification of Diseases and Related Health Problems (ICD)-10 Chapters: II (Neoplasms - codes C), IV , IX (Diseases of the circulatory system - codes I), and X (Diseases of the respiratory system - codes J). The ICD-10 blocks are used and their coding titles are listed in We next sought to test the effect of OCAAs compared to chronAge on risk factors and post assessment disease incidence, as measured by hospitalisation in the ORCADES cohort, where the outcome was thought In order to compare OCAA and chronAge, we first quantified the effect of chronAge on risk factors and disease , 7B. AlleHR.We tested for risk factor and disease associations with OCAA, using chronAge and sex as covariates. Results were then rescaled to be per year of chronAge effect by dividing the observed effect of OCAA by the effect of chronAge on the outcomes as identified at the previous step. This was taken trait-by-trait for risk factors, and a single effect for all disease groups and chapters: -0.0492 logDespite limited power for detecting OCAA-disease associations, 6/480 tests were statistically significant (FDR<10%) as were 19/90 OCAA-risk factor associations. We also found evidence of enrichment of positive effects of OCAA on both risk factors (81.1%) and disease (73%), with 43.3% and 22.3% being nominally significant (one sided p<0.05), respectively. Across clocks, the inverse variance-weighted mean effect of one year of OCAA on risk factors/disease was the same as 0.09/0.25 years of chronAge . Interestingly, the mean effect across all diseases of one year’s DNAme Hannum/Horvath CpGs OCAA was similar to one year of chronAge , but the effect on risk factors was much lower . Complete results are shown in In general, only associations with the Clinomics OCAA passed FDR, however DNAme Hannum CpGs OCAA, our own UPLC IgG Glycomics OCAA trained in ORCADES and GlycanAge were nominally associated with twelve ICD10 blocks, one more than Clinomics and DNAme Horvath CpGs OCAA . In contFor the analysis of the effect of OCAA on risk factors, Clinomics was excluded as its predictors  are often close to and designed to predict clinical endpoints and overlap with the risk factors considered here. Similarly, as the NMR Metabolomics assay contains a measure of creatinine, we excluded the association of both our own NMR Metabolomics OCAA trained in ORCADES and MetaboAge with this outcome. Finally, for the same reason we excluded the association of total cholesterol with NMR metabolomics, MetaboAge, MS Metabolomics, MS Complex Lipidomics and MS Fatty Acid Lipidomics OCAAs.The greater statistical power for risk factors results in considerably more significant associations at FDR<10% . DEXA anWe wanted to check if observed OCAA-health associations were driven by the associations of health with smoking and of OCAA with smoking. Our analysis fitting smoking status as a confounder suggests they were not , 9B.In principle, two OCAAs could have the same association effect size on disease, but one might be much more prognostic for the population as a whole than the other if it had much larger variation in its range. In order to determine which OCAAs could draw more meaningful distinctions between subjects in terms of health outcomes, we repeated the previous analysis using standardised OCAAs. Standardised OCAAs on risk factors had a narrow range, standardised NMR Metabolomics OCAA showed the greatest predictive power, with an IVW-average effect across all risk factors of 0.09 (SE ~0.01). Other clocks ranged from 0.07-0.03, with Hannum 2013 and Horvath 2013, MetaboAge and MS Fatty Acid Lipidomics OCAAs smaller still, spanning -0.01-0.01 . Conversely, CRP and total cholesterol were most predictable by standardised OCAA , whilst systolic blood pressure was least predictable .Standardised OCAA effects on disease showed a less uniform pattern : the IVWWe were interested to see if the effect of our OCAA-disease associations were consistent across the sexes. To mitigate issues with sample size, we restricted analysis to OCAA-disease block associations which were nominally significant (p<0.05) in the pooled analysis. Of the 107 nominally significant associations from the pooled analysis, 78 passed the criteria  to be assessed in each sex separately . Of thesFinally, we reduced dimensionality and assessed the underlying information about ageing being captured by different omics at the assay level, rather than simply the predictors selected for model inclusion. We constructed versions of our own clocks trained in ORCADES using a few principal components (PCs) of omics measures as predictors and repeated the previous analyses with their (standardised) OCAAs, estimating chronAge  and predWe have performed the most exhaustive comparison of different omics assays as potential biomarkers of age to date. We have shown firstly, it is possible to construct ageing clocks that produce highly accurate estimations of chronAge with a wide variety of omics biomarkers. Secondly, our own ageing clocks built using PEA proteomics, DNAme, UPLC IgG glycomics and clinical risk factors in ORCADES were able to estimate chronAge in independent populations. Thirdly, it is possible to achieve the same highly accurate estimation of chronAge using a substantial subset of core biomarkers from each assay. Despite finding only modest positive correlations between our OCAAs, we showed that different clocks overlap in the variation they explain in chronAge more than would be expected by chance if they were independently sampling from a latent set of complete predictors. We found associations of OCAAs with total cholesterol, C-reactive protein, BMI, creatinine, cortisol, FEV1 and systolic blood pressure. We found 6 statistically significant (FDR<10%) individual associations and strong evidence of enrichment of association of OCAA with incident disease collectively across our tests . We found Less variation in OCAA predictiveness across risk factors, than across diseases. Overall, we estimated that one year of OCAA has an effect of 0.09/0.25 years of chronAge on risk factors/disease incidence and showed that OCAA based on clocks built using a few principal components of omics were as prognostic as those presented with all available features.The correlation of our PEA proteomics, DNAme, UPLC IgG glycomics OCAs and chronAge were similar to correlation coefficients reported with published models , 17, 18.Unsurprisingly, DNAme-based clocks built in ORCADES were able to estimate age in both Scottish (Generation Scotland) and Estonian Biobanks (EBB), as the Hannum 2013 and Horvath 2013 epigenetic clocks have been used successfully in numerous populations. We showed for the first time that clocks built from Olink PEA-based proteomics replicate (in EBB and Croatia-Vis), while clocks built using the SOMAlogic . ConversFor a measure of BA to be clinically useful and efficient, effective age estimation based on as few predictors as possible is ideal. We substantially reduced the numbers of biomarkers from each assay that were included in our clocks and showed no dilution of performance across all of our own clocks. Enroth et al.  showed tThe extremely high correlations with chronAge reported, such as the r=0.97 of the Mega-omics OCA, highlight an issue that has been discussed in prior work: that if enough biomarkers were included in the model it would be possible to perfectly estimate chronAge and, by definition, fail to detect (distinct) BA. Lehallier et al.  showed tA useful BA must be an indicator of health status or outcomes beyond chronAge. We found DNAme-based OCAAs were better estimators of incident disease than risk factors, consistent with the known performance of the Horvath 2013 epigenetic clock. Several groups have shown Horvath 2013 OCAA to be associated with subsequent all-cause mortality , 34–37. It is perhaps not surprising that the Clinomics OCAA showed the strongest evidence of association with disease - it used common clinical measures thought to be prognostic of health. Nonetheless, the pattern is a reassuring proof of concept. The overall enrichment of OCAA-disease and -risk factor associations, strengthens the case for the notion of BA, trackable through omics markers.Jansen et al.  showed tMetaboAge stood out in several analysis: its low correlation with chronAge, subsequent low unique variance explained in chronAge and low overlap of variance explained in chronAge in pairwise comparisons across all other clocks suggest that MetaboAge may not be tracking chronAge. These combined with the nominal associations of MetaboAge OCAA with incident cardiometabolic outcomes despite limited power, add support to the argument that omics ageing clocks should be more outcome focussed and move away from training on chronAge.As by definition, having a BA of +1 indicates that the individual has the same functional capacity and risk of age-related disease as the average individual that is one calendar year older than them, indicating the effect of true BA is the same as 1 year of chronAge. Our estimate that the mean effect of 1 year of OCAA on disease incidence is the same as 0.25 years of chronAge is important. BA thus appears to be real and measurable and have effects of similar magnitude to chronAge, albeit our estimates are significantly diluted compared to chronAge, possibly