This comprehensive analysis illustrates the biological value of large, deeply phenotyped cohorts of unselected populations coupled with NGS data.Understanding the impact of rare variants is essential to understanding human health. We analyze rare (MAF < 0.1%) variants against 4264 phenotypes in 49,960 exome-sequenced individuals from the UK Biobank and 1934 phenotypes (1821 overlapping with UK Biobank) in 21,866 members of the Healthy Nevada Project (HNP) cohort who underwent Exome + sequencing at Helix. After using our rare-variant-tailored methodology to reduce test statistic inflation, we identify 64 statistically significant gene-based associations in our meta-analysis of the two cohorts and 37 for phenotypes available in only one cohort. Singletons make significant contributions to our results, and the vast majority of the associations could not have been identified with a genotyping chip. Our results are available for interactive browsing in a webapp ( Population-based association analyses of rare genetic variants with complex traits are limited by the availability of data from sufficiently large cohorts. Here, Cirulli et al. report gene-based collapsing analysis of exomes from 49,960 participants of the UK Biobank and 21,866 participants of the Healthy Nevada Project over a total of 4377 traits. In addition to identifying specific variants associated with traits, modern GWAS show that polygenic scores utilizing thousands of common variants together can explain a sizable portion of phenotypic variation and that genetic risk for one phenotype can help explain variation in another3.Over the past decade, we have witnessed the growing depth and breadth of genome-wide association studies (GWAS) leveraging genotyped common variants. It has been shown that the most useful and predictive insights about the genetic effects of common variants only begin to appear as sample sizes reach into the hundreds of thousands. Modern resources like the UK Biobank  of about 0.1%. It has been shown that as allele frequencies drop, the effect sizes of associated variants can increase beyond the limits imposed by natural selection on more common variants14. We have coupled these data to the exomes of 21,866 participants in the Healthy Nevada Project  who consented to research involving their electronic medical records15 . Of the 49,960 exome-sequenced individuals, 55% are female, and 40,468 are classified by the UKB as genetically of European ancestry (field 22006).We used two cohorts in this analysis Table . The fir15. The participants in this cohort are aged 18–89+, and 68% are female. We classified 17,238 of these as European ancestry using principal component analysis based on 184,445 representative common variants (see “Methods”).The second cohort included the exomes of 21,866 participants from the HNP  and had a MAF < 0.1%. We explored two gene-based collapsing models: (1) all non-benign coding and 2) only loss of function (LoF). The LoF model was used to identify associations where only putative LoF variants had an effect. In the coding model, we included 1,074,012 qualifying variants across 16,341 genes in the UKB cohort and 754,459 variants across 17,023 genes in the HNP cohort (see “Methods”). In the LoF model, we included 165,480 qualifying variants across 15,276 genes in the UKB cohort and 111,735 variants across 14,848 genes in the HNP cohort. There were 15,999 coding model genes and 13,474 LoF model genes that overlapped between the two cohorts. The median number of qualifying coding and LoF variants per gene in the UKB European ancestry population was 34 and six, and 22 coding and four LoF for the HNP cohort, respectively  to account for relatedness and population structure   in the discovery cohort, including 32 of the 47 associations from the meta-analysis and seven that were not significant in the meta-analysis. In the replication cohort, all 39 discovery phenotype/gene combinations showed directions of effect in that were consistent with the discovery signal, 32 (82%) achieved nominal significance (LMM p < 0.05), and 18 (46%) achieved statistically significant replication . Given that the sample size was much lower in the replication cohort, it is not unexpected that some of the discovery associations did not achieve formal replication significance.In addition to the meta-analysis, we wanted to clarify how often associations that were statistically significant in the UKB cohort alone would replicate in the independent HNP cohort. The success rate would inform our confidence in associations for phenotypes that were only measured in one cohort and not the other. We therefore also analyzed the European ancestry UKB data as the discovery cohort, with the HNP data as the replication cohort. By this method, we identified 39 associations that were statistically significant . These associations were therefore already strong in the European ancestry subset but appeared to need a larger sample size to push them to significance.We therefore identified associations that were only significant in the mixed ancestry analysis. We identified 17 from the meta-analysis across cohorts and seven more from the analysis of phenotypes available in only one cohort Table . In addiPCSK9 and APOB were associated with low density lipoprotein (LDL) levels, and rare LoF variants in TUBB1 were associated with platelet count.The vast majority of the significant gene-phenotype associations identified were consistent with the current knowledge in the field , including eye development, and a recent study found that the SMAD6 mouse homologue, Smad6, is essential for blood vessel function in the developing mouse retina22.Additional discoveries were novel. For example, we found that rare coding variants in p values in the meta-analysis but did not meet our criteria of having a better p value in the meta-analysis than in each individual cohort. For example, we observed a significant association between albumin levels and variants in the gene that encodes albumin, ALB , but the meta-analysis p value was higher than the UKB-specific p