Sscrofa genome 11.1). Copy number variations (CNV) were called using PennCNV. ME were classified into eight different classes according to the trio member (“Trio” meaning no assignment) and the allele on which ME was identified: TrioA/B, FatherA/B, MotherA/B, OffspringA/B. Most ME occurred due to systematic causes: (a) those assigned to the Father, Mother or Offspring occurred by null or partial null alleles characterized by heterozygote deficiency, varied with family size, involved a low number of loci (6506), and gathered most mismatches ; (b) TrioB errors varied with family size, covaried with SINEs, LINEs and CNV, and involved most ME loci  and mismatches ; and (c) TrioA errors were non-systematic ME with no sampling bias involving 1.2% of mismatches only and a low number of loci (1939). The influence of TrioB errors on the overall genotyping quality may be low and, since CNV vary among populations, their removal should be considered in each particular dataset. ME assignable to the Father, Mother or Offspring may be consistent within technological platforms and may bias severely linkage or association studies. Most ME caused by null or partial null alleles can be removed using heterozygote deficiency without affecting the size of the datasets.Up to 478 Gochu Asturcelta pig parents-offspring trios (61 different families) were genotyped using the Axiom_PigHDv1 Array to identify the causes of Mendelian errors (ME). Up to 545,364 SNPs were retained. Up to 40,540 SNPs gathering 292,297 allelic mismatches were identified and were overlapped with SINEs and LINEs ( Although not all genotyping errors result in Mendelian violation5, ME can be identified with certainty and factors contributing to their appearance, such as the presence of genomic alterations such as either short, SINEs, or long, LINEs, interspersed nuclear elements, studied6.When using SNP arrays data, Mendelian Errors (ME) can be defined as the identification of variant calls inconsistent with the rules of Mendelian inheritance. Although ME can reflect true genomic variation arising from de novo mutations, in the case of correct pedigree information they more likely result from genotype calling errors. At least in humans, estimates of the de novo mutation rate are assumed to be ∼ 108. Furthermore, although joint academic-industrial efforts have developed algorithms successful in improving genotyping accuracy, their performance may be lower if systematic errors of the array reaction exist or the typed sites are located in genomic regions carrying copy number alterations10 usually referred to as Copy Number Variations (CNV). CNV are DNA segments ranging in size from 50 base pairs (bp) to several megabases (Mb) in which insertion, duplication or deletion events have occurred12. CNV modify the number of A and B alleles and, therefore, their intensities from the array causing misclassification. Actually, a successful genotyping approach should depend on the relative relationship between the A and B alleles from the array10. In Illumina SNP arrays, the alleles at each position are determined according to the intensity of a probe signal at a specific marker compared to the expected intensity, and the normalized B-allele frequency13. The genotyping software accompanying Affymetrix SNP arrays compares the allelic intensities detected for every SNP with those obtained for a number of probe quartets, each of which is composed of a 25-base-pair either matching the target sequence or not (on the 13th base) for alleles A and B separately using a Mahalanobis distance classifier under a Bayesian framework15.Genotyping errors due to technical issues can vary with the genotyping platform17. Furthermore, the assessment of Mendelian Errors is considered useful to characterize the quality of genotype calling20. It may contribute to avoid undesirable effects on genetic analyses, such as reducing power in linkage and association studies and, particularly, causing confusion in studies aiming at the identification of rare variants or de novo mutations22.ME assessment can be informative on the accuracy of variant calling pipelines8. The greater the number of closely related individuals is in a pedigree, the more power and the higher accuracy the pedigree allows for error detection and estimation3. Gochu Asturcelta is an extremely endangered pig breed kept in Asturias (Northern Spain) belonging to the Celtic pig strain of the Iberian Peninsula23. Recovery program allowed to obtain a complex pedigree25 useful to follow Mendelian inheritance across parent–offspring trios and within full-sib litters. This research uses genomic profiles of a sample of Gochu Asturcelta data to ascertain the extent to which different factors such as technological issues, family structure or genomic features including CNV and the presence of interspersed nuclear elements can affect calling quality causing ME. The importance of systematic and non-systematic errors in SNP arrays data and the need of implementing marker-based quality-control measures are discussed.The identification of ME needs the availability of reliable and dense pedigrees formed by a sufficient number of parent–offspring trios as well as the assumption that ME can result from diverse error mechanisms including the inheritance of the maternal alleleData consisted of 492 Gochu Asturcelta individuals forming 478 parent–offspring trios that could be summarized into 61 different families . Individuals were obtained from 96 registered litters (formed by two known parents and their offspring in a farrowing season). The available pedigree is illustrated in Fig. 26 containing assays for 658,692 SNPs with SNP positions based on Sscrofa genome build 11.127. The software Axiom™ Analysis Suite v4.0.3  was used to create both standard genotypic .ped and .map files and intensity data useful for CNV calling. SNPs with ambiguous chromosome locations and SNPs located on either sexual chromosomes or mitochondrial DNA were excluded. Only animals with at least 95% of their SNPs called and individual SNPs with at least 99% call rate were considered. No thresholds for Minor Allele Frequency (MAF) or departures from Hardy–Weinberg (HW) proportions were applied to allow a correct identification of ME. Finally, a total of 545,364 SNPs with an average call rate of 99.79% were retained on the 18 Sus scrofa autosomes (SSC).Individuals were genotyped using the Axiom Porcine Genotyping Array (Axiom_PigHDv1) for pig genotyping28 was used to verify parentage in the pedigree.The program COLONY v.2.0.6.8—mendel option of the program PLINK v.1.9. The software iterates through all trios and all variants checking for these errors29. Loci in which ME were identified were hereafter referred to as ‘ME set’. Considering the parental genotypes (either homozygous or heterozygous), the origin of the ME was assigned to the Father, the Mother, the Offspring, or the “Trio” when it was not possible to ascertain on which member of the trio occurred the genotype calling error. Moreover, considering the frequency of the allele on which the Mendelian violation was identified at a locus, ME was further classified as either A  or B . Consequently, ME were classified into eight different classes: TrioA; TrioB; FatherA; FatherB; MotherA; MotherB; OffspringA; and OffspringB. These ME classes are expected to account for possible bias due to the sex of the parental allele inherited8 and the possible influence of changes in allele frequencies due to the genomic alterations6 in ME occurrence. Using the function—hardy of PLINK, heterozygote deficiency or excess ; and (b) all reproductive individuals were homozygous for the same allele but homozygous genotypes for the other allele were assessed in the offspring (ADIhom).Possible calling errors causing the appearance of either null alleles or false alleles on an SNP were also considered. SNPs included in the ME set were classified as Allele-Drop-Out  when no heterozygote genotypes were identified in the whole typed population but homozygous genotypes existed for both the A and the B alleles. In turn, SNPs were classified as Allele-Drop-In  when: (a) all reproductive individuals were homozygous for the same allele but heterozygous genotypes were identified in the offspring (ADI4 ME rates were quantified as: (a) Mean error rate per locus  and short interspersed nuclear elements (SINE) using the Sscrofa genome build 11.1.The 31 was used to perform CNV calling from the 18 autosomes of each individual in the dataset. PennCNV implements a hidden Markov model to detect CNV based on the log of the observed probe hybridization intensity divided by the expected probe hybridization intensity of SNPs (LRR) and the proportion of B alleles at an SNP (BAF). To adjust genomic waves, the—gcmodel option was used. Overlapping between the CNV and the SNPs having ME identified in each individual was assessed using the intersectBed function of the BedTools30 software.The program PennCNV32 of R environment. Correspondence analyses aiming at the assessment of the relationships between the eight classes of ME, CNV alterations, and annotated repeat elements were performed using the library FactoMineR33 of R environment. Eigenvectors computed on each individual using correspondence analyses were used to construct bidimensional dispersion plots using the library ggplot234 of R environment. Manhattan plots were constructed to illustrate data variation per chromosome and a count plot illustrating the relationships between the ME set loci and the eight classes of ME was created using the library ggplot234 of R environment as well.Gochu Asturcelta pedigree was visualized using the library visPedigreeSERIDA is adhered to the Ethical Committee in Research of the University of Oviedo (Spain) which ensures that all research with biological agents follows Good Laboratory Practices and European and Spanish regulations on biosecurity under the Regulation of February 13th, 2014 . In any case, blood and hair root samples used in this project were collected by veterinary practitioners working for the Gochu Asturcelta Breeders’ Association (ACGA), with the permission and in presence of the owners. For this reason, permission from the Ethical Committee in Research of the University of Oviedo was not required. In all instances, ACGA veterinarians followed standard procedures and relevant national guidelines to ensure appropriate animal care.