Vaccinium corybosum), composed of 1,847 individuals was phenotyped for eight yield and fruit quality traits over two years. Continuous genotypic based models performed as well as the best models. This approach also reduces the computational time and avoids problems associated with misclassification of genotypic classes when assigning dosage in polyploid species. This approach could be very valuable for species with higher ploidy levels or for emerging crops where ploidy is not well understood. To our knowledge, this work constitutes the first study of genomic selection in blueberry. Accuracies are encouraging for application of GS for blueberry breeding. GS could reduce the time for cultivar release by three years, increasing the genetic gain per cycle by 86% on average when compared to phenotypic selection, and 32% when compared with pedigree-based selection. Finally, the genotypic and phenotypic data used in this study are made available for comparative analysis of dosage calling and genomic selection prediction models in the context of autopolyploids.Estimation of allele dosage, using genomic data, in autopolyploids is challenging and current methods often result in the misclassification of genotypes. Some progress has been made when using SNP arrays, but the major challenge is when using next generation sequencing data. Here we compare the use of read depth as continuous parameterization with ploidy parameterizations in the context of genomic selection (GS). Additionally, different sources of information to build relationship matrices were compared. A real breeding population of the autotetraploid species blueberry ( Polyploidy events are not an exception in plants, as about 70% of Angiosperms and 95% of Pteridophytes underwent at least one polyploidization event . PolyploBecause speciation via ploidy increase can generate new phenotypic variability, this phenomenon is considered a powerful evolutionary source .The advent of high-throughput genotyping methods, associated with the development of genetic and statistical analysis tools, has generated significant genetic gains for diploid species . Howevere.g., i.e., the number of copies of each allele at a given polymorphic locus). Besides the existence of methods that allow accounting for ploidy effects  is a method to increase the efficiency and accelerate the selection process in breeding programs. GS is used to capture the simultaneous effects of molecular markers distributed across the genome, based in the premise that the linkage disequilibrium between causal polymorphisms and markers allow phenotype prediction based on genotypic values  by creating a more realistic representation of the effects of each genotypic class. Although the evidence of dosage effects in the expression of important economic traits exists  is used , or by creating more complex interactions between loci or alleles, such as dominance or epistasis  as a model. The cultivated SHB is an autotetraploid, presenting 2n = 4X = 48 chromosomes , scar diameter (mm), fruit diameter (mm), flower bud density (reported as buds per 20 cm of shoot), soluble solids content (oBrix), and pH were evaluated. The last three traits were phenotyped only in one year – soluble solids content and pH were phenotyped in 2014 and flower buds in 2015.The population used in this study encompasses one cycle of the University of Florida blueberry breeding program’s recurrent selection, comprising 1,847 SHB unique individuals. This population was originated from 124 biparental controlled crosses, from 146 parents that presented superior phenotypic performance (cultivars and advanced stage of breeding). Phenotypic data of eight yield and fruit quality-related traits were collected during two production seasons (2014 and 2015), when the plants were 2.5 and 3.5 years of age at the University of Florida Plant Science Research and Education Unit in Citra . Yield , weight (g), firmness  were randomly sampled to compose the measurement of fruit traits for each individual. Fruit weight was measured using an analytical scale . The FirmTech II firmness tester  was used to measure fruit diameter and firmness. The scar diameter was obtained by image analysis of the fruits using FIJI software (0 (AA), 1 (AB), or 2 (BB). For the tetraploid parameterization they were coded as 0 (AAAA), 1 (AAAB), 2 (AABB), 3 (ABBB), and 4 (BBBB). A third parameterization (assumption-free method) was used, which considered allele ratio 0 and 1.Genomic DNA was extracted and genotyped using sequence capture by Rapid Genomics . Polymorphisms were genotyped in genomic regions captured by 31,063 120-mer biotinylated probes, designed based on the 2013 blueberry draft genome sequence (2) were performed for pairwise LD estimation among SNPs within scaffolds, considering draft reference genomes (G2); ii) tetraploid (G4) and iii) ratio . The LD decay over physical distance was determined as the mean distance at the LD threshold of r2 = 0.2. To compare the LD among scenarios, the mean distances (Kb) and their interval confidences at r2 = 0.2 were compared. The diversity captured from each relationship matrix was obtained by PC using the R package adegenet v. 1.3-1 , and principal components (PC) analyses. Pearson correlation tests , we performed analyses considering different approaches to modeling the genotypic values in autotetraploid species , File S1A) were built considering a diploid model (G) were based on the incidence matrices of markers effects (X) according to X matrices under three assumptions regarding the additive marker allele dosage effect: i) a pseudo-diploid model, where all the heterozygous genotypes were assumed as one class, corresponding to a unique effect ; ii) an additive autotetraploid model, where each genotype had a specific value, and cumulative additive effect was assumed ; and iii) an assumption-free method based on the ratio of reads count for the alternative and reference alleles , where also a cumulative additive effect was assumed. For the construction of the relationship matrices based on marker data, the missing genotypes were substituted by the mean.The methods chosen to obtain the relationship matrices are shown in the id model  and autoThe six models described above  were fiti.e., Least Square means estimations; LSMeans) and the cross-validated breeding values (BV) predicted by the models for each validation fold. The goodness-of-fit for the different models was evaluated with measures of DIC  was considered constant for all methods and equal to 1.For each trait, models were compared based on their PA, stability (mean square errors), goodness-of-fit, and expected genetic gain. A 10-fold cross validation scheme was applied to compute model PA, for this the genotypes were assigned to ten groups, on each cross-validation step the phenotypic information for one of the groups was omitted  and predicted considering the model obtained from the remaining nine groups (training set). Because each validation group might have a different mean  are available from the Dyrad Digital Repository (accession number doi: 10.5061/dryad.kd4jq6h). Genotypic data and supplemental material are available at Figshare. Files include diploid, tetraploid and continuous genotypes, supplemental information 1 to 5, which includes: 1) description of the matrices used in the study; 2) model convergence figure; 3) LD distribution per parameterization; 4) principal components plots for each parameterization; 5) table of the predictive abilities, MSE, goodness-of-fit and beta for each parameterization. The authors ratify that all data necessary for confirming the conclusions of the article are present within the article, figures, and tables. Supplemental material available at Figshare: 2 = 0.2 at distances of 88.3 Kb, 92.6 Kb, and 98.2 Kb for the diploid, tetraploid and continuous models, respectively .Linkage disequilibrium decayed below rectively . No signi.e., pedigree-based or marker-based methods) in the PC analysis . The first two PC components of the marker-based (G) matrices were consistent across all matrices, explaining approximately 20% of the variation, G2 matrix captured 20.60% of the variation, while G4 captured 21.71%, and Gr captured 23.36% . The PC analysis results were consistent between pedigree methodologies as well. Approximately 38% of the variation was explained . The results obtained in the PC analysis did not justify a stratified sampling of cross-validation populations, since no evidence of sub-population structure was detected for any of the relationship matrices.Similarly, no major differences were found between parameterizations within methodology . However, the marker-based and pedigree-based results were not always significantly different from each other . Unlike the identity matrix, the use of pedigree-based matrix assumes that there is relationship  among individuals. The phenotypic PA obtained for the pedigree methods in 2014 yielded values from 0.20 (flower bud) to 0.49 (fruit firmness). As with marker-based methods, smaller values were observed for traits with lower heritability . For 2015, the PA results for the phenotypic-BLUP were 0.36, 0.38, and 0.42, for fruit weight, fruit scar, and fruit firmness, respectively. The PA values obtained for the same traits with pedigree-BLUP were 0.40, 0.45, and 0.49, respectively. No significant differences between the models’ stability were observed (Table S1).The use of pedigree-based relationship matrices generated higher phenotypic PA values for all the traits, when compared with the assumption of unrelated individuals  performed equally well as the best models  of 12 years (The results obtained for the expected genetic gain (EGG) are summarized in 12 years .i.e., genomic selection) were applied . Our results also explored models accounting for ploidy information and compared the use of genotypic data that is independent of assumptions regarding ploidy levels (continuous) to perform GS, avoiding the need for Our research showed empirical evidences that the use of continuous genotypic data from NGS can be effectively applied in GS models for autotetraploid species. This method was tested and compared with marker calling methodologies at the individual level in genome wide association studies  demonstrate the importance of considering dosage in the prediction of breeding values. However, this will depend on the trait analyzed, as previously reported by The results obtained for both models that assumed more than three genotypic classes (We also demonstrate the value of applying GS in a perennial fruit tree, blueberry. One cycle of blueberry breeding takes from 12 to 15 years until the release of a new cultivar , signifi


