MPr) for each model. Two distinct cross-validation (CV) schemes simulating two genomic prediction breeding scenarios were used. CV1 predicts the performance of newly developed lines, whereas CV2 predicts the performance of lines tested in sparse multi-location trials. Predictions for Zn in CV1 ranged from -0.01 to 0.56 for DH1, 0.04 to 0.50 for DH2 and -0.001 to 0.47 for the association panel. For CV2, MPr values ranged from 0.67 to 0.71 for DH1, 0.40 to 0.56 for DH2 and 0.64 to 0.72 for the association panel. The genomic prediction model which included G x E had the highest average MPr for both CV1 (0.39 and 0.44) and CV2 (0.71 and 0.51) for the association panel and DH2 population, respectively. These results suggest that GS has potential to accelerate breeding for enhanced kernel Zn concentration by facilitating selection of superior genotypes.Zinc (Zn) deficiency is a major risk factor for human health, affecting about 30% of the world’s population. To study the potential of genomic selection (GS) for maize with increased Zn concentration, an association panel and two doubled haploid (DH) populations were evaluated in three environments. Three genomic prediction models, M  incorporating main effects (lines and genomic) and the interaction between genomic and environment (G x E) were assessed to estimate the prediction ability ( Malnutrition arising from zinc (Zn) deficiency is a major risk factor for human health affecting nearly 30% of the world’s population . SeveralBreeding maize for increased Zn concentration may offer some relief. The Zn-enriched varieties can be widely accessible, will not require continued investment once developed, and they remain after the initial successful investment and research (Govindan 2011). Recently, maize varieties with 15–36% more Zn were released in Guatemala and Colombia . NeverthKernel Zn concentration is determined at the end of a plant’s life cycle, so GS can enable selection of promising genotypes earlier in the life cycle. This reduces the time and cost of phenotypic evaluation and may increase the genetic gain per unit time and cost .Initially, GS models and methods were developed for single-environment analyses and they did not consider correlated environmental structures due to genotype by environment (G x E) interactions  analyses in maize and each study has reported that Zn concentration is under the control of several loci. The phenotypic variation explained by those loci ranges from 5.9 to 48.8% . The panel represents wide genetic diversity for kernel Zn concentration  populations. DH1 was derived from the F1 generation of a mating between CML503, a high-Zn inbred (31.21 μg/g) with CLWN201, a low-Zn inbred (22.62 μg/g). DH2 was derived from the F1 generation of a mating between CML465, another high-Zn inbred (31.55 μg/g) with CML451, a moderate-Zn inbred (27.88 μg/g). DH1 and DH2 were comprised of 112 and 143 inbreds, respectively.The ZAM panel was grown at CIMMYT research stations in Mexico, during the months of June through September and November through March at Agua Fria in 2012 and 2013, and Celaya in 2012. Plot sizes and the experimental designs  in 2015 and 2017. In 2014 and 2015, both populations were evaluated in single-replication trials  using the standard CIMMYT laboratory protocol . The samhttps://www.maizegenetics.net and the SourceForge Tassel project https://sourceforge.net/projects/tassel. For each inbred, the pipeline yielded 955, 690 SNPs which were distributed on the 10 maize chromosomes. After filtering using a minor allele frequency of 0.05 and removing SNPs with more than 10% missing data, 181,889 (ZAM panel) and 170, 798  SNPs were used for genomic prediction.SNP identification was performed using TASSEL 5.0 GBS Discovery Pipeline with B73 (RefGen_v2) as the reference genome (2)  . An estiCorrelation coefficients between Zn and environments, descriptive statistics and phenotypic data distribution using boxplots were generated in R . Line meGenomic models used in this study were based on the reaction norm model which models the markers (genomic) by environment interaction  genotype/line tested in the ith  environment and thj genotype and the ith environment N denotes a normal random variable, iid stands for independent and identically distributed responses and In this study, environment is the combination of site and year (site-by-year) and the adjusted means (BLUES) to be used in the genomic prediction models are obtained by fitting the phenotypes Thus, models used in this study were derived from the baseline model above by subtracting terms or modifying assumptions and/or incorporating genomics/marker information. A brief description of the genomic models used in this study are given below.This model is obtained by retaining the first three components from the baseline model  while their underlying assumptions remain unchanged.mth  marker, th line at the mth marker and Another representation of the random main effect of line Therefore, th genotype in the thi environment and This model accounts for the effects of lines Models were first fitted to the entire data set to estimate variance components using the R-package BGLR . The infTwo distinct cross-validation schemes that mimic prediction problems that breeders may face when performing genomic prediction were used (MPr).In CV1 and CV2, a fivefold cross-validation scheme was used to generate the training and validation sets to assess the prediction ability for Zn within the ZAM panel and each DH population. The data were randomly divided into five subsets, with 80% of the lines assigned to the training set and 20% assigned to the validation set. Four subsets were combined to form the training set, and the remaining subset was used as the validation set. Permutation of five subsets taken one at a time led to five training and validation data sets. The procedure was repeated 20 times and a total of 100 runs were performed in each population. The average value of the correlations between the phenotype and the genomic estimated breeding values (GEBVs) from 100 runs was calculated for the ZAM panel, and each DH population for Zn in each environment and was defined as the prediction ability  were needed to explain 80% of the genotypes’ variance  and 3B. i.e., M2 and M3), the estimated variance due to environments was reduced to 9.46 for M2 and 8.11 for M3. Inclusion of the interaction term (genomic x environment) reduced the residual variance component by ∼30%, from an estimated residual variance of 7.01 in M1 to 4.92 in M3 suggesting that some components of differences among genotypes cannot be fully captured by the main effects of markers and environments. Similar trends were observed in DH1 and DH2 except for M1 of DH1 where the main effect of lines accounted for a slightly higher proportion of the total variance (38%) than the main effects of environments (35%).Variance component estimates for all models were derived from the full-data analysis . In the The residual variance component values for all models of DH populations were slightly higher than those for the ZAM panel , possiblMPr values for kernel Zn were estimated for the ZAM panel and DH populations  and CV2 (0.71) were observed in Tlaltizapan 2017 and 2015, all for DH1  correlation values between environments  and CV2 (0.72) were obtained in Celaya and Agua Fria 2012 . For the for DH1  and 7. T and CV2 ronments .MPr values obtained from each model, M1 had the lowest  accuracies in CV1 for the ZAM panel and DH populations  to 0.39 (M3) for the ZAM panel and from 0.43 (M2) to 0.44 for DH2 , which includes the interaction between markers (genomic) and the environment  for DH2  and 6. S for DH2  and 7. Hs for Zn  and 7. FAM panel , 0.51 toAM panel . For CV2AM panel , 6 and 7Overall, moderate to high prediction ability values for kernel Zn were observed for the ZAM panel and DH populations. This could be attributed to the heritabilities observed for kernel Zn  and 2. SAdditionally, the moderate to high prediction accuracies reported in this study shows that GS can be used in maize breeding to improve kernel Zn concentration. Assuming two possible seasons of Zn evaluation per year, the predicted genetic gains can be estimated from prediction accuracies and genetic variances of the training populations. The genetic variances for the ZAM panel, DH1 and DH2 were 12.38, 12.20 and 14.88, and prediction accuracies were 0.71, 0.70 and 0.51, respectively. If the inbreds in each predicted population are ranked based on their predicted Zn values and the top 10% selected, then their expected average Zn values can be estimated from the proportion of inbreds selected, their respective training population genetic variances, prediction accuracies and the time interval for evaluating the lines. With reference to this, the expected average values of Zn are approximately 31 μg/g for the ZAM panel, 30 μg/g for DH1 and 27 μg/g for DH2. These averages are higher than the averages of the respective training populations  suggesting that the prediction accuracies achieved are sufficient to select at least 10% of the predicted inbreds with higher Zn concentration.Data from both bi-parental populations and diverse collection of inbreds have been used for GS and based on cross-validation (CV), it has been established that prediction accuracies could also be affected by the relatedness between training and prediction sets .Cross-validation (CV) schemes are used in genomic prediction to estimate the accuracy with which predictions for different traits and environments can be made (vs. 143 lines), this was unexpected since DH1 and DH2 were grown in the same environments.Genotype by environment interaction is an important factor affecting kernel Zn concentration in maize and genomic prediction models that incorporate G x E may enhance the potential of GS for biofortification breeding. For different crop species and traits, genomic prediction models which incorporated G x E achieved higher prediction accuracies in both CV1 and CV2 schemes relative to models which did not include G x E . Such correlations can be exploited using multi-environment models to derive predictions that use information from across both the lines and environments . Thus, bHowever, the prediction accuracy values were of lower quality when genomic predictions were conducted across populations. For instance, when the ZAM panel was used as the training population, prediction accuracies for DH1, DH2 and DH1+DH2 were 0.15, -0.10 and 0.09, respectively. When DH1 and DH2 were used as a training and prediction set for each other, prediction accuracies were 0.08 and 0.16 (Unpublished data). These prediction accuracies are considerably lower than those reported in this study and the differences may be attributed to: (i) weak genetic relationships between the training and prediction population sets and (ii) different methods of analysis because the prediction accuracies reported in this study were partly achieved by modeling the random-effects environment structure to account for G x E while for the unpublished data, the random-effects environment structure of G x E was not included.The ability to predict kernel Zn concentration using high-throughput SNP markers including G x E interactions creates an opportunity for efficiently enhancing Zn concentration in maize breeding programs. For instance, during early generations of a breeding program, GS can be utilized to identify genotypes with favorable alleles when numbers of progenies and families are large. This could potentially reduce the resource-intensive evaluation process and advancement of false-positive progenies (


