Behavioral discrimination of kin is a key process structuring social relationships in animals. In this study, we provide evidence for discrimination towards non-kin by third-parties through a mechanism of phenotype matching. In mandrills, we recently demonstrated increased facial resemblance among paternally related juvenile and adult females indicating adaptive opportunities for paternal kin recognition. Here, we hypothesize that mandrill mothers use offspring’s facial resemblance with other infants to guide offspring’s social opportunities towards similar-looking ones. Using deep learning for face recognition in 80 wild mandrill infants, we first show that infants sired by the same father resemble each other the most, independently of their age, sex or maternal origin, extending previous results to the youngest age class. Using long-term behavioral observations on association patterns, and controlling for matrilineal origin, maternal relatedness and infant age and sex, we then show, as predicted, that mothers are spatially closer to infants that resemble their own offspring more, and that this maternal behavior leads to similar-looking infants being spatially associated. We then discuss the different scenarios explaining this result, arguing that an adaptive maternal behavior is a likely explanation. In support of this mechanism and using theoretical modeling, we finally describe a plausible evolutionary process whereby mothers gain fitness benefits by promoting nepotism among paternally related infants. This mechanism, that we call ‘second-order kin selection’, may extend beyond mother-infant interactions and has the potential to explain cooperative behaviors among non-kin in other social species, including humans. Kin selection is an evolutionary process promoting traits that provide fitness benefits for genetic relatives of individuals expressing them . EmpiricMandrills are non-human primates from Central Africa that live in large matrilineal societies, characterized by family units of philopatric, maternally-related and highly nepotistic females. Males, the dispersing sex, are only temporary residents in these groups, and the highest-ranking male sires a large proportion of infants born into different matrilines each year . Each cor~0.25; and see Appendix 1). This heightened facial resemblance among paternal half-sisters compared to maternal half-sisters, possibly resulting from genomic imprinting processes  than a mechanism based on direct selection on facial traits to provide cues of paternity. Using the father’s face as a template is also unlikely in mandrills because of the highly pronounced sexual dimorphism and morphological differences between adults and immatures in this species  and a non-descendant recipient (here the similar-looking infant). To address the question of how and whether such a behavior may evolve, we formally demonstrate an evolutionary mechanism by which mothers may gain fitness benefits from favoring nepotism between their own offspring and their offsprings’ paternal half-sibs, as infants themselves benefit from interacting repeatedly with kin, following predictions of the kin selection theory. The mechanism that we propose to call ‘second-order kin selection’, can be generalized beyond mother-infant interactions. While it is a novel explanation for the evolution of nepotism, second-order kin selection perfectly fits into the mathematical framework offered by inclusive fitness theory .We first show that, during infancy, paternal half-sibs resemble each other more than maternal half-sibs and non-kin do. We retrained VGG-Face, a popular algorithm for human face recognition, to recognize 112 individual mandrills from their face, independently of their position, lighting or facial expression, using approximately 12 k training pictures. These pictures were taken in the course of a long-term field project on a large, natural social group of mandrills in Gabon. We used this retrained model to predict facial distance  among 80 individually recognized infants (0–1 yr) of both sexes , represented by a total of 204 new pictures (1–10 pictures/infant), not used for training the model. Maternal identity was known for all 80 infants based on direct observations. Due to field constraints, paternal identity was determined genetically for only 32 infants. For the remaining 48 infants with unknown paternity, we differentiated between pairs of infants conceived during the tenure of the same alpha male from those conceived under tenures of different alpha males. Indeed, reproductive skew in mandrills is high, with alpha males monopolizing 60–70% of all conceptions . While iWe then restricted our image data set to those infant dyads for which we had detailed records on spatial associations routinely collected during behavioral observations (N=48 infants and their 30 mothers). Using generalized linear mixed models , we analyzed spatial associations (i) among infants ; (ii) between infants and other mothers ; and (iii) among mothers having infants at the same time  as a function of the residuals  of facial distance among infants, and controlling for matrilineal origin, maternal relatedness and rank, and infant sex and age (at the time of observations).2=1.71, p=0.19). Similar models on infant-infant and mother-infant grooming relationships were not performed because infants never groom each other, and, of a total of 560 mother-infant dyads, only 6 were observed grooming each other during the course of this study. An important implication of this last observation is that mothers probably bring infants into proximity to each other but do not invest into other social behaviors such as grooming (i.e. infants ‘do the rest’).We found that mothers associate significantly more with infants that look like their own infants compared to infants that do not , Table 2r=0.85, p<0.0001). Altogether, these correlational results support the hypothesis that mothers are responsible for the increased association observed among infants that depends on several factors, including infants’ facial resemblance.In mandrills, mothers constitute the main social partner for their offspring for several months or years. During their first year of life, infants are closely associated with their mother . To confIn other mammals, from rodents to humans, mothers often show some form of control over social opportunities and preferences of their offspring, and maternal behavior may impact both their infants’ neural and social development . In chimTo care for their own offspring, mandrill fathers need some form of paternity certainty because females mate with several males around ovulation . In someBecause paternal half-siblings share paternal, not maternal genes, we finally show how, under minimal assumptions, mothers can obtain fitness benefits by fostering interactions between their offspring and paternally-related siblings. Key steps to that conclusion are that infants engage in repeated interactions within dyads and that such interactions are mostly of two types, ‘repeatedly cooperate’ or ‘repeatedly defect’, so that only dyads of cooperators can repeatedly cooperate. Then, any maternal behavior that increases the likelihood that her offspring will repeatedly cooperate rather than repeatedly defect may be favored. This two-step reasoning does not require a statistical association (linkage disequilibrium) between alleles affecting the control of assortment of infants by mothers, and alleles affecting cooperation. To reach this conclusions, we will use a ‘one-generation’ formalism  suitableW is quantified as a covariance  . W is her expected number of (adult) offspring, expressed as a function w of her own behavior xf and of that xp of her social partners (neighbor-modulated fitness), and then as a function of gene effects underlying such behaviors  or not (xf = x0 when zf = 0) the allele under consideration). The covariance expression for selection then involves expected values of products of the mother’s and partners’ indicator variables, that can be interpreted in terms of relatedness coefficients, and linkage disequilibria when several loci are involved. In the present case, we will see that many such products can be ignored, allowing a simple characterization of selection on maternal behavior.In this formalism, selection on an allele affecting the mother’s fitness variance  between For simplicity, we assume effects on infant survival. We could alternatively assume effects on infant reproductive potential  but then the fitness of a mother should be measured as a weighted sum of numbers of offspring of different quality, which would complicate the model formulation without modifying its conclusions. Likewise, formal models including pairing processes are generally complex to formulate but such a formulation is not required to understand the key qualitative features of the present scenario.R indicating the ‘rewards’ received by two cooperating infants, P the ‘punishment’ payoff received by two defecting infants, T the ‘temptation’ payoff received by a defecting infant interacting with a cooperating infant, and S the ‘sucker’s’ payoff received by a cooperating infant interacting with a defecting infant, and w the probability of iteration ), rotated (range of degree: [–20 20]) and scaled (range of factor: [–0.7 1.2]). The entire training procedure was repeated 10 times. Eventually, after approximately 15 epochs, VGG-Mandrill was able to identify mandrill faces individually with a maximal accuracy of 93.42% (sem:±0.13), and high generalization capacity . VGG-Mandrill was then used to extract deep feature activation vectors, a compact  and informative representation of a mandrill face. Previous experiments revealed that face verification with mandrills was the most performant when using activation vectors from the first fully connected layer (after RELU nonlinear transformation), compared to using vectors from the second, or from both fully connected layers . The disχ2 difference  or 1  as a response variable. The SVM outputs the accuracy of the face verification task as well as the weight of each feature. We found that the classifier could verify mandrill identity from their face with an accuracy of 86.87% (sem: ±0.01). Weights were eventually used to calculate a weighted χ2 distance between every pairs of images in the test set. Pairwise distances were averaged for every different pairs of individual  to provide our final estimates of facial distance across pairs of infants (mean ± SD: 6.46±6.99 pairs of pictures for each pair of infants).Last, we used a linear support vector machine (SVM) to learn a distance metric, with the goal to find the feature weights that optimize face verification. We randomly selected 15 k pairs of images representing different individuals and 15 k pairs representing same individuals, and for each pair we calculated the ce as in . We thenTrained observers, blind to the study questions, perform daily behavioral observations on all individually-recognized mandrills using either ad libitum data or 5 min focal sampling. During each focal observation, we record social interactions including time spent grooming or receiving grooming from all focal’s groupmates. During these focal observations, we also record scans on one to three occasions to measure spatial associations between the focal animal and all groupmates that are located less than five meters away (termed ‘scan’). Since 2012, we obtained 9,305 scans from 48 infants and their 30 mothers during their offspring’s first year of life . We restricted our analyses on association rates to infant-infant (169±77 scans per pair), mother-infant (174±80) and mother-mother (174±91) dyads with at least 5 scans (and at least one scan for either partner) and when both individuals were alive and infants aged ≤1 yr. For each association rate, we matched the average facial distance of the corresponding pair of infants aged ≤1 yr. For mother-mother dyads, we also retrieved the frequency of grooming exchanged, recorded during their offspring’s first year of life and restricted our analysis to those dyads observed during at least 30 minutes .Behavioral observations further allowed to examine patterns of two conspicuous