Asian cultivars were predominantly represented in global rice panel selected for sequencing and to identify novel alleles for drought tolerance. Diverse genetic resources adapted to Indian subcontinent were not represented much in spite harboring useful alleles that could improve agronomic traits, stress resilience and productivity. These rice accessions are valuable genetic resource in developing rice varieties suited to different rice ecosystem that experiences varying drought stress level, and at different crop stages. A core collection of rice germplasm adapted to Southwestern Indian peninsular genotyped using SSR markers and characterized by contrasting water regimes to associate genomic regions for physiological, root traits and yield related traits. Genotyping-By-Sequencing of selected accessions within the diverse panel revealed haplotype variation in genic content within genomic regions mapped for physiological, morphological and root traits.Diverse rice panel (99 accessions) were evaluated in field and measurements on plant physiological, root traits and yield related traits were made over five different seasons experiencing varying drought stress intensity at different crop stages. Traits like chlorophyll stability index, leaf rolling, days to 50% flowering, chlorophyll content, root volume and root biomass were identified as best predictors of grain yield under stress. Association mapping revealed genetic variation among accessions and revealed 14 genomic targets associated with different physiological, root and plant production traits. Certain accessions were found to have beneficial allele to improve traits, plant height, root length and spikelet fertility, that contribute to the grain yield under stress. Genomic characterization of eleven accessions revealed haplotype variation within key genomic targets on chromosomes 1, 4, 6 and 11 for potential use as molecular markers to combine drought avoidance and tolerance traits. Genes mined within the genomic QTL intervals identified were prioritized based on tissue specific expression level in publicly available rice transcriptome data.The genetic and genomic resources identified will enable combining traits with agronomic value to optimize yield under stress and hasten trait introgression into elite cultivars. Alleles associated with plant height, specific leaf area, root length from PTB8 and spikelet fertility and grain weight from PTB26 can be harnessed in future rice breeding program.The online version contains supplementary material available at 10.1186/s12870-021-03272-3. Oryza sativa L.) is a staple food grown in 144 million hectares span across 114 countries  with higher average phenotypic value for RV and RB. Within this cluster, the accessions, PTB1,2, and 10 was found to be high yielding in trial 1–2, 4 and 5, also possess valuable alleles for root architectural traits. The average yield of PTB1 is 12 g, whereas N22 rice cultivar is only 7.2 g in Trials 1–2, whereas in trial 5, the average shoot biomass of landrace, Ponnaryan (PTB2) across years is 27.6 g, which is on par to rice hybrid, PHB71 (28.6 g), and better than N22, a known rice cultivar to be drought and heat tolerant with 20.3 g.Genetic related analysis of Panel I revealed nine different clusters, including seven clusters only with local rice landraces harbor valuable alleles to improve different agronomic traits in rice lines adapted to rainfed ecosystem. Interestingly, Aryan (PTB1) and PTB10 (Thekkancheera) are genetically closer, whereas accession, PTB2 (Ponnaryan) is genetically dissimilar to any high yielding cultivars evaluated in this study. To illustrate the genetic relatedness among diverse rice lines of Panel II, Principal coordinate analysis (PCoA) was performed Fig. .Fig. 2ThPopulation structure analysis among 81 rice genotypes with a set of 100 SSR markers determined optimum K value of two, representing the diverse panel group into two major clusters Fig. . In PaneThe linkage disequilibrium (LD) map of the diverse panel (Panel I and II) revealed 8.25% of 100 SSR markers studied exhibited an R2 value of ≥0.1. In Panel I, the LD was unevenly distributed at genome level and concentrated on chromosomes 1 and 5. Among 52 LD hotspots identified, includes 46 inter-chromosomal LD pairs and 6 intra chromosomal LD pairs. LD plots with SSR markers demonstrated significant LD blocks are shown in Fig. The marker-trait associations for the phenotypic traits measured in diverse Panels I and II. In Panel I, the marker trait associations with GLM and MLM approaches identified 83 , 7 (RM1048), 9 (RM1026), and 12 RM5715) governing both physiological and root structure under water limitation are valuable genomic targets for rice trait breeding approach. The RM246 QTL was found to be linked with submergence 68], salinity tolerance , root bi, salinit5 governiBased on the physical position of markers associated with key traits, genes underlying QTLs identified were tabulated Tables  and 7. IIn Panel I, four genomic regions, RM490, RM3825 (chromosome 1), RM1130 and RM1031 (chromosome 6) were not detected in Panel II. The peptidase C45 gene (LOC_Os01g12230) associated with chlorophyll stability index was highly expressed in flag leaf in Affymetrix  and tranBetween Panel I and II, interestingly four locus  harboring five genes  were common and identified to be linked to key plant physiological production traits in rice. Gene expression analysis revealed the gene (LOC_Os01g09550) associated with SLA to be highly expressed in internode of rice dwarf mutant Fukei71 , treatmeTissue specific expression data from global RNA transcriptome datasets revealed some genes not represented in Affymetrix arrays like, LOC_Os02g24205 associated with RV and SDW to be highly expressed in anther and pollen tissues , LOC_Os0In Panel I, analysis of 11 genes with stress transcriptome datasets revealed six genes  to be highly expressed in rice tissues. The LOC_Os01g12230 (CSI), LOC_Os04g23040  and LOC_Os06g51330 (SF) were highly expressed in leaf tissues under drought . IntriguIn Panel II, evaluation of rice stress related gene expression datasets revealed LOC_Os01g09550 and LOC_Os01g16414 associated with SLA and PH to highly express in leaf tissue and panicle under stress . These sAllele mining approach was employed with GBS data of selected 11 accessions  from a previous published literature . Field tOryza sativa ssp. Indica reference genome to 93 local landraces from Kerala, revealed alternative haplotypes in key genomic regions identified in this study  grown under nitrogen , lower drought susceptibility index , and higher grain yield and spikelet fertility . Certain accessions like PTB1, 2, 10, 13, 15, 29, 30 were found as genetic resources to stack different traits to improve rice plant performance and yielding ability under drought with optimized root system. GWAS analysis revealed rice genomic region to be associated with phenological, physiological and root traits measured across seasons in this study. GWA studies in rice were successful in identifying genomic regions for agronomic  and rootComparing two diverse panels, uncommon genomic targets between panels, Panel I (RM1031) and Panel II (RM0132 and RM5633) were selected. Mining of publically available transcriptome datasets reveled the gene, LOC_Os06g51330 underlying RM1031 region associated with SF will be an interesting target to increase seed setting under stress. The genes underlying Panel II specific targets (RM0132 and RM5633), LOC_Os01g16414 and LOC_Os04g23040 associated with plant height, leaf physiological and root architectural traits are valuable targets to improve rice productivity under stress. Co-location of genomic regions identified in this study with previous rice QTL/association mapping studies –75, 120 Eighty-one diverse rice accessions Panel 1) used for drought screening and identify marker trait association are listed in Supplementary Table  used forThe experiment was conducted at Regional Agricultural Research Station, Pattambi, Kerala Agricultural University, India (10°48′41.1″N 76°11′24.9″E), oldest rice research station in Kerala with managed irrigation facility for drought stress phenotyping studies. This experimental site is rice target environment and severely prone to heat and drought stresses. The selected diverse panel were grown in field site during wet season; Mundakan (August –December) of 2017  and dry season; Puncha (January –May), 2018 . The experiment was laid out in alpha lattice design with two replications in both control and irrigated treatments. In both seasons, wet  and dry , 10 g seeds of each genotype were planted in pots filled with soil, sand and cattle manure with equal v/v ratio. Twenty-one days old seedlings were transplanted to the open field at the rate of 2 seedlings/ hill. After 8 days of transplanting, gap filling was done and one healthy seedling was maintained per hill. Each genotype was transplanted in four rows of 2 m length with a spacing of 20x15cm. Management practices were followed as per package of practices recommendation of Kerala Agricultural University. Fertilizer dose of N: P: K in the ratio of 90:45:45 Kg/ha in three split doses @ 1/3 dose of each fertilizer at basal, tillering and at panicle initiation stages. Blast incidence was controlled by using Nativo @ 4 g/10 l of water to impose water stress, irrigation was withheld for 25 consecutive days at the panicle initiation stage. Measurements on morphological and physiological parameters were recorded after 10 days of stress imposition. After stress period, re-watering was done at reproductive stage and maintained till physiological maturity. During harvest, data on plant production traits were recorded in both control and drought treatments. To quantify the soil moisture and the level of water stress data on moisture content was estimated using gravimetric method.Physiological parameters such as leaf rolling score and drying score was done according to the Standard Evaluation System for Rice (SES) of IRRI, Philippines. Leaf rolling and drying scores were taken after 10 days of drought imposition as when leaf tissues showed drought symptoms during the time between 12 pm and 1 pm. Leaf temperature was measured using infrared thermometer  with an 8° field of view and equipped with a 10.5- to 12.5-μm band pass filter, as described . The meaA previous study  with ano13C values, negatively associated with WUE. An isotope ratio mass spectrometer (IRMS) interfaced with a suitable combustion system is used for the determination of ∆13C. The plants ability to discriminate against the heavy isotope of carbon (13C), resulting in the depletion of 13C content in biomass compared with atmospheric air during photosynthesis activity was used to measure CID [13C was measured as the ratio of the partial pressures of CO2 inside the leaf to that in ambient air (Pi/Pa), as follows: Δ13C = a + (b − a) Pi/Pa; where, a and b are isotope fractionations that occur during diffusion through stomata and carboxylation by Rubisco respectively.Five leaves from actively growing rice seedlings were collected for CID analysis 50 days after sowing. Fully opened leaves were dried in oven at 80 °C for 3 days and powered the leaves using ball and mill. The water use efficiencies (WUE) of 81 rice accessions studied in the study was measured using ∆sure CID . Δ13C waSPAD  was used to measure the chlorophyll content based on light absorbance and/or transmittance characteristics at 430 nm and 750 nm on a leaf tissue. Percent chlorophyll content was measured between 10.00 and 16.00 h on second or third fully expanded leaf lamina avoiding the mid-rib portion. Actual chlorophyll content was measured using lab based assay . The speAfter 70 days of planting data on plant height and biomass were collected. Then the brick wall in the root phenotyping structure was dismantled and strong jet of water was used to remove the excess soil adhering plant root system. The soil particles adhering close to rice roots were removed carefully and data on root length, root volume, root dry weight were measured as described . Root leA subset of 35 diverse lines , 35 cycles each of 94 °C for 30 s, 50–60 °C  for 30 s, 72 °C for 1 min with final cycle at 72 °C for 5 min and 4 °C for infinity. After PCR amplification, 3 μL of gel loading dye (Bromophenol blue) was added directly to the reaction tubes and spun for few seconds in a micro centrifuge. The PCR amplicons were resolved on 2% agarose gel stained with ethidium bromide along with marker (100 bp ladder). Electrophoresis  was performed using 1X TBE as buffer solution at 80 V with running time of 90 min. The gel was visualized under UV (312 nm) transilluminator in gel documentation system (Syngene G-box documentation system) and scored as 1 for presence or 0 for absence of specific allele.Gradient cycle was performed in Master Cycler gradient 5331  to optimize the annealing temperature suitable for SSR genotyping in rice diverse panel. A final sample volume of 20 μl was used for PCR reaction with reaction mixture contained 30 ng/μl DNA, 2.5 mM dNTPs, 30 ng forward and reverse primer each, 1 unit of Taq DNA polymerase with 10X reaction buffer and 25 mM MgClp-value < 0.05. The analysis was conducted by considering genotypes with and without admixture identified by software STRUCTURE at K = 2. The LD was estimated by considering the squared correlation coefficient (r2) between SSR markers and plotted as triangle plots to evaluate LD blocks in association genetic analysis. The marker-trait association was calculated using TASSEL 5 based on General Linear Model (GLM) based on Q-matrix based on the population structure and a Mixed Linear Model based on Q-matrix and the kinship-matrix (MLM) [p-value < 0.05 and r2 value > 0.1 were considered as significant markers for further analysis. To identify genes underlying major QTLs flanked by SSR markers, two-hundred Kb (both up and downstream) around the SSR marker physical positions were mined using Ricebas e[Population structure of 81 rice genotypes was estimated using a STRUCTURE software V2.3.4 based on Bayesian clustering algorithm . Five inix (MLM) . In bothRicebas e and QTL Ricebas e. In geneRicebas e, thus twRicebas e and drouRicebas e, yield uRicebas e and nitrRicebas e. The tisRicebas e.Oryza meyeriana subsp. Granulate from Wynad district: Chomala, Gandhakasala and PTB25 (Thonnuran) and PTB8  from Kasaragod representing O. sativa f. spontanea (weedy rice) represents novel alleles that would improve grain yield and stress resilience in elite rice cultivars [GBS data on eleven accessions including 9 landraces  variation among rice landraces for potential use as marker to introgress drought tolerance in future rice breeding program. Similar approach for comparing deep sequence data on elite and landraces/wild accessions revealed a SNP allele with higher frequency among upland rice variety to regulate lateral root density .The phenotyping data were analyzed statistically using the SAS program . Principal component analysis, principal co-ordinate analysis and cluster analysis were done using R environment of statistical computing . Predictors for leaf rolling and grain yield under stress were identified using bootstrap forest model integrated into JMP SAS software (v.15.1.0).Memorandum of Understanding between institutions are needed to collect the samples from Regional Agricultural Research Station, Pattambi, Kerala, India.Additional file 1: Supplementary Table 1. List of rice diverse panels (Panel I and II) evaluated in the study.Additional file 2: Supplementary Table 2. Correlation of phenological, physiological traits with yield under water stress in trial 1.Additional file 3: Supplementary Table 3. Correlation of phenological, physiological traits with yield under water stress in trial 2.Additional file 4: Supplementary Table 4. Correlation among morpho-physiological and root traits measured under rainfed condition in trial 3.Additional file 5: Supplementary Table 5. Marker-trait association identified with GLM analysis in diverse panel I phenotypic data measured under water stress.Additional file 6: Supplementary Table 6. Marker-trait association identified with GLM analysis in diverse panel II phenotypic data measured under water stress.Additional file 7: Supplementary Table 7. List of SSR markers used in genotyping diverse rice panels (Panel I and II).Additional file 8: Supplementary Figure 1. Variation in precipitation and relative humidity experienced by diverse rice panels evaluated in field trials 1–4. Trial 1(2017), Trial 2(2018), Trial 3(2011) and Trial 4 (2013).Additional file 9: Supplementary Table 8. SNP variation identified in 11 rice land race accessions used in this study.


