PTPRD (protein tyrosine phosphatase δ) was significantly associated with OC traits at the genome-wide significance level (p = 2.48 × 10−8). rs7856850 was also associated with OCD in a meta-analysis of OCD case/control genome-wide datasets (p = 0.0069). The direction of effect was the same as in the community sample. Polygenic risk scores from OC traits were significantly associated with OCD in case/control datasets and vice versa (p’s < 0.01). OC traits were highly, but not significantly, genetically correlated with OCD . We report the first validated genome-wide significant variant for OC traits in PTPRD, downstream of the most significant locus in a previous OCD GWAS. OC traits measured in the community sample shared genetic risk with OCD case/control status. Our results demonstrate the feasibility and power of using trait-based approaches in community samples for genetic discovery.Using a novel trait-based measure, we examined genetic variants associated with obsessive-compulsive (OC) traits and tested whether OC traits and obsessive-compulsive disorder (OCD) shared genetic risk. We conducted a genome-wide association analysis (GWAS) of OC traits using the Toronto Obsessive-Compulsive Scale (TOCS) in 5018 unrelated Caucasian children and adolescents from the community (Spit for Science sample). We tested the hypothesis that genetic variants associated with OC traits from the community would be associated with clinical OCD using a meta-analysis of all currently available OCD cases. Shared genetic risk was examined between OC traits and OCD in the respective samples using polygenic risk score and genetic correlation analyses. A locus tagged by rs7856850 in an intron of  Up to 50% of OCD cases have a childhood-onset (before the age of 18)2, which is more heritable than adult-onset OCD3. Two genome-wide association studies (GWAS) in clinical samples with mixed ages of OCD-onset and a meta-analysis of these studies did not identify genome-wide significant loci6. The most significant loci from previous GWAS include SNPs within DLGAP1, BTBD3, GRID2, and one close to PTPRD. Using obsessive-compulsive (OC) symptoms rather than a clinical diagnosis, a study of adult twins identified a genome-wide significant SNP in MEF2B (rs8100480)7. However, this SNP was not replicated in an independent sample5.Obsessive-compulsive disorder (OCD) is a common 9. We measured OC traits using the Toronto Obsessive-Compulsive Scale 8. This heritable measure10 includes negative scores that represent ‘strengths’  and positive scores that represent ‘weaknesses’ . We reasoned that a strength-to-weakness format would generate scores with a more normal distribution in a community sample8 than those observed with typical OCD scales and would therefore boost the power of genetic discovery11. Typical OCD trait measures generate J-shaped distributions because their format calls for ratings of symptoms from absence to presence (score of zero to a positive integer). A j-shaped distribution is especially likely when using typical OCD measures in a community sample where the prevalence of OC symptoms is low and most people would get scores of zero12. This j-shaped distribution can be replicated with the TOCS by collapsing the ‘strengths’  into scores of zero  of the most significant loci, estimating SNP-based heritability and genetic correlations of total OC trait scores with other medical/mental health disorders and traits. We also examined if the most significant loci from the previous GWAS of OC symptoms7 replicated in our study. Finally, we tested the hypothesis that OC traits in the community share genetic risk with OCD by examining individual genetic variants, genetic correlations, and polygenic risk between OC traits in Spit for Science and three independent OCD case/control samples.We conducted a GWAS of quantitative OC traits in a large pediatric, community-based sample: Spit for Science9. Briefly, the sample included 15,880 participants with complete demographic, questionnaire, and family information  from the 17,263 youth (6–18 years of age) recruited at the Ontario Science Centre over 16 months. Informed consent, and assent where applicable, were obtained using a protocol approved by the local Research Ethics Board at the Hospital for Sick Children. Participants provided a saliva sample in Oragene saliva kits  for genetic analyses. See the supplement for details.The Spit for Science sample is described in detail elsewhere10. Each item was scored on a 7-point Likert scale ranging from −3 (‘far less often than others of the same age’) to +3 (‘far more often than others of the same age’). A score of zero was designated as an average amount of time compared to same-age peers. The TOCS total score was standardized into a z-score to account for age, sex, and questionnaire respondent (parent or self). Details of z-score creation are described in the supplement. We tested the impact of the strength/weakness structure of the TOCS by re-scoring the TOCS to convert all negative scores for individual items to zero before summing scores . We also compared the TOCS to an additional OCD symptom measure with a j-shaped distribution: The Obsessive-Compulsive Scale of the Child Behavior Checklist (CBCL-OCS)13. Each of the eight CBCL-OCS items was scored on a scale of 0–2  and was summed to generate a total score (range: 0–16). This ‘collapsed’ TOCS total score, with a cluster of scores at zero, created a distribution similar to the CBCL-OCS . We excluded any samples with concentrations <60 ng/µl and insufficient quality based on agarose gels. We genotyped 5645 samples on the Illumina HumanCoreExome-12v1.0_B (HumanCore) and 192 samples on the Illumina HumanOmni1-Quad V1.0_B (Omni) bead chip arrays  at The Centre for Applied Genomics . There were 538,448 markers on the HumanCore and 1,140,419 markers on the Omni array.14. Sample exclusion and selection criteria are described in the http://bochet.gcc.biostat.washington.edu/beagle/1000_Genomes_phase3_v5a/). We excluded individuals who were non-Caucasian based on principal component (PC) analysis and included only one participant from each family  was conducted separately for each array using standard methods with PLINK v1.90R2 imputation quality (AR2 > 0.6) and used the standard genome-wide threshold of p ≤ 5 × 10−8. We also tested if any genome-wide significant variants from the analysis with the standardized scores were still significant using a non-standardized TOCS score. For these analyses, age, sex, respondent, and their 2- and 3-way interactions were used as covariates in addition to the above .GWAS was conducted using R (v3.5.1). Our primary analysis tested if imputed dosage and standardized TOCS total score were associated using a linear regression model that included the top three PCs and genotyping array as covariates. We included SNPs with a minor allele frequency (MAF) > 1%, allelic zeroinfl from the R package pscl (v1.5.2). This model was chosen because of the high proportion of zero scores that created a j-shaped distribution. The test is a mixture of two models: a negative binomial model, which contributes to zero and positive scores, and a logit model, which contributes to possible inflation of zero scores (point mass at 0) compared to what a negative binomial model predicts. These analyses used non-standardized scores for the collapsed TOCS and CBCL-OCS so the model adjusted for the covariates and the association of SNP allele dosage with the OC trait scores is tested against the null of having no effects on both the logit part and the negative binomial part using likelihood ratio tests.In secondary analyses, we evaluated the association between SNPs and the collapsed TOCS score, and between SNPs and CBCL-OCS, using zero-inflated negative binomial likelihood ratio tests, using the function 15 (fuma.ctglab.nl).We subsequently used FUMA to conduct a gene-based GWAS of the TOCS standardized total score with MAGMA using a Bonferonni correction for the number of protein-coding genes included16 (https://locusfocus.research.sickkids.ca/). We examined the 14 GTEx sets from brain tissue types and examined SNPs within ±1 Mbp of each SNP.We tested each genome-wide significant variant for co-localization with brain eQTLs using LocusFocus17 v1.91.2-beta (http://cnsgenomics.com/software/gcta/) with further exclusion of cousins and SNPs with AR2 > 0.9 and LDSC18  calculated from SNPs in HapMap3. We used LDSC19 to examine the genetic correlation of TOCS total scores with the 850 phenotypes available on LD Hub (http://ldsc.broadinstitute.org/ldhub/).We estimated SNP heritability using both GCTAp-values and effect sizes of the top variants from the present study in the only previous GWAS of OC symptoms7 of 6931 twins and sibs from the Netherlands Twin Registry .Finally, we examined the 6, (2) the Philadelphia Neurodevelopmental Cohort (PNC) from the Children’s Hospital of Philadelphia (CHOP)20, and (3) the Michigan/Toronto OCD Imaging Genomics Study21. See the supplement and Table For validation analyses, we investigated three independent OCD case/control cohorts: 1) the International OCD Foundation Collaborative (IOCDF-GC) and OCD Collaborative Genetics Association Studies (OCGAS) meta-analysis the Inte22 (see 23 (see supplement). First, we derived PRS for TOCS from the Spit for Science sample and tested their association with case/control status in the combined OCD cohorts . Second, we derived PRS from the combined OCD cohorts and tested their association with the standardized TOCS total score in the Spit for Science sample (target sample). We examined the potential shared genetic risk between the Spit for Science and the meta-analyzed OCD samples using genetic correlations estimated with LDSC19.To validate findings from the GWAS of the TOCS total score, we combined the GWAS summary statistics from each OCD cohort using a fixed-effect inverse variance meta-analysis. For completeness, we also conducted a meta-analysis of the summary statistics from the GWAS of the TOCS total score with the OCD samples using a modified sample size-based weighted meta-analysis method for combining continuous and categorical variablesPTPRD was significantly associated with TOCS total scores at the genome-wide level . There was no eQTL data for the SNP in PTPRD, rs7856850, in LocusFocus16, or in the most recent version of GTEX v824.We used 5018 participants for GWAS analyses after sample exclusion and selection . For both collapsed TOCS and the CBCL-OCS, the A allele was associated with both higher scores  and lower proportion of zero scores .When we analyzed the collapsed TOCS total score and the CBCL-OCS, the genome-wide significant locus for the TOCS total score rs7856850 was no longer genome-wide significant, although the remaining effect was in the same direction and had the same direction of effect (p = 0.05/19369 protein-coding genes = 2.58 × 10−6). The most significant genes were SH3GL2 ; RRN3 , and PDXDC1  using GCTA and h2 = 0.071  using LDSC when the