Picea abies (L). Karst.) for the traits related to wood tracheid characteristics. The study employed an exome capture genotyping approach that generated 178 101 Single Nucleotide Polymorphisms (SNPs) from 40 018 probes within a population of 517 Norway spruce mother trees. We applied a least absolute shrinkage and selection operator (LASSO) based association mapping method using a functional multi-locus mapping approach, with a stability selection probability method as the hypothesis testing approach to determine significant Quantitative Trait Loci (QTLs). The analysis has provided 30 significant associations, the majority of which show specific expression in wood-forming tissues or high ubiquitous expression, potentially controlling tracheids dimensions, their cell wall thickness and microfibril angle. Among the most promising candidates based on our results and prior information for other species are: Picea abies BIG GRAIN 2 (PabBG2) with a predicted function in auxin transport and sensitivity, and MA_373300g0010 encoding a protein similar to wall-associated receptor kinases, which were both associated with cell wall thickness. The results demonstrate feasibility of GWAS to identify novel candidate genes controlling industrially-relevant tracheid traits in Norway spruce.Through the use of genome-wide association studies (GWAS) mapping it is possible to establish the genetic basis of phenotypic trait variation. Our GWAS study presents the first such effort in Norway spruce ( The properties of the tracheids have large influences on the quality of the final products, and also on process economy andì sustainability, for solid wood as well as fibre-based products2. Tracheid morphology and cell wall structure influence the flexibility of wood and fibres, interactions among fibres, as well as the mechanical, physical and optical properties of the end-products3. Consequently, identifying the genetic background of different tracheid traits as a basis for breeding may bring benefits for both industry and society. Several papers have reported the phenotypic correlations, between tracheid cross-sectional dimensions and wood traits such as density in conifers6. A study of Norway spruce felled in the winter of 1989/1990 in central Sweden, found tracheid length dependent on the logarithm of cambial age and growth ring width, with density dependent on latewood percentage. Similar models for the influence of cambial age and ring width have been presented for tracheid length, width and wall thickness models of Norway spruce, Sitka spruce, Scots pine and loblolly pine7. Such results have indicated that changes in growth conditions over time acting mainly through crown development, will have an influence on wood structure development in Norway spruce6. However, these reports paid very little regard to the underlaying genetic factors influencing these phenotypes. Therefore, the dissection of the genetics impacting these relationships and the variations observed in tracheid properties will be of great value to any tree breeding program.Norway spruce is considered to be one of the most important multipurpose species. Its wood provides various solid wood products as well as pulp and paper products. It is considered one of the best raw-materials for the production of mechanical pulp for many types of paper grades8. Wood density and microfibril angle (MFA) are key indicators of wood quality as they influence strength and dimensional stability of solid wood9. However, combining productivity with wood quality is problematic due to negative genetic correlations between these traits10. One of the tools helping to understand these genetically complex variations in forest trees is the integration of extensive genetic and phenotypic data in order to discern the genetics underlying these traits13. Hence, knowing the genetic control of these variations, may lead to optimal breeding strategies for the improvement of both growth and wood quality traits.Various long-term breeding programmes for the species are already being pursued with the goal to identify genotypes with high productivity and wood quality14, hence the need to utilize techniques that target the whole genome15. The availability of an array of genomic resources has led to the reliable identification of Quantitative Trait Loci (QTLs), which in conifers are traditionally detected using suitable segregating populations such as, full- or half-sib progenies. More recently, GWAS, also known as Linkage Disequilibrium (LD) mapping, has been applied as an alternative approach of QTL detection from traditional pedigree-based mapping studies. GWAS accounts for historical recombination events in the natural population as compared to those observed in a pedigree based QTL mapping16. When confounding factors are taken into consideration, LD mapping provides greater resolution than pedigree studies, since it utilizes markers in strong LD with putative causative genomic regions17.With genomic resources now available, a large array of molecular markers has been available for the studying and understanding of complex traits. The majoritiy of these traits are known to be predominantly polygenic in nature, and affected by environmental effects16. However, rapid and heterogenous decay in conifer LD18 can be a source of concern as proximal markers can be completely unlinked and therefore offer no predictive power to the quantitative trait that may be residing physically close19. Together LD heterogeneity, population structure20, epistasis and Genotype x Environment interactions (GxE)21 are factors that if not carefully controlled can negatively impact QTL identification. The utilization of LD mapping in the dissection of genetic backgrounds underpinning complex traits has been shown in several systems, for example, complex solid wood properties in Norway spruce22, white spruce23 and Eucalyptus19, and detecting genes underlying various traits including ecological adaptations in Populus27. The dissection of these complex traits can benefit from the application of mathematical functions that account for the year-to-year variation across annual growth rings. The development of mathematical methods for the analysis of dynamic data has made it possible to develop functional mapping approaches29 that firstly model the phenotypes using curve-fitting methods and then utilize the parameters describing the curve (latent traits) for independent association analysis31.Many coniferous species are characterized by an outcrossing mating system and large population sizes which lead to a rapid LD decay within the genomes and low inbreeding coefficient32.Therefore, this study is novel in that it is, to our knowledge, one of the first to tackle the issue of dissecting the genetic background to tracheid properties in a conifer species. With the exception of a single study conducted in Arabidopsis thaliana, as a model system, for traits controlling fibre length33, the majority of the studies related to tree fibre related traits have focused on mostly microfibril angle genetics37. Hence our study seeks to form the bases upon which, the dissection of the genetic backgrounds to more complex and expensive traits, such as, tracheid dimensions can be investigated. Such traits are to a large extent determined by the genes acting during wood development39. Tracheid traits can also be regulated non-cell-autonomously by processes that take place in other organs and tissues. For example, the activity of the shoot apical meristem determines the availability of auxin in the cambium and developing wood41, whereas the photosynthetic activity in the needles influences the availability of sucrose for wood biosynthesis25. Therefore, combining the knowledge of candidate genes with their expression analysis will give more insights to the biological processes shaping tracheids.GWAS can also increase our knowledge on molecular processes controlling tracheid traits. Presently the majority of breeding programs have focused on the easy to measure phenotypic traits such as volume, straightness, disease resistance and spiral grain. Due to cost and time of measurement of traits related to tracheid dimensions most programs have not been able to select and advance such traits using marker assisted breeding42. These approaches are referred to as reduced representation approaches as there are used as proxies for whole genomic sequencing. In this study, we have used exome capture, aiming at maximizing the capture of exonic regions of the genome only, thereby increasing the coverage and depth of genic sequence in our variant detection study. The analysis provided 26 mostly novel candidate genes for regulation of various tracheid traits, which, along with their expression patterns, give new insights to the tracheid traits determination, and offer key markers for early genetic selection in Norway spruce breeding.The major goal of this study was to identify causative allelic effects of genomic regions contributing to wood tracheid traits using LD mapping on exome sequence capture data. Due to the large size of the Norway spruce genome (20 Gb) and its highly repetitive nature, it presents a challenge to use whole genome re-sequencing approaches for the development of molecular markers. Approaches aimed at reducing these genome complexities, especially by either eliminating or drastically reducing the repetitive sequences have been developed.10 and Hayatgheibi et al.43. Briefly, the mapping population for the association mapping population constisted of two progeny trails established 1990 in Southern Sweden:  and S21F9021147 aka F1147 ), composed of 1373 and 1375 half-sib families. A randomized incomplete block design with single-tree plots was employed for both trials. From the trials, 517 families in 112 provenances were selected for use in the investigation of wood tracheid properties. Wood increment cores with diameter of 12 mm were collected at breast height (1.3 m) from six trees from each of the selected families of each trial. A total of 5618 trees were sampled: 2973 trees from trial F1146 and 2645 from F1147.The association mapping population, phenotypic data and statistical analysis are described in Chen et al44 at Innventia, now RISE Bioeconomy, Stockholm, Sweden. SilviScan is an instrument for efficient measurement of radial variations in a multitude of properties from the same sample with high spatial resolution. High precision sample strips from pith to bark were produced from the increment cores and automatically scanned for radial variations in cross-sectional tracheid widths with a video microscope combined with image analysis, in wood density with X-ray transmission and in structural orientations with X-ray diffraction. From these data, information on radial variations of further traits were derived, such as wall thickness, coarseness and MFA of tracheids, and stiffness of wood (MOE). The locations of the annual rings were identified, as well as of their compartments of earlywood (EW), transitionwood (TW) and latewood (LW), using the “20–80 density” definition45, established for use in different types of studies48. Averages for all rings and their compartments were calculated for the traits and organised to be ready for use in continued genetic evaluations, such as the work on solid wood traits10, on tracheid traits49 and for wood traits22, genomic selection50 and influences of age and weather51. The traits addressed in the current work are listed in Table The radial variations of growth, wood and tracheid attributes from pith to bark were analysed using the SilviScan instrument43, including also pre-processing of the data for removal of outliers. A threshold value of MFA 20° for the fitted curves was chosen to define an age up to to which an inner core of wood with inferior timber properties occurred, here named the transition age MFATA43. From anatomical perspective, a threshold of 20° is on the high side, emphasizing a core of pronounced juvenility. We have decided to stay with this threshold level, because for the young trees investigated, the fitted curves for quite a few trees would not pass a low treshold, and they would have had to be discarded from the analysis. Thus, it works better for ranking. The averages of MFA for wood inside and outside this limit were calculated, MFACORE and MFAOUTER. This provided three latent traits for MFA.For MFA, central