Alpaca breeding takes place in the most entrenched areas of the Andes, where the conditions to implement genetic improvement programs are very difficult. Likewise, taking phenotypic records is limited in its ability to predict genetic merit accurately. For this reason, genomic information is shown as an alternative that helps to predict the genetic values of fiber traits more precisely. This study showed how genomic information increased precision by 2.623% for the fiber diameter, 6.442% for the standard deviation of the fiber diameter, and 1.471% for the percentage of medullation compared to traditional methods for predicting genetic merit, suggesting that adding genomic data in prediction models could be beneficial for alpaca breeding programs in the future.Improving textile characteristics is the main objective of alpaca breeding. A recently developed SNP chip for alpacas could potentially be used to implement genomic selection and accelerate genetic progress. Therefore, this study aimed to compare the increase in prediction accuracy of three important fiber traits: fiber diameter (FD), standard deviation of fiber diameter (SD), and percentage of medullation (PM) in Huacaya alpacas. The data contains a total pedigree of 12,431 animals, 24,169 records for FD and SD, and 8386 records for PM and 60,624 SNP markers for each of the 431 genotyped animals of the Pacomarca Genetic Center. Prediction accuracy of breeding values was compared between a classical BLUP and a single-step Genomic BLUP (ssGBLUP). Deregressed phenotypes were predicted. The accuracies of the genetic and genomic values were calculated using the correlation between the predicted breeding values and the deregressed values of 100 randomly selected animals from the genotyped ones. Fifty replicates were carried out. Accuracies with ssGBLUP improved by 2.623%, 6.442%, and 1.471% on average for FD, SD, and PM, respectively, compared to the BLUP method. The increase in accuracy was relevant, suggesting that adding genomic data could benefit alpaca breeding programs. The purpose of alpaca breeding is to improve the textile properties of the fiber . The texCurrently, in alpaca breeding, most advanced improvement programs rely on quantitative genetics using Best Linear Unbiased Prediction (BLUP) methods with a multi-trait animal model with repeated measurements . Fiber tA) and the genomic relationships matrix (G) in a combined matrix (H) and to develop a single-step Genomic BLUP (ssGBLUP) method. Matrix H can be understood as a modification of the regular pedigree relationships by including genomic relationships [H [Genomic selection implementation has recently evolved rapidly in many species. Meuwissen et al.  proposedionships . The ssGships [H ,10,11. Tships [H ,13. Possships [H ,15. Hencships [H . TherefoThe data were obtained from the PacoPro v5.10 software from the Pacomarca Genetic Center, which contains pedigree information from 1992 to 2020 and phenotypic data collected from 2001 to 2019. The fiber traits were the FD and SD as described by Gutiérrez et al. [A recently developed DNA microarray consisting of 76,508 SNPs from 37 pairs of chromosomes, 36 autosomal chromosomes, and one sex chromosome , was useA) and an ssGBLUP method based on a combined matrix (H) constructed from a matrix A and a genomic relationship matrix (G). Variance components were estimated using restricted maximum likelihood (REML) [Genetic parameters were, firstly, estimated for the three traits, and then the precision of the breeding values was analyzed comparing two methodologies. Two methods were used: A traditional BLUP method with phenotypic data and pedigree-based relationship matrix (d (REML) ,18.ď. They were assumed to be the true breeding values for prediction, averaging the regressed values of an individual when there were repeated measurements of the phenotype. These were obtained by fitting the fixed effect solutions estimated with the equation b, and X was the corresponding incidence matrix. The sex by physiological effect was defined as one level for males and two for females (empty or lactating). The vector y was FD, SD, or PM phenotypes.FD, SD, and PM traits were independently analyzed. For each of them, deregressed phenotypes were called ď using the model equation BLUP and ssGBLUP performance were compared by cross-validation as predictors of the de-regressed phenotypes M was equal to 2) is cI is the identity matrix of order equal to the number of permanent environmental subclasses, eI is the identity matrix of order equal to the number of records, A is the numerator relationship matrix according to the pedigree information, and H is a similar matrix to A that includes both pedigree-based relationships and differences between pedigree-based and genomic-based relationships [A−1 matrix must be replaced by H−1 [A22 is a numerator relationship matrix for genotyped animals, and G is a genomic relationship matrix [G−1 was obtained as the inverse of a combination of the G matrix and the corresponding A matrix, weighting them by 0.95 and 0.05, respectively [ionships . The ssGd by H−1 , such asp matrix . G−1 wasectively .ď defined above and the predicted breeding values obtained both by BLUP (PBV) and by ssGBLUP (GPBV). This procedure was carried out in fifty replicates.Records from a random sample of 100 animals out of the 431 genotyped ones were initially removed to be used later as the testing set, keeping the rest as a training set. BLUP and ssGBLUP were compared by computing the correlation between the true breeding values An additional analysis to check the use of genomic selection in non-genotyped animals was carried out. Phenotypes of 390 animals of the last year of recording were initially removed. Later the corresponding deregresed phenotypes were predicted from both BLUP and ssGBLUP methodologies and their accuracies were compared.In the next step, PBVs and GPVBs of genotyped animals were compared using a Pearson correlation. Given that selected animals can vary when selected by truncation using different criteria, the ranking of all animals using the two methods was compared by calculating a Spearman’s rank correlation between them. To better illustrate this, selection by truncation was simulated both with the PBV and GPBV, where genotyped animals were ranked by their genetic value for each trait, and the top 25% were selected . Thus, the percentage of the same animals that would be selected in both methods was calculated for each trait.lm, cor, cor.test and t.test functions of the R language [The SNP markers’ quality control was carried out using the R language . The lm,language  were uselanguage  were useThe mean heritabilities estimated across replicates by BLUP and ssGBLUP methods were not significantly different for FD, SD, and PM traits, as shown in p > 0.05).The mean prediction accuracy for FD, SD, and PM traits under BLUP and ssGBLUP methodologies is shown in The Pearson correlations of genetic values for fiber traits between BLUP and ssGBLUP were 0.975 for FD, 0.976 for SD, and 0.959 for PM. Meanwhile, the Spearman’s correlations show how much the individuals conserve their position in a ranking. They were 0.972 for FD, 0.970 for SD, and 0.956 for PM.The percentage of animals for FD, SD, and PM that would be selected using both methods with truncation selection of the best 25% of the animals is shown in Analyses comparing the accuracies of BLUP to ssGBLUP in non-genotyped animals showed that predictive ability increased by 1.331% and 0.522% for FD and SD, respectively, but decreased 46.288% for the PM trait.Alpaca management systems in Peru are traditional and are based on the exploitation of the Andean rangelands, where the management of other domestic animal species is difficult . In mostGenomics is undoubtedly a tool of increasing interest that has different uses in animal management . GenomicThe first studies that evidenced the importance of using molecular genetics regarding fiber traits were reported by Pérez-Cabal et al. . HoweverThis study showed that heritabilities estimated by BLUP and ssGBLUP were similar to those reported by Cruz et al. , in whicThe ssGBLUP methodology provided greater accuracy of prediction than BLUP, as reported in the case of carcass traits in chicken broilers ,30,31. THowever, it must be considered that these comparisons are not directly comparable to our results due to the differences between the size of the genotyped population, pedigree size, and the number of records in other species. In carcass traits of sheep, the increase in accuracy was 33.3% , while iIn the present study, the increase in accuracy was relevant even when the number of genotyped animals was small . The reference population size affects the prediction accuracy of breeding values ,42. AccuThe results comparing the predictive ability on non-genotyped animals showed much less improvement in the new methodology for FD and SD traits. This highlights the importance of genotyping the selection candidates. In fact, the prediction ability for the PM trait, with low heritability and fewer records, considerably worsened.Song et al.  explaineHowever, it should be noted that alpaca farming has not yet implemented artificial insemination as an intensive breeding technique, and natural mating is a common practice. This practice leads to the need for a high number of males to cover reproductive needs. Therefore, the ranking variation may be less influential due to the low number of females assigned to each male in the breeding campaign . Our resGenomic selection works best in populations where the effect of the marker comes from animals with data and whose marker-selected progeny go on to produce phenotype data and reenter the training population, which then becomes dynamic. Additionally, considering that the cost of genotyping in alpacas is high and that prediction accuracy is much higher when marker and phenotype information complement each other, data collection is still an important tool when implementing genomic selection. The eventual cost reduction may make its implementation more feasible.The use of genomic selection increased the accuracy of the genetic value estimation through the ssGBLUP methodology, showing its potential for the genetic improvement of fiber traits. Therefore, ssGBLUP is a new methodology to predict genetic values, especially for difficult-to-measure traits, such as the percentage of medullation. Moreover, it is necessary to genotype more animals to know the real possibilities of genomic selection in alpacas. Finally, it is important to encourage the recording of information from a larger population to extend the benefits of this methodology to a larger population and, thus, have more impact on alpacas.


