Strawberry shape uniformity is a complex trait, influenced by multiple genetic and environmental components. To complicate matters further, the phenotypic assessment of strawberry uniformity is confounded by the difficulty of quantifying geometric parameters ‘by eye’ and variation between assessors. An in-depth genetic analysis of strawberry uniformity has not been undertaken to date, due to the lack of accurate and objective data. Nonetheless, uniformity remains one of the most important fruit quality selection criteria for the development of a new variety. In this study, a 3D-imaging approach was developed to characterise berry shape uniformity. We show that circularity of the maximum circumference had the closest predictive relationship with the manual uniformity score. Combining five or six automated metrics provided the best predictive model, indicating that human assessment of uniformity is highly complex. Furthermore, visual assessment of strawberry fruit quality in a multi-parental QTL mapping population has allowed the identification of genetic components controlling uniformity. A “regular shape” QTL was identified and found to be associated with three uniformity metrics. The QTL was present across a wide array of germplasm, indicating a potential candidate for marker-assisted breeding, while the potential to implement genomic selection is explored. A greater understanding of berry uniformity has been achieved through the study of the relative impact of automated metrics on human perceived uniformity. Furthermore, the comprehensive definition of strawberry shape uniformity using 3D imaging tools has allowed precision phenotyping, which has improved the accuracy of trait quantification and unlocked the ability to accurately select for uniform berries. Fragaria × ananassa) are not true fruits. The red fleshy pseudocarp of a strawberry is formed from a swollen flower base or receptacle. The true fruits are, in fact, the achenes, which develop from a whorl of carpels and together form an aggregate-accessory fruit. The viability of both carpels and pollen has an important role in the resulting uniformity of berries1. Carpel position, density and viability dictate the shape, size and uniformity of a strawberry. Indeed, strawberry breeders have selected for high carpel densities in order to produce larger fruits1. Simple, classical studies, which remove all or part of the achenes from undeveloped pseudocarps has led to a cessation in the auxin “swelling signal” in the area beneath each achene and thus uneven fruit development2. In a similar fashion to achene removal, uneven pollination of the carpels, or absence of achene development, are the main causes of uneven pseudocarps3. Uneven successful pollination can be caused by damage to flowers through high temperature, frost or precipitation1. A late frost in spring could lead to carpel and other damage, resulting not only in malformation but also complete lack of strawberry development1. Strawberry flowers have a variable proportion of viable carpels and anthers between flower orders, both within a plant and also between different cultivars5. Indeed, primary fruit are more likely to be malformed due to the relatively lower quantities of viable anthers and pollen7.Strawberries  even allocation of viable carpels across the receptacle within the flower (2) ready access to pollen within flowers and (3) high fertility of carpels ensuring even successful pollination.In spite of the environmental factors known to influence uniformity, literature has shown that strawberry uniformity still has a large genetic component and can be improved through breeding12. Producing visually appealing strawberry fruit is one of the primary objectives in a strawberry breeding programme13. Shape uniformity is an essential trait of strawberry fruits due to the direct association with product quality and value14. Increasing the uniformity of berries can increase the proportion of marketable fruit as berry irregularity is one of the primary imperfections leading to culling and reduced marketable yield8.Strawberry is an important fruit crop with a global production of 9.25 million tonnes in 2017As there are no well-defined strawberry phenotyping guidelines for fruit uniformity, the current system used in the strawberry breeding programmes at NIAB EMR relies on visual assessments, which are subjective and laborious. Unlike morphological traits such as length, volume and colour, which can be accurately measured manually in a low-throughput manner, uniformity assessment is extremely subjective. As there is no quantitative method of generating phenotypic data for uniformity, the genetic determinants of strawberry uniformity are still unknown.15, apples16, watermelon17, cherries18 and mangos19. Basic shape traits such as length, width, aspect ratio and volume, and more sophisticated traits such as elliptic Fourier descriptors20 have been quantified and used to describe variation in fruit quality. 3D imaging has been successfully used for phenotyping the crop canopy22 and root architecture24, and a 3D strawberry phenotyping platform has been explored in our previous study25. With the 3D point cloud reconstructed based on the Structure from Motion (SfM) method26, basic size-related parameters have been measured in three dimensions allowing volume estimation with high accuracy27. Compared with shape and size evaluation, uniformity is a multidimensional trait, therefore it is not possible to quantify through 2D image analysis with a single viewing angle. The application of 3D image analysis for phenotyping the external qualities of fruit has not been sufficiently explored, and the basic, previously characterised, shape- and size-related parameters are not adequate for understanding uniformity.Computer vision has shown great potential to quantify external fruit quality and 2D imaging has been successfully implemented to measure the shape and size of fruits such as strawberries25 in order to define eight new external variables and investigate their importance on manual uniformity assessment. This method was applied to a multi-parental strawberry mapping population in order to quantify the genetic components underpinning strawberry uniformity.Here the application of a 3D phenotyping platform allows us to investigate the genetic basis of strawberry shape uniformity. The 3D image analysis software leverages the previously developed platform−1 (rate: 10 s every 45 min) using Vitex Vitafeed . The trial was located at NIAB EMR, Kent, UK (51° 17′ 24.202″ N 0° 26′50.918″ E). Two 150 m polytunnels with standard luminescence ‘3 year plastic’, 150 micron covers; external temperatures were: June 16(±4) °C, July 16(±4) °C, August 18(±4) °C, September 15(±4/3) °C and October 16(±3) °C. Pots were arranged in a randomised blocks design on the two central most gutters, pots were held in place using wire and cable ties. Due to the large scale of the experiment, replicate blocks were set up at intervals, plants were removed from the cold store on the following dates: block 1—29th May; block 2—20th June, block 3—2nd July, block 4—23rd July, block 5—1st August. A Natupol Koppert bumble bee hive was added into each polytunnel to assist even pollination. Where possible, three strawberries per plant were picked when ripe into egg boxes. The phenotyping took place over a single season in 2018. Picking for block one began on 11th July 2018 and picking for block 5 finished on 8th November 2018. Boxes were labelled with QR codes to assist tracking of genotypes. Strawberry shape uniformity was scored for individual berries on a scale from 1 (completely irregular and undulating fruit surface) to 9  with extensive training provided for all assessors. Strawberry shape was allocated into 9 categories: globose, globose-conic, conic, long-conic, bi-conic, conic-wedge, wedge, square and miscellaneous, strawberry shape categories were based on the definitions provided by strawberry breeders at NIAB EMR, some of these categories have been described before15. Manual uniformity and shape scores were recorded in the field book app 28, the QR scanning feature allowed quick access to the correct entry form.A multi-parental strawberry population was generated through crossing 26 diverse cultivars and breeding lines to create a population of 416 genotypes made up of 26 crosses each containing up to 16 individuals . Unlike the previous study, a webcam  was fitted at a height of 30 cm and horizontal distance of 25 cm away from the sample. QR codes on containers were scanned through the webcams allowing tracking of berries and automated labelling of image files. The imaging rig was placed inside a photography studio tent with constant LED illumination. The turntable rotated at a frequency of 50 s per full turn, and an image was captured every second. Six imaging platforms allowed concurrent imaging of replicate berries. The 3D reconstruction was implemented with Agisoft Photoscan , and in order to increase the accuracy and processing speed, all images were pre-processed by cropping to a smaller size (400 × 600 pixels). Background subtraction was achieved through arbitrary colour thresholding. The image processing software for webcam control and automated image pre-processing were written in C++ utilising the OpenCV Library29.The 3D imaging platform was a modified version of that developed by He et al.z axis. After rotation, the arbitrary threshold was applied again on the hue channel in order to segment the strawberry body and blue holder from the whole point cloud. The height of the holder was obtained by calculating the difference between the maximum and minimum values of the holder point cloud on the z axis. As the original coordinate system generated by SfM method has an arbitrary scale, each point cloud model needed to be standardised by the height of the holder, so that the sizes of all point clouds are comparable.In the pre-processing stage Fig. , each poEight uniformity-related traits were calculated from the point cloud data of the strawberry body after pre-processing. These are:Coefficient of variation (CV) of side view areas (CV_A) and the ratio between maximum and minimum side view area (Max_A/Min_A). All side views should be identical in a perfectly uniform strawberry. In order to eliminate the heterogeneity introduced from the calyx and the holder, only the points within the middle 50% of the body height of each point cloud were retained for analysis . A convex hull was fitted to each projected image and the contour area was calculated. For area metrics, two traits were obtained; the CV of side view areas (CV_A) and the ratio between the maximum and minimum area (Max_A/Min_A). An ideal uniform strawberry will have a value of zero for CV_A and one for Max_A/Min_A.sis Fig. . In ordeCV of principal orientations (CV_D). The major eigenvector indicating the main orientation was calculated by principal component analysis (PCA) for all 100 side view projected images, and the heterogeneity of the orientations of the projected images was quantified by calculating the CV of angles of the main orientation. Like CV_A, a perfectly uniform strawberry will have a value of zero for CV_D.Aspect ratio of the minimum bounding box (L/W). A lateral slice image was obtained by identifying the intersection between the plane in parallel with the x–y plane and point cloud . Visually, the circularities of the contours in horizontal slice images are high if the strawberry is uniform. Circularity (CIR) was calculated as previously described30:A and p are the area and perimeter of the convex hull respectively. For each point cloud, the circularity was calculated for the slice image with largest contour area.Straightness of centre axis (STR). The coordinates of the centroids for each horizontal slice image can be located by calculating the moment of the contour. The centroids can be connected as a straight line for a uniform strawberry. The centroids were calculated for all the slice images within the middle 80% of the body height, and the straightness of the central axis was characterised by:N (N = 80) is the number of slice images used for the analysis, di is the Euclidean distance between neighbouring slice images, and D is the Euclidean distance between the centroids of the top and bottom slice images.CV of curvature and the ratio between maximum and minimum curvatures (Max_C/Min_C). The principal curvature can be calculated for each point in the point cloud, which describes how much the curve deviates from a straight line at this point. It can be imagined that the 3D curve can be sliced orthogonally around the direction of normal in to 2D curve, and the maximum curvature k1 and minimum curvatures k2 are the two principal curvatures for the 3D curve31. The average curvature k, which is defined as the mean value of the magnitudes of principal curvatures in the two main directions was applied to quantify the curvature for a given point. As the curvature measurement is sensitive to noise, the point cloud surface of strawberry body was first smoothed by using Moving Least Squares (MLS) method32, which could reconstruct a smooth surface from the noisy point cloud. Sixteen patches of the points were selected evenly from the points forming the largest slice in parallel with the x–y plane  and the Bayesian Information Criterion (BIC). In general, a better model fit generates lower values for both AIC and BIC34. In order to identify the optimal variable combination related to manual assessment, stepwise AIC and BIC methods were applied35. The most significant variable was identified by comparing the criterion values of all models. Other variables were added successively and retained if the model fit was improved.Statistical analysis was performed using R (version 3.5.1) and the Genstat statistical package . Differences in uniformity traits within each shape groups were distinguished using ANOVA and Tukey post-hoc test. Pearson coefficients of correlation were calculated between all proposed uniformity-related traits. As the group labels are ordinal dependent variables, ordinal regression was used to evaluate the performances of all traits® IStraw35 384HT array36 (i35k). Marker curation was undertaken using the Affymetrix analysis power tools software and the SNPpolisher R package, parameters were 0.8 for minimum dish quality control, 96% minimum call rate and a call threshold of 0.01. A total of 18,790 markers were found to segregate across the multiparental population. Crosslink was used to generate linkage maps—a programme developed specifically for polyploid plant species37. A total of 34,949 markers were used to generate the consensus map. The map orders from 5 populations were combined to make the consensus map as detailed in the study of Cockerton et al.38Fragaria × ananassa chromosome number is denoted by 1–7 and the sub-genome number is represented by A–D as specified in ref. 39.DNA was extracted for each genotype from unopened leaflets using the Qiagen DNeasy plant mini extraction kit. Genotyping was conducted using the AxiomH2) was calculated using mixed model output parameters generated through the asreml function in the statistical package ASReml-R version 4.1.0.10640, whereby H2 = VG/VP, where VG is the genotype variance and VP is the total phenotypic variance. Narrow sense heritability was calculated using the vpredict function in ASReml-R h2 = VA/VP, where VA is the additive genotypic variance.The best linear unbiased estimate (BLUE) was calculated for all genotypes in order to correct for the influence of assessor, data and block, estimated scores were weighted by replicate number. Linear mixed-effects models were generated for each phenotypic trait with and without covariates. Grand scores for each genotype were calculated using mixed models to account for significant covariates. Broad-sense heritability (41. Genetic best linear unbiased prediction (GBLUP) was performed using the statistical package ASReml-R version 4.1.0.10640. 75% of genotypes were used as the ‘training’ population to predict the phenotype of the remaining 25% of genotypes used as the ‘test’ population, as advised by Erbe et al.42. Phenotype was weighted by the number of replicates. Predictive ability (PA) was defined by the correlation between the predicted and actual phenotypic score for the test population over 10 cross validation events, thus allowing us to determine the PA of the model. Prediction accuracy (PAC) was calculated by PAC = PA * √H2 as detailed in Gezan et al.43.A genetic additive relationship matrix was calculated for the population using r package snpReady44. A permutation test determined the significance threshold45. A two-step QTL analysis was implemented: the selection of cofactors was achieved through Simple Interval Mapping (SIM) proceeded by a multi-QTL model search using composite interval mapping (CIM)47. As a multi-parent population CIM works on parent relationships. Therefore the ‘FA2654’ × ‘FA8752’ cross was removed as it is not directly related through the parental cultivar network. All other crosses were interrelated and formed a single network . The Pearson’s linear correlation coefficients were calculated between all traits, and strong correlations were found amongst Max_A/Min_A, L/W and CIR , L/W and CIR scores indicating bi-conic strawberries have consistently circular horizontal transects at the middle point. Whereas for curvature uniformity measures (CV_C & Max_C/Min_C), globose fruit are the most uniform and miscellaneous fruit the least (data not shown). Both the manual uniformity score and CIR could discriminate miscellaneous shapes from the other shape categories Fig. .Fig. 4CiPrediction accuracy values varied between 0.05 and 0.32  of strawberries showed the best predictive ability for manual uniformity scores based on the ordinal regression model fit, when studying individual variables alone. A completely misshapen fruit with a severely undulating fruit surface will score a value of 1 for manual assessments, and these completely misshapen fruits were the easiest category to identify by eye, as they were clearly distinct from regular shapes. A low CIR value appears to represent the undulating misshapen and “miscellaneous” fruit Fig. . Miscell1 may help to further elucidate the mechanism of uniformity segregating in the multiparental population.One of the QTL represented by the focal marker AX.166521293 on chromosome 2B was found to be associated with CIR, this QTL was also associated with CV_C and Max_A/Min_A, each of which were found in the best fitting model used to describe the manual uniformity score. The focal SNP AX.166521293 was found to be present and significant in six progenitors and had an effect size of 10.96% on Min_A/Max_A. Therefore, this marker is a potential candidate for marker-assisted breeding in selection against completely misshapen and irregular strawberries. Furthermore, this work has highlighted a region of interest for further study to pinpoint the causative allele associated with reduced uniformity. Dissecting the contribution of genetic and environmental components believed to underpin strawberry uniformity; susceptibility to heat stress, carpel and pollen viability, achene position, size and distribution50. However, this study does not aim to identify the optimal feature combination to develop prediction model related to manual uniformity evaluation, but develop a new image-based quantification to replace the manual scale, because the ground-truth data are subjective and as such any large bias can reduce the robustness of model development. Moreover, the manual scale cannot be considered a comprehensive assessment as the parameter STR cannot be visually evaluated by eye. However, it must be said that if a trait cannot be detected by the human eye, then it is not a valuable trait for a strawberry breeder to select upon.The trait L/W shows little improvement on the overall combined trait model fit due to the high correlation with other traits including Max_A/Min_A and CIR, but it was still a good predictor of uniformity based on the model fit when studying individual variables alone. AIC- and BIC-based stepwise feature selection showed disagreement on the selection of the STR parameter. The difference between calculating AIC and BIC is that AIC does not account for the sample size, so when sample number is large, BIC applies larger penalty for complex models and leads to a simpler modelThe 3D point cloud analysis software is independent of the imaging acquisition system, and the uniformity-related traits can be extracted automatically in a high-throughput manner. However, the imaging collection throughput was 50 s per fruit and the 3D reconstruction has to be performed separately, which limits use to pre-breeding experiments. Due to the occlusion from the viewing angles, the strawberry nose cannot be fully reconstructed especially for globose-shaped fruit, which decreases the accuracy of STR measurements and also limits the study on automated shape classification. To increase the throughput and accuracy of 3D phenotyping, it is necessary to further develop the hardware with multiple cameras to allow more viewing angles or a structured light-based imaging system with a robotic arm, and also integrate the hardware driver with 3D reconstruction software. The current point cloud image analysis software