mother-infant behaviors, “follows” and “approaches” recorded as bouts during focal samplings of infants and their mothers. Finally, we reconstructed both male and female dominance hierarchies, using the outcomes of approach-avoidance interactions obtained during ad libitum and focal samplings and calculated using normalized David’s score as per . We diviMaternity was known for all 80 studied infants thanks to daily monitoring. Matrilineal identity was determined for all of them, including those 48 infants and associated 30 mothers used in the proximity analyses, thanks to detailed data on births and pedigree information available since the beginning of the project  and at CGenetic relatedness was determined using pedigree data obtained from paternity analyses performed on most adult mandrills and on a subset of immature individuals  to study variation in the averaged facial distance among pairs of 80 infants aged ≤1 yr as a function of the averaged distance in ages across all pairs of pictures collected on each studied pair of infants, their sex , whether or not they share a common mother , and whether or not they either share a common father and whether or not they were conceived during the tenure of the same alpha male . We excluded those infants (and the corresponding pairs) conceived during a month when the alpha position was disputed and unclear, resulting in 2,556 pairs of infants represented by 16,755 pairs of pictures (mean ± SD: 6.56±6.86 pairs of pictures for each pair of infants). We included an autocorrelated random effect with distinct levels for each pair, to represent correlations between pairs of facial distance values involving a same infant in both distances, with a single correlation value for all such pairs. Its correlation is equal to 1 between permuted pairs , equal to a correlation value ρ (fitted to the data) between pairs sharing one individual, and to 0 for non-overlapping pairs. The case where ρ equals 0.5 represents the case of two additive individual random effects, each with the same variance. The fitted correlation model is therefore more general than this particular case. It is formally identical to a random effect model for a “diallel” experiment as considered in quantitative genetics, where the same individual may be the “first” or the “second” member of a mating pair across different mating pairs, and more specifically assuming that in each case it would express the same effect on its offspring (implying that there is a single variance parameter to be fitted). The present random effect structure accounts for the analogous symmetry of effects on facial distance. The case where ρ=0.5 represents additive random effects from each individual  with negative binomial response family and log link to assess predictors of the frequency of spatial association among dyads of (i) infants (‘infant-infant’); (ii) infants and other mothers (“mother-infant”); and (iii) mothers having infants in the social group (“mother-mother”). The response is the number of observed spatial associations. We considered only pairs of infants (and associated mothers) born into the same cohort (conceived during the same reproductive season). Each fitted model includes an offset , six fixed effects, a random effect term representing the effect of two additive individual random effects under the constraints of symmetry, as for “general combining ability” in a “diallel” experiment, in models of symmetrical interactions (mother-mother and infant-infant), and standard individual-level random effects for mothers and for infants in the mother-infant model. The six fixed effects are whether the infants belonged to the same matriline , relatedness between mothers (continuous variable), mothers’ rank difference , infants’ age difference when behaviors (not photographs) were recorded (continuous variable in days and <365), infants’ sex , and the residuals of infant facial distance. Indeed, to assess the effect of infant facial distance unconfounded by the effect of infant age differences (at the time of pictures’ collection), we used the residuals of such distance obtained from the fit of an LMM as performed above and that included as predictor the age difference , and an autocorrelated random effect with the same “diallel” correlation structure as above .r=0.008, P=0.89; facial distance within same vs. different matriline, mean ± SD: 13.03±2.12, N=56 pairs of infants vs. 13.21±1.90, N=226 pairs of infants; and see Appendix 1).Both matrilineal origin and females’ relatedness were independent from facial distance values meaning that highly resembling infants were not more likely to be found within the same matriline than lowly resembling infants nor were they the offspring of highly related females .We standardized all continuous predictors to allow comparisons of the estimates and we preliminary checked that no significant first-order interaction occurred among all these explanatory variables (not shown) and kept full models as final models. In all these three GLMM, we further checked for possible hazardous multicollinearities between continuous predictors by calculating variances of inflation (VIF <2). For the two significant negative effects of facial-distance residuals, we excluded individuals one at a time and checked that there was no apparent error in the data for individuals most supporting the negative relationships. To validate the models, we finally verified that the magnitude of standardized residuals is independent of the fitted values. This study shows that, 60 years after the development of kin selection theory, new implications are still being uncovered. The authors report results of a long-term field study of a mandrill population in the forests of Gabon. Facial-pattern analyses and accompanying theoretical work support the hypothesis that females "socially engineer" relationships between their offspring and other offspring, based on facial resemblance. Via this mechanism, mothers appear to promote associations with individuals that are more likely to treat them as relatives, increasing the likelihood of future benefits from cooperative interactions. The authors suggest that their discovery could explain cooperative behaviours among non-kin in other social species, including humans. public reviews designed to be posted alongside the preprint for the benefit of readers; (ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.Our editorial process produces two outputs: (i) Decision letter after peer review:eLife. Your article has been reviewed by three peer reviewers, one of whom is a member of our Board of Reviewing Editors, and the evaluation has been overseen Christian Rutz as the Senior Editor. The following individual involved in the review of your submission has agreed to reveal their identity: James Higham (Reviewer #3).Thank you for submitting your article "Primate mothers promote proximity between their offspring and infants who look like them" for consideration by The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this decision letter to help you prepare a revised submission. The reviewers were unanimous in identifying the need for more data to support the conclusions as stated. The decision to invite resubmission was marginal -- please carefully revise your article, to address the points raised below. We are planning to send out the revised article for re-evaluation.Essential revisions:1) The manuscript should be revised to more accurately report the level of support for the hypothesis of phenotype-matching by mothers to promote kin-selected cooperation towards offspring. The evidence in support of the single hypothesis is currently overstated. A more balanced article would be considerably toned down, with appropriate notes of caution about the evidence for the proposed mechanism, critical tests, and alternative explanations. What is the evidence for potential benefits from sib interactions?2) A revision should help readers assess the likelihood of alternative hypotheses: paternal sibs look alike and associate more with each other than expected, but to what extent can we attribute maternal behaviour to explain this?eLife has adopted the STRANGE framework, to help improve reporting standards and reproducibility in animal behaviour research. In your revision, please consider scope for sampling biases and potential limitations to the generalisability of your findings:Note: Please note that https://reviewer.elifesciences.org/author-guide/journal-policieshttps://doi.org/10.1038/d41586-020-01751-5Reviewer #1 (Recommendations for the authors):The paper is generally clear and well-written.Lines 102-103: Please drop unnecessary acronyms: MHS and PHS. Seems designed purely to save having to type and they are confusing as 'S' of MHS refers to sisters, but 'S' of PHS refers to sibs.Line 115: Suggest a new paragraph to start from "In Charmentier…"Lines 142-143: Perhaps a little more context here would be helpful about the study population. For example, perhaps important to note that the group is semi-captive in the main text.Figure 1: My only significant difficulty is with interpreting Figure 2 -- the figure showing the main result. From the text from which it is referred to (lines 209 and 211), I'm looking for evidence of an association between females and offspring that look like their own offspring. None of the Y-axis categories seems to correspond to this group. The "variable of interest" is presented in pink as "facial distance". The legend was not helpful at all.Reviewer #2 (Recommendations for the authors):Suggestions to improve the paper.1. The spatial proximity data are interesting but suggestive and preliminary. I would therefore recommend toning down the interpretation in places. For example, I think the subheading "The driving role of mothers" is too strong. You haven't shown this. Also, the second paragraph of the Conclusion seems to be arguing strongly that spatial proximity scores, as measured here, must convert into benefits in the longer term. That might be the case but not necessarily so; for example, there might be local competition effects.2. I wasn't clear why you did not analyse any other data to try to test your hypothesis. In line 570 you state "Here, we exclusively studied association rates and no other social behavior such as grooming because scans were more numerous than any other behavioral records, and also because we reasoned that if mothers influence their offspring's social opportunities, this will mainly take the form of increased associations and not of an increased investment into potentially costly social behavior." I don't understand this reasoning. First, just because scans are more numerous is not a reason in itself to ignore other behavioural data. Were the other data too scant to be analysed? Did you analyse them, but not include them, because there was no pattern? A bit of transparency would be helpful here.Second, it doesn't seem a good reason to not analyse something because you 'think' that's not the way second-order kin selection will work. For example, mothers might bring infants into proximity with each other, and they do the rest; or mothers might be more active in the process. Presumably, you don't know until you examine the data. So neither of these reasons for excluding information are good ones.3. In the second paragraph of the conclusion you state that "a firm demonstration of the second condition [namely the occurrence of reciprocal interactions between the matched social partners] will necessitate years of detailed behavioural and demographic data and is beyond the scope of this paper"; this seems a bit odd -- like you are trying to convince the reader that you haven't done any other tests because they would all take years. Are there really no other tests that could be done in a shorter time frame, or with the data you have?4. Finally, I wondered about other factors that might contribute to the enhanced facial similarity in PHS compared to MHS. If I have understood correctly, paternal half-sibs are offspring born to different mothers fathered by the same α, whereas maternal half-sibs would be born to the same mother with different fathers. This would seem to imply that PHS is clustered temporally