intercept was constrained to 1. TOCS total score was not significantly associated with any phenotypes on LD Hub (see supplement).The heritability of the TOCS total score was 7 was nominally associated with TOCS total scores in the Spit for Science sample with the same direction of effect . This SNP is in the same region as our most significant locus (9p24.1), but not in LD . Another 16 of the reported most significant loci in den Braber7, including a variant in MEF2BNB (rs8100480) that was genome-wide significant in their sample, had effects in the same direction but were not significantly associated in the current sample , we had a total of 3369 cases and 8611 controls in our validation samples  when intercepts are constrained to 1. Figure R2 = 0.277%, p = 0.0045 at ρ = 0.003). Figure R2 = 0.24%; p = 0.00057 at ρ = 0.1).The genetic correlation between standardized TOCS total scores and OCD meta-analysis was Using a trait-based approach in a community sample, we identified a genome-wide significant variant associated with OC traits (rs7856850) that was also associated with OCD case/control status. Polygenic risk and genetic correlation findings showed sharing of genetic risks between OC traits in the community and OCD case/control status in independent samples.PTPRD that codes for protein tyrosine phosphatase δ. No eQTLs have been calculated yet for rs7856850 (GTEx V8)24. To validate this finding, we tested if this SNP was also associated with OCD in a meta-analysis of three independent cohorts. The significant association of rs7856850 with OCD case/control makes it the first variant associated with OC traits and OCD. For completeness, we presented genome-wide results for the meta-analysis of the OCD cohorts as well as a meta-analysis of TOCS total score with the OCD cohorts, where no genome-wide significant findings were revealed. However, the direction of effect for rs7856850 was in the same direction in all samples. The small size of the OCD cohorts likely precluded finding genome-wide significant SNPs. In the meta-analysis of OC traits and OCD, summary statistics were combined using sample size-based weights that were modified and calibrated to account for SNP heritability to reflect differences in power and ascertainment between continuous (OC traits) and categorical (OCD case/control) designs25. The low SNP heritability of TOCS severely down-weighted the OC trait sample size while up-weighting the already underpowered OCD case/control cohorts. Larger samples will be helpful to confirm the results from the present study.The genome-wide significant variant (rs7856850) associated with OC traits is in an intron of the consensus transcript of PTPRD. However, those variants were independent of the locus found in our study7. These observations support a possible role of the 9p24.1 region in OCD. The 9p region is also the location of one of the strongest linkage peaks in earlier genome-wide linkage studies of pediatric OCD27. Rare CNVs in PTPRD have been identified in cases with OCD21 and ADHD28. SNPs in PTPRD were genome-wide significantly associated with ASD29, restless legs syndrome30, and self-reported mood instability31. Ptprd-deficient mice show learning deficits and altered long-term potentiation magnitudes in hippocampal synapses32. PTPRD is expressed highly in the brain compared to non-brain tissues, especially in myelinating axons and growth cones34 in the prenatal cerebellum35. The presynaptically located PTPRD is involved in axon outgrowth and guidance36 and interacts with postsynaptic proteins such as Slitrk-2, interleukin-1 receptor, and TrK to mediate synapse adhesion and organization in mice38 and the development of excitatory and inhibitory synapses39. Members of the Slitrk and interleukin protein families have been associated with OC behaviors in humans and mice41.Previous GWAS of OCD symptoms or diagnosis identified variants that approached significance in the region around rg = 0.42, p = 0.095; 50)43. Lack of power is the most likely explanation for the absence of a significant result. Previous studies of other psychiatric disorders reported shared genetic risk between traits and diagnoses, with polygenic risk and genetic correlations similar to what we report for OC traits and OCD case/control status44. The shared genetic risk between OC traits and OCD supports the hypothesis that an OCD diagnosis could represent the high extreme of OC traits that are widely distributed in the general population. One implication of this finding is that population-based samples with quantitative trait measures can serve as a powerful complementary approach to case/control studies to accelerate gene discovery in psychiatric genetics.Our results show that OC traits in the community share genetic risk with OCD. Polygenic risk for OC traits was associated with OCD case/control status and vice versa. OC traits and OCD case/control status also were substantially, but not significantly, genetically correlated. This estimate is higher than reported in a recent study 45. A similar trend for lower SNP heritability in traits vs. diagnosis has been observed for ADHD46. The reason for the disparity in SNP heritability between traits and diagnosis is unclear as there are several differences that may play a role including informant (parent/self vs. teacher or clinician)46, type of measurement , consideration of impairment, and timing . Regardless of a non-significant SNP heritability for OC traits from our sample, we still identified and validated a genome-wide significant variant.SNP-based heritability for OC traits in the current sample was not significant in line with previous studies. Previous research reports lower SNP-based heritability for self-reported OC symptoms (0.058)10 . Our results indicate that the distribution of the OC trait measure impacts power to identify genome-wide significant associations. A ‘strengths’ to ‘weaknesses’ measure identified a genome-wide significant association. However, when we collapsed the ‘strength’ end of the TOCS distribution to zero, the significance of this variant was substantially reduced to below genome-wide significance, although the effect was in the same direction. The same effect was observed using another OC measure that generates a j-shaped distribution: CBCL-OCS. One implication of our results is that there is genetic information in the ‘strengths’ end of the distribution captured by the TOCS. This information would be lost in scales that only measure ‘weaknesses’, particularly in community samples where the prevalence of clinically significant OC symptoms is relatively low. Trait-based scales that capture ‘strengths’ and ‘weaknesses’ and have a less skewed distribution could improve power to identify genome-wide hits and variants associated with disorders, especially in population samples.The TOCS scale is similar to existing OC trait/symptom measures in item content but is unlike existing scales in that it measures OC traits from ‘strengths’ to ‘weaknesses’. As a result, the distribution of the total score is closer to a normal distribution than the j-shaped distributions typically observed with most symptom-based scales that rate behaviors from zero to a positive integerThe results of this study should be considered in light of its limitations. Although our sample was large enough to detect a genome-wide significant locus that was also significant in meta-analyzed OCD case/control cohorts, substantially larger samples will be needed to identify most of the contributing common variants. The current version of the TOCS measures OC traits cross-sectionally, which does not account for symptom waxing and waning and does not measure impairment directly. However, our polygenic risk and genetic correlation analyses show that OC traits and OCD share genetic risk, suggesting that the TOCS is capturing traits that are likely to be on a continuum with OCD.We identified the first genome-wide significant variant for OC traits that was also associated with OCD case status. Power to detect a genome-wide association was impacted by the distribution of the OC trait measure. OC traits and OCD share genetic risks supporting the hypothesis that OCD represents the extreme end of widely distributed OC traits in the population. Trait-based approaches in community samples using measures that capture the whole distribution of traits is a powerful and rapid complement to case/control GWAS designs to help drive genetic discovery in psychiatry.Supplemental Methods and Results


Secale cereale L.) is one of the most important cereals in Europe. Nevertheless, its germplasm collections are among the least numerous compared with cereals. There are only about 27,000 Secale accessions in 70 gene banks around the world. Despite extensive research on the molecular characterization of genetic resources, only a fraction of this collection has been described. The main objective of the presented study was to perform genotypic and phenotypic characterization of an obsolete gene pool represented by 100 accessions originated from 28 countries around the world and preserved in the gene bank of the Polish Academy of Sciences Botanical Garden – Center for Biological Diversity Conservation in Powsin. Genetic analysis using simple sequence repeat markers showed that the obsolete gene pool is relatively large. This indicates that different sources of variability were used in breeding programs. However, the genetic variation is in no way related to the place of origin. Despite the great differences in the genetic make-up, the collection showed a broadly common phenotype. This could result in a low level of interest among breeders in the stored germplasm, undervalued as a source of important but not easily observable traits, e.g., high disease resistance, which was found in some accessions.Common rye (The online version of this article (10.1007/s13353-020-00579-z) contains supplementary material, which is available to authorized users. Secale cereale L.) belongs to the Poaceae family and is one of the most important cereal crops cultivated in Europe. It is characterized by the ability to produce high yields even when grown under environmental stress conditions, i.e., low temperatures, drought, and low soil fertility. The presence of disease resistance genes reduces the need for intensive chemical protection of this crop . Therefore, rye ex situ collections are much smaller than wheat and barley ones, but single accessions may contain a large pool of genetic variation  to determine SSR based genetic diversity of obsolete gene pool of rye preserved in gene bank of Polish Academy of Sciences Botanical Garden – Center for Biological Diversity Conservation in Powsin, (ii) to evaluate the variation of agro-morphological traits, (iii) to develop a core collection, and (iv) to identify accessions with potential utility for breeding programs.The plant material consisted of 100 rye accessions stored in the gene bank in the Polish Academy of Sciences Botanical