Zea mays L.) has been scrutinized genetically and phenotypically due to relationship with other agronomically valuable traits . Heritable variation of PHT is determined by many discovered quantitative trait loci; however, phenotypic effects of such loci often lack validation across environments and genetic backgrounds, especially in the hybrid state grown by farmers rather than the inbred state more often used by geneticists. A previous genome-wide association study using a topcrossed hybrid diversity panel identified two novel quantitative trait variants controlling both PHT and grain yield. Here, heterogeneous inbred families demonstrated that these two loci, characterized by two single nucleotide polymorphisms (SNPs), cause phenotypic variation in inbred lines, but that size of these effects were variable across four different genetic backgrounds, ranging from 1 to 10 cm. Weekly unoccupied aerial system flights demonstrated the two SNPs had larger effects, varying from 10 to 25 cm, in early growth while effects decreased toward the end of the season. These results show that allelic effect sizes of economically valuable loci are both dynamic in temporal growth and dynamic across genetic backgrounds, resulting in informative phenotypic variability overlooked following traditional phenotyping methods. Public genotyping data show recent favorable allele selection in elite temperate germplasm with little change across tropical backgrounds. As these loci remain rarer in tropical germplasm, with effects most visible early in growth, they are useful for breeding and selection to expand the genetic basis of maize.Plant height (PHT) in maize ( Zea mays L.) (Triticum spp.) and rice (Oryza Sativa) through the introduction of dwarf loci, initially used as a breeding strategy to maintain grain yield lost through lodging (r = 0.61) with grain yield in commercial hybrids over subtropical environments. They proposed that an optimal taller PHT is a desirable maize ideotype with respect to yield, especially under subtropical heat and drought stress, as long as lodging is not an issue.Plant height (PHT) in maize has been subjected to many phenomic and genomic investigations since it influences plant architecture and agricultural performance, relating to other agronomically and economically significant traits in maize (http://archive.gramene.org/qtl/). Very few of these to our knowledge have been confirmed as QTL in independent studies across different genetic backgrounds and environments.The wealth of studies on maize PHT has demonstrated the complexity, dynamic pattern, and polygenic inheritance of this trait; a trait governed by a large number of loci but with minor effects -induced gene transcriptions in the absence of GA signaling (Dwarf3 gene (D3) of maize has significant sequence similarity to the cytochrome P450, which encodes one of the early steps in GA biosynthesis  limitations in detection ability of height related QTLs in diverse structure of mapping populations , (iii) rabph1) in maize) (A genome wide association study (GWAS) on testcrossed hybrids made between a diversity panel and topcrossed to a line from the Stiff Stalk heterotic group Tx714;  under van maize) . GRMZM2GPast GWASs have shown false positives due to cryptic population structure, familial relatedness, allele variants with low frequency or various allelic variants, as well as spurious associations between phenotypic variations and unlinked markers. For this reason, loci must be validated using different populations, environments (Outside of In this study for the first time (i) validated the temporal loci effects, first discovered using hybrid genetic background in GWAS, in HIFs generated from different parental crosses; (ii) implemented a unoccupied aerial system (UAS) platform to detect temporal changing of these loci effects on PHTs of HIFs; (iii) examined epistasis between these two loci; and (iv) characterized genetic architecture of their pleotropic effects on flowering times1 hybrids on-hand that were derived from these parents  and selfed (three to five times) up to obtaining NILs as HIFs. Until obtaining NILs, both loci  were maintained as heterozygote calls in each population  genotyping results  LH82 × LAMA, (2) Ki3 × NC356, (3) NC356 × Ki3, and (4) Tx740 × NC356 (recurrent parent × donor parent for populations 1 to 4), respectively, and selfed to generate F5 RILs . RILs wehttp://www.kbioscience.co.uk/) assays by Total genomic DNA was extracted from the frozen (−60°C) plant flag leaf tissue using a modified cetyltrimethylammonium bromide method . To desiR2 and lower than 0.2 LD was ignored. The MaizeGBD (http://www.maizegdb.org/) genome browser was used to determine plausible genes linked to SNPs. The Gramene database (http://www.gramene.org) was used for the identification of candidate genes.https://www.panzea.org/) website was used to extract sequence information of genes from publicly available maize germplasm to evaluate how the allele frequency of these SNPs differs over germplasm and time. For elite temperate material, the information on the years when germplasm was developed were obtained from expired plant variety protection (Ex-PVP) certificates available on the USDA-ARS Germplasm Resources Information Network (https://www.ars-grin.gov). Allele shift of the loci were illustrated as count-based frequency histogram  through release years of germplasms.The Panzea  for summer nurseries and Weslaco (26°09′32.7″N 97°57′36.1″W), Texas for winter nurseries from 2016 to 2019. All nurseries were grown based on range and row design with two replications per HIF. Each row plot (∼6.10 m long) in each range contained two row plots of two different HIFs. Plot rows were 3.05 m long for each HIF, and 18 seed were planted per HIF row plots. During the advancement of HIFs from 2016 to 2018, SNP1 and SNP2 calls were primarily maintained by selecting heterozygotes (X:Y) to advance and increase. For traditional and UAS phenotyping in College Station 2019, entire plots of X:X, Y:Y, and X:Y for each HIF were planted on the 12th of April, 2019, in two replicates. These HIFs were grown in a total of 18 ranges with 16 row plots each as well as parental lines and red stalker inbreds (Supplementary Table S3). Row plots of red stalker inbreds were used as planting indicators to verify that the planting was correct via orthomosaic because of their red stem and leaf color. Experimental designs were applied as a split:split:split plot design where the main split was replicate, the second split was population/genetic background, and the third split was genotype. Unless noted, all reported hand measurements and unoccupied aerial vehicle (UAV) flights were conducted when HIFs were grown near College Station in 2019.Days to anthesis (DTA) and silking (DTS) were recorded on a plot basis when 50% of the plants were showing anthers and silks, respectively, checking plots daily. Three different terminal PHT measurements were taken using a ruler including TH, FH, EH July 2nd, 2019, about 2 to 3 weeks after flowering. In addition, UAV (aka drone) PHT measurements were taken weekly from emergence to the end of the growth period. The flight dates were shown as day/month/year (dd/mm/yy). Grain yield was not taken as it has little value in the inbred lines screened which often are confounded by inconsistent pollination in the heat stress of Texas.UAV images of the field were taken using a DJI Phantom 4 Pro V2.0  at an above ground altitude of 25 m. The standard integrated camera resulted in images having a resolution of 72 DPI. DJI standard flight control software was used. Orthomosaics and point clouds were created with the images for each flight by using Agisoft Metashape V15.2 software . The captured images were at 72 dpi with 90% overlap and were used to create an orthomosaic and point cloud for determining the PHT as a function of time during the growth period. Ground control points were used during the flights to assist the data processing and reduce effects due to aberrations and the resulting georeferenced mosaics.Previous work has shown that various methods to measure inbred maize plants from the ground using point clouds produced similar results  to predict the best linear unbiased estimates (BLUEs) of SNPs. SNPs were fit as fixed effects to obtain BLUEs values for flights as well as for ruler measurements. Separate models with genotypes as random effects in an all random model were fit to obtain variance components. All components, except the SNPs and population, were always fit as random effects under the following mixed linear models in each model.First, each SNP was tested separately within each population . While oWithin this base model, response variable PHT and flowering time were also tested for SNP1 and SNP2 individually combining all data across populations 1, 2, and 3 . While oThe interactions of both SNPs and populations using the full factorial function were tested for both flowering time and for PHT from the ruler measurement and UAS flights temporally across populations 1 and 2 .EquatioHere, response variable Orthogonal contrasts were applied to r) for single environments (Repeatability (R) was calculated based on following formula with number of replication , canopy height measurements derived from UAV-point cloud data (Uav-chm.xlsx), Field map (Field Map.xlsx), and Experimental area  are available at https://doi.org/10.6084/m9.figshare.13046306.v4. UAV-point cloud data (.laz files), processing reports (.pdf files), tif files, belonging to 06/19/19, 06/21/19, 06/28/19, 07/02/19, 07/09/19 and 07/12/19 flight dates, are available at https://doi.org/10.6084/m9.figshare.13269953.v1.Unoccupied aerial vehicle (UAV)-point cloud data (.laz files), processing reports (.pdf files), tif files, belonging to 05/17/19, 05/30/19, 06/04/19, 06/11/19, 06/13/19 (mm/dd/yy) flight dates, are available at https://doi.org/10.25387/g3.14188481.Supplementary material is available at R2 values and flanking regions of the genes for the two SNPs. (c) Effects sizes for the two SNPs (tonne per hectare). (d) Polymorphic SNPs colocalized in LD blocks and haplotype variants based on two SNPs and (e) segregations of two SNPs in parental genotypes, advanced populations used in this study as follows: [LAMA (recurrent parent) × LH82], [Ki3 × NC356 (recurrent parent)], [Ki3 (recurrent parent) × NC356] and [Tx740 (recurrent parents) × NC356]. Supplementary Figure S2 Illustrations of canopy height measurements (CHM) obtained by extracting the digital surface model (DSM) from DTM. The orthomosaic obtained from the drone flight that was flown on June 28, 2019, is shown as an example in here. C2C (cloud to cloud) absolute distances (as meters unit) heatmap show the plant heights of HIFs in the point clouds of CHM after the extraction of point clouds of DSM from point clouds of DTM. Viridis color heatmap was used to illustrate the plant heights in the ranges and row plots as top view. The zoomed row plot illustrates the side view example of plant height differences between two heterogeneous inbred families developed from same population background comparatively; one of those has both favorable alleles , the other has unfavorable alleles . Supplementary Figures S3 and S4 contain the BLUEs for SNPs and the interaction of SNPs with populations obtained by Primer development and designs used in KASP genotyping are given in Supplementary Tables S1 and S2. Table S3 contains the number of row plots of HIFs with their population background and SNPs information. Supplementary Tables S4 and S5 contain the results of explained percent variations estimated by e.g., XX) calls in both SNPs, contributed by both NC356 and LH82 parents , increased all three ruler measures of PHTs . Tassel height differences between XX and YY calls were statistically significant across all populations  differences between favorable (XX) and unfavorable loci (YY) were 16–20 cm in early growing stages  but narrowed 3–5 cm by harvest time depending on when either SNP1 or SNP2 was tested in In Population variation σPop always eFor accuracy assessment, means and medians of each plot measured by ruler on July 2nd, 2019, were correlated with UAS-PHT captured on the same date, and a correlation coefficient was found to be 0.83 for either the median or mean correlated with UAS-PHT .LD decay distances calculated for each chromosome were found to be 1.5, 5.8, 4.5, 3.7, 4.5, 5.1, 4.5, 4.5, 4.9, and 5.7 kb for chromosomes 1 to 10, respectively . Candidate genes were determined based on the LD decay around the surrounding regions of SNP1  and SNP2  as well as their physical positions using the Maize Refgen v2 coordinates . SNP1  has a strong LD  has strong LD (3:4) developed from parental lines segregating for the two SNPs of interest , found the largest effect sizes of loci and interaction effects of loci  as well Both loci in this study were found to have pleiotropic effects on flowering  not observed in the initial GWAS  ,was first observed in maize mutant showing transformed phyllotaxy behavior , cytokinin-inducible type A response regulator is encoded by abph1, indicating that cytokinins play a role on aberrant phyllotaxy in maize  expression, which is polar auxin transporter for leaf primordia expression in maize, and auxin localization in embryonic leaf primordia in SAM  family that involves in crucial biological process such as the regulation of cell signaling, transduction, and cell formation  that work across genetic backgrounds, in inbred lines and throughout diverse environments, confirmed through this study. New UAS tools provided substantially more information and better screening for the effects of these alleles than the traditional terminal ruler height measurements in which they were discovered. To get a better understanding of QTV’s affecting complex traits such as PHT and grain yield in maize, a combination of high-throughput phenotyping and genotyping studies must be evaluated together, which will be critical for managing the phenotypic plasticity of complex traits.