together (within the tenure of the same α) whereas the MHS are necessarily spread out temporally . Is that the case, and can you rule out temporal clumping as a factor influencing patterns of variation in the data?Reviewer #3 (Recommendations for the authors):I think that an appropriate reworking would present the manuscript differently, as a demonstration that paternal siblings look alike and that they associate. The authors could then go on to explore different possible explanations for this using their association data, make the case that maternal behavior is the most plausible (but not the only) explanation, and present their model of how such behavior could bring fitness benefits. In my view, such a paper would be publishable, but the current presentation is problematic. Essential revisions:1) The manuscript should be revised to more accurately report the level of support for the hypothesis of phenotype-matching by mothers to promote kin-selected cooperation towards offspring. The evidence in support of the single hypothesis is currently overstated. A more balanced article would be considerably toned down, with appropriate notes of caution about the evidence for the proposed mechanism, critical tests, and alternative explanations.We have now revised our manuscript to tone down the interpretation of our results and give more weight to alternative mechanisms involving fathers. For example, we have changed the title (now: “Mandrill mothers associate with infants who look like their own offspring using phenotype matching”) to report more accurately our main findings, and changed/added several sentences/paragraphs throughout the manuscript (see details in the responses to referees below).What is the evidence for potential benefits from sib interactions?In two previous publications , we have demonstrated, both in captivity and in the wild, that mandrills bias their social behavior preferentially towards close maternal and paternal kin compared to non-kin. In particular, male and female juvenile mandrills affiliate more with their adult paternal and maternal half-sisters than any other adult females (except their mother); adult females disproportionally groom and associate, but also fight, more with their maternal and paternal half-sisters than with non-relatives. Overall, maternal and paternal half-sisters constitute privileged social partners in their group (an information that we have now added on L90-92). Although the multiple fitness benefits of social integration are widespread throughout the animal kingdom , in our specific case these kin-driven benefits are not yet known. Estimating fitness benefits of social behavior would necessitate long-term data collection on fitness proxies such as lifetime reproductive success, that are not available yet. Our results are, however, consistent with a wealth of studies showing kin-biased social behaviors, and the predictions of kin selection theory.2) A revision should help readers assess the likelihood of alternative hypotheses: paternal sibs look alike and associate more with each other than expected, but to what extent can we attribute maternal behaviour to explain this?vice versa). We have now included this information in the revised version of our manuscript and have discussed it (L201-207).In the revised version of our manuscript, we now thoroughly discuss alternative mechanism involving fathers . In addition, to address the extent to which maternal behavior contributes to infant-infant association, we have calculated the correlation between the raw rate of spatial association observed among pairs of infants as a function of the average rate of association recorded between each of these infant and the mother of the other infant. Appendix 1 – figure 3 highlights the clear relationship between the two variables: about 71% of the variance in infant-infant association is explained by the average association between each mother and the other infant (and Reviewer #1 (Recommendations for the authors):The paper is generally clear and well-written.Thank you for this positive comment.Lines 102-103: Please drop unnecessary acronyms: MHS and PHS. Seems designed purely to save having to type and they are confusing as 'S' of MHS refers to sisters, but 'S' of PHS refers to sibs.Changed as suggested .Line 115: Suggest a new paragraph to start from "In Charmentier…"Done.Lines 142-143: Perhaps a little more context here would be helpful about the study population. For example, perhaps important to note that the group is semi-captive in the main text.This is not the case .Figure 1: My only significant difficulty is with interpreting Figure 2 -- the figure showing the main result. From the text from which it is referred to (lines 209 and 211), I'm looking for evidence of an association between females and offspring that look like their own offspring. None of the Y-axis categories seems to correspond to this group. The "variable of interest" is presented in pink as "facial distance". The legend was not helpful at all.Figure 2 represents the estimates of the models . Consequently, a negative estimate  indicates a negative correlation between spatial association and that variable : individuals associate more with low values of facial distances (high resemblances). We have added this information in an updated legend  indicates a negative correlation between spatial association and that variable : individuals associate more with low values of “facial distance” (high resemblance)”).Reviewer #2 (Recommendations for the authors):Suggestions to improve the paper.1. The spatial proximity data are interesting but suggestive and preliminary. I would therefore recommend toning down the interpretation in places. For example, I think the subheading "The driving role of mothers" is too strong. You haven't shown this. Also, the second paragraph of the Conclusion seems to be arguing strongly that spatial proximity scores, as measured here, must convert into benefits in the longer term. That might be the case but not necessarily so; for example, there might be local competition effects.We fully agree with this comment. We have therefore changed the subheading “The driving role of mothers” to “Mothers and their offspring associate more with similar-looking other infants”. We have further toned down the interpretation of our findings throughout the text. For example, on lines 61-62; 245-253; 374-376.2. I wasn't clear why you did not analyse any other data to try to test your hypothesis. In line 570 you state "Here, we exclusively studied association rates and no other social behavior such as grooming because scans were more numerous than any other behavioral records, and also because we reasoned that if mothers influence their offspring's social opportunities, this will mainly take the form of increased associations and not of an increased investment into potentially costly social behavior." I don't understand this reasoning. First, just because scans are more numerous is not a reason in itself to ignore other behavioural data. Were the other data too scant to be analysed? Did you analyse them, but not include them, because there was no pattern? A bit of transparency would be helpful here.We agree with this comment too. We have now included similar models on grooming relationships between mothers as a function of their offspring’s facial distance. We show that mothers do not groom each other more when their offspring resemble each other more . In addition, at these young ages, infants do not groom each other yet; we did not thus add a similar model for infant-infant grooming relationships. Finally, in the mother-infant model, only 6 females groomed other infants over 560 mother-infant dyads, precluding any reliable statistical analysis, and suggesting that, as we proposed, females do not invest into social relationships with other infants. As noted by this reviewer below, mothers thus seem to simply bring their offspring into proximity to resembling infants who possibly do the rest. We have now introduced and discussed this new analysis (L184-193).Second, it doesn't seem a good reason to not analyse something because you 'think' that's not the way second-order kin selection will work. For example, mothers might bring infants into proximity with each other, and they do the rest; or mothers might be more active in the process. Presumably, you don't know until you examine the data. So neither of these reasons for excluding information are good ones.We agree and have addressed this comment by including new analyses on grooming relationships and have further discussed these new results .3. In the second paragraph of the conclusion you state that "a firm demonstration of the second condition [namely the occurrence of reciprocal interactions between the matched social partners] will necessitate years of detailed behavioural and demographic data and is beyond the scope of this paper"; this seems a bit odd -- like you are trying to convince the reader that you haven't done any other tests because they would all take years. Are there really no other tests that could be done in a shorter time frame, or with the data you have?This sentence was indeed awkward . However, to address this comment, at least partially, we have looked at the association rates of the study pairs of infants, a year later (when they were aged 1-2 yrs). Compared to infants that did associate at 0-1 yr, those that did not, associate three times less on average at 1-2 yrs. Note that the sample size of juveniles aged 1-2 yrs decreased drastically because some of them died and until 2020, we were unable to physically recognize some of these former infants once weaned (sample sizes are provided within bars and represent the number of pairs of 1-2 yrs juveniles). This result, although preliminary, suggests that early association during infancy may pervade later in life. We have added this new figure in Appendix 1 – figure 4; and see also: (L389-390).4. Finally, I wondered about other factors that might contribute to the enhanced facial similarity in PHS compared to MHS. If I have understood correctly, paternal half-sibs are offspring born to different mothers fathered by the same α, whereas maternal half-sibs would be born to the same mother with different fathers. This would seem to imply that PHS is clustered temporally together (within the tenure of the same α) whereas the MHS are necessarily spread out temporally . Is that the case, and can you rule out temporal clumping as a factor influencing patterns of variation in the data?This is a very good and accurate point: MHS are always at least a year apart while some PHS are, indeed, born in the same cohort, although not all of them as some males reproduced either a few consecutive years  or a few years apart, due to various patterns of male migration found in this population. In our data set of 80 infants, for example, MHS are, on average, 3.5 yrs apart while PHS are 0.5 yrs apart. This is the reason why, in our analyses of spatial association, we only considered dyads of infants born into the same cohort, an information that we have now justified in the revised version of our manuscript (L596-597). Our main results, that mother and infants associate more with similarly looking other infants, are thus found on cohorts of individuals that are all similar in age. Temporal clumping thus cannot explain our results .Reviewer #3 (Recommendations for the authors):I think that an appropriate reworking would present the manuscript differently, as a demonstration that paternal siblings look alike and that they associate. The authors could then go on to explore different possible explanations for this using their association data, make the case that maternal behavior is the most plausible (but not the only) explanation, and present their model of how such behavior could bring fitness benefits. In my view, such a paper would be publishable, but the current presentation is problematic.For the sake of clarity, most of the alternative scenarios were initially addressed in a Supplementary Table . Perhaps that the information, somehow, got lost. We have therefore discussed more widely these alternative mechanisms in the revised version of our manuscript .