Native chickens play a vital role in the rural economies of many countries and are considered a valuable genetic resource for use in the development of high-yielding breeds. Native breeds are used as foundation stocks for breeding through a system of crossbreeding with commercial breeds, taking advantage of heterosis. However, due to the constraints of their slow growth rate and low egg production, there is insufficient supply to meet consumer demand. Therefore, improving genetic knowledge is one way to solve these problems and achieve sustainable results. In this paper, a multi-trait animal model and a selection index are developed as a solution for this problem to improve growth and egg performance.p < 0.05) from 4 to 10 weeks of age and that this difference increased over the generations. The differences between BW0 and BrC6 by sex and generation were not significant (p > 0.05). The estimated heritability of body weight ranged from 0.642 (BW0) to 0.280 (BW10); meanwhile, the estimated heritability of BrC6 was moderate (0.284). For egg production traits, the estimated heritability of 240EP, 270EP, 300EP, and 365EP was 0.427, 0.403, 0.404, and 0.426, respectively, while the estimated heritability of AFE was 0.269. The genetic and phenotypic correlations among the growth traits (BW0 to BW10) were low to highly positive. The genetic and phenotypic correlations between growth (BW0 to BW10) and BrC6 traits were positive, and the genetic correlations between BW6 (0.80), BW8 (0.84), BW10 (0.93), and BrC6 were strongly positive. Genetic correlations among the egg production traits  were low to highly positive and ranged from 0.04 to 0.86. The genetic correlations between AFE and all egg production traits were low to moderately negative and ranged from −0.14 to −0.29. The positive genetic correlations between body weight  and egg production traits were found only in 240EP. The average genetic progress of body weight traits ranged from −0.38 to 30.12 g per generation for BW0 to BW10 (p < 0.05); the genetic progress was 0.28 cm per generation for BrC6 (p > 0.05). The average genetic progress of cumulative egg production traits ranged from 4.25 to 12.42 eggs per generation for 240EP to 365EP (p < 0.05), while the average genetic progress of AFE was −7.12 days per generation (p < 0.05). In conclusion, our study suggests that the body weight at six weeks of age (BW6), breast circumference at six weeks of age (BrC6), cumulative egg production at 240 days of age (240EP), and age at first egg (AFE) are the traits that should be used as selection criteria, as they have a positive effect on the development of growth and egg production.To improve the genetics of both growth and egg production, which are limitations in purebred native chickens, new genetic lines can be developed using an appropriate genetic approach. The data used in this study included 2713 body weight , breast circumference (BrC6), chicken age at first egg (AFE), and egg production  records covering the period 2015 to 2020. A multi-trait animal model with the average information-restricted maximum likelihood (AI-REML) and a selection index was used to estimate the variance components, genetic parameters, and breeding values. The results showed that males had significantly higher weights than females ( Nowadays, consumers are interested in good-tasting and healthy food enriched with naturally bioactive ingredients. Poultry meat is therefore widely consumed due to its components such as peptide proteins, fatty acids, and antioxidants ,2,3,4,5,In 2010, the Research and Network Center for Animal Breeding and Omics Research, Khon Kaen University, developed a commercial broiler x Thai native (Chee) named Kaimook e-san1 (KM1), which is Thai synthetic chickens . The goals of KM1 breeding are to promote growth performance and egg production compared to TNC. The growth of KM1 was significantly better than that of TNC; TNC was fed until it reached 12 weeks of age, while KM1 was fed until only 10 weeks of age to reach a 1.2 kg market weight . AdditioTherefore, the objectives of this study were (1) to estimate heritability, genetic correlation, and genetic progress using a multi-trait animal model in TSC (KM2) and (2) to create an appropriate selection index for four primary traits in terms of body weight, breast circumference, age at first egg, and egg accumulation with high genetic values for use as traits in a chicken-breeding program to improve animal genetics in the following generations.The study was carried out in the experimental farm of the Network Center for Animal Breeding and Omics Research, Faculty of Agriculture, Khon Kaen University, Thailand. The farm is located in the northeastern part of Thailand. The region experiences a hot tropical climate with temperatures ranging from 10 °C in winter to 41 °C in summer. This study was reviewed and approved for institutional animal care based on the Ethics for Animal Experimentation of the National Research Council of Thailand (No. IACUC-KKU-37/64).A flock of TSC was created by crossbreeding commercial broiler chickens and Thai native chickens called Kaimook e-san1 (50% native chicken). Mixing between the broiler and KM1 was used to create Kaimook e-san2, named KM2 (25% native chicken), in 2014. To date, KM2 has developed until the 6th generation. For the breeding plan, each cock was mated to five hens  by within-breed selection in a closed nucleus system, and then fertilized to produce eggs that hatched in sets. Three sets of the same parent pairs were bred, each incubated 1 week apart, followed by two more cycles of mating with alternating hens each round; each round produced three sets of offspring. In each generation, there were approximately 1500–1800 chicks, which were selected at 16 weeks of age to produce 30 cocks and 150 hens (effective population size = 100)  to fertilize the next generation. The selection process was divided into two steps: (1) Selection for the desired morphological characteristics of the breed  and (2) The morphology shown in When the chicks were hatched, their weights were recorded, and they were tagged with an ID number. This study was divided into three phases. During Stage 1 (0–4 weeks), called the starter phase, they were raised on a litter floor with brooders using incandescent lighting (24 h of light to 0 h of darkness). During Stage 2 (5–16 weeks), called the grower phase, they were moved to a growing house with a litter floor and raised in a house open to natural light. In the first 2 stages, feed and water were supplied on an ad libitum basis. During stage 3 (16+ weeks), called the mature phase, they were moved to individual battery cages, where lighting (18 h of light to 6 h of darkness) and feed were provided on a limited basis. Males were fed 150 g/bird/day, and females were fed 130 g/bird/day. Mating was accomplished by artificial insemination twice a week and eggs were collected from the hens daily. The chickens were fed a starter ration  from 0–4 weeks of age, a grower ration  from 5–16 weeks of age, and a layer ration  from 16 weeks of age to the end of the production period.Data were obtained from the Network Center for Animal Breeding and Omics Research, Faculty of Agriculture, Khon Kaen University. A total of 2713 animals with records of body weights, breast circumference, age at first egg, and egg production traits were gathered from six generations during the period of 2015 to 2020. A pedigree file was constructed by tracing back all generations of ancestors and included 3828 individuals born between 2014 and 2019. In the data-editing process, chicks at birth that did not meet the morphology requirements and with a birth weight of less than 26 g were discarded; additionally, we identify outliers for each trait (data more than 1.5 times the interquartile range below the first quartile).The traits considered included the birth weight (BW0); body weight at 4, 6, 8, and 10 weeks of age ; and breast circumference at 6 weeks of age (BrC6). The egg production traits included the age at first egg (AFE) and cumulative egg production at 240, 270, 300, and 365 days of age .The recorded data were validated and analyzed for the least square means, and statistical differences were compared by sex and generation using the post hoc test in the generalized linear model for an unbalanced analysis of variance (GLM procedure) using the SAS package. The variance components and genetic parameters, such as heritability and genetic correlation, were estimated using the average information-restricted maximum likelihood (AI-REML) , and theY is the vector corresponding to the phenotypic values for the growth  and egg production traits ; X and Z are incidence matrices related to fixed and random effects, respectively; b is the vector of fixed effects, including the chicken hatch set and sex; a is the vector of random additive genetic effects, assumed to be A is an additive relationship matrix and e is the vector of random residual effects assumed to be Multi-trait animal model:h = the accuracy of selection, i = the selection intensity (20% per generation), and gen = generation (year of birth of chickens). The generalized linear model for an unbalanced analysis of variance (PROC GLM) using the SAS package was used to compare the EBV by generation. The accuracy of the selection index (r) by sex was calculated based on the equation P = phenotypic variance–covariance matrix; G = genetic variance–covariance matrix, and A is an additive relationship matrix and v = the vector of relative economic weights corresponding to the traits considered in this study.Genetic progress (EBV) four traits: Body weight  from 4 to 10 weeks of age (p > 0.05). The body weight of KM2 tended to increase over the generations, especially in the fifth and sixth generations  to 0.280 (BW10). These values were moderate to high. Meanwhile, the estimated heritability of BrC6 was moderate (0.284).The estimated variance components and heritability of growth traits are shown in 2 for 240EP to 489.20 egg2 for 365EP. Meanwhile, residual variances ranged from 185.40 egg2 for 240EP to 660.40 egg2 for 365EP. The estimated heritability of egg production ranged from 0.427 (240EP) to 0.426 (365EP). These values were high. Meanwhile, the estimated heritability of AFE was moderate (0.269).For egg production traits, the additive genetic variances ranged from 138.10 eggGenetic and phenotypic correlations among growth, breast circumstance, and egg production traits are shown in Genetic correlations among the egg production traits  were mildly to strongly positive and ranged from 0.04 to 0.86. The genetic correlations between AFE and all egg production traits were mildly to moderately negative and ranged from −0.14 to −0.29. Similarly, the genetic correlations between AFE and body weight  and BrC6 traits were negative and ranged from −0.03 to −0.27. The phenotypic correlations between growth and egg production traits were slightly lower than the genetic correlations.p < 0.05). The genetic progress of the BrC6 trait was 0.33 cm per generation (p > 0.05) (p < 0.05), while the average genetic progress of AFE was −7.12 days per generation (p < 0.05) (The genetic progress per generation for (a) body weight and breast circumference and (b) age at first egg and egg production traits are shown in  > 0.05) a. The av < 0.05) b.The top 20% of the selection index values in male and female KM2 chickens are shown in To develop high growth and egg productivity for TSC, the crossbreeding of exotic and native chickens with genetic evaluation is needed for genetic selection. This study demonstrated that performing genetic selection during six generations caused an increase in body weight and egg production. The estimates of the heritability and genetic correlations between traits suggest that body weight at 6 weeks of age (BW6), breast circumference at 6 weeks of age (BrC6), cumulative egg production at 240 days of age (240EP), and age at first egg (AFE) are desirable traits to use in a selection index, as selection for these traits has a positive effect on growth and egg production. Finally, a multi-trait animal model and selection index can be used in this population of native chickens to improve growth and egg performance.The mean body weights of KM2 among generations demonstrated that the selection method used in our study was effective, as the body weights increased over the generations b. ComparFor egg production traits, the mean age at first egg was lower (AFE) and the average cumulative egg production increased  over the generations, especially from the fourth generation onwards . Both thAs shown in The heritability estimates for the traits in the present study were medium to high (ranging from 0.269 to 0.642; see The estimates of genetic and phenotypic correlations between the observed traits varied from low to high . The genThe study of genetic trends is a way to monitor the selection process. According to the genetic progress for body weight and egg production shown in We propose the use of a multi-trait animal model and selection index in this population of native chickens to improve growth and egg performance. Moreover, we determined that the method based on estimating the breeding value is the optimal method for genetic improvement. Body weight at 6 weeks of age, breast circumference at 6 weeks of age, cumulative egg production at 240 days of age, and age at first egg are the traits that should be used in a selection index.