value because there was only one carrier in the HNP cohort. In addition, we identified a novel association between LoF variants in the PAPPA gene and decreased height , but there were only two carriers in the HNP cohort and the p value rose above significance in the main LMM analysis that included relatives. While this gene has been previously implicated in GWAS of height, this is the first time that rare variants in this gene have been found to be significantly associated with height in a human population27. As one more example, our meta-analysis identified as significant (p = 2.5 × 10−20) the known association between LoF variants in TTN and the ICD10 code I48 for dilated cardiomyopathy28. However, there were only three expected case carriers, which failed our LMM threshold, and the Fisher’s exact test of unrelated individuals was just shy of significant (p = 6.9 × 10−10). Nonetheless, our main LMM analysis did identify an association between this gene and the related ICD10 code I42 for atrial fibrillation, a more common phenotype that included more expected variant carriers and thus passed our criteria .Mapping the precise effects of each contributing variant can elucidate the underlying biology of an association. We therefore performed association analyses of each individual rare variant to show the effects they had on the overall signal for each associated gene. For example, variants in els Fig. . The proein Fig. . Of the GFI1B have distinct effects on mean platelet volume , but we make the observation that some variants between zinc fingers 3 and 4 may have an effect in the opposite direction 31.Likewise, variants in different portions of ume Fig. . ConsistASGR1 and alkaline phosphatase AP levels. Previously, two LoF variants in this gene were found to be associated with AP levels, coronary artery disease, and non-HDL cholesterol32. In our analysis, the association is most strongly influenced by LoF variants, but many missense variants contribute to the signal as well . Together, our results indicate that while some significant associations had single variants that made major contributions to their effects, few were completely explained by individual variants.Finally, we investigated what proportion of the gene-based signals could be traced back to one causal variant as opposed to requiring the grouped effects of multiple rare variants to achieve significance. First, we found that only 16% of the significantly associated genes had a single variant within the gene whose 35. Most of these studies were designed around specific phenotypes and collected targeted, disease-specific samples. Analyzing thousands of traits in a biobank-scale population presents additional challenges due to the rarity of the variants, which can lead to false positive associations. The best practices that we suggest to produce reliable results include restricting to high quality regions of the genome, setting a very low MAF cutoff, and requiring that at least a minimum threshold of individuals carry qualifying variants in the analyzed gene. Our methodology reduces test statistic inflation, and our success is demonstrated by the independent replication of most results.Here we present an analysis to catalogue the effects of rare and unique coding variants on thousands of phenotypes across two large cohorts. Until now, rare variant analyses using next generation sequence data have been performed on a small number of phenotypes at a time such as in schizophrenia, developmental delay, and diabetesp value for the gene as a whole remained below 5 × 10−8 for 85% of the associations.Our analysis found that the majority of statistically significant gene-based associations were not driven by single explanatory variants. In fact, the signal for these associations was sufficiently dispersed over multiple variants per gene such that if aggregation at the gene-level was not utilized, 84% of the associated genes would not have had a single variant that exceeded the threshold for a multiple-testing correction. Furthermore, when the association for each gene was performed conditional on the single most significant variant in each gene, the 36. Such unique variants are not accessible by chip and were vital to our study’s success. In fact, 88% of our statistically significant associations generated higher, i.e., worse, p values–by a median of 2 orders of magnitude–when the singletons were removed from the analysis.Importantly, the associations identified in this analysis can only be obtained using sequencing techniques, as opposed to chip-based methods. All of the variants used in our analysis have a MAF below 0.1%, which is below the range of frequencies that can currently be comfortably imputed. Furthermore, 38% of the variants included in our analysis were singletons–only observed once in our dataset and never reported in gnomADIn genes where multiple rare variants contribute to the signal, we find that mapping the precise contributions of each variant in the context of the secondary and tertiary structures can reveal the most functional parts of the gene for the given phenotype and provide additional support for a statistical association Fig. . Mapping14. Some of our analysis differences included our use of a replication dataset, our more stringent MAF cutoff (1% vs. 0.1%), and collapsing model differences (LoF vs. both LoF and coding models). We also identified 5 of the 25 gene-based associations reported by Van Hout et al. 14. The associations that we did not confirm were largely due to differences in our analysis techniques: the p value cutoff (three associations), the MAF cutoff (five associations), the requirement for a minimum number of carriers , and restriction to high-confidence regions (three associations)  is able to more accurately predict phenotypic outcomes of polygenic variation towards an improvement in our understanding of human health.Many of the significant signals that we find reflect associations that had already been established in rare familial diseases , additional work focusing on these areas will likely identify