Hybrid potato breeding has become a novel alternative to conventional potato breeding allowing breeders to overcome intractable barriers  with the benefit of seed-based propagule, flexible population design, and the potential of hybrid vigor. Until now, however, no formal inquiry has adequately examined the relevant genetic components for complex traits in hybrid potato populations. In this present study, we use a 2-step multivariate modeling approach to estimate the variance components to assess the magnitude of the general and specific combining abilities in diploid hybrid potato. Specific combining ability effects were identified for all yield components studied here warranting evidence of nonadditive genetic effects in hybrid potato yield. However, the estimated general combining ability effects were on average 2 times larger than their respective specific combining ability quantile across all yield phenotypes. Tuber number general combining abilities and specific combining abilities were found to be highly correlated with total yield’s genetic components. Tuber volume was shown to have the largest proportion of additive and nonadditive genetic variation suggesting under-selection of this phenotype in this population. The prominence of additive effects found for all traits presents evidence that the mid-parent value alone is useful for hybrid potato evaluation. Heterotic vigor stands to be useful in bolstering simpler traits but this will be dependent on target phenotypes and market requirements. This study represents the first diallel analysis of its kind in diploid potato using material derived from a commercial hybrid breeding program. Hybrid vigor was detected for multiple traits in diploid hybrid potato. Additive gene action was most prominent in tuber yield and should be the primary target within hybrid breeding programs.Solanum tuberosum), a plant species once isolated to the continents of southern and central America, is now a crop that spans over 17 million hectares of crop-land worldwide (Potato (orldwide . It is torldwide . There ifixed at the creation of the F1), it is widely known for being a long process from generation of the nursery to variety release. Because the success of clonal breeding is highly dependent on the generation of enough novel genotypes in the F1, it takes as many as 9 years to sufficiently bulk tubers in conjunction with applying appropriate selection pressure  along with 2 of its simpler yield components, average tuber volume and total tuber number (TN), and partitioned their underlying genetic effects into additive and nonadditive components. This trait and genetic decomposition was done to inspect 2 broad questions: (1) Which tuber phenotypes in this population were responsible for the variation seen in TY and (2) are these yield phenotypes primarily under the control of additive or nonadditive gene action? This latter question holds particular weight as it gives insight on where the focal point of DHP breeding should lie. We put forward a 2-part modeling approach to utilize intrablock information to estimate the general and specific combining abilities of our hybrid parents and crosses, respectively. Our study presents the first diallel study in DHP using highly inbred parents derived from a commercial breeding program.tuberosum and several wild species  library available through the comprehensive R archive network  were made for each phenotype and hybrid over all trials , and the deviation from the expected mean of a cross, or the specific combining ability SCA; . These 2ijs is the SCA for parents’ i and j with fijr being their respective interaction with trial location, f. With the addition of these 2 random effects, the model will be denoted as fM going forward.This model can be expanded further to include hybrid cross-wise effects with the addition of the SCA and SCA by environment interaction. This final model then has the form:u. This includes the hybrid  to derive ratios of effects within traits and (2) to produce genetic correlations between traits and trial locations. These components were first used to derive several important genetic parameters including variation due to additive genetic effects , we perform a hypothesis testing procedure on the original M0  and Mf [M0 [Mf  models aH1, the hybrid genetic effect from our original model (3) should equal the sum of the GCA and SCA effects specified in model (5). Our modeling procedure for all multivariate models began with the estimation of variance components and production of genetic correlations on BLUPs in the univariate analogs (i.e. M0 and fM), which were used to initialize the unstructured covariance matrices for all random effects in model’s M0 and fM. All models were fitted through restricted  maximum likelihood (REML) using ASReml-R 4  suggests that TV showed the greatest stability of all 3 phenotypes.Using spatially corrected phenotypes from model (1), we extracted both marginal and conditioned hybrid BLUPs from model (3) . GeneralM0 and fM. TV not only exhibited the largest proportion of variance explained by SCA  . TN and eraction . Broad-svariance . The adderaction .fM. These were produced for GCA  with little difference between TY (0.55) and TN (0.53) . RelativTV=0.55) .When comparing the GCA and SCA quantiles for each trait and trial location, the GCA BLUPs were consistently larger than those SCA BLUPs. On average, any given GCA quantile was 2 times larger than its respective SCA quantile; this was true for all traits measured here (see Supplementary Table 4). These differences in magnitude between the estimated GCA and SCA effects could also be readily seen while examining the size of each variance component  or even fM and M0 were found to be significant yielding a probability less than 0.001 , all of which are prone to change across experiments. Even making comparisons between studies utilizing very similar genetic backgrounds can lead to divergent findings . While sgcaICC = 0.84) and SCA (scaICC = 0.75), the between-trial genetic correlations in Among our 2 yield components (i.e. TN and average TV), we found strong genetic correlations between each and TY for both the additive see  and nonaAmong certain market classes, our 2 yield components, average TV (or tuber size) and TN, often exhibit an inverted relationship due to the physiological and genetic limits of potato. For example, The large narrow-sense heritabilities and magnitude of the GCAs found here have major implications for breeders of DHP. To begin, the valid estimation of GCAs further validates the potential of potato in its conversion into an inbred–hybrid crop as purported before  shows thThe application of these results should be done with some qualification. One principal limitation of this study can be found in the number of trials conducted. All inference drawn here was based upon only 2 trials that took place over one season. This limits our findings to one particular year which narrows their interpretive weight and scope. Related to this, this study was performed on a particular composite experimental population and does not necessarily represent the heterotic potential of the entire tuber-bearing Solanum gene pool. Considering all this, this population is an appropriate candidate for the purpose of surveying the presence and potential of heterotic vigor in DHP which was the aim of this current paper.Finding evidence for heterotic effects in DHP does not yield much regarding the source of the effects identified here. The statistical models assume all underlying effects captured by the SCA term to be the product of cumulative dominance deviations across the genome, but there exist many other plausible sources of nonadditive variation. Since its conception, genetic theory has explained heterosis with a whole suite of models with many being broadly plausible  in key economic targets in hybrid potato.https://doi.org/10.25387/g3.16973293). File Phenotypes.csv contains the 3 aforementioned traits along with field trial row, column, and block indices for each observation. File Pedigrees.csv gives a hybrid identification number with each parental code.All trial and pedigree data utilized for the following analysis have been made available on GSA figshare .All authors of this publication have reviewed the topic of any potential conflicts of interest and have concluded that there are none relevant to the research discussed in this article. We feel that our original statement expressed this sentiment concisely.


Plant science has generated many discoveries and advances in genetics and genomics research. These contributions reflect the ingenuity and rigor of the plant science community as well as the rich diversity of plants and their biology. Plants are also vital in our daily lives as a source of food, fiber, and fuel and as a key component of the global carbon cycle. As such, the importance of plant research should not be underestimated, especially as society copes with changing climate and its impacts to crop productivity and sustainable energy.Arabidopsis as a model plant with the publication of the first Arabidopsis mutant screen  × A. suecica (4N) hybridization—to detect meiotic recombination, aneuploidies, and homologous exchanges between the progenitor subgenomes. Their results show that interploidy crosses can form viable and recombinant progeny and, therefore, may serve as a bridge between diploid and polyploid species. cis-regulatory variation. They use a creative p1/p2 gene enhancer system to detect structural rearrangements that were confirmed using Chromosome Conformation Capture (3C) experiments. Their results show that progressive rearrangements can form complex alleles resulting in diverged expression patterns. asay1) background of A. thaliana. They cross asay1 with a Columbia wild-type line and recover a progeny exhibiting chromoanagenesis, a phenomenon characterized by localized chromosomal shattering and reorganization. Overall, they observed hundreds of novel DNA junctions falling within the shattered region of Chromosome 1, with the pattern of junctions suggesting random reassembly of the pieces and a completely reorganized chromosomal region. Their study provides a first step in deciphering the molecular mechanisms leading to chromoanagenesis and its potential role in plant genome evolution. Together, these papers provide new insights into the mechanisms and role of ploidy and chromosome-scale variation in plants and give a glimpse of a rapidly expanding field.Four manuscripts from the Series focus on the mechanisms, evolution, and impacts of polyploidy and chromosomal-scale variation in plants. Avena sativa) using an impressive array of 9 multiparent families and a TILLING population. They identify a number of marker-trait association hotspots, likely the result of pleiotropic gene action, and discover a notable link between panicle architecture and disease susceptibility. Striga hermonthica, an endemic parasitic weed of maize in West Central Africa. Here, the researchers expose maize lines to standardized Striga exposure in the field and evaluate the number of emerged Striga and host plant damage data at 2 sites over multiple years. The authors find a relationship between the number of emerged Striga, host plant damage, and grain yield, and genome-wide association scans identify a number of SNPs underlying Striga resistance traits. These findings may afford opportunities for molecular breeding of Striga resistance in maize. Pistacia atlantica  and Pistacia integerrima  parental trees of an F1 mapping population. Genetic mapping analyses identify loci associated with tree size and shape, sex, and precocity as well as implicate a strong candidate for the ZZ/ZW sex chromosomes. Cannabis sativa, with a focus on disentangling evolutionary forces that have influenced the divergence of domesticated and feral (escaped domesticated lineages) populations. Population structure and evolutionary analyses support an Asian origin of C. sativa and provide some insight into the origin of marijuana- and hemp-type accessions. Selection analyses based on polymorphism data implicate the action of positive and balancing selection on a number of genomic regions. These regions may have been targets of domestication selection and signals of local adaptation in the US feral populations. Rubus argutus). The utility of the resource is demonstrated by its support of the first genotyping-by-sequencing-based linkage map of tetraploid blackberry and the identification of possible candidate genes for annual fruiting. This set of papers provides a clear picture of the importance and rapid adoption of marker-based methods and the importance of these tools in applied plant science.A second theme of the series arises from studies of the genetic basis of quantitative traits. Here, 7 manuscripts use genome-wide molecular markers to describe diversity and map genomic regions contributing to trait variation. Triticum aestivum). They conduct a time-series expression analysis of nitrogen remobilization in RNAi lines that have reduced NAM gene expression. They discover that nitrogen metabolism genes exhibit earlier and stronger expression in wild-type compared with mutant lines, providing insight into NAM-regulated and NAM-independent candidate genes and pathways involved in senescence and remobilization. A. thaliana. Their project centers on the potential role of Argonaute 1 (AGO1) in phenotypic buffering through its role in microRNA-mediated pathways controlling gene expression. They show that AGO1 buffers environmental and genetic variation for the relationship between rosette leaves and flowering time and use a creative bulk segregant analysis to identify HUA2 as an AGO-1-dependent buffering locus. Finally, ETHYLENE INSENSITIVE (EIN2) in seed development and size in Arabidopsis. They find that EIN2 is maternally expressed in endosperm through seed development, that methylation plays a role in the pattern of maternal-specific expression, and that EIN2 affects the temporal regulation of endosperm cellularization with consequences for seed size. This set of papers highlights functional genetic work in model and crop species, which helps to elucidate molecular mechanisms linking genotype and phenotype—a research program that we anticipate will continue to increase with improved transformation methods and genome assemblies.Three papers use molecular manipulations to evaluate the effect of candidate genes on plant phenotypes. We are pleased with the community response to our call for papers and hope that this outstanding set of publications showcases progress as well as motivates additional submissions. This is an open call for papers, and we encourage authors to continue to submit their work to GENETICS and G3. We would especially welcome commentaries and perspectives about rapidly advancing fields in Plant Science. New submissions can be directly submitted to either of the two journals; manuscripts will be subject to standard peer review and editing, and accepted articles will be added to the online collection.