peak regression mathematical functions were fitted to describe the MFA variation from juvenile towards mature wood, using procedures presented by Hayatgheibi et al.22. Total genomic DNA from 517 half-sib individuals was extracted using the Qiagen Plant DNA extraction kit . DNA was extracted from buds, when present, or from young needles, when buds were absent. DNA quantification was performed using the Qubit ds DNA Broad Range (BR) Assay Kit . DNA from randomly selected individuals was then electrophoresed on a 2% agarose gel. Probe design and evaluation is described in Vidalis et al.52. In breif, the exome capture method was implemented by the probe design that was based on a combination of sequenced genomic DNA, predicted gene annotations and de novo transcript assemblies. Exome capture was based upon the use of targeted oligonucleotides that bind to complementary genomic sequences. Sequencing was performed at Rapid Genomics, USA, using the Illumina sequencing platform. Sequence capture with average depth of 15× coverage was performed using the 40,018 diploid probes previously designed and evaluated for Norway spruce. Illumina sequencing compatible libraries were amplified with 14 cycles of PCR and the probes were then hybridized to a pool comprising 500 ng of 8 equimolarly combined libraries following Agilent’s SureSelect Target Enrichment System (Agilent Technologies). These enriched libraries were then sequenced to an average depth of 15× using an Illumina HiSeq 2500  on the 2 × 100 bp sequencing mode at Rapid Genomics, USA.DNA extraction, variant detection and annotation and population structure on the genomic data utilized in this study was previously describedP. abies reference genome v1.0 using BWA-MEM53. SAMTools v.1.254 and Picard (https://broadinstitute.github.io/picard) were used for sorting and marking of PCR duplicates. Variant calling was performed using GATK HaplotypeCaller v.3.6 as per the best practices protocol55 in gVCF output format (see https://www.broadinstitute.org/gatk/guide/best-practices for more information about GATK best practices). Samples were then merged into batches of ~ 200 before all 517 samples were jointly called.Raw reads were mapped against the 56. The resultant SNPs were annotated using default parameters for snpEff 457. Ensembl general feature format  information was utilized to build P. abies 1.0 snpEff database.GATK based Variant Quality Score Recalibration (VQSR) method was performed in order to avoid the use of hard filtering for exome/sequence capture data. For the VQSR analysis, two datasets were created: a training file and an input file. The training dataset was derived from a Norway spruce genetic mapping population with known segregating loci. The training dataset was designated as true SNPs and assigned a prior value of 12.0. The input file was derived from the raw sequence data using the above mentioned GATK’s best practices with the following parameters: extended probe coordinates by +100 excluding INDELS, excluding the LowQual sites, and keeping only bi-allelic sites. The annotation parameters QualByDepth (QD), MappingQuality (MQ) and BaseQRankSum, with tranches 100, 99.9, 99.0 and 90.0, were then applied to the two files for the determination of the good versus bad variant annotation profiles. After obtaining a VQSR for all raw data variant sites, the recalibration was applied to filter the raw variants. The SNP trimming and cleaning involved the removal of any SNP with MAF and “missingness” of < 0.05 and > 20%, respectively. These parameters were filtered out using VCFTools10. The EBVs at each cambial age were estimated using univariate, bivariate or multivariate mixed linear models in order to select the optimal model for each trait, based on a compromise of model fit and complexity. Akaike Information Criteria (AIC) was used to determine the fitness of different models. This resulted in use of a univariate linear mixed model for joint-site analysis as the bases for the analyses of all traits: lth tree from the kth family in ith site, u is the general mean, ith site and the jth block within the kth family and the random interactive effect of the ith site and kth family, respectively, Latent traits expressing how the traits developed with age were calculated in two steps. First, a breeding value approach was applied to refine data from influences not directly related to the genes, such as site and block effects. For this purpose, breeding values were estimated (EBV) for each annual ring separately , reducing site and block effects, but also the time trajectories, which were reconstructed as a final step by adding back the averages at each age. The variance and covariance components were estimated using ASREML 4.0 as described in Chen et al.For the tracheid dimension and coarseness traits, linear splines with multiple knots were fitted to the EBV refined time trajectories against cambial age  Fig. , general0, with linear segments between the knots at t = Ki , segments with slopes defined by the β1 to β1+m parameters, where βi = 0 if t < Ki-1. The knots are thus reflecting transitions between phases of different slopes in the development of the traits, and at each knot, the slope is changed according to the β of the next segment. Therefore, the times when the knots occur have to be properly defined in order to provide accurate descriptions of the data under investigation, and also their numbers in order to avoid overadaptation to data58. We found use of two knots the most suitable for tracheid dimension traits across the time intervals investigated. Hence, the linear spline model used was defined as:This is a continuous curve starting at the intercept β1 and K2 were adapted for each trait. Then, the intercept β0, and the slope parameters β1, β2 and β3 were estimated for each tree by standard least squares59. The four estimates were used as the latent trait in the subsequent QTL analysis conducted in R-studio60, and then analysed using the LASSO model in order to identify SNPs showing significant associations to the traits.In a first analysis, fixed values of K58, was applied to all latent traits for the detection of QTLs.The LASSO model as described by Li et al.yi is the phenotypic value of an individual i  for the latent trait, α0 is the population mean parameter, xij is the genotypic value of individual i and marker j coded as 0, 1 and 2 for three marker genotypes AA, AB and BB, respectively, αj is the effect of marker j , and λ (> 0) is a shrinkage tuning parameter.The LASSO model:62. For a marker to be declared significant, a SSP inclusion ratio (Frequency) was used with an inclusion frequency of at least 0.52 for all traits. This frequency inferred that the expected number of falsely selected markers was less than one (1), according to the formula of Buhlmann et al.63. Population structure was accounted for in all analyses by including the first five principal components based on the genotype data as covariates into the model. The LASSO regression has a limitation in that it might over-shrink the effect size of SNPs due to the use of a single tuning parameter for all the regression parameters64. The consequence is that the LASSO might significantly under-estimate the proportion of phenotypic variation (PVE) explained by a SNP65. To improve this, an adaptive LASSO approach64 was used alternatively to evaluate the PVE of a QTL (Methods S4):Stability selection probability (SSP) of each SNP was applied as a way to control the false discovery rate and determine significant SNPsIn brief, estimated breeding values (EBV) were computed for each annual ring by cambial age to reduce site and block effects . In a second step, linear splines were applied to reconstruct time trajectories based on annual ring EBV. Fix age values for two knots were determined, as the intercept and slope parameters, the latent traits, were fitted to the EBV describing the shape of the time trajectories of each individual tree.https://norwood.congenie.org69) hosted by ConGenIE (https://congenie.org/). After the identification of the QTL, the Norway spruce contigs linked to the significant SNPs were extracted from the web based database congenie (congenie.org/blast). The complete Norway spruce contigs that harboured the QTLs that were not annotated in the ConGenIE were used to perform a nucleotide BLAST (Blastn) search, using the option for only highly similar sequences (megablast) in the National Center for Biotechnology Information (NCBI) nucleotide collection database (https://blast.ncbi.nlm.nih.gov/Blast.cgi?).To assess putative functionality of SNPs with significant associations, gene ontology and network analysis of putative genes and their associated orthologs was performed against the NorWood v1.0 database  and β1 to β3 22 were implemented in the association mapping. Candidate gene loci were identified for MFACORE, MFAOUTER and MFATA, and for the intercepts β0, of the tracheid dimensions and coarseness of rings, EW, TW and LW.For all the other tracheid phenotypes: wall thickness, radial tracheid width, tangential tracheid width and coarseness, family means of βd/a|< 1.25). The remaining 23 markers were all determined to have an additive (|d/a|< 0.50) mode of inheritance  ranging between 0.01 to 3.79% Table , using aOUTER explaining 1.03% of the PVE. MA_10117352g0010_1285, a synonymous variant explaining 1.47% PVE associated with MFAOUTER, occurred within gene MA_10117352g0010 homologous to Arabidopsis ONE HELIX PROTEIN (OHP). The gene is highly expressed especially in needles and shoots in spruce  explaining 3.16% of the PVE and occurred within a gene encoding nuclear transcription factor Y subunit A-7 (NF-YA7)  Table . NF-Y isLW with seven significant associations, had the highest number of detected associations per trait. Two missense SNPs, MA_336364g0010_6123 and MA_64438g0010_10851 associated with TWrLW, explained a small proportion of the PVE observed 0.01% and 0.03%, respectively. MA_336364g0010 is homologous to the Arabidopsis INDUCER OF CBF EXPRESSION  (ICE2) regulating deep-freezing tolerance by inducing CBF1, CBF2 and CBF3 genes , a part of the ENTH/ANTH/VHS superfamily  involved in the regulation of HAK5-mediated high-affinity K+ uptake in calcium-dependent manner in Arabidopsis roots80. The confidence of the spruce model was low, but the gene was found highly expressed in developing shoots, buds and cones   Table . CNGCs aacross traits TWtTW and TWtRing  had three significant associations. The synonymous SNP MA_492000g0010_1672 had a high inclusion frequency (0.726) and explained the highest percentage of PVE (1.78%). The same SNP was associated with WTEW. The gene MA_492000g0010 is homologous to a tRNA synthetase beta subunit family protein, phenylalanyl-tRNA synthetase beta chain (Table MA_9563494g0010 named as Picea abies BIG GRAIN 2 (PabBG2)83 homologous to the BIG GRAIN 1 gene (OsaBG1) in rice87. OsaBG1 encodes a membrane protein regulating auxin transport and sensitivity, and positively affecting plant biomass and seed size. The gene belongs to a small family containing nine members in spruce86. Auxin has long been known to act as a key hormone essential for the induction of vascular strands, cambial growth and secondary wall deposition91. PabBG2 is highly expressed and specifically upregulated in the developing xylem  PtMYB8 expressed in spruce induced secondary cell wall thickening98. White spruce (P. glauca L.) PgMYB4 was associated with cell wall thickness and tracheid coarseness23, and has been shown to be highly expressed during secondary cell wall formation and lignification in both white spruce and loblolly pine99. MYB encoded by MA_80033g0010 could play a more indirect role in secondary wall regulation in spruce considering its expression  is responsible for double strand repair via somatic homologous recombination100. The spruce gene shows increased expression in organs with active meristems  and explained a relatively high PVE of 3.25% and 1.40%, respectively. It also had high inclusion frequencies for all three traits   family, which includes key enzymes in the monolignol biosynthetic pathway. However, the Arabidopsis homolog of MA_5g0010, At4g05160 does not encode an enzyme active on phenyl propanoid substrates but a fatty acyl CoA synthase involved in lipid and jasmonic acid biosynthesis101. MA_5g0010 is not expressed in the developing wood but it is highly expressed in young vegetative shoots and needles, including the infected needles  encoding a plant homeodomain (PHD) finger protein (Table . 