Garden – Center for Biological Diversity Conservation in Powsin (PASBG). The accessions represented 28 countries  and placed on Petri dishes on tissue paper at room temperature for germination. From the seedlings at the first leaf stage, fragments of about 1.5 cm were collected from the central part of the leaf blade. Each accession was represented by 6 bulk samples of 16 plants each. After harvesting, the material was frozen, freeze-dried (LABCONCO), and ground in a bead mill MM 100 (Retch). DNA was isolated from pooled samples using a Clean Plant PK DNA Purification Kit (CLEANNA) according to the manufacturer’s procedure. Concentration and purity of DNA were evaluated spectrophotometrically using NanoDrop 1000 (Thermo). Then, six pooled samples for each accession were combined in one in equal proportions and diluted to a working concentration of 20 ng/μl.Seventeen SSR markers available in the public domain  using a 36-cm capillaries array filled with POP-7 polymer. The length of fragments was assessed against the size standard GeneScan™ 500 LIZ™ Dye Size Standard (Applied Biosystems).2 experimental plots. All observation and measurements have been carried out for three consecutive growing periods: 2015/2016; 2016/2017; and 2017/2018. Seeds were sown in autumn every 2–3 cm in rows 17.5 cm spaced. Before sowing, seeds were treated with seed dressing (Funaben T). During growing season, morphological characters as well as basic phenological data have been evaluated. Each observation has been carried out at the recommended time. Sowing, flowering, and wax maturity dates make a possible calculation of the length of vegetation and grain filling period. Plant emergence, winter hardiness, snow mold resistance, powdery mildew resistance, brown rust resistance, and stem rust resistance were expressed in a 1–9 point scale. Plant height, penultimate leaf length, and spike length were measured for 20 plants per plot and expressed in centimeters. The number of grains per spike was the average number for 20 manually harvested and threshed ears. One thousand grains of each accession were collected at random, weighed to record the seed index, and expressed in grams.Phenotyping has been carried out on rye plants growing on 1.5 mThe length of the fragments was determined using the GeneMapper (Applied Biosystems) software. The amplified fragments from each accession were transformed into binary character matrix where the presence of a fragment of defined length was scored as 1, while its absence as 0. The obtained binary matrix was subjected to data analysis encompassing four key steps, i.e., evaluation of marker informativeness, genetic diversity analysis, genetic distance-based analysis, and population structure analysis.At first, an assessment of the markers’ informativeness was carried out and it included the calculation of polymorphism information content (PIC) and resolving power (Rp) coefficients (Prevost and Wilkinson I) were calculated  and Shannon’s information index , and hierarchical analysis of molecular variance (AMOVA)  values was set from 1 to 15, with ten independent runs for each K . An admixture model with correlated allele frequencies was employed. The CLUMPAK software was used to identify the number of real clusters in data (K). The optimal value of K was determined based on the posteriori probability of data for a given K and ΔK using a full search algorithm to obtain the best alignment of the cluster analysis  to simultaneous analysis of qualitative and quantitative morphological traits. The averages for the traits were also standardized and a proximity matrix by the Gower coefficient was developed Gower .Genetic and phenotypic dissimilarity matrices were correlated using the Mantel test Mantel . A conseAll the abovementioned data analyses were performed using the following software: Microsoft Excel 2016, XLSTAT Ecology , GenAlEx 6.501  to 80% (SCM118 and SCM028). The average value of PIC for all SSRs was 0.648. A multiplex A had a slightly higher PIC value (0.677) than a multiplex B (0.616). The maximum PIC was achieved by SCM101 (0.907), while the minimum value was found for SCM050 (0.00). The mean value of resolving power that describes the discriminatory potential of the markers was equal to 1.67 (multiplex A) and 2.54 (multiplex B). Detailed data is provided in Table I) . The lowest variation was among the accessions originating from Finland (uHe = 0.191 and I = 0.257) and the highest between those originating from Germany (uHe = 0.266 and I = 0.385) and Turkey (uHe = 0.280 and I = 0.390). The analysis of variance (ANOVA) did not show significant differences in uHe and Shannon index values among accessions grouped by country of origin. There were no significant differences in the values of both coefficients between the groups separated based on continents from which the analyzed accessions were originating. Since as much as 73% of the surveyed accessions came from Europe, they were also labeled according to the European regions. ANOVA showed significant region-related differences in values of Nei’s unbiased coefficient of variation. The lowest variation was observed within the accessions from Central Europe and the highest within the South European. The values of the I coefficient did not show any significant differences between the regions of Europe. The results of the analysis following the biological status of accession showed that the highest diversity occurred within the group of landraces (0.246), significantly higher than the calculated for cultivars (0.187). Shannon’s index values did not show statistically significant differences in biological status. The date of acquisition of the accessions to the gene bank did not significantly affect the differentiation coefficients.Accessions were classified according to geographical region, country of origin, biological status, and date of acquisition. For such groups, the diversity coefficients were calculated, i.e., Nei’s unbiased coefficient of variation (uHe) and Shannon’s information index  to 0.710 (“ATHENS ABRUZZI”–“BALBO”). The average distance between accessions was 0.433. Hierarchical grouping by Ward’s method was performed based on the distance matrix. It showed the presence of three main clusters consisting of 35, 18, and 47 accessions. Within all clusters, there were lower-order structures. In the results of grouping, there were no links with the country of origin, geographical area, or biological status Fig. .Fig. 2Hip = < 0.05. Ward’s grouping based on significant ɸPT values showed the presence of three groups composed of 5, 14, and 2 countries. Accessions from Germany, Austria, Romania, Czech Republic + former Czechoslovakia, and Poland formed the first group. The second group consisted of all other accessions except those from Spain and Turkey, which were classified in the third group .Analysis of molecular variance revealed no genetic structuring related to geographic regions. However, for the European accessions, 1.5% of variation in the genetic structure was correlated with five regions of Europe. In total, 3.4% of molecular variance originated from countries. Negative values for variation components indicated a lack of population structuring associated with the biological status and the year of acquisition (Table K), evaluated using the ΔK method, indicated K = 5 sub-populations  to an inferred population. The highest cluster membership coefficient (Q) values for five clusters were as follow 1–0.503 (“PORTO”); 2–0.467 (“FLORIDA BLACK WALLANCE SELECTION”); 3–0.573 (“SCHNIDT”); 4–0.282 (“BRIGODA DE MIRANDELA”); and 5–0.661 (“DWARF WINTER”). In geographical regions, the proportion of clusters was significantly different (p < 0.05) only for the fifth group. Its contribution was significantly higher in Asia than in Europe and both Americas. The differences in the proportion of all clusters were significant for countries. However, the HSD post hoc test distinguished homogeneous groups only for the third cluster. Its contribution was significantly higher in Iran than in Turkey, Belgium, and Belarus.SSR dataset was implemented in STRUCTURE. The most probable number of clusters .Dendrogram based on the results for oup Fig. . There woup Fig. . In accoade Fig. . As befoMicrodochium nivale (Fr.) Samuels & I.C. Hallett, causing snow mold, was stable because none of the accessions showed symptoms of infestation. The highest variability (11%) occurred within the number of grains per spike. On average, the lowest number of grains per spike was obtained for “DWARF WINTER” (29.67) and the highest for “DOBRENICKE KRMNE” (61.67). The variability of the other traits ranged from 1 to 7% and it was reflected as insignificant in the analysis of variance (Table p = 0.035) and region of origin (p = 0.013). The number of grains per spike was significantly higher for the accessions of Northern Europe than for those from Africa (p = 0.002). The other accessions formed a homogeneous group with an intermediate score. The variance analysis did not show significant differences between groups with different biological status or between individual accessions. On the other hand, all traits were significantly influenced by the climatic conditions in the years in which the field trials were carried out (p < 0.0001). Correlation analysis showed a moderate uphill relationship between two phenological traits i.e. grain filling period and the number of days to maturity (0.516). Plant height was moderately related to spike length (0.496) and the penultimate leaf length (0.393), as well as to winter hardiness (0.401). A weak uphill relationship was observed for stem rust resistance and grain filling period (0.261), spike length, and penultimate leaf length (0.240). A weak downhill correlation was found between stem rust resistance and spike length (− 0.303). In the remaining variants, the correlation coefficient was statistically insignificant  which is used to analyze simultaneously various types of data . The first three eigenvalues corresponded to 18.7% of the total variance . The quantitative traits were highly related to the first axis while the qualitative data were correlated with the second one. Consensus plot of analysis results, i.e., MFA centroid, of the first and second coordinates showed a significant distinctiveness of “POLLUX” cultivar Fig. . It was p < 0.0001). Generalized Procrustes analysis (GPA) was carried out to minimize the scale effect and to achieve a consensus configuration based on genotypic and phenotypic data. Scaling was the most efficient transformation method , the obtained result indicates that the rye breeding programs conducted in the years 1960–1980 were based on initial materials with a relatively high degree of differentiation. It is generally believed that the continuous selection and crossing of closely related cultivars have led to a narrowing of the gene pool on which modern breeding is based  Samuels & I.C. Hallett (snow mold), Oculimacula yallundae  Crous & W. Gams and Oculimacula acuformis  Crous & W. Gams, (eyespot disease), Gaeumannomyces graminis (Sacc.) Arx & D.L. Olivier , Blumeria graminis (DC.) Speer (powdery mildew), Rhynchosporium secalis (Oudem.) Davis (Rhynchosporium leaf spots), Puccinia recondita f. Dietel & Holw. , Puccinia graminis Pers. (stem rust), and Claviceps purpurea (Fr.) Tul. (ergot). All the examined accessions showed high or very high resistance to four observed diseases i.e. snow mold, powdery mildew, and brown and stem rust. Brown rust is one of the most popular, airborne rye fungal diseases and is regularly observed throughout its cultivation range ESM 2(PDF 107 kb)


