Although barley is mainly used for livestock feed and beverages, its use as human feed can enrich human diets with some health benefits. The development of new hulless varieties rich in β-glucans and micronutrients can enhance the use of barley as food, but little is known about the effects of the environment on these nutritional traits. In this study, we evaluated five Moroccan varieties and two elite breeding lines of barley at four locations in Morocco during the 2016–2017 and 2017–2018 cropping seasons. The results showed highly significant differences between genotypes for β-glucan, protein, iron, and selenium contents, as well as 1000 kernel weight, but not zinc content; significant to highly significant differences between environments for all traits except β-glucan content; and significant to highly significant interactions for all traits. The highest level of β-glucan content has reached 11.57% observed at the Sidi El Aydi site during the growing season 2017–2018 for the hulless variety Chifaa. This variety has shown the highest content of β-glucan (6.2–11.57%) over all environments except at Tassaout during the 2016–2017 seasons. The breeding line M9V5 has achieved significantly higher protein content at all the locations during the two growing seasons, ranging from 12.38 to 20.14%. Most hulless lines had significantly higher β-glucan and protein contents, but lower 1000 kernel weight. For micronutrients, the content ranges were 28.94 to 38.23 ppm for Fe, 28.78 to 36.49 ppm for Zn, and 0.14 to 0.18 ppm for Se, with the highest content for Fe and Zn shown by the breeding line M9V5 and Chifaa showing average contents of 33.39 ppm, 35.34 ppm, and 0.18 ppm for Fe, Zn, and Se, respectively. The GGE biplot confirmed the high and relatively stable content of β-glucan and acceptable micronutrient contents of the Chifaa variety and identified Marchouch as the most discriminant site to breed for biofortified barley varieties. Hordeum vulgare L) is considered one of the founding crops of old-world agriculture. Its cultivation began 10,000 years ago in the “Fertile Crescent”  and micronutrients  . Indeed,eraction .To overcome micronutrient deficiencies, many strategies have been implemented, including supplementation, dietary diversification, fortification, and biofortification of crop plants . CompareBiofortification is either achieved through an agronomic approach, or genetic selection using conventional plant breeding methods, or biotechnology techniques . The agrHordeum spontaneum are a great source of high contents of β-glucans and micronutrients  to capture the effects of genotypes, environments, and their interactions on nutritional and quality attributes of some Moroccan barley varieties and elite lines, (ii) to identify barley varieties with higher nutritional value for human consumption, and (iii) to recommend testing environments for efficient breeding for quality and nutritional attributes in barley.Five Moroccan barley varieties , along with two breeding lines , were obtained from the National Barley Breeding Program of the National Agricultural Research Institute of Morocco (INRA) .2) of each experimental unit, consisting of six rows of 5 m length and 0.25 m row spacing.These entries were grown in two replications in a randomized complete block design at each of the four Moroccan locations  during the 2016–2017 cropping season and at Marchouch, Annoceur, Jamaat Shaim, and Sidi El Aydi during the 2017–2018 cropping season. The soil characteristics across the studied sites exhibit distinct patterns. Jamaat Shaim and Sidi El Aydi sites are characterized by clay soil texture, while Marchouch has heavy clay soil. Conversely, the Annoceur and Tassaout sites exhibited a medium texture with sandy clay loam soils. The presence of a calcium content risk is evident in all soils except at the Marchouch site. In terms of pH levels, all soils tend to be slightly basic to basic, except for the soil at Jamaat Shaim, where the pH remains neutral. Importantly, none of the soils are saline. The soils at Jamaat Shaim and Marchouch sites have medium organic matter content, while those at the other stations are poorer. The soils at the Marchouch and Sidi El Aydi sites exhibit high contents of available phosphorus, while Annoceur, Jamaat Shaim, and Tassaout sites display medium contents. Notably, all sites feature high available potassium content (more than 300 ppm), except for the soil at the Marchouch site (110 ppm).Meteorological data for the experimental locations have been collected from the National Institute for Agriculture Research . CumulatDried and cleaned grains were ground using a laboratory cyclone mill  with a sieve of 1 mm. The grinding mill was cleaned before and after milling each sample to avoid cross-contamination. The moisture content of each sample was determined following the American Association of Cereal Chemists (AACC) approved method [44–40]-44–40.01 Moisture Modified Vacuum-Oven Method .The β-glucan content was determined according to a fluorometric method using a continuous flow analyzer . Prior to this determination, an acid extraction was performed using the procedure recommended by the European Brewery Convention . The extThe determination of microelement content was performed according to a standard protocol . The eleThe thousand kernel weight was evaluated by weighing samples of 1000 kernels from the harvest of each plot, and the protein content was determined on the whole grain using near-infrared reflectance (NIR)  spectroscopy according to the AACC approved method [39–10] .The analyses of variance for combined environments (location × year) and for each of the eight environments were conducted. The descriptive statistics, including the mean, LSD (5%), and CV (%), and tests of normal distribution were conducted for each trait in each environment. Data were also subject to GGE analysis, which uses principal component analysis to explain the maximum amount of variance in the original data by a few linearly transformed uncorrelated components. The resulting scatter plot based on PC1 and PC2 allows the visualization of mega-environments, the mean and stability of traits for each genotype and its comparison with the ideal genotype, the discriminating ability and representativeness of the environments, and the correlations among genotypes and environments . For eacThe combined analysis of variance showed highly significant differences among entries for all measured traits except for zinc content . FurtherBecause the interactions between entry × environment were significant to highly significant, an analysis of variance was conducted for each trait in each environment, which allowed comparison among the entries and the derivation of descriptive statistics.For β-glucan, the highest content of 11.58% was obtained for the variety Chifaa at the Sidi El Aydi station in 2017–2018. This variety has also given the highest contents in all environments except Tassaout and Jamaat Shaim in 2016–2017, where the variety Assiya has given similar contents . At AnnoThe protein content varied from 9.28% for the landrace Rabat 071 at Annoceur in 2017–2018 to 20.14 for the breeding line M9V5 at Marchouch in 2016–2017 . The higFor the thousand kernel weight, the highest values were recorded in the varieties Rabat 071 (41.13 to 59.30 g) and Adrar (42.46 to 54.31 g), while the lowest values were given by the breeding line M9V5 (30.15 to 39.88 g) and the variety Assiya (27.87–41.02 g) over the eight environments . The remRegarding the microelements, large variations were observed between entries and between environments. For iron content, it ranged from 17.49 ppm at Sidi El Aydi in 2017–2018 to 51.41 ppm at Marchouch in 2016–2017 for the same variety, Amalou . It appePearson's correlations between traits were only highly significant between TKW and protein content (-0.544) and significant between iron and β-glucan contents with a negative correlation of (−0.192) and between iron and protein with a positive correlation of (0.278) . The corThe GGE biplot analysis was conducted only for β-glucan and the micronutrient contents, and four different plots were selected for the purpose of this study: (i) the scatter plot of the “which-wins-where” pattern, which allows to identify different sectors by joining the winner genotype in each sector to identify different mega-environments and the best genotypes in each mega-environment; (ii) the ranking plot, which allows to evaluate the genotype performance and stability through the projection of the genotype performance on average environment coordinate (AEC) abscissa. The shorter the projection from the AEC is, the more stable the genotype is; (iii) the comparison plot, which allows to identify the environment with the most discriminating power among test environments; and (iv) the joint biplot, which allows to compare the variety Chifaa (recommended for human food) with the landrace Rabat 071, the widely grown landrace in Morocco.The two principal components of the biplot analysis for β-glucan accounted for 87.79% of the total variation . The scaFor iron content, the two first principal components explained 74.46% of the variation . The scaFor zinc content, 75.74% of the variation was accounted for by the two first principal components . Two megFor selenium content, the two first principal components explained 71.77% of the variation . The scahttps://www.harvestplus.org/home/crops). ICARDA and many countries, including Morocco, are working on the development of barley with high β-glucan, which allowed the release by INRA-Morocco of two hulless barley varieties, Assiya and Chifaa, with the latter having high β-glucan.Micronutrient malnutrition, referred to as “hidden hunger,” affects over two billion people in the world, including those living under harsh conditions. Iron and zinc deficiencies are among the most common and are posing serious health challenges in developing countries, mainly for resource-poor farming communities. Morocco is known for its highest per capita consumption of barley as food in the world, mainly by the rural communities in the mountainous and arid areas. Due to the predominant cereal-based diets and the limitations of diversifying the diets, biofortification through breeding micronutrient-rich varieties has become a research priority in many crop breeding programs. Several studies have promoted genetic biofortification as a more cost-effective and sustainable approach compared to agronomic biofortification, food diversification, or supplements , 40. TheThe results of the present study on five released barley varieties in Morocco and two breeding lines showed large genotypic differences in protein content, thousand kernel weight, β-glucan, and Fe and Se micronutrient contents, significant effects of environments except for β-glucan, and significant genotype × environment interactions for all traits, indicating the potential for selecting nutrient-enriched germplasm and the need for multi-environment testing for selecting more stable genotypes. Many authors have reported similar findings, highlighting the greater significance of genotypic effects over environmental effects on protein content , 41–43. Several studies have shown that the protein content is mainly under genetic control, with a slicing effect of the environment  and a hiIn this study, among micronutrients, Zn appears to be influenced more by the environment, while Se showed a high influence of the environment and a high genotype × environment interaction. Significant genotypic and environmental effects and their interactions were also reported for micronutrient contents in wheat . In otheThousand kernel weight is an important yield component and quality attribute of barley that is most appreciated by farmers. This study showed the predominance of genetic effects and high genotype × environment interactions. Similar conclusions were reported by Boudiar et al.  and BociMorocco lies within the Mediterranean region, known for high climate variability within and between the seasons. Therefore, a comprehensive evaluation of GE interactions requires more advanced statistical methods than the standard ANOVA. Since the genotype × environment interactions were significant to highly significant for all traits measured, selection and identification of lines with higher β-glucan and higher micronutrient contents will be facilitated by the identification of locations with the most discriminating power for plant selection during segregating populations and by the evaluation of the performance stability of the derived fixed lines using multi-environment testing. GGE biplot was used since it offers many features not shown in other stability analysis approaches, GGE biplot provides many visual interpretations, including crossover G × E interaction, comparing tested genotypes with ideal genotypes, and identifying environments with the most discriminating ability . In the Based on the results of the analysis of variance and stability analysis using the GGE biplot, the hulless barley varieties Chifaa and Assiya can be recommended for human consumption, with the former one having the advantage of being biofortified for β-glucan with acceptable levels of Fe, Zn, and Se contents. The two hulless breeding lines BFH129 and M9V5 can also be promoted as varieties for human consumption or used as sources of micronutrients to be crossed with Chifaa for the development of varieties combining high β-glucan and high content of micronutrients. For micronutrients, a special search for parental germplasm with high contents is needed to develop biofortified varieties. For the remaining varieties, the landrace Rabat 071 and the newly released varieties Amalou and Adrar can be considered for multiple purposes, but their use as human food will require a dehulling process which could reduce the micronutrients. Among the experimental stations used, Marchouch appears to be the most appropriate for selecting β-glucan glucans and for Se and Zn but not for Fe. As a breeding implication, this station can be used to select within segregating populations, but multilocational trials are needed for most advanced lines to select the ones combining high-quality and more stable varieties for quality and nutritional traits. For commercial production of biofortified varieties, it will be good to consider environments that allow the best expression of these nutritional traits. More research is needed on the stability and bioavailability of these nutrients during processing and consumption.Barley can be further promoted as a human food to partially replace the imported wheat grain. This could allow consumers to benefit from the nutritional value as well as the health claims attributed to barley. In this study, we characterized five barley varieties released in Morocco and two hulless breeding lines for their protein, β-glucan, Fe, Zn, and Se contents. We could identify genotypes suitable for human consumption and recommend the hulless variety Chifaa as a β-glucan biofortified variety for cultivation in Morocco and as a parental germplasm for breeding germplasm combining high β-glucan with high micronutrient contents using Marchouch as selection site for early generations, followed by a multi-environment evaluation of the fixed lines.The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.AA, MI, and FE: conceptualization. FE, AJ, and ZK: methodology. FE and ZK: software. AA and MI: validation. FE, ZK, and AA: statistical analysis. FE, AJ, and AA: fieldwork. FE and AE-B: laboratory analysis. FE and AA: writing draft manuscript. AA, MI, and ZK: editing and finalizing. All authors have read and agreed to the submitted version of the manuscript.