due to OCAA capturing only some aspects of BA, reflecting the types of assay and tissue, rather than BA itself. Better measures of BA seem worthy of pursuit, as do interventions that can reverse well-measured BA. The negative association between Melanoma and other malignant neoplasms of skin (C43-C44) and OCAAs for many clocks leads us to speculate that a less sedentary lifestyle is leading to lower OCAA, but also increased exposure to the sun. If replicated, this will highlight that skin BA and other BAs need not closely align, and we speculate this finding might also generalise across other organs.A strength of our work was the sheer number and range of assays and therefore omics ageing clocks whose performance we compared in the same individuals, whereas previous comparisons have been limited to DNAme-based clocks , 24, 51 The novel assessment of excess overlap between clocks is a strength of this work, as it has not previously been shown that, across multiple different omics assays, OCAs overlap more than would be expected by chance if they were ISLSP, indicating these clocks are tracking more common rather than complementary aspects of ageing. A further strength is the regularisation of effect sizes - we have measured the effect of OCAA per effect of year of chronAge - giving a natural and understandable scale. Another strength is its scope, with many clocks tested against many age-related diseases. Of course, this is also a weakness, as it reduces power after compensation for multiple testing. Nonetheless, the essentially agnostic view taken of individual disease groupings and clocks does mitigate the risk of publication bias.A limitation of this work is the relatively small sample size, both in terms of the number of individuals with multiple omics assays and within that, the number of incident hospital admissions over the follow-up period. Due to the low number of deaths in our sample we are as yet unable to test for the association of OCAA on mortality, as in previous studies. The issue of sample size combined with the exacerbation of multiple testing limited our power to assess the effect of OCAA measures on subsequent incident disease over and above common risk factors as in previous studies , 25, howAs our aim was to characterise the properties of ageing clocks trained on chronAge, derived from a wide range of omics assays, in order to understand what these measures are actually capturing as this has been understudied, we did not compare our clocks with those trained on mortality-based measures , 22. A sAs the omics data available for ORCADES is cross-sectional, we were unable to comment on the variation of OCAA within individuals over time. However, we were able to investigate the prognostic ability of single time point OCAAs on hospital admissions over a 10-year follow up. While not explicitly testing OCAAs for their association with healthspan as defined by Zenin et al. , by inclThe nature of our sample, a population isolate, means there is potential for local factors to influence our results. We have shown this is not the case for several of our omics clocks’ accuracies , as theyOf course, association does not imply causation. Although the use of a prospective cohort has reduced the risk of reverse causation, undiagnosed cases (at baseline) might still have contributed to the effects we observe, although confounding where a latent set of underlying traits is influencing disease susceptibility and the biomarkers is perhaps more likely. Nonetheless, even in the absence of causation, OCAA does appear to often be a biomarker of disease and underlying BA.In conclusion, our work has strongly further evidenced the existence of BA as distinct from chronAge, whilst highlighting a substantial part of the OCAA is noise. The data also suggested there may be more than one type of BA, as measured by different clocks and giving rise to differing amounts of disease susceptibility, most strongly implied by our evidence that skin age and heart age may move in opposite directions. We also highlight that some OCAAs (e.g. PEA proteomics) may capture specific risks and consequently associate with health, whilst others (e.g. DNAme- and IgG glycomics-based) may capture more generalised ageing. Our observation that clocks derived from few PCs of omics are less accurate in estimating chronAge but better able to predict risk factors, suggests that the search for BA should be pursued through salient features of biology. This supports the recent success of ageing clocks trained on all-cause mortality based measures , 22, DNAAnalyses were predominantly carried out using the Orkney Complex Disease Study (ORCADES) , a populWhole body imaging was performed on the Hologic fan beam DEXA scanner . Measures of body composition were derived from the DEXA scans using APEX2 software for bone, lean and fat tissue and APEX4 software for android, gynoid, visceral and lean fat mass content. 28 measures in the following broad categories: bone mineral density, bone mineral content, fat or lean mass percentages for head, trunk and limbs were selected for analyses. These were measures that did not use chronAge in their calculation and were also available in the UK Biobank. Measures were removed as outliers based on a z-score cut-off of 6 then pre-corrected for sex. Residuals were additionally subject to a threshold by removing outliers with a z-score cut-off of 3.preprocessNoob function in the “minfi” package was used for array normalisation to remove unwanted technical variation. M values were corrected for the technical covariates: plate number (as a random effect), season of venepuncture, year of venepuncture, plate position and 10 principal components of the control probes (as fixed effects) using GCTA-REML [The Illumina EPIC 850K array was used to measure DNA methylation levels in ORCADES. Quality control was carried out using the meffilQC pipeline  and minfCTA-REML .Instead of creating novel DNA methylation clocks when there are landmark clocks available in the literature, we constructed clocks based on Hannum and Horvath’s original epigenetic clocks, to compare with our other omics. As ORCADES used the Illumina EPIC 850k chip rather than the earlier 450k/27k chips used by Hannum and Horvath, our methylation clocks are subsets of Hannum and Horvath’s clocks. It has been shown that imputing probes that are absent from the 850k chip but present in the 450k/27k set leads to underestimation of both published ageing measures . Thus, fThe high throughput NMR metabolomics assay of EDTA plasma  quantified 225 metabolomics measures in molar concentration units. The measures include amino acids, ketone bodies, low molecular weight metabolites and numerous lipid and lipoproteins subclasses. In both ORCADES and Croatia-Korčula, metabolite measures were removed as outliers based on a z-score cut-off of 6, pre-corrected for sex and the use of statins as a binary variable. Residuals were additionally removed as outliers with a z-score cut-off of 3.Shotgun lipidomics and liquid chromatography tandem mass spectrometry (LC-MS/MS) was used to quantify the molar concentrations of 44 fatty acids as described previously . Fatty aThe glycan data have previously been described in detail by Kristic et al., for the ORCADES , Croatia1,102 proteins were measured using a proximity extension assay method   from EDTThis dataset consisted of 13 selected clinical measures that are routinely measured during visits with general practitioners and clinicians: albumin, fasting plasma glucose, calcium, uric acid, high density lipoprotein cholesterol, total cholesterol, triglycerides, height, weight, forced expiratory volume in 1 second (FEV1), and diastolic and systolic blood pressure.Non-targeted metabolomic and lipidomic features were detected and quantified using Metabolon as described previously . The HD4The ORCADES cohort has record linkage to hospital admission records (Scottish Morbidity Records: SMR01). The first occurrence of any hospital admission with ICD10 diagnosis, was taken as incidence. NHS Scotland records moved from ICD9 to ICD10 in April 1996, so diagnoses since ~12 years prior to assessment were captured. The disease groupings analysed included each ICD10 block within 5 Chapters thought a priori to associate with age II (Neoplasms - codes C), IV , IX (Diseases of the circulatory system - codes I), and X (Diseases of the respiratory system - codes J). For Chapter II only C codes  were included in our analyses. Chapters as a whole were also analysed, as were all the diseases from these chapters simultaneously. Incident disease was defined as the time of first hospital admission with a diagnostic code recorded (in any position in the admission record) for any disease within the grouping being analysed. For each disease grouping, subjects with recorded admission prior to the date of venepuncture were then excluded entirely in the subsequent analysis, as already prevalent.Outliers were defined based on z-score thresholds that varied between omics datasets depending on the distributions of the raw measures. Omics measures were pre-corrected for known batch effects and covariates (specified above) using fixed effects linear regression or other specified methods. A second pass z-score threshold on the residuals was used