Solanum lycopersicum L.) is a widely used model plant species for dissecting out the genomic bases of complex traits to thus provide an optimal platform for modern “-omics” studies and genome-guided breeding. Genome-wide association studies (GWAS) have become a preferred approach for screening large diverse populations and many traits. Here, we present GWAS analysis of a collection of 115 landraces and 11 vintage and modern cultivars. A total of 26 conventional descriptors, 40 traits obtained by digital phenotyping, the fruit content of six carotenoids recorded at the early ripening (breaker) and red-ripe stages and 21 climate-related variables were analyzed in the context of genetic diversity monitored in the 126 accessions. The data obtained from thorough phenotyping and the SNP diversity revealed by sequencing of ripe fruit transcripts of 120 of the tomato accessions were jointly analyzed to determine which genomic regions are implicated in the expressed phenotypic variation. This study reveals that the use of fruit RNA-Seq SNP diversity is effective not only for identification of genomic regions that underlie variation in fruit traits, but also of variation related to additional plant traits and adaptive responses to climate variation. These results allowed validation of our approach because different marker-trait associations mapped on chromosomal regions where other candidate genes for the same traits were previously reported. In addition, previously uncharacterized chromosomal regions were targeted as potentially involved in the expression of variable phenotypes, thus demonstrating that our tomato collection is a precious reservoir of diversity and an excellent tool for gene discovery.Tomato ( Solanum lycopersicum L.) is a major vegetable crop worldwide and a widely used model species in genomic studies and breeding. The rapidly increasing availability of genomic data has allowed researchers to bypass the limitations of a single reference genome and has facilitated development of a pan-genome that represents the genetic diversity of cultivated and wild tomato species [Tomato . In Sardinia in particular, together with artichoke, tomato is the most important horticultural crop species, with numerous landraces still cultivated locally by farmers. This is of special relevance if we consider that landraces might be directly used in marginal environments or exploited in future breeding programs as a reservoir of genes that underlie quality and productive traits that can be used to increase the performance of modern cultivars and to boost sustainable agriculture ,6,7,8,9.Genome-wide association studies have become a powerful tool to investigate and link the huge available amounts of genome sequence variation data with measurable phenotypic variations ,3,4. RecHere we used both conventional MLM and the two alternative models, FarmCPU and QTCAT, to scan an ad hoc assembled population of 120 tomato varieties that were mostly landraces for relevant associations among 2470 RNA-Seq–derived SNPs and traits of interest. In this “multi-omics” era, where DNA sequence data are easily acquired, phenotypic data are becoming the limiting factor. For this reason, we have here mainly addressed our efforts at thoroughly characterizing this population. Accordingly, the aim of the present study was to link the diversity observed at the phenotypic level with that observed at the transcribed genome level. To achieve this goal, we used three different GWAS approaches that provided an output of 536 significant associations related to plant and fruit morphophenological traits, fruit quality and response to climate. With the present study, we are acknowledging the value of this tomato collection for future gene-function, genome-editing and crop-breeding studies.Solanum lycopersicum L.) accessions that comprised 64 landraces from Sardinia, 8 landraces from other regions of Italy, 43 landraces from different countries and 11 vintage or modern cultivars  during 2012 and 2013. We conducted the open-field trial in 2012 at Oristano, according to a randomized complete block design with five replicates, 124 treatments (accessions) and four plants per plot. The field trial started in June and the full mature fruit were harvested in September. In 2013, we set up the second trial from January to July in a greenhouse in Ottava (Sassari); a randomized complete block design was again used, with three replicates, 126 treatments (accessions) and three plants per plot.http://tinyurl.com/n7k75m6).We used 26 conventional morphophenological traits  to charaIn addition to these conventional descriptors, we carried out digital phenotyping on the mature fruit collected in 2013 using the Tomato Analyzer software ,25. For We extracted the carotenoids from three replicates of each accession (one per block), at both the breaker stage , when the fruit are still rich in chloroplasts and the red-ripe stage, when the fruit accumulate large quantities of carotenoids, for a total of 762 samples. All of the harvested fruit at the breaker and red-ripe stages were healthy and uniformly colored within their stage. An example of the two ripening stages is shown in Frozen flesh samples from each fruit stage were rapidly homogenized and carotenoids extraction performed as described in . Carotenhttp://www.diva-gis.org/) was used to extract ecological data from the free-access database at http://www.diva-gis.org/climate.For each accession for which geographic coordinates were available (108 accessions), climatic data were also downloaded relative to each collection site. Bioclimatic variables were used for further comparative analyses with phenotypic and molecular data, as they are considered more biologically meaningful than monthly temperatures and rainfall. Indeed, they describe annual temperature and precipitation trends, seasonality or extreme environmental factors . The DIVA-GIS 7.5 software (http://broadinstitute.github.io/picard/) and only uniquely mapped reads were kept. SNPs were then identified based on the mpileup files generated by SAMtools [Transcriptome sequencing was performed on 120 of the 126 accessions cultivated during the green-house trial in 2013. The fruits used for RNA extraction were the same used for carotenoid extraction (see above) Total RNA was extracted using RNeasy Plant Mini kits  from full-ripe tissue samples previously frozen at −80 °C and homogenized. Strand-specific RNA-Seq libraries were constructed using a protocol described in  and 51-bSAMtools . The resijr is the observation of the ith genotype, in the jth environment and block replicate r, μ is the grand mean, Gi is the effect of the ith genotype, Ej is the effect of the jth environment, G*Eij is the interaction of the ith genotype with the jth environment, Br(Ej) is the effect of the rth replicate in the jth year and εijr is the random error.Ranges and mean values were calculated by site for each accession on quantitative conventional descriptors, Tomato Analyzer descriptors and carotenoid contents. Analysis of variance (ANOVA) was then performed on the conventional descriptors collected from the 122 accessions shared among the two sites. To test for significant variations among environments and genotypes and to estimate the size of the genotype by environment interaction (GxE) we used the model given in Equation (1):The adjusted means (as the best linear unbiased predictors (BLUPs)) and the broad sense heritability were also calculated across sites for the quantitative conventional descriptors and within sites for the Tomato analyzer descriptors and the carotenoid contents. To calculate these statistics, we have fitted a mixed linear model based on the restricted maximum likelihood method with years as fixed effects and genotypes and blocks as random effects using Equation (2): 2p = σ2g + (σ2g⋅e/ne) + (σ2ϵ/ne⋅nr) for heritability across the two sites and σ2p = σ2g + (σ2ϵ/ne⋅nr) for heritability calculated by site, and where σ2g is the genotypic variance, σ2p is the phenotypic variance, σ2g⋅e is the genotype by environment interaction, ne is the number of environments, σ2ϵ is the residual variance and nr is the number of replicates. The expression given in Equation (3) was used to calculate the statistics:lme4 and car) [All the above statistical analyses were carried out using R dedicated packages (and car) .pi is the frequency proportion of the descriptor state and b is the base of the logarithm. Most studies use natural logarithms, although some use base 2 (which makes no significant differences). Each value was normalized by dividing it by its maximum value  to keep the values between 0 and 1. Pearson’s correlations were estimated among the different traits using the cor standard R function and plotted using the corrplot package [ggbiplot package (https://github.com/vqv/ggbiplot). The Hopkins statistic [factoextra R package (https://cran.r-project.org/web/packages/factoextra/index.html).The Shannon–Weaver index was used to evaluate the diversity among the qualitative traits . GenAlEx package . Principtatistic  was usedtatistic . The EucST values and also proportion of shared alleles between pairs of sub-populations.Genetic diversity analyses were performed using GenAlEx 6.5  and Arlemantel.rtest function of the ade4 R package; 9999 permutations were used to evaluate the significance of the test. The phenotypic distances were those obtained to draw the clustering plot, while the genetic Nei minimum distances were obtained using the R adegenet [popr packages . A Mantel correlation test was then used to compare the genetic and phenotypic distances using the adegenet  and popradegenet package for the R software [2 < 0.2). To filter the dataset, we used the R package bigsnpr [To investigate population structure, we used the model-based clustering method, as implemented in Structure vs. 2.3.4 , and dissoftware . The firsoftware . The secsoftware . The met bigsnpr , to obta2 measure was calculated; in the second case, the r2 unbiased estimates were corrected by the structure of the sample (rs2), the relatedness of the individuals (rv2) or both (rvs2). These methods were implemented in the LDcorSV R package [LD-vignette included in the Bioconductor package snpStats implemented in R [2 data against the genetic distance and fitted the LD decay line as in [https://fabiomarroni.wordpress.com/). The