–4) were identified. Up to 24,312 (60.3%) loci had inconsistent calls in a single trio (hereafter referred to as ‘unique’6), substantially contributing (0.0446) to the total mean error rate per locus. Up to 37,183 SNPs  had ME in less than 10 individuals and 3,121 loci  had ME in more than 10 individuals. Up to 293 loci gathered 200 ME or more and 17 of them had at least 300 mismatches , were identified in the 478 parent–offspring trios available Table . Family PennCNV identified a total of 5,450 CNV across individuals summarized in 3549 CNV  included in the ME set, gathering 50,222 mismatches, were located within LINEs. These figures were lower for SINEs . Most ME loci located within interspersed nuclear elements were identified on porcine chromosomes 1, 9 and 2  gathered 227,970 mismatches  that could be assigned to various ME classes. Figure Although the TrioB error class gathered the higher number of both loci and mismatches in the ME set Table , both thMost typed loci did not show either heterozygote excess or deficiency Table . Only 2%Relationships between ME error classes and genomic features  were summarized via correspondence analysis Fig. . Dimensip) of deviation from the Hardy–Weinberg proportions (column B) for the eight classes of ME defined. Except for the TrioB class in which the minor allele tended to be in low frequency, MAF tended to be moderate. Loci having ME assigned to either the Father or the Mother tended to be in HW disequilibrium. However, this could not be assessed for either the Trio or the Offspring error classes, which mainly included loci in HW equilibrium. This pattern was the same for the variation of Figure het. Seventy-seven percent of them (2648) were unique. All ADIhet loci were in HW equilibrium with no deviation of excess or deficiency of heterozygotes  were classified as ADIhom. Although MAF was very low as well (ranging from 0.008 to 0.002), their behavior departed from that of the ADIhet loci: all ADIhom loci significantly departed from the HW proportions with high heterozygote deficiency  which gathered 75,843 mismatches  were classified as ADO . Loci classified into the TrioB error class had a particular behavior within the ME set: although they involve a large number of loci, both the number of mismatches per loci and the number of individuals having such errors at a given locus were very low. An extreme case of TrioB errors are the ADIhet loci in which one allele is fixed in the individual with offspring in data and the ME occur in heterozygous offspring with a “new” allele added to the parental genotypes. Furthermore, TrioB errors do not alter HW equilibrium and 10, therefore hindering the correct assessment of the allelic frequencies of an SNP38. Our results suggest that such genomic features can underlie the occurrence of TrioB errors10. Furthermore, the influence of this type of genotyping errors may be variable among datasets38. Although SINEs and LINEs may have a general influence on genotyping quality within species, CNV may not. Moreover, CNV are assumed to mirror particular population histories39 and their importance and influence on the occurrence of ME should probably be assessed for each particular genotyping project unless sufficient evidence exists for a particular CNV in a species.Furthermore, literature suggests that identification of ME should be more problematic when the frequency of the minor allele is lowses Fig. . SINEs a41, i.e. alleles with no fully codominant signal that do not always generate missing data. The ascertainment between null and partial null alleles may be difficult when sample size is small42. Furthermore, our results suggest that ME assigned to Offspring can be extreme cases of calling problems also affecting to ME assigned to the parental (Father and Mother) classes: the number of ME assigned to the offspring is very low and most loci with OffspringA and OffspringB errors have mismatches on the parental error classes  and the lower number of total mismatches (1.2%). They are in loci with the minor allele having moderate to high frequency, mainly in HW equilibrium, most of them with no heterozygote excess or deficiency, and no clear sampling bias Fig. . The ascp value) of ≤ 0.001 . However, applying such conventional quality filters may not always be advisable because of their major impact on the final number of loci gathered for analyses6. The relevant subject is how these standard control measures affect the information provided by SNPs that do not gather ME.Since ME do not occur by chance, researchers routinely incorporate marker-based quality control measures to limit for spurious findings. These measures frequently consist in removing SNPs with MAF lower than 0.05 and those that did not adjust to the HW expectation with a threshold  would imply the removal of a higher number of mismatches  than with using MAF and a lower number of total SNPs . In any case, the combined use of MAF (≤ 0.05) and HW thresholds (p < 0.001) would be inadvisable: although this strategy allows to remove the highest number of mismatches , it is done to the cost of removing the 40.8% of the SNPs  of the total dataset.From our data, fitting MAF ≤ 0.05 as a threshold cannot be recommended  appears necessary45, they can affect the quality of the SNP arrays data to a large extent. Approaching this via HW tests may cause the removal of an undesirable number of truly informative loci. The use of filters based on heterozygote deficiency can be considered an alternative. However, since differences in heterozygote deficiency can be caused by either extensive natural selection or inbreeding as well44, this should be considered with caution due to Allele Drop errors only affect only a subset of loci41. Mismatches assessed in our dataset are clearly biased to positive Our results suggest that removal of loci having ME caused by null or partially null alleles  should be approached before using SNP arrays data for further analyses. Since null genotypes could leave ‘footprints’ in SNP genotype data, including deviations from Mendelian inheritance and intense deviation from Hardy–Weinberg proportions46. Classification of ME into eight different error classes and the characterization of three Allele Drop sources of errors allow us to suggest that there are two main causes of most ME: (a) the presence at the locus location of genomic alterations such as CNV, SINEs and LINEs; and (b) the presence of null and partial null alleles. The presence of null alleles, although involving a limited number of loci affects a significant number of genotypes and can be challenging for association and linkage studies and the identification of rare variants in a genome. ME due to genomic alterations, however, can be particular for each population under study and may not have a strong influence on the results of either association or diversity analyses. Using In summary, our results contribute to the understanding of the causes and importance of Mendelian Errors in SNP arrays data confirming that all ME cannot be considered the same and that most ME probably occur due to systematic causesSupplementary Information.


PstI and MspI restriction enzymes for reducing genome complexity, was performed on a triticale diversity panel (n = 289). After filtering for biallelic loci with more than 70% genome coverage, and minor allele frequency (MAF) > 0.05, de novo variant calling identified 16,378 single nucleotide polymorphism (SNP) markers. Sequences of these variants were mapped to wheat and rye reference genomes to infer their homologous groups and chromosome positions. About 45% (7430), and 58% (9500) of the de novo identified SNPs were mapped to the wheat and rye reference genomes, respectively. Interestingly, 28.9% (2151) of the 7430 SNPs were mapped to the D genome of hexaploid wheat, indicating substantial substitution of the R genome with D genome in cultivated triticale. About 27% of marker pairs were in significant LD with an average r2 > 0.18 (P < 0.05). Genome-wide LD declined rapidly to r2 < 0.1 beyond 10 kb physical distance. The three sub-genomes  showed comparable LD decay patterns. Genetic diversity and population structure analyses identified five distinct clusters. Genotype grouping did not follow prior winter vs spring-type classification. However, one of the clusters was largely dominated by winter triticale. GS accuracies were estimated for forage yield using three commonly used models with different training population sizes and marker densities. GS accuracy increased with increasing training population size while gain in accuracy tended to plateau with marker densities of 2000 SNPs or more. Average GS accuracy was about 0.52, indicating the potential of using GS in triticale forage yield improvement.Triticale, a hybrid species between wheat and rye, is one of the newest additions to the plant kingdom with a very short history of improvement. It has very limited genomic resources because of its large and complex genome. Objectives of this study were to generate dense marker data, understand genetic diversity, population structure, linkage disequilibrium (LD), and estimate accuracies of commonly used genomic selection (GS) models on forage yield of triticale. Genotyping-by-sequencing (GBS), using  It can have different ploidy levels and genome compositions ranging from tetraploid to octoploid depending on the type of wheat parent involved in the hybridization (n = 42 = AABBRR) is the most commonly cultivated form because of its better genomic stability and superior agronomic performance , training population size, and marker density . It is aThe objectives of this study were to (1) generate a set of triticale markers using a diversity panel consisting of diverse breeding parents and representative triticale accessions from the National Small Grains Collection (NSGC); (2) characterize the marker set through genome mapping and LD analysis; (3) characterize diversity and population structure of the panel; and (4) evaluate the feasibility of GS in forage triticale breeding.2 was used in the 2018–2019 season while simple lattice with the same plot size was used in the 2019–2020 season. Forage biomass sample was manually harvested from two adjacent rows and total dry weight was converted to g m−2 scale.The experiment started in 2017–2018 season by characterizing 1400 accessions in a paired-row single observation plot of 1.5-m long and 40-cm wide at Gene Autry, Oklahoma, USA. A total of 289 diverse hexaploid triticale lines were selected out of the initial 1400 accessions on the basis of phenology, plant architecture, winter hardiness, biomass, and grain yield representing available diversity to compose an association mapping population. This population consisted of 39 cultivars or elite breeding lines developed primarily from the southern Great Plains, and 250 accessions from the NSGC that are being used as newly introduced parents in our breeding program. Altogether, there are 75 spring, and 196 winter-type lines in the panel. The panel was further evaluated in replicated trials at one location  in 2018–2019, and two locations  in 2019–2020. Triple lattice design with plot size of 2.25 mPstI–MspI method . The DNA concentration and quality of each sample were assessed before library construction using a nano-photometer (Thermo Fisher Scientific). Genome complexity reduction and multiplexed GBS library construction were performed based on the Raw sequence data in FASTQ format were processed using ipyrad_v.0.9.45 software  with ther2) based on loci that have been mapped on the wheat and rye reference genomes (r2) was plotted against the physical distance (bp) between markers to determine the LD decay distance (bp). Differences in LD patterns and decay distances were evaluated and compared among the three sub-genomes . It was assumed that r2 values higher than 0.1 are likely to be caused by genetic linkage. Mean r2 values were calculated for every 500 bp interval window. The interval at which the r2 values fall below 0.1 was taken as LD decay distance. This LD decay distance was supported by fitting a nonlinear smoothing curve . The expected value of r2 under drift-recombination equilibrium with a low level of mutation and an adjustment for sample size n, was calculated using Hill-Weir’s equation as follows  and distance, which can be calculated as C = ρ*distance.The LD pattern was estimated by using squared allele frequency =1i.e., hierarchical clustering, k-means clustering, and principal components analysis (PCA). Principal components (PCs) were further analyzed by using discriminant analysis of the principal components (DAPCs).Three population clustering methodologies