The Mediterranean basin countries are considered secondary centres of tomato diversification. However, information on phenotypic and allelic variation of local tomato materials is still limited. Here we report on the evaluation of the largest traditional tomato collection, which includes 1499 accessions from Southern Europe. Analyses of 70 traits revealed a broad range of phenotypic variability with different distributions among countries, with the culinary end use within each country being the main driver of tomato diversification. Furthermore, eight main tomato types (phenoclusters) were defined by integrating phenotypic data, country of origin, and end use. Genome-wide association study (GWAS) meta-analyses identified associations in 211 loci, 159 of which were novel. The multidimensional integration of phenoclusters and the GWAS meta-analysis identified the molecular signatures for each traditional tomato type and indicated that signatures originated from differential combinations of loci, which in some cases converged in the same tomato phenotype. Our results provide a roadmap for studying and exploiting this untapped tomato diversity. Solanum sect. Lycopersicon species, through the anthropogenic selection pressure exercised progressively on the genetic variability present in the closest wild relatives of the crop during domestication, the diversification during tomato cultivation history, and more recently, introgressions from wild relatives by modern plant breeding activities. Since its arrival to Europe (beginning of the 16th century), the tomato was rapidly adopted into Spanish and Italian diets  was calculated as E (X) = μx = ∑ [xi·p (xi)], and the variance as [inline graphics]x2 = ∑ [xi·- μx]2·p (xi)]; where xi is the scoring number and p (xi) the proportion of accessions scored as xi. In the case of binary variables (recorded as 0 or 1), the mean (μx) or expectancy [E (X)] was calculated as E (X) = μx = p (x1), and the variance as [inline graphics]x2 = p (x1) [1- p (x1)], where p (x1) is the proportion of accessions scored as 1. Quantitative trait variability was evaluated with the coefficient of variation (CV), and qualitative variation index (IQV) as the ratio of the total number of differences in the distribution to the maximum number of possible differences with the same distribution [Mean, standard deviation, variance, and maximum and minimum values were calculated for each quantitative trait using summarytools 0.9.4 . In the ribution . A heatmribution  with cen2) test with Bonferroni false discovery rate (FDR) for pairwise nominal and ordinal comparisons of the proportions using Rstudio [ijd) were calculated to analyse the departure of each category from the expected values. Residuals with |ijd| > 4 have an approximate P-value < 0.001, and |ijd| > 2 have an approximate P-value < 0.05 [Trait variation between countries for normal quantitative traits was assessed with an one-way ANOVA, and pairwise mean comparisons between countries were performed with Tukey’s honestly significant difference (HSD) test (p < 0.05) using the Rstudio package . Non-nor Rstudio . Standare < 0.05 . The rese < 0.05 . For tese < 0.05 .The overall correlation between all traits was calculated using the corrplot package  with SpeFruit external colour, Distal fruit end shape, Fruit internal structure, Fruit shape, Fruit shape homogeneity, Proximal fruit end shape, Plant & inflorescence architecture, Use and country of origin) and twelve groups of continuous quantitative variables . Traits in each group are indicated in n-dimensions  [j,n= j,i is the contribution of the variable j to the dimension i, and λi is the eigenvalue of the dimension i. The contribution cut-off for a variable j was calculated assuming a uniform variable contribution, so that the value of expected contribution for the variable j to the dimension i is 1/Y, where Y is the total number of variables.A multifactorial analysis (MFA) was performed to assess common factors that explained European tomato variability with all quantitative and qualitative groups of traits with less than 10% missing data together with passport data , in regas  . The conThe hierarchal clustering on principal components (HCPC) was performed using the Factominer R package  on the sBalloon plots comparing phenotypic and genetic clusters described in ref.  were conhttps://www.ncbi.nlm.nih.gov/sra) under accession numbers PRJNA722111 and PRJNA774172. Fastq files were evaluated for sequencing quality. Sequences were mapped to the tomato reference genome version 2.50 together with the same subset of SNPs selected from the European accessions genotyped by ref. [l) of 30 and Number of nearest neighbours (k) of 20  data used in this analysis is based on 1118 European accessions which were already genotyped , plus da by ref. , re-sequ by ref. , 8 paren by ref. , and 350 by ref.  as descr by ref. . The SNP by ref.  algorith by ref.  with defk) of 20 . Optimal<fig id="f11">Dataset S7<graphic mimetype="image" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="uhac112f11"/></fig><fig id="f12">Dataset S8<graphic mimetype="image" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="uhac112f12"/></fig><fig id=<xref ref-type="supplementary-material" rid="sup1">>Dataset S9<graphic mimetype="image" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="uhac112f13"/></fig><fig id="f14">Dataset S10<graphic mimetype="image" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="uhac112f14"/></fig><fig id="f15">Dataset S11<graphic mimetype="image" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="uhac112f15"/></fig><fig id="f16">Dataset S12<graphic mimetype="image" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="uhac112f16"/></fig><fig id="f17">Dataset S13<graphic mimetype="image" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="uhac112f17"/></fig>r2 values for intrachromosomal pairwise marker comparisons using a sliding window size of 50 markers. The baseline r2 value, as evidence of linkage, was calculated at the 95th percentile of the null distribution of inter-chromosomal root transformed r2, using it only on markers showing a MAF >0.01 according to [r2 value using ggplot2 [2 value.The genetic diversity analysis was performed with Tassel 5.0 . Nucleotrding to . Intrach ggplot2 . Block lLeS, FPS, EFC and SPS) was transformed to a binary variable. Ordinal variables were treated as quantitative variables. Only SNPs with MAF > 5% were used in the GWAS, PCA, and kinship calculations. The optimal PC needed to control for population structure of each trait in each trial was determined using a Bayesian information criterion (BIC) included in GAPIT. The kinship matrix was calculated using all SNPs in the panel by an additive efficient mixed model (EMMA) algorithm implemented in GAPIT. The direction of the effect was calculated with respect to the minor allele.GWAS was performed using the R package Genomic association and prediction integrated tool (GAPIT) version 3.0  using a 2 test statistics divided by the expected median of the corresponding χ2 distribution at individual loci for each trait [P < 10−5 were considered to have significant evidence of association.The meta-analysis was performed using a sample-size based approach, using corrected p-values and a fixed effect model in METAL . p-valuech trait  in each A candidate region carrying lead SNP  was defined based on a window size length of intra-chromosomal LD decay  and centr2 higher than the baseline r2 value 0.1349. Overlapping LD blocks or close (within 100Kb) were combined to conform an associated loci using beddtools [Loci were then defined by merging lead SNP candidate regions that physically overlapped and found in an associated LD block as follows: for each lead SNP, regional LD was calculated within the candidate gene region using the imputed SNP matrix. LD blocks were defined for SNPs with e within Kb were cGenomic regions including each lead SNP ± 1 Mb were compared against positions of previously published QTLs, GWAS analyses, and genes cloned with known effects on natural variability . Those tPhenotypic and genotypic data integration was performed using MFA-HCPC analysis as described above but including the genotypic matrix of associated SNPs as tenth class of qualitative variables. The number of clusters was selected to maximize the number of accessions per cluster classified previously in each phenocluster. Those allele/SNP combinations with a frequency higher than 60% and with an enrichment p-value<0.0001 were considered representative of a cluster.GWAS Meta-analyses, linkage block data, candidate genes, and the most prevalent genotype in each phenocluster, were summarized using the Fuji-plot script developed by  but withWe thank Universitat Illes Balears, the Greek Gene Bank (GGB-NAGREF), Università degli Studi Mediterranea Reggio Calabria, the CRB-Leg (INRA-GAFL)”, the Genebank of CNR-IBBR  and ARCA 2010 for seed sharing. CNR-IBBR also acknowledges the seed donors, the Leibniz Institute of Plant Genetics and Crop Plant Research, Maria Cristina Patané  and La Semiorto Sementi SRL, as well as Mrs. Roberta Nurcato for technical assistance. IBMCP-UPV acknowledges Maurizio Calduch (ALCALAX) for technical assistance and Mario Fon for English grammar editing. This work was supported by European Commission H2020 research and innovation program through TRADITOM grant agreement No.634561, G2P-SOL, grant agreement No. 677379, and HARNESSTOM grant agreement No. 101000716. Clara Pons and Mariola Plazas are grateful to Spanish Ministerio de Ciencia e Innovación for postdoctoral grants FJCI-2016-29118 and IJC2019-039091-I/AEI/10.13039/501100011033; Joan Casals to a Serra Húnter Fellow at Universistat Politècnica de Catalunya.A.G and A. J. M conceived and coordinated the study. M.J.D, S.S, J.C and J.P designed the Phenotypic characterization kit. M.C, A.M, A.K, M.J.D, S.S, J.C, S.G and St.G selected and provided seeds from genebanks and seed collections. C.P, J.C, S.P, L.F, A.R, J.L.R, A.R, M.R.F, M.P, At.K, K.N, M.E.P, M.S, M.C, J.F, S.S, M.J.D, S.G, A.M, G.G, St.G and D.Z phenotyped the collection. C.P, J.C, S.G and A.M curated phenotypic data. C.P sampled plant material for DNA, performed DNA isolation for GBS assay. P.Z, J.B, J.Cñ, M.B, R.F processed GBS data and developed the GBS marker dataset. CP performed all analyses, prepared all figures and drafted the manuscript with A.G and A. J. M. All authors participated in the final manuscript revising and approved the version to be published.https://www.ncbi.nlm.nih.gov/sra)under accession numbers PRJNA722111 and PRJNA774172. Datasets 6 and 7 are available at Zenodo (https://zenodo.org/) under DOI: https://doi.org/10.5281/zenodo.5720772GBS data raw are available in SRA SRA (The authors declare that they have no conflicts of interest.Horticulture Research online.Web_Material_uhac112Click here for additional data file.