Managing beneficial Mendelian characteristics in dairy cattle breeding programs implies that the correlated genetic effects are considered to avoid possible adverse effects in selection processes. The Mendelian trait polledness in cattle is traditionally associated with the belief that the polled locus has unfavorable effects on breeding goal traits. This may be due to the inferior breeding values of former polled bulls and cows in cattle breeds, such as German Simmental, or to pleiotropic or linkage effects of the polled locus.We focused on a variance component estimation approach that uses a marker-based numerator relationship matrix reflecting gametic relationships at the polled locus to test for direct pleiotropic or linked quantitative trait loci (QTL) effects of the polled locus on relevant traits. We applied the approach to performance, health, and female fertility traits in German Simmental cattle.Our results showed no evidence for any pleiotropic QTL effects of the polled locus on test-day production traits milk yield and fat percentage, on the mastitis indicator ‘somatic cell score’, and on several female fertility traits, i.e. 56 days non return rate, days open and days to first service. We detected a significant and unfavorable QTL effect accounting for 6.6% of the genetic variance for protein percentage only.Pleiotropy does not explain the lower breeding values and phenotypic inferiority of polled German Simmental sires and cows relative to the horned population in the breed. Thus, intensified selection in the polled population will contribute to increased selection response in breeding goal traits and genetic merit and will narrow the deficit in breeding values for production traits.The online version contains supplementary material available at 10.1186/s12711-021-00652-z. Bos taurus autosome 1 (BTA1). The four dominant allelic variants cause polled phenotypes and scurs, and the recessive wild-type variant causes the horned phenotype [C) and “Friesian” (PF) variants are predominant in polled animals from European dairy and dual-purpose cattle breeds [Bovine polledness is a Mendelian trait OMIA 000483-913) that was discovered as early as 1902 [henotype . The idee breeds . The Men3-913 thaThe broad availability and use of the high-throughput genotyping technology in cattle have enhanced the discovery of new Mendelian genetic characteristics during the past decade . Most ofThe basis of genetic trait associations is either pleiotropy or linkage . From thVariance component (VC) estimation methods that incorporate single or multiple marker genotype information to infer QTL effects at a given chromosomal segment or position appear to be suitable to test for correlated effects of the polled locus , 23. In matrices . These metection , 25. WheOur aim was to derive complete polled genotypes in a complex pedigree to be able to calculate the IBD probabilities and to test for pleiotropic or linked QTL effects of the Mendelian polled locus on production and reproduction traits in German Simmental cattle via univariate and bivariate VC estimation.The polled status in German Simmental cattle is routinely recorded via a collaboration between farmers and the Bavarian milk recording organization . Only poAvailable polled labels for ancestors of the selected cows in the final dataset were taken from the literature , 26, 27,A comparison of the unchanged initial genotypes as reported from the routine records and the genotypes after inference showed a significant amount of non-registered (e.g. progeny of gene- or progeny-tested homozygous polled sires or dams) and also falsely-registered (e.g. polled progeny from horned matings) animals  probabilities between the alleles at the polled locus of any two founders were assumed to be zero. Genotypes at the polled locus were treated as a tri-allelic marker. Hence, polled alleles of founder animals from breeds that predominantly carry the “Celtic” polled allelic variant such as Simmental, Brown Swiss and most beef breeds are initially coded differently than those of founder animals from breeds that predominantly carry the “Friesian” polled allelic variant, such as Holstein and Jersey [The k et al.  with a sentclass1pt{minimaage in R , and wasTest-day production traits included milk yield (MY), protein percentage (P%), fat percentage (F%) and the health indicator somatic cell score (SCS). In total, 58,262 test-day records from lactations 1 to 7 recorded from 2005 to 2015 were included for parameter estimation. Female fertility traits were binary 56 days non-return-rate (NRR-56), days open (DO) and days to first service (DFS) for cows. NRR-56, DFS and DO were calculated from routine insemination data collected from 2004 to 2014. Only the first four lactations were considered for parameter estimation of female fertility traits. Descriptive statistics for all analyzed cow traits are in Table Variance components for all cow traits were estimated in single trait animal models using the software package DMU and the implemented AI-REML algorithm . The basFixed effects considered in the models for the test-day traits were lactation, herd-test day and calving season. Days pregnant and calving age (linear regression) and days in milk  were considered as covariates.A threshold model using a logit link function was defined for the NRR-56 trait without changing the structure of fixed and random effects described above. Fixed effects in the models for the fertility traits were the combined effects of herd-year, type of insemination-year, and lactation-calving age.The extended linear univariate QTL models  statistic:h models . LRT tesAlthough the outlined VC approach appears to be straightforward for our research question, to our knowledge no previous studies have proved its applicability using either simulated or real data. As flexible stochastic simulation packages to simulate precise genomic trait architectures are available , 31, we Table Standard errors of the heritabilities for all production and fertility traits from both models With regard to the The estimated direct QTL effects of the polled locus on production traits MY, F% and on SCS were consistently low and not significant. Comparison of the univariate with the bivariate QTL models indicates that the QTL effects from the bivariate models are smaller for the moderately heritable production traits. All the estimates of QTL effects for the fertility traits were non-significant and those from the corresponding bivariate models were considerably smaller, thus following the same trend as for the production traits.The estimates for phenotypic and genetic correlations Table  support The results for the preliminary simulation study showed a general suitability of our approach for the detection of predefined QTL effects  of the polled locus to the genetic variance of P%. In consequence, we refuted any concerns for possible unfavorable effects of the polled locus on production, udder health and female fertility traits in German Simmental cattle. At first sight, this contrasts with previous studies in the Simmental population based on the comparison of breeding values between polled and horned sires , 21. HowBased on our results, there is no general antagonistic or detrimental effect of the polled alleles on productivity, udder health and female fertility. The formerly general and presently partial inferiority of polled animals is most likely due to genetic drift caused by a small number of polled Simmental founders or ancestors from beef type cattle with inferior genetic values in dairy traits. Strong associations due to pleiotropy or tight linkage would imply unidirectional effects across breeds that are unaffected by selection over time. In contrast, associations due to genetic drift have a random nature and can be altered by selection and recombination. Thus, the closing gap in production traits between polled and horned animals in Simmental  and HolsOur results also give further support to previously published simulation results on breeding strategies for polledness e.g. , 38–41),–41,38–41It should be noted that selective breeding for polledness in German Simmental is an ongoing dynamic process that has significantly changed and still changes the frequency of the polled allele in the population. Therefore, given that the estimates of QTL effect and variance depend on allele frequency, we recommend monitoring the effects of the polled locus in the future, when the proportion of polled animals has substantially increased. In addition, although non-significant, the estimated QTL effects for NRR-56, DFS and DO reflect significant proportions of the overall estimated genetic variance for each of these traits. This may be due to a lack of power to accurately estimate the effects of the polled locus on these traits, and a larger sample size should be used in future studies.Nonetheless, we were able to show that a relationship matrix based on polled genotypes (NP chips , 47, we c and Pf based on genotype data could help to answer the remaining questions concerning different family effects in association with the polled trait [Given that the number of polled animals in the population is constantly growing and that young animals are much more frequently genotyped in the current genomic breeding schemes, it will therefore be much easier to analyze extensive datasets in German Simmental cattle and other breeds by using the methodological framework developed here. In this regard, explicitly modelling and dissecting more precisely the potentially diverging effects of the known polled variants such as Ped trait , 48.Our results reveal no direct pleiotropic or linked QTL effects of the polled locus on the studied production traits MY and F%, on the udder health indicator SCS and on the female fertility traits, NRR56, DO and DFS, in German Simmental cattle. Only one statistically significant direct QTL effect of the polled locus on P%, was detected. Thus, further selection on polledness is not expected to result in negative side effects on breeding goal traits. Based on our results, we conclude that any remaining inferiority of polled cows and bulls will be reduced by increasing the dissemination of the polled alleles and by intensive simultaneous selection on breeding goal traits and polled genotypes in the German Simmental population.Additional file1. Rscripts used for the Inference of polled genotypes in the full pedigree.Additional file 2. Rscripts used for the calculation of Additional file 3. Preliminary study on variance component models for polledness. Preliminary study on variance component estimation for the trait polledness including a QTL relationship matrix based on inferred genotypes from the pedigree in univariate models.Additional file 4. Preliminary simulation study. Preliminary stochastic simulation study to validate the variance component estimation approach.Additional file 5. Variance component estimation results from bivariate analysis for simulated and real data for the trait polledness.