regression line is based on [SneP, a program designed to easily estimate effective population size from genome-wide SNP data or directly from LD levels [To further evaluate the structure of the population, we estimated the familial relatedness and linkage disequilibrium (LD) levels of the tomato collection by calculating the pairwise kinship coefficients as obtained from TASSEL 5.2.42 . We then package . Pairwisted in R ,46. We tne as in , adaptinbased on  and the D levels .2 value) across our tomato population above which loci can be assumed to be associated, we used the method from [To identify the critical value of LD , genotype (G) and genotype × year (G × Y) interactions as effects of the model revealed significant differences among the genotypes between the years, except for FWG, NFI and LWI (p < 0.001) was also observed for G and G × Y interaction effects (2B) calculated across sites and by site also varied among conventional traits  differences were observed in both 2012 and 2013 when looking at the differences among the genotypes, as shown by the mean, maximum and minimum values of the conventional quantitative traits  . Almost 2B = 92.21%) and the lowest for the perimeter of the longitudinal section (h2B = 36.65%)  . The meaFor the carotenoids, the total content was higher at the ripe stage than the breaker stage, as expected . At the The heritability of the carotenoids was on average lower than the other phenotypic traits, with β-carotene showing the highest values at both breaker and ripe stages  .p < 0.0001) with the area, perimeter and maximum width (Width_M_H_L and Width_M_H_T) of the transverse and longitudinal sections of the fruit . Significant correlations were also observed between carotenoid content and fruit color and size. In particular, β-carotene content was negatively correlated  to the mean fruit weight (FWG) at both the breaker stage and the ripe stage and it was positively correlated with the degrees Brix at the breaker stage  and the ripe stage . Fewer correlations were detected between bioclimatic variables and the plant/fruit traits, some of which were of particular note, such as those between the traits that described fruit size  and bio3 , bio8 (Mean Temperature of Wettest Quarter), bio12 , bio13 (Precipitation of Wettest Month), bio 16 (Precipitation of Wettest Quarter) and bio18 (Precipitation of Warmest Quarter). The observed correlations varied from −0.45 (p < 0.001) for correlations between FLE and bio3 and bio8, to −0.27 (p < 0.005) for correlations between area and perimeter with bio12, bio13 and bio16  and low puffiness. Cluster C is mainly comprised of varieties of small/medium size, elongated fruit, with thick pericarp and intermediate phenotypes for sugar content.The main features of the three groups can be deduced from the loading of the different traits on the first two principal components . BrieflyST) among the groups was 0.07 on average and was always statistically significant. Moreover, the proportion of shared alleles indicated higher similarity between SLR and ELR than between CV and ELR (The genetic differentiation (F and ELR . On the  and ELR .p < 0.001) among populations. This thus indicates that more than 96% of this diversity is due to the genetic variation among accessions within groups, either from different countries of the world or from a single country or region .The partitioning of the SNP variance (AMOVA) indicated that only 4% of the total variation was due to a statistically significant differentiation . Indeed, if we do not consider the admixed individuals, the same accessions were attributed to the green and red genetic groups shown in Based on the Bayesian approach, the most likely number of genetic groups within this tomato population was three , while tp = 2 × 10−4; 9999 permutations), the genetic and phenotypic clustering do not completely overlap  was higher than the r2 measure corrected for population structure (rs2), kinship (rv2) and both population structure and kinship (rvs2). These were seen as r2 > rs2 > rv2 > rvs2; rv2 and rvs2 LD were very similar overall and within each chromosome  and rv2 (1.37 Mb) than for standard r2 (1.06 Mb). This output was more evident within chromosomes for the rs2 decay.We plotted the rromosome  and the Some differences were also observed for LD decay among the different chromosomes, with lower LD decay distances for chromosomes 1, 3, 8 and 10, while the highest decay distances were for chromosome 5  that encode transcription factors  were detected within 203 genes, 23 of which are genes  was detected for fruit size and shape  , which was detected by the three GWA models as associated to several traits  and was located within the gene Solyc02g081700, which encodes a proteasome subunit α type protein, 13 kb from an ANANTHA gene  is located within the gene it size) . This geTHA gene .Recruiting Motif (TRM) gene that has been previously shown to influence final fruit shape [Among the genomic regions that influenced variation of fruit traits, an MTA on chromosome 4 (ch4_31) was 28 kb from a TONNEAU1 it shape .FASCIATED gene is located. FASCIATED has been extensively studied in tomato, where mutations to this gene result in altered floral meristem size and locule number [On chromosomes 6 to 10, we detected more relevant MTAs for fruit size and shape, of which six were located within genes that encode transcription factors and others were either located near genes that influence fruit setting  or in regions where no genes determinant for the investigated fruit traits were found . On chroe number ,56,57,58Solyc01g079620, a SlMYB12 gene that influences accumulation of the yellow-colored flavonoid  in tomato fruit [Among the associations detected for quality traits, we found a relevant region on chromosome 1 (at 71 Mb) where three SNPs (ch1_38-40) were associated to fruit color and β-carotene content . These Sto fruit . These MSolyc02g081700, above mentioned for its association with different fruit size and shape traits and one located within Solyc06g066320, the gene encoding for a transcription factor homolog of Arabidopsis thaliana IWS1 (INTERACTS WITH SPT6) [Other relevant MTAs were detected for sugar content and other fruit quality traits. Among these, one is located within the gene TH SPT6) . This geSolyc01g108020, a gene that encodes a thioredoxin M3, and one on chromosome 10 within Solyc10g084400, which encodes a glutathione S-transferase (AP2/ERF). Moreover, on chromosome 11 we detected a SNP (ch11_49) within a DELLA gene (Solyc11g011260) that encodes a gibberellic acid insensitive (GAI) transcription factor  within nsferase . These Mn factor . The gend tomato ,63,64,65Solyc04g080730, at 31 kb from Solyc04g080780 and 35 kb from Solyc04g080790, two BEL-like homeodomain genes [Arabidopsis and tomato [Solyc11g010490 at 82 kb from the J-1 gene, a MADS-box 512 transcription factor implicated in the differentiation of the pedicel abscission zone and the 513 maintenance of the inflorescence meristem [Solyc11g071680, which encodes a serine/threonine kinase (homolog of the Arabidopsis TOUSLED). This is particular interesting because it encodes a serine/threonine protein kinase that is required for leaf and flower development and is involved in the regulation of RNA interference [Marker-trait associations for leaf size were also identified that included an MTA for leaf length within in genes . BEL-LIKd tomato ,67,68. Mmeristem ,70 and arference . These fSolyc02g071510, a transcription factor GTE12, which is associated with bio13, bio15 and bio16 . The T allele of ch2_130 is private to the exotic landraces group  and were both identified within  protein . This pr LOCUS C ,74.Solyc05g015510 that it is associated with precipitation  and encodes SlySBP10 a SQUAMOSA promoter-binding protein  by activating a central flowering repressor that is encoded by flowering locus C [Solyc07g055660, located 26 kb from a heat-stress transcription factor; Solyc08g076930, a transcription factor for jasmonic acid 3 that is 72 kb from a RAMOSA transcription factor  to be a worldwide sample. On the other hand, the higher levels of genetic diversity observed within modern and vintage cultivars underlines that the breeding programs might have led to inclusion of different loci in these accessions ,16.Previous studies performed on tomato landraces and commercial cultivars using SNP markers revealed comparable levels of diversity and similar levels of heterozigosity when looking at groups of landraces from different regions ,81,82,83The genetic distance observed among the groups of varieties was not high, which is consistent with the nature of domesticated tomato, for which high phenotypic but low genetic variation has been shown previously ,11,16,85The population structure and the linkage disequilibrium analyses performed prior to GWAS have further shown that our population was adequate for association analysis. While the LD levels and LD decay on some chromosomes were comparable to those from previous studies, these were quite variable among chromosomes ,82,87. IAmong the methods used to perform these GWAS, FarmCPU and QTCAT shared the highest numbers of associations while the standard MLM method, showed the least number of shared associations with the other two methods. These data indicate that some associations are highly robust and detected by all methods and that weaker associations might best be identified using multiple strategies. In particular we also chose to use QTCAT to determine whether the associations detected were comparable with those from previous consolidated GWAS models ,19,20,21Differently from many GWA studies that rely on the genomic variation, the present study was based on the transcriptome variation among genes and accessions. The RNAseq based SNPs genotyping has been proven to be valuable for QTL mapping and functional and evolutionary studies ,93. ConcThe GWAS results have mainly targeted genomic regions that underlie genes relevant for fruit size and shape traits. This is a direct consequence of our phenotypic analysis, which mainly focused on fruit traits, in line with the relatively thorough phenotypic characterization, as either detected by conventional or digital phenotyping. This is also a good way to evaluate the appropriateness of the present collection for GWAS studies. Indeed, a large number of associations were detected with data from the Tomato Analyzer, which was also successfully used in previous studies ,86,100. Arabidopsis CLAVATA3), which underlies the fasciated mutant phenotype [Additional traits identified by these authors as predictive for these categories were fruit shape index, distal end protrusion, width widest position, proximal end blockiness, rectangular, distal angle and proximal eccentricity. Here, we also detected many of these associations, such as that with Proximal_angle_macro near OVATE or with fruit-shape-related traits near FASCIATED. However, interestingly, along with these associations, we also detected