2009). Homeodomain genes encode transcription factors central in the regulation of plant developmental processes102. OBE1 and OBE2 redundanlty regulate meristem establishment and maintenance in Arabidopsis . The spruce OBE2 gene is ubiquitous and highly expressed in vegetative and reproductive organs  proteins. WAKs have been previously reported to be associated with average ring width and the proportion of earlywood in white spruce23 and with MFA in Populus26. The gene is expressed primarily in early season needles and late season stem from vegetative shoots but there is no detectable expression in developing xylem (Fig. A total of five significant associations were identified for coarseness traits explaining moderate to high PVE ranging from 0.78 to 3.62% (Table 30, with our study representing a major advance by using 178,101 SNPs with a functional mapping approach. The relatively small number of associations is comparable to other association studies of complex growth traits in forest trees, were a few associations are detected with a relatively small proportion of the genetic variation being explained106. It can be argued that many of the alleles causing variation for polygenic traits may be either rare or have small effects and current GWAS methods lack the power to detect them, thus the small number of significant associations108. The small number of associations being reported could also be largely due to the small sample sizes in these studies for such complex traits. Theoretical work has also shown that alleles of large effect are unusual, with allele effect having been suggested to follow a negative exponential distribution pattern109. Thus the magnitude of the detected allele effects follows a truncated exponential distribution110. Therefore, the detection of alleles with small effects is difficult when compounded with the small population size. The small number of significant associations can also be attributed to the genotyping method, which is a complexitiy reduction genotyping method. The limitation of the genotyping employed in our study has also been noted in other studies111, in that some of the alleles impacting a trait might not be within the captured regions that we targeted. If the sampled markers do not include the casual allele or if the LD between the marker and the casual allele is incomplete the power of detection is drastically reduced26. The statistical power required to detect associations between molecular markers and a trait is heavily dependent upon the sample size113. Due to the challenges of developing large populations for GWAS in conifers, the majority of the studies utilize a few hundred individuals from natural populations, which limits the statistical power of GWAS. It was reported that in order to capture 50% of genetic variaon for growth traits in an association mapping study, it would require roughly 25,000 individuals to be analysied14. Therefore, the relatively small association population size results in low statistical power, thus rendering small to medium effect QTLs statistically non-significant and very difficult to detect. Our study had 517 martenal trees to perform GWAS upon, thus rendering a small number of significant associations. Missing heritability will remain an issue in association studies as long as population sizes are kept in the range of hundreds14. However, improvements made to statitstical methods are now potential viable options, which are being developed and utilize a combination of information from multiple populations using Meta-GWAS and Joint-GWAS115. These approaches are now being applied in some recent forest tree studies113 and could be the next level of analysis using our application of latent traits on these complex traits.This work presents the first genome wide dissection of wood tracheid traits in Norway spruce. A total of 30 significant associations were detected for all investigated traits. These associations have identified a set of genes that could be exploited to alter wood tracheid traits for improving solid wood properties for its use in industrial processes. Previous studies utilizing a LASSO penalized analysis approach were limited in the nature and number of molecular markers availableSupplementary file1


A) (A-AM). After genomic selection was introduced into breeding programs, it was anticipated that VC estimates from A-AM would be biased because the effect of selection based on genomic information is not captured. The single-step method (H-AM), which uses an H matrix as (co)variance matrix, can be used as an alternative to estimate VC. Here, we compared VC estimates from A-AM and H-AM and investigated the effect of genomic selection, genotyping strategy and genotyping proportion on the estimation of VC from the two methods, by analyzing a dataset from a commercial broiler line and a simulated dataset that mimicked the broiler population.The traditional way to estimate variance components (VC) is based on the animal model using a pedigree-based relationship matrix (H-matrix, regardless of whether selective genotyping is applied or not in the selection process. For simulated populations under genomic selection, estimates of genetic variance from A-AM were also significantly overestimated when the effect of genomic selection was strong. Our results suggest that VC estimates from H-AM under random genotyping have the expected values. Predicted breeding values from H-AM were inflated when VC estimates were biased, and inflation differed between genotyped and ungenotyped animals, which can lead to suboptimal selection decisions.VC estimates from H-AM were severely overestimated with a high proportion of selective genotyping, and overestimation increased as proportion of genotyping increased in the analysis of both commercial and simulated data. This bias in H-AM estimates arises when selective genotyping is used to construct the We conclude that VC estimates from H-AM are biased with selective genotyping, but are close to expected values with random genotyping.VC estimates from A-AM in populations under genomic selection are also biased but to a much lesser degree. Therefore, we recommend the use of H-AM with random genotyping to estimate VC for populations under genomic selection. Our results indicate that it is still possible to use selective genotyping in selection, but then VC estimation should avoid the use of genotypes from one side only of the distribution of phenotypes. Hence, a dual genotyping strategy may be needed to address both selection and VC estimation. A) based on pedigree is widely used as the relationship matrix in the animal model, and in this paper, the animal model that uses the A matrix as (co)variance matrix for BV is denoted as A-AM. To predict BV from A-AM, variance components (VC) including genetic variance and residual variance are needed to solve the mixed model equations (MME). In many cases, VC need to be estimated from the available data. If a population is under selection based on phenotype and pedigree information, if all the data that drive selection decisions are included in the model, and if the genetic architecture of the trait is close enough to the assumption of an infinite number of loci contributing to the trait, VC estimated by restricted maximum likelihood (REML) from A-AM are unbiased [Accurate and unbiased prediction of breeding values (BV) is essential for an efficient breeding program to select parents of future offspring and maximize genetic gain. The linear mixed animal model  is commounbiased .During the last decade, affordable high-throughput genotyping products for livestock have enabled breeding companies to use dense markers such as single nucleotide polymorphisms (SNPs) to aid prediction of genetic merit and select superior breeding stock –5. Instex for BV . Howeverx for BV –10. The enotyped .In many livestock breeding programs, H-AM is now used for genomic evaluation , 11, 12,If we use H-AM to estimate VC, another concern is the effect of the genotyping strategy on bias in VC. Selective genotyping has been reported to lead to bias in prediction of BV , and we In this paper, we report VC estimated by A-AM and H-AM by using a commercial broiler dataset for which a large number of individuals are genotyped. The first and main objective of this study was to report, backed-up by a simulation study, that H-AM provides (highly) overestimated genetic variances when genotyping is selective and a sufficiently large proportion of individuals is genotyped. The secondary objectives were to investigate whether A-AM, or H-AM with random genotyping, could provide unbiased estimates of VC, and how genotyping strategy, proportion of genotyping and VC estimates affect predicted BV.1 is a vector of ones.The relationship matrix based on pedigree is denoted The difference between H-AM and A-AM is the (co)variance structure for BV, where the quations :\documenatrix as , 17:\docwhere The commercial broiler data were provided by Cobb-Vantress Inc. , and the trait was body weight (BW) measured in grams at a fixed age after birds were hatched. This purebred broiler line was first selected for several selection rounds (SR) based on predicted BV from A-AM, followed by a number of SR based on predicted BV from H-AM. Table For the commercial dataset, the genotyping strategy and proportion of genotyping differed between males and females (see below). Therefore, part of the strategy used to investigate the effects of genotyping strategy and proportion of genotyping compared analyses from the whole dataset with analyses for each sex. The model for this analysis is as follows:where  package . Fixed aFor the commercial data, we studied the effects of genotyping strategy and proportion of genotyping on VC estimates from A-AM and H-AM by using different subsets of the dataset.t test for the difference between two means as In the first set of analyses, we compared VC estimates from A-AM and H-AM for the whole dataset, and for males and females separately, and linked differences in VC estimates to differences in genotyping strategies and proportions of genotyping in these datasets. First, we computed mean and standard deviations for BW per SR for genotyped and ungenotyped males and females, and second, we plotted the distributions of standardized BW, which were obtained within SR, of genotyped and ungenotyped males and females. As will be shown in the Results section, females were preselected among the heavy individuals based on own BW, but this was less the case for males, and the proportion of genotyping was higher in females than in males. VC estimates were compared between A-AM and H-AM for the three groups , and related to the different genotyping strategies and proportions of genotyping used in each group. Significance of differences was tested by computing a In the second set of analyses, we compared VC estimates from H-AM for different subsets of the data that were constructed to either enhance or mitigate the effects of selective genotyping and proportion of genotyping, and thus to better disentangle these two effects. Subset 1 increased the effect of selective genotyping in males, while Subsets 2 and 3 modified the proportion of genotyping by either removing genotyped or ungenotyped animals. These subsets were constructed as follows.In Subset 1, selective genotyping was increased in males and proportion of genotyping was decreased, by using only the genotypes of the heavy males among all genotyped males . This made the genotyping