N = 639,063  across ancestries. OUD cases were defined as having a lifetime OUD diagnosis, and controls as anyone not known to meet OUD criteria. We estimated SNP-heritability (h2SNP) and genetic correlations (rg). Based on genetic correlation, we performed MTAG on OUD, alcohol use disorder (AUD), and cannabis use disorder (CanUD). A leave-one-out polygenic risk score (PRS) analysis was performed to compare OUD and OUD-MTAG PRS as predictors of OUD case status in Yale-Penn 3. The EUR meta-analysis identified three genome-wide significant  lead SNPs—one at FURIN  and two OPRM1 variants . Rs1799971 (p = 4.91 × 10−08) and another OPRM1 variant  were identified in the cross-ancestry meta-analysis. Estimated h2SNP was 12.75%, with strong rg with CanUD  and AUD . The OUD-MTAG resulted in a GWAS Nequivalent = 128,748 and 18 independent GWS loci, some mapping to genes or gene regions that have previously been associated with psychiatric or addiction phenotypes. The OUD-MTAG PRS accounted for 3.81% of OUD variance  compared to 2.41%  explained by the OUD PRS. The current study identified OUD variant associations at OPRM1, single variant associations with FURIN, and 18 GWS associations in the OUD-MTAG. The genetic architecture of OUD is likely influenced by both OUD-specific loci and loci shared across SUDs.Despite the large toll of opioid use disorder (OUD), genome-wide association studies (GWAS) of OUD to date have yielded few susceptibility loci. We performed a large-scale GWAS of OUD in individuals of European (EUR) and African (AFR) ancestry, optimizing genetic informativeness by performing MTAG  with genetically correlated substance use disorders (SUDs). Meta-analysis included seven cohorts: the Million Veteran Program, Psychiatric Genomics Consortium, iPSYCH, FinnGen, Partners Biobank, BioVU, and Yale-Penn 3, resulting in a total  Opioid use disorder (OUD) has a serious negative impact on public health and is a leading cause of preventable death . AlthougOPRM1 [KCNG2, KCNC1, APBB2, CNIH3, RGMA, and OPRM1 [g) with other substance use disorders (SUDs)  and psychiatric disorders  [Genome-wide association studies (GWAS) examining single nucleotide polymorphism (SNP) effects on OUD have been underpowered , 9. NeveOPRM1 . Earliernd OPRM1 , but theLarge-scale GWAS meta-analyses have advanced discovery of novel loci for SUDs , cannabis use disorder (CanUD) –12. Thisg between genetically-related traits  to increase the equivalent sample size. MTAG is an attractive option for boosting power for sets of similar traits like SUDs [Multi-trait methods   have theike SUDs , 14, andike SUDs . MTAG caWe conducted a large-scale GWAS meta-analysis of OUD in samples of African (AFR) and European (EUR) ancestry individuals. We maximized the informativeness of the available samples by performing a multi-trait analysis that incorporates SUDs that are highly genetically correlated with OUD.https://r5.finngen.fi/). GWAS of OUD from iPSYCH [N = 84,877 , a total EUR N = 554,186 , and an overall N = 639,063 . Other than Yale-Penn, this study involved de-identified data. The work was approved as appropriate by the Central Veterans Affairs (VA) institutional review board (IRB) and site-specific IRBs, including Yale University School of Medicine and VA Connecticut, and was conducted in accordance with all relevant ethical regulations. Cohort-specific summaries of AFR and EUR OUD subjects are presented in Table The meta-analysis includes summary statistics across seven cohorts examining OUD case vs. OUD control status in AFR and EUR ancestry individuals. We included both published and unpublished OUD GWAS. Previously published GWAS include data from Yale-Penn , 6, 7, Pm iPSYCH , BioVU [m iPSYCH , and new2) and genomic inflation (λGC) were used to evaluate potential bias influenced by heterogeneity between cohorts or by population stratification. Included GWAS summary statistics were limited to variants present in at least 80% of the analysis-specific effective sample size . The 80% effective sample size inclusion threshold ensured that variant effects present only in smaller cohorts did not disproportionately influence the overall results. This effectively meant that a variant needed to be present in MVP, PGC-SUD, and at least one additional cohort for it to be included  OPRM1 variants in low LD (r2 < 0.1).Data from the 1000 Genome Project (1000 G) phase 3  was used2SNP), and to characterize OUD genetic correlations (rg) using LD score regression (LDSC) [p = 9.26 × 10−04 (0.05/54). LDSC analyses were not performed in AFR and cross-ancestry meta-analyses because of the inability to use an LD reference panel for recently-admixed populations such as African-Americans or for analyses integrating datasets from diverse ancestry groups [EUR OUD GWAS summary statistics were used to estimate SNP-heritability (hn (LDSC) . LDSC ann (LDSC) . Effectiy groups .Based on LDSC estimates of genetic correlations with OUD, a joint-analysis that included the EUR OUD GWAS and GWAS summary statistics for AUD  and CanUp = 3.87 × 10−05 (0.05/1291).To examine phenome-wide relationships for OUD and the OUD-MTAG analysis, and to compare their relationships with other clinically-relevant outcomes, we performed phenome-wide association studies (PheWAS) in BioVU , a cohorPRS are described in Supplemental Materials. Briefly, a leave-one-out PRS analysis was performed by excluding the EUR and AFR Yale-Penn 3 (YP3) cohorts from the respective OUD GWAS and OUD-MTAG analyses allowing for YP3 OUD cases and controls to be used as ancestry-specific PRS target samples.p = 9.54 × 10−10) mapped to FURIN on chromosome 15, one of two GWS SNPs in the FURIN gene . The second strongest association was with the OPRM1 functional variant . An additional OPRM1 variant was also identified . In these analyses, each variant fell below GWS when conditioning on the effect of the other ; although, there were no statistically significant differences in effect estimates for the respective OPRM1 variants conditioned vs. unconditioned effects. No GWS variants were identified in the AFR GWAS , an intronic variant. Rs1799971 was also GWS in the cross-ancestry meta-analysis (p = 4.91 × 10−08), and is not in strong LD with rs9478500 . The top FURIN association in EUR (rs11372849) was uninformative in three of four AFR ancestry cohorts and did not meet the threshold (80% of Neffective) we set to be included in the analysis. The second top FURIN association (rs17514846) fell below GWS in the cross-ancestry GWAS (p = 6.00 × 10−08).The cross-ancestry OUD GWAS identified two GWS risk variants mapping to FURIN (p = 3.09 × 10−07) and OPRM1 (p = 3.59 × 10−07) were significant in EUR gene-based analysis  estimate was 12.75% (s.e. = 1.1%) in EUR using effective sample-size adjusted prevalence rates and a population prevalence of 0.021 [GC = 1.18), intercept = 1.01 (s.e. = 0.011), and attenuation ratio = 0.05 (s.e. = 0.049).The liability scale SNP-heritability (hof 0.021 . Genome-p ≤ 9.26 x 10−04) genetic correlations with 40 traits including substance use, SUDs, psychiatric traits, pain outcomes, physical health, and sociodemographic characteristics  [g = 0.82; p = 1.14 x 10−47) [g = 0.77; p = 6.36 x 10−78) [g = 0.14; p = 8.15 x 10−03) [OUD showed statistically significant p ≤ 9.2 x 10−04  10−214) , suggestx 10−47)  and AUD x 10−78) . Modest g = 0.52; p = 2.89 x 10−18) and PTSD , lower back pain , inability to work due to being sick or disabled , and scores on the Townsend Deprivation Index . OUD was negatively genetically correlated with measures of sexual activity , indices of educational attainment  and cognitive performance , and levels of past month “Heavy Do It Yourself” physical activity , among others  and AUD  in EUR. The OUD-MTAG analysis resulted in an increase in effective sample size from the original EUR OUD GWAS Neffective = 56,994 (GWAS mean x2 = 1.18) to an equivalent sample size of N = 128,748 (GWAS mean x2 = 1.40). The increase resulted in the identification of 18 independent GWS OUD-MTAG risk loci  on chromosome 11 mapping to both TMX2 and CTNND1. The second strongest was with NICN1  on chromosome 3. Additional GWS associations included FOXP2 , PDE4B , SLC39A8 , NCAM1 , RABEPK , PLCL2 , and FTO . One of the FURIN variants identified in the EUR OUD GWAS was also GWS in the OUD-MTAG . The top OPRM1 association was with rs1799971 (p = 1.39 x 10−06). Of the 18 GWS loci, three were GWS in the AUD GWAS and one was GWS in the CanUD GWAS used for MTAG (Table ).The top OUD-MTAG association was with rs11229119 p = 7.0 x 10−11 p ≤ 0.05/15,927 = 3.14 x 10−06) genes  with 46 traits including the largest previously published GWAS of OUD to date at rg = 0.98 (p = 1.22 x 10−77) [The OUD-MTAG GWAS was significantly genetically correlated (Bonferroni p = 2.12 x 10−69). Additional top OUD associations included tobacco use disorder , chronic pain , alcohol-related disorders , mood  and anxiety  disorders, viral hepatitis C , and suicidal ideation or attempt , amongst others , substance addiction and disorders , and alcohol-related disorders . OUD-MTAG also demonstrated significant associations with mood  and anxiety  disorders, chronic pain , viral hepatitis C , and suicidal ideation or attempt  , the EUR OUD PRS was a significant predictor of OUD  with the PRS specifically accounting for 2.41% of OUD variance (Supplementary Table p = 2.0 x 10−16) explaining 3.81% of OUD variance.In Yale-Penn 3 (YP3) EUR , both AFR-derived and EUR-derived OUD PRS were generated. The EUR-derived PRS  explained a small proportion of OUD variance in YP3 AFR but the AFR-derived PRS  was not a significant predictor.In YP3 AFR . This study is the first to provide evidence of a GWS single-variant GWAS association between FURIN and OUD. We support findings from previous OUD GWAS implicating OPRM1 as a risk locus for OUD [OPRM1 associations that remained statistically significant in a cross-ancestry analysis of AFR and EUR populations. We add evidence of gene-based associations with OUD and provide estimates of OUD SNP-heritability and genetic correlations with numerous traits. Further, we apply a multi-trait approach for OUD genetic discovery utilizing the high degree of genetic correlation across SUDs  to increase power, yielding an equivalent sample size of 128,748 and 18 GWS OUD-MTAG risk loci. PheWAS of OUD and OUD-MTAG demonstrated similar patterns of associations across the phenome, and the OUD-MTAG PRS explained a larger amount of variance in OUD case status (3.81%) compared to the OUD PRS (2.41%), suggesting that the OUD-MTAG and OUD analyses are capturing similar phenomenon.We present a large genetic study of OUD, with an overall sample size of 639,063  individuals (OPRM1 and FURIN), and 18 loci identified in the OUD-MTAG analysis . The OUD-MTAG loci did not include OPRM1. The absence from the MTAG analysis of any statistically significant association mapped to OPRM1, a locus that should be highly-specific to OUD, was unexpected.Compared to other complex psychiatric traits, there are comparatively small samples available for genetic analyses of SUDs, particularly those involving illegal substances  , 9. A stFURIN was associated with OUD risk in both SNP-based and gene-based analyses. FURIN  encodes the endoprotease furin enzyme that serves a primary role in regulating synaptic neuronal activity, including the synthesis of brain-derived neurotropic factor and regulation of neurotrophin levels in the brain [FURIN has been associated with multiple psychiatric outcomes including schizophrenia [FURIN SNPs associated with OUD are in strong LD (r2 = 0.91); the second strongest FURIN association, rs17514846, has been significantly associated with multiple cardiovascular and hypertension outcomes [FURIN gene-level association being driven by rs17514846 was reported between FURIN and opioid addiction [FURIN gene region, there was significant association between rs11372849  and opioid addiction. Accumulating evidence linking FURIN and opioid outcomes, including the FURIN GWAS associations reported herein along with evidence of gene-based