further associations linked to SlCLV3 . These show that this combined regional and global tomato collection is useful for targeting genes with prominent roles in fruit. As an example, a single SNP (ch2_151) was identified within a gene that encodes a proteasome subunit α type protein that appears to have a key role during fruit setting ,108. ThiTogether, these results suggest that the diversity of this germplasm is sufficient to gain further insights into the role and function of genes that underlie important traits. Indeed, further associations were found in regions that were previously targeted as important for fruit color, carotenoids and sugar content, while others targeted novel genes that might be of relevance for quality traits, such as fruit color and β-carotene content on chromosomes 1 and 4 ,111,112.Using GWAS on SNPs from RNA-Seq extracted from mature fruit, we did not expect a direct implication for most of the present polymorphisms in the expressions of genes involved in plant development and phenology. However, we would expect a relevant number of these genes to be in LD  with others expressed in the fruit tissue. The significant RNA variation has indeed allowed the identification of associations for growth habit. These were often at short distances from transcription factors that have determinant roles in the regulation of plant growth and development, including tomato ripening and responses to environmental stimuli ,114,115.Solyc06g066320, the gene encoding for a IWS1 transcription factor, is particularly interesting because this protein is highly conserved in different organisms (from humans to yeast) in Arabidopsis it is required for gene expression induced by brassinosteroids [gene spanning nearly 0.5 Mb, is enriched of genes encoding for different transcription factors, also related to fruit development and ripening [Arabidopsis, as well as in the establishment of polarity of lateral organs, including leaf and flower, in Arabidopisis, rice and other species [Solyc06g066320, together with its simultaneous associations with different traits , suggest a possible pleiotropic role for this gene. Being pleiotropy a target for many GWA studies, variable strategies have been designed for the identification of causal variants and their roles that will help to extract the huge amount of information that is being produced [As revealed by a recent study from Wu and colleagues , genes osteroids ,116. Morripening ,117. In  species ,119,120.produced ,121,122.Previous studies based on climatic data have revealed gene expressed regions that might be involved in the answer to different environments ,127,128.Solyc02g071510, which encodes a transcription factor (GTE12) that in Arabidopsis is implicated in the cytosolic Ca2+ increase after application of stress, such as heat, drought and cold [Among the most interesting results, we found MTAs identified on chromosome 2 for variables related to precipitation, such as ch2_130, with its “T” allele being private to the exotic landrace population. This MTA is located within and cold ,130. Thrand cold ,128,131.Further associations were detected across the remaining chromosomes in genomic regions that appear to be involved in plant responses to climatic changes. As an example, we indicate the MTAs located within a gene that encodes a heat stress transcription factor or within SlMYC2, a jasmonic acid transcription factor involved in fruit chilling tolerance in tomato . Others The present results indicate that we can select the accessions that carry the polymorphic alleles to create specific crosses with controlled fruit size and shape and, for example, improved quality characteristics or that might be linked to specific climate responses.We demonstrate here the value of exploring the genetic diversity of local and regional accessions, to exploit the variation of this genetic diversity reservoir to gain knowledge and facilitating crop improvements. Our results indicate that, as well as the relevant associations detected for fruit size, shape and quality, there is correlation between the expression of fruit traits and other traits related to plant development and flowering, and the likely response to climate variations. As recently demonstrated in tomato, the human-driven selection for specific traits during domestication has resulted in direct effects on other secondary traits or indirect influences in the expression of other genes to linkage drag ,133. Som


Pinus sylvestris L.), a genomic prediction study was conducted with 694 individuals representing 183 full-sib families that were genotyped with genotyping-by-sequencing (GBS) and phenotyped for growth and wood quality traits. 8719 SNPs were used to compare different genomic with pedigree prediction models. Additionally, four prediction efficiency methods were used to evaluate the impact of genomic breeding value estimations by assigning diverse ratios of training and validation sets, as well as several subsets of SNP markers.Genomic selection (GS) or genomic prediction is a promising approach for tree breeding to obtain higher genetic gains by shortening time of progeny testing in breeding programs. As proof-of-concept for Scots pine (Genomic Best Linear Unbiased Prediction (GBLUP) and Bayesian Ridge Regression (BRR) combined with expectation maximization (EM) imputation algorithm showed slightly higher prediction efficiencies than Pedigree Best Linear Unbiased Prediction (PBLUP) and Bayesian LASSO, with some exceptions. A subset of approximately 6000 SNP markers, was enough to provide similar prediction efficiencies as the full set of 8719 markers. Additionally, prediction efficiencies of genomic models were enough to achieve a higher selection response, that varied between 50-143% higher than the traditional pedigree-based selection.Although prediction efficiencies were similar for genomic and pedigree models, the relative selection response was doubled for genomic models by assuming that earlier selections can be done at the seedling stage, reducing the progeny testing time, thus shortening the breeding cycle length roughly by 50%.The online version contains supplementary material available at (doi:10.1186/s12864-020-07188-4). Genomic prediction or genomic selection (GS) was proposed by Meuwissen et al.  as a metThe application of GS requires phenotypic data and marker information of a training population (TP) of individuals that are used to develop prediction models to calculate genomic estimated breeding values (GEBV). GEBV are then validated through a validation population (VP) of individuals, or selection candidates, which are genetically related to the TP, and for which only marker data is available to predict their own GEBV –5. SincePractical application of GS in animal and crop breeding programs would not have been possible without the rapid and cost-effective development of next-generation sequencing (NGS) technologies which, consequently, have accelerated the discovery of thousands of single nucleotide polymorphisms (SNP) markers –11. AmonPicea glauca (Moench) Voss  BSP)  BSP) , interiol.] BSP) , 70, Norl.] BSP) , 81, 82,l.] BSP) , 83, 84,Douglas)  and mariDouglas) , 67. GS Douglas) . In the imations .r1) has been widely used but normally shows lower values than r2,r3 and r4 for different traits (r2) and theoretical accuracy (r3) have been used less frequently [r3 [Predictive ability  which generally showed higher values than r1,r2,r3 (r4 (0.65–0.75), followed by r3 (0.59–0.68), r2 (0.49–0.70), and r1 (0.19–0.43) that had the lowest values as expected. Nevertheless, using r4 ) may inflate the prediction efficiency due to that the individuals in the validation population used to estimate EBV (EBVVP) were a proportion of the individuals in the full dataset (y) used to estimate PEBV (PEBVy) and therefore the correlation between them was generally higher [Another method extensively used has been the prediction accuracy  exhibited the lowest r1,r2 and r3 . EM was the most efficient imputation method in a wheat breeding GS study [r2,r3, and r4, when EM imputation algorithm was combined with GBLUP and BRR. In contrast, r1 was almost equal for each trait when BL model was used regardless of the imputation method used. We speculate that the slighly better performance of GBLUP-EM and BRR-EM could be due to that GBLUP and BRR respectively assume that QTL allelic effects are normally and multivariate normally distributed, and in addition the EM imputation method uses a kinship-based imputation algorithm which also assumes that marker genotypes follow a multivariate normal distribution [For species such as Scots pine with large and complex genomes  but withGS study  with GBSribution , 33. Thur1 among GBLUP, BRR and BL for growth and stem straightness traits, but with larger prediction bias  when BL was used. In a different maritime pine study with three generations, larger prediction bias were detected for PBLUP than for GBLUP or BL [r1 (0.16–0.44) and r4 values (0.58–0.77) observed for all of them [r4. On the contrary, genomic models  performed better than PBLUP in terms r1  in eucalyptus hybrids, yet pedigree errors were observed in the populations studied, resulting in the underestimation PBLUP estimates [Traits of interest in tree breeding programs have different genetic architecture; thus, different genomic prediction models to evaluate prediction efficiencies may be used . In a twUP or BL . Several of them . Howeverstimates . The autr4 (0.75–0.84) for all traits, however in terms of r1,r2 and r3 GBLUP, BRR, BL and PBLUP showed similar prediction efficiencies for growth and wood quality traits , belonging to the Swedish tree improvement program at Skogforsk (The Forestry Research Institute of Sweden) was used. The trial consists of 184 full-sib families and 7240 trees (F1-generation), generated from a partial diallel mating design of 40 plus-trees (F0-generation). The progeny trial was established in 1971 by Skogforsk as a randomized single tree plot design, divided into 14 post-blocks . A more Growth traits were measured in the 7240 progeny trees whereas wood properties were estimated in a subset of 694 progeny trees. Height (Ht) was measured when the progeny trees were 10 (Ht1) and 30 (Ht2) years old. Diameter at breast height (DBH) was also measured twice, at ages 30 (DBH1) and 36 (DBH2). In 2011, increment cores at breast height were extracted and processed by Silviscan . From the Silviscan analysis, three traits were used in this study: microfibril angle (MFA), static modulus of elasticity (MOEs) and wood mean density (DEN). In addition, dynamic modulus of elasticity (MOEd) predicted by Hitman ST300  was also used in the current study. Wood traits are further described in Hong et al. [Ⓡ Plant II kit  was used to extract genomic DNA from vegetative buds or needles from the 694 progeny trees and 46 parents. DNA concentration was determined with Qubit Ⓡ 2.0 fluorometer . Then, three genomic libraries for GBS were prepared following the procedure