novel associations, as they have proved useful in some prior studies42. Finally, many phenotypes included in this study had a small number of cases, which reduced the power for discovery, and will no doubt become better powered as more of these large-scale population studies are completed.Our analysis has a number of limitations. The analysis included rigid criteria for variant qualification and grouped variants at the most basic level, the gene. Future studies in this dataset can utilize more complex weighting algorithms as opposed to rigid cutoffs and can explore different ways of grouping rare variants, such as by gene family or by exonhttps://ukb.research.helix.com/) to facilitate these discoveries.This analysis presents one of the first forays into a new standard for human genetics research. As the sample sizes of cohorts with extensive phenotypic data and next generation sequencing grows, both through publicly available cohorts such as the UKB and population-based screening efforts such as the Healthy Nevada Project, we are now able to investigate the biological impact of rare variants with the same fine-tuned precision with which we currently assess the effects of common variants. A wealth of discoveries await us as we embark on this next phase of incorporating rare genome sequencing information into truly personalized medicine. We provide an interactive browser of our results as a resource to the human genetics community (43 of the UKB PLINK-formatted exome files (field 23160), a field-standard and stringent method. It has been reported that this version is missing some variants, specifically in regions of the genome with alternate haplotypes, but we proceeded with this version as most genes are unaffected, we restricted to the Genome in a Bottle high-confidence regions of the genome as described below, and a new FE file had not yet been released at the time of this writing45. We also used the imputed genotypes from GWAS genotyping (field 22801-22823). The HNP study was reviewed and approved by the University of Nevada, Reno Institutional Review Board , and all participants provided informed consent. The HNP samples were sequenced at Helix using the Exome+ assay, a proprietary exome that combines a highly performant medical exome with a microarray-equivalent SNP backbone into a single sequencing assay (www.helix.com)46. Data were processed using a custom version of Sentieon and aligned to GRCh38, with variant calling and phasing algorithms following GATK best practices47. Imputation of common variants in the HNP data was performed by pre-phasing samples and then imputing. Pre-phasing was performed using reference databases, which include the 1000 Genomes Phase 3 data. This was followed by genotype imputation for all 1000 Genomes Phase 3 sites that have genotype quality values <20. Imputation results were then filtered for quality so that only high precision imputed variant calls were reported.We conducted the UKB research using the UK Biobank Resource under Application Number 40436. We utilized the FE version48. Coding regions were defined according to Gencode version GENCODE 29, and the Ensembl canonical transcript was used to determine variant consequence50. Variants were restricted to CDS regions. Genotype processing was performed in Hail 0.2.2151.Variant annotation was performed with VEP 95.353. We also ran a LoF model that only included LoF variants . We used a MAF cutoff of 0.1%. To pass the MAF filter, the variant must be below that frequency cutoff in all gnomAD populations36 as well as locally within each population analyzed: for the UKB exomes, this includes unrelated European, African, East Asian, South Asian, and other ancestry groups. For the HNP exomes, this includes unrelated European, African, East Asian, South Asian, Latino, Native American, and other ancestry groups. For the HNP exomes, only PASS calls were used in the analysis, with an average depth of 62.9×. Qualifying variants were also restricted to the high-confidence regions of the genome as defined by the Genome in a Bottle resource for NA1287844.For the collapsing analysis, samples were coded as a 1 for each gene if they had a qualifying variant and a 0 otherwise. We defined “qualifying” as coding  and not Polyphen or SIFT benign 55. ICD-10 diagnosis code phenotypes were coded with 1 if participants had the ICD-10 code recorded at least once in their series of Electronic Health Records (EHR), and otherwise with a 0; controls were restricted to one sex when appropriate.HNP phenotypes were collected from Epic/Clarity EHR data. Microsoft SQL Server was used as a backend database for record storage. SAS 9.4 M5 with SAS/ACCESS to SQL Server was used to perform ETL on these data in preparation for analysis, also in SAS using Base SAS and SAS/STAT. UKB phenotypes were processed using the Neale lab modified version of PHESANT, which rank-transforms quantitative traits to normally distributed data and divides categorical traits into binary sets; HNP quantitative phenotypes were also rank-normalizedPhenotypes were chosen to have at least 50 cases for binary traits (with at least 5 from each cohort) and 400 phenotyped individuals for quantitative traits across the combined UKB and HNP cohorts. For HNP phenotypes, there was an additional step where the pre-transformed median of quantitative traits was taken when multiple measurements were available for a person.56. Briefly, this method builds a LMM using common variants to account for the effects of relatedness and population stratification. The covariates included are age and sex. In the HNP analysis, the Helix bioinformatics pipeline version for alignment and variant calling was also included as a covariate in the model to account for batch effects across different versions of the pipeline.We used BOLT-LMM for the main statistical analysisr2 < 0.6) to a set of 184,445 variants. This set of variants was genome-wide, including both coding and noncoding regions.A representative set of LD-pruned, high-quality common variants were identified for both the creation of principal