was able to characterise many key external traits, which are important for strawberry breeding, however, the measurement of other parameters such as achene density must be investigated through an improved phenotyping platform in future studies.52, however, there are currently no papers, which report QTL associated with strawberry uniformity. Here, we provide a phenotyping platform, which has facilitated the assessment of the genetic components underlying strawberry uniformity for the first time. The use of a multi-parental population has allowed the study of a diverse set of germplasm and has ensured that resulting QTL have a greater relevance for breeders when compared to alleles identified in bi-parental studies. Furthermore, implementation of an additive genomic selection model indicates that strawberry shape uniformity—particularly CIR may be somewhat improved using a genomic selection approach. Overall, 20 of the QTL were found to have an effect on uniformity in more than one of the 26 progenitors indicating that there has been limited linkage decay between the causal allele and marker, and that the relationships have been maintained across generations. Furthermore, the QTL on chromosome 2B was observed three times across different uniformity traits, such traits are only partially correlated and thus describe discrete components, as such this allele can be seen to have a major role in uniformity.Papers detailing strawberry fruit quality QTL report genetic alleles associated with multiple fruit quality traits including fresh weight, metabolites, external colour and firmnessFragaria vesca and may act as a surrogate model for the cultivated octoploid strawberry Fragaria × ananassa. In F. vesca, fruit shape is primarily controlled by phytohormones54. Auxin increases the width of fruit and by contrast gibberellic acid (GA) increases the length of a strawberry, whereas abscisic acid (ABA) down regulates both auxin and GA and thus reduces fruit expansion54. GA-deficient F. vesca mutants were found to have a “short” or globose berry shape, which, through the application of GA to the berry, could be restored to result in a “long” or long-conic fruit shape54.Unlike uniformity the mechanism controlling fruit shape has been studied extensively in the wild strawberry; It is clear that breeders wish to select for greater berry uniformity however the confounding relationship between shape and uniformity must also be considered. For example, square, wedge and wedge-conic strawberries may have high 2D symmetry but not 3D symmetry. UK breeders primarily aim to select for conic or long-conic fruit, whereas globose, square, wedge and miscellaneous berries are classified as undesirable and bi-conic, globose-conic and conic-wedge fruit are seen as acceptable shapes . Here we provide an objective measure (CIR) that can be used to discriminate the least desirable berries—miscellaneous or misshapen berries and select for regular fruit shapes.56.Broad-sense heritability scores were between 36.6 and 64.1 for automated uniformity metrics, whereas narrow sense heritability varied between 2.1 and 51.4, as such, some traits were found to have a higher heritability than manual uniformity. These values indicate the proportion of variation segregating in the study population, however improvement in the heritability may also be caused, in part, by more accurate phenotypic measurements. In particular, high heritability was observed for CV_D, which indicates the angle of a strawberry related to whorl of carpels (Table In this study, we detail a comprehensive dissection of the traits underlying strawberry uniformity and show that the visual perception of a strawberry can be represented by five metrics. The generation of an objective measure of uniformity has allowed the assessments of genetic components in a multi-parental breeding population. We show uniformity has a strong genetic component that can be improved by breeding and identify genetic components controlling uniformity that are present across a wide array of germplasm.Defining Strawberry Shape Uniformity using 3D Imaging and Genetic Mapping


The objective of the study was to investigate the possibility of utilizing an early litter size trait as an indirect selection trait for longevity and to estimate genetic parameters between sow stayability and litter size at different parities using a linear-threshold model for longevity in Thai Large White (LW) and Landrace (LR) populations.The data included litter size at first, second, and third parities  and sow stayability from first to fourth farrowings (STAY14). The data was obtained from 10,794 LR and 9,475 LW sows. Genetic parameters were estimated using the multiple-trait animal model. A linear-threshold model was used in which NBA1, NBA2, and NBA3 were continuous traits, while STAY14 was considered a binary trait.Heritabilities for litter size were low and ranged from 0.01 to 0.06 for both LR and LW breeds. Similarly, heritabilities for stayability were low for both breeds. Genetic associations between litter size and stayability ranged from 0.43 to 0.65 for LR populations and 0.12 to 0.55 for LW populations. The genetic correlation between NBA1 and STAY14 was moderate and in a favorable direction for both LR and LW breeds .A linear-threshold model could be utilized to analyze litter size and sow stayability traits. Furthermore, selection for litter size at first parity, which was the genetic trait correlated with longevity, is possible when one attempts to improve lifetime productivity in Thai swine populations. Sow longevity is a key productivity indicator for commercial pig breeding herds, involving sow productivity and the efficiency and profitability of the swine operation. Generally, the annual culling rate of sows is approximately 50% in commercial herds, of which 15% to 20% of culled sows can produce only one litter of piglets  suggesteThe genetic evaluation of animals for longevity traits presents a challenge in animal breeding. Because these traits are not normally distributed and the functional trait is recorded at the end of the female’s production life, they can be difficult to properly analyze when one is estimating genetic parameters and conducting genetic evaluations. In some countries, traditional methods of genetic evaluations based on a linear model are used for these traits; however, they have been reported as being unsuitable for nonlinear data . MeanwhiIdeally, the lifetime traits would be evaluated in the commercial female . However, in most cases, the commercial female does not have a pedigree because pooled semen is used to generate replacement gilts. Without a pedigree, the genetic parameters cannot be estimated. In this situation, the best population in which to evaluate lifetime traits consists of purebred animals from the multiplication phase of production. It is important to obtain accurate estimates for the genetic association between litter size and longevity traits to improve the productivity of pigs using traditional and modern selection methods within the breeding scheme. Therefore, the objective of the study was to investigate the possibility of utilizing an early litter size trait as an indirect selection trait for longevity and to estimate genetic parameters between sow stayability and litter size at different parities using a LTM for longevity in Thai LW and LR populations.No Institutional Animal Care and Use Committee (IACUC) approval was needed because the data used for this study was obtained from an existing database. However, details about gilt and sow management have been provided so that readers have this information with respect to typical Thai environmental conditions.Field data was accumulated from three farms in a large commercial production system located in the central and northeastern parts of Thailand. Data from the Sow Tracker Version 3.4.2   reproduc2, which met or exceeded National Pork Board [ad libitum (full feeding) from the day of farrowing on. All gestation and farrowing diets met or exceeded National Research Council [Replacement gilts were selected based on structural body conformation   and the rk Board  recommenrk Board . Gilts w Council  recommenGenetic parameters were estimated by the multiple-trait animal model. A LTM was used in which NBA1, NBA2, and NBA3 were continuous traits and STAY14 was considered a binary trait. The following linear-threshold animal model was used in the analysis:y is the vector of observations of the actual observed value for NBA1, NBA2, and NBA3, and the unobservable liability for STAY14; β is the vector of fixed effects ; a is the vector containing random additive genetic effects for the animal; e is the vector of random residual effects; and X and Z are the correspondent incidence matrix of the fixed effects and additive genetic, respectively. The distribution of a and e was assumed to be normal, with zero means and variances following a~N and e~N, respectively, where A is the numerator relationship matrix, G and R are the additive genetic and residual variance-covariance matrix across four traits, I is an identity matrix, and ⊗ is the Kronecker product operator. For binary data, the parameters were not identifiable. Therefore, the residual variance was fixed to 1 and the threshold was set to 0, which is a standard assumption in categorical data analysis [where analysis .The (co)variance components and genetic parameters were estimated utilizing Bayesian inference methods that were implemented using the program THRGIBBS1F90 , with a The data description for the four traits evaluated in the present study is presented in Heritability and variance component estimates based on posterior means are shown in The heritability estimates for the stayability trait in the present study were low: 0.05±0.01 in LR sows and 0.05±0.01 in LW sows. Additionally, that been reported the heritabilities for stayability tend to increase with parity up to Parity 3 . In the Posterior mean estimates for genetic and phenotypic correlations (±standard error) between litter size at different parities are presented in The genetic and phenotypic correlation estimates between litter size and the stayability trait are shown in However, Tholen et al  reportedIn conclusion, a LTM could be utilized to analyze litter size and sow stayability traits. Selection for litter size at first parity as a potential early indicator trait, which was the genetic trait correlated with longevity, is possible when one is attempting to improve lifetime productivity. Therefore, efforts to improve sow productivity through a genetic evaluation program in a commercial pork system should include both litter size and sow longevity.