Vigna subterranea [L.] Verdc.) considered as leading legumes in the tropical regions due to its versatile advantages. The main intent of this study was to find out the high yielding potential genotypes and considering these genotypes to develop pure lines for commercial cultivation in Malaysia. Considering the 14 qualitative and 27 quantitative traits of fifteen landraces the variation and genetic parameters namely, variability, heritability, genetic advance, characters association, and cluster matrix were determined. ANOVA revealed significant variation for all the agronomic traits (except plant height). Among the accessions, highly significant differences (P ≤ 0.01) were found for almost all the traits excluding fifty percent flowering date, seed length, seed width. The 16 traits out of the 27 quantitative traits had a coefficient of variation (CV) ≥ 20%. A positive and intermediate to perfect highly significant association  was found between yield and its related traits. The trait dry seed weight per plant (g) had the highest GCV = 59.91% and PCV = 59.57% whereas the trait fresh pod weight (99.55%), dry seed weight (98.86%), and yield (98.10%) were highly heritable. The genetic advance recorded the highest for dry seed weight (122.01%) and lowest (3.97%) for plant height. To validate the genetic disparity, an unweighted pair-group produce with arithmetic mean (UPGMA), principal component analysis (PCA), heatmap, and H’-index was performed considering 27 quantitative traits. The constructed dendrogram showed five distinct groups of accessions. Genotypes G2, G3, and G9 from Group IV consider as promising lines which gave 70.05% higher mean yield compared to grand mean yield (1180 kg ha−1) with desirable traits. Group II had a maximum number of accessions while group III and group V had one of each. However, findings declared that the availability of genetic variance will be beneficial for this crop improvement through cross breeding as well as the plant breeders to prefer desirable traits in V. subterranea L. Verdc. for further breeding purposes.As a crop for the new millennium Bambara groundnut ( The present binomial name Vigna subterranea (L.) Verdc was suggested by Verdcourt, 19803 and chromosome number is 2n = 2x = 224. This crop occupied 3rd position after Arachis hypogea and Vigna unguiculate in African continent5 though it is used as food supplement instead of profitable crops due to its low ranked6. The word “Bambara’ comes from a place name near Timbuktu in central Mali, West Africa and the word “groundnut’ is the causes of it pods setting occur under the ground soil, hence jointly its common name is ’Bambara groundnut’7. Due to rich in carbohydrate (63–65%), protein (18–20%) and oil (17–18%), Bambara groundnut has been defined as a fully well-adjusted food for human feeding8. According to9 it contains 32.72% essential amino acids and 66.10% non-essential amino acids. The seed is regarded as a balanced food because when compared to other food legumes, it is rich in iron and its protein contains high level of lysine and methionine10. Lysine is the major essential amino acid and represents 10.3% of the total essential amino acid10. Bambara groundnut can fulfill the regular demand of protein for the marginal users where the animal protein is badly available due to its high cost11. Seeds of Bambara groundnut contain considerable amount of minerals such as Ca: 260 mg; K: 1723.25 mg; Fe: 3.6 mg and Na: 75.25 mg of each 100 g dry (weight) seed, moreover due to rich in potassium (K) it also have ability to lessen diabetes by prompting the insulin hormone12. The undeveloped fresh seeds of Bambara groundnut can be boiled to make pudding (Moi-Moi or Okpa)13; fodders to feed animals14 and extract of leaves has medicinal values as anti-vomiting agent15. In many developing countries where cultivation of other major crops is difficult, but Bambara groundnut can be accommodated nicely due to its drought tolerant and low diseases-insects infestation nature16 and as legume it can fix atmospheric nitrogen via nodulation17. This crop has the capacity to give high yield with low input and mostly grown by female in sole culture without any modern techniques18 and 10–40% of their total yield they sold in market rest is used by themselves19. According to FAOSTAT20, the annual production is about 42,023 kg ha−1, of which Africa produces half, with Burkina Faso occupied the major producing country. Bambara groundnut can well adapt to the tropical area like Malaysia, where cultivation of major crops  are increasingly challenging due to drought and unpredictable rainfall patterns21. The average yield of Bambara groundnut is low 0.650–0.850 t ha−122, 28.96 g plant−112, and 0.38–1.6 t ha−123. The production of Bambara groundnut is mainly limited due to lack of improved cultural techniques24 and improvement of Bambara groundnut was neglected for many years by researchers because of the lack of available fund and unprivileged effort on its improvement25. Although numerous efforts have unsuccessful for the varietal improvement of Bambara groundnut through hybridization due to cleistogamous and autogamous nature of flower and knowledge gap on its reproductive biology as under developing crop26. On the aspect of Malaysia, the breeding approach of Bambara groundnut is undetermined, and no commercial high yielding variety is available, so the requirement is to discover commercially high yielding cultivar for certain rising areas27. Germplasm screening considering the agronomic variables is the initial attempt to identify the targeted characters of interest28. This current research reveals the genetic divergence of fifteen Bambara groundnut accessions to discover the existing variation and the selection to develop high yielding pure lines for this crop improvement. Accordingly, this study provides an evidence on Bambara groundnut diversity among the landraces that introduced from Africa (Nigeria) to Asia . All the modern applicable techniques may be applied for the betterment of this ongoing cultivated crops, but the dual approaches like conventional breeding linked with molecular breeding is highly successive over the solely use of one approach. However, traits improvement can be possible through direct selection with valuation of different genetic parameter analysis. The extent of selection approach exceedingly inspire by heritability and genetic gain estimation is the commanding tools for the enhancement of a certain traits29. Hence, the core intent of this study was to determine the inherent variation of Bamabara groundnut landraces using both qualitative and quantitative traits via valuation of characters association, variance component and different genetic parameters, resulting the identification of high yielding potentials from which pure lines will be developed for commercial cultivation.Bambara groundnut is a future emerging legume grown in Africa and Asia, is usually known as a poor man’s crop or as “Women´s Crop”30.The research work has experimented under the Institute of Tropical Agriculture and Food Security (ITAFoS), University Agricultural Park, Universiti Putra Malaysia (UPM), Malaysia. Based on the Global Positioning System (GPS) the research location was 2°58ˊ54.0˝N latitude and 101°42ˊ53.8˝E longitude. The seeds of accessions were sown in open field conditions during the 2018–2019 cropping season. The soil PH is 6.6 to 7.5 with sandy loam to clay loam type . Fifteen accessions of Bambara groundnut were selected for this current research work, all representing the African accessions collected from the local market of Nigeria. Land races of Bambara groundnut used in this research was listed in Table .30. During the growing season the recommended intercultural practices like land preparation, land clearing, weeding, irrigation, and fertilizer were practiced. The recommended fertilizer rates  and all portion of Phosphorus and Potassium were applied during field preparation hence, 70% N was applied at 5 weeks after sowing31.The experiment was conducted in a randomized complete block design (RCBD) with three replications. The experimental plot comprised of two rows measuring 1.6 m × 0.80 m. The distance between plant to the plant 30 cm, row to row 50 cm, plot to plot 1.5 m and the distance between replication was 2.0 m according to Unigwe et al32 data were recorded from 5 randomly selected plants of each plot at several growth stages in the field and post-harvest data in the physiology lab.Twenty-seven quantitative and 14 qualitative characters Table  were con33 correlation coefficient formula. The genotypic and phenotypic variation were calculated as per following the formula given by Singh and Choudhary34. The coefficient variation of phenotypic (PCV) and genotypic (GCV): were estimated as per formula given by Khan et al.23 also relative differences was estimated using the formula (RD) = Relative difference between PCV and GCV. The estimated values of PCV and GCV were categorized by Robinson et al.35 and Khan et al.23, like as between 0 and 10% for low, 10–20% for intermediate and greater than (≥ 20%) for high. Broad sense heritability  (as a percentage of mean): was calculated with 5% selection intensity (K) following the method of37. Genetic advance is categorized as between 0 and 10% for low, 10% to 20% for intermediate and more (> 20%) than for high, following the formula given by Khan et al.23. K for constant also indicates the intensity of selection. According to Adewale et al.38 the rate is 2.06 at the point when the K is at 5%. Genetic Gain (%) = Estimated as genetic advance (GA) × 100; it is also categorized39 as between (0 to 10%) for low, (10 to 20%) for intermediate and (≥ 20%) for high GA. Based on the Euclidian Distance Method also Dices’s and Jaccard’s similarity of coefficient data was analysed for investigation of genetic diversity. In addition to this, based on the Unweighted Pair Group Method using Arithmetic Average (UPGMA) and following the algorithm & sequential, agglomerative, hierarchic, and non-overlapping (SAHN) method the genetic inter-relationship (showing dendrogram) among the Bambara groundnut were estimated. For this analysis NTSYS version 2.1 , Exeter Software, Setauket, NY, USA software40 were used. Using similar software, the principal component analysis (PCA) was done to produce two dimensional (2D) plots though, multivariate statistical packages (MVSP) was used for PCA biplot loading. However, the Shannon diversity index is a synonym for the Shannon equitability index and evenness was calculated using the formula given by Shannon41 and Hennink and Zeven42. For germplasm selection, rank summation index (RSI) was estimated using Onwubiko et al.43; Mulumba and Mock44 reported formula. For correlation networking, pattern search plot we used MDP tools while for heatmap study we used ClustVis bio tools.The SAS  version 9.3 was followed to test the significant differences using the analysis of variance (ANOVA) procedure at the level of LSD; P ≤ 0.05 and to compare among the mean of significant of traits. The correlations between the quantitative variables were determined using PearsonThe frequency of distribution of some qualitative variables are summarized in Fig. For crop upgrading plant breeders considered yield and its related traits as a controlling parameter. In current research, to select the best performing accessions for next breeding program a total number of 27 numerical traits of 15 Bambara groundnut landraces were analysed. The analysis of variance revealed the significant variation, mean, standard error of mean (SE.m), standard deviation (St.Dev), and coefficient of variation (CV%) displayed in Table −1). The days to 50% flowering varied from 31 to 49 days after sowing (DAS) while 66.67% of the accessions gave 50% flower before 40 days after sowing. Most of the landraces (80%) took more than 120 days to maturity