were used dudi.pca function of the adegenet package in R  term sβsX and the random  component sSZ form the mixed model expression of the smooth spatial surface, i.e., f  = sβsX + sSZ, where the vector of random spatial effects s has covariance matrix S. The vector U comprises the mutually independent sub-vectors of random row and column effects accounting for discontinuous field variation, with design matrix uZ =  [rZ|cZ] and covariance matrix U = diag . The vector G contains the random genotypic effects and gZ is the associated design matrix. We assumed independent genotypic variance, i.e., g ∼ N, with G = 2gIgσ. The vector e consists of spatially independent residuals with distribution e ∼ N. Since spatial analysis significantly reduced residual error, final analysis to obtain predicted means for each environment was done using spatial analysis.Data were analyzed in two steps. In the first step, data from each year and location were analyzed separately using agricolae/R package  for lattijkY = μ + iG + jE + ijGE + ijke, where ijkY is the observed mean, μ is the grand mean, iG is the effect of the ith genotype, jE is the effect of the jth environment (locations and seasons were considered as separate environments), ijGE is the effect of the ith genotype in the jth environment, ijke is the random error. Genotypes were considered as fixed effects while environments were considered random.In the second step, combined analysis was performed on adjusted means from each season and location using the following mixed linear model: mixed.solve function with marker design matrix, GBLUP using kinship. BLUP with additive relationship matrix, and GUASS model using Euclidean distance matrix, were tested with 103 iterations each. GS accuracy was evaluated as the average correlation value between GEBV and phenotypic estimated breeding value (PEBV).GS was performed using the ridge regression best linear unbiased prediction (rrBLUP) R package . Three d > 0.05, a total of 16,378 biallelic SNPs were identified . Homologous groupings of markers were identified by mapping sequence reads to the wheat . A total of 204,106 SNP variants were identified via de novo variant calling. Missing value percentage was close to 86% before some low coverage SNPs were filtered out . After filtering for biallelic SNPs with at least 70% genome coverage, and MAFhe wheat , and ryeChromosomal locations of SNPs were determined based on the locations of the SNPs mapped to the wheat genome. All the SNPs mapped to the D genome of wheat are hereafter considered as R genome SNPs as triticale does not have a D genome. Out of the 7430 SNPs mapped to wheat reference genome, 31% (2284), 40% (2995), and 28.9% (2151) SNPs were located at A, B, and R sub-genomes, respectively. Similarly, out of the 9500 high-quality SNPs mapped to the rye reference genome, about 6670 SNPs were potentially located on the R sub-genome of triticale .r2 > 0.18 (P < 0.05). The r2 values from the whole genome LD were separated into the respective genomes to compare LD patterns. LD based on the whole genome dropped quickly to r2 < 0.1 at about 10 kb (P < 0.05), while the B and R genomes had 25% and 27% of marker pairs in significant LD. The A genome had the shortest LD block (4 kb) while B and R genomes had similar decay distances in the range of 10 kb . The first three PCs explained 19% of genetic variation  by winter triticale.Three different methods were used to infer population structure and allelic diversity of the panel. Hierarchical clustering, PCA and DAPC all showed five clusters. Genotypes were grouped into five clusters based on Euclidian distance matrix and Wad.Dariation . For k-mat k = 5 , indicatFurther analysis using DAPC was conducted to capture more genetic variation. Result from DAPC showed that about 95% of the total genetic variation was explained by the first 200 PCs . The DAPThere was high and erratic field heterogeneity that was corrected with spatial smoothing using 2D tensor product splines . Spatial analysis showed about 24.5% higher efficiency in reducing random error compared with lattice design. There was significant variation between genotypes, environments, and the interactions between the two. Broad sense heritability was 42% and 47% for lattice and spatial analysis, respectively. Best linear unbiased estimates were used for estimating GEBV of individuals.Individuals and markers were randomly assigned into training and validation populations. Prediction accuracy of forage yield (dry matter) increased with increasing training population size when a large number (7000) of markers were used . The RRBThis study showed that GBS is an effective marker development technology for triticale breeding and genetics research. GBS is especially suited for genetic improvement of orphan crops like triticale through combined marker discovery and genotyping of large populations, even in the absence of a reference genome , which mr2 statistics) of the allelic states of two given polymorphic loci . K-means clustering handles large dataset better compared with hierarchical clustering  under project PRJNA715663 and will be accessible upon release at http://www.ncbi.nlm.nih.gov/bioproject/715663.All Supplementary materials and relevant data are available at figshare at


The search for genomic regions related to adaptive abilities preserved in the chicken gene pool of two breeds, which have not been under intensive selection pressure, is of great importance for breeding in the future. This study aimed to identify key candidate genes associated with the adaptation of chickens to cold environments (using the example of the Russian White breed) by using molecular genetic methods. A total of 12 key genes on breed-specific ROH (runs of homozygosity) islands were identified, which may be potential candidate genes associated with the high level of adaptability of chickens to cold environments in the early postnatal period. These genes were associated with lipid metabolism, maintaining body temperature in cold environments, non-shivering thermogenesis and muscle development and are perspectives for further research.® chips. The search for homozygous regions by individual chromosomes was carried out using the PLINK 1.9 program and the detectRuns R package. Twelve key genes on breed-specific ROH islands were identified. They may be considered as potential candidate genes associated with the high adaptive ability of chickens in cold environments in the early postnatal period. Genes associated with lipid metabolism , maintaining body temperature in cold environments , non-shivering thermogenesis  and muscle development (METTL21C) are perspectives for further research. This study contributes to our understanding of the mechanisms of adaptation to cold environments in chickens and provides a molecular basis for selection work.It is well known that the chicken gene pools have high adaptive abilities, including adaptation to cold environments. This research aimed to study the genomic distribution of runs of homozygosity (ROH) in a population of Russian White (RW) chickens as a result of selection for adaptation to cold environments in the early postnatal period, to perform a structural annotation of the discovered breed-specific regions of the genome (compared to chickens of the Amroks breed) and to suggest key candidate genes associated with the adaptation of RW chickens to cold environments. Genotyping of individual samples was performed using Illumina Chicken 60K SNP BeadChip The future of chicken breeding is aimed at improving the welfare of chickens, especially when the conditions for rearing chickens are as natural as possible. With such a rearing system, chickens will be exposed to various physical and climatic stresses , infectious diseases and social stress. Additionally, birds kept in such conditions should have high adaptive abilities and stress resistance, which should not be accompanied by an increase in production costs and the risk of diseases .Local breeds of chickens may have valuable genetic variants for breeding and selection of new breeds and commercial crosses in conditions of changing market demands. Local breeds of chickens are carriers of specific sets of genes responsible for adaptation to harsh environmental conditions, various diseases, etc. Advances in genetics and biotechnology make it possible to quickly transfer the unique genetic variants that have arisen in local breeds of chickens during the adaptation process to the local habitat into the genomes of commercial international high-performance breeds to preserve their outstanding properties in new habitat conditions. To increase the frequencies of adaptive alleles in commercial strains, it is also possible to use genomic selection methods .Most of the research works in the field of thermoregulation genetics are devoted to adaptation to hot climate conditions, since the main areas of developed poultry farming  are concentrated in regions with high average annual temperatures ,5,6,7,8.Low temperature is the main environmental factor that limits the growth of animals and can threaten their survival . Neverthhttp://vniigen.ru/ckp-geneticheskaya-kollekciya-redkix-i-ischezayushhix-porod-kur/ (accessed on 1 February 2022)). Simultaneously, the excellent adaptability of RW chickens to cold environments in the early postnatal period has not been sufficiently studied from the viewpoint of molecular genetic mechanisms.One of such breeds with high adaptive abilities in low temperatures, starting from the age of one day, is the Russian White breed of chickens contained in the bioresource collection “Genetic Collection of Rare and Endangered breeds of chickens” of the Russian Research Institute of Farm Animal Genetics and Breeding-Branch of the L.K. Ernst Federal Research Center for Animal Husbandry  . ROH islGallus gallus domesticus) in the early postnatal period to cold environments, using the example of a population of Russian White chickens.The main purpose of this study was to search for candidate genes associated with high adaptive abilities of chickens (http://vniigen.ru/ckp-geneticheskaya-kollekciya-redkix-i-ischezayushhix-porod-kur/ (accessed on 1 February 2022)) at the age of 43 weeks.The object of research was chickens of the Amroks and Russian White breeds  versus the generally accepted 30–33 °C ,14.At the initial stage of the selection work, the heterogeneity of chickens was established on the basis of their adaptive ability at low temperatures and their divergence into two alternative forms: thermoresistant and thermosensitive. The increased thermoregulatory ability, providing thermoresistance, was inherited by the dominant type. Chicks that had hypothermia in conditions of low temperatures were recessive homozygotes. Because of the individual selection of parental pairs, the thermoresistance property in the population was stabilized in subsequent generations. The dominant type of inheritance of adaptive ability at low temperatures has been confirmed by crossing RW chickens with other breeds. It was found that the property of resistance to low temperatures of day-old chicks of the new population of the RW breed is transmitted to the hybrid .Physiological studies have shown that selection by the function of thermoregulation at low temperatures led to the development of biologically appropriate adaptations to these temperatures in the body, providing an energy-saving effect. RW chicks showed a decrease in the relative weight of the lungs as a mechanism for reducing heat transfer. On