Quantitative autistic traits are common, heritable, and continuously distributed across the general human population. Patterns of autistic traits within families suggest that more complex mechanisms than simple Mendelian inheritance—in particular, parent of origin effects—may be involved. The ideal strategy for ascertaining parent of origin effects is by half-sibling analysis, where half-siblings share one, but not both, parents and each individual belongs to a unique combination of paternal and maternal half-siblings. While this family structure is rare in humans, many of our primate relatives, including rhesus macaques, have promiscuous breeding systems that consistently produce paternal and maternal half-siblings for a given index animal. Rhesus macaques, like humans, also exhibit pronounced variation in social functioning.2P) between sons that could uniquely be attributed to their shared genetics (σ2g), expressed as σ2g/σ2P (or the proportion of phenotypic variance attributable to genetic variance), as well as narrow sense heritability (h2).Here we assessed differential paternal versus maternal inheritance of social functioning in male rhesus macaque offspring (N = 407) using ethological observations and ratings on a reverse-translated quantitative autistic trait measurement scale. Restricted Maximum Likelihood mixed models with unbounded variance estimates were used to estimate the variance components needed to calculate the genetic contribution of parents as the proportion of phenotypic variance (σGenetic contributions and heritability estimates were strong and highly significant for sons who shared a father but weak and non-significant for sons who shared a mother. Importantly, these findings were detected using the same scores from the same sons in the same analysis, confirmed when paternal and maternal half-siblings were analyzed separately, and observed with two methodologically distinct behavioral measures. Finally, genetic contributions were similar for full-siblings versus half-siblings that shared only a father, further supporting a selective paternal inheritance effect.These data are correlational by nature. A larger sample that includes female subjects, enables deeper pedigree assessments, and supports molecular genetic analyses is warranted.Rhesus macaque social functioning may be paternally, but not maternally, inherited by sons. With continued investigation, this approach may yield important insights into sex differences in autism’s genetic liability. Autism spectrum disorder (ASD) is diagnosed on the basis of two core symptom domains: persistent social interaction and communication difficulties and the presence of restricted, repetitive behaviors . ASD repPatterns of autistic traits within families suggest that more complex genetic mechanisms than simple Mendelian inheritance—in particular, parent of origin effects—may be involved . HoweverThe ideal strategy for ascertaining parent of origin effects would be to estimate genetic contributions to social functioning in a population of paternal half-siblings and maternal half-siblings, where paternal half-siblings do not share a mother (and vice-versa) . While tThe present study therefore tested for differential paternal versus maternal inheritance of social functioning in a sample of male rhesus monkeys, while avoiding the confounds that have impeded such efforts in humans. Male monkeys were chosen to study in our larger research program because of ASD’s male-biased prevalence , and, hevice-versa, which prevented any shared environmental effects of the father and mother masquerading as genetic effects [Because each son belonged to a group of paternal half-siblings and to a group of maternal half-siblings, we could estimate the genetic contributions of the father and mother to each son’s phenotype without knowing parental phenotypes. This had two advantages. First, because mothers’ phenotypes were never measured, we circumvented the confound that females (but not their sons) show lower phenotypic expression of genetic susceptibility , 14. Sec effects .We used two statistically correlated , but metN = 407 male rhesus macaques (Macaca mulatta) aged 1.17–20.7 years (mean ± SD = 3.30 ± 1.67). Subjects had been born and reared at the California National Primate Research Center (CNPRC) in Davis, CA. Subjects lived outdoors in any one of the 24 half-acre (0.2 ha) field corrals. Each corral measured 30.5 m wide × 61 m deep × 9 m high and contained up to 221 animals of all ages and both sexes. Subjects were tattooed as infants and dye-marked periodically to facilitate easy identification for husbandry- and research-related procedures. Monkeys had ad libitum access to Lixit-dispensed water. Primate laboratory chow was provided twice daily, and fruit and vegetable supplements were provided weekly. Various toys, swinging perches, and other forms of enrichment were provided in each corral, along with outdoor and social housing, thereby providing a stimulating environment.Subjects were a total of The present investigation collated behavioral data obtained previously from five study cohorts . Subjects in these study cohorts had been selected independent of genetic relatedness, on the basis of the following criteria: male, socially housed in outdoor field corrals, medically healthy, not simultaneously enrolled in another CNPRC project, and previously enrolled in CNPRC’s BioBehavioral Assessment Program  as infanThe CNPRC houses approximately 4000 rhesus monkeys. A center-wide reproductive management plan has been in place for more than three decades to ensure an outbred colony. The formation of new corrals occurs regularly, and animals from multiple corrals are often combined to further prevent inbreeding. These decisions are guided by a geneticist. It was thus critical to determine the genetic parentage of subjects, which was accomplished using an established panel of microsatellite markers designed to identify maternity and paternity , 35.An individual’s rank may impact social behavior in nonhuman primates . We therN = 376 subjects from four of the five study cohorts  while they were in their home field corrals as previously described [Unobtrusive behavioral observations had been previously conducted on escribed , 20, 23.escribed . After cescribed .N = 264 subjects from three of the five study cohorts . Observers rated each subject on a 36-item original mSRS [mSRS-R scores had been previously obtained on nal mSRS , which wnal mSRS . RatingsWe first collated all available data for each subject. As noted above, measures that differed in the observation or calculation method between study cohorts  were Z-scored within cohort prior to analysis, whereas measures that employed the same ascertainment method across study cohorts (namely mSRS-R score and variables such as age) were not Z-scored. A small number of subjects had been studied twice . For these animals, we uniformly discarded their earliest data and retained their most recent data for analysis here.We then used CNPRC’s multigenerational pedigree records to identify the father and mother for each subject. These data included a small number of full-sibling pairs. For our initial analyses, we uniformly discarded the youngest animal of the full-sibling pair to enable retention of one animal (the eldest) for the half-sibling analyses. Using the same pedigree data, we measured the inbreeding coefficient (F) for 40 random offspring as well as both of their parents. We also estimated the coefficient of relatedness (r) between each of these animals with every other animal included in this subset. While F is defined in terms of the probability of identity in state of different pairs of alleles, r measures the probability that alleles drawn at random from the same locus in each of two subjects will be identical by descent . DiffereFor the two behavior measures  we then generated two new datasets, and calculated the number of paternal half-siblings and the number of maternal half-siblings for each subject independently for each measure. Subjects with no paternal and no maternal half-siblings were then removed in each dataset. Thus, in each dataset each subject had at least one maternal or paternal half-sibling, and no full-siblings , 43.2P) between sons that could uniquely be attributed to their shared genetics (σ2g), expressed as σ2g/σ2P (or the proportion of phenotypic variance attributable to genetic variance), and narrow sense heritability (h2) following [Restricted Maximum Likelihood (REML) mixed models with unbounded variance estimates were used to estimate the variance components needed to calculate the genetic contribution of parents as the proportion of phenotypic variance  increase σ2g/σ2P for one parent and decrease them for the other. Furthermore, variance estimates for mothers and fathers can be tested against each other. Thus, σ2g/σ2P captures more information than a traditional estimate of narrow-sense heritability (h2), despite the two measures being mathematically related [2 are influenced by a wide range of effects, which combined with its strict definition in terms of purely additive haplotypic gametic contribution, limit interpretation. By focusing on additive genetic variance, and gametic potential, h2 essentially assumes that allele–allele interactions , gene–gene  interactions, and gene–environment interactions  are not contributing to estimates of genetic variance. However, this is rarely the case, and so dominance and epistatic effects tend to inflate h2, whereas phenotypic plasticity can inflate or deflate it, depending on study design [2 does not imply genetic determinism [2 is often far higher than concordance rates [2g/σ2P, which is broader but more meaningful in interpretation, has distinct meanings within its dynamic range, and cannot exceed its theoretical limits.Different genetic scenarios have different dynamic ranges and expected values for σ2g/σ2P . For ins related , 45. Absy design , 43. Furerminism : for exavice-versa [2g/σ2P and h2 attributed to each), we used an F-test of the variance components with their respective degrees of freedom. The variance of σ2g/σ2P was calculated and used to calculate a Z-score and associated P value following [2g/σ2P and h2, h2 shares the same Z-score and P value.A critical advantage of this approach is that potential confounding environmental effects can be included in the model, and the variance components representing the half-sibling group , are estimated after these confounds are taken into account . All mice-versa . The powce-versa , 46. To ollowing . Given t2g/σ2P, h2, and their tests was performed as described above.Best practice in linear (and mixed) model design is to stress-test the models to detect potential confounding effects . InIn the non-social equivalence score and mSRS-R datasets, 11 (4.0%) and 7 (3.6%) of the subjects, respectively, were not raised by their birth mother , yielding a total of N = 12 unique animals. To ensure that these subjects were not introducing an artefact, we repeated the analyses above excluding these individuals. Doing so did not change the results. We therefore present the analyses from the full data sets, especially as doing so is the conservative biological and statistical approach .N = 24). The same mixed models were used to generate variance components, σ2g/σ2P, and h2. We did not have enough full-sibling pairs to calculate meaningful tests for the σ2g/σ2P, but we could test the variance component estimates against those from the half-sibling data. These F-tests enabled us to test whether full-siblings differed in the magnitude of their variance components (and hence σ2g/σ2P) from paternal and maternal half-siblings.Finally, given the apparent selective genetic contribution from fathers but not mothers, we generated a trimmed dataset for full-siblings. Full-siblings are rare given the rhesus monkey’s promiscuous breeding system. Nevertheless, we were able to analyze full-sibling data for one of our measures: the non-social equivalence score  as the BLUP, which is given as the deviation from the population average.