associations with opioid addiction [he brain . Geneticophrenia , 28 and ophrenia , 30. Theoutcomes , 32, andoutcomes . A statiddiction . In a taddiction , reflectOPRM1 genetic variation in opioid addiction and OUD [OPRM1 as a risk factor in a cross-ancestry analysis. The top association in the EUR OUD GWAS was with the OPRM1 coding variant (rs1799971) identified in an earlier OUD GWAS, all cases of which are also included in the current study, plus an additional OPRM1 variant (rs79704991) in low-LD with rs1799971 (r2 = 0.02). Two OPRM1 variants were also GWS in the cross-ancestry OUD GWAS . Rs9478500 was previously GWS for opioid addiction in EUR [OPRM1’s complex haplotype structure and the potential for multiple independent OPRM1 risk loci influencing risk for OUD and SUDs dating to 2006 [OPRM1 variants  demonstrated that these variants are not independent as indicated by each variant falling below GWS when conditioned on the other; however, the variant effects remained nominally significant and there were no significant differences in the conditioned vs. unconditioned effect sizes. Future studies of larger cohorts with diverse ancestral backgrounds will be needed to distinguish the effects of OUD risk alleles across the OPRM1 region, including the known-functional rs1799971 variant which may or may not be the variant motivating previous findings.Our findings support previous OUD GWAS implicating  and OUD , 34 and  to 2006 . Conditi2SNP) of 12.75% (Z = 11.28) compared to the previous largest OUD GWAS  [We found an estimated SNP-heritability  and psychiatric conditions , with lower correlations for measures of substance use , suggesting that OUD is more akin to measures of substance dependence than use per se. OUD was genetically correlated with multiple forms of chronic pain  and indicators of impairment  and significantly genetically correlated with socioeconomic hardship (Townsend Deprivation Index) and lower levels of educational attainment. These patterns of genetic correlation are consistent with and may reflect high rates of co-occurrence of OUD with SUDs and psychiatric disorders in epidemiologic studies , 36. Beyg = 0.82) and AUD (rg = 0.77) and increased by an order-of-magnitude the number of GWS risk loci detected. While this provides proof-of-concept for this approach given that many of the loci identified via OUD-MTAG have previously been implicated with psychiatric and substance use outcomes, OPRM1 was not GWS in the OUD-MTAG analysis, so increased detection may have come at the cost of specificity for OUD. However, only 4 of the 18 OUD-MTAG GWS associations were GWS in the respective AUD and CanUD GWAS used as MTAG instruments, so the MTAG results did not simply reflect the findings from AUD and CanUD GWAS. The OUD-MTAG PRS also outperformed the OUD PRS in predicting OUD case status in the Yale-Penn holdout analysis  indicating that the OUD-MTAG is capturing OUD risk, and that the OUD-MTAG PRS is more powerful than the OUD PRS alone as also reflected by the comparative number of GWS loci identified in the respective GWAS.We examined the utility of using MTAG to increase the information available from the limited number of genotyped OUD subjects. The OUD multi-trait analysis was feasible given the high genetic correlations with CanUD (rg = 0.98) with OUD [OPRM1) that were found in the OUD single-trait analysis that are also influencing risk.A PheWAS across 1291 clinical outcomes also demonstrated convergent patterns of association between OUD and OUD-MTAG with common comorbidities , supporting that these two analyses capture genetic factors that underlie similar clinical presentations and related impairment. Additionally, summary data from the OUD MTAG analysis including multiple SUDs was highly genetically correlated  .Supplemental MaterialsSupplemental FiguresSupplemental Tables


The root system is a key component for plant survival and productivity. In particular, under stress conditions, developing plants with a better root architecture can ensure productivity. The objectives of this study were to investigate the phenotypic variation of selected root- and yield-related traits in a diverse panel of spring barley genotypes. By performing a genome-wide association study (GWAS), we identified several associations underlying the variations occurring in root- and yield-related traits in response to natural variations in soil moisture. Here, we report the results of the GWAS based on both individual single-nucleotide polymorphism markers and linkage disequilibrium (LD) blocks of markers for 11 phenotypic traits related to plant morphology, grain quality, and root system in a group of spring barley accessions grown under field conditions. We also evaluated the root structure of these accessions by using a nondestructive method based on electrical capacitance. The results showed the importance of two LD-based blocks on chromosomes 2H and 7H in the expression of root architecture and yield-related traits. Our results revealed the importance of the region on the short arm of chromosome 2H in the expression of root- and yield-related traits. This study emphasized the pleiotropic effect of this region with respect to heading time and other important agronomic traits, including root architecture. Furthermore, this investigation provides new insights into the roles played by root traits in the yield performance of barley plants grown under natural conditions with daily variations in soil moisture content. Hordeum vulgare L.) is the fourth most important crop in terms of grain production  and have been the focus of interest of plant breeders and researchers for decades. As these traits have been explored only to a limited extent because of their low heritability and environmental sensitivity, they have eventually become the target of traditional breeding approaches; however, expensive and labor-intensive phenotyping procedures are required to successfully explore these quantitative traits . GenomicRecently, genome-wide association study (GWAS) has been recognized as a powerful approach to identify genes that regulate complex traits in crops . GWAS alThe objectives of the present study were as follows: (i) to investigate the phenotypic variations of selected root- and yield-related traits in a diverse panel of spring barley genotypes, (ii) to reveal their genetic architecture by using LD-based GWAS, and (iii) to analyze the potential association between traits linked to root- and yield-related properties of the studied accessions. To assess root architecture in the field, the electrical capacitance characteristics of barley roots and manual root traits were evaluated.A total of 149 spring barley genotypes (accession panel) were used in phenotypic and genotypic analyses. The studied accessions were obtained (with few exceptions) from the Polish Breeding Companies. A majority of the barley accessions (85) were registered as cultivars, and 54 accessions were classified as breeding lines. To increase the diversity of the panel, four RILs (seed collection of Institute of Plant Genetics Polish Academy of Sciences – IPG PAS) and eight BC lines (six from IPG PAS collection and two from NordGen collection) were also included. We focused mainly on two-row spring barley accessions from breeding companies’ collections to reduce the confounding effects of population structure in terms of origin, growth, and row types . The comThe study was conducted over three growing seasons  on the experimental fields located at IPG PAS in Poznań, Poland (52°24′30″N 16°56′03″E). All trials were performed in three replications, in randomized blocks, and under standard fertilization and cultivation conditions. The experimentalplots consisted of five rows of 150-cm length that were spaced 20 cm apart. Seeds were planted within each row at 15 cm from each other. In each of the replications, there were 10 individual plants. Before sowing, the seeds were treated with the systemic fungicide Funaben. During the experiment, spraying was carried out as needed to protect the plants against diseases or pests required. Standard agricultural practices of barley production were followed for field management, including irrigation, fertilization, weed control, and pestmanagement. More information in terms of soil fertilization can be found in As shownin Once plants reached full maturity, they were harvested, preserved, and then subjected to biometric analysis in which the following agronomic traits were evaluated: total biomass (g), root biomass (g), root length (cm), number of productive tillers, total number of tillers, plant height (cm), average length of spike (cm), average number of grains per spike (g), average weight of grains per spike (g), weight of grains per plant (g), and weight of a thousand grains (g). A complete list of the analyzed agronomic traits with their abbreviations as well as the methods used for their evaluation is provided in At harvest, plants were cut at the soil level and dissected into two main components: roots and shoots. A hose fitted with a fine spray head was used to carefully remove the soil substrate, and the roots (from a 1 m deep piece of soil 1 m deep)were gently teased apart. Because the soil used in this experiment was highly dispersible, excessive manipulation was not required to remove soil particles which can often result in accidental loss of roots.Electrical capacitance (picofarad—pF) was measured three times at each of the following stages: tillering stage (T1), stem elongation stage (T2), and heading stage (T3). As described by http://www.lgcgroup.com).The GWAS panel was genotyped using the GBS method as described by https://github.com/ekg/freebayes#readme). The obtained variants were further filtered based on the following criteria: (a) the read count for the locus must exceed eight reads, (b) genotypes must have been observed in at least 66% of samples, and (c) the minimum allele frequency across all samples must exceed 5%. No missing SNP data imputation was performed. The IBSC_v2 (https://plants.ensembl.org) genome assembly was used as the reference (Ensembl Plants rel. 49). The sequencing, variant calling as well as SNP filtering were provided by LGC Genomics.For the GBS analysis, a pilot project was performed in order to assess the optimal enzyme combination, type of library, and number of reads per sample. PstI/MspI were selected as restriction enzymes (ddRAD approach). Briefly, 200 ng of each DNA sample (10 µl volume) was digested with the restriction enzymes and ligated to unique barcode adapters in the main project. Sequencing was carried out on an Illumina NextSeq 500/550 platform , and several runs were performed to obtain ~3 M of 75-bp single-ended reads per sample. Data were quality-trimmed, and variant discovery was done with Freebayes v1.0.2-16 software  and used for the hierarchical clustering of accessions to visualize the population structure. LD was estimated for each pair of markers as r2 value in linear regression, with one marker used as the response and another one used as the regressor. Based on the LD matrix, the hierarchical clustering of markers was performed in R software using the group average (UPGMA) agglomerative method.The kinship coefficient matrix (based on Dice similarity coefficients) was processed P value was lower than 0.05.GWAS was carried out by combining the phenotypic data for the 149 barley accessions  and the genotypic data generated using the GBS approach. It was carried out using the method developed by P values corrected as in GWAS. Loci for each chromosome were named following the pattern described by N, N being the total number of LD blocks).Based on the significant markers from GWAS, clusters of markers, called LD blocks, were constructed as groups characterized by LD exceeding the mean LD within the chromosome. The second GWAS based on these LD blocks was performed using mean values over all experiments. The analysis of the effects of the LD blocks was carried out using the analysis of covariance in Genstat 21, with the variants within an LD block as