to detect further outliers for a subset of assays and all missing values were removed. The residuals produced from covariate correction were then scaled and centred to have a mean of zero and a standard deviation of one to ensure that effect sizes of any variables included in the models were comparable.The individuals in the ORCADES cohort were split into 75% training, 25% testing. For the analysis comparing clock performance across omics platforms the testing 25% of samples were taken preferentially from the pool of individuals that possess measures for all of the omics platforms. Tenfold cross validation in the training sample was used to select the shrinkage parameter, λ, for the penalised regression that was estimated to produce the model with the minimum mean squared error. Models were constructed using three different procedures implemented using the glmnet  and careAs stochasticity is present in the procedure, the variables selected for model inclusion will vary depending on the individuals selected to be in the training sample, clock construction was repeated 500 times and the features selected for inclusion and the correlation between chronAge and age estimated by the model were recorded to ensure that the model performance results presented here are representative and not an outlier due to individuals at extreme ends of distributions contributing to the training sample and rare model being used to draw conclusions (data not shown).This model that was presented with all of the features from all of the omics platforms. The dataset itself was created by merging all of the corrected omics measures  after platform level quality control, again standardising all features to have a mean of zero and standard deviation of one. The clock was created using the same construction procedure outlined above.These models were constructed per omics assay. The elastic net regression algorithm was presented with only those predictors that were selected for model inclusion in >95% of the 500 iterations of clock construction for the relevant omics platform. This reduced set of predictors then underwent clock construction as described above.prcomp function in R. These PCs were then presented to the elastic net algorithm and clocks built.To ensure that the differences in variance explained in chronAge by different omics clocks is not due to the discrepancy between the number of features available and hence the number of features selected for model inclusion across omics types but rather a genuine difference in the information about ageing captured by different omics; clocks were built using principal components (PCs) of the relevant omics platform as features. The first 3, 5, 10 and 20 PCs were extracted from the covariate corrected scaled and centred omics data at the platform level using the http://dnamage.genetics.ucla.edu/, MetaboAge was calculated using the online calculator metaboage.researchlumc.nl. GlycanAge was calculated as the residuals from the regression of GP6, GP62, GP14 and GP15 separately for each sex on chronological age at venepuncture.To compare clocks trained in ORCADES to existing clocks in the literature we calculated four published ageing clocks trained on chronAge: the DNA methylation clock described by Hannum et al., (Hannum 2013) , HorvathPairwise Pearson correlations between 10 of our OCAAs were calculated, Mega-omics OCAA was excluded from this and all between clock comparisons as it contains predictors spanning multiple assays.Y) and age estimated by clock i while controlling for all of the other k clocks. Part correlations were calculated using the spcor.test function in the “ppcor” package in R [k clocks together, the R2 from the following model:The unique variance in chronAge explained by each clock, age in R . The porY is chronAge and X1…k are age estimated by clocks 1 to k, was used to partition the total variance of chronAge further into that which remains unexplained by the 10 clocks (1 – R2) and that which is explained by overlapping clocks:Where R2 of a bivariate model. The overlap, calculated by subtraction, is specifically the variance in chronAge explained by both of the clocks in the pair. This is unlike overlap calculated in the previous step, where we were only able to state that this variance was not unique to a particular clock but unable to deconstruct further.To gain a more detailed insight into the relationship between clocks we carried out pairwise comparisons. Following the same procedure as outlined above, the unique variance in chronAge explained by each clock in the pair is the squared part correlation of chronAge and age estimated by one clock while controlling for age estimated by the other clock in the pair. The variance remaining unexplained by either of the clocks was 1 – R2 from a bivariate model, with chronAge being the dependent variable and the estimated ages from the two clocks in the pair the independent variables. The variance explained in chronAge (iv) by each clock (i) individually was the univariate R2 from the regression of estimated age on chronAge. The expected variance in chronAge explained by two clocks by chance (E) was calculated as follows:We assessed whether the combined variance in chronAge explained by pairs of clocks deviated from what would be expected by chance if both clocks were independently sampling from a latent set of predictors (ISLSP) of chronAge. The combined variance in chronAge explained by both clocks together was calculated as the multiple iv. With the null hypothesis that the two clocks are independent samples from the latent set of complete predictors and thus explain partly overlapping information about age. The expected left unexplained after the addition of the second clock is thus (1 – v1)(1 – v2).The idea being that the variance in chronAge not already explained by the first clock is 1 − O) from expected (E) across pairs of clocks, this deviation was re-scaled. As the magnitude of iv effects the possible range of values O could take. The theoretical minimum variance explained (minE) by two clocks is the variance explained by the larger of the two clocks alone . The theoretical maximum (maxE) is v1 + v2 or 1 if v1 + v2 > 1 (the clocks are explaining entirely non-overlapping variance). Comparisons containing clocks with high iv will have a much smaller range of possible O than those with low iv so directly comparing the magnitude of the deviation of observed from expected is not ideal. The results presented are on a scale of excess overlap calculated as follows:To allow for the comparison of the deviation of observed variance explained in chronAge  was modelled using a Cox proportional hazard model  and the with standardised risk factors (units of phenotypic standard deviation) were carried out using linear regression with chronAge and sex fitted as fixed effects covariates. To restrict the burden of multiple testing we only tested the association of OCAAs on risk factors or disease blocks which showed a statistically significant association (effect size >0) with chronAge at outset (Benjamini-Hochberg FDR<10%) and had >5 incident cases (disease blocks). We tested the effect of OCAAs on each disease grouping using the same model as for chronAge, including chronAge and OCAA as effects. OCAA was not standardised but observed effect sizes were rescaled (divided) by the effect of chronAge, using the same model, enabling a comparison of the effect of one year's OCAA with one year's chronAge, with a value of 1 denoting the same effect. False discovery rate was again determined using the Benjamini-Hochberg method (FDR<10%).Across both risk factors and disease, we found that large estimated effects arose in the context of large SEs. To facilitate visualising the results we had most confidence in we applied a shrinkage method, imposing a prior assumption on the distribution of beta  to the likelihood of our observed beta, shrinking resultant estimates with larger SEs more towards 0.Individual tests of association generally had limited power due to multiple testing and the low variance of OCAA (compared with chronAge). We therefore considered the results of each OCAA across multiple outcomes by inverse variance weighting (IVW) observed results for individual outcomes. The covariance amongst outcomes and predictors, mean that the independence assumption for meta-analysis (or sign testing) is violated. Whilst this should not bias estimates, their precision will be overstated. We consider these results to be descriptive, and not conformable to formal testing. We use “~” to denote SEs calculated under the violated independence assumption, but still consider these useful to give a sense of magnitude. Conversely, for the same reason, the formal tests we perform (FDRs) are likely to be conservative.We repeated these analyses with standardised OCAAs to compare the prognostic ability of different OCAAs at a population level, across risk factors and diseases and with our PC clocks OCAAs.There is neither research ethics committee approval, nor consent from individual participants, to permit open release of the individual level research data underlying this study. Please contact the QTL Data Access Committee (accessQTL@ed.ac.uk) for further information if required. Access to data from GS:SFHS is by application (access@generationscotland.org) and a managed process. Access to data from the Estonian Biobank is by request and managed by the Estonian Committee on Bioethics and Human Research.Supplementary NoteSupplementary FiguresSupplementary Table 1Supplementary Table 2Supplementary Table 3Supplementary Table 4Supplementary Table 5Supplementary Table 6