σ2g/σ2P, significance tests of σ2g/σ2P, and calculation of h2, involved several further steps [Mixed models were performed in JMP 15 Pro for Windows. Further calculations of σNote that this statistical approach is essentially equivalent to an “Animal Model”  with a pP > 0.05). Average pairwise relatedness (r) among dams and among sires were also not significantly different . The average pairwise relatedness among all parents was low, 0.016, consistent with values expected for sixth-degree relatives. Average relatedness estimates of dam-offspring and sire-offspring pairs also did not significantly differ , indicating that heritability estimates are unlikely to be influenced by differences in inbreeding and relatedness levels from the parents and/or unequal levels of relatedness between the offspring and their dams and sires.Differences between the F values among the dams and among the sires were not statistically significant , paternal genetic contribution was strong  and highly heritable (h2 ± SD = 0.777 ± 0.270); whereas maternal genetic contribution was weak and not significantly heritable  , and is evidenced by the BLUPs for fathers versus mothers ; whereas when maternal half-siblings were considered separately (N = 73), maternal genetic contribution remained non-significant , confirming a selective paternal effect. Furthermore, for non-social equivalence score, we assessed a small set of full-siblings (N = 24). The maximum expected σ2g/σ2P for full-siblings is 50%. This combined parental genetic contribution was σ2g/σ2P = 25.2% (h2 = 0.503 ± 0.553) , but not significantly different from those for fathers , consistent with a lack of maternal effect and a selective paternal effect.We then used “sensitivity analysis”, which is a formal process to rule out false discovery due to subject selection or model design in human medical research , thereby2g/σ2P = 25.5%; Z = 3.08; P = 0.0021) and highly heritable (h2 ± SD = 1.020 ± 0.331); whereas maternal genetic contribution was weak and not significantly heritable  , and is evidenced by the BLUPs ; whereas when maternal half-siblings were considered separately (N = 45), maternal genetic contribution remained non-significant .Finally, we performed the same main and secondary analyses for the mSRS-R score, which showed the same pattern: When fathers and mothers were analyzed together , paternal genetic contribution was strong , we observed the same consistent and highly significant inheritance pattern in male rhesus monkeys. Specifically, paternal genetic contributions were strong for sons who shared a father, whereas maternal genetic contributions were weak and nonsignificant for sons who shared a mother. This difference between paternal and maternal genetic contributions was highly significant. Corresponding paternal and maternal heritability estimates followed the same pattern. Importantly, these findings were detected using the same scores from the same sons in the same analysis; confirmed when paternal and maternal half-siblings were analyzed separately; and observed in both behavioral measures. Finally, full-siblings showed significantly higher σ2g/σ2P. Thus, because the same sons are considered, the phenotypic variance to be explained  is the same for mothers and fathers, but the greater spread in BLUPs for fathers leads to a greater genetic variance . Furthermore, both fathers and mothers show a unimodal spread in BLUPs, consistent with a polygenic mechanism. If only one or two genes were contributing to this variability, then we would expect to see a clumped distribution of BLUPs. A further important consequence of these findings is that if the lower σ2g observed in mothers was simply due to there being more mothers than fathers in the analysis, then the range of BLUPs would have to be equal to or greater than that for fathers. However, the narrower range of BLUPs found in mothers rules out this possibility.These population-level interpretations are supported by the BLUPs for mothers versus fathers. Fathers show a much greater range of BLUPs than mothers for the same sons, which directly leads to differences in σIn humans, there is evidence that quantitative autistic traits might be selectively inherited from the father , but thi2g/σ2P from the theoretical maximum, which makes inferring the mode of genetic transmission here difficult. Nevertheless, the observation that maternal genetic contribution is negligible and significantly lower than paternal genetic contribution (at least for sons), cannot be explained unless Y-linked genes or maternally-silenced autosomal genes are involved. However, the fact that the paternal genetic contribution is less than the maximal theoretical value than would be predicted by Y-linked (100%), or a paternally-inherited imprinted (50%), effects, argues for a complex blend of causal factors, which remain to be identified.Gene-by-environment interactions and incomplete penetrance lower σThis study had limitations that warrant comment. First, it is important to acknowledge that animal models are approximations that enable study of human phenomena; spending more time alone and having a higher autistic-like trait burden is not the same as having ASD. Thus, how well these monkey findings ultimately generalize to humans, particularly in light of species differences in life history strategies, remains to be determined. Second, paternal half-siblings can be born in the same year, whereas maternal half-siblings are almost always born in different years. There thus may be systematic differences in the temporal structure of paternal versus maternal half-sibling sets which might affect heritability estimates. Third, behavioral measures were not available from parents to further validate these findings. However, if postnatal parent–offspring social interactions were responsible for phenotypic similarities between half-siblings, then we would observe the exact opposite pattern of results, because rhesus monkey fathers engage in little if any parental care , whereas2g/σ2P were roughly similar between full-siblings and half-siblings that only shared a father, providing intriguing support for a paternal origin effect, consistent with [This study reports that social functioning is paternally but not maternally inherited by sons in a primate species closely related to humans. σent with . The uni


Many genes associated with asthma explain only a fraction of its heritability. Most genome-wide association studies (GWASs) used a broad definition of ‘doctor-diagnosed asthma’, thereby diluting genetic signals by not considering asthma heterogeneity. The objective of our study was to identify genetic associates of childhood wheezing phenotypes.We conducted a novel multivariate GWAS meta-analysis of wheezing phenotypes jointly derived using unbiased analysis of data collected from birth to 18 years in 9568 individuals from five UK birth cohorts.ANXA1], p<6.7 × 10-9), associated exclusively with early-onset persistent wheeze. We identified rs75260654 as the most likely causative single nucleotide polymorphism (SNP) using Promoter Capture Hi-C loops, and then showed that the risk allele (T) confers a reduction in ANXA1 expression. Finally, in a murine model of house dust mite (HDM)-induced allergic airway disease, we demonstrated that anxa1 protein expression increased and anxa1 mRNA was significantly induced in lung tissue following HDM exposure. Using anxa1-/- deficient mice, we showed that loss of anxa1 results in heightened airway hyperreactivity and Th2 inflammation upon allergen challenge.Forty-four independent SNPs were associated with early-onset persistent, 25 with pre-school remitting, 33 with mid-childhood remitting, and 32 with late-onset wheeze. We identified a novel locus on chr9q21.13  provided most of the funding for this study. Three-quarters of children hospitalized for wheezing or asthma symptoms are preschool-aged. Some will continue to experience breathing difficulties through childhood and adulthood. Others will undergo a complete resolution of their symptoms by the time they reach elementary school. The varied trajectories of young children with wheezing suggest that it is not a single disease. There are likely different genetic or environmental causes.Despite these differences, wheezing treatments for young children are ‘one size fits all.’ Studying the genetic underpinnings of wheezing may lead to more customized treatment options.Granell et al. studied the genetic architecture of different patterns of wheezing from infancy to adolescence. To do so, they used machine learning technology to analyze the genomes of 9,568 individuals, who participated in five studies in the United Kingdom from birth to age 18. The experiments found a new genetic variation in the ANXA1 gene linked with persistent wheezing starting in early childhood. By comparing mice with and without this gene, Granell et al. showed that the protein encoded by ANXA1 controls inflammation in the lungs in response to allergens. Animals lacking the protein develop worse lung inflammation after exposure to dust mite allergens.Identifying a new gene linked to a specific subtype of wheezing might help scientists develop better strategies to diagnose, treat, and prevent asthma. More studies are needed on the role of the protein encoded by ANXA1 in reducing allergen-triggered lung inflammation to determine if this protein or therapies that boost its production may offer relief for chronic lung inflammation. HLA region), 2q12 (IL1RL1/IL18R1), 5q22 (TSLP), and 9p24 (IL33)  . Overall4 (IL33) .However, despite undeniable successes, genetic studies of asthma have produced relatively heterogeneous results, and only a small proportion of the heritability is accounted for . One parOne way of disaggregating asthma diagnosis is to use data-driven methods to derive subtypes in a hypothesis-neutral way . For exaThe Study Team for Early Life Asthma Research (STELAR) consortium  brings tValidated questionnaires were completed on multiple occasions from infancy to adolescence . A list never/infrequent wheeze (52.4%); (2) early-onset pre-school remitting wheeze (18.6%); (3) early-onset middle-childhood remitting wheeze (9.8%); (4) early-onset persistent wheeze (10.4%); and (5) late-onset wheeze (8.8%). These latent classes were used in the subsequent GWAS.In the pooled analysis among 15,941 subjects with at least two observations on current wheeze, we used latent class analysis (LCA) to derive wheeze phenotypes from birth to age 18 years . A detaiGenotyping, quality control, imputation, and exclusions are described in Appendix 2: Genotyping and imputation. Analyses were performed independently in ALSPAC, MAAS, and the combined IOW-SEATON-Ashford . We used SNPTEST v2.5.2  with a fAnnexin A1 (ANXA1), exclusively associated with early-onset persistent wheeze (see Results section). We therefore proceeded with studies to identify causal variants and explore the biological mechanisms underlying this locus (see Appendix 3: Post-GWAS: rs75260654 (ANXA1) for more details). To this end, we firstly identified the most likely causative SNP using Promoter Capture Hi-C (PCHi-C) loops. We then ascertained genotype effect on gene expression and assessed the potential biological function of ANXA1 in asthma. Finally, we used a murine model of house dust mite (HDM)-induced allergic airway disease to investigate whether ANXA1 was important in regulating immune responses to a clinically relevant aeroallergen and used knock-out mice to derive further in vivo functional data to support our GWAS finding.Our GWAS identified a novel locus in chr9q21 nearby We included a total of 9568 subjects with European ancestry: ALSPAC, n=6833; MAAS, n=887; SEATON, n=548; Ashford, n=348; and IOW, n=952. Demographic characteristics of the participants in STELAR cohorts included in this analysis and a flowchart are shown in –5. After clumping, we identified 134 independent SNPs uniquely associated with different phenotypes (p<10–5): of these, 44 were exclusively associated with early-onset persistent, 25 with early-onset pre-school remitting, 33 with early-onset mid-childhood remitting, and 32 with late-onset wheeze , but some of the SNPs in this region were also associated with the early-onset mid-childhood remitting phenotype (p<10–4).We conducted three GWASs  in parallel and results were meta-analysed. The distribution of the minor allele frequencies was consistent across genotyped datasets (mean SD 0.01). A circular Manhattan plot and a QQ plot are shown in t wheeze . Scatter–5). Following close inspection of all plots, we short-listed 85 independent SNPs (NAALADL2) identified as top hit for early-onset pre-school remitting wheeze was reported as an associate of severe asthma exacerbations, but only at nominal level , we used locus zoom plots (LZPs) for the 134 independent SNPs , p<6.7 × 10–9. The ANXA1 locus was the only GWAS-significant locus that had not previously been associated with asthma or atopic traits, with one previous study showing an association with FEV1/FVC and bronchodilator response in smokers  were associated with early-onset persistent wheeze at genome-wide significance (GWS), with an additional SNP rs78320984 almost reaching GWS  across wheeze phenotypes are shown in AXNA1 and overlapping the known regulatory region ID ENSR00000882742 on Chromosome 9: 73,173,001–73,173,200. This region is active in the GI tract, M2 macrophages, neural progenitor cells, and trophoblasts, but is repressed in T lymphocytes including CD4+ CD25+, Treg, and CD8+ cells.Variant Effect Predictor (VEP) prediction shows the SNP rs75260654 (C changed to T) to be located downstream of three protein-coding transcripts of ANXA1 in naïve B cells  and to increase expression in lymphoblasoid cell lines   . In the eQTL catalogue, rs75260654 is identified as an eQTL of ANXA1 in various immune cells  including T cells, monocytes, fibroblasts, whole blood, Th2 memory cells, naïve B cells. rs75260654 is also an eQTL of ANXA1 in monocytes that were stimulated with R848 (agonist of TLRs 7 and 8) and a human seasonal influenza A virus  . This SN A virus  (at nomi A virus . In the s (ANXA1  as well ANXA1 (p=6.02 × 10–23) and ALDH1A1 (p=1.11 × 10–19) at FDR = 0. No significant trans-eQTLs were observed.Additional supporting evidence regarding the significance of the T-allele on the expression of these genes was provided using eQTLGene Consortium meta-analysis of 24 cohorts and 24,331 samples . This meANXA1 interacts directly with genes enriched for asthma (including IL4 and IL13) and inflammatory regulation  showing its significance in dysregulation of the immune response  mice given HDM over 3 weeks developed significant airway hyperresponsiveness (AHR) compared to PBS control mice. Mice deficient in anxa1 had significantly worse lung function (greater airway resistance) compared to WT-treated mice . To identify the most likely causative variant, we investigated the overlap of the associated SNPs with PCHi-C interactions to demonstrate that SNP rs75260654 overlapped a region interacting with the ANXA1 promoter. Using eQTL data, we identified that the risk allele (T) of rs75260654 associated with early-onset persistent wheeze is also associated with ANXA1 expression. Further investigation of the biological function of ANXA1 revealed that it interacts with genes enriched for asthma (including IL4 and IL13) and inflammatory regulation . In functional mouse experiments, anxa1 protein expression increased and anxa1 mRNA was significantly induced in lung tissue following HDM exposure, suggesting that the pro-resolving anxa1 may play a role in regulating the pulmonary immune response to allergen. Concurrently, by utilising -/-anxa1 deficient mice we demonstrated that loss of anxa1 results in heightened AHR and Th2 inflammation upon allergen challenge, providing important in vivo functional data to support our GWAS finding.Herein, we present a comprehensive description of the genetic architecture of childhood wheezing disorders. Using a novel approach applied to a unique dataset from five UK birth cohorts, we identified subsets of SNPs differentially associated across four wheezing phenotypes: early-onset persistent , early-onset pre-school remitting , early-onset mid-childhood remitting , and late-onset . We found little evidence of genetic associations spanning across different phenotypes. This suggests that genetic architecture of different wheeze phenotypes comprises a limited number of variants likely underpinning mechanisms which are shared across phenotypes, but that each phenotype is also characterised by unique phenotype-specific genetic associations. Importantly, we identified a novel locus in chr9q21 nearby ANXA1 is a 37 kDa glycoprotein with potent anti-inflammatory and pro-resolving properties that are mediated by interaction with a specific G protein-coupled receptor FPR2  as an associate of this disease subtype, but not of doctor-diagnosed asthma  but is less likely to be an issue when primary outcome is determined by deep phenotyping. This is indirectly confirmed in our analyses. Our primary outcome was derived through careful phenotyping over a period of more than two decades in five independent birth cohorts, and although comparatively smaller than some asthma GWASs, our study proved to be powered enough to detect previously identified key associations . Precise phenotyping has the potential to identify new risk loci. For example, a comparatively small GWAS (1173 cases and 2522 controls) which used a specific subtype of early-onset childhood asthma with recurrent severe exacerbations as an outcome identified a functional variant in a novel susceptibility gene d asthma . This imd asthma , but repinitions .The importance of the precise outcome definition was highlighted in our previous studies in ALSPAC which explored genetic associates of wheeze phenotypes derived by LCA . Our curANXA1 findings in PIAMA cohort , identified SNP rs75260654 as the most likely causative variant, and demonstrated that the risk allele (T) confers a reduction in ANXA1 expression. In mouse experiments, ANXA1 expression increased in lung tissue following allergen exposure, suggesting that the pro-resolving ANXA1 may play a role in regulating the pulmonary immune response to allergen. Using ANXA1-deficient mice, we demonstrated that loss of ANXA1 results in heightened AHR and Th2 inflammation upon allergen challenge, providing important in vivo functional data to support our GWAS finding. Targeting these pathways to promote the clearance of chronic inflammation in persistent disease may represent an exciting therapeutic prospect.In conclusion, using unique data from five UK birth cohorts, we identified subsets of SNPs differentially associated across four wheezing phenotypes from infancy to adolescence. We found little evidence of genetic associations spanning across different phenotypes. We discovered a novel locus in chr9q21 uniquely associated with early-onset persistent wheeze  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 3 peer reviewers, including Anurag Agrawal as the Reviewing Editor and Reviewer #1, and the evaluation has been overseen by Molly Przeworski as the Senior Editor.Thank you for submitting your article "A meta-analysis of genome-wide association studies of childhood wheezing phenotypes identifies ANXA1 as a susceptibility locus for persistent wheezing" for consideration by The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.Essential revisions:1) Please provide more details on the meta-analysis strategy and rationaleConsider the other comments for greater clarity of the workReviewer #1 (Recommendations for the authors):The work is interesting, novel, and technically rigorous. Some suggestions follow regarding the experimental mouse model part of the manuscript, which needs further editing for clarity and standardisation of terminology.1. Figure 4A, font size needs to be increased.2. Line 289-291 needs correction. 