which varied from 122 to 141 DAS. The genotype G1 marked as short duration line with a total day to maturity of 119 DAS (Table −1) (Table −1) was recorded for the genotype G9 (2226.30 kg ha−1) followed by G3 (1557.61 kg ha−1), G6 (1414.67 kg ha−1) and G10 (1250.73 kg ha−1)  were shown in the Tables  were sho−1) revealed positive and perfect (r = 1.00) highly significant association with DPW  while positive and highly strong ( 0.75 ≤ r < 1) significant association was found with DSW , NSP  and HSW  per plant  provides overall as well as comparative abundance profile across landraces at different variable levels Fig. a. Variat−1 reported higher genotypic  and phenotypic  variance while the lower genotypic (0.11) and phenotypic (0.24) value was recorded for the trait internode length. The traits such as TNP (PCV 19.48% & GCV 18.53%), PW (PCV 14.55% & GCV 10.95%), NSP (PCV 19.92% and GCV 18.95%), SL (PCV 16.37% & GCV 9.30%), SW (PCV 13.61% & GCV 7.29%) and shell% (PCV 6.05% & GCV 4.50%) showed below 20% of phenotypic coefficient of variation (PCV) and genotypic coefficient of variation (GCV). For the improvement of this crop further selection could be done considering the traits having GCV ≥ 20%  which indicated high degree of variability among these traits although the variation is due to the effect of additive genes. Due to the lower GCV values (≤ 10%) the vegetative traits  and yield traits  indicated the limited chance of selection based on respective traits due to the effect of environment on their phenotypic expression.The output of genetic components analysis was compiled in Table −1 and noticed that the variation present among the traits due to the effect of gene and have a better response to direct selection. On the other hand, the traits with higher difference in between their PCV and GCV values indicated the wider genetic variability due to environmental effect and not better feedback to direct selection for the improvement of traits.The relative difference (RD) is the ratio of GCV in association with the respective PCV and the estimated RD values varied from 0.22% (fresh pods weight) to 57.46% for plant height  for cluster IV whereas the cluster I (9.34%), cluster II (31.44%), cluster III (50.08%) and cluster V ( 12.95%) produced lower (−) yield.In this study, the homogenized data was used to calculate the Euclidean distances among the 15 Bambara groundnut accessions and a UPGMA dendrogram was designed Fig. . To discA heatmap is a data imagining practice that displays extent of a phenomenon as color in two dimensions. The variation in color may be by hue or intensity, giving noticeable visual indications to the reader about how the phenomenon is clustered or varies over space. It visualizes the relative patterns of high-abundance features against a background of features that are mostly low-abundance or absent. Heatmap analysis of agro-morphological descriptors were carried out to show a chromatic evaluation of the Bambara groundnut genotypes. The heatmap analysis constructed double dendrograms, the 1st dendrogram on the vertical direction, an arrangement that represent the Bambara groundnut accessions, and the 2nd dendrogram on the horizontal direction representing traits that influenced this diffusion. Dendrogram 1 showed two major group, group (a) linked to two genotypes Maikai (G2) and Giiwa (G9) while the group (b) comprised of rest 13 genotypes Fig. . Dendrog−1) with high and positive value of coefficient of variation. The second principal component (PC2) associated with the traits D50%F, PH, NP, NL, FPW, DPW, DSW, HI (maximum 0.447) and YLD (kg ha−1) accounted for 10.64% of the total variation. About 8.78% of the total variation was detected for Principal Component 3 (PC3) and displayed differences based on PH, NB, NS, NP, NL, NNS, IL (largest 0.472), BFW and BDW. The principal component 4 (PC4) accounted for 8.19% of the total variation and consisted mostly of the traits of D50%F, NS, NP, NL, HSW, and Shell% (maximum 0.444). The variation (6.75%) was found for principal component 5 (PC5) and comprised with D50%F (maximum 0.479), DTM, DSW and YLD (kg ha−1). The last principal component 6 (PC6) accounted for 5.69% of the total variation and consisted of the traits DTE, DTM, NP, NL, BDW, NIP, PW and SL up to this principal component covered close to 86% of the cumulative variation. The two-dimensional  for the twenty-seven traits shown in Table H = 1.00 ) values of evenness was marked for the traits like fifty percent flowering date, maturity date, plant height, nodes number per stem, internode length, pod width, seed width, shelling percent whereas minimum (EH = 0.95) was for biomass dry weight, dry pod weight, dry seed weight, yield kg ha−1.The Shannon–Weaver diversity index was used to assess the phenotypic diversity for each trait. The estimation of the Shannon–Weaver diversity index (H) and Evenness , consequently the ranked values of traits were summed to estimate the total performance of individual accessions. In this way, the accession with the lowest RSI score indicates the superior yielding potential. The values of RSI ranking of Bambara groundnut with the traits of positive and strong significantly correlated traits with yield namely fresh pod weight, dry pod weight, no. of seed per plant and dry seed weight per plant are represented in Table −1), G9 (2226 kg ha−1), G3 (1557 kg ha−1), G6 (1414 kg ha−1), G10 (1250 kg ha−1), G1(1101 kg ha−1) were identified as high yielding accessions among the population evaluated with lowest RSI values 4, 8, 16, 21, 23, and 28 were observed, respectively while the entries G13 had the lowest yield (558 kg ha−1) with highest RSI values of 59.Pearson's correlation coefficients were estimated for all traits to define the traits of a positive or negative association with yield. The traits which were strongly positively associated with yield were used to compute Rank Summation Index (RSI) to lead high yielding Bambara groundnut selection from the population. Furthermore, correlations within a couple of traits are probably significant when the ideal values of the coefficient (%) are higher than 0.20.45 he found significant variation among all the qualitative traits. We recorded three types of growth habit and similar observation were noticed by Ntundu et al.1 in Tanzania, Khan et al.23 in Malaysia and Azam-Ali et al.46 in Cameroon. We categorized the vegetative growth of Bambara groundnut namely, bunches type, semi bunches type and spreading type which is matched by the result of Doku47 and highly significant difference among the qualitative trait was noted by Egbadzor et al.48.The existence of a significant qualitative variation was found for all the qualitative traits, supported by Gbaguidi et al.1 and Aliyu et al.49 in Vigna subterranea (L.) Verdc and the cowpea (Vigna unguiculata L)50. The estimated high coefficients of variation (CV) in our study is the indication of vast scale of heterogeneity confirmed by Goli et al.51 in Bambara groundnut. We found D50%F close to 39 days but52 noticed close to 68 days in Ghana. The indeterminate53 nature of flower bearing make it vital issue for adjustment mechanism to an environment54. The inconsistency of flowering time was reported by51 from 38 to 68 days; Massawe et al.10 from 64 to 76 days; Masindeni55 reported 43–80 days and Ouedraogo et al.8 from 32 to 53 days. Several climatic issues photoperiod, temperature, altitude and soil structure as well as genotypic nature is responsible to bearing flower in Bambara groundnut28 and reported flowering happened between 36 to 53 days. In our study, genotype G1, G2, G3, G8, G9 and G10 identified as early flowering lines; early flowering ensures early maturity56. A significant difference (P ≤ 0.01) was recorded for maturity (119.67 to 141.33) days is supported by Goli et al.51 and Masindeni55 and due to diverse cultivar along with multi-environmental factors maturation time varied from 90 to180 days57. Plant hight had no significant variation, supported by Ntundu et al.1 in Tanzania and Shegro et al.28 in south Africa. The yield and yield related traits like TNP, NSP, FPW, DPW, DSW, PL, PW, NMP, NIP and HSW showed high genetic discrepancy, similar variation stated by Shegro et al.28 with a recommendation of variation happened due to effect of genotype by environment interaction Bambara yield. Hundred seed weight varied from 177.52 g to 360.15 g, is a vital factor for the measurement of morphological traits linked to yield58 it also influences the yield directly. The yield of Bambara groundnut was recorded from 146.6 to 2678.6 kg ha−1 by Gbaguidi et al.45; average 703.3 kg ha−1 by FAO20; 1058.8 kg ha−1 by Dansi et al.59 in west Africa whereas we calculated from 588.98 to 2991.77 kg ha−1. Typically, FAO20 estimated average yield Bambara groundnut is lower than our estimated yield 1180 kg ha−1.The estimated 27 quantitative traits showed a massive genetic variation and similar variation was confirmed by Ntundu et al52. For superior genotype’s selection programme consideration of correlation matrix can be a great scale of measurement60. Strong and positive significant correlation for total number of pods (TNP) was identified with the traits NMP, DPW, NSP, DSW and Yield this result is consistent with the study of Pranesh et al. 61 and Jonah et al.62. We got moderate and positively high significant association of plant hight (PH) with TNP, NMP, FPW and yield can be proposed the selection based on these traits may be beneficial for yield enhancement of this crop as well as fodder production for animal feeding. Similar recommendation was stated by Mohammed52 in Cote d’Ivoire and63 in Cameroon. Nankar et al.64 described correlation network in tomato phenotypic diversity and pattern search plot that was also supportive to my findings. Relative abundance study using area plot and pie chart revealed richness of traits diversity for each genotype. The traits possessing higher extent in both area plot and pie chart indicates that these traits are highly contributed to governing the diversity among the landraces.In plant breeding correlation matrix is a prominent approach for the judgement of degree of the association between two or more variables, is supported by Mohammed65. Consideration of both heritability and genetic advance is more effective over the uniquely use of heritability67. Like the previous reporters Adebola et al.68 findings, we disclose higher phenotypic variance values than genotypic variance for all traits, indicates the trait expression govern by the environment. The obtained GCV and PCV value was categorized based on the suggested index of 0%-10% for low, 10–20% for moderate and ≥ 20% for high variation70. Intermediate to strong genetic advance with heritability was found for all yield related traits except seed width and shelling% is the indication of the traits have significant potential in the selection process due to low environmental influences, supported by Meena et al.71. The improvement of the traits with low heritability and genetic advance can be boost over heterosis breeding this is supported by Usman et al.70. The value of relative differences between GCV and PCV had higher for the trait plant hight, seed width, days to 50% flowering, and seed length is the sign of higher environmental effect and the improvement of these traits are tough via direct selection whereas the trait with lower difference is the symbol of lower influence by the environment which may give desirable strong and significant output in crop improvement program, is supported by Umar et al.29 and Usman et al.70. Direct selection can be effective considering the traits having low relative differences72. Considering the heritability and genetic advance index37 like as more than 60% for high, 30–60% for moderate, and 0–30% for low, we found the traits BFW (Hb = 97.68% GA = 88.82%), BDW(Hb = 89.91% GA = 106.66%), FPW (Hb = 99.55% GA = 93.54%), DPW(Hb = 98.10% GA = 113.94%), DSW (Hb = 98.42% GA = 122.01%) and yield (Hb = 98.10% GA = 113.94%) were highly heritable together with high genetic advance value, recommended that for crop improvement direct selection can be effective based on these traits with effect of additive genes; similar findings documented by the previous researchers73. Low to moderate heritability and genetic advance values may hindrance in the trait’s betterment due to high environmental effects over the genetic effects on its stated by Ridzuan et al.74. So, only an effective selection can be gained picking the traits with higher GCV, PCV, HB, and GA meaning that effect of additive genes is sufficiently robust than environmental effect70.For the selection program variation presents among the traits was taken into consideration which depends on the degree of heritability. To know the projected gain from selection, valuation of genetic advance with heritability can be a significant approach of crop improvement. Various research findings reported that the selection may be effective for a specific trait improvement using available genetic variation with the degree of heritability76 stated that they constructed same type of cluster and found significant variation regarding morphological traits in Bambara groundnut. The study of Unigwe et al.30 explored the four distinct groups of Bambara groundnut genotypes in south Africa using UPGMA model. The timing of flowering duration is a motivational factor for the final yield also play a positive role to the best yield of the group and selection could be effective from this class noted by Tourél et al.77. Flowering in Bambara groundnut is indeterminate up harvesting stage explained by Kumaga et al.78. However, early flowering has been considered as a well agronomic trait of crops to quick maturity, uniform yield as well as generally crop production78 thus, accessions that have early flowering criteria should be treated as best to production of Bambara groundnut79. The groups achieved from the cluster analysis of quantitative characteristics illustrate the performances of Bambara groundnut accession cultivated in Benin would be the future guideline for this crop improvement80. The clustering and characterization of accessions considering their agro-morphological traits and genetic similarity would be the crucial issue to identification and selection of the best parents for hybridisation81. Additionally, cluster IV produced 70.05% higher mean yield than the average grand mean yield of 1180 kg ha−1 while the other groups gave lower yield and this finding were supported by Onwubiko et al.73. Therefore, current research represents significant information to the plant breeders based on their similarity and grouping of accessions through univariate and multivariate methods. The heatmap analysis depicted the depth of correspondence among morphological traits evaluated of Bambara groundnut genotypes and this result was constantly supported by Virga et al.82Five clusters were constructed based on the 27 quantitative traits at 1.16 of the distant coefficients that indicates a degree of diversity among the genotypes. The cluster V considered as potential group of genotypes for the crop betterment associated with high yielding capacity. The findings of previous researchers39. The first axes (PC1) elucidate utmost portion of total variation in any PCA83. In our findings first principal component (PC1) accounted more proportion of variation (45.88%) than PC2 (10.68%). Similar result was identified by Mohammed et al.84 of total variation at 19% (PC1) and 14% (PC2) in Bambara groundnut. The results of several researchers like Usman et al.70, Farhad et al.85 & Maqbool et al.86 supported our findings. Shegro et al.28 grouped the 20 Bambara groundnut accessions by PCA analysis using quantitative traits. For yield improvement the selection PC1 was revealed as the most powerful criterion concluded by the work of Adéoti et al.87 and Mih et al.88. In my research total pod numbers, mature pods number, seed number, dry seed weight and yield kg/ha occupied high values in PC1. This finding supported by Stoilova & Pereira80 described that the most significant components for yield are the pods number and seeds number per plant. The cluster analysis together with principal component analysis explored the common association among landraces in terms of seed yield and related agronomic traits.The principal component analysis (PCA) is the re-validation instrument of cluster analysis. To estimate the total variation, exist in a set of characters, PCA is effective noted by Johnson90. Olukolu et al.22 reported H’ Index of nineteen qualitative traits (0.1 to 0.15) and twenty-eight numerical traits (0.09 to 0.16) of Bambara groundnut that supported our findings. Bonny et al.76 evaluated the diversity in qualitative traits of Bambara groundnut landraces of similar findings with our result.Shannon’s diversity index (H) is another index that is generally used to categorize the species diversity in a certain community. Shannon’s diversity index is an account for both richness and evenness present in the species also used for a wide diversity of fields. The estimated H’ Index varied from 2.57 for dry seed weight per plant to 2.71 for plant height, maturity date and shelling percent among the phenotypic traits. In our study the observed diversity index value was more than 2.50 for most of the traits evaluated and highest the value indicates higher diversity, though H’ index ranges typically from 1.5 to 3.5 but rare can be reaches 4.5.43 and Ajala et al.91, he got two high yielding genotypes from 33 accessions using the RSI method. Other agronomic evaluations in this study like clustering and principal component analysis also had confirmed the accuracy of the selective index result.The result from correlation studies revealed the traits that have a positive significant correlation with yield also give valuable information in a breeding program for the selection of high yielding Bambara groundnut. Similar findings were concluded by Onwubiko et alVigna subterranea [L.] Verdc.) yield and it related traits can be gained via selection with the valuation of different genetic parameters analysis like GCV, PCV, HB, and GA. Based on the recorded data and considering the supplementary analysis  it can be state that a considerable degree of variation exist in almost all the agronomic traits evaluated in this study. Moderate to perfect significant association was noted between the yield and its related traits. Additionally, this research also depicts selection criteria using the traits which had strongly positive correlation with grain yield. More than 20% PCV and GCV values was estimated for all traits excluding the traits like TNP, PW, NSP, SL, SW, and shell% beside this, the six traits like DSW, DPW, FPW, BDW, BFW, and Grain yield showed high (≥ 20%) genetic advance (as percentage mean) with high heritable values. However, it can be declared that a higher extent of divergence was detected among tested landraces based on H’-index (1.57–2.71) as well as Euclidian distance clustering (into five group). Considering all statistical findings, the genotype G2, G3, G8 and G9 identified as high yielding promising lines and can be use as distance parents for hybridization program. We suggested that further research can be conducted to gain the homogeneity of genotypes based on yield and its contributed traits improvement. Concurrently, we must provide emphasis on intensive research of these potentially high yielding lines, together with conventional breeding and molecular approaches.From the present study an evident has been established that the improvement of Bambara groundnut (


TLR1A gene.Natural antibodies (NAb) are antibodies that are present in a healthy individual without requiring previous exposure to an exogenous antigen. Selection for high NAb levels might contribute to improved general disease resistance. Our aim was to analyse the genetic background of NAb based on a divergent selection experiment in poultry, and in particular the effect of a polymorphism in the TLR1A were determined for chickens in generations 5, 6 and 7. The data were analysed using mixed linear animal models.The study population consisted of a base population from a commercial pure-bred elite white leghorn layer line and seven generations of birds from a High and Low selection line. Birds were selected for total KLH-binding NAb titer . An enzyme-linked immunosorbent assay was performed to determine NAb titers in blood plasma for IgTotal and the antibody isotypes IgM and IgG. NAb titers were available for 10,878 birds. Genotypes for a polymorphism in TLR1A C allele was 0.45 in the base population and 0.66 and 0.04 in generation 7 of the High and Low line, respectively. The TLR1A polymorphism had large and significant effects on IgTotal and IgM. Estimated genotypic effects suggest full dominance of the TLR1A C allele. Significant TLR1A by generation interactions were detected for IgM and IgTotal.The heritability estimate for IgM was 0.30 and higher than that for IgG and IgTotal (0.12). Maternal environmental effects explained 2 to 3% of the phenotypic variation in NAb. Selection for IgTotal resulted in a genetic difference between the High and Low line of 2.4 titer points (5.1 genetic standard deviation) in generation 7. For IgM, the selection response was asymmetrical and higher in the Low than the High line. The frequency of the TLR1A gene on IgTotal and IgM NAb was confirmed. Furthermore, we provide experimental verification of changes in allele frequencies at a major gene with dominant gene action on a quantitative trait that is subjected to mass selection. TLR1A by generation interactions indicate sensitivity to environmental factors.The effect of a polymorphism in the The online version contains supplementary material available at 10.1186/s12711-022-00715-9. Diseases pose serious and increasing challenges to livestock production. Diseases not only affect production efficiency, and thus environmental impact, but also consumers’ attitude towards food products of animal origin. In recent years, poultry layer production has shifted in several countries from battery cages to free roaming housing systems. This has increased contact between chickens, resulting in a higher risk of pathogen spreading , 2. AnotThere are several aspects that complicate selective breeding for disease resistance. First, elite breeding animals are usually kept under strict hygienic conditions and, as a result an animal’s ability to fight pathogens is not or poorly expressed. Second, traits that capture differences in disease resistance between animals must be identified. Selection should preferably not be for resistance to one or a few diseases but for enhanced immune competence that results in a broad  disease resistance. Furthermore, traits that reflect general disease resistance should be easy and cheap to measure and ideally early in the animal’s life.Escherichia coli [Natural antibodies (NAb) could be of value in selection for general disease resistance. NAb are antibodies that are present in a healthy individual without requiring previous exposure to an exogenous antigen . NAb arehia coli . Therefotoll-like receptor 1 family member A (TLR1A) gene, in which a polymorphism was identified as the most likely causal variant affecting the level of NAb [TLR1A gene. Using this large data set, we performed a detailed study of the genetic effects , maternal environmental effects, sex effects on NAb, and specifically the role of the TLR1A polymorphism. Furthermore, the availability of TLR1A genotypes provided a unique opportunity to quantify the effect of mass selection on allele frequencies at a major gene for NAb titers.In previous studies on poultry, we showed that NAb against KLH are heritable , 13 and l of NAb . Studiesl of NAb , 15 