the first days after hatching, thermoresistant chicks in conditions of low temperatures showed a slight decrease in body temperature, which caused a slowdown in physiological processes, including respiratory function. Reducing the intensity of breathing slowed down the development of the lungs and reduced heat transfer .Additionally, because of the selection under hypothermic stress conditions, genotypes of day-old chicks with snow-white down appeared .Obviously, the selection of neonatal RW chicks by the stress response to sublethal low temperature affected the mechanism of neuroendocrine regulation of the melanogenesis process, which is controlled by pituitary, thyroid, steroid and sex hormones, resulting in a mutation in the down color of the day-old chicks. Our assumption about the influence of neuroendocrine status on the down color of neonatal chicks is confirmed by studies evaluating 10-week-old RW chicks with a snow-white down color at the day-old age, according to the degree of functional reserves of the adrenal glands detected by administrating adrenocorticotropic hormone (ACTH). It was found that the average concentration of corticosterone in the blood of chicks with a snow-white down color in response to ACTH injection was 82.6 ng/mL, while in other chicken gene pools, it was significantly lower (24.0–52.3 ng/mL) . AdditioIt is also known that one of the protective mechanisms in chicken embryos under conditions of lowering the incubation temperature is an increase in the volume of their allantois-amniotic fluid . It is tRecent studies have been devoted to the conduction of genome-wide association studies (GWAS) using single-nucleotide polymorphism (SNP) genotyping and the Illumina 60K BeadChip chip ,21. SeveThe evaluation of the thermoresistance of RW chickens at low temperatures was carried out compared with chickens of the Amroks breed .There was no selection work for adaptation to low temperatures in this breed. Amroks is a dual-purpose breed of chicken, and its egg production is about 200–220 eggs per year. Amroks chickens originate from the USA, specifically in Plymouth, Massachusetts. They are a crossbred of the Black Java, Black Cochin and Dominique chickens. Amroks chickens are known from 1848 as “Barred Rock Chickens”. They have the same origin as the Barred Plymouth Rock. After the Second World War in 1948, these chickens were imported to Germany, where their egg qualities and adaptability to environmental conditions were improved. The breed standard was approved in the late 1950s. The breed was named Amroks .Design of the study is schematically presented in the ® chips. All genotypes of chickens were filtered according to the effectiveness of genotyping. Only SNPs located on autosomes from GGA1 to GGA28 were used for analysis, and SNP genotypes in less than 95% of samples were excluded from the analysis. The analysis used the following parameters for filtering SNPs: --maf 0.05, --geno 0.02, --hwa 0.0001. To eliminate the effect of sex chromosomes on the subsequent estimation, SNP markers located on them were excluded.The material for genomic DNA extraction was blood samples taken from the vena axillaris of chickens . Blood samples were collected by trained lab personnel following the RRIFAGB ethical guidelines for minimizing any possible bird discomfort or distress. DNA was isolated by conventional methods using phenol–chloroform extraction. The quantity and quality of DNA samples were tested on a NanoDrop 2000 spectrophotometer. Genotyping of individual samples was performed using Illumina Chicken 60K SNP BeadChipTo assess intrapopulation genetic diversity and ROH analysis, PLINK 1.9 software was used ,26. AddiST) values were computed using eigensoft 6.1.4 software [The analysis of multidimensional scaling was carried out in PLINK 1.9. Visualization was conducted in R using the standard “plot” function. The Admixture v1.3 program was used to establish the population structure of the chicken groups under study, and visualization was conducted in R using the “pophelper” package . To assesoftware .The search for homozygous regions by individual chromosomes was carried out using the PLINK 1.9 program and the detectRuns package in R according to the following parameters: window size, 15 SNPs; window overlap threshold, 0.1; minimum number of SNPs in the region, 15; maximum number of heterozygous SNPs in the window, −1 .ROH (ROH-based inbreeding coefficient with SD). The number and length of ROH for each individual were estimated and then averaged for each individual within each breed. Additionally, we calculated the coefficient of genomic inbreeding and inbreeding based on ROH (FROH). The number of ROH in the genomes of the two study breeds was studied using the following ROH length classes: 0.5–1, 1–2, 2–4 and 4–8 Mb. To determine the proportion of the genome covered by different ROH segments, we calculated the sum of ROH for the following different minimum lengths: >0,5, >1, >2, >4 Mb.PLINK 1.9 was used to estimate FThe putative ROH islands were identified as overlapping homozygous regions shared by more than 50% of the analyzed individuals within each breed. A threshold of 0.5 Mb was applied for the minimum size of the overlapping length, since shorter segments of 0.5–1.3 Mb in size predominate in the genome of Russian White chickens.http://www.ensembl.org/index.html (accessed on 1 February 2022)). The adjustment of the position before the GRCg6a assembly was carried out using a file containing information about polymorphisms included in the Cobb Vantress Chicken 50 k biochip, which was provided by Illumina. The results obtained from the Ensemble Biosmart browser for each genomic region of the breeding signatures were sifted manually and compared with the corresponding published studies to determine the main candidate and other genes of interest. Additionally, with the help of Chicken QTLdb, a search was carried out for known QTL that overlap with the ROH islands identified in our studies.To determine the prospective candidate genes in genomic regions, genomic localization of regions defined as ROH islands was used. The boundaries of these regions, localized in the chromosomes of the reference assembly GRCg6a, were used as a list of queries for extracting chicken genes and their orthologs in humans, rats and cattle using the Ensembl Genes release 103 web database and the Ensembl BioMart data analysis tool  and the standard error of the averages (±SEM) were calculated. The reliability of differences was assessed by Student’s t-criterion. The differences were considered statistically significant at An MDS graph to estimate the genetic relationships between the chicken breeds under study was used . The resThe calculations of the CV error for the different numbers of clusters (from 1 to 5) showed that the optimal number of clusters (K) was equal to 2. The admixture clustering at K = 2 clearly distinguished the RW and Amroks populations at K = 2 .The results of the genetic diversity indices are presented in HOM was unreliable and not significant, so there may be inconsistent indicators with different approaches to calculations)  .From the general database of genotypes obtained as a result of genome-wide genotyping and subsequent SNP filtering, chromosomes with the most relevant candidate genes were selected. FST (0.160 ± 0.004) was calculated for the selected chromosomes. Its value was significantly higher than in the genome-wide analysis (0.118 ± 0.0023). This is due to the peculiarities of the architecture of the genome of breeds formed under the influence of selection in the RW breed for cold tolerance in the early postnatal period.The total length of ROH in the RW breed genome was mainly represented by a set of short segments , which indicates a low level of inbreeding in this population; the average length of ROH was 3749.6 ± 111.3 Mb per individual.Descriptive statistics for the runs of homozygosity (ROH) by ROH length class in the breeds of Russian White and Amroks chickens are shown in During the ROH analysis, 12 RW-specific regions on 8 chromosomes were identified, overlapping with 32 known QTL associated with plumage density, stress resistance, lung percentage, bursa, spleen and thymus gland mass, egg yolk weight, unsaturated fatty acid content in muscles, body temperature, abdominal fat mass, feed intake and blood oxygen saturation. In Amroks chickens, 5 specific regions on 4 chromosomes were found to overlap with 19 known QTL associated with egg and meat productivity, as well as reproductive qualities. Thus, the area of 50.5–54.0 Mb on GGA2 in Amroks chickens is associated with QTL for body weight at 14 and 28 days  ; the areThe effect of chicken genome heterogeneity on the formation of ROH segments was also investigated by comparing ROH on macrochromosomes (GGA1 to 5), intermediate chromosomes (GGA6 to 10) and microchromosomes (GGA11 to 28). The largest number of ROH islands shared by more than 50% of the analyzed individuals was found on macrochromosomes. There were 18 regions with a length of 0.62–3.89 Mb; a total of 3 ROH islands with a length of 0.54–1.32 Mb were found on intermediate chromosomes. Six ROH were detected on microchromosomes. A total of 27 ROH islands were identified, which covered 45.71 Mb of the genome and were distributed across 12 chromosomes . ROH islands in selected populations may indicate areas of the genome that have undergone artificial selection. In homozygous regions on 12 chromosomes of RW chickens, the presence of 404 genes associated with selection effects on the breed was revealed .The well-known QTL found using Chicken QTLdb in the regions common to Amroks and RW chickens confirm this assumption. A total of 6 regions on 5 chromosomes were identified, common to both study breeds, overlapping with 16 known QTL. Selective changes mainly covered QTL for growth, egg productivity, egg quality and reproductive qualities of chickens, since selection work for increasing productive rate indicators in both breeds was carried out. Thus, the regions 48.88–48.93 Mb on GGA2 and 2.14–4.51 Mb on GGA5, because of selection pressure in the RW and Amroks breeds, were associated with multiple QTL for live weight at 21 and 28 days  . The areTwelve prioritized candidate genes on breed-specific ROH islands  were ideThe study of the mechanisms of thermoregulation of RW and Amroks chicks in the early postnatal period showed that RW chicks have higher adaptive abilities in low-temperature conditions. Amroks chicks under the influence of cold stress maintain their temperature homeostasis mainly due to the consumption of lipid reserves of the body. This was evidenced by a decrease in the value of the RQ by 3.2% .p < 0.05) and, accordingly, a higher level of oxygen consumption.RW chickens under the influence of cold stress maintain their temperature homeostasis mainly due to the consumption of carbohydrate reserves of the body. This was evidenced by an increase in the value of the RQ by 3.0% . ObviousSOCS3, NDUFA4, TXNRD2, IGFBP1, IGFBP3), maintaining body temperature in cold environments , non-shivering thermogenesis  and muscle development (METTL21C) are essential , maintaining body temperature in the cold , non-contractile thermogenesis  and muscle development (METTL21C) are of the greatest interest.Candidate genes associated with lipid metabolism (This study contributes to our understanding of the mechanisms of thermal resistance in chickens in the early postnatal period and provides a molecular basis for selection work in this field.