In summary, we accurately estimated the NAFLD status in UKB using common serum traits and anthropometric measures, which empowered us to identify 90 GWAS NAFLD loci, build NAFLD PRS, and discover a significant causal effect of NAFLD on CAD.The prevalence of non-alcoholic fatty liver disease (NAFLD), now also known as metabolic dysfunction-associated fatty liver disease (MAFLD), is rapidly increasing worldwide due to the ongoing obesity epidemic. However, currently the NALFD diagnosis requires non-readily available imaging technologies or liver biopsy, which has drastically limited the sample sizes of NAFLD studies and hampered the discovery of its genetic component. Here we utilized the large UK Biobank (UKB) to accurately estimate the NAFLD status in UKB based on common serum traits and anthropometric measures. Scoring all individuals in UKB for NAFLD risk resulted in 28,396 NAFLD cases and 108,652 healthy individuals at a >90% confidence level. Using this imputed NAFLD status to perform the largest NAFLD genome-wide association study (GWAS) to date, we identified 94 independent  is underdiagnosed due to limitations in diagnostic techniques. We estimated NAFLD in the UK Biobank based on serum and anthropometric measures, which helped identify 90 NAFLD GWAS loci, build NAFLD polygenic risk scores, and discover a causal effect of NAFLD on coronary artery disease. The degree of steatosis (fat in the liver) can be measured through different imaging techniques, mainly using abdominal magnetic resonance imaging (MRI) and magnetic resonance spectroscopy (MRS).It is estimated that over 25% of adults worldwide have non-alcoholic fatty liver disease  have remained small,Our prediction approach that leverages shared genetics between fatty liver disease and other metabolic disorders is also in line with the current change in the nomenclature from NAFLD to MAFLD,The leading cause of death from NAFLD is coronary artery disease (CAD), with an estimated 5%–10% of people with NAFLD dying from CAD.cis-expression quantitative trait loci (eQTL) results were obtained from the GTEx portal in the version of dbGaP Accession phs000424.v8.p2. The Kuopio Obesity Surgery (KOBS) cohort was recruited at the University of Eastern Finland and Kuopio University Hospital, Finland. All individuals gave written informed consent, and the study protocol was approved by the local ethics committee. We analyzed the liver RNA sequencing (RNA-seq) and genome-wide SNP data from 259 KOBS participants.This research has been conducted using the UK Biobank Resource under application number 33934. The GTEx coronary artery In UKB, the true NAFLD cases were first identified using the following ICD9/10 codes: 571.5, 571.8, 571.9, K74.0, K74.6, K75.8, and K76.0, as in previous large administrative data-based studies of NAFLD prevalence and incidence.To evaluate the accuracy of this model, we performed a 100-fold cross-validation in UKB. In more detail, we randomly split the individuals into 100 groups, so that the training groups contained 99% of the individuals and the remaining independent 1% of the individuals were estimated based on the trained model. To estimate the importance of different predictors, we also trained a random forest model using the same predictors that we used in the NAFLDS model. The random forest was employed using the “randomForest”2, sex, BMI, top 20 genotype PCs, array type, and center ID as covariates. To decrease genetic heterogeneity and avoid confounding due to multiple ethnicities and population substructures, only unrelated participants of European ancestry were included in the analysis. We also excluded individuals with liver disease other than NAFLD from the GWAS, PRS, and MR analyses using ICD9/ICD10 codes: 571.1–4, 571.6, 572.0, 572.8, 573.3, 573.8–9, K70.0–4, K70.9, K71.0–2, K71.5–9, K72.0–1, K72.9, K73.0–2, K73.8–9, K74.1–5, K75.0, K75.2–4, K75.9, K76.1–3, K76.6–9, and K77.0. To fulfill a two-sample MR requirement, we performed the CAD GWAS among the individuals who do not have a solid estimation of NAFLD status .We used a linear mixed model implemented by BOLT-LMM to identify the associations between the genetic variants and selected traits  while taking into account the population structure in UKB. The CAD individuals were identified using the ICD9/10, as described by Khera et al.cis-eQTLs in the KOBS liver RNA-seq cohort, we first estimated the gene expression using Kallisto. Only the genes that had an estimated TPM > 0.1 in more than 90% of the KOBS liver samples were retained for the analyses. We also performed a 2-pass alignment using STAR and estimated the following technical factors: mitochondrial reads percent, mRNA reads percent, uniquely mapped rate, 5′ bias, and 3′ bias. Then we adjusted the gene expression for the technical factors, RIN, first 3 genotype PCs, and 20 SVAs. The cis region was defined as 1 million bases up/downstream of the transcription start site of the target genes. Using a permutation-based multiple test correction employed by fastQTL, we identified 260,748 significant cis-eQTL SNP-target gene pairs passing false discovery rate (FDR) < 0.05.To identify the cis-eQTLs and the GTEx coronary artery cis-eQTLs and filtered out the shared SNPs that might affect both the liver and coronary arteries. The cis-eQTLs that only exist in one of these tissues were identified as the tissue-aware cis-eQTLs. When using the imputed NAFLD status as the exposure variable, we identified the variants that are significant both in the NAFLD GWAS and liver-aware cis-eQTL analysis in the KOBS or GTEx cohort. The identified SNPs most likely affect the liver health status, reflected by the imputed NAFLD status. Then we linkage disequilibrium (LD) pruned (R2 = 0.2) the overlapping SNPs and treated the non-redundant SNPs as instrumental variables (IVs). When testing the causal effect of CAD on NAFLD, we included both the UKB CAD GWAS SNPs and the CARDIoGRAMplusC4D CAD GWAS SNPscis-eQTLs. We LD pruned (R2 = 0.2) the CAD GWAS cis-eQTLs and treated the independent CAD GWAS SNPs as IVs. Next, we used MR-PRESSOUsing the summary statistics that we obtained from our GWAS analysis, we explored the causal relationship between NAFLD and CAD . We first overlapped the KOBS/GTEx liver ,,To impute the NAFLD status using available traits in UKB, we first identified the NAFLD and healthy control individuals using the same ICD9/10 codes see  for NAFLUsing a 100-fold cross-validation, we assessed the performance of NAFLDS, FLI, and HSI on predicting the NAFLD status. Moreover, we calculated the positive predictive value (PPV) and negative predictive value (NPV) of different cutoff points and set −1.5/1.5 as the low/high cutoff points of NAFLDS. These cutoff points were selected to call NAFLD cases and controls at a >90% confidence level, as is evidenced by the fact that when we applied the high cutoff point (1.5), we identified 1,188 NAFLD cases, of which 93%  were true NAFLD cases, and when we applied the low cutoff point of −1.5, we identified 1,406 NAFLD-free individuals, of whom 92%  were correctly categorized. Thus, utilizing both the high 1.5 and low −1.5 cutoff points, our NAFLDS model diagnosed the binary NAFLD status of 2,391 individuals  at a high accuracy .2, and BMI). This simplified model (NAFLDS_simple) also outperformed FLI, his, and any predictor alone in the 100-fold cross-validation  and 108,652 healthy control subjects (NAFLDS < −1.5) in UKB. We then performed a GWAS analysis on the two traits .2 < 0.2). Comparing to the previously identified suggestive or significant NAFLD GWAS loci,TM6SF2 and PNPLA3  variants in 2 independent LD blocks , which is 13 times more loci than reported in the previous NAFLD GWASs together  when compared with the NAFLD status , we identified substantially more significant GWAS variants for the imputed NAFLD status than in the NAFLD status GWAS analysis (see above). All in all, we identified 94 NAFLD GWAS loci for the imputed NAFLD status, with 5,187 significant (p < 5E-8) variants in 94 independent LD blocks (Rtogether  and S3. 606563), SAMM50 (MIM: 612058), and PNPLA3 (MIM: 609567)  , while w 609567)  and S3.Tcis-eQTLs identified from 208 liver RNA-seq samples from GTEx and 259 liver RNA-seq samples from KOBS. In total, the GWAS variants regulated 50 liver eQTL target genes (eGenes) in either GTEx or KOBS as cis-eQTLs (FDR < 0.05). Among the 50 liver eGenes, 19 were observed in both GTEx and KOBS. These results provide potential targets for co-localization analyses in the future fine mapping studies to further confirm the co-location between the GWAS and cis-eQTL variants at each of these loci. HLA) genes that replicated the recent report by Yoshida et al.Next, we overlapped the GWAS loci obtained using the imputed NAFLD status with liver Since the NAFLDS is predicted using 14 different predictors, we further investigated whether the imputed NAFLD status  helps to identify GWAS loci that cannot be identified by the individual predictors of the NALFDS model. We chose five predictors to represent the five categories of predictors  and then performed GWASs for these 5 representative predictors and compared their GWAS variants with the significant imputed NAFLD GWAS variants. 