Resolving population genetic structure is challenging, especially when dealing with closely related or geographically confined populations. Although Principal Component Analysis (PCA)-based methods and genomic variation with single nucleotide polymorphisms (SNPs) are widely used to describe shared genetic ancestry, improvements can be made especially when fine-scale population structure is the target.This work presents an R package called IPCAPS, which uses SNP information for resolving possibly fine-scale population structure. The IPCAPS routines are built on the iterative pruning Principal Component Analysis (ipPCA) framework that systematically assigns individuals to genetically similar subgroups. In each iteration, our tool is able to detect and eliminate outliers, hereby avoiding severe misclassification errors.http://bio3.giga.ulg.ac.be/ipcapsIPCAPS supports different measurement scales for variables used to identify substructure. Hence, panels of gene expression and methylation data can be accommodated as well. The tool can also be applied in patient sub-phenotyping contexts. IPCAPS is developed in R and is freely available from  Single Nucleotide Polymorphisms (SNPs) can be used to identify population substructure, but resolving complex substructures remains challenging . Owing tST) is satisfied. FST is commonly used to measure genetic distance between populations. The software developed to perform ipPCA has some shortcomings though. Notably, it is limited to a MATLAB environment, which is not freely available. Also, outliers can severely disturb the clustering analysis. These limitations are addressed in IPCAPS [The identification of substructure in a genome-wide association study sample of healthy controls or patients is a clustering problem. Conventional population structure analyses use Bayesian statistics to show relationships amongst individuals in terms of their so-called admixture profiles, where individuals can be clustered by using ratios of ancestral components, see also . The iten IPCAPS , which isimSNP: a synthetic dataset containing SNPs and population labels.ipcaps: a function for unsupervised clustering to capture population structure based on iterative pruning.rubikClust: a function for unsupervised clustering to detect rough structures and outliers.cal. PC.linear: a function for linear PCA.ST calculation between two groups.fst.hudson: a function for average FST calculation for all SNPs between two groups.fst.each.snp.hudson: a function for Fplot.3views: a function to create scatter plots in three views.top.discriminator: a function to detect top discriminators between two groups.The R package IPCAPS provides one synthetic dataset and seven functions:See the IPCAPS reference manual for details of the functions, arguments, default settings, and optional user-defined parameters.The IPCAPS package implements unsupervised strategies that facilitate the detection of fine-scale structure in samples, extracted from informative genetic markers. For general populations, information regarding substructure may come directly from SNPs. For patient samples, general population structure should first be removed via regressing out ancestry informative markers prior to clustering. The latter is incorporated in IPCAPS. Currently, IPCAPS accepts three data input formats: text, PLINK binary , and RData  steps need to be taken. These are not supported by IPCAPS itself but can easily be performed in PLINK (1.9) . SuggestST) is lower than a threshold, and EigenFit is lower than a pre-specified cutoff. The EigenFit criterion is defined by the differences between the logarithms of consecutive eigenvalues, sorted from high to low.Rather than performing two-means clustering in PCA-space, at each iteration, IPCAPS clustering potentially involves the consecutive application of 2 clustering modules. The first, which we call rubikClust, is applied in the 3-dimensional space determined by the first three principal components (axes) at an iteration step. It involves applying rotations in 3D by consecutively performing rotations around PC1, PC2, PC3, and may provide more than 2 clusters. Notably, this approach also allows for rapid identification of outliers. When samples cannot be divided into 2 groups in this way, the existing R function mixmod (package Rmixmod) is used for latent subgroup detection. In particular, earlier computed PCs (untransformed) at a particular iteration are subjected to multivariate Gaussian mixture modeling and Clustering EM (CEM) estimation , allowinfiles, which they are internally merged again for iterative PCA. Extra attention is paid on efficient PC calculation [All IPCAPS results are saved in a single directory including textual information about cluster allocations, and visual information such as PC plots and hierarchical trees of group membership. Due to memory restrictions in R, large datasets  may need to be split into multiple files and loaded into computer memory via the IPCAPS option culation , also reThe analysis procedure using IPCAPS proceeds as follows: Firstly, genotype data are loaded and are analyzed automatically by the function ipcaps. Secondly, cluster membership is returned once clustering process is done. Clusters containing few members are counted as outlying individuals. Lastly, top discriminators between clusters are identified.bed.file <− “simSNP.bed” #the bim file and the fam file are requiredsample.info <− “simSNP_individuals.txt”column.number = 2output.path <− “result”clusters <− ipcaps # 1) perform clustering . Note that input files must to be in the working directory.print (clusters$cluster$group)table # 2) Check clustering result.bim.file <− “simSNP.bim”top.snp < −top.discriminator head (top.snp)# 3) Identify top discriminators between groups, for example, group 4 and group 5.Usage example:ST = 0.005 [We simulated genotype data for 10,000 independent SNPs and 760 individuals belonging to one of three populations  and 10 outliers . The pairwise genetic distance between populations was set to F = 0.005 . Ten out = 0.005 . Two-dim = 0.005 . These p = 0.005 . After d = 0.005 , includiFine-scale resolution of population substructure can be captured using independent SNPs once all redundancies are filtered out. In this work, we have introduced a flexible and efficient R package to accomplish an unsupervised clustering without prior knowledge, in the search for strata of individuals with similar genetic profiles. The tool performs well in fine-scale and broad-scale resolution settings. The IPCAPS routines allow a relatively easy extension to input data derived from transcriptome or epigenome experiments.Project name: IPCAPShttp://bio3.giga.ulg.ac.be/ipcapsProject home page: Operating system: Platform independentProgramming language: R version > = 3.0.0Other requirements: Dependency R packages; RMatrix, expm, fpc, Rmixmod, LPCM, apcluster, rARPACK, igraphLicense: GPLv3


Estimate genetic parameters, the rate of inbreeding, and the effect of inbreeding on growth and egg production traits of a Thai native chicken breed Lueng Hang Kao Kabinburi housed under intensive management under a tropical climate.Genetic parameters were estimated for weight measured at four weekly intervals from body weight at day 1 (BW1D) to body weight at 24 weeks (BW24) of age, as well as weight at first egg, age at first egg (AFE), egg weight at first egg, and total number of eggs (EN) produced during the first 17 weeks of lay using restricted maximum likelihood. Inbreeding depression was estimated using a linear regression of individual phenotype on inbreeding coefficient.Direct additive genetic effect was significant for all traits. Maternal genetic effect and permanent environmental hen effects were significant for all early growth traits, expect for BW24. For BW24, maternal genetic effect was also significant. Permanent environmental hen effect was significant for AFE. Direct heritabilities ranged from 0.10 to 0.47 for growth traits and ranged from 0.15 to 0.16 for egg production traits. Early growth traits had high genetic correlations between them. The EN was lowly negatively correlated with other traits. The average rate of inbreeding for the population was 0.09% per year. Overall, the inbreeding had no effect on body weight traits, except for BW1D. An increase in inbreeding coefficient by 1% reduced BWID by 0.09 g (0.29% of the mean).Improvement in body weight gain can be achieved by selecting for early growth traits. Selection for higher body weight traits is expected to increase the weight of first egg. Due to low but unfavorable correlations with body weight traits, selection on EN needs to be combined with other traits via multi-trait index selection to improve body weight and EN simultaneously. The poultry industry provides an important protein source and household income to many smallholders in Thailand. The Thai native chicken (TC) is the most popular local breed among rural farmers due to its relative superiority with respect to disease resistance and adaptation to harsh environmental conditions compared to non-local breeds. Also, it is considered an important economic resource for rural people. Its importance to the Thai economy was clearly exposed during the avian influenza outbreak in 2004 when the Thai poultry industry could not import or export poultry products and the industry relied entirely on TC.There are 12 breeds of TC in Thailand. The TC is small in size, ranging in weight from 1 to 1.5 kg by 2.7 months of age, and also has low egg production, with 30 to 40 eggs (20 to 24 chicks) per hen per year, which is well below the annual household requirement. Yenjai and Sinnak [In 2003, the Department of Livestock Development (DLD) in Thailand established a breed-improvement program for a popular TC breed known as Lueng Hang Kao Kabinburi (LHKK) chicken to improve its productivity. The main objective of this improvement program was to maintain the breed characteristics. Several years of within-line selection and random mating resulted in improved uniformity of breed characteristics. However, the improvement in performance in relation to growth and egg production was limited and requires further attention.Establishment of a selection program for improvement in growth, egg and chick production requires the estimation of genetic parameters for these traits. Using within-line selection on a small poultry population is expected to increase the rate of inbreeding in the population, and the increase in rate of inbreeding may lead to undesirable effects such as a decline in performance and loss of vigor . TherefoGrowth and egg production data used in this study were from the native breed improvement program. The native poultry breed improvement program was initiated in 2003 at the Kabinburi Livestock Research and Breeding Center, DLD in Thailand. The Livestock Research Centre is located in the Eastern region of Thailand and it is situated between 14.0478° North latitude and 101.3725° East longitude. It receives an