strategy in males similar to the genotyping strategy in females, but with a lower proportion of genotyped animals. Genotyped males were ranked according to BW records, then genotypes from the heaviest 3000 individuals were kept to compute a new In Subset 2, the effect of selective genotyping was decreased in females by randomly removing genotypes of females. Instead of using all the 14,838 genotyped females, two random subsets of 8000 and 4000 individuals were sampled from genotyped females, and In Subset 3, selective genotyping in males, as in Subset 1, and proportion of genotyping were increased simultaneously. This was achieved by using the same heaviest 3000 genotyped males from Subset 1, combined with decreasing the number of ungenotyped males by removing their phenotypes, so that, relatively, the proprortion of selectively genotyped males increased. To reach a proportion of genotyping of 30% in males, a random subset of 7000 ungenotyped males were sampled. Using phenotypes from these 7000 ungenotyped males together with the heaviest 3000 genotyped males in Subset 1, VC were estimated for males only using H-AM with the H-AM estimates in these three different subsets were compared to the H-AM estimates for the unedited data in the same group . Differences in VC estimates were tested with the same t-test as described above to test the difference between two means.After analyzing subsets of the commercial data, a population that mimicked the population structure and selection process of the broiler breeding program was simulated using the ADAM software . The firThe simulation was based on a historical population with a genome consisting of 40 K SNPs and 2 K quantitative trait loci (QTL), and the patterns of linkage disequilibrium (LD) decay along distances between each pair of SNPs were generated such that they were similar to that observed in the commercial data. Genetic effects were the sum of the QTL effects, which were sampled from a normal distribution. For both sexes, the total genetic variance in the base population was assumed to be 9644, and residual variance was assumed to be 24,798. Phenotypes were simulated as body weights (BW) which was the sum of additive genetic effects  selection before SR 30 cannot be accounted for, because the phenotypes on those generations were not included in the analysis, and (2) the simulated data are generated based on a finite locus model in which genetic variance can be lost due to fixation of QTL under selection. A-AM cannot correctly model this change of genetic variance, because it assumes an infinite locus model in which allele frequencies at underlying loci do not change. For these reasons, we expected the A-AM genetic variance estimates to be lower than those of the base population. Additional file  selectioPhenotypes, genotypes and true BV of all birds were saved for all scenarios. Therefore, VC can be estimated after simulation using different models and under different genotyping strategies. This includes scenarios in which genomic selection was based on selective genotyping, but estimation of VC used a random set of genotypes, or vice versa. Full pedigree, and phenotypes of birds in SR31-40, were used to estimate VC in both A-AM and H-AM. To investigate the effects of genotyping strategy and proportion of genotyping on VC estimates from H-AM, different proportions  of either a selected or random set of birds in SR31-40 were assumed to be genotyped, and these genotypes were used to construct the In the commerial data, we evaluated predictive ability, computed as the correlation between predicted BV and phenotypes corrected for fixed effects  of either preselected or random birds to be genotyped and using genotypes of selected birds to compute the The results of the analyses with A-AM in populations under genomic selection (Scenarios 1 and 2) showed that For the commercial data, predictive ability and bias of predictions were compared among the three models: A-AM, H-AM and H-AM (VC-A), which is H-AM using VC estimates from A-AM. Table Generally, bias of predictions was smaller for males than for females, and all biases indicate inflation of predictions. Bias was largest for females with H-AM, but males were close to unbiased with H-AM. Furthermore, levels of bias differed between genotyped and ungenotyped birds, but this was not consistent across models: bias was larger for genotyped than for ungenotyped birds  with A-AM, but bias was smaller (close to absent) for genotyped than ungenotyped birds with H-AM (VC-A). There was no clear connection between predictive ability and bias of predictions.For the simulated data, prediction accuracy and inflation were compared between three H-AM using different VC: VC estimated in H-AM or A-AM, or VC of the base population Tables , 8. The The first and main aim of our study was to show that there is a (large) overestimation of the genetic variance by H-AM with selective genotyping at a sufficienly high proportion of genotyping. Using the broiler dataset, we showed that the proportion of genotyping is higher for females than for males, and genotyping is performed only on heavy animals Table , 2. The Few studies have used H-AM for VC estimation. One study estimated VC in pigs  and repoH-AM is A-AM with genotype data added for part of the individuals, and if the proportion of genotyping is low, it is logical that H-AM and A-AM lead to similar results and that the amount of bias in H-AM from selective genotyping depends on the proportion of genotyped animals. We were able to show this effect clearly: with the commercial data, we created an edited dataset with 5.57% of selectively genotyped males, and observed no difference between the variance estimates obtained with H-AM and A-AM Table . In the Based on our simulation studies, we could separate two effects related to selective genotyping: (1) the effect of using selective genotyping in the genomic selection; and (2) the effect of using a selective sample of genotyped individuals in the ed Table . Hence, ix Table . Hence,  of DNA” , i.e., ttimation  and overtimation  of genettimation . In our The second objective of this study was to investigate the hypothesis that A-AM could provide biased estimates of genetic variance in populations under genomic selection, and whether H-AM with random genotyping can be an alternative approach. This is based on the simulation study for which we know the underlying QTL and genetic variances. One issue in interpreting the results from the simulation study is that, as described in Methods, we expect genetic variance estimates to be lower than that of the original base population because (1) the selection history up to SR 30 was hidden by not including phenotypes from these SR in the analysis; and (2) we used a finite-locus model to simulate genomic selection causing the genetic variance to decrease across generations due to the change in allele frequencies at the QTL. Thus, we compared VC estimates both to the original base population variance (9644), and to the A-AM estimate for populations under A-AM selection (Scenario 3), which estimated genetic variance at 8206 Table .The results with A-AM in populations under genomic selection (Scenario 1 and 2) showed that A-AM estimates were clearly biased upwards in Scenario 1, but in Scenario 2 the conclusion is ambiguous because the genetic variance estimate did not differ significantly from the variance of the base population, but did differ significantly from the A-AM estimate in the population without genomic selection Table . The difWe verified whether H-AM with random genotyping could be an alternative for estimating genetic variance in populations under genomic selection and we found that the estimates were significantly smaller than the variance of the base population for Scenarios 2 and 3, but for all scenarios they did not differ from the Scenario 3 A-AM estimate for a population that was not under genomic selection. If one assumes that analysis of this data should retreive the variance of the base population, these lower estimates are difficult to explain. However, if one assumes, for the reasons explained above, that analysis of this data should result in estimates of genetic variance smaller than the variance of the base population, then the Scenario 3 A-AM estimate and all H-AM estimates with random genotyping fit this expectation. We concluded that the results from H-AM with random genotyping remain ambiguous, but suggest that H-AM with random genotyping may be more appropriate than A-AM for the estimation of genetic variance in populations under genomic selection.As already noted above, H-AM with a low proportion of genotyping should perform similarly to A-AM. Since we have established that A-AM overestimates genetic variances for populations under genomic selection, at least clearly in Scenario 1 with strong genomic selection, then H-AM with a low proportion of genotyping should also overestimate genetic variances. Indeed, in Scenario 1 estimates of genetic variance from H-AM with random genotyping tend to increase when the proportion of genotyping decreases. This trend is small and not significant, but in line with the overestimation by A-AM. We also observed that H-AM with 10% random genotyping is sufficient to make a significant downward adjustment on the overestimate of A-AM in the scenario with genomic selection using selective genotyping . The results from using H-AM with random genotyping in the population under genomic selection with selective genotyping (Scenario 1), suggest that a dual genotyping strategy could be used: selective genotyping for estimation of BV using H-AM, which is known to lead to high accuracy, and a random genotyping for estimation of VC.Use of inappropriate VC can generate bias in predicted BV , with “bOur findings imply that populations under genomic selection need a proper strategy to collect genotypes, which should ensure that the current statistical models can provide unbiased VC estimates and thus unbiased prediction of BV. We showed that genotyping a high proportion of heavy birds in broiler breeding caused strong overestimation of the genetic variance, which also leads to bias in predicted BV. In addition, we showed that A-AM also overestimates the genetic variance in populations under genomic selection, although in our results this was clearly significant only in the scenario with the strongest genomic selection. Finally, our results suggest that H-AM with random genotyping could provide plausible estimates of VC. As in one of our scenarios, it is possible to combine selective genotyping for the purpose of selecting breeding animals, with random genotyping for the purpose of estimating VC. In addition, other selective genotyping strategies besides random genotyping have been considered in the literature, for instance genotyping both low and high extreme phenotypes was described for prediction of BV by , 15. It In this study, we showed that estimates of genetic variance from a single-step animal model, in which genomic and pedigre relationships are combined (H-AM), can be severely overestimated when using selective genotyping at a high proportion. We found that, for populations under genomic selection, genetic variances estimated from the pedigree-based animal model (A-AM) were also biased. This is due to the inability of A-AM to capture the effect of genomic selection. When we used a random genotyping strategy, genetic variance estimates from H-AM were close to expected values. In addition, we demonstrated that predicted breeding values were biased when using inappropriate variance estimates, and more so for ungenotyped than for genotyped animals, which would lead to suboptimal selection across genotyped and ungenotyped animals. Therefore, we recommend to ensure that individuals from the whole distribution of phenotypes are genotyped.Additional file 1: Figure S1. Genetic variance by generation in the simulated data computed from the variance of true breeding values in each generation.