2 < 0.8) with the significant GWAS variants of these predictors. We then tested the associations between the 110 GWAS variants with all 14 predictors and identified a set of 8 variants in tight LD (R2 > 0.80) in a 33-kb region on chromosome 17 that are not associated with any predictors . These 8 imputed NAFLD GWAS variants belong to the same LD block overlapping the Growth Factor Receptor Bound Protein 2 (GRB2) gene, suggesting this gene as a possible underlying gene, though further fine mapping and functional studies are warranted to identify the actual regional NAFLD gene. GRB2 region that do not overlap with the GWAS loci detected by any predictors . Noteworthy, as there are other regional variants that do not reside in this LD block that are associated with waist-hip ratio adjusted BMI and waist-hip ratio in previous GWASs,GRB2 region thus shows genetic effects on multiple metabolic traits beyond the imputed NAFLDS.When comparing the imputed NAFLD GWAS variants with the GWAS variants of the predictors, 110 GWAS variants of imputed NAFLD are specific to the imputed NAFLD and do not overlap or are not in LD (R2 from 0.2 to 0.8) and p value thresholds (p from 5 × 10−8 to 0.1) using the polygenic score function in plink.2 < 0.8 and p < 0.1 as the best thresholds, because this provided the most significant difference in the PRS between the imputed NAFLD cases and the imputed controls and also identified the highest OR between the 10th decile versus the 1st decile. Finally, we applied this model to the validation set and observed a concordant difference of the PRS between the NAFLD cases (identified by ICD codes) and control subjects (verified by MRI data). −15), while the OR of the 10th decile when compared to the 1st decile is 2.1.To investigate how the imputed NAFLD GWAS variants predict individual risk of NAFLD, we constructed a PRS model for the imputed NAFLD status in UKB. To train and build the NAFLD PRS model, we separated UKB into 3 independent groups: training set , test set , and validation set . The validation set contained all the case/control individuals whose NAFLD status was verified by ICD codes or MRI scan while training, and the test set contained the individuals whose NAFLD status were imputed by the NAFLDS model. We first performed a GWAS analysis of the imputed NAFLD status in the training set to establish the effect sizes (beta) of all variants on the NAFLD risk. Then we investigated the effectiveness of the PRS model in the test set using different combinations of LD pruning thresholds . In UKB, we treated the 5,187 significant GWAS variants of the imputed NAFLD status as the candidate IVs. To perform GWAS in two independent cohorts required by a two-sample MR setup, we also performed a GWAS analysis of CAD among the individuals who do not have a predicted NAFLD status . With only 17,188 CAD cases in UKB, we identified fewer significant CAD GWAS SNPs (n = 841 without LD pruning) than in the imputed NAFLD GWAS . Therefore, we also included the reported known CAD GWAS SNPs from the large Cardiogram meta-studycis-eQTLs in their respective tissues. We used RNA-seq data of 259 liver biopsies from KOBS to identify the liver cis-eQTLs. We also downloaded the cis-eQTLs identified in the liver and coronary artery tissue from GTEx v.8 and excluded cis-eQTL SNPs that overlapped between the liver and coronary arteries to avoid including as IVs these SNPs that function as cis-eQTLs in both tissues. In total, 58,147 shared cis-eQTLs were identified in both KOBS and GTEx liver cohorts, and 464,236 cis-eQTLs were identified in the GTEx coronary artery samples. We then obtained our final list of candidate IVs for NAFLDS and CAD by overlapping the respective cis-eQTLs with the significant (p < 5E-8) NAFLDS or CAD GWAS SNPs.Moreover, the IVs used in an MR analysis should preferably have a known function to decrease horizontal pleiotropy, as pleiotropy can lead to misleading MR results.2 ≤ 0.2) that are associated with NAFLD status in UKB and are liver, but not coronary artery, cis-eQTLs (FDR < 0.05). We identified a significant positive causal effect  of NAFLDS on CAD in UKB. To reduce the potential of pleiotropy, we used MR-PRESSO,2 ≤ 0.2) that are both CAD GWAS SNPs and coronary artery, but not liver, cis-eQTLs. Using MR-PRESSO to correct for the potential horizontal pleiotropy, we did not find a significant causal effect of CAD on NAFLDS .To further verify the direction of the causal effect of NAFLD on CAD, we performed a similar two-sample MR using ALT as the surrogate of liver health. Similarly, we observed a significant causal effect of high ALT level on the risk of CAD  using MR-PRESSO, while the reverse causal effect (CAD→ALT) remained insignificant . Therefore, we further utilized MR-egger and verified the causal effect of ALT on CAD. In more detail, MR-egger also identified the significant causal effect of ALT on CAD , while no sign of pleiotropy was observed (p.pleio = 0.15). In summary, we identified the IVs for the MR analyses using the GWAS SNPs of imputed NAFLD and CAD in the UKB and refined these IVs to those with functional evidence in their respective tissues by selecting the tissue-aware IVs. Our tissue-aware bi-directional MR analysis demonstrated that NAFLD causally increases the risk of CAD and did not identify any evidence of reverse causality .We used the UKB cohort to develop an estimation model of NAFLD. By combining the relevant serum traits , our imputed NAFLDS achieved a high accuracy on NAFLD (AUC = 0.89) and outperformed the existing FLI,When identifying NAFLD predictors using elastic net, we used the serum HbA1c and T2D status to represent glucose metabolism instead of serum glucose levels, because the glucose levels were not taken after overnight fasting in UKB, which may bias them. It is suggested that serum glucose and lipid levels are independent predictors for NAFLD,2 < 0.2), which is 13 times more loci than reported previously in NAFLD GWASsSince the NAFLDS model showed a high accuracy in predicting NAFLD cases and healthy control subjects when employing the cutoff points of −1.5/1.5, we used it to predict 28,396 NAFLD cases and 108,652 healthy individuals at >90% confidence level in UKB. This sample size increased the power of our GWAS analysis and resulted in the discovery of 94 independent NAFLD GWAS loci . We recognize that the use of ICD coding may underestimate the prevalence of NAFLD, which perhaps contributes to the relatively low OR in the PRS. Nevertheless, both the GWAS and PRS analyses demonstrate that the NAFLD status imputed by our NAFLDS model greatly increased the power in identifying genetic variants associated with the risk of NAFLD for future follow-up studies.Using the effect sizes estimated from the GWAS variants, we also built and tested a PRS model that predicted the NAFLD risk in UKB. In the validation set where individuals’ NAFLD case/control status was verified by the ICD codes or MRI data, the individuals who have a higher NAFLD PRS score (top 10%) exhibited an OR of 2.1 when compared to the individuals who have the lowest (bottom 10%) NAFLD PRS score. The NAFLD individuals also show a significantly higher NAFLD PRS score compared to the healthy control subjects . Accordingly, using the tissue-aware cis-eQTL GWAS SNPs as IVs and applying MR-PRESSO should reduce the potential pleiotropy and thus improve the robustness of the MR analysis.It is difficult to distinguish the specific contribution of NAFLD on CAD from the other risk factors shared by NAFLD and CAD. For example, obesity is a known risk factor for both NAFLD and CAD. Thus, it is important to avoid the potential horizontal pleiotropy in the MR analysis when investigating the causal relationships between NAFLD and CAD. Here, we included BMI as a covariate to identify the GWAS variants that are associated with NAFLDS/CAD without being mediated by the obesity status (BMI). Furthermore, we combined the GWAS variants and tissue-aware ,Table S1cis-eQTL IV SNPs with potentially fewer pleiotropic effects, an approach not used before to address this key MR limitation.Although we used independent samples for training and validating NAFLDS in the UKB, the FLI and HSI models were originally trained in other cohorts than UKB.cis-eQTLs to identify the GWAS SNPs that affect gene expression preferentially in the liver or coronary arteries. Using these tissue-aware cis-eQTL GWAS SNPs as IVs and applying MR-PRESSO to avoid the potential pleiotropy, we identified the putative one-way causal path from NAFLD to CAD. This result was further supported by the observed causal path from ALT to CAD using the same tissue-aware IV design in MR. Our non-invasive NAFLD model in the UKB cohort can next be implemented to other large biobanks to further investigate these results and advance our understanding of genetic predisposition to common metabolically driven liver damage and ultimately NAFLD.In summary, we used key clinical metabolic measurements to build the NAFLDS model that is easy to employ and outperforms the existing NAFLD estimation model in UKB. When some serum traits, such as HbA1c, triglycerides, and cholesterol, are not available, our NAFLDS_simple model can be used to predict the NAFLD status. Using the imputed NAFLD status in UKB, we identified 94 independent NAFLD GWAS loci, of which 90 have not been identified before. Moreover, the power boost from the sample size  also helped us successfully build a PRS model that shows a significant difference between the NAFLD cases and healthy controls. Furthermore, we combined the GWAS variants and tissue-aware