average annual rainfall of about 1,532 mm and average daily temperature varied from 15°C to 40°C within the five years recorded from 2003 to 2007. The initial parent stock of 70 cocks and 350 hens were sourced across the country as the founding gene pool of LHKK chickens. Each cock was mated to five hens by artificial insemination. Fertile eggs were collected for two weeks and hatched in the incubator as the same batch. Each hen produced about 60 day-old chicks per year. Annually, 70 males and 350 females were selected as replacement birds of each generation. Birds kept for dual purposes and they were selected based on breed characteristics, including the type of comb, face color, eyes, beak, skin, tail, shank, body plumage, neck, and saddle plumage.2) and 24 hours of light schedule. They were fed ad libitum with a ration containing 190 g of crude protein (CP) and 11.7 MJ metabolizable energy per kg. For the next 18 weeks, chicks were moved to grower pens and were allowed to scavenge outside during daytime and were sheltered inside at nighttime, at a rate of 8 chickens per one square meter (m2). The light schedule was 12 hours per day and feeding was ad libitum with a ration containing 150 g of CP and 11.9 MJ metabolizable energy per kg. At 22 weeks of age, 70 cocks and 350 hens were selected as replacements and moved to individual battery cages. The light schedule was steadily increased by 1 hour per week until it reached 16 hours per day. Some fresh grass was fed as supplementation during starter and grower periods. During the laying period, they were fed with layer feed at a daily rate of 120 g for hens and 150 g for cocks (from 22 weeks of age until culling) with 160 g CP and 12.1 MJ metabolizable energy per kg. Birds were vaccinated for Marek’s disease, New Castle disease, Infectious Bronchitis disease and Fowl Pox disease as recommended by DLD.During the first 3 weeks after hatching, all chicks were housed in deep-litter housing with an average of 7.5 chicks per square meter  and total number of eggs produced during the first 17 weeks of lay. The birth weight at hatch was defined as the individual body weight at day 1  of the chick when measured on the first day of age and is the starting point for recording growth performances. Subsequently, individual BW of chicken were measured at 4 (BW4), 8 (BW8), 12 (BW12), 16 (BW16), 20 (BW20), and 24 (BW24) weeks of age. The BWs were measured in grams at 4 and 8 weeks of age and in kilograms thereafter. The BW at first egg (BWFE) is the BW (in kilograms) of the hen when the first egg laid while hens were in the individual battery cage. Age at first egg (AFE) is the age of the hen (in days) when her first egg was laid. The EWFE is the weight (in grams) of the first egg of an individual hen. Number of eggs (EN) is the total number of eggs per hen from the start of lay until the end of the 17th week after the start of lay.Phenotypic records within three standard deviations from the population mean were kept for this analysis. Main fixed effects identified were year, hatch within year and sex of bird. There were five years of hatch (from 2003 to 2007) with an average of 17 hatches per year. The BW traits were recorded when the LHKK project started and the egg production traits of LHKK were recorded a year later. Thus, all BW traits were recorded for five generations, only four generations of data were recorded for AFE and EWFE, and three generations of data for EN. Trait observations without hen identification were removed, which accounted for 1% to 6% of the observations. Parent cocks and hens were known for each bird, and five generations of pedigree were available for all birds with records. The total number of individual birds in the pedigree was 17,883 and the number of birds with records was 11,588, and they descended from 486 cocks and 1,461 hens. The same pedigree was used for all traits in estimating genetic parameters.The PROC Mixed procedure in SAS program  was usedGenetic parameters were estimated using restricted maxi mum likelihood methods by fitting an animal model in the WOMBAT software package . The infY = observation’s vector of the trait; b = vector of fixed effects ; a, m, and pe are the vectors of direct additive genetic effect, maternal genetic effect and permanent environmental hen effects, respectively; e = vector of random residual effect; X, Z1, Z2, and Z3 are incidence matrices relating records to the fixed, direct additive genetic effect, maternal genetic effect and permanent environmental hen effects, respectively.Where: The variance components for the random effects were de noted asA is a numerator relationship matrix. The covariance between direct additive genetic effect and maternal genetic effect was assumed to be zero.where The log likelihood ratio test was used to evaluate the sig nificance of fitting various random effects in the models. A chi square distribution with one degree of freedom was used as the critical test statistic. The inclusion of the effect was considered significant when twice the difference in the Log likelihood of nested models differing by one random factor was greater than the critical value of 3.84 (α = 0.05). Significant random effects (p<0.05) were included in the final model. Estimated heritabilities and genetic correlations were based on the best model identified in the previous step. Estimated genetic parameters and the log likelihood for each model are given in WOMBAT was also used to estimate inbreeding coefficients  based onAverage BW of LHKK chickens increased with age, ranging from 31 g for BW1D to 2.12 kg at BW24 . They reModel D, including direct additive genetic, maternal genetic effect and permanent environmental hen effects was the most appropriate (p<0.05) for BW1D, BW4, BW8, BW12, BW16, and BW20. Model B, including direct additive genetic and maternal genetic effect was most appropriate for BW24, and model C, including direct additive genetic effect and permanent environmental hen effects was the best model for AFE. Model A, including direct additive genetic effect only was found best for BWFE and EWFE. The heritabilities were generally low to high and standard errors were low . Direct Genetic correlations between growth traits from BW4 to BW24 were all positive . GeneticGenetic correlations between AFE and other traits were variable. The genetic correlations between AFE and growth traits were positive, except for BW4, BW8, and BW12. High genetic correlations were estimated between AFE and BW1D (0.63) and BWFE (0.72). Genetic correlations between EWFE and growth traits were high positive and ranged from 0.37 to 0.71. EN was negatively correlated with all traits and the correlations ranged from −0.10 to −0.73. Negative genetic correlations between growth traits and EN indicate that selection for higher growth rate would have reduced the number of eggs laid. Estimated correlations between maternal genetic effects were high between growth traits and ranged from 0.65 to 1.00 .The permanent environmental hen effect had positive correlations for growth traits, and ranged from 0.57 to 0.99 . Low negOverall, phenotypic correlations were positive between growth traits . The BW1The number of inbred birds, average inbreeding level and change in trait value per 1% of inbreeding, across generations, varied for traits . Egg proGenetic trends of growth traits expressed in units of genetic standard deviation unit (GSU) are shown over a five year period in Estimated mean BWs of LHKK chickens were similar to average growth performance of four other indigenous chicken breeds managed under tropical Thailand conditions ,7. The LHeritabilities of direct additive genetic effects were higher than maternal heritabilities and permanent environmental hen effects ratio for all growth traits, except for BW1D. This is because preovipositional maternal effects influence egg size and egg quality and thereby, influences chick weight at hatch . TherefoGenetic maternal and permanent environmental hen effects influence on growth and egg production traits were explored. Both maternal genetic effect and permanent environmental hen effects influenced all growth traits except for BW24. This is in agreement with the study by Ghorbani et al  on nativThe genetic correlations between growth traits were moder ate to high and in agreement with Dana et al  and LwelTotal EN was negatively correlated with growth and re production traits. Lwelamira et al  and NiknPositive genetic correlations between BW at different ages and BWFE and EWFE from this study indicate that selection for higher growth would increase BWFE and EWFE. Niknafs et al  reportedImproving BWFE would increase AFE and EWFE due to positive genetic correlations. Boonkum et al  and El-LThe AFE and EWFE had a high positive genetic correla tion of 0.66, which was in agreement with values reported for native chicken by El-Labban et al  and SangEstimated genetic correlations among the eleven traits of LHKK chickens suggest that the associations between growth and egg production traits are generally antagonistic. Chicken with heavier BW at maturity and heavier egg weight would increase household income. Selection for improving BW will lead to heavier individual egg weight of LHKK flock. However, it will have an unfavorable influence on the other egg production traits (AFE and EN). Selection for increased BW would result in increased egg weight through late sexual maturity and would lead to lower egg numbers. On the other hand, negative genetic correlations between EN and other traits indicate that hens with a lighter BW will reach sexual maturity earlier, and they can produce more eggs and chicks. Furthermore, lighter hens would lower feed cost due to lower consumption of feed. Therefore, a multi-trait selection strategy is required to achieve optimal growth and egg production in LHKK chickens.Rate of inbreeding estimated in this study was not high enough to have any detrimental effect on performance or vigor of LHKK chickens. However, inbreeding of males and females had mixed influence on growth traits on LHKK chickens across 5 years. An increase of 1% of coefficient inbreeding decreased BW1D (−0.29 g). Niknafs et al  and RahmIncrease in inbreeding level also increased the BW measured after 4 weeks of age. Niknafs et al  reportedIn conclusion, LHKK could be used as a dual purpose chickens. Moderate heritability estimated for growth and egg production traits suggested that both groups of traits could be improved through selection. However, antagonistic relationships shown in the phenotypic and genetic correlations between growth and some egg production traits, and effect of inbreeding rate suggested that both traits could not be improved simultaneously using additive selection. Therefore, multi-trait selection strategy using both groups of traits need to be implemented.