described in Pan et al. [The commercial NucleoSpin n et al.  by usingprocess_radtags module of Stacks v.1.40 [Pinus taeda v1.0 [mpileup command of SAMtools over all the samples simultaneously, with default parameters, and converted into VCF matrix using BCFtools v0.1.19 [vcfutils in BCFtools with default parameters, the SNPs within 3bp around an indel or with mapping quality <20 were filtered out; using Vcftools v.0.1.12b [Thereafter, paired-end raw reads of each GBS library were cleaned and demultiplexed by the s v.1.40  on the beda v1.0  referenceda v1.0  with defeda v1.0 . SNP mar v0.1.19 . Further.0.1.12b , only SNcodeGeno function in synbreed package [A.mat function implemented in rrBLUP package [Finally missing genotypic data were imputed using two imputation methods to compare their prediction efficiencies. Random (RND) imputation with the  package  in R  were available for all progeny trees in the trial, therefore univariate single site spatial analysis were performed in ASReml 4 standalone [andalone , with thandalone , 103. Woandalone –107.Best Linear Unbiased Prediction (BLUP). The following model was used for PBLUP and GBLUP:y is the vector of the adjusted phenotypic data for each trait, b is the vector of fixed effects (intercept), a is the vector of random effects and e is the vector of residual effects, which is assumed to follow a normal distribution as I is the identity matrix. X and Z are the incident matrices of b and a.where a (additive genetic effects) from Eq. A is the numerator relationship matrix (NRM). Briefly, the diagonal elements (i) of A were estimated according to Lynch and Walsh [In the PBLUP, the vector nd Walsh  as:2\dog and h are the parent of individual i.where i and j and were estimated as:The off-diagonal elements are the relationship between individuals a is assumed to follow a normal distribution with expectations of G is the genomic realized relationship matrix (RRM) estimated according to VanRaden [For the GBLUP, the vector VanRaden  as:4\doM is the matrix of genotyped samples, P is the matrix of allele frequencies with the jth column given by 2(pj−0.5), where pj is the observed allele frequencies of the genotyped samples. The elements of M were coded as 0, 1 and 2  for the estimation of the G matrix with function kin from the synbreed package in R in the case of RND imputed data, and with the function A.mat from the rrBLUP package in R, for the EM imputed data. PBLUP and GBLUP analyses were conducted in ASReml-R version 4.1.0.106.where Bayesian models. BRR and BL were implemented using the BGLR function from the BGLR package in R [age in R . In briey is the vector of n adjusted phenotypes, 1n is the vector of ones, μ is a scalar denoting the intercept, W is the incidence matrix for the m vector of marker effects, and e is the vector of residual effects that follow a multivariate normal distribution m from Eq. p is the number of markers, dfm is degrees of freedom and Sm is the scale parameter. Residual variance is assigned as dfe degrees of freedom and scale parameter for residual variance Se [m from Eq. λ2 is assigned as λ2∼Gamma. Finally, the residual variance is assigned as dfe is degrees of freedom and Se is the scale parameter for residual variance [where iance Se . For thevariance .For the Bayesian methods, GEBV in the VP were estimated as,ith tree at the jth locus and jth locus.where Model convergence and prior sensitivity analysis. Bayesian algorithms were extended using Gibbs sampling for estimation of variance components. The Gibbs sampler was run for 20,000 iterations with a burn-in of 1,000 iterations and a thinning interval of 100. The convergence of the posterior distribution was verified using trace plots.Cross validation. For all traits, pedigree based (PBLUP), genomic models  and imputation method (EM and RND), a 10-fold cross-validation analysis was implemented, i.e., 90% of individuals randomly selected for the TP and 10% in the VP. BRR and BL were tested with eleven different sets of SNP markers randomly selected . Additionally, to evaluate the performance of the pedigree versus genomic prediction models, different sizes of TP and VP were used. All individuals were randomly split into four different proportions of TP/VP, 80%, 70%, 60% and 50%  for TP and the rest as VP. Each analysis was replicated 10 times.Prediction efficiency of traditional and genomic genetic evaluations. Prediction efficiencies of pedigree and genomic models were evaluated and compared based on the predictive ability, predictive accuracy, theoretical accuracy, and prediction accuracy.r1) was defined as the Pearson product-moment correlation between the EBV of the individuals in the VP (EBVVP) and their adjusted phenotypes (y). i.e., r1=corr.The predictive ability (r2) was estimated as the r1 scaled by h , i.e., r2=corr/h [The predictive accuracy /h .PEV is the prediction error variance of the VP, and Gii is the diagonal element of the ith individual in the G matrix for GBLUP model or in the case of PBLUP model Gii=Aii, i.e., the diagonal element of A matrix [The theoretical accuracy  was estimated for PBLUP and GBLUP as A matrix .r4) was estimated as the Pearson product-moment correlation between the EBV of the individuals in the VP (EBVVP) and the PEBV estimated with all 697 progeny trees (PEBVy), i.e., r4=corr [The prediction accuracy  , 91.To avoid fold effects, all the methods were estimated within each fold and averaged across folds and replicates .Heritability estimation. Pedigree- and genomic-based narrow sense heritabilities (h2) were estimated for PBLUP and GBLUP as:where Relative selection response of GS. Assuming that selection response is inversely proportional to the length of the breeding cycle, the relative selection response (RSR) of GS to the traditional pedigree-based selection (PS) can be estimated as a ratio (RSRGS:PS) between the efficiency or accuracy method and the breeding cycle time in years [in years .8\documrGS and rPS are the efficiency of GS and PS, respectively and CLPS and CLGS are the breeding cycle lengths of PS and GS, respectively.where RSRGS:PS−1)∗100 [RSRGS:PS, two GS approaches were assumed to reduce the breeding cycle, by shortening the period of field progeny testing needed for phenotypic evaluations. In addition, for the first approach the cycle length was reduced to 18 years considering that female flowering starts at 15–18 years in Scots pine [The percentage of increase in selection efficiency of GS was estimated as (S−1)∗100 . In ordeots pine , whereasots pine  would prRSRGS:PS was estimated considering the four different prediction efficiency methods described in the previous sections  and both breeding cycle reduction approaches.Additional file 1 Supplementary information. The additional file contains detailed information about the initially performed spatial analysis and the


Owing to increasing medical expenses, researchers have attempted to detect clinical signs and preventive measures of diseases using electronic health record (EHR). In particular, time-series EHRs collected by periodic medical check-up enable us to clarify the relevance among check-up results and individual environmental factors such as lifestyle. However, usually such time-series data have many missing observations and some results are strongly correlated to each other. These problems make the analysis difficult and there exists strong demand to detect clinical findings beyond them. We focus on blood test values in medical check-up results and apply a time-series analysis methodology using a state space model. It can infer the internal medical states emerged in blood test values and handle missing observations. The estimated models enable us to predict one’s blood test values under specified condition and predict the effect of intervention, such as changes of body composition and lifestyle. We use time-series data of EHRs periodically collected in the Hirosaki cohort study in Japan and elucidate the effect of 17 environmental factors to 38 blood test values in elderly people. Using the estimated model, we then simulate and compare time-transitions of participant’s blood test values under several lifestyle scenarios. It visualizes the impact of lifestyle changes for the prevention of diseases. Finally, we exemplify that prediction errors under participant’s actual lifestyle can be partially explained by genetic variations, and some of their effects have not been investigated by traditional association studies. Thus, while the essential purpose of EHR is to document the history of patient care for reimbursement, the accumulated EHRs have been gathering a great deal of attention for advanced purposes. For example, the analysis of EHRs enables us to facilitate clinical decision support, to predict condition-specific clinical processes, and to improve clinical treatment plans beyond traditional clinical encounters. In particular, periodic time-series EHRs collected in, e.g., annual medical check-ups, can clarify trends of changes in check-up results depending on lifestyle and social status. However, such time-series data have many missing observations and some results are strongly correlated to each other. These problems make the analysis difficult.Recently, the continuously increasing cost of medical care has received significant attention. The center of the idea aimed at curbing this trend is using electronic health records (EHRs) to detect signs and preventive measures of diseases such as diabetes. EHRs consist of patient’s medical information, e.g., survival modeling, are utilized when we focus on the time-to-event to evaluate the effect of treatments or occurrence of events for some endpoints, e.g., the mortality of cardiovascular diseases. On the other hand, there exist statistical techniques, which utilize statistical models to mathematically formalize the generative process of the data under assumptions. They can elucidate hidden mechanism of the target system and also incorporate the effect of time such as dynamic Bayesian networks (DBNs) , W/H-ratio = 1.05, BMI = 30, and Smoking status for male, and W/H-ratio = 0.95, BMI = 30, Body Fat Percentage (BFP) = 35, and Smoking status for female. The simulation results are illustrated in We compare the predicted and actual time-series blood test values of participants when improving or corrupting their body composition values and lifestyles. In this experiment, we postulate two situations; (i) a healthy person suddenly corrupted their body composition values and lifestyles, and (ii) an unhealthy person started to improve their body composition values and lifestyles. At first, we extracted a healthy male and female, and predict blood test values using the estimated parameter values and (i-a) actual In these figures, we assume that the body composition values and lifestyles suddenly changed to unhealthy condition at the first medical check-up year. From the simulations, participants in Dyslipidemia Group seem to be the most affected. The male