Oncorhynchus tshawytscha), a region of major effect for migration timing has been discovered that harbors two adjacent candidate genes , but there has been limited work to examine the association between these genes and migratory phenotypes at the individual, compared to the population, level. To provide a more thorough test of individual phenotypic association within lineages of Chinook Salmon, 33 candidate markers were developed across a 220 Kb region on chromosome 28 previously associated with migration timing. Candidate and neutral markers were genotyped in individuals from representative collections that exhibit phenotypic variation in timing of arrival to spawning grounds from each of three lineages of Chinook Salmon. Association tests confirmed the majority of markers on chromosome 28 were significantly associated with arrival timing and the strongest association was consistently observed for markers within the rock1 gene and the intergenic region between greb1L and rock1. Candidate markers alone explained a wide range of phenotypic variation for Lower Columbia and Interior ocean‐type lineages , but less for the Interior stream‐type lineage (5%). Individuals that were heterozygous at markers within or upstream of rock1 had phenotypes that suggested a pattern of dominant inheritance for early arrival across populations. Finally, previously published fitness estimates from the Interior stream‐type lineage enabled tests of association with arrival timing and two candidate markers, which revealed that fish with homozygous mature genotypes had slightly higher fitness than fish with premature genotypes, while heterozygous fish were intermediate. Overall, these results provide additional information for individual‐level genetic variation associated with arrival timing that may assist with conservation management of this species.Recent studies have begun to elucidate the genetic basis for phenotypic traits in salmonid species, but many questions remain before these candidate genes can be directly incorporated into conservation management. In Chinook Salmon ( We used the program Haploview v4.2 to visualize haplotype blocks independently for each population  and selectively neutral set of SNPs that could be used as a covariate for population structure and relatedness in further analyses, we used a previously published panel of 298 SNP markers and a single sex marker from the Chinook sdY region  function implemented in the GAPIT R package, which allows for the inclusion of fixed and random effects to account for population structure and relatedness, respectively  as implemented in the GAPIT R package to test the effect of multiple markers simultaneously .To determine whether there was an association between fitness and candidate markers on chromosome 28, we used a subset of data that were available from Johnson Creek that included reproductive success . It is important to point out, however, that there are limitations of using R2 values to explain the percent of variation in models with binary response variables . Therefore, we also include results from a genomic relatedness‐matrix restricted maximum likelihood (GREML) method implemented in the genome‐wide complex trait analysis (GCTA) software to estimate the variance in arrival timing explained by genetic variance  for the Interior stream‐type population and also added year as a fixed effect. Power calculations using the GCTA‐GREML power calculator indicated <10% power to estimate VG/VP with high accuracy  and 2011 . Models were run separately for each sex and with sexes combined. The best fit model for sexes combined, males, and females included predictors of quadratic arrival time, sex, year, age, and one (out of two) of the candidate markers on chromosome 28 , supporting previous studies identifying this candidate region of the genome  and in selection for the trait in certain years compared to others . Previous research has shown that ocean and stream conditions directly affect migration timing (Anderson & Beer, Across all three populations, there was evidence that heterozygotes at the most significant candidate markers demonstrated early arrival timing which provides information regarding the mode of inheritance patterns beyond previous studies (Prince et al., While balancing selection may maintain phenotypic variation for both early and late migration phenotypes through disruptive selection related to high stream temperatures in the middle of migration (Narum et al., As suggested by Waples and Lindley , gainingNone declared.Supporting informationClick here for additional data file.


Salvelinus alpinus) is a species of high economic value for the aquaculture industry, and of high ecological value due to its Holarctic distribution in both marine and freshwater environments. Novel genome sequencing approaches enable the study of population and quantitative genetic parameters even on species with limited or no prior genomic resources. Low coverage genotyping by sequencing (GBS) was applied in a selected strain of Arctic charr in Sweden originating from a landlocked freshwater population. For the needs of the current study, animals from year classes 2013  and 2017  were used as a template for identifying genome wide single nucleotide polymorphisms (SNPs). GBS libraries were constructed using the PstI and MspI restriction enzymes. Approximately 14.5K SNPs passed quality control and were used for estimating a genomic relationship matrix. Thereafter a wide range of analyses were conducted in order to gain insights regarding genetic diversity and investigate the efficiency of the genomic information for parentage assignment and breeding value estimation. Heterozygosity estimates for both year classes suggested a slight excess of heterozygotes. Furthermore, FST estimates among the families of year class 2017 ranged between 0.009 – 0.066. Principal components analysis (PCA) and discriminant analysis of principal components (DAPC) were applied aiming to identify the existence of genetic clusters among the studied population. Results obtained were in accordance with pedigree records allowing the identification of individual families. Additionally, DNA parentage verification was performed, with results in accordance with the pedigree records with the exception of a putative dam where full sib genotypes suggested a potential recording error. Breeding value estimation for juvenile growth through the usage of the estimated genomic relationship matrix clearly outperformed the pedigree equivalent in terms of prediction accuracy (0.51 opposed to 0.31). Overall, low coverage GBS has proven to be a cost-effective genotyping platform that is expected to boost the selection efficiency of the Arctic charr breeding program.Arctic charr ( Genomic information is being incorporated at an ever increasing rate in aquaculture breeding programs, guiding selection decisions  and realImplementation of genomics in both animal and plant breeding programs is commonly performed through the usage of single nucleotide polymorphism (SNP) arrays  and are composed from a multiplex including a large number of animals. Low coverage GBS offer an attractive solution that offers for both high SNP density and cost-effective genotyping  farming is a small but growing industry with ample margin for production scaling. Sweden is the second largest producer of Arctic charr worldwide with a production volume of approximately 1,310 tons  and rainbow trout (Oncorynchus mykiss), genomic resources for Arctic charr have only started to become available very recently  of the Swedish Arctic charr breeding program. The breeding nucleus is located in the facilities of Aquaculture Center North  where selection is operated using non-overlapping generations of approximately 4 years each. As such, the 2017 year class was formed through artificial crosses among selected animals from the 2013 year class. In the current study, 171 animals from year class 2013 (selected based on DNA availability) and 759 animals from year 2017 were genotyped using GBS. Genotyped animals from year class 2017 originated from crosses between 11 sires and 13 dams. Due to the aforementioned breeding scheme the genotyped animals of 2017 year class consisted of 13 full-sib families and two pairs of paternal half-sib families. Family size ranged between 10 – 90 offspring.According to available pedigree data, five sires and four dams from the genotyped animals of year class 2013 were the parents of eight genotyped families from year class 2017. In particular, two families had both their putative parents genotyped, six had only one parent genotyped (two families had only their dam and four families had only their sire genotyped) and the remaining five families had neither of their parents genotyped . MoreoveA tissue plug of approximately 3 mm diameter from each fin clip was used for DNA extraction. Prior to extraction, the tissue was air dried overnight to remove all traces of ethanol and then DNA was extracted following a salt-based extraction protocol  below -0.05 were removed using the KGD v0.8.7 software.Reads of low quality (average phred33 score below 30) were discarded using trimmomatic v0.39 (https://github.com/AgResearch/KGD). The constructed GRM was the equivalent of G5, as described in A genomic relationship matrix (GRM) was estimated using the KGD v0.8.7 software  was performed in order to investigate the population structure of the two separate year classes. PCA was conducted through applying the singular value decomposition (SVD) algorithm to the estimated GRM. Finally, aiming to investigate in more depth potential genetic clustering of the genotyped families (year class 2017), the discriminant analysis of principal components (DAPC) was performed  was used for selecting the optimal number of clusters (K) based on the elbow method (Mean expected (He) and observed (Ho) heterozygosity were estimated separately for animals from year class 2013 and 2017 using the KGD v0.8.7 software. Heterozygosity metrics were estimated both on the raw scale and by taking into account the read depth. Moreover, FSNPs passing QC filters were used for parentage assignment purposes. Assignment to most probable sire and dam was performed taking into account the sequence depth of the corresponding individuals as described in y is the vector of recorded phenotypes. X, Z and T are the incidence matrices relating phenotypes with fixed and random effects. b is the vector of the fixed effects (intercept and age), u the vector of random animal effects ∼N, I the identity matrix and Heritability estimates of juvenile weight and length were obtained using the constructed GRM. Variance