wereThe birds used in this study were from a commercial pure-bred elite white leghorn layer line provided by Hendrix Genetics . The study population consisted of a base population of birds from the same layer line, that had not been selected for NAb, and seven subsequent generations that were divergently selected for total KLH-binding NAb titer , i.e. from High and Low selection lines.The base population consisted of two batches of chickens. The first batch was previously described and used in studies by van der Klein et al.  and BergIncubation of eggs and housing of chickens for the selection lines (from generation 1 onwards) was at the research facility “Carus” from Wageningen University & Research, according to standard production practices. In each generation, our aim was to select 25 males and 50 females per line, but for some generations the number of selected parents deviated slightly  was used, as described by Berghof et al. . For thiFor the base population, samples from males and females were analysed on different ELISA plates . For gentoll-like receptor 1 family member A (TLR1A) gene, which harbors a single nucleotide polymorphism (SNP) that has been identified as the most likely causal variant [TLR1A SNP, based on whole genome sequence data, were available for the base population and the frequency of the imputed TLR1A C allele was 0.45 [TLR1A SNP for chickens in generations 5, 6 and 7 were determined based on a TaqMan assay. For this purpose, DNA was isolated from blood samples, and 4 µL of the normalized DNA sample was mixed with 8 µL of TaqMan mix that included primers and probes for the SNP. The FAM and VIC fluorescent dyes were used for the G and C allele probes, respectively. Polymerase chain reactions (PCR) were run for 40 cycles at 95 °C for 15 s and at 60 °C for 1 min. Genotypes were called using the QuantStudio™ Design and Analysis Software v1.4.3 (Thermo Fisher Scientific Corporation). Samples with a high FAM signal were classified as homozygous G, those with a high VIC signal as homozygous C, and samples with an approximately equal signal for both FAM and VIC were classified as heterozygous. When birds could not be unequivocally assigned to one of the genotypic classes their genotype was set to missing.A genomic region on chromosome 4 that is known to be significantly associated with NAb titers contains the  variant . This SNwas 0.45 . The genIn a previous study, we identified significant maternal environmental effects for IgM NAb titers, but not for IgTotal and IgG, and found no evidence for maternal genetic effects . The avaThe first model contained a random maternal  effect and a random additive genetic effect:Heritability and maternal environmental effects were estimated as:To test if the maternal environmental effects affected NAb titers significantly, the data were also analysed using a model without a random dam effect:th dam and th dam. The assumed (co)variance structure of the random effects was:To investigate if in addition to maternal environmental effects, maternal genetic effects play a role, the data were analysed using the following model:The likelihood ratio test (Eq. ) was useResponse to selection was estimated by averaging the estimated breeding values obtained from Model (1) of all individuals in each line-by-generation combination.To estimate genetic and phenotypic correlations between the three NAb traits, bivariate analyses were performed. Furthermore, to determine if the genetic background of the NAb traits differed between the sexes, titers of males and females were analysed as different traits in bivariate analyses. The bivariate models used were as described for Model (1) but without the effect of sex. The likelihood ratio test (Eq. ) was useTLR1A polymorphism, phenotypes and TLR1A genotypes from chickens in generations 5 (n = 1105), 6 (n = 681) and 7 (n = 978) of the selection experiment were used in the following model:TLR1A SNP . Additive genetic and maternal environmental variances in these analyses were fixed at the estimates obtained from Model (1) based on the complete data set. Estimates of TLR1A genotypic effects of Model (5) were reparametrized into additive and dominance effects as:To estimate the effect of the TLR1A and sex, TLR1A and Line, TLR1A and Generation by adding the respective interactions in Model (5).Significance of additive and dominance effects was tested using the !CONTRAST statement in ASREML 4 . AdditioNatural antibody titers for IgTotal, IgM and IgG for 10,878 chickens were available for the current study. A little less than half of the observations were from chickens in the base population. Average NAb titers for the High and Low lines and each generation are in Table Remarkably, the standard deviation of the NAb titers was larger in the Low line than in the High line and this difference tended to increase over generations. E.g., in generation 7 the phenotypic standard deviations for IgTotal were 1.45 and 1.02 in the Low and High lines, respectively.Model comparisons showed that a model with maternal environmental effects (Model (1)) had a significantly better fit than a model without (Model 2)): the likelihood ratio was 26.3 (p < 0.001) for IgTotal, 11.4 (p < 0.001) for IgG, and 25.9 (p < 0.001) for IgM. Adding maternal genetic effects to the model (Model (4)) only resulted in a significant improvement for IgM: the likelihood ratio was 3.2 for IgTotal, 2.2 for IgG, and 6.5 for IgM (p = 0.04). The estimate of the maternal genetic variance for IgM was close to 0 and the estimated genetic correlation between direct and maternal genetic effects was negative and outside the parameter space (− 3.79). Therefore, we concluded that a model with maternal environmental effects (Model (1)) provides the best fit for IgTotal, for IgG, and for IgM. Results for this model are in Table ): the liThe heritability estimate for IgM (0.30) was considerably higher than those for IgG and IgTotal (0.12). All three NAb isotypes were significantly affected by maternal environmental effects, which explained 2 to 3% of the phenotypic variation. The ELISA plate effect, which included effects of generation or batch, was highly significant for all three NAb isotypes. When plate was included as a random effect in the model, it explained 22, 18, and 14% of the phenotypic variation for IgT, IgM, and IgG, respectively. Furthermore, there was a significant but relatively small effect of sex on IgM (p = 0.04), but not on IgTotal or IgG. Males had 0.13 titer point lower IgM levels than females.Bivariate analyses showed strong genetic correlation estimates of IgTotal with IgM (0.91) and IgG (0.94) Table . IgG andTable Figure entclass1pt{minimaentclass1pt{minimaentclass1pt{minimaC allele was 0.71, 0.64, and 0.66 in generations 5, 6, and 7, respectively, in the High line . The CC and CG genotypes had more than 1 titer point higher IgM NAb levels than the GG genotype. As for IgM, the CC and CG genotypes had higher IgTotal NAb titers than the GG genotype. For IgG, the heterozygous CG genotype had higher titers than the GG and CC genotypes. For IgM and IgTotal, both additive and dominance effects of TLR1A were significant. For IgG, the additive effect was not significant, but the dominance effect was (p = 0.015).Table TLR1A polymorphism with Sex, Line, or Generation were detected. The TLR1A effect on IgM and IgTotal suggested full dominance of the C allele and, therefore, only two genotype classes (GG and CG/CC) were distinguished for the interaction analyses. For IgM, a significant (TLR1ATLR1A polymorphism (CC/CG versus GG) was larger in males (1.15) than in females (0.92). For IgTotal, neither a significant  than in the High line (0.89).For IgG, no significant interactions of the TLR1ACC/CG and GG genotype classes was 0.43, 0.23, and 0.71 titer points in generations 5, 6, and 7, respectively. For IgM, the difference between the CC/CG and GG genotype classes was 0.70, 1,22, and 1.28 titer points in generations 5, 6, and 7, respectively.Remarkably, highly significant . ThTLR1A gene. Our results provide unique experimental verification of changes in allele frequencies at a major gene with dominant gene action on a quantitative trait that is subjected to mass selection.To our knowledge, our study is the first divergent selection experiment for a non-antigen specific immune trait, i.e. natural antibodies. The large number of phenotypes collected over multiple generations allowed a detailed study of the maternal environmental and genetic factors that affect NAb and, in particular, the effect of a polymorphism in the In a previous study, we estimated genetic parameters for NAb using data on 3689 chickens from the base population . In the f et al. , i.e. 0.f et al. . When maf et al.  reportedOur analyses did not show evidence for maternal genetic effects on IgG and IgTotal, in spite of the large number of records included and collected over multiple generations. The standard error for estimates of the genetic correlation between direct and maternal genetic effects suggests that there is little information in the data to estimate this parameter when the model also includes a maternal environmental effect (Model (4)). For IgM, a model with a maternal genetic effect did result in a significantly better fit (p = 0.04) than a model with only a maternal environmental effect. However, the segregation of a gene with a major effect on IgM and allele frequencies that change during the course of selection, violates the assumption of equal genetic variance. As a result, a model with more degrees of freedom, as for the model that includes maternal genetic effects, could result in a better fit, even in the absence of true maternal genetic effects.Estimates of genetic and phenotypic correlations reported in the current study are similar to those estimated by Berghof et al.  for the In the study by Berghof et al. , the effTLR1ATLR1A effect was larger in males than in females, which is consistent with the larger additive genetic variance for IgM in males than in females . The estimate of the genetic correlation for IgTotal between males and females differed from 1 (0.78). This indicates that the effects of genes affecting IgTotal NAb titers differ between males and females. Sexual dimorphism has been described for e.g. body weight in broilers, with genetic correlation estimates ranging from 0.89 to 0.94, depending on age [It is generally accepted that males and females differ in their immune response, with females tending to mount stronger innate and adaptive immune responses than males, which can be due to the actions of genes, possibly due hormonal differences between males and females . In the es Table . This scg on age . Howeverg on age ).Previously, Berghof et al.  suggesteThe selection experiment described in the current study was designed based on the work of Wijga et al.  who estiA) in the High line and − 1.19 points (2.59σA) in the Low line. Furthermore, the phenotypic difference in generation 7 was similar to the genetic difference; phenotypic and genetic differences between the High and Low lines for IgTotal were 2.64  in the Low line and + 1.13 titer points (+ 1.92σA) in the High line. Asymmetric selection responses have been observed in several divergent selection experiments and have several possible explanations [TLR1A polymorphism with a full dominance mode of gene action. By generation 7, the response that was due to changes in allele frequency at the TLR1A gene was -0.65 titer points in the Low line and + 0.21 titer points in the High line. Selection response due to genes other than TLR1A was very similar for the High (− 0.95 titer points) and Low lines (+ 0.92 titer points).Selection for IgTotal resulted in correlated responses for IgM and IgG. The difference in the magnitude of the direct and correlated selection responses is due to the genetic correlation between the selection criterion and the correlated trait and