4E shows methacholine-induced bronchoconstriction, not allergen-induced reduced lung function.3. Was baseline resistance (pre-methacholine) different? Any differences in the slope of the curve?4. Airway remodelling changes should be described if possible.Reviewer #2 (Recommendations for the authors):Even though the study described by Granell et al. is an impressive piece of work with robust sets of evidence that support the derived conclusions, this reviewer has some suggestions the authors might want to consider:1. A more detailed description of the association models applied to test the association with wheezing phenotypes, including the covariates used, is encouraged.2. Which approach was used for the meta-analysis? Did you take into account the heterogeneity among studies to choose fixed or random-effect models to be applied?3. Could you please provide an explanation of the genomic inflation found for the persistent wheezing phenotype ?4. Table E4: Could you please explain the reason for inclusion/exclusion?Reviewer #3 (Recommendations for the authors):1. Regarding the definition of wheeze phenotypes from infancy to adolescence, were transitions across different phenotypes over time possible? Was time-varying phenotype of concern? Did the authors evaluate the accuracy of phenotyping using metrics such as sensitivity, specificity, and positive and negative predictive values? And how much of the variation in each wheeze phenotype can be explained by genetic profile?2. In order to identify primary and secondary independent loci, can the authors perform the conditional analysis? Further, considering the LD structure, credible set analysis, and colocalization analysis can help identify potential causal variants.3. Can the authors provide further explanations on the biology using pQTL and TWAS?4. Can the authors use DEPICT or other tools to identify relevant tissues/cell types that are highly expressed?5. The heterogeneity in the genetic profile for the wheeze phenotypes can be better illustrated, such as by using a scatter plot to show the differences in genetic effect sizes. A heterogeneity p-value can be computed. In addition, what was the genetic correlation between the wheeze phenotypes? Essential revisions:1) Please provide more details on the meta-analysis strategy and rationalePlease see Reviewer 2 Q2.Consider the other comments for greater clarity of the workReviewer #1 (Recommendations for the authors):The work is interesting, novel, and technically rigorous. Some suggestions follow regarding the experimental mouse model part of the manuscript, which needs further editing for clarity and standardisation of terminology.1. Figure 4A, font size needs to be increased.Figure 5A (4Ain previous version) has been updated.2. Line 289-291 needs correction. 4E shows methacholine-induced bronchoconstriction, not allergen-induced reduced lung function.We have corrected this in line 322 of the Revised Manuscript.3. Was baseline resistance (pre-methacholine) different? Any differences in the slope of the curve?No statistically significant difference was observed in resistance at baseline. However, a leftward shift in the curve was observed.4. Airway remodelling changes should be described if possible.We did not assess changes in remodelling in these experiments. Here, we utilised a 3-week HDM challenge model, whereas to fully characterise changes in airways remodelling, mice would need to be assessed later than 3 weeks post HDM.Reviewer #2 (Recommendations for the authors):Even though the study described by Granell et al. is an impressive piece of work with robust sets of evidence that support the derived conclusions, this reviewer has some suggestions the authors might want to consider:1. A more detailed description of the association models applied to test the association with wheezing phenotypes, including the covariates used, is encouraged.We agree with this reviewer’s comment, and we have made this clearer in the Methods section on lines 185-7 Revised Manuscript:“We used SNPTEST v2.5.2(26) with a frequentist additive multinomial logistic regression model (-method newml), using the never/infrequent wheeze as the reference and without including any covariates.”2. Which approach was used for the meta-analysis? Did you take into account the heterogeneity among studies to choose fixed or random-effect models to be applied?These details were already available in the Supplement, and they can now be found in Appendix 2: GWAS Meta-analysis:“We used the option SCHEME STDERR in METAL to implement an effect-size based method weighted by each study-specific standard error in a fixed-effects model.”3. Could you please provide an explanation of the genomic inflation found for the persistent wheezing phenotype ?We would interpret Figure 1—figure supplement 1  as an association between SNPs and Persistent wheeze rather than inflation. For instance, we did not observe any deviation in the other phenotypes. We now report the phenotype specific genomic inflation factor for each wheezing phenotype in lines 222-5 Revised Manuscript:“We observed slight deflation of the meta-analysis pvalues in our summary statistics. Genomic Inflation Factor (λ) for Early-onset Pre-school Remitting=0.96, Early-onset Mid-childhood Remitting = 0.94, Late-onset=0.96, and Early-onset Persistent wheezing=0.97.”And have added this text in Appendix 2: Genetic Control:“The genomic inflation factor (λ) was calculated using the scipy.stats.chi2 module in Python. The chi-squared test statistics from the meta-analysis p-values were first obtained. Then the observed median chi-squared statistic from the calculated chi-squared test statistics were calculated. Finally, the genomic inflation factor (λ) was derived by dividing the observed median chi-squared statistic by the expected median chi-squared statistic.”4. Table E4: Could you please explain the reason for inclusion/exclusion?Thank you, this was not properly explained in the main text. Individuals with missing genetic data, as well as related and non-European individuals were excluded. We have now made this clear in line 212-4 Revised Manuscript:“Individuals with missing genetic data, as well as related and non-European individuals were excluded. Comparison of included vs. excluded individuals across cohorts (per cohort and time point) is shown in Appendix 1 and Appendix 1-Table 4.”Reviewer #3 (Recommendations for the authors):1. Regarding the definition of wheeze phenotypes from infancy to adolescence, were transitions across different phenotypes over time possible? Was time-varying phenotype of concern? Did the authors evaluate the accuracy of phenotyping using metrics such as sensitivity, specificity, and positive and negative predictive values?No, LCA does not allow transitions across different phenotypes over time.Was time-varying phenotype of concern? Did the authors evaluate the accuracy of phenotyping using metrics such as sensitivity, specificity, and positive and negative predictive values?We have explored time-varying phenotype in the context of LCA in our recent paper :883-893. doi: 10.1164/rccm.202108-1821OC. PMID: 35050846; PMCID: PMC9838626.) where we compared the robustness of LCA and PAM phenotypes. In this paper we show that latent class analysis- and Partition Around Medoids (PAM) clustering of spell-based phenotypes appeared similar, but within-phenotype individual trajectories and phenotype allocation differed substantially. The spell-based approach was much more robust in dealing with missing data, and the derived clusters were more stable and internally homogeneous than those derived by LCA. If anything, any instability of LCA phenotypes over time would reduce the power of the current study, yet we still identified genome-wide significant associations unique to specific wheeze clusters. None-the-less future studies should explore the utilisation of PAM derived phenotypes in genetic analysis of allergic disease.And how much of the variation in each wheeze phenotype can be explained by genetic profile?We have not explored this question in this paper, but we will do so in our follow-up paper.2. In order to identify primary and secondary independent loci, can the authors perform the conditional analysis? Further, considering the LD structure, credible set analysis, and colocalization analysis can help identify potential causal variants.3. Can the authors provide further explanations on the biology using pQTL and TWAS?4. Can the authors use DEPICT or other tools to identify relevant tissues/cell types that are highly expressed?The reviewer makes a number of helpful suggestions of additional post-GWAS analyses that could be undertaken on our results. Given the size of the manuscript and the extensive incorporation of mouse model data for the ANAX1 loci, we feel that adding further post GWAS analyses to the supplementary data is beyond the scope of this paper and we are currently working on a new follow-up paper that will focus on fine mapping of these loci.5. The heterogeneity in the genetic profile for the wheeze phenotypes can be better illustrated, such as by using a scatter plot to show the differences in genetic effect sizes. A heterogeneity p-value can be computed.We agree with the reviewer’s suggestion and have produced scatter plots to illustrate the heterogeneity in the genetic profile of the wheezing phenotypes. We have added these plots in Figure 2—figure supplement 1 and the following text has been added in lines 230-3 Revised Manuscript:“Scatter plots in Figure 2—figure supplement 1 show the heterogeneity in the genetic profile of the wheeze phenotypes. The plots show that all signals were phenotype-specific at p<10-5 and only nominal associations were shared across wheezing phenotypes. More details on how these plots were derived can be found in Appendix 2: Heterogeneity scatter plots.”In addition, what was the genetic correlation between the wheeze phenotypes?We identified exclusive subgroups of SNPs associated with each wheezing phenotype, and only two locus were shared with Persistent and Early-onset Mid-childhood Remitting Wheezing (chr17q12 and chr17q21) therefore the genetic correlation between the wheeze phenotypes is likely to be small.