Through simulation studies, we showed that type I error was correctly controlled by FamBAC for various variant weighting schemes (0.0371 to 0.0527). However, FamKAC type I error rates based on approximation methods were deflated (max 0.0376) but improved by permutation tests. Our simulations also demonstrated that burden test FamBAC had higher power than kernel test FamKAC when high proportion (e.g. ≥ 80%) of causal variants had effects in the same direction. In contrast, when the effects of causal variants on the censored trait were in mixed directions, FamKAC outperformed FamBAC and had comparable or higher power than an existing method, RVFam. Our proposed framework has the flexibility to accommodate general nuclear families, and can be used to analyze sequence data for censored traits such as age-at-onset of a complex disease of interest.We propose a set of family-based burden and kernel tests for censored traits (FamBAC and FamKAC). Here, censored traits refer to time-to-event outcomes, for instance, age-at-onset of a disease. To model censored traits in family-based designs, we used the frailty model, which incorporated not only fixed genetic effects of rare variants in a region of interest but also random polygenic effects shared within families. We first partitioned genotype scores of rare variants into orthogonal between- and within-family components, and then derived their corresponding efficient score statistics from the frailty model. Finally, FamBAC and FamKAC were constructed by aggregating the weighted efficient scores of the within-family components across rare variants and subjects. FamBAC collapsed rare variants within subject first to form a burden test that followed a chi-squared distribution; whereas FamKAC was a variant component test following a mixture of chi-squared distributions. For FamKAC,  Recent advances in next generation sequencing (NGS) have expanded the scope of disease gene mapping from common variants to rare variants. Common genetic variants identified through genome wide association studies (GWASs) often explain only a small proportion of disease heritability. Rare genetic variants, here defined as alleles with a minor allele frequency (MAF) less than 1%–3%, tend to be functional and have stronger effects than common genetic variants, and may account for some of the missing heritability. This transition of disease gene mapping from common variants to rare variants has resulted in active development of new methods that test the association between rare variants and human complex traits. As these variants are rare, single variant association tests, a standard approach for common variants in GWASs, are underpowered unless effect sizes of rare variants or total sample sizes are very large . To overAlthough population-based designs are widely used in GWAS, family-based designs have several advantages for rare variants association analyses. First, due to the low minor allele frequencies (MAF) of rare variants, they may not be sampled adequately in the unrelated samples in the population-based designs. On the contrary, family-based designs are likely to increase the chance of sampling causal rare variants because of the shared genetic content within families , 8. Secoe.g.  = Swim, W be a k × k diagonal weight matrix with W = wm being the weight of the mth variant. Also let 1n be a vector of n 1s and 1k be a vector of k 1s. For the burden test, FamBAC collapses efficient scores of multiple rare variants into a single burden variable for each subject to form a test statistics as below:Sw are asymptotically distributed as a normal distribution with mean 0 under the null hypothesis. It is also reasonable to assume that under the null hypothesis the efficient scores of the offsprings in each trio are independently identically distributed. Then, according to the central limit theorem, TFamBAC is asymptotically distributed as χ2 distribution with degree of freedom 1.The within-family efficient score of each variant from each subject Swim, , was then × cov, where Sw1, Sw2, …, Swk are the efficient scores of the k variants from Sw. This quadratic form is shown to follow a mixture of χ2 distribution as:1, …, λk are the eigenvalues of V = nW2cov. For computing efficiency, Davies exact method [χ2 distribution to obtain p-values. Alternatively, one can also perform permutation tests to obtain empirical p-values.For the kernel test, FamKAC can be written as a quadratic statistic which sums over the marginal effects of individual rare variants as below:t method  can be uw1, …, wk be the weights of the k variants, which are pre-specified without using the outcome and reflects the relative contribution of the variants to the test statistic. There have been several weighting schemes proposed to date [wB = Beta [p refers to the MAF of a given variant.It has been shown that decreasing the weight of non-causal variants and increasing the weight of causal variants can yield improved power. To improve the performance of the tests in detecting effects of very low frequency variants, it will require choosing a suitable variant weighting scheme that would up-weight the variants with lower frequencies. Let  to date , 5, 17. 1/p(1−p)  and the https://personal.broadinstitute.org/sfs/cosi/), with parameters that mimicked linkage disequilibrium and allele frequency distributions observed in European population. We then divided this 30 kb gene into 100 segments of size 300 base pairs to represent subregions of the gene . Assuming 10 exons are within a gene, we randomly select 10 segments from the 100 segments to form a haplotype size of 3 kb to represent the exome captured in whole exome sequencing. This 3 kb region was subsequently fixed for all replicates generated by simulation. Using the pool of 20,000 haplotypes of size 3 kb, we generated genotype data for a population of 1,000,000 parent-offspring trios. Specifically, for each parent-offspring trio, we first randomly sampled two haplotypes without replacement from the 20,000 haplotype pool to form genotype data for each parent, and then randomly sampled one haplotype from each parent to generate genotype data for the offspring. The variants considered to be rare were those with population MAF from COSI less than 0.01. Consequently, 69 rare variants were found within the selected 3 kb region.To evaluate the validity and performance of our proposed methods FamBAC and FamKAC, we conducted a series of simulation studies of parent-offspring trios, and assessed Type I errors and statistical power under various parameter settings. We designed our simulation of sequence data based on the exome of a gene region, which is similar to the process described in Jiang et al. . That isAAO ≤ AAE, the subject was designated as affected with event time at AAO and event indicator Δ = 1; when AAO > AAE, the subject was designated as unaffected with censoring time at AAE and Δ = 0. To evaluate type I error rates, the AAO data were generated under the null hypothesis, where rare variants within the 3 kb region do not affect the AAO distribution. Thus, we drew random samples of AAO from a normal distribution N, where μAAO and VAAO were the pre-specified mean and variance of AAO. Similarly, AAE was randomly sampled from another normal distribution N where μAAE and VAAE were the pre-specified mean and variance of AAE. For simplicity, we assumed the variance of AAO and AAE were the same (VAAE = VAAO = 100). To ensure that the AAO data were generated within a reasonable life span (e.g. within 100 years of age), we assumed that the mean AAO (μAAO) was at the age of 45. The mean AAE (μAAE) was determined to be at an age that can theoretically lead to a given censoring rate. For example, to reach 10% censoring rate, μAAE was estimated at 26.88. For each parameter setting, we generated 10,000 replicates of 2,000 trios to estimate type I error rate.To simulate AAO of a disease as a censored trait, we assumed that every subject will eventually encounter the disease given sufficient follow-up time. Therefore, in order to create the censoring scenario, we generated AAO and AAE separately for each offspring. When VAAO) was contributed by all causal rare variants within the 3 kb region (Vg), polygenes (Vp), and residual environmental factors (Ve). We fixed the total AAO variance VAAO at 100 and the residual variance Ve at 75. This led to various combinations of Vg and Vp for the remaining variance (Vg + Vp = 25). We set different proportions of causal rare variants (Pcv) to obtain the number of causal rare variants (kcv) in the region. We randomly selected kcv causal rare variants from all rare variants within the 3 kb region. Assuming all causal rare variants contributed equally to AAO, the genetic variance contributed by each causal rare variant was obtained from pm was the population MAF for the mth variant from COSI. The AAO data of the offspring in the ith trio was then generated from the following model, μAAO was the overall AAO mean fixed at 45, gim was the genotype score of the offspring in the ith trio at the mth variant, and P and E were the random polygenic and environmental effects, randomly drawn from N and N, respectively. Similar to type I error simulations, AAE was randomly sampled from N, where VAAE was fixed at 100 and μAAE was determined at the age to theoretically achieve a given censoring rate. To evaluate the power of proposed tests, AAO data were generated under the alternative hypothesis, where rare variants within the 3 kb region contribute to the AAO distribution. We assumed that the total AAO variance . By fixing all other parameters, we compared statistical power of the proposed methods FamBAC and FamKAC for different censoring rates, variance of AAO contributed by all causal rare variants (Vg), proportions of rare variants to be causal (Pcv), and number of trios, respectively. Second, we simulated another scenario in which the kcv causal rare variants had effects in mixed directions on AAO. Within the kcv causal rare variants, we randomly selected a proportion of causal rare variants with positive effects on AAO, which would lead to later AAO (positve directon), and the rest of causal rare variants with negative effects on AAO (negative direction). We compared our proposed methods FamBAC and FamKAC with RVFam [Our simulation studies covered two scenarios regarding to the directions of effects of the th RVFam  for threw0 = 1 no weight; (2) p from COSI; (3) Gb  the density function of Beta distribution at population MAF p; and (5) Beta. We investigated five rare variant weighting schemes for FamBAC and FamKAC, including (1) w0, wB, and wp and wp and p-value for each