the relative size of the additive genetic standard deviation of the correlated trait  resistance compared to Low line chickens [In addition to the correlated responses in KLH-binding IgM and IgG, we have previously reported positive phenotypic correlations between NAb and natural auto-antibody isotypes that bind different antigens at different ages –28, and chickens . In addiTLR1A gene as a result of selection will affect the variance explained by this major gene , which was confirmed in the current study but with different estimates; the heterozygous advantage as suggested in the current study was not found by Berghof et al. [Based on a GWAS in the base population, we identified a genomic region on chromosome 4 with a major effect on IgM NAb . Based oenotypes . Chickenf et al. . Howeverf et al. . Berghoff et al.  also repf et al. .TLR1A with sex, line, and generation. Interactions of the TLR1A polymorphism with background genes might result in an interaction between TLR1A and line. The  genes via changes in temperature  and posre  and andTLR1Aes . St. StTLR1Aen bursa . Therefons e.g. , 40). Al. AlTLR1AG allele because of dominance of the C allele  selection is expected to remove alleles with deleterious effects from the population. However, pure line individuals are mostly kept under strict hygienic conditions in breeding nuclei, and therefore their immune response is not seriously challenged and purifying  selection will not be effective. Furthermore, results from the selection experiment show that mass selection of pure line individuals for increased NAb levels will not be able to completely remove the TLR1ATLR1A allele (CC), differences in the performance of crossbred offspring will not be affected by the TLR1A polymorphism because of the dominant gene action. Therefore, we hypothesize that, in crossbreeding schemes, most of the alleles with detrimental effects on immune response remain undetected and that this may lead to the accumulation of deleterious mutations in elite purebred lines. This might not be limited to genes related to disease resistance but might also apply to other genes if genotype-by-environment interaction plays a role and mutations show dominant gene action. One might argue that this is not a problem since it does not affect the performance of commercial crossbred offspring. However, in the long term, accumulation of alleles with deleterious effects on disease resistance in the pure lines will gradually increase the risk of disease outbreaks in the pure lines. Such a disease outbreak can have far-reaching consequences for commercial breeding companies. Recently, it has been shown that deleterious alleles can be identified based on whole-genome sequence analyses (e.g. [The reported es (e.g. ). TherefTLR1A gene on IgTotal and IgM NAb and provide experimental verification of changes in allele frequencies at a major gene with dominant gene action on a quantitative trait that is subjected to mass selection. The observed asymmetrical selection response could be attributed to segregation of the TLR1A polymorphism. TLR1A by generation interactions suggest high sensitivity to environmental factors and illustrate the importance of choosing the appropriate environment when selecting for NAb and, possibly, for disease resistance in general. Results from this study suggest that in crossbreeding systems, with pure-line animals kept under strict hygienic conditions, there is no purifying selection against alleles with detrimental effects on disease resistance and a dominant mode of gene action. Therefore, alleles with detrimental effects on disease resistance might accumulate in pure line animals.We successfully selected chickens for high and low NAb titers. Results confirm a major effect of a polymorphism in the Additional file 1: Table S1. Number of selected sires and dams for the High and Low selection lines.Additional file 2: Figure S1. Standardized realized selection differentials for males in the High and Low selection lines. Figure S2. Standardized realized selection differentials for females in the High and Low selection lines.Additional file 3: Figure S3. Average inbreeding in the High and Low selection lines. Figure S4. Realized and expected selection response for IgTotal in the High and Low selection lines. Figure S5. Realized and expected correlated selection response for IgM in the High and Low selection lines. Figure S6. Realized and expected correlated selection response for IgG in the High and Low selection lines.Additional file 4: Figure S7. Additive genetic variance due to the TLR1A polymorphism as a function of the C allele frequency. Frequency of the TLR1A C allele (based on imputed genotypes) was 0.45 in the base population [pulation , 0.04 anAdditional file 5: Figure S8. Realized and expected changes of TLR1A allele frequencies. Theoretically expected changes in TLR1A frequencies due to mass selection for IgTotal.


The purpose of this study was to investigate the origin of the genetic variation in the prevalence of bovine digital dermatitis (DD) by comparing a genetic analysis of infection events to a genetic analysis of disease status. DD is an important endemic infectious disease affecting the claws of cattle. For disease status, we analysed binary data on individual disease status , whereas for infections, we analysed binary data on disease transmission events . The analyses of the two traits were compared using cross‐validation. The analysis of disease status captures a combination of genetic variation in disease susceptibility and the ability of individuals to recover, whereas the analysis of infections captures genetic variation in susceptibility only. Estimated genetic variances for both traits indicated substantial genetic variation. The GEBV for disease status and infections correlated with only 0.60, indicating that both models indeed capture distinct information. Together, these results suggest the presence of genetic variation not only in disease susceptibility, but also in the ability of individuals to recover from DD. We argue that the presence of genetic variation in recovery implies that breeders should distinguish between infected individuals versus infectious individuals. This is because epidemiological theory shows that selection for recovery is effective only when it targets recovery from being infectious. In applied animal breeding, disease status data are often analysed using simple linear mixed models, where the binary record of the individual is linearly related to its breeding value. Generalized linear mixed models, such as threshold models, are also used and are statistically much more appropriate  or presence  of the disease in the individual Gianola, . BecauseGianola, , but benGianola, ..However, neither linear models nor the commonly used generalized linear models consider the underlying dynamics of the transmission of infectious diseases in the population  which ignores these transmission dynamics. For example, whether such a breeding value would be different from a breeding value based on epidemiological models, and whether or not it is predictive of response to selection . The R0 depends on the susceptibility of individuals to become infected, and on their ability to recover from being infectious. Note, we allow for a distinction between being infected (i.e. having disease status 1) versus. being infectious (being able to infect others). Because a disease status of 1 usually means that an individual is infected, the breeding value of a DSM is a function of both the susceptibility of the individual, and of its ability to recover from being infected . For the analysis of infection events, we will use a generalized linear mixed model, hereafter referred to as infection model , which has received very little attention in livestock genetic improvement.22.1The data used in this study were collected from November 2014 to April 2015. The cows were just moved indoors before the start of this observation period. Observations on the DD‐status of the hind feet of individual cows were recorded during that period from 12 dairy farms from across the Netherlands, which previously had been reported endemic for DD with mean herd‐level prevalence of at least 20%. The number of cows per farm ranged from 88 to 189, and DD status of cows was recorded (in principle) at 11 observation moments per farm  Other factors that may affect the prevalence of DD on farm were recorded also Table . These i2.2DSM = 0, 0.5 or 1, 0 indicating two non‐infected feet, 0.5 indicating one infected foot and 1 indicating two infected feet. Thus, an animal had up to 11 observations, one for each of the 11 observation moments . Parity was expressed in 5 classes: 1 through 4, and greater than or equal to 5. Days in milk was converted to months in milk (MIM). All the above data restructuring were done using the R programming software R‐studio , where an M0 score was converted into 0, while feet with M1, M2, M3, M4 or M4.1 were converted into 1. Next, the scores of the two‐hind feed at an observation moment were averaged, so that animals were scored as y2.3F = 1 or 2 non‐infected feet at the start of the observation interval. Hence, the resulting phenotypic measure (yIM = C/F) was either 0, 0.5 or 1, with a binomial total of F = 1 or 2 susceptible feet. For example, an animal with two non‐infected feet at the previous observation moment (TO) and a single infected foot at the current observation moment had a y‐value of 1 infected foot/2 feet = 0.5, and F = 2. Parity was defined in the same way as for the DSM but with fewer factor levels.For the analysis of infection events, only data on individuals that had at least one non‐infected foot at the beginning of an observation interval are informative . Hence, while we used all informative records from the full original data set, the number of records is smaller for the analysis of infection events than for the analysis of disease status. Thus, only animals with at least one non‐infected foot at the previous observation moment were included in the dependent variable for an observation interval. The resulting total number of records for the IM was 6,099, which is only half of that for the DSM. The M‐scores were converted to cases per foot. These cases (C) were considered as “successes” in binomial trials, with binomial total Genotype data were filtered as described in detail in Biemans et al. . Only SN2.4p‐value < 0.10 , time of observation (TO) and breed of the animal. We included fixed effects with a 10 Table .p‐value > 0.10, and were thus omitted from the model, except for the Farm*TO interaction which was included as a random effect (see below).Interactions between significant fixed effect terms from Table G) constructed according to method 1 of , farm*TO and farm*parity, respectively, and a is the unknown vector of random animal genetic effects , with σa2is the additive genetic variance. ϵ is the vector of independent residuals. The total number of observations for the DSM was 12,195.The final model was built with stepwise addition of fixed and random effects. Random effects of farm by observation interaction and farm by parity interaction were included to correct for the random variation from sampling duration and state of animals from particular farms due to their managemental protocols. A random non‐genetic animal effect  was included to account for the repeated measures on same animal and its biological disposition. A random genetic effect was included with a genomic relationship matrix (A), using a∼N, to compare the additive genetic variance and heritability between models with genomic versus pedigree‐based relationships.We also fitted the DSM with a pedigree‐based (based on 4 generations) relationship matrix  due to its genotype. For example, an animal with a GEBV of 0.1 will have a 10 per cent point higher probability of being infected due to its genotype than the average cow. The