Sorghum bicolor) is a paramount breeding objective, as is increasing drought resistance (DR), because sorghum is grown mainly in drought-prone areas. The genetic basis of grain nutritional traits remains largely unknown. Marker-assisted selection using significant loci identified through genome-wide association study (GWAS) shows potential for selecting desirable traits in crops. This study assessed natural variation available in sorghum accessions from around the globe to identify novel genes or genomic regions with potential for improving grain nutritional value, and to study associations between DR traits and grain weight and nutritional composition.Increasing grain nutritional value in sorghum , and plant height (PH) in sorghum through GWAS of 163 unique African and Asian accessions under irrigated and post-flowering drought conditions. Several QTLs were detected. Some were significantly associated with DR, TKW, PH, protein, and Zn, Mn, and Ca contents. Genomic regions on chromosomes 1, 2, 4, 8, 9, and 10 were associated with TKW, nutritional, and DR traits; colocalization patterns of these markers indicate potential for simultaneous improvement of these traits. In African accessions, markers associated with TKW were mapped to six regions also associated with protein, Zn, Ca, Mn, Na, and DR, suggesting the potential for simultaneous selection for higher grain nutrition and TKW. Our results indicate that it may be possible to select for increased DR on the basis of grain nutrition and weight potential.This study provides a valuable resource for selecting landraces for use in plant breeding programs and for identifying loci that may contribute to grain nutrition and weight with the hope of producing cultivars that combine improved yield traits, nutrition, and DR.The online version contains supplementary material available at 10.1186/s12864-023-09613-w. Sorghum bicolor (L.) Moench, one of the five main cereal crops globally [globally , plays aglobally . Sorghumglobally . Mann etglobally  hypothesglobally , 6. Asiaglobally . Landracglobally , 9, Eritglobally , Sudan [globally , and Indglobally . These aglobally , 14.Developing sorghum cultivars with enhanced grain nutritional quality is an important target for many breeding programs . SorghumMore than 90% of sorghum growing and 85% of processing  are concentrated in warm semiarid areas . DroughtMaintaining high productivity in crops exposed to environmental stresses depends on inherent mechanisms to avoid, adapt to, or tolerate stresses . The graDespite massive research efforts, the genetic mechanisms underlying DR and mineral contents remain poorly understood. As well, most recent studies on sorghum have investigated grain yield, quality, or stress tolerance–related traits in isolation , 30, 31.We examined the mineral or nutritional content, thousand kernel weight (TKW), protein content, and plant height in 163 germplasm accessions grown under normal conditions and three DR traits in plants grown under post-flowering drought conditions. We investigated the genetic control of these traits and their associations using a genome-wide association study (GWAS) approach. We also established a method for screening cultivars for DR on the basis of their grain weight and nutritional profile under normal conditions without the need for complicated field testing.To the best of our knowledge, this is the first attempt to explore the phenotypic and genotypic associations between sorghum grain weight, nutritional potential, and DR traits intended to identify valuable germplasm or to establish screening methods to develop drought-tolerant cultivars with high yield potential in terms of PH and TKW and nutrition without field testing. It provides a valuable resource for elucidating loci associated with traits of interest that may be useful in understanding mechanisms that contribute to grain nutrition and weight and in future breeding programs that seek to produce sorghum cultivars that combine high grain yield potential, nutrition, and DR.Allele frequencies and polymorphic information content (PIC) values are summarized in Supplementary Table Cluster analysis explore differences and similarities between geographic groups showed that the accessions formed nine groups, in two separate clusters. The first cluster contained the Sudanese cultivars, the Central, North, and East African accessions, and a sub-cluster of accessions from South and East Asia Fig. a. IntereWe also performed principal component analysis (PCA) to investigate the relationships among accessions. PC1 and PC2 captured ~ 73.7% of the genetic variation , withering at 4 days after drought (WI_4DAI), and withering at 6 days after drought (WI_6DAI) were significantly higher in African than in Asian accessions Fig. b. After Among all accessions, protein content correlated positively with Mg (0.50**), P (0.48***), and Fe (0.20*) contents Fig. a. TKW coWe conducted PCA and hierarchical cluster analysis of phenotypic and mineral traits to explore associations between traits and regions of origin. PC1 and PC2 accounted for 24.1% of the variation in all populations, 25.2% in African, and 32.2% in Asian  based on trait similarity Fig. . The tra10(P) values, as reflected by low scores following the null hypothesis line. Particularly for Al, P, and TKW marker trait associations (MATs), mrMLM produced higher -log10(P) values than expected yet showed greater statistical power, as reflected by the higher number of significant associations detected (Tables We performed a genome-wide association study (GWAS) of all accessions and of African accessions 105), but not of Asian accessions, as their numbers were low (< 100). We used two algorithms, the multi-locus random-SNP-effect mixed linear model (mrMLM) and the FAST multi-locus random-SNP-effect mixed linear model (FASTmrMLM) . GWAS re, but notd Tables  and S10.The complete list of markers significantly associated with phenotypes is provided in tables In all accessions, the MTAs for the DR traits DR_B and WI_6DAI (10 MTAs) were distributed on Chrs. 1, 2, 4, 6, 7, 8, 9, and 10; most were identified by both mrMLM and FASTmrMLM. In African accessions, the drought-related traits were associated with 14 SNPs on all chromosomes except Chrs. 4 and 7 Tables  and S10.In all accessions, markers related to minerals, WI_6DAI, protein content, and TKW co-located in the region from 0.379 to 58.557 Mbp on Chr. 1. Markers related to WI_6DAI were co-located with markers related to PH and P on Chr. 4 (51.656–60.751 Mbp), with markers related to PH and Zn on Chr. 10 (1.1327–3.9496 Mbp), and with markers related to Zn on Chr. 7 (2.4337–6.5319 Mbp). DR_B-related markers co-located with markers related to PH, Zn, and protein content on Chr. 9 (40.8101–55.9349 Mbp), and with markers related to minerals and PH on Chr. 8 (58.1326–62.1117 Mbp) , as defined by Habyarimana et al. [In all accessions, 14 markers had a significant effect  for all significant markers identified by GWAS on all chromosomes. The WI_6 DAI SNPs  associated positively with P, Mn, Na, Fe, Mo, B, and Se and negatively with TKW, whereas DR_B  SNPs associated positively with TKW and Zn and negatively with protein content and eight minerals. Protein content SNPs associated positively with Ca, and PH SNPs associated positively with TM_6 DAI, P and Mn  and animal (biomass) consumption. Knowledge of its nutritional, yield traits (PH and TKW), and DR genetic diversity and their associations would benefit breeding programs for improvement of yield and quality traits in drought-prone areas. We evaluated traits related to yield potential  and seed quality or nutrition (16 macro- and micro-elements and protein content) in plants grown without drought, as well as DR traits  in plants grown under post-flowering drought. We explored the genetic diversity of these traits in order to identify landraces for use in breeding better sorghum cultivars and to assess the feasibility of predicting DR in plants grown under controlled conditions by evaluating their mineral and potential yield profiles.Genetic studies have investigated polymorphism in sorghum genotypes and assessed the extent of genetic variability. However, few studies have explored the genetic diversity in sorghum nutrition and yield traits values. This study provides a comprehensive and detailed evaluation of genetic diversity in sorghum through analysis of collections with known geographic origins by using high-throughput diversity array technology (DArT) markers across the entire genome.The sorghum accessions used represent an excellent genetic diversity resource covering a wide range of geographical and ecological zones. Accessions showed the greatest similarity in SNP markers to others from the same geographic region Fig. . The relThe PCA plots and hierarchical clustering indicate that the North African (center of origin) accessions are the most divergent from the other accessions; accessions from Africa appear to have little within-group diversity, whereas the Asian accessions have greater within-group diversity Fig. a–d. SorgAccording to AMOVA, 90% of the total variation occurred within and 10% occurred among populations (region of origins). The higher variation within regions of origin suggests lower human selection pressure on landraces than on sorghum grown elsewhere in these regions; strict selection by breeders for crop improvement might have contributed to reduce within-accession variation. As genetic diversity indicates potential to adapt to environmental change , sorghumWe studied the phenotypic variation of 16 grain mineral contents and several phenotypic traits. We could not confirm any clear relationship between molecular and morphological traits. This could be attributed to the relatively small sample size and the fact that phenotypic traits are influenced by environment. Ayana et al.  found no− 1; Fig. The control of macro- and micronutrient homeostasis in plants has been extensively studied; however, the loci that control natural ionomic variations in the grain of sorghum are still largely undetermined. The discovery of genes or genomic regions associated with grain nutritional content and TKW may accelerate the development of new biofortified cultivars. Accessions showed a broad range of mineral concentrations Tables ; Fig. 2.2+ acts as a control ion in a wide variety of plant cells, and Ca signaling potential is linked to the ability of cells to maintain high concentration gradients [Mg, Fe, S, and Cu had relatively low levels of variation Fig. , indicatradients . Anotherradients . GermplaProtein content varied widely (6–15%). Asian accessions had the highest content. Both environment and genotype affect protein content . AfricanS. bicolor retain the most similarity to wild ancestors, and high protein, mineral contents, and TKW may have been inadvertently counter selected during cereal domestication when high starch and grain yield were selected. Furthermore, human selection in these areas for different food or feed uses influenced the patterns of grain composition distribution among genetic groups; porridges from different regions require different grain compositions. The significant correlations between PH and grain yield and TKW were reported in sorghum by Kante [The yield parameters of average PH and TKW tended to be higher in African accessions, especially from North and West Africa; grain weight is a crucial determinant of yield-related traits in cereals. Accessions from those areas are promising sources of genetic material for high TKW and mineral concentration. Among sorghum cultivars, those of by Kante , which sby Kante , 51.Tannin 1 gene, which controls the presence of tannins in the grain, was found to be correlated with several bioclimatic factors [Our diverse association panel’s protein, TKW, PH and mineral content ranges may help improve sorghum yield potential and quality. Furthermore, adaptation to different environments drives grain composition differences between genetic groups. Evidence of this adaptation was recently found in tannins in sorghum grain when a variant of the  factors .Correlations between traits are of great importance for successful selection in breeding programs. Here, protein