replicate, and computed type I error based on 1, 000 replicates, a reduced number of replicates due to the computational burden. The type I error was improved from 0.0330 to 0.0424, which was encouraging to re-assure the validity of FamKAC. Finally, our simulation showed that population MAF p and sample MAF w0 and Vg, number of trios, proportion of rare variants to be causal (Pcv)  . We chosVg or number of trios increased  for FamBAC while other proportions (Pcv = 30% − 100%) have similar power, ranging from 71.1% to 80.8% . Similarly, the other parameters were fixed at the default values of 30% censoring rate, n = 2, 000 trios, and 30% of causal rare variants. We showed that FamBAC has decreasing power as the ratio of causal rare variants with different directions of effects increases (e.g. 20/80 and 50/50) for all Vg settings. FamKAC has fairly constant power across different positive/negative direction settings under a fixed Vg = 5 or Vg = 10  is small for all simulation studies , it is known to be more stable and computational efficient than the likelihood ratio test. Moreover, using efficient score statistics instead of score statistics has the advantage of removing the effects of nuisance parameters. Finally, our proposed tests have the flexibility to use different variant weighting schemes of one’s choice. Like other burden and kernel tests, p-values can be obtained analytically based on the corresponding distribution of the test statistics, χ2 distributions for FamKAC. Alternatively, permutation tests can be employed to increase the accuracy of p-values, particularly for FamKAC.We propose a set of family-based burden and kernel tests, referring to as FamBAC and FamKAC, for identifying association between rare variants and time-to-event outcomes. Both methods were built upon the efficient scores of parameter for the within-family genotype component derived from the frailty model. Our proposed methods utilized several known statistical properties. First, the orthogonal decomposition of offspring genotype scores into between- and within-family components is applicable to nuclear families of any sizes with and without parental genotype data. It is known that testing the effect of within-family component on the trait is robust to the potential population stratification . Second,χ2 distributions for FamKAC. This deflated type I error may also explain why FamKAC had lower power than we would expect in simulation studies. Since Davies’ approximation method can compute p-values efficiently, one may want to screen all genes using Davies’ method for FamKAC first, and then conduct permutation tests on selected genes meeting a conservative threshold (e.g. Davies p ≤ 0.1) for confirmation.The simulation studies under the null hypothesis showed that type I error was well controlled for FamBAC but deflated for FamKAC under different simulation settings. However, the validity of FamKAC is still supported by permutation tests. This deflation is likely due to the fact that we used Davies’ approximation method  to generFor the performance of FamBAC and FamKAC, their statistical power is generally aligned with what we anticipated for burden and kernel types of methods. That is, FamBAC performed better than FamKAC when majority of causal rare variants have same direction of effects, and conversely FamKAC outperformed FamBAC when causal rare variants have effects in mixed directions. This is anticipated as the burden tests directly collapse all rare variants without distinguishing the sign of their effect. On the other hand, since FamKAC is a variance component test using the second central moment statistics, it captures the absolute effects of all rare variants. Therefore, FamKAC is robust to the scenario of variants with mixed direction effects to the trait. Considering rare variants are most likely new mutations, it is not realistic to assume that all of causal rare variants affect the trait in the same direction. Since the direction of the variant effect is unknown and can be varied by genes, in practice, both tests should be performed. With the complementary property of both tests, it is also possible to implement a combined test of FamBAC and FamKAC using the similar strategy proposed in SKAT-O  in the fVg) and individual causal rare variant (Vcv) are correlated with the proportion of causal rare variants (Pcv) in the gene. Under fixed Vg, the Vcv decreases as Pcv increases, while under fixed Vcv for all causal rare variants, Vg increases as Pcv increases. In practice, these three parameters are unknown for a given gene. However, it is probably reasonable to assume that Pcv is lower in the full gene sequence from whole genome sequencing but larger in the exome of the gene [Pcv from 10% to 100% in related to Vg and Vcv. Under the 10% causal variant scenario, the power of both tests were generally lower than other proportions regardless of fixing Vg or Vcv  maintained similar power across different Pcv, while FamKAC (kernel test) showed decreasing power as Pcv increased (e.g. 80.09% power for Pcv = 30% vs. 79.9% for Pcv = 90%). The above power pattern for the scenarios with Pcv ≥ 30% may be explained by how burden and kernel tests handle the effects of rare variants. When Vg is fixed, the higher Pcv will result in lower Vcv. Since burden tests collapse the effects of all rare variants, Vg is preserved within each subject regardless of the different quantity of Vcv. On the other hand, kernel tests compute variance components for each variant prior to summing over variants. Therefore, the smaller Vcv may lead to smaller test statistics and then lower power. In contrast, when Vcv is fixed for all causal rare variants, the increase of Pcv is equivalent to the increase of Vg. As expected, burden test (FamBAC) shows increasing power as Pcv increased. Conversely, under fixed Vcv for all rare variants, the variance component that capture the variation among rare variants is likely small between different Pcv, which may explain the relatively consistent power observed in FamKAC. While the proportion of causal rare variants (Pcv) is a key factor to influence the power of both tests, we conclude that FamBAC is more sensitive to the total genetic variance (Vg), and FamKAC is more sensitive to the effect size of individual variant (Vcv).The genetic variance contributed by all causal rare variants  model is the most commonly used method for analyzing survival outcomes. In this study, we chose to use the frailty model instead of Cox PH model due to the consideration of the random polygenic effects shared within families. It is important to include the ploygenic effects as a random effect in the model because a complex disease is mostly affected by multiple genes rather than a single gene. For this reason, the frailty model, a generalization of the Cox PH model, fits better than Cox PH for family-based design. Regarding the applications of our proposed FamBAC and FamKAC, it is worthwhile to note the differences between our proposed methods for censored traits and commonly used methods for quantitative traits. Using AAO of a disease as an example, when AAO is treated as a quantitative trait, it is equivalent to a case-only design where only affected subjects with AAO are included. With the biased sampling on affected only, the distribution of AAO is likely to be skewed, and large sample size from affected subjects will be required to achieve sufficient power. The quantitative trait methods focus more on the amount of AAO changes if one carries the minor alleles of rare variants in the gene. On the other hand, our proposed tests that treat AAO as a censored trait can include the observed event time (AAO) from affected subjects and censoring time (AAE) from unaffected subjects. Our results focus on the relative risk (hazard ratio) of developing the disease at any age between the risk allele carriers and the non-carriers for the gene of interest.Vg) and the proportion of causal rare variants (Pcv) in the region have more impact on the statistical power, which have been captured in our simulation studies. Since the full 30 kb haplotype sequences were simulated based on the European genome, they should have captured the similar LD structure and allele frequencies in the European genome. Similarly, the exonic region we assembled should still capture the LD structure within each exon as well as across exons. Overall, our simulated sequence data should have sufficient representation of the sequence structure in the European genome for method evaluation. Second, we assume that all causal rare variants have equal effect size, which is unlikely in the real data. If Vcv varies across rare variants in the gene, this should not affect the performance of FamBAC since the Vg (sum of Vcv effects in the gene) is more relevant to FamBAC. Based on our simulation results shown in Vg, we would anticipate FamKAC shown higher power for the scenario of higher proportion of variants with higher Vcv than the scenario of higher proportion of variants with lower Vcv. Third, our proposed methods focus on testing rare variants within the region of interest, not including common variants. Common disease common variants (CDCV) and common disease rare variants (CDRV) [Some limitations are existed in this study. First, we used an arbitrary sequence length in our simulation studies. Our intention was to mimic the exonic region of a gene by generating 10 exons made up from 10 equal sequence length (300 bp). In reality, the number of exons and exon length may vary in/between genes. However, these arbitrary settings should not impact the pattern of simulation results much because the proposed method are gene-based test that aggregate all rare variant effects in the gene. The total genetic variance (s (CDRV)  are two s (CDRV)  but haveWe have proposed two rare variant association tests, FamBAC and FamKAC, for censored traits under family-design. Although we used parent-offspring trios throughout most part of this paper to describe our methods, as shown in the supporting information section, both methods can be applied to nuclear families of any sizes with and without parental genotypes. We have also shown the complementary property of FamBAC and FamKAC through simulation studies. In conclusion, as the genetic basis of AAO for human complex diseases remains an important area of research, our proposed methods can facilitate the analysis of family-based next generation sequencing data to identify gene associated with disease AAO.S1 Appendix(PDF)Click here for additional data file.S2 Appendix(PDF)Click here for additional data file.S3 Appendix(PDF)Click here for additional data file.