fixed effects and random non‐genetic effects are interpreted in the same way.The variance components and GEBV were estimated using the ASREML‐w software  resulting from 11 times of observation, Farmk∗Periodt is the random interaction term of farm and period, PEi is the random non‐genetic animal effect accounting for repeated observations on the same animal , and Ai is the random genetic animal effect, and the last term is the known offset. The offset logEt+ItottNtΔt was included to account for the infection pressure coming from the infected cows at the start of the observation interval, for the effect of infectious material accumulated in the environment, and for the length of the observation interval  is the total number of infected claws at the start of the observation interval, and E(t) represents the infection pressure from the environment  between farms and periods , the fraction of infected feet in the farm*period was included in an offset in the IM. The IM wass et al.  for detaPEi and Ai in the IM are measures of the susceptibility of the animal. Specifically, Biemans et al.  given the same exposure in both. To calculate this heritability, we used a residual variance of π2/6 on the liability scale, which is the variance of the standard Gumbel distribution. We took this approach because a generalized linear model such as the IM with a complementary log‐log link function is equivalent to threshold model with a Gumbel distribution of the residual on the underlying scale , and the average was used as the corrected phenotype,For the DSM, the observed phenotypes were corrected for non‐genetic effects by fitting a linear model with only the fixed effects of Eq. 2; yobs and the predicted GEBV,The predictive ability of the DSM was measured by the correlation between For the IM, the cross‐validation procedure is not straight forward because the correlation between predicted and observed records depends on the value of the fixed effects  was 0.268 , except for farm*parity. The fixed effects of TO, lactation levels and months in milk were significant, but farm and breed did no longer have a significant effect on the disease status in the model including random effects (Table The estimated additive genetic variance was 0.052 (±0.008), phenotypic variance was 0.19 (±0.006), and heritability , phenotypic variance was 0.192 (±0.0056), and heritability was 0.268 (±0.05). There was no significant difference in the heritability estimates between the model based on genomic relationship and the model based on pedigree.3.3p < .001), months in milk (MIM) (p < .001) and period (p = .039) were significant . The resulting values were centred around 1 and ranged from 0.26 and 3.43  was 0.28 Figure , and Speσu32 because most records on an individual came from the same parity. The resulting accuracy was 0.28/√0.381 ≈ 0.45. This means that the DSM‐based GEBV predict the true breeding value for an individual's disease state with an accuracy of ~45%.In the denominator of this expression, we did not apply averaging to 3.5The weighted Pearson's correlation (ρ) coefficient between corrected phenotypes and predicted probabilities was 0.20 Figure . Note thTable The phenotypes used in the DSM versus IM correlated by 0.753, indicating that disease status (the DSM y‐variable) is positively correlated with, but clearly different from, becoming infected or not (the IM y‐variable). Similarly, the GEBV from both models are positively correlated (0.629), but the correlation is clearly different from 1. Hence, both models capture different traits. Overall, the correlation for DSM predictions is higher than for IM predictions, which probably reflects the substantial difference in the number of informative records. The IM better predicts disease transmission than disease status (0.20 versus 0.09), while the DSM better predicts disease status than disease transmission (0.28 versus 0.22).44.1The main purpose of this study was to present and compare estimated genetic parameters and breeding values for the binary disease status and infection events. Disease status was analysed with a linear mixed model (DSM), while infection events were analysed with a generalized linear mixed model. For both traits, we used all informative records from a data set on the endemic claw disease DD in dairy cattle. Both traits showed significant and substantial additive genetic variance and a significant correlation between the GEBV of the two traits and observed phenotypes in the cross‐validation. These results demonstrate the presence of heritable variation, both in DD disease status and in susceptibility to become infected with DD.4.2The estimated heritability of binary disease status from the DSM was 0.27, which is in the upper range of values found in the literature . More ii stays in the infected state (1) while the denominator is the average duration of a full cycle from susceptible to infected and back to susceptible. This expression relates the breeding value from the linear model to epidemiological parameters and shows that an individual's breeding value for disease status includes the genetic component of its susceptibility (βi) and its ability to recover from being infected (αi), where the relative contribution of both components depends on the prevalence of the disease. Note that the GEBV from the DSM predict individual disease status in a population where the prevalence (and thus the “infection pressure”) is the same as in the population from which the GEBV are estimated. The GEBV do not predict response to selection at the population level, because the DSM may capture recovery from infected but non‐infectious states and because it ignores indirect genetic effects originating from transmission dynamics M in epidemiology, but might have removed some genetic variation, because an offset is treated as a known effect, and thus not estimated simultaneously with the genetic effects. While the DSM does not explicitly account for the number of infected herd mates, their effect is captured by the fixed Farm effect and the random farm*period effect, at least partly.Moreover, both the DSM and the IM did not consider potential genetic variation in the propensity of (infected) herd mates to infect the focal individual. The IM accounted for the number of infected herd mates by including a known offset equal to i.e. animals becoming infected), whereas the ordinary threshold model is typically used to analyse the binary disease status observed at any time point.The IM used here is fundamentally different from the classical threshold model  and on the given farm, a random effect for farm X period was included in the model . This systematically controlled for effects of the period (duration) on the given farm from which the animal (and subsequently the susceptible hind foot) originates.4.7The relevance of a distinction between infected individuals (having disease status 1) and infectious individuals (being able to infect others) was a key motivation for this study. In the Introduction, we stated that the prevalence of an infectious disease depends on the duration that individuals are infectious, while the duration that they are infected is irrelevant. In Appendix 5The above comparison of the DSM and the IM shows that the DSM captures both the disease susceptibility of an individual and its ability to recover from being infected, whereas the IM captures susceptibility. The difference in GEBV of both models suggests that genetic variation in disease status for DD originates not only from genetic variation in susceptibility, but also from genetic variation in recovery. With genetic variation in recovery, the distinction between infected individuals and infectious individuals becomes relevant for response to selection if selection is focused on recovery along with susceptibility. Hence, a genetic analysis of recovery events would be interesting, particularly when data are available on the infectiousness of the infected individuals. A joint analysis of susceptibility (e.g. with the IM used here) and recovery of individuals from being infectious would provide insight into the genetic effects underlying the prevalence of an infectious disease, and thus in potential response to combined selection on susceptibility as well as recovery.Appendix S1Click here for additional data file.Appendix S2Click here for additional data file.


In addition to possessing great agronomic and cultural value, Mexican highland maize represents a good system for the study of local adaptation and acquisition of adaptive phenotypes under cultivation. In this study, we characterize a recombinant inbred line population derived from the B73 reference line and the Mexican highland maize variety Palomero Toluqueño. B73 and Palomero Toluqueño showed classic rank-changing differences in performance between lowland and highland field sites, indicative of local adaptation. Quantitative trait mapping identified genomic regions linked to effects on yield components that were conditionally expressed depending on the environment. For the principal genomic regions associated with ear weight and total kernel number, the Palomero Toluqueño allele conferred an advantage specifically in the highland site, consistent with local adaptation. We identified Palomero Toluqueño alleles associated with expression of characteristic highland traits, including reduced tassel branching, increased sheath pigmentation and the presence of sheath macrohairs. The oligogenic architecture of these three morphological traits supports their role in adaptation, suggesting they have arisen from consistent directional selection acting at distinct points across the genome. We discuss these results in the context of the origin of phenotypic novelty during selection, commenting on the role of  Climatic trends and a need to reduce the level of agronomic inputs have fostered interest in the development of crop varieties that show stable performance in the face of diverse, and potentially unpredictable, environmental challenges. One approach to enhance stress tolerance in the cultivated genepool is to explore diversity at the extremes of a crop’s distribution . Thousanlocal adaptation. Local adaptation is defined formally as superior performance of local genotypes in their native environment vs nonlocal genotypes (genotype × environment interaction (GEI), i.e. the degree to which the relative performance of a given variety compared with others depends on environmental conditions  was domesticated from Balsas teosinte , PT is considered ancestral to the broader Mexican highland maize group and a progenitor of more productive modern highland landraces  is a traditional popcorn distributed in the Mexican highlands, notably in the valley of Toluca, at elevations from ∼2,100 to ∼2,900 m.a.s.l. . Althougandraces . PT has andraces . Subsequandraces  and charandraces . In contandraces . B73 wasandraces  and remaandraces .In this work, we characterize a mapping population generated from the cross of B73 and PT. We demonstrate local adaptation in PT and characterize associated genetic architecture by evaluation of our mapping population in lowland and highland field sites. We identified quantitative trait loci (QTL) linked to phenology, morphology, and yield components, including evidence of QTL× environment interaction (QEI). Overall, morphological QTL were stable across environments with either little QEI or mild scaling effects. We observed stronger QEI associated with yield components. We found evidence for relatively complex genetic architectures associated with putatively adaptive morphological traits. We discuss the implications of these results with respect to the origin of adaptive variation during rapid local adaptation in cultivated species.1 was generated from the cross between the reference inbred line B73 and pollen pooled from several individuals of PT, an open pollinated landrace endemic