content was positively correlated with TKW, Cu, Mg, P, S, and Zn. These results suggest that the best strategy may be to combine selection for micronutrients, protein, and TKW in Asian accessions in a single agronomic background. However, for cultivars with improved micronutrient profiles to benefit human nutrition, the cultivars must carry farmer-preferred grain traits such as earliness, seed size, and color . Other sThe wide phenotypic variation in minerals, protein content, and TKW indicates that our accessions panel is well suited to dissecting the genetic basis of high nutritional value and TKW in sorghum.We found no correlations between TKW, PH, protein, and most elements, except for the negative correlation of Mn and B and the positive correlation of Al, Se, and Ca. However, PH QTLs co-located with QTLs for Mn, P, Na, Si, Se, Mo, and DR on Chrs. 4 and 10 and WI_6DAI on Chr. 8  in drought-prone areas. We found that accessions from different regions possess different unique traits. Although we used an unequal number of accessions from different geographical regions, these results could be a trigger for more detailed studies to map the different traits to different geographical regions which can strongly help and impact the breeding programs. Moreover, despite the concentration on African sorghum germplasm in breeding programs, the Asian accessions showed interesting variability not existing in the African germplasm that can impact sorghum breeding.To the best of our knowledge, this is the first attempt to identify the phenotypic and genotypic associations between sorghum grain weight and nutritional potential and DR traits. Our results indicate that it may be possible to select for DR without field testing by selecting for high grain mineral contents  and yield potential. Although additional validation is necessary, this method can be used as a preliminary selection strategy in breeding programs.Sorghum bicolor) accessions used here have been deposited in the gene bank of the Arid Land Research Center, Tottori University, Japan , 2 from Southeast Asia (Cambodia and Myanmar), 26 from South Asia , and 2 from Southwest Asia (Iran and Israel). The remaining African accessions came from North Africa (75 accessions from Sudan and Morocco), Central Africa , East Africa , West Africa (5 accessions from Nigeria) and South Africa .The 163 sorghum ; each pot contained three replicates of each accession. All pots were filled with Tottori sand dune soil mixed with fertilizers ) 5 g/pot; NPK fertilizer 14–14–14, 5 g/pot). A corrugated sheet was laid under the bottom of the pots, and the roots were restricted so they could not emerge from the pot. Seeds were sown in the first week of June in 2017 and 2018. In the drought treatment, pots were irrigated normally until heading, then irrigation was stopped for 10 days. In the first year and during these 10 days of drought, we observed the plant senescence rate and scored it on a 6-point scale . The withering index was measured at 4 (WI_4DAI), 6 (WI_6DAI), and 10 (WI_10DAI) days after irrigation in 2017 and at 4 (DR_A) and 10 (DR_B) in 2018 Fig. . In bothPlant height (PH) was measured in cm at the heading. After the drought treatment, we re-irrigated the pots normally and observed the recovery from drought at 3 and 5 weeks. From the normally irrigated pots, without drought in season 2017, grains were harvested, TKW was measured in g, and seeds were used for mineral and protein content analysis.Matured seeds were collected from irrigated plants, and stored at the desiccator for drying before analysis. Seeds were then ground for measurement of the N content of about 50 mg of sorghum kernel powder with a CN Corder . We recorded the N: C ratio, then calculated the N content as a percentage, and converted that value to the crude protein content by multiplying the N content by 5.95 .3 in a high-performance microwave digestion system , Kawasaki, Japan; http://www.milestonesrl.com). Digested samples were diluted 1:1000 before injection into an inductively coupled plasma-mass spectrometry (ICP-MS) system .Macro-  and micro-  mineral contents were quantified. About 200 mg of mature seeds was digested with concentrated HNOData were tested for normality and homogeneity of variance before analysis by the Shapiro–Wilk test and Levene’s test, respectively. Analysis of variance (ANOVA) and mean comparison (Tukey’s test) were performed in GenStat software, 18th edition . A combined analysis of variance was carried out across 2 years and best linear unbiased prediction (BLUP) was used in subsequent analysis.Morphological data for all 163 accessions were clustered by using the between-groups linkage method of squared Euclidean distance in SPSS v. 25 software . Hierarchical cluster analysis based on average values of all traits and dendrogram visualization of the results in hierarchical cluster analysis was visualized in R program, 2018 .http://statgen.ncsu.edu/powermarker/). Branch support values were determined by performing bootstrap analysis with 1000 replicates. Power Marker was used to calculate the allelic number (NA), genetic diversity, and polymorphism information content (PIC) values of each marker. Population structures were evaluated in the R program v. 2018 [Genetic distances were estimated by using Nei’s distance , and phy v. 2018 . Princip v. 2018 . Analysi v. 2018 .To maximize the differences and to detect those differences in all traits among groups, the current research applied a multivariate method, Visual Infusion Phlebitis (VIP) scores was performed based on the Partial Least Squares Discriminant Analysis (PLS-DA) using Metaboanalyst (ver.5) software.DNA from the leaf tissue of 2-week-old seedlings of each line was extracted by using a modified CTAB method . Samples10(P) values was visualized on quantile–quantile (Q–Q) plots to test the fitness of GWAS models for all traits [Drought data sets, seed weight, and nutrition-related traits were used to identify SNPs associated with all traits. GWAS analysis was performed separately on 158  of the 163 accessions and on the 110 African accessions using 8938 SNP markers with < 20% missing data and ≤ 0.05 minor allele frequency. GWAS was performed in R software for multi-locus GWAS, namely mrMLM version 4.0.2 , 84. Thel traits , and thel traits , 93.https://ritchielab.org/software/phenogram). Manhattan and Q–Q plots were plotted in R.To summarize all the GWAS results, significant marker-trait associations were plotted in PhenoGram software (Biomedical and Translational Informatics Laboratory: Below is the link to the electronic supplementary material.Supplementary Material 1Supplementary Material 2


Introduction: Compared to Genome-Wide Association Studies (GWAS) for common variants, single-marker association analysis for rare variants is underpowered. Set-based association analyses for rare variants are powerful tools that capture some of the missing heritability in trait association studies.Methods: We extend the convex-optimized SKAT (cSKAT) test set procedure which learns from data the optimal convex combination of kernels, to the full Generalised Linear Model (GLM) setting with arbitrary non-genetic covariates. We call this extended cSKAT (ecSKAT) and show that the resulting optimization problem is a quadratic programming problem that can be solved with no additional cost compared to cSKAT.Results: We show that a modified objective is related to an upper bound for the p-value through a decreasing exponential term in the objective function, indicating that optimizing this objective function is a principled way of learning the combination of kernels. We evaluate the performance of the proposed method on continuous and binary traits using simulation studies and illustrate its application using UK Biobank Whole Exome Sequencing data on hand grip strength and systemic lupus erythematosus rare variant association analysis.Discussion: Our proposed ecSKAT method enables correcting for important confounders in association studies such as age, sex or population structure for both quantitative and binary traits. Simulation studies showed that ecSKAT can recover sensible weights and achieve higher power across different sample sizes and misspecification settings. Compared to the burden test and SKAT method, ecSKAT gives a lower p-value for the genes tested in both quantitative and binary traits in the UKBiobank cohort. Genome-wide association studies (GWAS)  while controlling for stratification by taking into account non-genetic covariates. Given non-genetic and genetic covariates denoted by g ∈ {0,1,2}pn subjects, where m and p are the non-genetic and genetic dimensions, we collect the data into a dataset We consider a genetic association study of sample size G, we are interested in testing for an association between the trait and genetic information. We use the standard frequentist hypothesis testing framework  with τ ≥ 0 and K a valid kernel function, the vectorized output NKij = K is the so-called kernel matrix encoding affinity between the individuals through the gene set. When β ∼ N)w, where we assume w ∈ △p, with △p being the set of probability vectors of dimension pw allows us to design a test that fits the domain knowledge by weighing the contribution of individual variants according to the weight vector w. From here on, we will only consider weighted linear kernels w ∈ △p.The family of SKAT-like testing procedures, including their many descendants ; 2014 , where vi is the maximum likelihood conditional variance of iy under the null model. For binary outcomes, λ0 be the eigenvalues of A, denoted by λ0 = eig(A).In this case, we can reformulate Eq. p-value function as follows:0 analytically, but it can be evaluated numerically up to arbitrary precision using Davie’s method  = exp(−x), which leads to the so-called softmax weighing function. In practice, one could choose lϕ from a pool of candidates  using cross-validation on the train set ((lϕ(x) = x2, which reduces to using the aggregation method in l,j = lϕ, Dl = diag(Φl), and F, and w is then learned using multiple kernel learning techniques  and linear regression. Here, we solve the full case when X is non-zero and for any valid conditional response model that comes under the GLM framework with a canonical link function. They proposed to split the data into a train and a validation set, D = Dtr ∪ Dts, where Dtr is used to finding w and Dts to perform the hypothesis test using w. The learning procedure of w is defined through the following objective:λ0 as functions of Kw through w. The induced optimization problem becomesw* in Eq. As shown in theorem 1.1, we may rewrite p-value ; w) ≤ u(w), where u(w) is tight and convex. Here, we have explained explicitly the dependency of p0(q) on w.As pointed out in alue Eq.  on the tp-value is upper-bounded throughJ(w) differs from the cSKAT objective (Eq. b = diag(B), the diagonal of B as a vector, instead of J(w) ≥ 1, then the upper bound is exp(−J(w)/8). Since the function f(x) = exp(−x/8) is decreasing, we observe that minimizing f is equivalent to maximizing J(w), which again reduces to a QP problem similar to Eq. 22. The result shows that the modified cSKAT objective is a principled objective and that maximizing it is equivalent to minimizing an upper bound on the p-value, and furthermore, as the objective grows in value, the upper bound decreases and reaches zero as J(w) → ∞ exponentially fast. In particular, for any a ≥ 0, fixing a minimum level p0(Q(w); w) ≤ 10a− can be certified as long as In theorem 1.2, we show using large deviation theory , we need to know how the genetic and non-genetic covariates are related to the output. We simulate the data using a model of the relationship of the GLM form as follows:g others , for a dp, and SKAT, where we set the weights using β-pdf, as outlined in β) in the figures and experiments, in terms of estimated Type I error and power. For the marginal distributions of the non-genetic and genetic covariates (Xρ and Gρ), we either generate them synthetically or use the empirical distributions of the UKBiobank dataset through using available datapoints (patients) relating to the gene PARK7 with non-genetic covariates of age (in