Pigeonpea, a tropical photosensitive crop, harbors significant diversity for days to flowering, but little is known about the genes that govern these differences. Our goal in the current study was to use genome wide association strategy to discover the loci that regulate days to flowering in pigeonpea. A single trait as well as a principal component based association study was conducted on a diverse collection of 142 pigeonpea lines for days to first and fifty percent of flowering over 3 years, besides plant height and number of seeds per pod. The analysis used seven association mapping models  and further comparison revealed that FarmCPU is more robust in controlling both false positives and negatives as it incorporates multiple markers as covariates to eliminate confounding between testing marker and kinship. Cumulatively, a set of 22 SNPs were found to be associated with either days to first flowering (DOF), days to fifty percent flowering (DFF) or both, of which 15 were unique to trait based, 4 to PC based GWAS while 3 were shared by both. Because PC1 represents DOF, DFF and plant height (PH), four SNPs found associated to PC1 can be inferred as pleiotropic. A window of ± 2 kb of associated SNPs was aligned with available transcriptome data generated for transition from vegetative to reproductive phase in pigeonpea. Annotation analysis of these regions revealed presence of genes which might be involved in floral induction like Cytochrome p450 like Tata box binding protein, Auxin response factors, Pin like genes, F box protein, U box domain protein, chromatin remodelling complex protein, RNA methyltransferase. In summary, it appears that auxin responsive genes could be involved in regulating DOF and DFF as majority of the associated loci contained genes which are component of auxin signaling pathways in their vicinity. Overall, our findings indicates that the use of principal component analysis in GWAS is statistically more robust in terms of identifying genes and FarmCPU is a better choice compared to the other aforementioned models in dealing with both false positive and negative associations and thus can be used for traits with complex inheritance. However in order to reach SDG-2 standards and commitments, it is necessary to prioritize nutrition in addition to food security. Pulses are important in combating malnutrition, as in addition to providing a sustainable production system, they are the crucial component of human diet (http://www.fao.org/resources/infographics/).The United Nations 2nd Sustainable Development Goal (SDG-2) aims to eradicate hunger and malnutrition globally by 2030. The goal has become even more challenging in the current context of the Covid-19 pandemic, which has devastating effect on agricultural sector that by 2030, the number of hungry peoples may exceed 840 million, with the majority (above 381 million) from the Asian (Cajanus cajan (L.) Millsp.), is a highly nutritious grain legume. Although it is a perennial plant, but primarily cultivated as an annual crop with sowing to flowering duration ranging between 60 to 180 days. While long duration varieties have higher yield potential, lately, a significant shift towards shorter duration varieties has occurred so as to accommodate them in diverse cropping systems. Hence, the development of short duration varieties with comparable yield potential is compelling need of the hour. Cultivation of short duration varieties also enables farmers to escape adverse growth conditions, such as drought, severe winter, and disease incidences. The main trait that can be targeted for developing varieties with a specific duration is the number of days to flowering. Thus, biotechnological interventions could be deployed to speed up the development of short-duration varieties4.Pigeonpea  can better capture the genetic basis of flowering than bi-parental mapping populations since it uses the natural population that represents all allelic combinations arising out of historical recombinations. The GWAS approach has been shown to be effective in identifying novel genes and QTLs for multiple traits in diverse germplasm of rice and wheat19. The availability of a 62 K SNP chip and hyper variable markers covering the complete pigeonpea genome, together with low cost sequencing costs, enables efficient GWAS analysis in pigeonpea24.To develop shorter duration varieties of pigeonpea, it is essential to understand the mechanisms underlying flowering time variations and its adaptation to different ecologies. Though efforts have been made to characterise the 27. The current work used an association panel of 142 accessions in order to identify candidate genes and markers for flowering-related traits in pigeonpea.Previous reports on GWAS in several legume crops have focused on domestication related loci, resistance against fusarium wilt, and days to flowering in chickpea; days to flowering and maturity in soybean, but a computationally robust analysis is still needed to decipher association and develop markers with high confidence for flowering related traits in pigeonpea28. As a result, we conducted a PCA based GWAS to discover genetic factors regulating crop architecture with emphasis on flowering. The effectiveness of the approach in identifying significant genes associated with pigeonpea flowering and related traits was further validated through annotation of the flanking regions using transcriptome data of ICPL 20338 accession (PRJNA752250). Thus, the present study was undertaken with the following objectives: (i) Single trait based GWAS using seven association mapping models  to identify novel genes to flowering and related traits (ii) PC based GWAS to improve the accuracy and robustness of single trait GWAS and investigate pleiotropy (iii) Genomic prediction and (iv) Annotation of significantly associated SNPs.Principal Component Analysis (PCA) is a powerful dimension reduction and an unsupervised linear transformation technique which aims to extract critical information from phenotypically complex traits while reducing the redundancy in variables and preserving the information parallelly. It reduces a large set of initially correlated variables to a much smaller set of uncorrelated or orthogonal variables termed as PCs. GWAS using PC scores as dependent variables are more reliable and robust than single trait based, and it can reveal possible pleiotropy with increased powerhttp://bbi.irri.org/products. Analysis of variance and broad sense heritability of the data was calculated using Indostat (version 8.1). The whole study complies with relevant institutional, national, international guidelines and legislation.A collection of 142 accessions representing a global pigeonpea germplasm collection, which includes landraces and breeding lines (mostly of Indian origin) Table , were prhttps://www.ncbi.nlm.nih.gov/). These datasets were downloaded and processed to remove adapters and poor quality reads through Trimmomatic version 0.3629 using default parameters. High quality reads were mapped to the pigeonpea reference genome of cultivar ICPL 87119 available at NCBI using the BWA tool version 0.7.1730. Samtools version 1.10 and Freebayes version 1.3.1 were used further for variant calling with minimum 10 × depth as basic criteria32. InDels were removed and SNPs covering minimum of 80% reads were filtered. All 142 .vcf files generated were merged to a single .vcf file through samtools, which was used to develop hapmap through Tassel 5.033. SNPs that showed a Missing Allele Frequency of more than 0.05 were not considered for Hapmap preperation, which ultimately included 168,540 SNPs.The raw sequence data of all 142 lines used in the present study is available at NCBI  was set from 1 to 10 with the burn-in period, and the number of Markov Chain Monte Carlo (MCMC) replications after burn-in were both set to 100,000 under the “admixture mode”. Five independent runs were performed for each k number. Finally, the structure was developed using the STRUCTURE harvester vA.239. The delta K method developed by Evanno et al.40 was used to determine the optimal value of k.Population structure is known to affect association studies, but we need to look after its impact. The power to detect population structure is highly dependent on the number of loci utilised33 based on the nucleotide diversity (π), Watterson estimator (θ), and Tajima’s D index43.The diversity estimation was done using Tassel 5.0PCA was conducted using R software version 4.0.0. Reduced DOF and DFF, but increased SPP, are desirable for the generation of short duration varieties with higher yield potential. Given the intricate inheritance and genetic correlation of quantitative traits, some trade-off is inevitable. As various quantitative phenotypic variables were measured in different units reflecting different types of interpretations, for statistical validity, the original variables were standardized (with mean 0 and variance 1) before attempting PCA. The detailed description about the PCA statistics and their loadings are provided in Table 50. These models are distinct in their basic structures and components included as fixed and random effects. Except GLM all the models include mixed effect structures, that is both fixed and random effect components. PC scores were used as covariates or fixed effects in PC based GWAS. CMLM, ECMLM and SUPER generally exhibit higher statistical power as compared to the MLM51. Amongst all models, MLMM and FarmCPU algorithms are for multiple loci analysis. FarmCPU model is designed to control both false positives and false negatives as compared to other models51. Table GWAS was conducted using the GAPIT package version 3.0 in R software, which employs Bonferroni correction to define statistically significant MTAs. For this study seven association models were implemented namely General Linear Model (GLM), Mixed Linear Model (MLM), Multiple Loci MLM (MLMM), Compressed MLM (CMLM), Enriched CMLM (ECMLM), Fixed and Random Model Circulating Probability Unification (FarmCPU) and Settlement of MLM Under Progressively Exclusive Relationship (SUPER) algorithm52 for ridge-regression based genome-wide regression. The Ridge Regression Best Linear Unbiased Prediction (RRBLUP) model for genome-wide regression assumes the following form,y is the vector of phenotypic values; X and Z are the design matrices for fixed and random effects respectively; b and u are the coefficient vectors of fixed and random effects respectively; u is assumed to follow normal distribution I being the identity matrix. The random effect coefficient u is used to represent the marker effects associated with Z being the matrix of genotypes. The variance components Genomic prediction (GP) modelling was done through the rrBLUP (v4.6) package in R softwarein-house transcriptome data of early flowering genotypes (ICPL 20338) (PRJNA752250) present in two biological replicates, through the BLASTn program. Initially the raw reads were filtered through trimmomatic version 0.3629 to remove the poor quality reads with default parameters. Cleaned reads were de novo assembled using Trinity (version 2.1.1)53 with default parameters. Differentially expressed genes (DEGs) were identified using the edgeR package in the bioconductor environment through R script. These DEGs were filtered on the basis of p value (0.001), FDR < 0.05 and on the basis of fold change of the fragments per kilo-base of transcript per Million fragments mapped (FPKM) value (± 2). CD-hit web server (http://weizhong-lab.ucsd.edu/cd-hit/) was used to remove duplicates. The FPKM values of genes corresponding to vegetative leaves (VL), reproductive leaves (RL), shoot apical meristem (SAM) and reproductive buds (Bud) were retrieved to construct heatmap using Morpheus web server (https://software.broadinstitute.org/morpheus/).For annotation analysis, a window of ± 2 kb was used for each associated SNP for annotation analysis. The selected windows were looked at for similarity searches within the assembled This article does not contain any studies with animals performed by any of the