of the Mexican highlands. The PT accession used was CIMMYTMA 2233, MEXI 5 (https://doi.org/10.18730/GAKPV) obtained from the International Center for Maize and Wheat Improvement (CIMMyT) seed bank, originally collected near the city of Toluca, Mexico State  at 2597 m.a.s.l. A single B73×PT F1 individual was crossed as male to multiple B73 ears to generate a large BC1 population, capturing a single haplotype of PT. The BC1 was then self-pollinated for 5 generations from ear-to-row to produce 120 BC1S5 families, giving a theoretical average of 25% PT genome content to 75% of B73 per family. The same initial crossing strategy was used to generate material from the cross between B73 and the open-pollinated Conico/Celaya accession CIMMYTMA 1872, MICH 21 (https://doi.org/10.18730/GA8DU). B73×Mi21 stocks were further backcrossed to B73 with phenotypic selection for sheath pubescence to produce a segregating BC5S1 stock. The progenitor B73×PT and B73×Mi21 F1 individuals described here are the same as those used in a previous report to derive introgression stocks segregating the Inv4m inversion polymorphism  with a 30-Hz frequency for 30 s. After grinding, 300 μl of UEB1  buffer were added and the solution was mixed in a Thermomixer at 38°C for 10 min. Two microliters of PureLink RNAse were added and the mix was left incubating for 30 min. After incubation, samples were separated by centrifugation at 14,000 rpm for 10 min at room temperature. Two hundred and fifty microliters of supernatant were recovered and collected in a 1.5-ml tube. Forty microliters of 3M sodium acetate, pH 5.2, and 450 μl of isopropanol were added per tube, and samples incubated for 20 min at 4°C. A further centrifugation step was performed  and the supernatant was discarded. Pellets were washed 2 times with 250 μl of 70% ethanol. The supernatant was discarded, and the pellet was left to dry for 30 min. When the pellet was dry, it was resuspended in 100 μl of milliQ water. DNA was quantified by spectroscopy and adjusted to a concentration of 20 ng/μl. DNA was analyzed by DArTSeq (https://seedsofdiscovery.org/about/genotyping-platform/) laboratory in CIMMyT, generating ∼30,000 reads per sample.Fifty micrograms of leaf tissue for 100 BC DArTSeq  at the S1S5 families with more than 30% of missing SNP information were removed, leaving a total of 98 BC1S5 families used to estimate the genetic map. SNP calls were processed using genotype-corrector , and in the lowlands during 2015 and 2016 at 54 m.a.s.l. in Valle de Banderas, Nayarit  as the number of days from planting to when 50% of the plants in the row were shedding pollen; days to silking (DTS) as the number of days from planting to when 50% of the plants in the row had visible silks; anthesis-silking interval (ASI) as the difference between male and female flowering; plant height measured from the base of the plant to the to the base of the tassel; ear height (EH) measured from the base of the plant to the first ear node; tassel branch number as the number of primary branches in the tassel; tassel length (TL) as the distance from the base to the tip of the tassel; ear weight (EW) as the weight of the whole dried ear, including cob and grain; ear length measured from the base to the apex of the ear; ear diameter (ED) measured at the middle of the ear; kernel row number counted at the middle of the ear; kernels per row counted from the base to the apex of the ear; total kernel weight (TKW) as the weight of all dried grain shelled from the ear and weighed without the cob; anthocyanin pigment intensity (INT) was evaluated at the internode immediate below the highest ear of the plant on a semiquantitative scale ranging from 0 (completely green internode) to 4 (intense red or brown coloration); sheath pubescence was evaluated as a hair score calculated from 2 semiquantitative measures taken at the third internode from the top of the plant: macrohair density ranging from 0 (glabrous) to 4 (very hairy), and macrohair pattern, the distribution of the macrohairs on the internode, from 0 (glabrous sheath), 1 , 2 (heterogeneous sheath pubescence) to 3 (uniform sheath pubescence). Plants with pattern 0 or 1 where given a hair score of 0 for absence of the medial sheath pubescence characteristic of Mexican highland maize. Plants with pattern 2 or 3 where given the density value as the hair score.The 120 families of the BCmeasured . Traits total kernel number (TKN), TKW, and TL] were further adjusted on a per block basis to a spline fitted using R/stats::smooth.spline against row number to reduce spatial variation at the subblock scale. Spline fitting was not applied to any block containing less than 50 plots. The final dataset contained 4 years of data for location Hi , and 2 years of data for location Lo . For each continuous phenotypic trait, a mixed linear model was fitted using restricted maximum-likelihood with R/lme4::lmer. To fit the model, a location-year variable was generated to represent location by year combinations, and a location-year-block variable was generated to represent location, year, and block combinations, such that:Preparation of trait data and QTL mapping were performed using R Statistics . Data coEI BLUPs . We alsoEI BLUPs .The BLUPs for continuous traits, the medians for count traits and the mode for semiquantitative scale traits were used as phenotypic inputs for QTL mapping. Phenotypic scores were selected/combined to perform four distinct analyses: (1) GEN: the genotype main effect (G) of the mixed linear model for continuous traits and the median/mode across all plots for other traits; (2) Lo: G + G×E term for Lo for continuous traits and the Lo median/mode for other traits; (3) Hi: G + G×E term for Hi for continuous traits and the Hi median/mode for other traits; (4) GEI: the difference between Hi and Lo GEI BLUPs for continuous traits and the difference between Hi and Lo median/mode for other traits.χ2 distribution with 1 degree of freedom  with R/qtl::scanone  was performed to measure the association between genetic variation and the elevation of native environment for landrace accessions across Mexico, as previously described . The dat1S5 families. When generating the BC1, we used a single F1 individual as a male to pollinate several B73 females, ensuring that a single PT haplotype was captured from the open-pollinated donor accession. As a consequence our mapping population was bialleleic, i.e. segregating for B73 and a single PT allele at any given locus. BC1S5 families were genotyped using the DArT Seq platform (http://www.diversityarrays.com/) and a final set of 918 markers and 98 BC1S5 families were used for QTL mapping. On average, the BC1S5 families conformed to the expectation of ∼25% PT genome in a B73 background, with homozygosity >98%  and highland  field sites over several seasons. Lowland trials were conducted during the dry season from November to March with supplemental irrigation. Highland trials were conducted in a rain-fed field in the standard Highland cycle from April to November. We collected data on a range of phenological, morphological and agronomic traits , measured in chronological days, was delayed in the highland site by 71 days. Overall, plants in the highlands were shorter in stature  and produced smaller ears  bearing fewer grains   . Averagend field .1S5 families. For certain traits, such as ear hieght, there was a significant GEI effect, but limited rank-changing among the lines  and lowland (Lo) fitted values, an analysis for the genotype main effect (Gen) and an analysis for the GEI effects. Across the 4 analyses, we identified 44 distinct QTLs  and two linked to tassel branch number  candidate gene  consistently in both environments with no evidence of QEI  and R1  locus that has previously been linked to the production of leaf blade macrohairs in temperate inbred maize  to Mexican highland maize . Although the four macrohair loci were individually sufficient to induce leaf sheath macrohair production, we hypothesize that their combined effect  is necessary to approach the levels of pubescence of the PT parent.For all macrohair QTL, the PT allele was associated with greater leaf sheath pubescence. We previously reported difficulty in mapping sheath macrohairs in a PT×lowland landrace Faluation . We intealuation . Consist1S5 population, we also produced pubescent near isogenic lines (NILs) by phenotypic selection and recurrent backcrossing to B73. Here, we initially used several different Mexican highland landrace donors. Material generated from the PT relative Conico (accession Michoacan 21) consistently showed the greatest pubescence and was prioritized for backcrossing and genotypic analysis. A BC5S1 family showed 3:1 segregation of pubescent to glabrous plants, indicating the action of a single, dominant locus . We selected two strongly pubescent and three strongly glabrous individuals for genotyping using the DArT-Seq platform. The pubescent individuals carried a large block of Mi21 introgression across chromosome 3 that was absent from glabrous plants  with respect to yield components, with individual BCevations .Panicum virgatum L.) in which the majority of QTL associated with adaptive traits showed conditional positive effects in their home environment with little or no detectable effect or cost in other environments  insertion upstream of ZmRap2.7, although the absence of the MITE alone did not explain late flowering Vgt1 alleles  was hypothesized to have originated in highland teosinte mexicana and to have been transferred to cultivated maize by introgression , an ortholog of the Arabidopsis XIPOTL1 gene encoding for a phosphoethanolamine N-methyltransferase (PEAMT). PEAMT catalyzes the transformation of phosphocholine to phosphatidylcholine  with the PT allele promoting pigmentation. The B1 gene encodes a bHLH transcription factor that regulates the temporal and tissue-specific expression of genes that produce anthocyanins  a leaf blade macrohair mutation in temperate maize ; (2) a lteosinte ; (3) intnd maize ; and (4)nd maize . Identifnd maize . In thispiration . In addipiration , a potenhttps://www.biodiversidad.gob.mx/diversidad/proyectoMaices). That said, PT has contributed to the broader Mexican highland group  and Mi21 (CIMMYTMA 1872) are available from the CIMMyT Germplasm Bank https://www.cimmyt.org/resources/seed-request/. All other materials are available from the corresponding author subject to SMTA compliance (https://mls.planttreaty.org/itt/index.php), costs of propagation, and export if outside of Mexico. The following data sets can be found online on Figshare: https://figshare.com/articles/dataset/B73xPT_QTL/16608517. Phenotypic data: BLUPs and fitted values estimated for diverse traits for 98 B73×PT BC1S5 families. Genetic map: genetic map of the B73×PT BC1S5 mapping population. QTL LOD profile: LOD profile of the multi-QTL models for different traits for each set of phenotypic data. Effect Plots: estimated effect of the QTLs detected for a set of phenotypic data using fitted values. Reaction norms: contains reaction norms estimated for all the QTLs detected in Lo and Hi phenotypic sets using fitted values. Elevation eGWAS: results of the eGWAS analysis.Additonal supplemental figures and supplemental information referenced in the text are available at G3 online.jkab447_Supplementary_Figure_S1Click here for additional data file.jkab447_Supplementary_Figure_S2Click here for additional data file.jkab447_Supplementary_Figure_S3Click here for additional data file.jkab447_Supplementary_Figure_S4Click here for additional data file.jkab447_Supplementary_Figure_S5Click here for additional data file.jkab447_Supplementary_Figure_S6Click here for additional data file.jkab447_Supplemental_MaterialClick here for additional data file.jkab447_Supplemental_InformationClick here for additional data file.jkab447_Supplemental_MaterialClick here for additional data file.