years), sex (one-hot encoded), and 10 principal components from the full genetic whole-exome sequencing (WES) dataset and sample without replacement (due to the size of the database the id violation is negligible) from these patients 1,000 times in order to get 95% confidence intervals for We benchmark ecSKAT against the burden test using the sum as an aggregation, uniform SKAT, where the weights are equal to 1/α = 10−1, 10−2, 10−3, and perform this experiment 1,000 times to get 95% confidence intervals. We see that all algorithms control the Type I error for big α but struggle for α = 10−3. However, it should be noted that for all algorithms in all plots, the current significance level falls within the confidence interval.In the Type I error experiment , we showα and large sample size, the other SKAT methods , Uniform) also have maximum power, while the burden (Sum) test fails to perform well, probably because it assumes all weights to have the same sign, which is not true here. For the misspecified case (row 2), we see that only SKAT ) and ecSKAT manage to perform well with their performance improving in the sample size and decreasing with smaller α. From this, we can see that ecSKAT performs best and SKAT variants perform well in terms of power for the correctly specified case, while only ecSKAT and SKAT ) perform well in the misspecified case, probably due to the data-dependent nature of how they reweigh each genetic covariate.Finally, for the power experiment , we lookTPTEP2-CSNK1E and ZDHHC8  in 73,424 individuals and systemic lupus erythematosus (SLE) (binary trait) with 966 cases and 4,296 controls, adjusting for sex, age, and 10 principal components. We restricted our analysis to the predicted loss-of-function   variantse tested , 2.1. We showed that the weighted annotation method of 2. We completed the analysis of cSKAT for the case of an arbitrary GLM model when the covariates are non-zero, showing that the objective can be solved using a similar QP procedure as the original cSKAT algorithm, retaining the same computational complexity.p-value as a function of w and that this bound is tight as the objective goes to infinity, indicating that cSKAT is a principled objective since it relates well to the objective of the study .3. Finally, we showed that a slight modification of the cSKAT objective is related to an upper bound on the This study generalised the cSKAT formulation to general GLM models with non-genetic covariates and showed that this formulation, while being considerably more general and applicable in practice as compared to the linear model, the no covariate setting of p-value for the genes tested in both quantitative and binary traits.Simulation studies showed that ecSKAT can recover sensible weights and achieve higher power across different sample sizes and misspecification settings. In real data analysis, we applied the method to both the binary (SLE) and quantitative (hand grip strength) traits in the UKBiobank cohort. Compared to the burden test and SKAT method, ecSKAT gives a slightly lower w* and the size of the training and validation sets. Furthermore, given a fixed dataset size n, we would like to analyze the optimal training set size, which would be of interest in practice. Finally, we would like to perform more large-scale experiments, in particular, on the newly released 500-k WES cohort of UKBiobank (In the future, we would like to theoretically analyze the power in terms of the true value


Cropping efficiencies were in the range of 30.8–67.4 g/cm2/year while nut weight and nut yield varied from 5.9 to 10.5 g/year and 477.8 to 939.4 kg ha‐1 year‐1 in the fourth to sixth years after planting, respectively. The Beninese progenies outperformed the Brazilian progenies for cropping efficiency. GCA effects were more important than SCA effects. Narrow‐sense heritability ranged from 0.47 (CE) to 0.80 (NW). Canopy spread in the north‐south direction correlated  strongly with cropping efficiency at the genotypic level. Among males, BE203 showed positive GCA effects for cropping efficiency, TCSAv, and nut yield, whereas A2 and SG273 showed positive GCA effects for nut weight. Among females, SG287 showed negative GCA effects for TCSAr. Our study provides evidence that, cashew tree size and nut quality are under genetic control and the identified clones represent a suitable genetic resource pool to increase productivity.High cropping efficiency implies that high yields are obtained from reasonably sized trees. We studied the general and specific combining ability (GCA and SCA) of selected cashew clones of Brazilian (A), Beninese (BE), and Ghanaian (SG) background for cropping efficiency and nut weight in the early years of bearing. Using North Carolina II mating design, four clones were crossed as males to three best clones recommended for farmers. The 12 F The center of origin is north‐eastern Brazil but was introduced to West Africa by the early Portuguese settlers in the 16th century  the combining abilities of selected Brazilian, Beninese, and Ghanaian cashew clones for vigor, cropping efficiency, and nut weight and (ii) the genotypic and phenotypic correlations between growth and cropping‐efficiency‐related traits.22.11 progenies. On the basis of the origin of male parent, the 12 F1 progenies generated were classified into genotype groups: Ghana 1 , Ghana 2 , Benin , and Brazil  progenies. The Ghana 1 and 2 served as the standard varieties/controls. The male and female parents in this study were selected based on preliminary germplasm evaluation experiment conducted at Wenchi and Bole research stations in Ghana from 2004 to 2012. Selection criteria included vigor, nut yield, and nut weight . The three female parents were the top three best performing local clones recommended for farmers. These clones were selected from a historical local germplasm collection plot assembled in the mid‐1990s. A description of the characteristics of parental clones utilized is given in Table (active ingredient – cypermethrin) @ 150 ml/ha ) to control insect pest from August to October annually.The plant material used in this experiment involved two exotic and five local parental clones. The exotic parental clones selected for the study were BE203 from Benin and A2 from Brazil while the local (Ghana) parental clones selected for this study were SG273, SG224 ,SG266, SG276, and SG287 Table . Using N2.2In general terms, vegetative and reproductive traits were assessed based on the cashew descriptor list of the International Board for Plant Genetic Resources . In the second stage, progenies were classified into four genotype groups viz, Benin, Brazil, Ghana 1, and Ghana 2 based on the origin of male parent germplasm. The combined means of the three progenies within each genotype group for cropping efficiency and nut weight were determined. At this stage, genotype groups were considered as fixed factor and replicates as random factor. The general and specific combining ability (GCA and SCA) effect, narrow‐sense heritability, additive variance, dominance variance, and environmental variance were estimated using restricted maximum likelihood (REML) methods in AGD‐R  software  following tests for normality . In the first stage, all 12 FYijk is the observed value; µ is the population mean effect; kb is the block effect; im is the male GCA effect ; jf is the female GCA effect ; * fjmi  is the SCA effect, and eijk is the residual effect. The differences among means were tested by Duncan's Multiple Range Test (DMRT) at the 5% probability level. The genetic and phenotypic correlation coefficients between two traits, i and j, were estimated using METAR‐R statistical package  for all traits considered in this study. Partitioning of the progeny effect into male, female, and male × female interaction components indicated that the female effect  was not significant for any trait except increase in trunk cross‐sectional area at the reproductive stage (TCSAr). Therefore, the contribution of the female effect to the overall variability observed for the other traits among progenies was generally negligible. However, the effect of GCA for males was highly significant for all traits studied except TCSAr and consequently contributed the most to the variation observed among the progenies. On the other hand, the SCA effect was also significant for all traits, with the exception of cropping efficiency and canopy spread in the east‐west (CSew) and north‐south (CSns) directions. The ratio of the additive (GCA) to dominance (SCA) variance was also much higher than unity for most traits and indicated the predominance of GCA effects in controlling most traits. Genotype group effect was also significant for the nut weight and cropping efficiency.The genotypic effect was highly significant  were observed for all traits  direction, a range of 2.8 in SG266 × SG273 to 4.6 m in SG287 × BE203 was recorded, whereas in east‐west (CSew) directions it varied from 1.9 in SG276 × BE203 to 3.8 m in SG266 × SG224, respectively. Again, the Beninese progeny SG287 × BE203  were comparable but superior to the Beninese and Ghana 2 progenies , SG287 × BE203, SG287 × SG273, SG287 × A2, SG266 × A2, SG266 × SG273, and SG266 × SG224 were the top six outstanding progenies , nut yield, canopy spread (CSns), and cropping efficiency for the Beninese male parent BE203.(Table 3.4rg) and phenotypic (rp) correlation estimates among traits indicated that cropping efficiency had significant correlation with canopy spread in the north‐south direction  and nut yield . However, no significant correlation with vigor  at the vegetative  and reproductive  stages were observed, respectively. Additionally, nut weight negatively correlated with nut yield  at the genotypic level  has been reported  heritability estimates observed for all the tested traits implied that environmental influence was minimal and considerable selection responses can be expected. In Brazil, Cavalcanti, de Resende, et al.  reportedWith the exception of the non‐significant correlation between TCSA and cropping efficiency, the genetic correlations observed in our study are consistent with what has been found in other tree crops. Genetic correlations were reported between cropping efficiency and yield  were comparable to the Brazilian and Beninese progenies for nut weight and cropping efficiency, respectively. However, based on these two traits, six progenies, SG287 × BE203, SG287 × SG273, SG276 × SG224, SG266 × A2, SG266 × SG273, and SG276 × BE203, were outstanding, revealing the ability to combine targeted traits into a single background. Additive effects seemed to play a dominant role in the inheritance of most traits and breeding should therefore focus on cashew parents that are general combiners. Among the males, BE203 showed significant GCA effects for cropping efficiency, nut yield, TCSA at the vegetative stage, and canopy spread in the north‐south direction, whereas A2 and SG273 demonstrated significant positive effects on nut weight. Among the females, SG266 and SG278 showed positive and negative GCA effect on TCSA at reproductive stage respectively. Cropping efficiency correlated strongly  with canopy spread in the north‐south direction at the genotypic level. The identified clones constitute a suitable genetic resource pool for improving cashew productivity.Our study demonstrated that the potential vigor of cashew tree is under genetic control and the Brazilian (A), Beninese (BE), and Ghanaian (SG) clones had different merits as potential parents for TCSAv, TCSAr, cropping efficiency, and nut weight. The comparatively higher nut yields in the range of 477.8–939.4 kg/ha and cropping efficiency varying from 30.8 to 67.4 g/cmThe authors declare that there is no conflict of interest.PKKAG and MB conceived the manuscript. PKKAG collected and analyzed the data, wrote the manuscript. AA, AO, and FP all assisted with data collection and provided significant editorial and analytical advice.The authors acknowledge the support of the Deutsche Gesellschaft für Internationale Zusammenarbeit GmbH (GIZ) with funding through the African Cashew Initiative (ACi) and the Cocoa Research Institute of Ghana (CRIG).Table S1Click here for additional data file.