Antirrhinum majus) grown in a greenhouse. Our results brought evidence of a strong SAR syndrome for which magnitude did not vary between lines. They also brought evidence that its magnitude was not associated with the global DNA methylation percentage for five of the six traits under study. The magnitude of stem elongation was significantly associated with global DNA demethylation. We discuss the limits of this approach and why caution must be taken with such results. In-depth approaches at the DNA sequence level will be necessary to better understand the molecular basis of the SAR syndrome.Genetic and epigenetic variations are commonly known to underlie phenotypic plastic responses to environmental cues. However, the role of epigenetic variation in plastic responses harboring ecological significance in nature remains to be assessed. The shade avoidance response (SAR) of plants is one of the most prevalent examples of phenotypic plasticity. It is a phenotypic syndrome including stem elongation and multiple other traits. Its ecological significance is widely acknowledged, and it can be adaptive in the presence of competition for light. Underlying genes and pathways were identified, but evidence for its epigenetic basis remains scarce. We used a proven and accessible approach at the population level and compared global DNA methylation between plants exposed to regular light and three different magnitudes of shade in seven highly inbred lines of snapdragon plants ( In nature, organisms are often confronted with heterogeneous environments. Phenotypic plasticity is the capacity for an organism to produce several phenotypes in response to changes of its biotic and abiotic environments ,2,3. WheThe most documented case of phenotypic plasticity in plants is their shade avoidance response (SAR) . It is aPIF, HAT, ATHB2, or YUC [Arabidopsis, maize, tomato, and tobacco [Stellaria longipes to two light treatments (3.7 and 0.7 red/far-red ratio). They followed stem elongation and methylated cytosine content using high-performance liquid chromatography (HPLC) and found a correlation between the stem elongation response and levels of demethylation. They also submitted plants to 5-azacytidine treatment (a demethylating agent) to confirm the implication of DNA methylation in stem elongation and possibly in the SAR [Arabidopsis thaliana. Knowledge about the epigenetic basis of SAR, therefore, remains incomplete. Whether it can be generalized to other plant species remains to be addressed. SAR is a syndrome involving complex traits governed by multiple pathways associated with multiple genes, e.g., , or YUC . For exa tobacco ,32,34,41 tobacco . There i tobacco ,42,43,44 tobacco ,46. TatrAntirrhinum majus. Extensive work on developmental genomics was done on A. majus [A. majus line to shade treatments, which allowed us to study a fixed genetic background between individuals within lines, thereby excluding or extremely reducing the effect of genetic variation. Although epigenomics methods such as whole-genome bisulfite sequencing (WGBS) can provide detailed information at the scale of the cytosines over the whole genome [A. majus reference genome was published in 2019, after we conducted this study [Here, we present results from our investigation of the epigenetic basis of the shade avoidance response in seven highly inbred lines of A. majus , which wA. majus ,49. Its A. majus ,51,52 anA. majus . It is, e genome , these ais study . Other ais study ,57,58,59is study ,63,64,65is study . Shoot aAntirrhinum majus L. (snapdragon) is a hermaphroditic, self-incompatible, short-lived perennial plant from the Plantaginaceae family, which produces annual inflorescences with zygomorphic flowers. In this study, we used seven highly inbred lines of A. majus. These lines were generated by producing successive generations of self-fertilization. Their inbred genetic background was, therefore, mostly fixed at the homozygous state and was used as genetic stock to generate particular variants through inter-line crosses. Six lines were obtained from the John Innes Center , namely Ji2, Ji7, Ji75, Ji98, Ji522, and Si50. The seventh line was provided by the Technical University of Cartagena , namely E165. N = 140) grew under controlled environmental conditions with a large input of natural sunlight that was supplemented by artificial lighting  when the photoperiod was <16 h and/or when the light intensity was <400 watt/m2. Temperature was maintained below 30 °C by high-pressure water cooling and above 15 °C by central heating. Seeds were sown on 8 March 2017 in a mixture compost . Soon after all seeds germinated, seedlings were transplanted in individual 9 × 9 cm pots containing the same mixture compost (28 March 2017). Plants were watered approximately twice each week. Shade treatments were applied on 6 April 2017 when all seedlings had developed one or two internodes. We subjected the seven lines to four treatments  using three different types of shading nets. For logistical reasons, shade treatments were applied under controlled conditions as blocks that were not replicated. Five plants per line were subjected to each treatment. Plants were randomly distributed within treatments. We estimated the degree of shade produced by nets using spectrophotometer acquisitions  experimental station in Moulis (France). A total of 20 plants per line (isitions .2)/sum of dry masses of the five leaves (kg).We estimated the shade avoidance response 40 days after applying shade treatments (16 May 2017). Phenotypic measurements included plant height in cm, number of branches, number of floral buds, number of internodes, and stem diameter in mm. Internode length was calculated as the average stem length in cm per internode (plant height/number of internodes). Five fully developed leaves were collected and directly scanned on a regular scanner  after harvest to avoid any water loss , phosphodiesterase I , and alkaline phosphatase type III . The global percentage of methylation was determined by HPLC [v/v) and 5 mM acetic acid in water according to the methods of Gourcilleau et al. (2010). Controls for this procedure included co-migration with commercial standards (Sigma-Aldrich), confirmation by enzyme restriction analysis [At the end of the cultivation experiment, the shoot apex of each plant was harvested, frozen in liquid nitrogen, and conserved at −80 °C until epigenetic analyses. Frozen shoot apices were ground to powder using Tissue Lyser II , which grinds sample tissues by high-speed shaking of plastic tubes containing samples and stainless-steel beads. Total DNA was extracted from plant tissue using the Biosprint 15 DNA Plant kit , which is an automated method. DNA was enzymatically hydrolyzed into nucleosides and was analyzed by high-performance liquid chromatography (HPLC) following a previously published protocol ,70 with  by HPLC  using a analysis , and tesp-values), the amount of variation explained by the relationship (R2), and an indicator of the goodness of fit of the models (AIC) were used to estimate which models had the best fit. We then used this information to define the phenotypic reaction norm of traits.All statistical analyses were performed in R version 3.4.3  using thAs a first step, we explored the global effect of single factors on the data using unifactorial generalized linear models (GLM) for the sake of clarity and comparison with later results, with shade treatments considered as a continuous variable. We estimated the effects of (1) shade treatments (as a continuous effect) on phenotypic trait values, (2) shade treatments on global DNA methylation (%mC), and (3) the relationship between phenotypic trait values and %mC. We performed multiple comparison tests between treatments using Kruskal–Wallis tests when we found a significant effect of shade treatments on a trait. As a second step, we used generalized linear mixed models (GLMM) to include the random effect of lines in order to estimate genetic variation amongst lines. We tested for the effect of shade treatments (continuous fixed effect) on trait values, for the effect of lines (random effect) on the trait response to shade (line effect on the slope), and for the effect of lines (random effect) on trait values measured in full light conditions . We also tested for the effect of shade treatments (continuous fixed effect) on %mC, for the effect of lines (random effect) on the changes of %mC in response to shade , and for the effect of lines (random effect) on the %mC measured in full light conditions . We then tested for the effect of %mC (continuous fixed effect) on trait values, for the effect of lines (random effect) on the relationship between phenotypic trait values and %mC (line effect on the slope), and for the effect of lines (random effect) on trait values measured at baseline levels of %mC . Finally, in order to take into account that only plants reacting to shade are expected to undergo changes in %mC, we tested for a relationship between the magnitude of the trait responses to shade (using the slope of trait reaction norms) and the magnitude of %mC changes in response to shade (using the slope of the %mC reaction norm). This linear relationship was evaluated using a generalized linear model.N = 140) showed that the relationship between shade and trait values was better fitted by a quadratic function for all traits except for the number of branches . Up to 45% shade, the relationship with shade was significantly linear for most traits; plants grew taller, developed longer internodes, and featured more branches, while leaves were characterized by higher SLA. No such linear effects were detected for stem diameter and number of flowers  on the same four traits  and no significant effect of shade on stem diameter and number of flowers , revealed that about 15% of cytosines were methylated in the shoot apex of A. majus lines. We found no significant change in global DNA methylation percentage (%mC) in response to shade when we analyzed pooled data across lines (p = 0.87) and data of each line separately  on %mC. Measurements of %mC in full light conditions did not vary amongst lines, as illustrated by the lack of line effect on the intercept . Lines also did not vary in how their %mC varied with shade, as illustrated by the lack of difference between the slopes of %mC response to shade .The HPLC analysis, which was based on parately . These rp < 0.001). This means that trait values measured for %mC baseline levels varied significantly between lines in these four traits . These results were confirmed when we included the identity of lines as a random effect in a GLMM . Lines vr traits . The sloR2 = 0.5792, p = 0.0469; p-value because it is close to the significance threshold.In order to take into account the fact that only plants reacting to shade are expected to undergo changes in %mC, we compared the slope of the reaction norm to shade—the magnitude of phenotypic plasticity—to the slope of %mC modifications associated with shade for each trait. We found a negative relationship for internode length , where shade led to more compact plants with more branches [Our results brought evidence for phenotypic plasticity in response to shade in highly inbred lines of r plants ,27. Althr plants . In contbranches . This miA. majus that are used for the genetic study of plant development and the selection of horticultural varieties. These lines underwent multiple generations of self-fertilization and cultivation in controlled conditions, which resulted in reduced levels genetic diversity. They also likely underwent genetic and phenotypic divergence from natural populations. Phenotypic responses to light changes were already described in Antirrhinum cultivars [A. majus artificially selected lines. One originality of our results resides in showing that the reaction norms of lines were comparable, although these lines were characterized by different phenotypes. This finding implies the existence of standing genetic variation for the traits involved in the shade avoidance response, as well as the absence of such variation for the type or magnitude of the shade avoidance response.It is important