under unhealthy condition gradually increases TG and LDL and finally becomes Dyslipidemia when he is 64 years old. In contrast to the postulated unhealthy condition, actual status at 64 years old is not Dyslipidemic. Furthermore, ALT and Uric Acid are weakly increasing to suspected areas, and HbA1c, BDs, WBC, RBC, Hemoglobin, and Hematocrit are also weakly increasing in the Male in contrast that they are almost not varied in the Female. In both persons, BPs and HbA1c are not affected by the changes in body composition values and lifestyles used here.zk,t and (ii-b) postulated zk,t to conduct them to be healthy persons. Here, we consider healthy conditions as AC = 75[cm], W/H-ratio = 0.80, BMI = 20, and non-smoking status for male and W/H-ratio = 0.70, BMI = 20, BFP = 20, non-smoking, non-drinking and exercise every day. The simulation results are illustrated in Next, we extracted unhealthy male and female and calculated their predicted blood test values, and (ii-a) actual zk,t is normalized to mean 0 and variance 1 in the application, the rate of change of the effect by the jth lifestyle  to the ith hidden variable in this case is almost −1, where gi,j is the ith row and the jth column element of G and zk,t,j is the jth element of zk,t. On the other hand, smoking and drinking were changed from 0 to 1. The same applies to the opposite case. Similar to the previous experiments, Dyslipidemia Group seems to be the most affected blood test values. In these experiments, the male under unhealthy condition gradually decreases TG and LDL and finally becomes healthy from Dyslipidemia when he is 72 years old. In contrast to the previous experiments, ALT, Uric Acid, HbA1c, BDs, WBC, RBC, Hemoglobin, and Hematocrit are weakly affected both in the Male and Female. In both persons, BPs and HbA1c are not affected.In these experiments, when healthy male participant was changed his lifestyle to unhealthy one; AC, W/H-ratio, and BMI, were changed from less than 75, 0.80, and 20, to 95, 1.05, and 30, respectively. Also, their means in Male participants are approximately 85, 0.9, and 24, respectively. Because each element of observedvalue—predictedvalue). In some participants, there is a clear discrepancy and we assume that they are due to unobserved or unused information such as genetic effects. In the Hirosaki cohort study, genetic data was also obtained in 2014 and 2015, and we focused on detecting associated SNPs for these discrepancies.Finally, we analyzed the prediction errors among the predictive and observed values  in the male result. Additionally, rs887829 is popularly associated with total bilirubin levels [p = 4.17E − 8) in the female result. However, in contrast to the results of rs887829, the strong association of rs662799 with nonHDL cholesterol levels could not be detected when using the observation values (p = 2.34E − 2). In the middle and right of this figure, novel effects of SNPs were suggested in the associations of the prediction errors. rs4607103 and rs3817198 are known to have an association with type II Diabetes [p = 3.23E − 5) and PWV (p = 3.63E − 5) were indicated in the male results, respectively. Similarly, rs907612 and rs2371767 were indicated to have associations with Uric Acid levels (p = 1.00E − 5) and HDL cholesterol levels (p = 2.57E − 5) in the female results, respectively, but these SNPs are known to have associations with monocyte and granulocyte [It is known that rs662799 is associated with higher TG and cholesterol levels  and partn levels  and partDiabetes  and BreaDiabetes , respectnulocyte  and Waiszk,t as clinical target values to be controlled as listed in e.g., γGT and C-reactive protein, could be effective items to investigate chronic diseases, but they have been collected recently in this cohort. On the other hand, it may be interesting to analyze blood test values such as vitamins and hormones for other diseases.Generally, we could control some blood test values in response to the changes of body composition values, lifestyles, and social status. In this study, we focus on chronic diseases such as Diabetes and high blood pressure and selected 38 blood test values listed in yk,t − yk,t−1 and zk,t − zk,t−1, absolute correlation values are lower than the correlations among yk,t and zk,t. This indicates that blood test values are not suddenly changed according to the changes of other values . Thus, we can assume that lifestyle changes gradually impact blood test values.From H and G in the proposed SSM, similar to the results obtained in xk,t and environmental factors zk,t and assumed to be similarly varied. Additionally, we can see that body composition values can control some indicators for chronic diseases, e.g., Dyslipidemia Groups, but simultaneously and oppositely regulate RBC Groups. It is quite natural that chemical compounds are mutually regulated or share the same chemical pathways and they should be simultaneously affected even if we change only a part of the blood test values.From the results of estimated matrices Then, we postulated the case of healthy or unhealthy persons suddenly changing their body composition values and lifestyles and predicted time-transitions of their blood test values. In both males and females, indicators for Dyslipidemia such as total cholesterol, LDL, HDL, and TG, are strongly affected by changes in body composition values such as AC, W/H-ratio, and BMI. These results are intuitively natural because serum cholesterol levels show the lipid profiles in blood whereas body composition values show subcutaneous and visceral fat. Thus, the amount of cholesterols should have a mutual relationship with the body composition values. On the other hand, HbA1c and BPs are slightly affected by the changes of body composition values and lifestyle changes. Exercise could be one of several factors related to and prevent diabetes; however, even if you exercise, you still may get diabetes due to other factors. For example, HbA1c levels can increase if the participants started exercising but were still eating and drinking too much. As a result, their clear relationship cannot be detected in the experiments and we consider these values could be gradually changed over time based on their entire lifestyle. In contrast, ALT, Uric Acid, and BDs values are varied only in males in relation to the changes of indicators for Dyslipidemia. We assume that these blood test values are likely to be changed in males and these values are stable enough in females to reveal the change of trends in our dataset. Consequently, we conclude that the body composition values and lifestyle changes can be a clinical target of Dyslipidemia and the risk of the disease might be controlled by keeping them within regular ranges. Our quantitative results using the mathematical simulation are consistent with types of intuitively reasonable knowledge and capable of updating them in more detail. For other blood tests that are targets of other diseases, such as Diabetes, we have to add other controllable targets in environmental factors. For example, time of eating dinner and composition of the meal can be good factors.A, H, and G. In this case, we consider that other factors, which are not included in the model, could affect to their relationships or observation variables. To clarify these factors, finally, we evaluated genetic effects for the correction of prediction errors among the predictive values under the participant’s actual conditions and observed values. We discovered some interesting findings about detected SNPs. In the results of SNPs with known traits, rs662799 has a strong association with higher TG and cholesterol levels [p = 5.25E − 5). However, a strong association could not be detected when using observation values (p = 2.34E − 2). In the results of SNPs with novel traits, the association of rs907612 with uric acid levels was detected by using either prediction errors or observation values. However, associations of rs4607103, rs3817198 and rs2371767 with total protein, PWV and HDL cholesterol, respectively, were detected only by using prediction errors. rs4607103 was suggested to be associated with type II Diabetes [e.g., age, exercise, smoking history and drinking history, using a large number of participants . Such approaches could clarify the minute effects of SNPs that could not be detected using traditional genome wide association studies, which condition a few covariates or principal components and capture SNPs with higher effect size. Because our approach can utilize time-series information for detecting associated SNPs, it could especially enhance the detection power in contrast to the traditional non-time series approaches.Although the observation data are well predicted for the most part, if predicted values still vary from the observation data in only some participants, they do not share the estimated relationships represented by l levels  and thisDiabetes  and DiabDiabetes , 27. TheDiabetes , 25 by czk,t and it is difficult to include all obtained SNPs in the proposed model. Thus, extended models or more efficient algorithm might be required to include SNPs effects. For example, the calculation using linear mixed models are computationally efficient and it can be applied with designing appropriate covariance structure for time-series data. In addition, since random effects could correct for population structure and family relatedness also in SSM, it can be extensions of the proposed model. Some other factors can also affect to the changes of blood test values. The analysis including SNP effects, random effects, and other factors is one of future works. Our proposed model can express linear relationships under the Gaussian assumption among blood test values and environmental factors, but many natural processes, e.g., chemical reaction networks and biological signal processing, are generally represented as nonlinear structures. Moreover, the observation noises of some blood test values could not depend on the Gaussian noises, for example, TG levels can suddenly increase due to eating habits before medical check-up. The use of a nonlinear structure and non-Gaussian assumption has the potential to clarify more detailed relationship among them.The extension of the model to include the presence of SNPs enables us to evaluate their effect to the blood test values and to predict them more accurately. However, such extension makes the calculation heavier depending on the dimension of S1 AppendixThe detailed solution of estimating parameter values using the EM-algorithm for the proposed model.(PDF)Click here for additional data file.S1 Figyk,t of included and excluded samples for both dataset.The histograms of ages and 38 observation values (PDF)Click here for additional data file.S2 Figp  for both dataset.The lowest BIC score of each system dimension (PDF)Click here for additional data file.S3 Figxk,t and zk,t generated from F for both dataset.The regulatory relationships among (PDF)Click here for additional data file.