Systemic iron status affects multiple health outcomes, however its net effect on life expectancy is not known. We conducted a two-sample Mendelian randomization (MR) study to investigate the association of genetically proxied iron status with life expectancy.n = 1,012,240) and individual survival to the 90th vs. 60th percentile age . We used the inverse-variance weighted method to estimate the effect of a 1-standard deviation (SD) increase in genetically predicted serum iron on each of the life expectancy outcomes.Using genetic data from 48,972 individuals, we identified three genetic variants as instrumental variables for systemic iron status. We obtained genetic associations of these variants with parental lifespan (P = 3.00 × 10−3) fewer years of parental lifespan and had odds ratio 0.81  for survival to the 90th vs. 60th percentile age. We did not find evidence to suggest that these results were biased by pleiotropic effects of the genetic variants.We found a detrimental effect of genetically proxied higher iron status on life expectancy. A 1-SD increase in genetically predicted serum iron corresponded to 0.70 (95% confidence interval [CI] −1.17, −0.24; Higher systemic iron status may reduce life expectancy. The clinical implications of this finding warrant further investigation, particularly in the context of iron supplementation in individuals with normal iron status. Systemic iron status can be assessed using biomarkers such as serum iron, ferritin, transferrin (inversely related to iron status) and transferrin saturation. Levels of these biomarkers vary considerably between individuals, and can be altered through clinical intervention. Small changes in iron status have been suggested to have protective and detrimental effects on different disease processes . HoweverWhile observational studies have associated higher iron status with reduced life expectancy, such a study design is limited in its ability to draw causal inference due to the possibility of confounding and reverse causation . For exaThe Mendelian randomization (MR) approach can overcome these limitations by using randomly allocated genetic variants as instrumental variables for studying the causal effects of modifying systemic iron status . As germ22.1P < 5 × 10−8) with four biomarkers of iron status  in a pattern with concordant effects on overall iron status [HFE, rs1799945 – HFE, rs855791 – TMPRSS6) [HFE may inhibit iron absorption by inducing hepcidin production. In the setting of iron depletion, TMPRSS6 may stimulate iron absorption by inhibiting hepcidin production.As instrumental variables for systemic iron status, we selected single-nucleotide polymorphisms (SNPs) that had genome-wide significant associations P < 5 × 0−8 with n status ,4,7. Then status ,4. This TMPRSS6) ,4,7. PutTMPRSS6) . Briefly2.2n = 1,012,240) [Large-scale genetic association studies on individual lifespan are not available. However, parental lifespan is a readily available phenotype that can be used as an outcome in a genetic association study because parents share half of their genetic code with their offspring . As the 012,240) . These s012,240) , and wer012,240) . Data onAs a secondary outcome, we obtained genetic associations of these instruments with odds of individual  survival to a sex and birth cohort-specific 90th percentile age vs. 60th percentile age  [All included genetic association studies were conducted in European populations and adjusted for principal components of ancestry, thereby minimizing confounding by population stratification. All studies received relevant ethical approval and participant consent and their data are publicly available, as detailed in the supporting citations .2.3For the three genetic variants, we used the ratio method to estimate the instrumental effect of higher iron status on life expectancy. This method divides the SNP-outcome effects by the SNP-exposure effects and uses first-order weights to obtain the standard error of the causal effect . To poolP-value to assess the strength of evidence for pleiotropy [MR estimates may be biased by horizontal pleiotropy if the genetic variants proxying iron status influence longevity through a pathway independent of iron status. To test the null hypothesis of no pleiotropy, we calculated Cochran's Q for heterogeneity and used the eiotropy . We alsoeiotropy . As theseiotropy . This apAll analyses were implemented using the TwoSampleMR package of R .3P = 3.00 × 10−3), −1.64 for ferritin , −0.54 for transferrin saturation , and 0.78 for transferrin . The odds ratio for survival to the 90th vs. 60th percentile age was 0.81 for iron , 0.63 for ferritin , 0.86 for transferrin saturation , and 1.21 for transferrin . Individual variant MR estimates were all consistent with a deleterious effect of higher genetically predicted iron status on lifespan and longevity with no statistical evidence of heterogeneity .Genetically predicted higher iron status across all biomarkers was associated with reduced life expectancy , Fig. 2.4We found that higher genetically predicted iron status was associated with reduced life expectancy. This effect was consistent across all four biomarkers of systemic iron status, and across the outcomes of lifespan and longevity. The lack of heterogeneity across the genetic instruments and the consistency of evidence across statistical sensitivity analyses suggests that our findings are unlikely to be biased by pleiotropic effects of the variant used to proxy systemic iron status. While the association of genetically predicted transferrin levels with longevity in the secondary analysis was the only result where the confidence intervals overlapped the null, this is likely attributable to inadequate statistical power, and the direction of the point estimate was concordant with the other results.Taken together, these findings suggest that associations between higher iron status and increased mortality risk reported in prior observational studies may reflect causal relationships . Our finHFE lowers low-density lipoprotein cholesterol levels, while the iron status raising allele at rs1799945 in HFE raises systolic and diastolic blood pressure [Our findings should be interpreted in context. Despite our efforts, the MR estimates may still be biased by pleiotropic effects of the genetic variants on life expectancy through pathways independent of iron status. Prior work has shown that the iron status raising allele at rs1800562 in In conclusion, our genetic evidence suggests that an increase in systemic iron status around the population mean may reduce life expectancy. While randomized-controlled trials are required to provide definitive evidence of clinical effect, and further research is required to validate the clinical implications of our findings, caution may be prudent when supplementing iron without a clear clinical indication, such as in individuals with normal iron status.The funding source had no role in the design, acquisition of data, analysis, interpretation or write up of this study.Wellcome Trust 4i Programme (203928/Z/16/Z) and British Heart Foundation Centre of Research Excellence (RE/18/4/34215) at Imperial College London.DG is funded by the ID and DG designed the study, performed statistical analyses, interpreted results, wrote the manuscript, edited the manuscript for intellectual content, and take responsibility for the integrity of the study.DG is employed part-time by Novo Nordisk. ID has no conflicts of interest to declare.