The genotypic values were estimated using spatial adjustment in a two-stage analysis. First, we performed the individual analysis for each site/irrigation treatment combination. The best linear unbiased estimates (BLUEs) were used to calculate the Harmonic Mean of Relative Performance (HMRP) as a drought tolerance index for each testcross. The second stage was a joint analysis, which was performed using the HMRP to obtain the best linear unbiased predictions (BLUPs) of the index for each genotype. Then, GWAS was performed to determine the marker-index associations and the marker-Grain Yield (GY) associations for the two irrigation treatments. We detected two significant markers associated with the drought-tolerance index, four associated with GY in drought condition, and other four associated with GY in irrigated conditions each. Although each of these markers explained less than 0.1% of the phenotypic variation for the index and GY, we found two genes likely related to the plant response to drought stress. For these markers, alleles from landraces provide a slightly higher yield under drought conditions. Our results indicate that the positive diversity delivered by landraces are still present on the backcrosses and this is a potential breeding strategy for improving maize for drought tolerance and for trait introgression bringing new superior allelic diversity from landraces to breeding populations.Current climate change models predict an increased frequency and intensity of drought for much of the developing world within the next 30 years. These events will negatively affect maize yields, potentially leading to economic and social instability in many smallholder farming communities. Knowledge about the genetic resources available for traits related to drought tolerance has great importance in developing breeding program strategies. The aim of this research was to study a maize landrace introgression panel to identify chromosomal regions associated with a drought tolerance index. For that, we performed Genome-Wide Association Study (GWAS) on 1326 landrace progenies developed by the CIMMYT Genetic Resources Program, originating from 20 landraces populations collected in arid regions. Phenotypic data were obtained from early testcross trials conducted in three sites and two contrasting irrigation environments, full irrigation (well-watered) and reduced irrigation (drought). The populations were genotyped using the DArTSeq Maize is a major crop cultivated in many regions of the world and is one of the most important cereals for food production, occupying around 193 million hectares in 2018 . That coUnder abiotic stress conditions, tolerance is the ability of the plant and its yield component traits to maintain grain production during an extended period under that stress . Betweenper se phenotypic data or, seldomly, testcross hybrids. For crops such as maize where hybrids are used to exploit heterosis, the testcross is an important strategy in research for the development of improved hybrids, as it represents the real genotypic constitution of the crop grown by farmers. Additionally, early generation testing (EGT) of semi-inbred lines as testcrosses is a common strategy used in maize breeding programs, being a cross between a tester inbred or F1 with a S2 or S3 experimental line. The aim is to take advantage of early selection for yield potential, avoiding future testing costs on unpromising families . Subtropical adapted maize landraces are from areas between 800 and 1800 m above sea level  – Nayarit State, the CIMMYT experiment station at Tlaltizapán (TL) – Morelos State and the INIFAP station at Los Mochis (LM) – Sinaloa State. In each location there were two treatments, drought (DR) and well-watered (WW). A sparse phenotyping arrangement of genotypes in location was used due to restrictions on field space and seed amount, but the landraces ancestry was balanced in each location. That means, we did not test the entire set of 1332 genotypes in all locations, using so an unbalanced distribution of genotypes across location. The distribution of the testcross entries was: 266 tested only in TL (∼13 progenies of each 20 landraces), 266 tested only in LM (∼13 of each 20 landraces), 265 tested in TL and LM (∼13 of each 20 landraces), 529 tested in the 3 locations (∼26 of each 20 landraces). Thus, the TC1 experiment consisted of 6 combinations of location and treatment (3 locations and 2 irrigation treatments). The experiment was designed in a way that each location contains three plots of the tested germplasm in that specific location, one plot in irrigated condition and two plots in drought condition. The irrigated (WW) treatments were planted in a modified augmented design with systematically distributed spatial check plots arranged in a chess knight move, the field implementation of this design were based mainly on the studies of –1), considering the differences in plots size at each site.The TC1 trials were planted on January 14, 20, and 30 of 2016 at TL, SI, and LM, respectively. Harvest dates for the trial, in the same order, were June 4, 10, and 27. Winter planting dates were chosen because this is the dry season for much of Mexico, especially the western half of the country. Using drip irrigation in all the sites, we were able manage the amount of water delivered to each treatment. In all locations we used a two-row plot, in TL and SI were 4.5 m long with 0.75 m between the rows, and in LM, 4 m rows spaced by 1 m. The agronomic treatments  were applied following common local practices. The irrigation in the drought treatments was interrupted 3 weeks before the expected anthesis date at each location, and after flowering terminated one more irrigation was done to ensure the grain filling. The plots were harvested by hand, the ears threshed by machine, the weight and moisture grain of each plot were taken, and the plot grain production was adjusted to 13% of grain moisture and converted to 1000 kg per hectare  – Nayarit State, replacing the SI TC1 location, the 2 irrigation treatments were WW and DR, and the 3 testers were the hybrid CML264/CML311 and the lines CML373 and CL501801. All TC2 trials used two-rows plot of 4 m length, with 0.75 m between rows, harvest was accomplished using a Wintersteiger Classic plot combine at TL and PV location, and a New Holland TR 88 2-plot combine at LM, both combines provide measures of the grain weight and grain moisture of each plot. The agronomic treatments and water supply were managed the same as for the TC1 trials.1 for genotyping all the 1326 BC1S1 and 174 BC1S2 lines and the recurrent parent CML376. The genotyping was carried out in the Genetic Analysis Service for Agriculture  facility at CIMMYT, Mexico. A bulk of 10 seeds of each BC1S1 and BC1S2 line was sampled. A genomic representation was generated by digesting nuclear DNA with a combination of two restriction enzymes, PstI (CTGCAG) and NspI (CATG), and ligating individual barcodes adapters to identify the origin of each fragment after a samples pooling. Successfully amplified fragments were sequenced using the sequencer Illumina HiSeq2500 . Then, the SNP calling for those fragments was produced by the DArTsoft analytical pipeline  where the markers were identify de novo by comparing the sequences of fragments present in genomic libraries of samples previously processed, this process identifies and calls SNP markers against an existing library and it is totally independently of any reference genome. After this process, a set of filtering parameters were then applied to select high-quality markers for this study. To obtain markers positions on the chromosomes, the DNA fragment sequences were BLASTed against the Zea mays L. reference genome (B73 RefGen_v4). This procedure resulted in a genomic profile of 47,047 SNPs. Of this total, the markers of uncertain mapping or non-nuclear were still discarded, resulting in a profile of 32,592 SNPs with excellent quality that were positioned on the maize reference genome.We used DArTseq technology , developThe 32,592 SNPs were named, for this study, by their order in the genome from M1 (the first mark in chromosome number 1) to M32592 (the last marker in chromosome number 10). A final quality control procedure was applied, and markers with minor allele frequency (MAF) <5% were excluded and a call rate ≥95% used. Thus, markers with more than 5% of missing data were excluded. The imputation of missing data was performed by the Wright method carried out using the R package snpReady  using thFor analysis of the phenotypic data, a two-stage process was performed. In the first stage, adjusted yield means of genotypes were obtained by location, and then in the second stage a mixed model was fit