MaizeCUBIC is a free database that describes genomic variations, gene expression, phenotypes and quantitative trait locus (QTLs) for a maize CUBIC population (24 founders and 1404 inbred offspring). The database not only includes information for over 14M single nucleotide polymorphism (SNPs) and 43K indels previously identified but also contains 660K structure variations (SVs) and 600M novel sequences newly identified in the present study, which represents a comprehensive high-density variant map for a diverse population. Based on these genomic variations, the database would demonstrate the mosaic structure for each progeny, reflecting a high-resolution reshuffle across parental genomes. A total of 23 agronomic traits measured on parents and progeny in five locations, where are representative of the maize main growing regions in China, were also included in the database. To further explore the genotype–phenotype relationships, two different methods of genome-wide association studies (GWAS) were employed for dissecting the genetic architecture of 23 agronomic traits. Additionally, the Basic Local Alignment Search Tool and primer design tools are developed to promote follow-up analysis and experimental verification. All the original data and corresponding analytical results can be accessed through user-friendly online queries and web interface dynamic visualization, as well as downloadable files. These data and tools provide valuable resources on genetic and genomic studies of maize and other crops. Zea mays) is one of the most diverse crops containing tremendous variation, making it a perfect model for both genetic and genomic studies in plants. Next-generation sequencing technology (NGS) is a powerful and cost-effective approach for discovering genetic variation and rapidly becoming a desirable choice for population-level genomic studies. The first-generation haplotype map of maize was constructed in 2009, millions of SNP obtained by low-coverage sequencing among 27 diverse maize inbred lines  integrates multi-omics data sets of 527 maize elite inbred lines, including genomic, transcriptomic, metabolic and phenotypic information  and the development cycle is usually long .However, the analytical tools developed for the population genomics research in current databases are left behind, especially the lack of tools for visualization of genetic mapping results and further follow-up analyses. Moreover, most databases mentioned above are designed to natural un-related panels, in which individuals with unknown kinship are selected. Such populations always have obvious population structure and low-frequency functional alleles, which limit its efficacy in the genetic mapping of complex agronomic traits. Geneticists and breeders attempt to use diverse genetic mating designs in a controllable hybridization to develop large-scale synthetic populations that are more efficient for genetic mapping. RIL populations derived from bi-parental crosses, such as the B73 x Mo17 (IBM) population , are traCompared to them, data in MaizeCUBIC were collected from a well-designed large maize population named complete-diallel plus unbalanced breeding-derived inter-cross (CUBIC). A total of 1404 offspring inbreds are derived from two rounds of complete-diallel design among 24 founders, following with six generations of open pollination and another six generations of selfing . CUBIC phttps://github.com/Tfrain/Cubic). The data in the MaizeCUBIC database contains published variations of the CUBIC population and newly identified structure variations (SVs) and novel sequences of the population, the integrated processes are roughly shown in MaizeCUBIC is developed using the framework Struts 2 , boostraThe CUBIC population consists of 1404 progenies descended from 24 Chinese elite inbred lines . The genhttp://plants.ensembl.org) using BWA-MEM  (http://www.ebi.ac.uk) and B73 plastid genome sequences (v3.25) using basic local alignment search tool (BLAST) , including unmapped reads from both parents and progeny, were used to generate novel non-B73 sequences. Due to the large number of unmapped reads, we divided these sequences into several batches when using MaSuRCA v3.13,)  software(v4.6.5)  software5, includ (BLAST) , 148 916,  softwaThe tools in our database can be grouped into three general classes according to its related information: variation map , GWAS catalog  and variation application .The mosaic structure for progenies in population was successfully reconstructed using a modified hidden Markov model . It is aGenome browser is a utility enabling researchers to easily browse sequences, genes and genetic variations in various regions. It was built on one of the most widely used genome visualization tools JBrowse , a JavaSIn our database, each SNP or indel is labeled with a unique identifier . The first string ‘chr’ represents chromosome and the second string indicates the polymorphic type . The subsequent number is the chromosome coordinate of a variation. ‘chr1.s_1234’ means the SNP with the coordinate of 1234 bp at chromosome 1. In this interface, users can fetch the genotypes directly through entering the variation ID . Also, iA subset of 391 progeny was randomly selected from the CUBIC population for RNA-sequencing, and the gene expression quantification and eQTL mapping results were collected. The expression data with Reads Per Kilobase per Million mapped reads (RPKM) normalization can be searched and downloaded when a list of samples and gene symbols provided through this tool. Moreover, the RPKM-normalized gene expression data and the eQTL mapping results for the 391 samples are also provided on the DOWNLOAD page.Single-variant-based GWAS and haplotype-based GWAS were used for dissecting genetic mechanisms of over 23 agronomic and yield traits in the population . We inteP < 2.79E-8) for user’s interested traits can be queried by limiting genomic coordinate or gene ID  have been crossed to another diverse 30 paternal testers, and over 8000 F1 combinations were obtained and phenotyped across the same five environments. The CUBIC-based F1 hybrid data provide large-scale resources for exploring the genetic of heterosis and facilitate the agricultural data-driven plant breeding design via state-of-the-art technologies such as genomic selection, genome editing and machine learning. All these data will be integrated into this database in the near future. Therefore, the MaizeCUBIC will continuously provide valuable resources for maize community as well as researchers in many other fields.Jianxiao Liu and Jianbing Yan designed and supervised this study. Jingyun Luo, Haijun Liu, Yingjie Xiao and Xiaqing Wang collected data and performed most of the data analysis. Jingyun Luo, Chengcheng Wei and Shikun Cheng worked on the database construction. Jingyun Luo, Jianbing Yan and Jianxiao Liu wrote the manuscript. All authors critically read and approved the manuscript.The authors declare no competing financial interests.Database URL:http://cubicmaize.hzau.edu.cnPaper_baaa044Click here for additional data file.


Reproductive performance is critical for efficient swine production. Recent results indicated that vulva size (VS) may be predictive of reproductive performance in sows. Study objectives were to estimate genetic parameters, identify genomic regions associated, and estimate genomic prediction accuracies (GPA) for VS traits.Sus scrofa chromosome (SSC) 2 (154–157 Mb), 7 (107–110 Mb), 8 (4–6 Mb), and 10 (8–19 Mb). For Yorkshire, genomic regions on SSC 1 (87–91 and 282–287 Mb) and 5 (67 Mb) were identified. All regions explained at least 3.4% of the genetic variance. Accuracies of genomic prediction were moderate in Landrace, ranging from 0.30 (VH) to 0.61 (VA), and lower for Yorkshire, with 0.07 (VW) to 0.11 (VH). Between-breed and multi-breed genomic prediction accuracies were low.Heritability estimates of VS traits, vulva area (VA), height (VH), and width (VW) measurements, were moderately to highly heritable in Yorkshire, with 0.46 ± 0.10, 0.55 ± 0.10, 0.31 ± 0.09, respectively, whereas these estimates were low to moderate in Landrace, with 0.16 ± 0.09, 0.24 ± 0.11, and 0.08 ± 0.06, respectively. Genetic correlations within VS traits were very high for both breeds, with the lowest of 0.67 ± 0.29 for VH and VW for Landrace. Genome-wide association studies (GWAS) for Landrace, reveled genomic region associated with VS traits on Our findings suggest that VS traits are heritable in Landrace and Yorkshire gilts. Genomic analyses show that major QTL control these traits, and they differ between breed. Genomic information can be used to increase genetic gains for these traits in gilts. Additional research must be done to validate the GWAS and genomic prediction results reported in our study. Female reproductive traits, such as the number of piglets born, are well known to have low heritability, with estimates of around 0.10 . FinallyRecently, Romoser et al.  suggesteg = 0.07) and age at first farrowing (rg = 0.24). In addition, these authors reported a negative genetic correlation between VW and total number of piglets born (rg = − 0.33). Although it seems that there is clear genetic variation in VS traits in pigs, these correlations were weak and incongruent. Finally, the age of gilts when VS was measurement in Knauer et al.  and SSC 10 [8–19 Mb] for VA in Landrace). It is important to note that, in all analyses, all markers were used during training, while taking into consideration the marker selection process of BayesB. However, only those within their respective QTL were used for prediction. Therefore, all marker effect estimates were conditional on the whole genome. This is important to avoid bias in the marker estimates. These results are in accordance with other studies that estimated marker effects using all markers and then predicted breeding values based on QTL SNPs only , 68. TheIn this study we characterized the genetic and genomic bases of VS traits at approximately 23 weeks of age in purebred gilts. Results suggest that VS traits are lowly to highly heritable in pigs, which may be different at different ages during gilt development. In general, VS traits were less heritable in Landrace compared to Yorkshire pigs. For both breeds, VS traits were highly genetically correlated, indicating that selection for one VS trait would result in major changes in the other VS traits. Several genomic regions associated with VS traits were identified. Common QTL were found for all VS traits, but these differ between breeds. This could indicate and corroborate the fact that these traits are all genetically similar. In addition, relevant candidate genes related to characteristics of development of the reproductive organs, reproduction and productive characteristics are located within the identified QTL in this study, supporting our findings. Results show that genomic selection for VS traits is possible in purebred pigs although results for Yorkshire show only limited predictive ability of using markers. In general, genomic prediction within breed is advised, and using only SNPs within QTL regions showed greater accuracies for all traits. Our findings suggest that genomic information can be used to increase genetic gains for these traits in gilts. Additional research must be done to validate the GWAS and genomic prediction results reported in our study, and also to evaluate the use VS traits as indicator traits for reproductive performance in pigs.Animal Care and Use Committee approval was not obtained for this study because analyses were performed on existing data obtained as part of routine data recording in a commercial breeding program. All farms in this study are operating in line with the regulations on protection of animals.A total of 475 Landrace and 708 Yorkshire gilts from two lines for each breed from a commercial farm  were used for this study. All animals were reared under the same controlled conditions. After the completion of this study, all animals remained in the herd for commercial production purposes. A 19-generation pedigree including 5749 individuals was available for these animals. The estimated mean inbreeding of animals in the pedigree, removing animals without any inbreeding, was of 2.8 and 4.5% for Landrace and Yorkshire, respectively.At 23.8 (SD = 0.9) weeks of age, all animals had VS measurements collected using an Ultra Tech digital calipers , following the same measurement procedures described by Graves et al.  and RomoDNA was isolated from tail or ear tissue using the ReliaPrep 96/KingFisher tissue kits . Individuals were genotyped using the PorcineSNP60 BeadChip. Prior to statistical analysis, genotyping quality was assessed, and samples/SNPs were removed. Genotypes with GenCall scores below 0.20 were replaced with the average genotype of the SNP within breed. Markers with minor allele frequencies below 1% were removed, and individual samples and SNPs with a call rate below 0.8 were excluded. The number of SNPs that remained in the data set was 37,155 SNPs and no animals were removed.Yijkl is the observed phenotype of individual k at the ith level of Li and the jth level of CGj; Li is the ith level of the fixed-effect of line; CGj is the jth level of the fixed-effect of contemporary group; BWk is the linear covariate of body weight of the kth animal; ak is the animal random effect of the kth animal, assuming A is the additive numerator relationship matrix based on the pedigree; dl is the random common-environment effect of the lth litter, assuming I is the identity matrix; and eijkl is the random error term associated with Yijkl, assuming Genetic parameters for VS traits were estimated for each breed separately using the following animal model:L, CG, and BW (covariate). Bayesian genomic prediction methods [In order to identify associations between genetic markers and VS traits, genome-wide association studies (GWAS) were performed by breed. In addition to fitting SNP effects as random effects in a multi-locus model, the model included the fixed effects of  methods  were use methods .https://www.animalgenome.org/jbrowse/). QTL regions explaining at least 4% TGVM were discussed in this study, including the identification of candidate genes within these QTL.Putative candidate genes within identified QTL regions and in the neighboring upstream and downstream 3-Mb regions were identified based on the Sscrofa10.2 genome assembly, using the JBrowse tool from the National Animal Genome Research Program  while the remaining fold was used as the validation dataset. This was repeated until all n folds were used for validation. For within breed, 4- and 6-fold cross-validation were used for Landrace and Yorkshire, respectively. For multi-breed, a total of 10 folds (the sum of the within-breed folds) were used for cross-validation. In this strategy, the validation dataset included data on only one breed, whereas SNP effects were trained using both breeds. These folds were created based on sire families. In each fold, daughters from 5 randomly selected sires were grouped in order to increase the relationship within folds. The average (SD) number of animals in each fold was 119 (8.7) and 118 (12.3) for Landrace and Yorkshire, respectively.Genomic predictions of VS traits were performed using BayesB, BayesC, and BayesC0 (π = 0) using the same model described for GWAS. GPA were estimated using three training and validation strategies: (1) within breed, (2) between-breed, and (3) multi-breed. For within and multi-breed strategies, an An additional strategy was used for the within-breed approach based on the GWAS results. For this, genomic prediction was performed using different SNP sets. First, GPAs were calculated using all SNPs were used (referred as to ALL) using BayesB. Then, based on the results using ALL, SNPs sets were created based on QTL regions identified for each trait and breed. Therefore, depending on the trait and breed, different number of SNP sets were used. With this, GPAs were calculated using only SNP estimates and genotypes from each of these QTL, separately. Finally, GPAs were calculated using SNP estimates and genotypes from markers outside these QTL and referred to as REST. For REST, SNPs within 3 Mb from the limits of the QTL were removed to avoid SNPs in some degree of LD with the QTL to capture any unwanted effects .GPA was calculated differently depending on the strategy. For the between-breed analysis, this was calculated as:y∗); and h2 is the heritability of the trait in the breed used for validation.Where For the within- and multi-breed strategies, GPA was calculated as the weighted average across folds as:ri is the correlation of GEBV with y∗ of the ith fold, ni is the number of animals in the ith fold, and h2 is the trait heritability estimate of the breed used in the validation dataset.Where


The development of rice cultivars with desirable traits is essential. The decision-making is a crucial step in rice breeding programs. Breeders can make efficient and pragmatic decisions if an organized pedigree visualization platform is available for the accessions and cultivars in rice breeding germplasm. In the present study, the available data of all the rice varieties released by Rice Research and Development Institute, Sri Lanka, and the related landraces and genotypes were arranged in Pedimap, a pedigree visualization tool. Pedimap can showcase pedigree relationships, phenotypic, and molecular data. The identity by descent probabilities were calculated using FlexQTL software and included in the Pedimap database. The parentage selection based on the variations of phenotypic traits, selection of marker alleles for molecular breeding, and detection of the founders of genetic effects can be swiftly conducted using Pedimap. However, the power of harnessing the value of Pedimap for making breeding decisions relies on the availability of data for the traits, markers, and genomic sequences. Thus, it is imperative to characterize the breeding germplasms using standard phenomic and genomic characterization procedures such as the assessment of before organized into Pedimap. Thereby, the worldwide breeding programs can benefit from each other to produce improved varieties to meet global challenges. Half of the world population consumes rice as the staple food2. Currently, the demand for rice is rapidly increasing due to the growth of the human population3. However, the current rice production cannot meet the increasing demand causing severe food security issues. The biotic and abiotic stresses also exert a negative influence on rice production4. The rice farming is also a way of living for many people, especially in numerous Asian countries5. At present, 1.8 million Sri Lankan families engage in rice farming over 870,000 hectares6. The annual rice production in Sri Lanka is approximately 2.3 million metric tons (MT), which is insufficient to fulfill the domestic rice demand of 3.0 million7. Hence, the Sri Lankan government spends about USD 400 million to import rice annually8.Rice is one of the major crops in the world, with an annual production over 700 million metric tons11, adverse soil conditions such as salinity12, and pest and disease attacks13. The biotic and abiotic stresses in rice farming can be controlled using numerous agronomic practices such as irrigation, drainage, fertilization, and the application of pesticides. However, the rate of success of the controlling methods is limited13 due to the unpredictable nature of climate change, soil degradation, variations in pest dynamics, and development of pest resistance14. Therefore, breeding is considered as the most successful strategy to produce high yielding and stress-resilient rice varieties15. The improved rice genotypes can also contain the traits for higher consumer preference and organic farming16. In the past, the rice varietal improvement was conducted with classical breeding techniques, which are tedious, lengthy, and less feasible in cases such as breeding for pest resistance and submergence tolerance. However, the marker-assisted breeding (MAB) is employed in modern breeding programs to introgress valuable genetic loci from landraces and traditional varieties18 and the desirable haplotypes of Quantitative Trait Loci (QTL) to the improved rice varieties21.The rice production is mainly affected by drought and irregular rainfall patterns caused by climate change22. In the decisioning process, initially, the market trends based on consumer and other stakeholder preferences must be recognized23. Subsequently, the novelty and the uniqueness of the breeding objective must be assessed before the execution of the breeding program22.The decision-making process in a breeding program is crucial for successful outcomes. The formulation of decisions before breeding is a multi-step process that consists of the identification of breeding priorities, determination of the genetics and estimated breeding values (EBV) of target traits, and employment of pre-breeding methods if required. The economic and technical feasibility, number of parents for crosses, number of selfing and outcrossing cycles, length of the breeding program/cycles, and identification of the selection methods must also be assessed24. The parental selection depends on the number of prioritized traits for breeding. When multiple characteristics are to be introgressed, the breeders require a prioritized order of parents for stepwise crossing and selection26. The decision-making process in breeding is entirely based on the available information on phenotypes, genotypes, pedigree, EBVs of key traits, available budget, field and greenhouse space, desired time-to-market, etc. Although the data for decision-making for breeding are indispensable, haphazardly collected information would provide less value to the breeders. In many conventional breeding programs, most of the data are recorded in field notebooks and stored in the breeding stations, while very little information is available as computerized databases. If an organized database containing all the essential information for the rice varieties released and the parental genotypes used in breeding, the decisions can be easily made.The selection of suitable varieties or individual plants as parents and the determination of the selection methods are the two most critical aspects in planning breeding programs27. Pedimap is a pedigree visualization software. The data needed can be imported to Pedimap from FlexQTL, or with some custom script from any other database program. Pedimap is used by many contemporary plant genetics and breeding programs worldwide. As stated in Voorips et al.28, Pedimap can be used to record and utilize breeding history. Pedimap illustrates the available phenotypic and genetic data through pedigrees. All the information, including parentage, qualitative and quantitative data, marker alleles/genotypes, and the calculated identity-by-descent (IBD) probabilities can be presented in Pedimap. Currently, breeders prefer to use pedigree visualization tools like Pedimap since it allows them to access the large pool of genetic and phenotypic data quickly and generate pedigrees that are essential in making breeding decisions.The construction of a database with all the necessary information from varieties and their parents promotes the capacity of data sharing, mining, visualization, and retrievalIn Sri Lanka, Rice Research and Development Institute (RRDI) is the sole organization conducting the rice breeding programs for the national needs. Therefore, in the present study, we report an attempt to organize the information of the released varieties and the parental genotypes of RRDI breeding programs as a Pedimap based database, which is a valuable step to take accurate breeding decisions and speed up the process of releasing novel varieties.Yala and Maha seasons of Sri Lanka, two main rice growing seasons, based on the two different seasons of monsoonal rains. Yala season is generally drier29), plant height, basal leaf sheath color, and additional color patterns, recommended type of the land, level of phosphorus deficiency tolerance, amount of brown rice recovery, milling recovery, head rice recovery, amylose content, gelatinization temperature, the weight of 1,000 grains, shape of the grain, pericarp color, the weight of a kg, the color of the buff coat and resistance/susceptibility to pests and diseases; brown planthopper (BPH), bacterial leaf blight and rice blast disease were recorded under phenotypic data . The male and female parents and the order of crosses were taken under pedigree history. The average yield of the rice plants, the maturity period in different growing seasons (A Pedimap input data file is created in MS Excel 2019), and the data file is exported as a tab-delimited text (.txt) file . FlexQTL can also generate a complete Pedimap input data file.Next to the header, the pedigree section is entered, as shown in Fig. We used the examples 1 and 2 given in Table 36 for yield, maturity period, and plant height. The EBVs were calculated for all rice cultivars according to the following formula. The true breeding values (TBV) and the accuracies of EBVs based on the correlation with TBVs were also calculated37. The representative heritability (H2) estimates for the traits  were obtained from breeding records available at RRDI and other published sources40.2 = heritability of the trait; P = trait values of the individual or cultivar; The selection of parental cultivars for the examples 1, 2 and 3 . Twenty-four cultivars contain the PDT ranks of high, moderate, and sensitive Fig. a. The saA sample crossing scheme is shown in Fig. The calculations revealed that the EBV-yield of the cultivar At307 is 1.03, which received the second rank that justifying its selection for the cross selected in example 1. The cultivar Bg450 is also ranked at 29th position in terms of its EBV-yield. Thus, Bg450 also brings a higher genetic effect for yield. However, Bg450 carries genes for extended maturity (EBV-maturity period rank of 62); however, At307 got the rank 20th; hence, the progeny has a chance to receive genes for a shorter maturity period. For plant height, At307 and Bg450 got rank of 50th and 24th positions, respectively, indicating that progeny would have a strong basis for shorter plant height, which is desirable to prevent lodging and increase the fertilizer use efficiently Table  online.TIn example 2, our selection of Bg407H and At307 is firmly validated by the EBV-yield ranks of first and second received by these two cultivars, respectively. However, the EBV-maturity period of Bg407H was ranked 55th, and EBV-plant height was ranked 67th, indicating that Bg407H would bring favorable genes for extended maturity and taller plants. However, At307 got rank 20th for the EBV-maturity period and 15th for plant height, causing decreasing genetic effects on extended maturity period and tallness of the plants Table  online.Yala and Maha seasons, respectively. The perfect correlations between EBV and TBV of Bg250 regarding the maturity period, indicates that it is an ideal parent to provide the genetic basis for shorter maturity period to the progeny. However, Bg250 and Bg252 provide a higher genetic effect for taller plants than that of At362  available in the breeding germplasm at RRDI . The GEBVs would facilitate the efficient introgression of desirable traits to new varieties through MAB with efficient background and foreground selection schemesThe pedigree visualization with variations of phenotypic and molecular data using Pedimap is a user-friendly tool to plan rice breeding programs with higher accuracy and resource optimization. The present study explains the applicability of Pedimap as a decision-making tool to streamline the rice breeding programs in Sri Lanka and the calculated EBVs highly supports to the validity of decisioning based on Pedimap. However, it is also important to note that accurate characterization of the breeding germplasm for phenotypic and molecular data is the critical prior step to harness the value of Pedimap for breeding.Supplementary Information 1.