Phenotypic variability results from interactions between genotype and environment and is a major driver of ecological and evolutionary interactions. Measuring the relative contributions of genetic variation, the environment, and their interaction to phenotypic variation remains a fundamental goal of evolutionary ecology.Asclepias syriaca, in northern Michigan. We first measured resistance and resistance traits of 14 maternal lines in two common garden experiments (field and greenhouse) to detect genetic variation within the population. We carried out a reciprocal transplant experiment with three of these maternal lines to assess effects of local environment on phenotype. Finally, we compared the phenotypic traits measured in our experiments with the phenotypic traits of the naturally growing maternal genets to be able to compare relative effect of genetic and environmental variation on naturally occurring phenotypic variation. We measured defoliation levels, arthropod abundances, foliar cardenolide concentrations, foliar latex exudation, foliar carbon and nitrogen concentrations, and plant growth.In this study, we assess the question: How do genetic variation and local environmental conditions interact to influence phenotype within a single population? We explored this question using seed from a single population of common milkweed, We found a striking lack of correlation in trait expression of the maternal lines between the common gardens, or between the common gardens and the naturally growing maternal genets, suggesting that environment plays a larger role in phenotypic trait variation of this population. We found evidence of significant genotype‐by‐environment interactions for all traits except foliar concentrations of nitrogen and cardenolide. Milkweed resistance to chewing herbivores was associated more strongly with the growing environment. We observed no variation in foliar cardenolide concentrations among maternal lines but did observe variation among maternal lines in foliar latex exudation.Overall, our data reveal powerful genotype‐by‐environment interactions on the expression of most resistance traits in milkweed. This study explores how local growing environment and genetic variation dictates plant trait expression within a small population. We measured within‐population genetic variation in resistance traits of common milkweed and then compared those traits with patterns of resistance in the naturally growing milkweed population. Our findings suggest that genotype‐by‐environment interactions and environmental variation strongly influence phenotypic expression in this population of common milkweed and that standing genetic variation alone has a limited role in phenotypic variation. Phenotypic variability results from both genotype and environment and is an important driver of many ecological and evolutionary processes  and their herbivores have become a model system for studying the ecology and evolution of plant–herbivore interactions  to compare relative effects of genetic and environmental variation  in Pellston, MI . Within the old‐field, the growing environments of the genets vary in plant community cover type, proximity to structures and dirt roads, and proximity to the forest edge . The old‐field is maintained by semi‐annual mowing and hosts 32 genets that have been studied annually since 2007 . The 14 genets that we selected for our experiments were those that produced enough seed in 2018 for all three experiments in 2019. The distance between neighboring genets varies from approximately 5 m–20 m. The seeds were at least half‐siblings (multiple seed pods from unknown fathers for each genetic mother). Seeds and seedlings were classified by their maternal genotype and are referred to as “maternal lines” hereafter.To explore how milkweed genotype and environment influence resistance phenotypes, we designed three experimental groups: a field common garden, a greenhouse common garden, and a reciprocal field transplant. The seeds for all experiments came from fourteen genets of The field common garden and greenhouse common garden were both randomized block designs, and the same experimental design was replicated in the field and greenhouse. The reciprocal field transplant consisted of three maternal lines, each grown “at home” and “away.”We measured defoliation, arthropod abundances, and plant size  weekly from all common garden plants (12 weeks from 3 June–21 August 2019) and reciprocal transplant plants (10 weeks from 19 June–21 August 2019) and monthly from the naturally growing maternal genets. We collected samples for foliar chemistry from all plants once in mid‐July, in the middle of the growing season (methods below). This set of traits represents known drivers of insect performance, but we acknowledge that many other plant traits we did not sample likely also contribute to the insect abundance recorded in this study.3.23.2.1® 360 potting soil in Deepots®. Seedlings were grown in a controlled growth room  for the month of April 2019. Seeds were planted in April to ensure that plants were large enough to withstand field conditions by June, when local ramets emerge at UMBS. We transported plants from Ann Arbor to UMBS on 1 May 2019 to complete an additional month of greenhouse growth while outside conditions were still too cold. Plants were then either maintained in the greenhouse (greenhouse common garden) or transferred outside to the field common garden on 1 June 2019. This timing matches the typical phenology of the local milkweed population at UMBS .Plants for the field and greenhouse common gardens were grown from seed for one month at the University of Michigan in Ann Arbor before transfer to UMBS. Seeds were cold‐stratified for 6 weeks, treated with household bleach (5%), germinated in petri dishes for 1 week, and then planted in Sungro Metro‐mix3.2.2A. syriaca plant, we measured weekly arthropod abundance and plant height, leaf number, defoliation, and base stem diameter . In mid‐July, we measured foliar latex exudation and collected tissue to measure foliar concentrations of cardenolides, carbon, and nitrogen.The randomized block design of 18 blocks, each containing one individual of 14 maternal lines resulted in 252 plants total per common garden. Each plant was grown in an 18 cm × 16 cm pot held on benches (greenhouse) or set into the ground such that the topsoil of the pot was level with the ground (field). Each block consisted of two rows of 7 plants. Within each block of the field common garden, plants were spaced 1 m apart and 1.5 m separated each block. A 12.68 m × 32.53 m fenced exclosure surrounded the field common garden to protect plants from deer and rabbit browsing. The greenhouse common garden plants were arranged on benches so that plants were not touching. Plants were watered ad libitum and fertilized using Osmocote controlled‐release fertilizer (14:14:14 N:P:K)  once in May and once in July. From each 3.3A. syriaca at UMBS and also originated from spatially separated locations (at least 50 m apart) within the UMBS population. Based on the past 10 years of sampling, genet 14 has relatively high foliar nitrogen concentrations , genet 20 has relatively low foliar nitrogen concentrations , and genet 44 has relatively high foliar carbon concentrations  . Seeds from each of the three maternal lines were planted in the soil and location of all three of the original maternal genets. We chose to use only 3 maternal lines and 5 replicates at each location to minimize disturbance on the milkweed population growing in the old‐field. At each of the three maternal genet growing locations, 5 seedlings from each of the three maternal lines  were grown in soil from that maternal location . Therefore, at each maternal location, we grew offspring plants from the “matching” maternal line (“at home” seedlings) and two “non‐matching” maternal lines (“away” seedlings).We chose three of the 14 maternal genets to provide seed for reciprocal field transplants. We chose genets that spanned the known range of nutritional quality in A. syriaca plant; foliar chemistry samples were collected once on 21 August. Stem diameter was not measured due to the small size of plants.Because we wanted to use soil from each maternal genet location, the reciprocal transplant experiment started later than the common garden experiments. Seeds were planted in 18 cm × 16 cm pots on 13 May 2019 at UMBS in the soil of their reciprocal transplant destination. Seedlings were grown in the greenhouse until large enough to withstand outside conditions and were placed in the field on 19 June. Replicate seeds per maternal line were established within the spatial boundaries of each of the maternal genets. Each transplant location  hosted 5 replicate plants of each of 3 maternal lines, totaling 15 plants at each maternal genet location  in a 3 × 5 plant grid with 0.5 m separating each plant. We randomized the order of the plants at each of the three locations. Plants were protected by a wire open‐top cage to block deer and rabbit browsing but allow access by insects. We measured arthropod abundance and plant height, leaf number, and defoliation weekly (10 weeks from 19 June to 21 August 2019) for each 3.4To measure trait variation in the naturally growing milkweed population, we sampled 5 individual ramets (randomly selected in June) from each of the 14 maternal genets  on three dates . We measured size , defoliation, and arthropod abundance for each ramet. We collected foliar chemistry samples and measured latex exudation once in mid‐July.3.5To assess the contributions of genetic variation and environment to resistance to herbivory, we estimated defoliation by chewing herbivores from each plant in the common gardens, the reciprocal transplant experiment, and the maternal genets. We visually categorized each leaf longer than 1 cm into one of the following defoliation levels: no defoliation, 0%–5%, 5%–30%, 30%–50%, 50%–70%, 70%–90%, >90% defoliated. To estimate the overall percentage of defoliation per plant, we multiplied the number of leaves in each defoliation level by the median value of the level  and summed the values. This sum was then divided by the total number of leaves on that plant. The final value represents the overall estimation of percent defoliation for that plant. This method has a long history in the literature and correlates strongly with independent estimates of defoliator activity Hunter, .3.6We performed chemical analyses  on foliar samples from half of the blocks (blocks 10–18) in the two common gardens. We analyzed a subset of the samples due to project time constraints. We analyzed foliar chemistry in July because insect diversity and density are highest during July and this month represents the time period during which milkweed chemistry is most likely responsive to plant–herbivore interactions Agrawal,  Appendi.We analyzed foliar cardenolide concentrations using established methods , we analyzed our data in several ways. First, to examine trait variation among maternal lines over time , we analyzed each location separately. Second, to explore more explicitly any genotype‐by‐environment interactions, we built models that included data from both common gardens (= environments) simultaneously, but that either averaged or summed trait values over time for each individual milkweed (to remove the repeated measures). Models that included repeated measures and multiple locations simultaneously would