to note that we did not characterize the shade avoidance response in natural populations. Our results were obtained in highly inbred lines of ultivars ,49,76. CA. majus genome (~400 to 500 Mb). This is almost three times more than A. thaliana with ~5% for ~135 Mb, and the same order of magnitude as several other species, e.g., Brassica oleracea with ~16% for ~700 Mb, Lepidium sativum with ~15% for ~380 Mb, and Primula vulgaris with ~14% for ~500 Mb [A. majus lines, we found no differences in terms of %mC. Our results also showed that there was no variation of %mC with trait values. Our results, therefore, contrast with those from the literature that often report %mC variation within and between genotypes, and %mC variation associated with developmental processes [A. majus lines. Alternatively, %mC variation might be linked to genetic variation in A. majus lines, but their genetic differentiation might be too weak to detect %mC differences.Our results showed that 15% of cytosines were methylated in the  ~500 Mb . Our fin ~500 Mb . At the  ~500 Mb ,78. As drocesses ,80,81. TWe found different results for the number of branches. Its association with %mC varied between lines . This liA. majus lines. When several genotypes are investigated, this lack of direct association between shade and %mC might reflect that plants not reacting to shade are not expected to undergo %mC changes. When we took this information into account, our results showed that the increase in internode length in response to shade was negatively correlated to changes in %mC in response to shade. This supports the hypothesis that the magnitude of this reaction norm was associated with the global demethylation of the genome in response to shade. However, no such significant relationship was detected for the five other traits under study. Stem elongation in response to shade is commonly found in the shade avoidance response of plants [Stellaria plants [A. majus based on our assessment. This is because (i) it is widely acknowledged as a highly plastic trait influenced by light in most plant species and was one of if not the most responsive to shade in our study, and (ii) we have to keep in mind that DNA methylation is tissue-specific [We found consistent levels of %mC across shade treatments when using models that did not account for potential genetic variation between f plants ,27. Thisf plants ,36,37. Sa plants . One migspecific ,84. Evencis regulatory elements [A. majus. This will require assessing the control of gene expression and TE mobility via DNA methylation. Before going into such a thorough investigation, our finding will, however, need to be replicated. This is because our data must be interpreted with caution. The correlation between epigenetic changes and plasticity was found in only one trait and, although the size effect was large, and the relationship was statistically significant, the rejection probability was close to the threshold limit of non-significance (p = 0.0469). This test would not remain significant after a Bonferroni correction, but such a correction is also known to generate false negative results. The global DNA methylation percentage of a genome is also difficult to interpret because it provides us with an average value across the genome. As a consequence, new methylation and demethylation events occurring in similar proportions are masked by each other. The lack of %mC differences that we observed between lines and between shade treatments might hide changes in DNA methylation, resulting in the same amount of hypomethylated and hypermethylated genes and non-coding regions . This level of detail might be revealed by epigenome sequencing  at the DNA base or genomic region level [A. majus [Our finding of a correlation between plasticity and methylation changes does not imply causality. It simply opens up the question of this causality. It is widely acknowledged that gene expression can be controlled “directly” via methylation of gene promoters or bodies . Gene exelements . The mecon level ,66. WithA. majus , the feaA. majus imply the existence in this species of a shade avoidance syndrome, which is an ecologically significant case of phenotypic plasticity widely found in plants. They also imply that the global genome demethylation of these lines was possibly associated with stem elongation in response to shade. Our study, therefore, supports the hypothesis that epigenetic variation might play a role in an ecologically significant phenotypic plastic response in plants. However, no such result was found for the other traits of the syndrome. Assessing this type of association by measuring global DNA methylation percentages can be done at the population level, at which ecological relevance can be tested. However, our study underlined some limits of this approach, and caution must be taken with the results that it generated. Further evidence at the DNA sequence level appears necessary before a solid conclusion can be drawn.For the past few decades, the interest in epigenetic modifications and their role in the regulation of adaptive genes with ecological significance grew widely. Some studies provided evidence that DNA methylation has the potential to play a role in local adaptation and fast adaptive responses ,86. The


This study investigated if the allele effect of a given single nucleotide polymorphism (SNP) for crossbred performance in pigs estimated in a genomic prediction model differs depending on its breed-of-origin, and how these are related to estimated effects for purebred performance.SNP-allele substitution effects were estimated for a commonly used SNP panel using a genomic best linear unbiased prediction model with breed-specific partial relationship matrices. Estimated breeding values for purebred and crossbred performance were converted to SNP-allele effects by breed-of-origin. Differences between purebred and crossbred, and between breeds-of-origin were evaluated by comparing percentage of variance explained by genomic regions for back fat thickness (BF), average daily gain (ADG), and residual feed intake (RFI). From ten regions explaining most additive genetic variance for crossbred performance, 1 to 5 regions also appeared in the top ten for purebred performance. The proportion of genetic variance explained by a genomic region and the estimated effect of a haplotype in such a region were different depending upon the breed-of-origin. To illustrate underlying mechanisms, we evaluated the estimated effects across breeds-of-origin for haplotypes associated to the melanocortin 4 receptor (MC4R) gene, and for the MC4Rsnp itself which is a missense mutation with a known effect on BF and ADG. Although estimated allele substitution effects of the MC4Rsnp mutation were very similar across breeds, explained genetic variance of haplotypes associated to the MC4R gene using a SNP panel that does not include the mutation, was considerably lower in one of the breeds where the allele frequency of the mutation was the lowest.Similar regions explaining similar additive genetic variance were observed across purebred and crossbred performance. Moreover, there was some overlap across breeds-of-origin between regions that explained relatively large proportions of genetic variance for crossbred performance; albeit that the actual proportion of variance deviated across breeds-of-origin. Results based on a missense mutation in MC4R confirmed that even if a causal locus has similar effects across breeds-of-origin, estimated effects and explained variance in its region using a commonly used SNP panel can strongly depend on the allele frequency of the underlying causal mutation.The online version of this article (10.1186/s12864-018-5126-7) contains supplementary material, which is available to authorized users. The  animals , 4. As rncreases , 6.Moreover, crossbred genomic information is composed of a mosaic of genomic regions inherited from the different parental breeds . As a result, depending from which breed-of-origin an allele was inherited from, it might have different effects. These different allele effects can be due to: (1) quantitative trait loci (QTL) may be in linkage disequilibrium with different single nucleotide polymorphism (SNP) depending from which parental breed the QTL was inherited , (2) theSeveral studies support that effects of SNPs may be breed-specific. Firstly, in many cases, estimated effects of SNPs in an association study for a given breed are not replicated by studies in other breeds –12. SecoWith crossbreeding, SNPs can be observed in the different genetic backgrounds. Estimation of background specific effects, however, requires that the SNP alleles present in the crossbred animal are assigned to one of the parental breeds. Recently, we have developed a procedure that enables breed-of-origin assignment of alleles in three-way crossbred animals , 17. Knopc (< 0.70), tracing the breed-of-origin of alleles and using this information in a genomic best linear unbiased prediction model that accounts for breed-specific SNP effects for crossbred performance (BOA model) tended to show better predictive abilities compared to models in which SNP effects are assumed to be the same across breeds [pc of 0.82, a heritability for crossbred performance of 0.43 and no better predictions observed when using the BOA model; average daily gain (ADG) with an rpc of 0.61, a heritability for crossbred performance of 0.26 and better predictions observed when using the BOA model; and residual feed intake (RFI) with an rpc of 0.62, a heritability for crossbred performance of 0.19, but not tested previously with the BOA model. To illustrate how the effect of SNP-alleles in crossbred pigs depend on their breed-of-origin, we evaluated the estimated effects across breeds-of-origin for the melanocortin 4 receptor (MC4R) gene which has a missense mutation that is known to affect BF and ADG.For traits with low heritability (< 0.20) and low rs breeds . This iss breeds , 19, bacThe data consisted of three purebred-based pig populations; S, LR, and LW, and one crossbred population (S (LR x LW) or S (LW x LR)). S is a synthetic sire line created as a combination of Large White and Pietrain. LR is a Landrace based dam line and LW is a Large White based dam line. All pigs were genotyped using one of the three following SNP panels: Illumina PorcineSNP60.v2 BeadChip (60 K.v2), Illumina PorcineSNP60 BeadChip (60 K), or Illumina PorcineSNP10 BeadChip (10 K). Pigs genotyped with the 60 K or 10 K chips were imputed to the 60 K.v2 panel using FImpute Version 2.2 software  with defBWon is the on-test body weight, BWoff is the off-test body weight, BF and ADG are the previously described traits, b1, b2, b3, and b4 are the linear coefficients of the regression on covariates, and e is the RFI. The numbers of available genotypes and phenotypes per trait and per population are summarized in Table Performance from purebred pigs were available from 52 nucleus and combined crossbred purebred system (CCPS) farms recorded from August 2005 until August 2016. Performance from crossbred pigs were available from 7 CCPS and experimental farms recorded from January 2009 until March 2016. Phenotypes for BF and ADG were measured in most of the purebred and crossbred pigs. BF for purebred pigs was measured on average at 173 days of age using an ultrasound instrument, while BF for crossbred pigs was measured on the carcass using a probe, named “capteur gras maigre” , crossbred pigs were slaughtered when they achieved 120 kg (at an average age of 169 days). BF was measured approximately at the third to fourth rib from the last rib position. ADG for purebred pigs was calculated as the difference of on-test body weight at an average age of 60 days and off-test body weight at an average age of 173 days divided by the phase length. ADG for crossbred pigs was calculated as the difference of on-test body weight at an average age of 70 days of age and body weight at end of the finishing period, which was on average 120 kg, divided by the phase length. RFI was obtained as the estimated residual term from the following regression model :\documenGS) similar to Wang et al. . HoweverTo