components were estimated using AIREMLF90  was calculated and benchmarked against the accuracy of EBVs using traditional pedigree-based best linear unbiased prediction (BLUP) . AccuracGEBVs were estimated with GBLUP EBV is the vector of (genomic) estimated breeding values and h is the square root of the previously estimated heritability. In order to have meaningful comparisons between BLUP and GBLUP we used in both cases the heritability estimated through the genomic relationship matrix.The prediction accuracy was approximated as:https://doi.org/10.25387/g3.12151725.Obtained sequences are available in the form of FASTQ files from NCBI repository under project ID PRJNA607181. Information regarding the utilized barcode and the sequencing lane of each library is available in supplementary file S1. Genotypic and pedigree information is available in the supplementary files S2 and S3. Phenotypic information for the genotyped animals of 2017 year class is available in the supplementary file S4. Supplemental material available at figshare: More than 1.35 billion SE reads were produced. Approximately 97% of the above reads (∼1.32 billion) had an average phred33 score above 30 and were kept for downstream analysis. Reads without the expected enzymatic cut site and no recognizable barcode were discarded, resulting in ∼964 million SE reads. Thereafter reads were merged in 662,978 unique tags that were used as a template for SNP identification. We removed 2 animals (year class 2013) that were genotyped insufficiently (mean coverage < 0.1X). In total 17,652 SNPs were identified across 928 genotyped individuals. 70 SNPs with mean coverage below 0.5X were removed. The mean SNP coverage across animals was approximately 5X. Finally, 3,134 SNPs did not fulfill the chosen Hardy-Weinberg equilibrium (HWE) threshold and were removed. Overall, the final dataset was comprised of 14,518 SNPs  genotypeAmong the genotyped full-sib pairs, six animals from four families had low genomic relationships with their full-sibs (below 0.10) and were removed from subsequent analysis. The mean genomic relationship among all full-sib pairs was 0.42 . Furthero) when the read depth was not taken into account was 0.21 and 0.19 in year class 2013 and 2017 respectively, while the corresponding estimates of expected heterozygosity (He) were 0.19 and 0.17. When the read depth was taken into account the Ho was found equal to 0.32 and 0.31 for year class 2013 and 2017 respectively. Moreover, the He for the above year classes was 0.29 and 0.28. FST based calculations were performed among the families of year class 2017. The median FST ranged between 0.009 – 0.066, with the lowest estimate being observed between families 7 and 8 and the highest between families 7 and 11  was genotyped. The results suggested that the identity of the putative dam in family 2 was most probably incorrectly recorded. On the other hand, the putative sire of the same family was confirmed by 98.9% of the offspring. Regarding the other seven families, the genotypic data verified the available parentage pedigree records. Between 97.8–100% of offspring in each family were in accordance with the pedigree data. EMM was below 1% for all families apart from family 2 were EMM for the putative dam was approximately 5.4% .The estimated heritability for juvenile weight and length was 0.21 (SE 0.07) and 0.19 (SE) respectively. The corresponding full sib effect for weight and length was 0.11 (SE 0.08) and 0.12 (SE 0.08). Genetic correlation between weight and length was 0.94 (SE 0.13). For the subsequent breeding value accuracy estimation only the weight was used due to the high correlation between the two traits. The mean accuracy using GBLUP was 0.51, while the corresponding accuracy using pedigree BLUP was 0.31. In the case of GBLUP the obtained accuracy of the cross validation scheme used ranged between 0.42 – 0.56, while in the case of pedigree BLUP accuracy ranged between 0.28 – 0.35 .Genomic technologies can be utilized to estimate relationship values among the selection candidates at a higher resolution than a traditional pedigree-based analysis would allow. Relationships based on pedigree records will assign the same value to all full-sib members of individual families, making it impossible to utilize the within family variance for selection. Therefore, the information obtained through genomics can result in an increased accuracy of breeding values and a more efficient management of inbreeding.A plethora of recent studies have already highlighted the advantages of including genomic information in aquaculture breeding programs  the obtained heterozygosity values were higher in comparisons to previous studies where we used GBS type methodologies in studying the diversity of farmed tilapia populations from commercial hatcheries with no pedigree recordings .Nevertheless, it would seem premature to infer that due to the excess of heterozygotes the existing genetic diversity in the Arctic charr breeding program is sufficient. Interestingly, according to literature an excess of heterozygotes has been observed in small and relatively recently founded populations of both animals and plants with separate sexes . NaturalST metric is commonly applied in studying the genetic differentiation among populations . The obtained comparisons among the genotyped families indicated low genetic differentiation among the tested families (0.009 – 0.066) which appears to be in line with the history of the breeding program.Despite the fact that the Arctic charr breeding program has been operational for almost 40 years, no prior study attempted to investigate the genetic diversity of the selected population. The Fulations . Therefoe.g., families 5,7 and 9,10) and inseparable in comparison to other families that were more distantly related according to pedigree records. Overall, through GBS application informative decisions regarding selection crosses can be obtained in the future even in the absence of pedigree records.Moreover, in the current study we attempted to study in higher depth the genetic differentiation among the tested animals using PCA and DAPC. Applying PCA to the animals from the 2013 year class did not indicate the existence of distinct clusters. On the other hand the corresponding application of PCA in animals from year class 2017 provided indications regarding the existence of distinct clusters. The underlying algorithm of PCA aims to summarize the total variation of the tested dataset in a reduced dimension. Furthermore, a major advantage of PCA is its computational efficiency. Nevertheless, the above approach is not optimal for distinguishing different clusters and PCA does not qualify strictly speaking as a clustering algorithm. DAPC on the other hand retains the computational advantages of PCA, but at the same time offers higher resolution for detecting genetic clusters . The analysis conducted in the subset of families with at least one parent genotyped (8 families) demonstrated that the derived genomic information was sufficient for verifying putative parentage records. Apart from the obtained discrepancy between genotypes and pedigree for the putative dam of family 2, the obtained genotypic information verified the pedigree parentage records implying that low coverage GBS is an effective approach for parentage assignment as already demonstrated in red deer (Moderate heritabilities (∼0.20) for juvenile growth-related traits were obtained in the current study suggesting that ample genetic variance exists for further improving the Swedish Arctic charr strain. A high common full-sib effect was found (0.11) which might be confounding with the additive genetic variance. As such the obtained heritability might be underestimated. Nevertheless, it has to be pointed out that a small number of families (13) was used in the current study. In addition, the number of animals genotyped per family varied widely (16 – 90). Therefore the obtained heritability metrics should be treated with caution.Even though genomic selection through GBS related approaches has proven to consistently outperform pedigree BLUP in aquaculture species  parentage records and estimate genomic breeding values. Further studies focusing on application of GBS in studying resistance of Arctic charr to common encountered diseases would be deemed particularly promising since it would be possible to exploit the within family variance during selection. Finally, studies aiming to combine low coverage GBS with single-step BLUP approaches (


Tree breeding has focused on increasing stem volume growth with a cost to fecundity. However, fecundity is important in maintaining the fitness in natural stands and facilitating cross‐pollination to advance breeding populations. Understanding the inheritance of fecundity and the genetic relationship between fecundity and growth is essential to understand the constraints of evolution in natural population and design an optimal selection strategy to balance breeding for growth and fecundity. Inheritance of female fecundity and the genetic relationship between fecundity and growth in radiata pine were investigated using a large Australia‐wide progeny test, planted on eight sites involving 279 control‐pollinated families. It was found that fecundity of female cones was highly heritable with an estimated heritability of 0.39–0.61, but genetically correlated with growth (−0.30 to −0.39). This indicates that improvement in tree growth alone could reduce the fecundity, thus to break the possible evolutionary constraint in natural population. To maintain fecundity for breeding purposes and minimize the interruption of the evolutionary constraint between fecundity and growth, use of a restraint selection index to impose no change of fecundity is developed in current breeding, while dissecting the genetic basis of adversely correlated traits at loci level is required for optimal long‐term strategy. Increases in grain yield have been achieved mainly at the expense of stem biomass due to the genetic selection of flowering genes shifting the proportion of vegetative growth to reproductive growth, and the gibberellic acid hormone system preventing plants from collapsing by reducing plant height  in Canberra indicated that on average a 16% mean annual increment (MAI) of volume was diverted to cone and pollen production and this is equivalent to a reduction 2.4 m3 of wood on an average MAI = 15 m3 plantation  between 0.30 and 0.64 for fecundity traits have been estimated from clonal seed orchards in radiata pine , in Florida , a clonal repeatability of 0.50 and an individual‐tree heritability of 0.13 were observed for