not converge.4.14.1.1We used generalized linear mixed models (PROC GLIMMIX) to assess genetic variation in plant growth and resistance to chewing herbivores (defoliation level). For the common garden analyses, we included block and individual plant ID  as random variables to account for repeated measures from individual plants and any effects of autocorrelation within blocks. Week was a continuous variable while plant ID, maternal line, and block were class variables. Because plants began to senesce and reduce in size by the end of the season, our models included a quadratic term for time. Overall, our models assessed the effects of maternal line on variation in plant growth  and resistance (defoliation) over the growing season. Significant interactive effects of maternal line and week on character traits represent genetic variation in rates of growth or resistance to herbivory.Because we collected data from maternal genets only once each month, we used month as a class variable in analyses of variation in growth and resistance of maternal genets. Otherwise, we followed the same model structure as above without a block term.To analyze data from the reciprocal transplant experiment, we used a similar glimmix model structure but removed the random block term and assessed the effects of maternal line, week, transplant location, and their interactions on plant traits.4.1.2Aphis asclepiadis, but would not converge for the second species, Myzocallis asclepiadis. Therefore, we used a generalized linear model (PROC GENMOD) with a binomial distribution and logit link function for M. asclepiadis. Because proc genmod in SAS does not recognize random effects, we designated plant ID  as a repeated effect and accounted for variation among blocks by assigning block as a main effect.Most insect species were encountered too rarely to analyze separately, and aphids were by far the most abundant herbivores that we encountered Appendix. AccordiNext, for those common garden plants that hosted aphids, we used a mixed model (PROC MIXED) and log‐transformed aphid population counts to assess genetic variation for resistance to aphids among maternal lines over time. We held block and plant ID  as random variables and used plant ID  as the repeated subject term.A. asclepiadis on only six maternal genets, and only two of those genets hosted A. asclepiadis on three or more replicate ramets. Therefore, we did not have enough representation to perform meaningful statistical tests for A. asclepiadis population growth among maternal genets. Myzocallis asclepiaidis appeared only in the month of August and was observed on 23 maternal ramets. Five of the nine maternal genets represented had three or more ramets with aphids, and therefore, we restricted our analysis to those 5 maternal genets (17 ramets). We log‐transformed M. asclepiadis numbers and examined differences in M. asclepiadis populations among maternal genets using a general linear model (PROC glm). Because M. asclepiadis only appeared in August, no month term or repeated measure was required.Finally, for the naturally growing maternal genets, we recorded 4.1.3To assess genetic variation in foliar chemistry traits , we used a generalized linear mixed model (PROC glimmix) and held plant ID, maternal line, and block (for common gardens) as class variables. Block and plant ID  were random variables. We log‐transformed cardenolide data prior to analysis to meet assumptions of homogeneity of variance. For the maternal genets and reciprocal transplant experiment, we used general linear models (PROC glm). We held plant ID and maternal line as class variables for the maternal genets, and plant ID, maternal line, and location as class variables for the reciprocal transplant milkweeds. Because we only used chemistry data from one date (mid‐July), no week term was required.4.1.4As noted in our predictions (above), we would expect positive correlations in the trait values measured from maternal lines/genets among experimental locations if genetic variation alone dominates trait expression. We therefore correlated milkweed traits  (a) between common gardens, and (b) between each common garden and the natural field population of maternal genets. We first calculated average trait values for each maternal line at each location. We then calculated the slopes of the regressions for each trait across locations, using the means  as data points. Regression statistics were calculated using Excel for Mac version 16.33. We calculated average defoliation values for August for each maternal line/genet. We used the data from August because the majority of defoliation occurred in August and defoliation in June and July was rare. For foliar cardenolide concentrations and latex, we used the chemistry data collected in July. To calculate plant growth means, we used data from early and mid‐season, excluding end‐of‐season data due to plant senescence Appendix. We calc4.2Having calculated seasonal averages for defoliation, foliar chemistry, and initial plant growth (above), we could then build models that included data from both common gardens simultaneously to explore genotype‐by‐environment interactions. For each trait value in turn, we used a general linear mixed model (PROC mixed) with maternal line and location as fixed effects and block as a random effect. We assessed the effect of maternal line (genotype), growing location (environment), and the interaction between maternal line and growing location on trait expression to estimate the strength of genotype‐by‐environment interactions.While we originally considered maternal line as a fixed effect and block as a random effect in our models because M.D. Hunter has researched the same maternal lines for the past 12 years, we can gain insight into the importance of environmental variation by reversing that designation , suggesting that there may be determinants of resistance in addition to genetic variation for naturally growing milkweeds. In support of this, milkweeds in the reciprocal transplant experiment accumulated defoliation at different rates among transplant locations  whereas maternal lines were defoliated about equally , irrespective of location . Overall, these results indicate that both maternal line (common garden experiment) and local growing environment  contribute to the variation in resistance to chewing herbivores. As expected, insect densities and defoliation levels were negligible in the greenhouse common garden and are not reported here.Maternal lines grown in the field common garden expressed genetic variation for resistance, accumulating defoliation at different rates  in the field common garden. Likewise, after colonization, we observed no genetic variation in A. asclepiadis population densities among maternal lines , indicating that genetic variation may not account for resistance against A. asclepiadis population growth. Population sizes of A. asclepiadis were too low on both the maternal genets and the reciprocal transplant milkweeds to provide insight.We observed no evidence for genetic variation in A. asclepiadis, we found no evidence for genetic variation in Myzocallis asclepiadis colonization among milkweed maternal lines in the field common garden . After colonization, we observed no genetic variation in M. asclepiadis population levels among maternal lines . In contrast, we did observe variation among five maternal genets (those with aphids present on multiple ramets) in the population densities of M. asclepiadis , suggesting that local environment may be a significant determinant in M. asclepiadis population growth. Unfortunately, population densities of M. asclepiadis were too low on reciprocal transplant milkweeds to provide any additional insight.As with 5.3F13, 101 = 1.11, p = .3568; F13, 98 = 1.53, p = .1212, respectively; Figure y = 0.216x + 0.1749, R2 = 0.004, p = .8269). In contrast, foliar cardenolide concentrations did vary among naturally growing maternal genets . Consistent with results from both common gardens, milkweeds in the reciprocal transplant experiment did not vary in cardenolides among maternal lines . However, unlike the maternal genets, cardenolide concentrations did not vary among transplant locations either . In the models in which we combined data from the two common gardens , nor any evidence of a genotype‐by‐environment interaction . However, foliar cardenolide concentrations were much higher in the greenhouse than the field common garden . That lack of correlation likely reflects a significant interaction between the environment (greenhouse versus field) and maternal line that we detected in the model containing both common gardens . Unlike the effects on foliar cardenolides, we observed no small‐scale effect of block on latex exudation . However, foliar nitrogen concentrations varied substantially among the maternal genets . Nonetheless, in models in which we included both common gardens simultaneously  and garden location  on variation in foliar nitrogen concentration. There was no significant genotype‐by‐environment interaction  and garden location  were both significant predictors of foliar carbon concentration. Their interaction was marginally nonsignificant , suggesting that genetic variation is a major driver of differences in foliar carbon concentrations among field‐grown milkweeds.In contrast to foliar nitrogen concentrations, maternal lines expressed substantial genetic variation in foliar carbon concentration in both the field and greenhouse common gardens  whenever we counted insects. We then analyzed our size estimates using the same techniques described above. Because measuring phenotypic variation in plant size was not a primary goal of our study, we restrict the presentation of these analyses to Table 6Our results suggest that milkweed resistance traits are influenced substantially by their growing environment and by genotype‐by‐environment interactions and that standing genetic variation plays a smaller role in phenotypic variation in this population of common milkweed. We recorded considerable phenotypic variation in traits of the maternal genets in the naturally growing population, but only a subset of those traits varied among maternal lines in the common gardens. We observed significant effects of growing environment and genotype‐by‐environment interactions on trait expression, with evidence of G × E interactions in all recorded herbivore resistance traits except for foliar nitrogen, foliar carbon, and foliar cardenolide concentrations Table . Our resSimilar to previous studies of milkweed Agrawal,  and othepersonal communication), differences in age of the maternal lines and the maternal genets may affect their relative resistance to defoliation as well as other aspects of phenotype.At local scales, if certain maternal genets have experienced more herbivory over time than others, the effects of transgenerational resistance to herbivory Agrawal,  could coA. syriaca at UMBS differs in resistance traits from other milkweed populations at regional scales ; data curation ; formal analysis ; funding acquisition (supporting); investigation (lead); methodology ; project administration (lead); writing‐original draft (lead); writing‐review & editing . Mark D. Hunter: Conceptualization ; data curation ; formal analysis ; funding acquisition (lead); investigation (supporting); methodology ; project administration (supporting); writing‐original draft (supporting); writing‐review & editing .