Traditional selection in livestock and crops focuses on additive genetic values or breeding values of the individuals. While traditional selection utilizes variation between individuals, differences between gametes within individuals have been less frequently exploited in selection programs. With the successful implementation of genomic selection in livestock and crops, estimation and selection for gametic variation is becoming possible.if) of 1.5, showed larger variance than GEBV/2, indicating that greater future genetic gains can be obtained using an index that includes gametic variances. We also used the relative coefficient of variation to estimate with 95% confidence the sample sizes required to observe 90% variability of the progeny for lifetime net merit .The gamevar.f90 software is designed to estimate individual-level variance of genetic values of gametes for complex traits in large populations. The software estimates the (co)variances of gametic diversity as well as other diversity parameters that are useful for selection programs and mating designs. The calculation is carried out chromosome by chromosome and can be easily parallelized. The gamevar.f90 program is written in Fortran with efficient computing algorithms in a user-friendly software package with easily-handled input and output files. Finally, we applied the program to estimate gametic variance for hundreds of bulls for lifetime net merit, productive life, and livability. The RPTA (relative predicted transmitting ability), assuming a future selection intensity (Collectively, we develop an efficient computer program package, gamevar.f90, for estimating gametic variance for large numbers of individuals. The novel information on gametic variation will be useful in future animal and crop breeding programs. Traditionally, selective breeding programs and mating designs are based only on the estimated breeding values (EBVs) of individuals, aiming for the genetic improvement of additive merit. The EBV represents the sum of additive effects of all genes. The individual’s EBV is an average of its parents’ EBVs plus an independent effect from Mendelian sampling caused by random recombination and separation of homologous chromosomes . Mendelientclass1pt{minimak et al. , with diThe gametic variance entclass1pt{minimaong term  recommenThe entclass1pt{minimaentclass1pt{minimaIn this study, we implemented our recently developed method into the gamevar.f90 software that efficiently estimates gametic variance for complex traits in large populations. Basically, gamevar.f90 calculates individual-level genetic statistics per chromosome such as EBVs, (co)variances of gametic diversity, and coefficients of relative variation, as well other genetic components useful to estimate the relative selection index (such as RPTA) for designing selective mating programs and progeny tests.j and k, of an individual i, with the true allele substitution effect αj and αk, can be calculated from the variance of a binomial distribution as ij = n(pjk − pjpk)αjαk, and p = q = 0.5 and n = 1. Thus, the total variance is computed across all N heterozygous loci for trait x as x and y can be computed using the same matrix P . Loci with genetic distances greater than 50 cM on the same chromosome, are assumed to be independent. If the recombination rates between the SNP markers are directly used instead of cM, the off-diagonal elements of the P matrix will be Pjk = 0 when the rate is ≥0.5.The gamavar.90 program estimates the (co)variance of all possible gametic values that can be generated from an individual genome and meiosis process using data on phased genotype, allelic substitution effect, and recombination rate between variants. Since only the heterozygous loci of an individual will contribute to entclass1pt{minimas et al. ), as \doThe gamevar.f90 software also calculates the chromosome-level statistic HOM = entclass1pt{minimaentclass1pt{minimaM[α1…αN]′, where M is a matrix of genotypes coded in − 1,0 and 1 for aa, Aa and AA, with rows corresponding to individuals and column to markers.The gamevar.f90 program directly uses allele effects of the markers estimated from existing genomic evaluations. Since the allele effects have been estimated, gamevar.f90 can also calculate the genomic breeding values (it computes by chromosomes) according to Meuwissen et al.  as M[α1…https://github.com/djordand2008/gamevar.f90). Gamevar.f90 also requires some pre-processed files as input, such as allelic substitution effects and phased genotypes, as well as the chromosome information with recombination rate/genetic distance between markers. The program can optionally produce up to five easily-handled output files in text format for the (co)variance of gametic diversity, EBV, CRV and HOM by individuals. To reduce memory required by the program, output files are written during the analyses so that memory can be reused. In additional to the manual, ready-to-run example files are also provided in the package.A parameter file is required to run gamevar.f90. The parameter file provides some user-specified options, including file names. The program automatically performs an initial check of the parameters from the input file, such as the options defined by users, initial data descriptions, warnings, stoppings, cases of incorrect inputs, and output messages. Parameters are annotated in more details in the user’s manual , assuming a future (gametic) selection intensity (if) of 1.5, for the 100 best bulls for lifetime net merit, showed greater variance and greater density beyond the center of its distribution compared with the GEBV/2, indicating that greater future genetic gains (represented by the means of the criteria) can be obtained with this index :https://github.com/djordand2008/gamevar.f90Operating system(s): Linux and Unix Programming language: Fortran Other requirements: None License: GPL-v3 Any restrictions to use by non-academics: No (free software).Additional file 1. Manual of Gamevar.f90. A word document describing the manual of the software.Additional file 2. Source code of Gamevar.f90. An Fortran source code file for gamevar.f90.Additional file 3. Executable file of Gamevar.f90. An executable file for linux system.