authors.2) of PH, SPP, DOF and DFF were 0.5449, 0.5897, 0.6593 and 0.7094 respectively  in the original data Table . The fir54 or to determine evolutionary relationship. In our analysis, the value of π was 0.03573. The Watterson estimator (θ), which is an estimation of the population mutation rate was 0.19898. Usually both π and θ ranges in between 0–1, where the inclination toward 0 indicates presence of less diversity. Pigeonpea is an often cross pollinated crop but harbours less diversity, especially in the landraces and cultivated varieties due to progressive bottlenecks during domestication and breeding12. Our study was based on only 142 landraces and cultivated varieties mainly of Indian origin, and hence the lesser diversity could be explained. Similar results were reported by other groups also56. The Tajima’s D in our population was − 2.84379. Tajima's D is computed as the difference between two measures of genetic diversity: the mean number of pairwise differences and the number of segregating sites, each scaled so that they are expected to be the same in a neutrally evolving population of constant size. When Tajima’s D value is less than 0, it means abundant rare alleles are present, suggesting a possible selective sweep and population expansion.The nucleotide diversity (π) is a reflection of genetic diversity which can be used to monitor diversity and genetic variation in crops and related species57. Cluster 1 comprised 28 accessions, while clusters 2 and 3 comprised 63 and 51 lines respectively, in the population structure analysis  with a substantial level of admixture, probably the result of its pollination behaviour (often cross pollinated), which is in accordance with previous reportssis Fig. .Figure 2In all the aforementioned models, GWAS for quantitative traits like DOF and DFF revealed the association of 19 and 22 non-redundant SNPs distributed on chromosomes 2 and 6 respectively, in the 2017–18 data. However, no significant association was found for PH and SPP. A descriptive summary of GWAS on DOF and DFF in 2017–18 is provided in Tables 2 was 0.158. This LD level usually indicates that there is nearly no linkage between the markers after this; thus, we defined SNPs within a 1.5 Mb window as a single locus. So all of the associated SNPs scattered on scaffold NW_017988637.1 were treated as a single locus for further analysis as it’s of only 5 kb in size. Out of 24 SNPs 812678863:261: +, 812678807:41: + and 812679326:250: + were on the same scaffold and hence treated as single loci and the whole scaffold was considered for annotation purposes.Cumulatively, trait based and PC1 based GWAS on DOF and DFF revealed the association of 22 SNPs with DOF/DFF traits and were selected for further annotation analysis Table . InteresThree SNPs  were found to be associated with both PC1 and either DOF/DFF or both, while four SNPs  were found to be associated exclusively with PC1 scores, reflecting the ability of PC based GWAS in identifying novel associations. As PC1 is the representative of DOF, DFF and PH, these unique SNPs might be regulating plant height in addition to DOF and DFF. As PH is arrested once flowering starts in the case of determinate lines, the role of these SNPs in regulating plant height can’t be ignored. Further, among these novel associations identified by PC, 593701379:271 was annotated as F box protein , whereas deviation depicts the presence of association between testing markers and trait. Upward side deflation of lines represents a false positive association, while a false negative is represented by downward deflation. If the line is close to 1:1 ratio with a sharp upward deviated tail, it indicates that both false positives and false negatives were controlled and the presence of true associations can be inferred61. Usually most of the SNPs follow a uniform distribution as they are not in LD with a causal polymorphism, but the few that are in LD with a causal polymorphism will produce significant p values arising as ‘tail’.While association mapping make use of historical recombination to unravel marker trait association, it’s difficult to control false positives arising due to linkage disequilibrium (LD), family relatedness and population stratification. CMLM, ECMLM, MLMM, and SUPER, showed approximately 1:1 ratio, better than the remaining models, i.e., GLM, MLM, and FarmCPU  and LD based false positive associations (in the case of GLM), as well as being a multi-loci based model, it was appropriate for dissecting the complex traits  for each line was estimated using all the SNPs and 500 randomly generated train/test sets. The average correlation between the observed DOF and the predicted DOF by GP was 0.46, 0.51 and 0.46 in a model with no significant markers included as fixed effects during 2017–18, 2018–19, and 2019–20, respectively , which is suggestive of its involvement in SAM to bud transition. Under low auxin concentration, AUX/IAA binds to ARFs, ultimately inhibiting the further downstream genes, whereas when present in higher concentrations, auxin binds to TIR1 (F box protein which is a component of E3 ubiquitin ligase). After auxin binds to TIR1, it gets activated and cleaves AUX/IAA, thereby freeing ARFs and ultimately leading to expression of auxin responsive genesbud Fig. .80, which is generated by polar auxin transport involving regulators such as the auxin efflux carrier PIN-FORMED1 (PIN1) and the PINOID (PID) kinase, which controls PIN1 activity82. Interestingly, another SNP (633271872:58: +) was present in the genic region of PIN like transcript variants, which is reported to mediate auxin efflux dependent developmental processes as mutants of these showed defective auxin transport83. Also, PIN is believed to regulate flowering timing by altering auxin activity in collaboration with ARF. Likewise, a F box protein SKIP23 is a must for induction of downstream auxin responsive genes84 and was found in the vicinity of SNP (593701379:271: +). As it was found to be highly expressed in VL, it is hypothesised to degrade AUX/IAA and release ARF (found near other SNP i.e. 785047004:88: +).Establishment of a high concentrations of auxin is required for floral induction85, few reports suggest its role in auxin as well as abscisic acid signalling87. Although it is not clear how this gene influences flowering, we presume that it regulates flowering by hormonal regulation, mainly by regulating ABA and auxin. Another SNP, 164755426:80: + was found near the cytochrome P450 89A2 subunit. CYP715 (a cyt p450 gene family member) appears to function as a key regulator of flower maturation, synchronising petal expansion and volatile emission88. Similarly, this SNP might be involved in the maturation of reproductive buds to flowers.Similarly, SNP (376936577:87: −) was very close to a serine/threonine protein phosphatase 2A. Although serine/threonine protein phosphatase 2A is known to participate in various stress signals89. The spl11 rice mutants were found to display delayed flowering under long-day conditions. As per a previous report, mutating a U box protein in rice (SPL11) leads to delayed flowering. Transcript (TRINITY_DN33874_c0_g1_i3) might regulate flowering time as in rice, but interestingly its expression was higher in VL only suggesting that it may regulate flowering through a different mechanism from that of rice.A transcript (TRINITY_DN33874_c0_g1_i3) present besides SNP (35373484:284: +) was annotated as a U-box domain-containing protein. The SPIN1 (SPL11-INteracting protein 1) gene has been reported to regulate flowering time in rice and it is ubiquitinated by SPL11 (a U box protein)90, SNF5/BSH subunit91, the nuclear actin-related protein ARP492, BRAHMA (BRM), or SPLAYED (SYD). Both of these latter proteins are ATPases of Arabidopsis SWI/SNF complexes and have been shown to participate in the control of flower development and flowering time95. Likewise, the SWI3B protein interacts with the flowering regulator FCA90. Several reports regarding the involvement of epigenetic mechanisms in flower induction regulation are available, and TRINITY_DN34453_c0_g3_i10 might play a similar role. Interestingly, SNP 760222832:55: + was found in the vicinity of RNA methyltransferase (TRINITY_DN35027_c3_g2_i12). Many epigenetic regulators have already been reported to regulate flowering timing97. TRINITY_DN35027_c3_g2_i12 was found to express constantly in VL, Mer and Bud but the expression increased in RL, suggesting its role in flowering induction.Another transcript, TRINITY_DN34453_c0_g3_i10 annotated as chromatin structure remodelling complex protein BSH was found in the vicinity of SNP (35373484:284: +). Several components of chromatin remodelling complexes are evolutionarily conserved in plants, such as the SWI3 subunitsIn the current study, PC based GWAS was found to be superior over trait based and multi-loci based models for DOF and DFF in Pigeonpea, analysed using 142 accessions and 168,540 SNPs. PC transformation of the traits revealed that PC1 captured 58% of the variation, while PC1 and PC2 cumulatively captured 85% of the variation, suggesting PC1 and PC2 were sufficient enough for GWAS in our datasets. Cumulatively, GWAS revealed the association of 22 SNPs with DOF, DFF or PC1, out of which 15 were solely identified by trait based GWAS, 3 by both trait based as well as PC based GWAS, and 4 SNPs were found to be associated only through PC based GWAS. The 4 SNPs found to be associated with PC1 might be pleiotropic as PC1 also represented PH besides DOF and DFF. One of these 4 SNPs is annotated as F box protein, which plays a vital role in auxin signalling during growth and development, so these 4 SNPs can be inferred as pleiotropic to DOF, DFF and PH. Many of the associated SNPs were in the vicinity of vital genes like Auxin responsive genes like ARF, F box protein, U box protein, PIN like transcripts, chromatin remodelers, RNA methyltransferase the homolog’s/ortholog’s, many of which have been previously reported to regulate floral transition in other plant species. A few uncharacterized genes were also found, which are novel and need further characterization in order to decipher their function and role. Associations found in the present study suggest a functional basis of the associations in the regulation of flowering, and hence these genes are excellent candidates for further validation through bi-parental analysis followed by mutagenesis, genome editing, and other approaches. In conclusion, PC based GWAS is effective in deciphering pleiotropy and complex traits over trait based GWAS. Furthermore, the study can be taken forward by combining the PCA and the Multiple Dimension Scaling method to handle both quantitative and qualitative phenotypes as inputs for association mapping models.Supplementary Information.