fecundity . High family heritability and genetic gain for cone and seed production were also observed in a combined provenance and family trial of P. tecunumanii  and New Zealand (nine trees) was used in the Australian national mating and testing programme between 1976 and 1987. Trials in this period are referred to as the Australia‐Wide‐Diallel series (AWD). The AWD trials used a series (21 sets) of six‐parent disconnected half‐diallels as the mating design with 15 possible crosses for each set Griffin, . Trees f, viz second rotation radiata pine sites (2nd PR) and ex‐pasture crop sites (Pasture). Ex‐pasture sites were usually more fertile than the second rotation sites due to pasture improvement. The experiments were planted in 3–4 replicates with 4–5 tree row plots except for one site (VRC054) in which single tree plots were used. One‐ and two‐year‐old green cones and brown cones were counted from ground level for all the trees  at a similar age of trials (8 year 6 months to 10 years)  had a normal or an approximate normal distribution, whereas the transformed scores at three sites  were still largely skewed towards zero. These three sites had a higher percentage of trees (>25%) that were not producing any cones. Consequently two additional analyses were conducted. A binomial analysis, treating fecundity as a threshold trait  were conducted for the three sites  and diameter at breast height (DBH) data:Univariate and bivariate single‐site analyses, and across‐sites bivariate analyses were conducted using CONE and DBH, for all eight sites.Binomial analyses where CONE was treated as a threshold trait (fecundity score) for RAD199, RAD211 and RAD203.Percentage of trees bearing cones was analysed after arcsine square‐root transformation for RAD199 and RAD211. The same model for square‐root transformed total cone number was used for the arcsine square‐root transformation percentage.The second method used to model the data circumvents the requirement for the normal distribution of the data. All analyses were undertaken using the ASReml program  analyses site by site to estimate genetic correlation between DBH and CONE. For individual site bivariate analyses, the mixed linear model was formulated as.DBH, GCACONEGCA are GCA effect vectors for DBH and CONE, respectively. The incidence matrix, Zg becomes.where Zg=ZgDBHZgCone=∑⊕Zi.The variance–covariance structures for GCA and residuals become:GGCADBH∗Cone is GCA variance and covariance matrix among DBH and CONE within single site, σeDBH2 is the residual variance for trait DBH and σeDBH∗Cone2 is the residual covariance between DBH and CONE. Genetic variance‐covariance matrices for SCA effects were estimated in a similar fashion as for GCA.where 2.5An eight‐site model was conducted to test whether there was: (1) a single common versus heterogeneous error variances among sites; (2) a uniform (same) genetic correlation among sites; and (3) a single common variance either for GCA or SCA effects versus heterogeneous GCA and SCA variances. The statistical model used is similar to the model for individual site of univariate analyses, but included site effects. Results from across‐sites univariate analyses were used as initial values for the across‐sites bivariate analyses.2.6We also performed a multiple‐trait, multiple‐sites analysis to estimate between‐trait genetic correlations, accommodating among‐site heterogeneous variances for residual, GCA and SCA, for both traits. The model used to estimate bivariate DBH and CONE correlation for combined sites is.yS1DBHandyS1Cone are vectors containing phenotypic observations for DBH and CONE at site 1 etc. The incidence matrix relating observations to GCA is similarly partitioned as.Where Zg=ZS1DBH00ZS1Cone⋱=∑⊕Zi.The variance–covariance structure for GCA is.G01 is the between‐trait GCA variance and covariance matrix within‐site 1 and G12 is GCA covariance matrix between DBH and CONE between sites 1 and 2 which has elements:where σgcaDBH122 is an inter‐site covariance for DBH (sites 1 and 2) and σgcaDBH∗Cone122 is an inter‐site, inter‐trait. The variance–covariance structure for SCA effects was formulated in the same way. Finally, the residual variance–covariance structure was formulated as.where where.R1=σe1DBH2I1DBHσe1DBH.ConeI1DBH.Coneσe1DBH.ConeI1DBH.Coneσe1Cone2I1Cone.Models that were able to converge were those assuming a uniform between‐site genetic correlation at the level of GCA effects. These correlations were 0.6 and 0.9 for DBH and CONE, respectively and were applied with and without restraining the ratio of GCA variance to SCA variance.The combined site analyses were completed for all eight sites and for five sites  having a normal or nearly normal distribution of the transformed total cone number.Narrow‐ and broad‐sense heritabilities were estimated for individual and combined sites. Individual narrow‐sense heritability was calculated, assuming GCA variance estimates a quarter of the additive variance with a coefficient of relatedness of 0.25 among progenies of a single full‐sib family as.for the individual site analysis and,2gca is the estimate of GCA variance component, σ2sca is the estimate of SCA variance component, σ2gca.site is the estimate of GCA by site interaction variance, σ2sca.site is the estimate of SCA by site interaction variance, and σ2e is the residual variance estimate. Standard error of heritability was estimated by a Taylor's series expansion  is the height of a standard normal curve at the truncation point xp and Φp is the proportion of the population incidence of fecundity. The h2o is the estimated heritability of observable fecundity score, h2 is the underlying heritability of the unobservable liability  is the additive genetic covariance between DBH and CONE, and 2gi and σ2gjσ are the GCA variances for DBH and CONE, respectively.where cov , wood stiffness, branch index (BIX) and stem sweep; the index does not consider fecundity  selection based on DBH only, but calculating the indirect effect on fecundity.The direct and correlated responses were calculated as:(B) index selection based on DBH with the restriction of no change on fecundity.P1 and P2 are phenotypic measurements of DBH and fecundity on which selection is to be based, and b1 to b2 are the corresponding weighting factors of the vector to be determined. The b vector derived is , the genetic responses (gain or loss) for DBH and CONE were also modelled and plotted.where P* is the phenotypic variance for selection trait of DBH, G* is the additive genetic variance–covariance between DBH and fecundity, G** is the additive genetic variance–covariance matrix between selected traits excluding restricted traits, 33.1p < .05). Dickson et al. (p < .05). Thus, it seems that regions of lower and middle rainfall and lower elevation, such as southern Victoria and South Australia, are synonymous with higher fecundity rates at earlier ages. We did not observe a relationship between site type (viz. a 2nd rotation or ex‐pasture sites) with cone setting in these eight sites. To model the effect of site related climate and soil on cone setting, a more comprehensive sampling of sites with varying factors is recommended.Among the eight sites surveyed, the average number of cones ranged from 2.6 RAD211) to 17.1 cones per tree (RAD208) . Female cones (ovulating cones) develop two ovules on its upper side of each cone scale and accept pollination in the spring. Fertilization and seed development are a long process and take up to two years after pollination. Therefore, both first‐year cones and second‐year cones are usually co‐existing on trees and can be easily recognized in radiata pine. Both the age of onset of reproduction (precocity) and abundance of flowers (and cones) each year affect fecundity of plants  may contribute this extreme estimate for RAD203.Treating total cone number as a threshold trait for the three sites  produced a similar estimate of heritability for RAD199 and RAD211 (0.50 and 0.43 respectively). However, the estimated heritability for RAD203 was outside the bounds of the parameter space (>1.0). The higher cone incidence (73.9%) at RAD203 and very high observable binomial heritability estimate . However, tree breeding aiming for growth traits only may interrupt the evolutionary constraint, that balance growth rate and fecundity, maintained in natural populations. By interrupting this evolutionary constraint, therefore, there would be a cost in long‐term adaptation of the domesticated tree species. Radiata pine plantations outside the native range not only comprise the commercial planting estate, but also comprise ex situ conservation stands ) and reduced flowering in advanced generations of conifer breeding (such as radiata pine) are a large concern for tree breeding programmes. In radiata pine, it was observed that flowering was delayed and the numbers of flowers were reduced after mating of related trees  but a negative gain in CONE of 1.76 (19.6%) under selection intensity of 1%. If we maintain CONE (zero change), the genetic gain for DBH would be reduced to 3.76 mm (1.9%). Figure % but a n7Fecundity as measured by precocity and abundance of female cones is highly heritable in radiata pine with a moderate to high heritability about 0.39–0.61. Fecundity had a lower genetic variation and genotype by site interactions relative to DBH, based on this study. With such high heritability, fecundity can be increased or reduced genetically by selective breeding.Site had significant impact on fecundity, mainly due to the amount of rainfall of the tested sites in this experiment. Drier sites had higher fecundity.vice versa). However, a selection strategy for increasing wood production while maintaining acceptable fecundity should be developed for short‐ and long‐term breeding programme. Fecundity should be included as an objective trait in long‐term breeding programmes and in ex situ conservation stands, such as radiata pine.Moderate but negative genetic correlations between fecundity and growth (average of −0.39) were observed in this large sample of the Australiasian radiata pine population. This indicates that there is an evolutionary constraint in natural populations and ex situ conservation populations that imposes stabilizing selection of growth and fecundity. Selective breeding to improve tree growth rate alone could reduce fecundity of radiata pine (and In the short term, a constrained selection index approach is recommended to maintain the fecundity level while maximizing genetic gain for growth rate. For long‐term breeding strategy and ex situ conservation, dissecting the genetic basis of the negatively correlated traits at the gene level is required.