infer breed-of-origin of alleles in crossbred pigs we used the breed of origin of alleles approach (BOA approach) developed by Vandenplas et al.  and assuGS) was fitted  [GS) with three breed-specific partial relationship matrices (A model) . The thrVanRaden . The breMS is a matrix containing breed-specific allele content for purebred S pigs . MCB is a matrix containing breed-specific allele content for crossbred pigs , so that alleles not assigned a breed-of-origin were set to missing, meaning that they had an entry of zero in the centred matrix represented by (MCB − 1pS′); pS is the vector of breed S specific frequencies of the counted allele ( and GLW)( are defined similarly to GS), then it can be concluded that the coefficient of linkage disequilibrium, D, is significantly different from zero and that pair of loci are in linkage disequilibrium [Under a back-solving approach, all SNPs are considered simultaneously in the model, therefore, the effect of a QTL is likely distributed across all SNPs that have a nonrandom association with the QTL. For this reason, it is recommended to calculate the proportion of variance explained by a group of SNPs in nonrandom association instead of reporting effects of single SNPs . Groups ilibrium . Breakpoilibrium .i-th LD block was calculated as in Wang et al. [i is genetic value of the i-th LD block, j is a vector of genotypes of the j-th SNP for all purebred individuals of the same breed, j-th SNP within the i-th LD block that contains n SNPs, xnis the mean number of SNPs across LD blocks and niis the number of SNPs of the i-th LD block. With the back-solving approach we can identify peaks that explain the most variance, in our case, we took the top 10 LD blocks for comparison across scenarios.Percentage of genetic variance explained by the g et al. :\documenhttps://www.ncbi.nlm.nih.gov/genome/gdv/?org=sus-scrofa) and based on literature.Putative candidate genes within the top 10 LD blocks and in the neighbouring upstream and downstream 1-Mb regions were identified based on the Sscrofa11.1 genome assembly, using the NCBI Map Viewer (m) and allele G is the wild type allele . The MC4Rsnp was imputed in pigs that were not genotyped for it and the breed-of-origin of both alleles were inferred with the BOA approach. After quality control we had information for 7469 S, 3257 LR, 12707 LW, and 2763 crossbred pigs. Allele frequencies of the MC4Rsnp were computed in each of the purebred populations and in the crossbred population considering breed-of-origin. In order to build LD blocks that co-segregate with the MC4R gene, linkage disequilibrium was tested between the alleles of MC4Rsnp and the alleles from all the other loci in the SSC1 of the crossbred population [To illustrate the mechanisms underlying breed-of-origin specific estimated SNP effects, we investigated the estimated effects across breeds-of-origin for haplotypes associated to the MC4R gene, and the allele substitution effects for the MC4Rsnp itself. The MC4R gene has a missense mutation that is known to have a strong effect on BF and ADG . The genADGij was the pre-corrected ADG phenotype of crossbred pig j, ADG phenotypes were corrected for fixed effects listed in Table MC4RsnpS, MC4RsnpLR, and MC4RsnpLW were the centered allele content of MC4Rsnp (0 or 1) of crossbred j for breed-of-origin S, LR, and LW, respectively; bS, bLR, and bLW were the unknown allele substitution effect of MC4Rsnp for breed-of-origin S, LR, and LW, respectively; i, assumed to be normally distributed ~ N and eij was the random residual effect assumed to be normally distributed ~ N, followed by ADG (0.60–0.69), while BF showed the highest rpc (0.71–0.89). Because of the limited number of RFI records from LR pigs, genetic parameters estimated in this population had very high standard errors, therefore, estimates are not shown and were not further used in this study.Estimated variance components and standard errors for BF, ADG, and RFI using the BOA model are shown in Table The number and size of the LD blocks are shown per breed-of-origin in Table Figures LD blocks that appeared for both purebred and crossbred performance in the top 10 with most explained genetic variance, are shown per breed-of-origin in Table As LD blocks across breed-of-origin were not the same, because of different patterns of linkage disequilibrium, comparisons across breeds for crossbred performance were done regarding whether the top 10 LD blocks across breeds overlapped or were less than 1-Mb distance apart Table . These rA similar comparison was made across breeds for purebred performance. For BF, there was one common region between the top 10 LD blocks from S and LW and there were two common regions between the top 10 LD blocks from LR and LW. For ADG, there was one common region between the top 10 LD blocks from S and LR. For RFI, comparisons could be only made between S and LW because the SNP-allele effects for the LR population were not estimated, there was one common region between the top 10 LD blocks from S and LW. These regions can be observed in Figs. Putative candidate genes within the top 10 LD blocks either for purebred or crossbred performance and in the neighbouring upstream and downstream 1-Mb regions were identified based on the Sscrofa11.1 genome assembly and based on literature. The MC4R was identified as a candidate gene for ADG and BF. The MC4R gene was previously associated with feed intake and growth rate in pigs, as well as with BF –33. The The StAR-related lipid transfer domain containing 13 (STARD13) was identified as a candidate gene for BF. The STAR gene family is involved with lipids and lipid hormones binding to be exchanged between biological membranes . STARD13The porcine insulin-like growth factor binding protein (IGFBP-5) was also identified as a candidate gene for BF. IGFBP-5 is a focal regulatory factor during the development of several key cell types, e.g., myoblasts and neural cells . The IGFWe did not identify any candidate gene for RFI. For RFI, there are few GWAS studies in pigs and they all revealed different regions associated with this trait , 40, 41.m allele is highly prevalent (0.81–0.85), whereas in the LR population or among alleles originating from LR in the crossbred population, the m allele is almost absent (0.06–0.11).From all evaluated candidate genes, only for the MC4R gene the underlying causal mutation is known. Allele frequencies of this MC4Rsnp were quite similar between observed frequencies in purebred compared to crossbred pigs, but considerable differences were observed between breeds within the purebred or between breeds-of-origin within the crossbred Table . In the m or w allele of MC4Rsnp. The only exception was a haplotype that was observed in 83 crossbred pigs originating from S, in 260 crossbred pigs originating from LR, and in 1993 crossbred pigs originating from LW. This haplotype carried the m allele for all these crossbred pigs, except for two who received the haplotype from S and carried the w allele. These two cases, however, may simply be genotyping errors and were not used further for the MC4R analysis. Therefore, after including the MC4Rsnp in the LD block we still observed 74 different haplotypes. From the 74 haplotypes, 44 were observed in the S breed-of-origin, 19 in the LR breed-of-origin and 31 in the LW breed-of-origin.For S breed-of-origin, the MC4Rsnp was in LD with 31 flanking loci, which resulted in a LD block from 158.9 to 161.5 Mb. For LR breed-of-origin, the MC4Rsnp was in LD with 49 flanking loci, which resulted in a LD block from 158.8 to 163.3 Mb. For LW breed-of-origin, the MC4Rsnp was in LD with 42 flanking loci, which resulted in a LD block from 158.9 to 162.6 Mb. For comparison across breed-of-origin, we only considered the overlapping SNPs across the three LD blocks which resulted in a block of 31 SNPs (158.9–161.5 Mb). It is worthwhile noting that this MC4R based block contains the LD block spanning 158.9–160.2 Mb that was identified to be associated with ADG and BF in S and the LD block spanning 159.2–160.2 Mb associated with ADG and BF in LW. The block contained 74 different haplotypes, each unique haplotype was always exclusively co-segregating either the m allele had different effects compared to haplotypes co-segregating with the w allele . Haplotypes co-segregating with the m allele, in general, had a positive effect, while haplotypes co-segregating with the w allele had a negative effect. Effects of specific haplotypes were similar if they originated from the S or the LW population, however, their effects were smaller if they originated from the LR population . For each breed the average effects of the m and w allele, weighted according to the haplotype frequencies, are shown as red numbers in Fig. m allele for a w allele has an effect on ADG of − 2.5 g/d, − 0.5 g/d and − 1.6 g/d, when the allele originates from S, LR, or LW, respectively. Using the MC4Rsnp itself, the effect of substituting an m allele for a w allele at MC4Rsnp was − 22.60 g/d, − 14.21 g/d, or − 21.67 g/d, when the allele originated from S, LR or LW, respectively. Figure In Fig. FST was previously estimated by Sevillano et al. [The objective of this study was to show how the effect of SNP-alleles, estimated in a genomic prediction model using commonly used SNP panels, varies when observed in different genetic backgrounds. With crossbreeding, the effects of SNP-alleles can be observed both against purebred and crossbred background. Moreover, the degree of allelic differentiation among the three populations estimated with Weir and Cockerham’s o et al.  and werepc (< 0.70) the BOA model tended to show better predictive abilities [pc estimates with pedigree information from Godinho et al. [pc and heritability for crossbred performance were as expected.To observe the estimated effects of SNP-alleles for crossbred performance in different genetic backgrounds, we traced the breed-of-origin of alleles in crossbred animals and estimated breed-specific SNP effects from the solutions of a BOA model for three traits. For traits with low heritability (< 0.20) and low rbilities . Therefoo et al. , BF, ADGo et al.  as it waThe proportion of total genetic variance explained was calculated for each LD block instead of reporting effects of single SNPs. The LD blocks built based on alleles originating from the S paternal population were, on average, longer than the LD blocks built based on alleles originating from the maternal LR and LW populations. This is in line with linkage disequilibrium estimations made by Veroneze et al.  using thpc for that trait, as the correlation between allele substitution effects of the causal variants of two traits is expected to be the same as the genetic correlation between two traits [pc (0.37–0.60) and had at most only one LD block that appeared for both purebred and crossbred performance, while BF showed the highest rpc (0.71–0.89) and had 4 to 5 LD blocks that appeared for both purebred and crossbred performance.Within the same breed-of-origin, we observed some LD blocks that appeared for both purebred and crossbred performance in the top 10 with most explained genetic variance. Across traits, this number of common LD blocks in the top 10 is expected to be related to the ro traits . Our respc values below unity. However, as shown in Table pc values below unity is the presence of genotype by environment interactions (GxE) [For LD blocks that appeared for both purebred and crossbred performance in the top 10 with most explained variance, we observed that they explained a similar percentage of additive genetic variance. Despite the fact that percentages of additive genetic variance were quite similar, differences in allele frequencies between purebred and crossbred can explain rns (GxE) , 43. GxEns (GxE) , 43, as m allele of MC4Rsnp Additional file 2:Proportion of genetic variance for average daily gain explained by the top 10 LD blocks for purebred and crossbred performance by breed-of-origin. (PDF 214 kb)Additional file 3:Proportion of genetic variance for residual feed intake explained by the top 10 LD blocks for purebred and crossbred performance by breed-of-origin. (PDF 215 kb)