Fecundity is an important economic trait of sheep that directly affects their economic and productive efficiency. Our study aimed to identify single nucleotide polymorphism (SNP) loci associated with sheep puberty or litter size which could be used in future breeding programs to improve fertility. We obtained one SNP on GNAQ gene (NC_040253.1: g.62677376G > A), which was associated with litter size in Cele Black sheep. Perhaps this SNP could be a criterion to screen polytocous sheep.Fecundity is an important economic trait in sheep that directly affects their economic and productive efficiency. Our study aimed to identify SNP loci associated with sheep puberty or litter size which could be used in future breeding programs to improve fertility. Genomic DNA was obtained from Hetian and Cele Black sheep breeds and used for reduced-representation genome sequencing to identify SNP loci associated with pubertal initiation and litter size. Selective signatures analysis was performed based on the fixation index and nucleotide diversity, followed by pathway analysis of the genes contained in the selected regions. The selected SNP loci in the genes associated with pubertal initiation and litter size were validated using both sheep breeds. In total, 384,718 high quality SNPs were obtained and 376 genes were selected. Functional annotation of genes and enrichment analysis identified 12 genes associated with pubertal initiation and 11 genes associated with litter size. SNP locus validation showed that two SNP on PAK1 and four on ADCY1 may be associated with pubertal initiation, and one SNP on GNAQ gene (NC_040253.1: g.62677376G > A) was associated with litter size in Cele Black sheep. Our results provide new theoretical support for sheep breeding. Puberty is a critical stage, when animals first acquire fertility and involves complex interactions ,2,3. In Selection, both artificial and natural, has generated the many sheep breeds existing worldwide. During selection, the frequency of certain alleles within the population increases and the surrounding associated chromosomal regions show a decrease in polymorphism due to the hitchhiking effect, a phenomenon known as selective signatures . SequencThe Cele Black Sheep and the Tian Sheep are two local breeds in Northwest China, characterized by high and low fertility, respectively . Hetian Sheep are one of the most important domestic animals in Xinjiang. Improving the productivity of sheep plays an important role in improving the quality of life of local people and is one of the important goals of sheep breeding. However, the low fertility of most domesticated sheep is one of the factors limiting the development of sheep breeding in Xinjiang . In thisA total of 342 blood samples from a natural population of healthy ewes between the ages of three and four years were collected and stored at −80 °C until use. The exploratory cohort for selection signatures analysis comprised 54 Cele Black sheep  and 26 Hetian sheep . To validate the relationship of SNP loci with litter size we used a validation cohort of 77 monotocous Cele Black sheep , 68 polytocous Cele Black sheep . These 145 Cele Black sheep (CL-1 and CL-2) and 117 Hetian sheep (Group HT-1) were used as a cohort for validating the relationship of SNP loci with puberty. Genomic DNA was extracted using the standard phenol/chloroform extraction method. DNA integrity was verified using a 1% agarose gel and the OD 260/280 ratio was obtained by NanoDrop™ spectrophotometer . The DNA concentration was measured using a Qubit (Thermo Fisher Scientific). This study was conducted in accordance with the specifications of the Ethics Committee of Tarim University of Science and Technology. All the sheep were collected from Xinjiang Kunlun Luyuan Sheep Farm in Cele County, Hotan City, Xinjiang Province.Library construction was performed using the genotyping-by-sequencing (GBS) method. Genomic DNAs from CL and HT were digested with restriction endonucleases MseI and SacI  before being ligated with specific adaptors. An aliquot of the product was pooled and purified with AMPure XP Beads . Polymerase chain reaction (PCR) enrichment was then performed using the high-fidelity polymerase KOD-Plus-Neo . All products were pooled and loaded onto an electrophoresis gel overnight with Certified Megabase Agarose  at low pressure. Products in the range of 380–480 bp were purified using a gel extraction kit . Libraries were pooled according to the target downstream data volume and paired end 150 bp (PE150) sequencing was performed using the Illumina HiSeq platform . Each library contains 40 samples and we matched the clean reads individually to the barcodes and remnant restriction sites at both ends.Quality control and data filtering were performed using fastp software  with then is the number of alleles and iP and jP denote the ith and jth allele frequency, respectively.The frequency, number of alleles, and nucleotide diversity (π) were calculated using vcftools software and the polymorphism information content (PIC) values of the SNP data were calculated using the following equation:2) to reveal the population relationship between HT and CL. The average number of nucleotide differences (K) was calculated as the sum of nucleotide variations of all individuals divided by the number of individuals.Genetic diversity (GD) and observed heterozygosity (Ho) were calculated using PLINK . We usedNeighbor-joining trees were constructed using PHYLIP  and dataSelective signature analysis was performed using HT as the reference and CL as the target population in order to identify genes associated with pubertal initiation and litter size. The differentiation index (Fst) and nucleotide diversity (π) were calculated by vcftools, parameter: —fst-window-size 100,000—fst-window-step 50,000, for each interval between the two breeds (100 KB as a window and 50 KB as a step to divide the genome). Plots were drawn using the R package cmplot. Using the methods described by Li et al. , regionsFor SNP validation, we used CL-1, CL-2, and HT-1 groups. CL-1 and CL-2 were used to validate the relationship of SNP loci with litter size. CL-1, CL-2, and HT-1 groups were used to validate the relationship of SNP loci with puberty. SNP typing was performed by the Beijing Compass Biotechnology Company, China. The SNP loci located in screened genes that were associated with sheep puberty or litter size were validated for genotype-litter size correlation in CL-1 and CL-2 using Fisher’s exact test method . The genWe obtained 41.859 G of clean data, 0.076–1.174 G per sample, with an average of 0.523 G. An average of 4.36 million reads were retained per sample among the clean reads; q20 was higher than 98.03%, q30 was higher than 92.92% and the GC content was stable between 45.305 and 47.734%. Overall, our sequencing data yielded a high-quality score and a total of 384,718 high-quality SNPs were identified.The population structure was assessed using CL and HT. Both the evolutionary tree and PCA results identified two distinct groups A,B. The PLCE1, SPIRE1, DIAPH2, GABRA3, GNAQ, GHR, DNMT3B, PLCB1, ABCC8, PTGFR, PAK1, ADCY1) were associated with pubertal initiation and 11 genes  were associated with litter size. From these genes, we selected 25 SNP loci with high Fst values for further validation. Protein interaction analysis revealed interactions between PTGFR, LHCGR, GNAQ, GHR, PLCE1, LIF, PLCB1, ADCY1, BMPR2, and TGFB2  and four SNP loci in ADCY1  showed significant differences (p < 0.05) between HT-1 and the CL-1 and CL-2 groups. These SNPs were thought to be associated with primiparous initiation. Another SNP locus in GNAQ showed highly significant differences (p < 0.01) between CL-1 and CL-2, suggesting that this SNP locus was associated with litter size in Cele Black sheep. The heterozygous genotype GA at the NC_040253.1: g.62677376G > A locus on the GNAQ gene was more favorable for producing polytocous sheep  with little size records and performed preliminary validation of SNP loci associated with puberty using Hetian sheep (HT-1) and Cele Black sheep (CL-1 and CL-2).The assessment of genetic variation within breeds/populations can be an important basis in the improvement and development of sheep breeding ,42. SpecTo determine whether Hetian and Cele Black sheep populations are suitable to the selection signatures analysis, we conducted a population structure analysis. The resulting data showed that Hetian sheep and Cele Black sheep have different ancestral genetic backgrounds.Selective signatures can reveal genomic regions subject to selection that are associated with phenotypic traits acquired during biological evolution ,45. In tPAK1 and four SNP loci in ADCY1 potentially associated with pubertal initiation in sheep, and one SNP locus in GNAQ that was significantly associated with litter size in sheep. P21-activated kinase-1 (PAK1) is an important mediator of estrogen’s cell survival functions [ADCY1 gene may promote the initiation of proestrus in sheep by controlling meiosis. Previous studies have shown that the GNAQ gene is significantly associated with little size in Kazakh, Chinese Merino and Hu sheep [GNAQ gene was more favorable for producing polytocous sheep. However, the age at pubertal initiation for each sheep was not collected and therefore further validation of the identified loci associated with pubertal initiation is needed.Selection signatures analysis identified two SNP loci in unctions . In addiunctions . Pak1 isunctions . Therefounctions . TherefoHu sheep . Our stuPAK1 gene and four SNP loci on the ADCY1 gene that may be associated with pubertal initiation in sheep based on reduced-representation genome sequencing using selection elimination analysis. One SNP (NC_040253.1: g.62677376G > A) locus on the GNAQ gene was found to be associated with lambing number in sheep. Hetian sheep had higher genetic diversity than Cele Black sheep. Our results provide new theoretical support for sheep breeding.We identified two SNP loci on the


Arabidopsis thaliana) circadian clock is the evening complex (EC), a protein complex composed of EARLY FLOWERING3 (ELF3), ELF4, and LUX ARRYTHMO (LUX). Formation of the EC depends on ELF3, a scaffold protein that recruits the other components of the EC and chromatin remodeling enzymes to repress gene expression. Regulating the cellular distribution of ELF3 is thus an important mechanism in controlling its activity. Here, we determined that the cellular and sub-nuclear localization of ELF3 is responsive to red (RL) and blue light and that these two wavelengths have apparently competitive effects on where in the cell ELF3 localizes. We further characterized the RL response, revealing that at least two RL pathways influence the cellular localization of ELF3. One of these depends on the RL photoreceptor phytochrome B (phyB), while the second is at least partially independent of phyB activity. Finally, we investigated how changes in the cellular localization of ELF3 are associated with repression of EC target-gene expression. Our analyses revealed a complex effect whereby ELF3 is required for controlling RL sensitivity of morning-phased genes, but not evening-phased genes. Together, our findings establish a previously unknown mechanism through which light signaling influences ELF3 activity.Circadian clocks facilitate the coordination of physiological and developmental processes to changing daily and seasonal cycles. A hub for environmental signaling pathways in the Arabidopsis ( Light signaling pathways converge on the evening complex scaffold protein EARLY FLOWERING3 by controlling its cellular and sub-cellular localization and regulating circadian clock gene expression. Circadian clocks are internal biological timekeeping mechanisms that integrate light, temperature, and other stimuli with predictable daily changes so that internal physiological responses are coordinated with the external day–night cycle. In plants, the circadian clock is responsible for regulating growth, the floral transition, metabolism, and the response to both biotic and abiotic stressors . AccordiThe plant circadian oscillator is composed of a series of interlocking transcriptional–translational feedback loops . Within Arabidopsis thaliana) ELF3 is proposed to intrinsically localize to the nucleus through a nuclear localization signal (NLS) within the C-terminus of the ELF3 protein  and GIGANTEA (GI) in sub-nuclear structures . The co-Alongside associating to sub-nuclear structures with evening-phased proteins, ELF3 also localizes in sub-nuclear structures with TANDEM ZINC-FINGER PLUS3 (TZP), a morning-phased protein . The locELF4 is directly regulated by RL, UV-B, and far-red (FRL) light-signaling pathways  to RL pulses. Together, this work reveals a mechanism for how light signaling influences the activity of ELF3.Here, we further investigated how light signaling governs ELF3 activity. We observed that the cellular localization of ELF3 is responsive to pulses of RL and blue light (BL), and these wavelengths have opposite, and potentially competing effects on the sub-nuclear distribution of ELF3. We also identified phyB-dependent and phyB-independent effects of RL on ELF3, indicating other RL photoreceptors must also regulate ELF3 localization. Finally, we investigated the effect of these light pulses and the 35S::YFP:ELF3 (previously described in −2/s−1 (µmol henceforth) of RL or BL at ZT7 (zeitgeber time) and then immediately imaged at ZT10. As a control, ELF3 seedlings were transferred to the dark for the duration of the light pulse. Regardless of the light treatment, there was at least one focus per hypocotyl nuclei in all nuclei that were imaged (We first tested whether the cellular distribution of ELF3 to foci was responsive to light. Arabidopsis seedlings expressing e imaged . Direct e imaged . After ae imaged . There we imaged . Comparie imaged . TogetheThe observed effects of RL and BL on ELF3 foci dynamics could be indirect and instead reflect changes in the nuclear/cytoplasmic (N/C) partitioning and/or stability of ELF3. To test this, we first measured the N/C ratio of ELF3 in the dark or after the respective light pulses described above. Under all light treatments, ELF3 was localized to the nucleus and cytoplasm . The celTo confirm that the effects of RL on the sub-nuclear distribution of ELF3 occurred independently of changes in the stability of ELF3, we analyzed total protein levels of ELF3 under the different light treatments. There was no discernible difference in the total protein levels of ELF3 between samples transferred to the dark or pulsed with RL . FurtherAs RL and BL caused opposite effects on the localization of ELF3, we next investigated whether there was competition between RL and BL on ELF3 localization. As the effect of both RL and BL was more strongly observed on the sub-nuclear localization of ELF3 foci, we characterized the response of ELF3 foci to light pulses with different spectral ratios of RL and BL. Seedlings were pulsed with either equal ratios of RL and BL (25 µmol:25 µmol), predominantly BL (40 µmol BL:20 µmol RL) or predominantly RL (25 µmol RL:12 µmol BL). Together, these white light (WL) pulses were termed WL(=), WL(BL+), and WL(RL+), respectively. The application of the light pulses was carried out as described above.ELF3 was localized between the nucleoplasm and foci regardless of the spectral composition of the WL pulse . HoweverTo confirm that the BL intensity used in the WL(RL+) could promote foci formation, we analyzed the number of foci per nuclei after a 12-µmol monochromatic BL pulse. As with a 25-µmol monochromatic BL pulse, a 12-µmol monochromatic BL pulse promoted foci formation and there was no significant difference in the effect of a 12-µmol BL pulse compared with a 25-µmol BL pulse . This suphyB circadian phenotype is dependent on the intensity of RL. Below intensities of 10 µmol RL, the phyB mutant has no discerned circadian phenotype, while increases in the intensity >10 µmol progressively increased the severity of the phyB phenotype , while the line with a wild-type (WT) phyB allele will be referred to as ELF3 (B+). The cellular and sub-nuclear localization of ELF3 (B−) was then imaged in seedlings either transferred to the dark or pulsed with 25 µmol RL following the approach described above.To identify whether phyB directly regulated the cellular localization of ELF3, the phyB mutation. The N/C ratio of ELF3 (B−) in the dark was decreased by ∼40% compared with ELF3 (B+) (As with ELF3 (B+), ELF3 (B−) was localized to the cytoplasm and nucleus in the dark. However, the nuclear accumulation of ELF3 was severely compromised by the LF3 (B+) . The locLF3 (B+) . The N/CLF3 (B+) . The disLF3 (B+) . ELF3  that had no focus, there was ∼2 foci per nucleus for ELF3 (B−) in the dark (n = 18), while the mean number of foci per nucleus increased to ∼5. However, the number of foci per nucleus in ELF3 (B−) after a RL pulse was highly variable, with the number of foci varying from 1 to 12 per nucleus  seedlings were pulsed with FRL. A FRL pulse promotes the rapid photoconversion of phys from their active Pfr form to the inactive Pr form . SeedlinphyB mutation were associated with changes in the functional activity of ELF3, we measured the expression of genes that have been established as targets of ELF3. The expression of ELF3 targets was measured in a WT, ELF3 (B+), and ELF3 (B−) background at ZT10 either in the dark or after a 25-µmol RL pulse. The RL pulse was applied as described above.To understand whether the cellular or sub-nuclear changes in the localization of ELF3 following the RL pulse and PRR9, as the expression of PRR9 is RL-responsive and phyB was proposed to inhibit ELF3’s ability to repress the expression of PRR9 (PRR9 in either ELF3 (B+) or ELF3 (B−) was unchanged compared to WT (PRR9 was strongly induced by a RL pulse in the WT background (PRR9 was not induced by a RL pulse in either the ELF3 (B+) or ELF3 (B−) background. Instead, the expression of PRR9 decreased in both backgrounds following a RL pulse (PRR9 expression between ELF3 (B+) and ELF3 (B−), suggesting that ELF3’s ability to antagonize RL-induction of PRR9 expression is not repressed by phyB in the evening.We first focused on the expression of  of PRR9 . At ZT10ed to WT , consisted to WT . The expckground . In contRL pulse . There wGI, an evening-phased gene whose expression was found to be induced by RL (GI was repressed in ELF3 (B+) and ELF3 (B−) compared with WT at ZT10 in the dark, although the repressive effect of ELF3 (B−) was weaker than ELF3 (B+) , but not ELF3 (B−) where GI expression decreased slightly following a RL pulse (GI expression in response to RL between WT and ELF3 (B+) revealed a similar degree of induction in the two backgrounds  . As prevRL pulse . Comparikgrounds . TherefoLUX and TIMING OF CAB1 EXPRESSION (TOC1), further direct targets of ELF3/EC repressive activity (TOC1 was repressed in ELF3 (B+)  and there was no significant difference in the repressive activity when compared to ELF3 (B+). A RL pulse strongly repressed the expression of TOC1 in WT, ELF3 (B+), and ELF3 (B−), and there was no clear difference in the degree of repressive effect between the three genotypes  and ELF3 (B−) compared to WT  and ELF3 (B−) after a RL pulse, respectively  in the dark, even though the localization of ELF3 to foci in the phyB mutant background was strongly impaired  and XBAT35 at warm temperatures ; 2021b. In summary, we have found that light signaling pathways converge on ELF3 through controlling the cellular and sub-nuclear localization of ELF3. Together, these results provide an interface to understand how light signals could influence the activity of the plant circadian clock and subsequently facilitate the mechanism of entrainment. We also highlight the recent work of A.thaliana) lines used here are in the Wassilewskija-2 (Ws-2) or Columbia-0 (Col-0) background. The elf3-4 LHY:LUC, 35S::YFP:ELF3 , phyB-10 (Ws-2) and 35S::GFP:ELF3 (Col-0) lines have all been described previously (35S::YFP:ELF3 (elf3-4/phyB-10) line was generated by crossing the 35S::YFP:ELF3 (elf3-4) line into the elf3-4/phyB-10 double mutant that was generated during the course of this work. The genotyping primers used in this work are described in the All Arabidopsis (eviously . The 35SSeeds of the appropriate line were surface sterilized and plated onto 1× Murashige and Skoog (MS) plates with 1.5% w/v phytoagar, 0.25% w/v sucrose and 0.5 g/L MES with a pH of ∼5.7. The top quarter of the MS agar plate was removed to allow seedlings to grow vertically. Seeds were then stratified at 4°C for 3 days before being transferred to a growth chamber with 85 µmol of WL and a constant temperature of 22°C. Seedlings were then grown vertically for 6 days under a short-day (8/16) photoperiod. Experiments were started at ZT7 (1 h before dusk) on Day 7. Seedlings were transferred to a custom-built LED stack and pulsed with RL, BL, or FRL at the intensities and duration stated in the text. For RL and BL pulses, control plates were transferred to the dark at ZT7, while for FRL control plates were kept under WL for the 15-min duration of the FRL pulse. For all samples, the temperature was kept constant at 19°C for the duration of the light pulse. Samples were then imaged immediately for up to 1 h following the cessation of the light pulse.The Leica Zeiss 710 confocal laser scanning microscope with Plan-Apochromat 63×/1.4 Oil DIC M27 objective and Zen 2011 SP4 confocal software  was used to collect images. Arabidopsis seedlings were submerged in deionized water on clear white slides. For all imaging reported here, the YFP fluorochrome was excited at 514 nm and emission detected between 525 and 615 nm. The pinhole was set to airy one for all constructs. The same laser setting was used for all images collected during this work, regardless of the mutant background: laser power = 4%, master gain = 695, digital gain = 2.6, and digital offset = 23.40. All images were collected as Z-stacks, with a Z-stack slice depth of 0.4 µm. All presented images are min/max projections of compiled Z-stacks.Foci were counted from compiled Z-stacks projected as a 2.5D min–max image in the Zen 2011 SP4 software. These counts were then validated by scoring each image of the compiled Z-stack for foci. To calculate the N/C ratio, compiled Z-stacks were imported into ImageJ (version 1.51W)  where thLHY::LUC (WT), 35S::YFP:ELF3 (elf3-4), and 35S::YFP:ELF3 (elf3-4/phyB-10) seedlings were grown as described in the light pulse section described above. On the seventh day, seedlings were either transferred to the dark or pulsed with 25 µmol of RL for 3 h at ZT7 before ∼100 mg of seedlings were harvested and snap-frozen in liquid nitrogen at ZT10. RNA was harvested using the Qiagen RNeasy Plant Mini Kit before DNase treatment and subsequent clean-up was performed using the Zymo clean and concentrator kit. Then, 1 µg of cDNA was generated using SuperScript IV  and diluted to a concentration of 1.25 ng/µL. Manufactures’ recommendations were followed for all protocols.Ws-2 All reverse transcription-quantitative PCR (RT-qPCR) was performed on an ABI StepOnePlus machine using the StepOne Plus version 2.3 software package. Fast SYBR GREEN  was used for all qPCRs. The efficiencies of primers were determined for an annealing temperature of 60°C. Primers sequences are described in 35S::GFP:ELF3 (Col-0) were surface sterilized, plated onto 1× MS plates with 0.8% w/v agar, 0.25% w/v sucrose, and 0.5 g/L MES with a pH of 5.7. Seeds were subsequently stratified for 3 days before being transferred to a short-day growth cabinet with 125 µmol of WL and a constant temperature of 22°C. Seedlings were subsequently grown for 17 days before light treatments (as described above) were started at ZT7 on Day 18. The seedlings were thoroughly grinded and total proteins were extracted with the buffer containing 50 mM Tris–HCl, pH 7.5, 150 mM NaCl, 1 mM EDTA, 0.1% Nonidet P-40 (v/v), 1 mM PMSF, 1 mM DTT, 2 mg/mL Chymostatin, 2 mg/mL Leupeptin, 2 mg/mL Pepstatin, 2 mg/mL Aprotinin, 50 mM MG132, 50 mM MG115, 50 mM ALLN, 2 mM NaF, and 2 mM Na3VO4. About 10 µL GFP tagged ELF3 proteins (added with SDS loading buffer) were separated by 8% SDS-PAGE. Immunoblotting was performed using a 1:2,000 dilution of the primary anti-GFP antibody  or a 1:2,000 dilution of the primary anti-Actin . The 1:3,000 horseradish peroxidase (HRP)-linked anti-rabbit IgG was used as secondary antibodies. Experiments were repeated twice, with similar results observed on each occasion.Seeds of https://www.arabidopsis.org/) as follows: EARLY FLOWERING3 (ELF3): AT2G25930; phyB: AT2G18790; LUX ARRYTHMO (LUX): AT3G46640; EARLY FLOWERING4 (ELF4): AT2G40080; GI: AT1G22770; TIMING OF CAB EXPRESSION1 (TOC1): AT5G61380; PRR9: AT2G46790; PROTEIN PHOSPHATASE2A (PP2A): AT1G13320.Accession numbers are as described by TAIR (The following materials are available in the online version of this article. The cellular localization of ELF3 is light responsive. Light treatments do not influence the stability of ELF3. Equal ratios of RL and BL induce a wide range of responses. Monochromatic BL pulses induce foci formation independently of light intensity. The nuclear accumulation of ELF3 is not changed by 10 or 15 µmol RL pulses. Photoactivated phys are required for ELF3 nuclear and sub-nuclear accumulation. Genotyping primers. qPCR primers.kiac072_Supplementary_DataClick here for additional data file.