Glycine max) cultivars, we conducted a multilocus genome-wide association study (GWAS) on HSW of soybean based on phenotypic data from 20 different environments and genotypic data for 109,676 single-nucleotide polymorphisms (SNPs) in 144 four-way recombinant inbred lines. Using five multilocus GWAS methods, we identified 118 QTNs controlling HSW. Among these, 31 common QTNs were detected by various methods or across multiple environments. Pathway analysis identified three potential candidate genes associated with HSW in soybean. We used allele information to study the common QTNs in 20 large-seed and 20 small-seed lines and identified a higher percentage of superior alleles in the large-seed lines than in small-seed lines. These observations will contribute to construct the gene networks controlling HSW in soybean, which can improve the genetic understanding of HSW, and provide assistance for molecular breeding of soybean large-seed varieties.Hundred-seed weight (HSW) is an important measure of yield and a useful indicator to monitor the inheritance of quantitative traits affected by genotype and environmental conditions. To identify quantitative trait nucleotides (QTNs) and mine genes useful for breeding high-yielding and high-quality soybean ( Glycine max (L.) Merr.] is an important source of edible oil and plant protein for humans. Soy-based foods are popular in the international market, and the demand for soybeans is increasing. Hundred-seed weight (HSW) is an important agronomic trait related to soybean yield and food quality. Therefore, locating the quantitative trait locus (QTLs) related to HSW and exploring valuable alleles associated with the QTLs have important theoretical and application value for improving soybean yield and quality.Soybean [2  and simple sequence repeats (SSRs), is the main method for detecting QTLs controlling HSW in soybean. Quantitative trait loci associated with HSW have been identified in F2 , recombi2 , and bac2  populati2 , composi2 , and inc2 . Genome-2  was receDrifting and selection of genes during crop breeding and domestication produce complex population structures, which can cause false positives. Statistical methods to circumvent this issue include case–control studies, transmission imbalance tests, structured associations, principal component analysis (PCA), and hybrid models. A newly developed GWAS statistical method based on a mixed linear model (MLM) can overcome the above difficulties and address fixed and random effects flexibly. Common variables from STRUCTURE  and PCA Genome-wide association study has been used to detect QTLs related to HSW in soybean. We used 144 RILs with four-way hybridization (FW-RILs) to obtain phenotypic data for the HSW and SNP genotype data across multiple environments. The multilocus GWAS method was used to detect quantitative trait nucleotides (QTNs) associated with HSW. We identified potential candidate genes and common QTNs associated with large-seed lines across multiple methods or in multiple environments, laying the foundation for high-yield soybean breeding.1 (Kenfeng 14 × Kenfeng 15) was used as the female parent, and F1 (Kenfeng 19 × Heinong 48) was used as the male parent to produce four-way hybrid F1 seeds. From 2010 to 2012, the seeds were planted in Harbin in the summer and in Hainan in the winter. Four-way RILs (144 lines) were obtained by continuous self-crossing for six generations using the single-seed descent method.In 2008, the four soybean varieties Kenfeng 14 (HSW 21 g), Kenfeng 15 (HSW 18 g), Kenfeng 19 (HSW 19 g), and Heinong 48 (HSW 25 g) were used to prepare double hybrid combinations. Kenfeng 14, Kenfeng 15, Kenfeng 19, and Heinong 48 were derived from the crosses Suinong 10 × Changnong 5, Suinong 14 × Kenjiao 9307, Hefeng 25 × (Kenfeng 4 × Gong 8861-0), and Ha 90-6719 × Sui 90-5888, respectively. In 2009, FThe 144 FW-RILs were planted in 20 environments; details of the planting environments are summarized in Mean, variance, standard deviation, minimum, maximum, skewness, and kurtosis of FW-RILs were calculated in each environment. Analysis of variance was performed on phenotypic data from the 20 environments using the following model:Gi is the ith genotype effect, Ej is the jth environment effect, GEij is the genotype × environmental effect, and εij is the error effect following N.where μ is the grand average, The mean squared difference was estimated by applying the mean square of each variation source, and the estimated generalized heritability was calculated using the following formula.h2 is the generalized heritability of gene × environment interaction,2 is the error variance, e is the number of environments, and r is the number of repetitions in each environment. Data were analyzed using the general linear model method in Statistical Analysis System (SAS) 9.2, North Carolina.where 2O, 1 μL RNAase (10 mg/mL) was added, and samples were stored at −20°C. Single-nucleotide polymorphism genotyping was performed by Beijing Boao Biotechnology Co., Ltd.  using the SoySNP660K BeadChip. A total of 109,676 SNPs were obtained on 20 chromosomes after mass filtration based on the following criteria: minor allele frequency (MAF) of the SNP was identified (MAF > 0.05), maximum missing data rate <10%  of all SNPs located within 10-Mb physical distance, and the physical distance of LD decay was estimated as the position where r2 dropped to half of its maximum value.Linkage disequilibrium (LD) analysis was performed using TASSEL 5.0  to calcuK ranged from 1 to 10.Population structure was analyzed after filtration on 5,000 SNPs evenly distributed on 20 chromosomes using STRUCTURE 2.3.4 . The numP value parameter was set to 0.01, except that of FASTmrEMMA was set to 0.005. In the final stage, the critical LOD value of significant QTNs was set to 3. All matrices involved in these five methods were calculated using mrMLM.GUI3.0.Genome-wide association studies were performed using the software mrMLM. GUI (version 3.0). There are five multilocus GWAS methods in the mrMLM package that can be used to identify significant QTNs, namely, mrMLM , FASTmrMAmong the significant QTNs identified, some were detected in a variety of environments or methods. These significant QTNs are referred to as common QTNs. The positive and negative of the QTN effect values were used as the criterion for selecting superior alleles. If the QTN effect value is positive, the genotype of code 1, which was obtained by GWAS, is the superior allele; if the QTN effect value is negative, the other genotype is the superior allele. The percentage of excellent alleles is calculated as follows: for each line, the percentage of excellent alleles is the number of common QTNs carrying excellent alleles divided by the total number of common QTNs; for each common QTN, the percentage of alleles among the 144 FW-RILs is equal to the number of rows containing the alleles divided by the total number of rows. Heat maps were generated using the R package Complexheatmap .The physical locations of generic QTNs identified by the above methods were marked. Intervals containing each of the common QTNs were then selected on the Phytozome website. These intervals are determined by the LD decay rate. Highly expressed genes at certain locations based on relevant traits were selected. This step was done on the BAR website. Finally, the genes were matched to those in the Kyoto Gene and Genomics Encyclopedia (KEGG) to analyze gene expression pathways and identify potential candidate genes.We measured HSW phenotypic data for 144 FW-RILs and the four parent lines in 20 environments. The phenotypic values for the four parents are given in K (K = 1-10) and identified two subgroups (selected K = 2) based on the ΔK value  lines, which were artificially configured with good linkage relationship, whereas the natural population has no linkage relationship. The number of parents of the FW-RILs is limited, and the population structure is not obvious compared with the natural population composed of many germplasm resources. Thus, the false-positive rate of FW-RILs is lower than that of the natural population; the statistical power and QTL positioning accuracy are higher.R2) ranged from 6.9 to 13.2%. R2 ranged from 13.2 to 31.2%. Although high-density SNP markers were applied in these experiments, statistical methods . psaG is a nuclear-encoded gene corresponding to PSI subunit V of spinach (Glyma.08G365900 is related to mannose phosphate isomerase (PMI), and the PMI (manA) gene encodes a mannose-6-phosphate transferase, catalyzing the conversion of 6-phosphoric acid mannose to fructose 6-phosphate. Cells can metabolize fructose via the glycolytic pathway (manA) indirectly controls the HSW of soybean plants. Glyma.20G240000 is associated with hexosaminidase (Hex). During fruit ripening, β-Hex releases a large amount of free N-glycans from glycoprotein polypeptide chains (N-glycans are important signaling molecules that promote fruit ripening (Based on the common QTNs detected in this study and the annotated pathways associated with these QTNs, we identified three genes that may be associated with HSW in soybean . Glyma.0 spinach . Further spinach . Photosy pathway , renderie chains . Free N-ripening , possiblripening . We therWe identified 31 QTNs using a five-multilocus GWAS method. An analysis of the common QTNs identified three candidate genes. By analyzing the ratio of superior alleles in large- and small-seed lines, we established that the superior alleles of these common QTNs might be useful for molecular marker-assisted breeding of soybean plants with larger seeds.https://figshare.com/s/84eb97ad5c5e523072e5.The genotyping data is available at WL and HN conceived and designed the experiments. JS, XT, YW, YF, XL, JW, CY, SJ, and XS performed the field experiments. SL and ZT performed the genome sequencing. ZQ, KZ, and HN analyzed and interpreted the results. ZQ and HN drafted the manuscript and all authors contributed to manuscript revision. WL acquired the funding. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


However, counterexamples are occasionally reported, calling for a deeper understanding on the evolution of additive variance. In this study, we propose adaptive divergence followed by population admixture as a source of the additive genetic variance of evolutionarily important traits. We experimentally tested the hypothesis by examining a panel of ∼1,000 yeast segregants produced by a hybrid of two yeast strains that experienced adaptive divergence. We measured >400 yeast cell morphological traits and found a strong positive correlation between h2 and evolutionary importance. Because adaptive divergence followed by population admixture could happen constantly, particularly in species with wide geographic distribution and strong migratory capacity , the finding reconciles the observation of abundant additive variances in evolutionarily important traits with Fisher’s fundamental theorem of natural selection. Importantly, the revealed role of positive selection in promoting rather than depleting additive variance suggests a simple explanation for why additive genetic variance can be dominant in a population despite the ubiquitous between-gene epistasis observed in functional assays.Fisher’s fundamental theorem of natural selection predicts no additive variance of fitness in a natural population. Consistently, studies in a variety of wild populations show virtually no narrow-sense heritability ( The two parental strains differ by ∼0.5% at the genomic sequence level and experienced adaptive divergence according to an analysis of a set of principle component traits  . These tH2 and narrow-sense heritability h2 were estimated for each of the 405 traits according to a previous study  explained by additive (VA) and nonadditive (or epistatic) effects (VNon-A), and h2 = VA/VP. Because normalized trait values were considered, VP of the different traits was within an ∼1.5-fold range, whereas h2 spanned a >100-fold range . The broad-sense heritability us study  (supplemus study  online. of 0.240 E. In thild range  online. 