jointly considering residual weights estimated in the first stage. The spatial corrections were applied in the first stage, using a first-order autoregressive process (AR1⊗AR1) to model the covariance structure of error. The spatial modeling approach was chosen due to some factors as the high number of genotypes tested, the augmented design with repeated check plots applied and the large size of the field trial.The WW treatments were designed in a complete spatial model, with no replicates of the test genotypes, and the replicated checks were systematically distributed in the field, so these trials were fitted with the following model in the first stage:y is the vector of phenotypic data, arranged by Row x Column (plot field position), X is the design matrix of fixed effects; τ is the vector genotypes effects , and eventual spatial factors row and columns (as explained below); and e vector of errors [e∼N]; R is the covariance matrix of e. The spatial analysis does a decomposition of the e into a vector ζ of spatial correlated effects, and a vector η of spatially independent residuals, in this spatial approach of modeling the residual we assume the error variance as:where var[η] is assumed to be independent (σ2In), and the var[ζ] is the correlated spatial error, and we assume a first-order autoregressive, (AR1⊗AR1) (The AR1⊗AR1) , so the AR1⊗AR1) :col) and AR1(ρrow) are the first-order autoregressive correlation matrices for columns and rows. The components col) and (ρrow) are the autocorrelation parameter for field columns and field rows:where AR1 that captures the correlation between each pair of plots.cor1,2 is the correlation between the plot 1 and plot 2, and η is the number of plots. Therefore, as said before, the final error variance structure for the first stage models is the sum of the correlated spatial error and the spatial independent error:Where X also contains the block design, and the vector τ also contains the fixed effects of replicates.The DR treatments follow the same logic for the error variance structure. The difference between the analysis of WW treatments and DR treatments is in the fixed section of the model, in the drought management there were replicates, so the factor replicate was added as fixed effects. Therefore, the model is similar, but This analysis provides a variogram plot of the residuals that shows in a geographic form any spatial pattern that may be a result of systematic variation due to the row and columns effects. Depending on the variogram of each trial, the row and column may be included as a fixed effect factor . The WalOnce all the individual trials were fitted to its specific model, we obtained the genotypes adjusted means (BLUE) of each location and irrigation treatment irrigation treatment.With the adjusted mean of each genotype in each combination of location and irrigation treatment irrigation treatment, we applied the Harmonic Mean of Relative Performance Index (HMRP) as a measure of genotype drought tolerance in each location. This index, proposed by HMRPij is the harmonic mean relative performance of genotype i in site j; GYwwij is the BLUE of grain yield under well-irrigation treatment of genotype i in the site j; GYdrij is the BLUE mean of grain yield in the drought condition of genotype i in site j; j.where For the joint analysis (second stage), we used a linear mixed model to compute the best linear unbiased predictions (BLUPs) of the HMRP, grain yield in drought (GYDR) and well-watered (GYWW) conditions for each genotype fitting the following model:y is the vector of BLUEs obtained in the first stage; β is the vector of fixed effects of site, g is the vector of the genotypic values of the lines, i is the vector of the random effects of the interaction sites by genotype, e is the random errors vector. X, Z, and W are the design matrix for β, g, and i. The weighting method was based on squared standard errors from stage one BLUES. As the weight of HMRP, we used the mean of squared standard errors of the well-watered and drought experiments, as the index use both adjusted means (WW and DR BLUES) as its components. The significance of the fixed effect of location was estimated using Wald statistic, and for the random effects, genotypes, and genotypes x location interaction, by Likelihood Ratio Test (LRT). The broad-sense heritability, for the joint analysis, was estimated as:where l is the number of sites, 3, in this study and r is the number of replicates. All the phenotypic analyses were performed using the ASReml-R v.3 package  was performed using the Fixed and Random Model Circulating Probability Unification in the R package FARMCPU  for threg is the vector of adjusted phenotypic observation ; β is the vector of the fixed effects of intercept, single markers, and the three first principal components used for population structure control; u is the vector of random additive effects; e is the vector of random residual; the X is the design matrix of fixed effects; Z is the genotype incidence matrix driven by VanRaden’s genomic relationship matrix (GRM) , where G is the VanRaden’s GRM. A Multiple-test threshold adjustment was performed by a permutation method to determine the SNP significance level. This procedure was carried out in FARMCPU using the FarmCPU.P. Threshold function. The same GWAS process was done with the BC1S1 and BC1S2 populations, using the phenotypic data from TC1 and TC2, respectively.where ix (GRM) , so u∼N(r2 parameter (square of the correlation coefficient between two loci), using Synbreed R package (r2 of each pair of markers with the distance between them to generate a curve of LD decay (The linkage disequilibrium for the BC1S1 data was measured by the  package . The LD LD decay .3) databases, a search into maize reference genome (B73 RefGen_v4) was carried out to check the surroundings of GWAS-significant markers. This search was done inside a window of 200k base pairs (bp) centered on the markers, i.e., in a chromosome segment of 100k bp going up and downstream of the markers, we used this window size based on the LD decay obtained to our population and on the maize LD decay reported in the literature. Gene ontology is not thoroughly consistent across platforms, and MaizeGDB notation was chosen as primary. The functional annotation of the genes found inside those windows was analyzed and related to a drought tolerance process.Through the MaizeGDB  and NatiThe purpose of using landraces in a breeding program is to bring novel positive alleles to a breeding population. As the recurrent parent CML376 was also genotyped, it was possible to check if favorable alleles in our semi inbred experimental lines are derived from the CML376 or the landraces. The allele frequency of SNPs associated with the traits, and derived from the landraces, was calculated for the entire population, and stratified for each landrace family group. This allowed verifying if specific landrace is a potential donor for the allele of interest.On the other hand, regarding selection, it is also important to know the distribution of the superior allele across the landrace progenies in the breeding population, regardless of its origin, landraces or the elite recurrent parent line. The superior allele is the one that causes a positive variation in the trait of interest. Therefore, we also calculated, for the significant SNPs, the frequency of the alleles with positive effects for each landrace progeny.To evaluate the population in terms of favorable alleles content, the 20 families were ranked by the sum of the ranks of the superior allele frequency for each SNPs. Two rankings were made, one considering all significant SNPs, regardless of the origin of the superior allele, and the other considering only the markers where the superior allele is from landraces. Thus, with the former we assess which landraces could be more suitable for selection and with the latter, which landraces would be the best source of superior new alleles.Validation was performed by two approaches. In the first, we compared the results of GWAS over BC1S1 and BC1S2. In the second, we compare the BLUPs obtained from the TC2 yield trials with the genetic value estimative of BC1S2 individuals based on the effects of significant SNPs, obtained from BC1S1 GWAS. The genetic values estimative were calculated by multiplying the allele substitution effect vector by the marker matrix of BC1S2 individuals, as shown in the model below:1 is the vector of allele substitution value for each significant SNP. The prediction ability was measured by Pearson Product-Moment correlation y2 is the vector of BLUPS of the genotypes in the TC2. This procedure was carried out to the tolerance index and grain yield in both irrigation treatments.where Based on the variograms surface trends, specific spatial factors were chosen as fixed effects to better fit the model to each site/irrigation treatment . In all p-value threshold, both with a p-value of 0.15 (data not shown).Landraces progenies and the genetic check (Tester × CML376) distributions of grain yield means  and the Overall, the heritabilities were low, considering the joint analysis for the drought-tolerance index, GY in drought (GYDR) and well-watered (GYWW) conditions, as expected for such complex traits . The herThe Harmonic Mean of