the classifying factor and eigen scores of accessions as covariates, with Positional and functional interpretation of GBS and GWAS results was carried out in relation to two annotation sources (Gene Ontology and InterPro database) provided in Ensembl Plants and the protein descriptions in the nr protein database in OmicsBox 2.1.2 . Genes cMonthly precipitation (March–August) ranged from 9.8 to 127.1 mm Table 1.A total of 149 accessions were evaluated in triplicate under field conditions in 2017, 2018 and 2020 at IPG PAS. Eleven agronomic traits were analyzed by GWAS to identify new candidate loci associated with root traits.Electrical capacitance measurements on rootsThe distribution of the analyzed traits and the results of the comparison of annual means are shown in The results of electrical capacitance measurements that were carried out in all years of the experiments are shown in P value < 0.05) were observed between traits associated with tillering (NPT and TNT) and TB   Table 4.Genotyping of the GWAS panel using the GBS method revealed a total of 5739 SNPs Figure 3P value) of 0.05  were detected for 8 of 14 analyzed traits at a significance threshold  resulted in the largest increase in PH (3.09 cm) among three associated marker combinations. This combination was observed in the smallest number of accessions – 9 out of 135. The largest decrease in PH (4.01 cm) was identified for the combination G/G G/G C/C C/C A/A G/G C/C G/G C/C A/A of LD-b_4H_31 block.Seven LD blocks were associated with GNSp, three of which were located on chromosome 2H, three on chromosome 4H, and one on chromosome 7H (LD-b_7H_58). Two LD-b_7H_58 SNP combinations resulted in the largest decrease in GNSp (1.29–1.31 g). LD-b_4H_31 included six SNP combinations, of which the combination C/C G/G C/C C/C A/A G/G C/C G/G C/C A/A resulted in a GNSp increase of 0.70 g.Six associations (except one on Un) were identified for GWSp on chromosomes 2H, 3H, and 7H. The SNP combination of the block LD-b_7H_58, which included nine accessions, showed the largest negative allelic effect (−0.11 g). The marker combination (T/T G/G) linked to the GWSp block (LD-b_2H_17) resulted in the largest increase of GWSp (0.1 g).Two LD blocks were detected for this trait. The largest negative allelic effect (−3.08 g) was found for one of two SNP combinations of LD-b_1H_3. The SNP combination (A/A A/A) of LD-b_2H_6 resulted in the largest increase of TGW (1.56 g).Seven LD blocks significantly associated with RB were identified on chromosomes 2H, 4H, 5H, and 7H. In general, eight SNP combinations with positive allelic effects on RB were detected. Combination G/G A/A C/C of LD-b_4H_26 showed the largest allelic effect, resulting in 0.41g increase in RB compared to the general mean RB value of all accessions. This combination was identified in 8 of 146 accessions identified for this block. Twelve combinations with negative effects on the studied trait were identified with the largest decrease in RB (−0.25 g) for the SNP combination T/T G/G A/A of LD-b_7H_58  and tillering showed a significant increase. In the same year, plants developed much higher root biomass than that in the other two years. Concurrently, the lowest mean root depth values were observed in 2018, which indicated that the studied plants did not develop long roots to search for water resources in the soil. The soil moisture content recorded at the tillering stage (T1) was the highest, which directly contributed to the development of tillers. Under these conditions, plants did not develop deep root systems as the existing root structure allowed sufficient access to water resources. In 2018, a rapid decrease in soil moisture content was recorded only in the heading phase, when the studied plants were mainly in the maturation stage. According to the “balanced growth” hypothesis , some plContent and weather conditions provided information on the dynamic changes in water availability during the course of the field study. The average monthly precipitation values varied across the three years of our investigation; for example, extremely low number of days with rainfall were recorded in 2020. These differences in monthly weather conditions across the year influenced the yield performance of the studied plants to some extent. The studied accessions were not as stable as those grown under greenhouse conditions, but they were rather subject to dynamic, rapid changes in the environment. The monitoring of soil moisture content showed that spring drought occurred under field conditions in both 2017 and 2020, even though rainfall was relatively high in 2017. However, as several constraints limit water use, the use of water resources by plants is not solely dependent on rainfall e.g., .In this study, the highest values for electrical measurements were observed in 2018, when accessions developed many tillers and both total and root biomass increased. As previously reported, a strong correlation exists between the electrical capacitance of the root and the weight of the root system in crops . TherefoAlmost all common methods used to observe the below ground parts of the plant have their own advantages and disadvantages . Some apThe results of the study showed a negative correlation between RD and traits linked to plant biomass (TB and RB), which is consistent with the observations of In this study, the results of a LD-based GWAS for 11 phenotypic traits related to plant morphology, grain quality, and root apparatus in a collection of spring barley were presented. Small association panels were observed to increase both type 1 and type 2 error rates, resulting in the failure to detect true associations while producing a higher rate of false-positive associations . The numLD blocks were constructed based on the average LD extent in our material for each chromosome. The LD-based approach has been shown to be a reliable tool for analyzing quantitative traits in different crops . An advaIn the present study, two root-related LD blocks were of particular interest because of their colocalization and concomitant allelic effect on plant height and other yield-related traits. LD-b_2H_7 was associated with PH, whereas LD-b_7H_58 was related to three traits  that directly contributed to yield performance. Colocalization of root architecture loci with other traits is a well-known phenomenon . The allPPD-H1 locus was identified as the major determinant of response to long photoperiods in barley (PPD-H1 locus encodes the pseudo-response regulator (HvPRR37) gene which is orthologous to the Arabidopsis gene PRR7. This gene is part of the plant circadian clock and its activity increases the expression of VRN-H3, the main promoter of flowering, when photoperiods increase above 12 h  in association with earliness. The responsive/sensitive allele of PPD-H1 has been associated with increased yield in sites where earliness is advantageous . The yield effect can be explained by the pleiotropic effects of the responsive PPD-H1 allele. This allele shortens the total growing period, prolongs the time of grain formation, and increases grain size. On the other hand, the nonresponsive ppd-H1 allele has been shown to be associated with an increase in yield-related traits at sites where lateness is preferred to achieve higher yields   was usedbidopsis . Althougbidopsis , the assFPF1). To the best of our knowledge, this gene annotation has not been reported previously as the gene associated with both root growth and flower development in spring barley. This finding suggests that this region is associated with the previously mentioned spot on chromosome 2H that harbors flowering genes. AtFPF1 in Arabidopsis encodes a small 12.6-kDa protein that regulates flowering and is involved in gibberellin signaling. It is expressed in apical meristems immediately after the photoperiodic induction of flowering. Genetic interactions of this gene with flowering time genes and floral organ identity suggest that it may be involved in modulating the competence to flower. FPF1 is mainly expressed in roots and flowers and, to a lesser extent, in leaves. OsRAA1, a homolog of FPF1 in rice, showed 58% similarity to AtFPF1 at the amino acid level. Overexpression of OsRAA1 induces pleiotropic phenotypes in transgenic rice plants, such as altered leaf shape, heading time, and root development (Arabidopsis and 1100 genes in rice. A possible role in pollen development has been demonstrated for a type of the RLK gene (LecRLK). Disruption of the gene LecRK-IV.2 leads to the formation of smaller pollens that remain stuck together and cannot be detached from the dehiscent anther, eventually resulting in male sterility . The second LD-based block contained SNP markers associated with the following genes: HORVU7Hr1G099520, ENSRNA049488629, ENSRNA049488633, and HORVU7Hr1G101010. ENSRNA049488629/ENSRNA049488633 was annotated as a U2 spliceosomal RNA. In eukaryotic cells, pre-mRNA splicing is critical for processes such as expression of intron-containing genes, remodeling of protein–protein interaction networks, and regulation of transcript levels (Eight genes harbored by the chosen LD blocks (LD-b_2H_7 and LD-b_7H_58) were discovered using selected annotation tools. The SNP markers within the block LD-b_2H_7 were associated with the following genes: HORVU2Hr1G007220, HORVU2Hr1G007340, HORVU2Hr1G007350, and HORVU2Hr1G008240. Wall-associated kinase-like 1 is a HORVU annotation associated with HORVU2Hr1G007340. No association was found for HORVU2Hr1G007220. Both HORVU2Hr1G007340 and HORVU2Hr1G007350 genes were associated with wall-associated kinase-like 1. Wall-associated kinases (WAKs) are receptors of the plasma membrane. They appear to be crosslinked with the cell wall material and can be detected in the cell wall by electron microscopy. WAKs are required for cell expansion and are involved in the response to pathogens. Their expression is activated by several environmental stimuli. According to the available data, WAKs play a role in cell expansion . For exaelopment . This obterility . In the terility , the funterility . Moreovet levels . These it levels . Plants t levels . An intet levels .A. thaliana—GSO1 in coordination with GSO2—regulates root growth by controlling cell division and cell fate specification and controls seedling root growth by modulating sucrose response after germination (Therefore, AS in living plants may be a result of their adaptation to the new environment during land colonization . Thus, tmination .In summary, the findings of our study offer new insights into the role of root traits in the yield performance of barley plants growing under natural field conditions where soil moisture varies day to day. The barley association panel analyzed in this study showed high variability in most of the agronomic traits. The results demonstrated the importance of the region on the short arm of chromosome 2H in the expression of root- and yield-related traits. Furthermore, the selected SNP markers with high translation effects showed great potential for further investigations of the adaptation of roots to drought. This study also highlighted the pleiotropic effect of the region with respect to heading time and other important agronomic traits, including root architecture. Because barley is widely considered as a model plant, the potential regions of interest identified in our study can be further validated to determine their application in barley breeding programs and to develop drought-tolerant genotypes in different species.The original contributions presented in the study are included in the article/Conceptualization, PO, KM and AK. Methodology, PO and PK. Software, MM. Validation, PO, MM, PK and AK. Formal Analysis, MM and PK. Investigation, PO and MK. Resources, PO and KM. Data Curation, MM and PK. Writing – Original Draft Preparation, PO. Writing – Review and Editing, KM, MM, PK and AK. Visualization, MM. Supervision, AK. Project Administration, PO, KM and AK. Funding Acquisition, PO, KM and AK. All authors contributed to the article and approved the submitted version.