5 traits  online.R) between trait value and cell growth rate among the 734 segregants. Following a previous study . We measured the growth rate of each segregant under the same condition as for trait measurement  online. us study , the absl traits A.h2 and evolutionary importance for the large number of traits enabled a direct test for the hypothesis. Consistent with the hypothesis, we found a strong positive correlation between h2 and trait importance estimated by RTGR among the 405 yeast traits . Traits of stage A1B and stage A tended to have small RTGR, suggesting less selective constraints on the morphology of the two stages. Importantly, the positive correlation between  stage A D. In addtegories E.h2 and 1−CV . In nearly all cases, the trait variance explained by detected QTLs was close to h2  for each of the traits. A total of 2,505 QTLs were detected for 393 traits  online, h2 of a trait was highly correlated with the number of QTLs  by following a previous study  interactions observed between genes in functional studies . A previ of ∼50% . In the  of ∼50% .h2 ∼ 0.6 in the yeast segregant population, and observed a rapid reduction of h2 (s = 0.01 (or = 0.001) to be fixed in a diploid population of N = 100,000, where N is the population size and s is the selection coefficient  were amplified by polymerase chain reaction for these segregants. By comparing the results with the genotypes provided by Dr L. Kruglyak, we found that some percentage of segregants in plates 8 and 9 were mismatched, and there was no pattern to rescue the segregants in a row or a line, which may be the result of contaminations. We then focused the segregants in the left nine plates with right genotypes in the next experiments.4 cells per well and taken images by IN Cell Analyzer 2200  with 100× objective lens. There were two technical replications for each segregant, and segregants A11_01 and A11_96 were cultured, stained, and imaged in every experiment as a technical control.The morphological traits of each segregant were measured following Ohya’s protocol with some modifications . Brieflyhttps://github.com/bmbolstad/preprocessCore).CalMorph software was used to analyze images to quantify yeast morphology, and 405 quantitative traits were derived . Segregar = 2) using the mean normalized values of 734 segregants . The average normalized values of growth rate were taken as the fitness of each segregant, and listed in Strains were grown in YPD medium to saturation phase at 25 °C for 2 or 3 days, then diluted 1:100 to 100 μl fresh YPD medium at 96-well plate. Two replications of each segregant were placed in the same 96-well plate. The 96-well plates were put on Epoch2 Microplate Spectrophotometer (BioTek) and incubated at 25 °C with shaking. The absorbances at 600 nm of each well were determined per hour. The measurements lasted 24 h and all strains reached saturation phase. The H2), narrow-sense heritability (h2), additive QTL, and the variance explained by QTL of each morphological trait were calculated by methods consisted with Bloom et al.’s study (2013). Briefly, H2 was calculated by normalized values of traits of segregants with two replications. H2 was estimated as H2, h2 was calculated by the average normalized values of traits of segregants with two replications. And, segregants with only one replication were also included in other situations. Narrow-sense heritability was estimated as h2 (H2 and h2 were calculated by delete-one jackknife both.Because the segregant panel was produced by Bloom et al., broad-sense heritability ( package . When coulate h2 . SEs of r is the Pearson correlation coefficient between the genotypes and trait values. Significant genetic markers were detected from four rounds using different LOD thresholds corresponding to a 5% FDR, which were 2.68, 2.92, 3.72, and 4.9, respectively. A multiple regression linear model was estimated by taken each QTL as independent variables of each trait, and the total phenotypic variance explained by additive QTL was the square of the multiple regression coefficient. The results were listed in Additive QTL of each trait was detected using the stepwise forward-search approach developed by R between the trait values and the growth rates in YPD medium across 734 segregants was used as a proxy of RTGR for each trait. The results were listed in For each trait, the average normalized values of replications of each segregant were calculated. For segregants with only one replication, the trait values were the normalized values from the only measurement. The absolute Pearson’s i in A11_01 and A11_96, respectively. A large CV distance means the environmental robustness of the trait would be different for different segregants, so we excluded 105 traits with CV distance index >0.2 to obtain a set of traits with consistent measurements. There were 293 traits left. The results were listed in Coefficient of variations for each trait was calculated using raw data of replications of A11_01 and A11_96, respectively. Traits with large CV may indicate their indeterminacy, so we excluded seven traits with CV >0.4 when using CV as an index of trait importance. To evaluate the repeatability of two groups, we used a distance index between two groups of CV as msaa085_Supplementary_DataClick here for additional data file.


Previous research has shown that not only falls, but also fear of falling (FoF) influences health-related quality of life (HrQoL) negatively. The EQ-5D  is a frequently used instrument to determine HrQoL in clinical studies and economic evaluations, but no previous study compared the association between FoF and the EQ-5D index with the association between FoF and the EQ-VAS. Moreover, factors that influence the association between FoF and HrQoL are rarely examined. Thus, this study aimed to examine the association between FoF and HrQoL and to examine factors that mediate the association.N = 309). Linear and logistic regression analyses were performed, adjusting for sociodemographic variables, frequency of falls, number of chronic conditions, functional mobility (Timed up-and-go test), and subjective functional capacity . Multiple regression models were used to test the mediating effects.FoF  and HrQoL  were assessed in a sample of community-dwelling older persons (≥70 years) participating in the baseline assessment of a randomized controlled trial (Moderate or high FoF was prevalent in 66% of the sample. After adjusting for covariates, FoF was negatively associated with the EQ-5D index, but not with the descriptive system or the EQ-VAS. Subjective functional capacity partly mediated the association between FoF and the EQ-5D index and completely mediated the association between FoF and the EQ-VAS.FoF was negatively associated with the EQ-5D index. As subjective functional capacity mediated the association between FoF and HrQoL, future interventions should account for subjective functional capacity in their design. Due to the demographic change, the older population is projected to increase . As thisSince many health systems move beyond the idea of mere survival but focus on maintaining the best possible health status, overarching concepts like HrQoL have become more important in describing the impact of health conditions or the effects of interventions. HrQoL is subjective and depends on a variety of physical, emotional, and social-cultural factors , 11. It As a recent systematic review by Schoene et al.  confirmen = 182) and Taiwan (n = 193), Hsu et al. . For all analyses, the significance level was set to 0.05.N = 309 participants were included with a mean age of 78.68 (standard deviation [SD] 5.31) years. The majority was female (73.46%) and had an intermediate or high educational level (67.96%). Approximately half of the sample was married or was living in a partnership (45.31%), whereas one third was widowed (35.92%) and one tenth was divorced (11.97%). Only a small proportion was single (5.83%) or permanently living separated (0.97%). On average, participants reported 2.52 (SD 1.56) chronic conditions. The mean scores on the LLDFI function and disability scales were 57.34 (SD 7.94) and 70.66 (SD 11.98), respectively. In the previous 6 months, 40.78% of the sample experienced at least one fall. Among those who fell, the average number of falls was 1.61 (SD 1.21). Low FoF was reported by 33.98%, whereas 52.75% reported moderate FOF and 13.27% reported high FoF. The mean EQ-5D index was 0.84 (SD 0.15) and the mean EQ-VAS was 70.91 (SD 16.46). Furthermore, differences between people experiencing at least one fall and those without falls were not significant (data not shown).Sociodemographic and clinical characteristics are presented in Table S = 0.17 (p < 0.05) and 0.43 (p < 0.05). Furthermore, associations between FoF and the living situation and functional mobility were weak . Moreover, FoF correlated strongly with function  and moderately with disability . No significant correlation was found between FoF and age, gender, educational level, the number of chronic conditions, or the number of falls (p > 0.05).In bivariate analyses Table , Spearmap < 0.05). These associations became non-significant after adjusting for chronic conditions, functional mobility, and subjective functional capacity (Model 2).Results of the logistic regression models for the association between FoF and the dimensions of the EQ-5D descriptive system are presented in Table p < 0.001; Table p < 0.01; Model 2).After adjusting for sociodemographic variables (Model 1), linear regression revealed a significant negative association between FoF and the EQ-5D index , whereas the number of comorbidities , and the levels of function  and disability  significantly predicted the EQ-VAS.In Model 1, higher FoF was significantly associated with a lower EQ-VAS score  and to − 0.018 after controlling for disability . The association between FoF and the EQ-VAS was completely mediated by function as the coefficient of FoF increased from − 1.589 to a non-significant effect of − 0.489  after controlling for function. After controlling for disability, the coefficient of FoF increased from − 1.589 to − 0.989 , indicating a partial mediating effect of disability on the association between FoF and the EQ-VAS.Figures In this sample of community-dwelling older persons from Germany, 66% had moderate or high FoF. After adjustment for sociodemographic characteristics, chronic conditions, functional mobility, and subjective functional capacity, FoF was significantly associated with HrQoL measured by the EQ-5D index. This confirmed previous findings . The curAs already found in another study , higher Contrary to previous studies, where falls and FoF were associated and both had a significant relationship with HrQoL , 34, no The mediating effect of function and disability in the current study emphasizes the importance to maintain daily and social life activities in older people. Thus, addressing these factors in interventions may lead to a reduction of FoF and an improvement in HrQoL. A randomized controlled trial from the Netherlands evaluated a home-based cognitive behavioural program to encourage older persons in performing activities of daily living . The intEven though different studies have already reached consensus regarding the association between FoF and HrQoL , to our . As the current study used cross-sectional data, mediation models may not reflect the true direction of influence. Instead of being a consequence of FoF, functional limitations may lead to FoF, or even both directions of influence exist. Due to the conceptual overlap between function and disability, separate mediation models for function and disability were calculated, which precludes investigating their independent contributions. However, the mediation results of this study may serve as basis for future studies, which could, for example, investigate the causal relationship between FoF and HrQoL more closely using longitudinal data or by calculating more complex path models. In addition, the sample size of N = 309 was relatively small, thus results may not be generalizable to the older population at risk of falling in Germany. Furthermore, the selected sample reached better EQ-5D index values compared with normative values for the general German population of the respective age group [This study has some limitationsge group . This isge group , 62. TheFoF was a significant negative predictor of the EQ-5D index, whereas FoF did not predict HrQoL measured by the EQ-VAS. This is probably attributable to the different concepts underlying the EQ-5D index and the EQ-VAS. Furthermore, function and disability were shown to mediate the association between FoF and HrQoL. Therefore, future interventions should account for function and disability in their design.