the Relative Performance index measures drought tolerance. Its values are around 1.0, with higher values being more tolerant genotypes and lower values less tolerant. Regardless of site, the HMRP mean is always close to 1.0 because it is relative to the population in each site . The genThe use of principal components (PC) as covariates into the GWAS model was necessary as the population structure might result in a spurious marker-trait association. This necessity was confirmed by comparing the QQ-plots of the GWAS model with no PC as covariates and the QQ-plots of models containing it (QQ-plots not shown). The QQ-plots showed a better adjustment in the models that consider the correction for the first PCs. However, the sum of variance explained by the first three PCs was just 5.58%, distributed by 2.76, 1.44, and, 1.38%, evidence that the population was not very structured. Plotting the first two PCs no clear stratification in the population is noted p-values of the marker-trait association in a Manhattan plot and the quantile-quantile plots) are shown in p-values against the distribution of expected p-values, i.e., under the null hypothesis, that there is no association between the SNP and the trait. This is a simple tool to check how well the chosen GWAS model absorbed the population structure (covariates in the model). The dots on the upper right section of the graph are the SNPs with some association with HMRP. We expected that most markers would not be associated with the trait. Looking at the QQ-plot, most markers have a p-value observed close to the expected null hypothesis, which can be verified by the line-shaped markers’ p-values that are aligned to the red line .The association results  were very close to the Bonferroni correction (0.05/N = 9.01 × 10–6) for the three GWAS procedures, considering the logarithmic transformation −log10. Two markers associated with HRMP were found (r2), and 0.07 and 0.08% of the grain yield phenotypic variation under drought and well-watered treatments  that did not have any individual in one of the homozygous classes . The altThe significant markers found using in this research were not stable across traits in the first generation (BC1S1). The two SNPs associated with the drought tolerance index were not associated with grain yield. Likewise, none of the SNPs associated with grain yield were in both irrigation treatments. Furthermore, none of the 10 SNPs found by GWAS in BC1S1-TC1  were sigThe correlation between the predicted values of the BC1S2 estimates based on the effects of allelic substitution of significant SNPs obtained from BC1S1 GWAS, and the observed BLUPs was –0.11 for the drought tolerance index, –0.20 for grain yield under drought treatments and –0.23 under well-watered.r2 against the distance of base pairs  trends. Therefore, the spatial modeling used in the first stage analysis was crucial to avoid, or minimize, their effects on the means, mainly in the WW treatments, where augmented design was carried out. Thus, we expected lower heritabilities in these trials (WW) than in the DR treatments, even though drought-stressed trials usually are more affected by field spatial trends , resultiAs the TC1 used a single tester, the tester by genotype interaction could not be separated from the estimated genotypic value (BLUEs and BLUPs). Thus, the first generation (BC1S1) data are biased by this interaction, which is acceptable since it is not feasible to perform a testcross trial with more than 1,000 genotypes crossed to a group of testers. In the second testing year this was minimized, as with only 174 BC1S2 lines, testcrossing with three testers was possible.Similar values are found when comparing the grain yield means and the drought tolerance index for the BC1 progeny testcrosses and the genetic checks (Tester crossed with CML376). However, the distribution suggests a superior performance for the BC1 progenies for grain yield , and forThe usage of HMRP and other index based on harmonic mean is recognized for stress tolerance studies . The HarWe believe that the final number of SNPs  was relatively low considering the start point of 47,074 markers comparing with others maize GWAS published , mainly Many researchers use correlated traits to study drought tolerance such as seedling survival rate under drought , ear lenThe LD decay reported in maize varies considerably among populations . In landWe focused our search for genes in a window of 100k bp around the 10 trait-associated SNPs. The LD extent for our population (50–300k bp) is in accordance with those observed in the literature. This is the expectation for material that are neither landrace nor elite inbred line, since the former normally has a shorter LD decay than evidenced in our experimental lines and the latter, a larger LD decay . Except loci could help to elucidate their role and could be included in a genomic selection set. These two markers (M232 and M11032) could be used in our experimental lines to drive the selection in the next generations and be a basis for further investigations. This is especially so for the M11032 SNP, where the alternative allele originates from the landrace  where the superior allele is from the landraces, which demonstrates new diversity for drought tolerance alleles in the experimental lines. In this context, the landraces NVOL46, COAH20, COAH21, CHIH87, and COAH78 would be the main sources for exploring this diversity since they have a higher frequency of these favorable alleles.For the BC1S1 testcrosses, the lack of significant SNP correlation among the irrigation treatments may be a sign of differential expression of genes under contrasting water environments. For example, Likewise, there was no correlation of significant SNPs between the BC1S1 and BC1S2 generations. This is to be expected since selection was made in BC1S1 to generate the BC1S2 we induced a non-random allele frequency change between the generations that can affect the results as the new generation is diverging from the first . Thus, iAdditionally, the use of allelic substitution effects estimated using the BC1S1 data to predict the phenotype of BC1S2 generation as a validation method was not successful. The correlations between the predicted values and the observed BLUPs were low and negative to the three cases . The allele frequency also interferes with the average effect of the allele. Therefore, the selection and its incumbent frequency allele changes, also results in allelic effect deviations estimated in BC1S1, which could not be constant through generations of selection. Moreover, the heritabilities were low in BC1S1 (0.04–0.17), and the significant SNPs explain less than 0.1% of phenotypic variation, that is indicative of the complexity of quantitative traits as drought tolerance and grain yield, that reduces the efficiency of using a few SNPs for prediction, even if they are significant. Finally, there is the effect of the tester on the BC1S1 phenotypic data since the interaction tester by landrace progenies could not be estimated in this case. Thus, the allelic substitution effect is biased for this specific testcross and to use it to predict the genotypic values of a second testcross involving another tester may be unfeasible.Early testcross is a strategy to take advantage of early selection for yield and other traits that avoids excessive resource spending on unpromising families, and it can also be applied in a pre-breeding program. However, as The frequency of allele from landrace over the entire population are the same values of the MAF , once thThe general rank  gives thThe GWAS analysis was able to identify genomic regions associated with drought tolerance in early generation testcrosses, although they were not stable over generations or irrigation treatments. Two promising putative genes that encode proteins related with the physiological plant response to abiotic stress are located close to the significant SNPs found in this study. Some alleles from the landraces provide a slightly higher yield under drought treatments. These results indicate that the diversity delivered from landraces x elite inbred line backcrosses can play an important role for improving the maize tolerance to drought, and for trait introgression bringing new superior allelic diversity from landraces to breeding populations. As we expected for a complex trait such as drought tolerance, we did not find a unique marker with a large genetic effect, but a handful of regions that can be transferred to advanced and adapted breeding populations using landraces as origin of positive variants.https://hdl.handle.net/11529/10548540.Publicly available datasets were analyzed in this study. This data can be found here: dataverse: PB wrote the manuscript, analyzed the genetic and field data, prepared the genetic markers data, and participated in the conceptual design of the research. TM and MW created the conception of the study. TM and MA designed and carried out the experiments. VV-M and ES-I were responsible for the field operations. TM, RF-N, KS, and GG reviewed the manuscript. JB supported and reviewed the statistical and spatial analysis. CP conducted the genotyping process in the laboratory and the first marker data preparation. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.