components and for the random effects and trait heritability in the BOLT-LMM mixed model for collapsed gene analyses and individual variant analyses. Inclusion in this set required MAF >1%, imputation with reasonable accuracy in UKB (information score (INFO) >0.7) and high coverage or imputation in Helix samples  >0.95), LD-pruned  than the p values for either individual cohort, and, for binary traits, at least 10 expected case carriers overall for each analyzed gene.Meta-analysis was performed using the weighted PLINK 2.0 with age, sex, and the first 10 European-specific principal components  included as covariates59. When binary, these phenotypes were analyzed by Fisher’s exact test using PLINK. Fisher’s exact test was chosen over logistic regression due to its robustness and lack of test statistic inflation in situations with a small number of case carriers. The Fisher’s exact test was also used to identify associations when <10 cases were expected to be carrying qualifying variants based on the overall prevalence. We then performed meta-analysis on the summary stats from each separate PLINK analysis, again using the weighted Z-score p value from METAL57. Information on which analyses were performed for which phenotype can be found in Supplementary Data BOLT-LMM determined that 1438 phenotypes had 0 heritability based on the 184,445 common variants in the main European ancestry UKB analysis, and 359 phenotypes in the HNP cohort . Analyses of rare variants in these phenotypes therefore could not be completed using the BOLT-LMM algorithm. When quantitative, these phenotypes were analyzed by linear regression of unrelated European-ancestry individuals using Information on additional models tried, including a SKAT model and CADD-based cutoffs, can be found in the Supplementary Note.−10. For replication analyses, Bonferroni correction was set for the total number of tests done for that particular replication.A conservative Bonferroni correction for multiple tests was set for 17,365 coding model genes  plus 16,650 LoF model genes  × 4377 total phenotypes, thus 3.4 × 10p value per significant gene/phenotype combination. We then performed the gene-based collapsing analysis including that lead variant as a covariate and observed how the p value for the gene as a whole changed (see Supplementary Data We also performed a conditional analysis to test the effects of individual variants on the overall significance of the statistically significant associations. We identified the lead variant contributing to each gene-based signal, i.e., the variant with the best 62.Gene plots were made using trackViewer and Protter and annotated with Pfam domains v. 32.0Further information on research design is available in the Supplementary InformationPeer Review FileReporting SummaryDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2


The genetic architecture of skull shape has been extensively studied in mice and the results suggest a highly polygenic and additive basis. In contrast few studies have explored the genetic basis of the skull variability. Canalization and developmental stability are the two components of phenotypic robustness. They have been proposed to be emergent properties of the genetic networks underlying the development of the trait itself, but this hypothesis has been rarely tested empirically. Here we use outbred mice to investigate the genetic architecture of canalization of the skull shape by implementing a genome-wide marginal epistatic test on 3D geometric morphometric data. The same data set had been used previously to explore the genetic architecture of the skull mean shape and its developmental stability. Here, we address two questions: (1) Are changes in mean shape and changes in shape variance associated with the same genomic regions? and (2) Do canalization and developmental stability rely on the same loci and genetic architecture and do they involve the same patterns of shape variation? We found that unlike skull mean shape, among-individual shape variance and fluctuating asymmetry (FA) show a total lack of additive effects. They are both associated with complex networks of epistatic interactions involving many genes (protein-coding and regulatory elements). Remarkably, none of the genomic loci affecting mean shape contribute these networks despite their enrichment for genes involved in craniofacial variation and diseases. We also found that the patterns of shape FA and individual variation are largely similar and rely on similar multilocus epistatic genetic networks, suggesting that the processes channeling variation within and among individuals are largely common. However, the loci involved in these two networks are completely different. This in turn underlines the difference in the origin of the variation at these two levels, and points at buffering processes that may be specific to each level. In recent years, studies exploring the genetic basis of skull shape have proliferated due to advances in high-throughput phenotyping techniques  and genoPhenotypic robustness can be defined as the ability of an organism to buffer the impact of internal  and external factors  on the phenotype. It has been the subject of a vast literature  were assessed using the symmetric component of shape variation: the 132 coordinates of the 44 3D landmarks were submitted to a full Procrustes superimposition with object symmetry  leading yi ∼qN  where, for a locus with an additive effect, mi = μ + iaα with ia is the allele dosage, α the q-dimensional additive effect, and S the residual variance–covariance matrix  . This diyik−mik) , weightiAlthough the relatedness among individuals is standardized in the mapping population used here , we applGenome-wide association mapping were run on the 67 PC from the symmetric shape to estimate which loci contribute additively to the mean shape variation. Such an association should be assessed preferentially using a multivariate model because the effect of loci generally does not align with the PCs  and locil associated with the average distance from each individual to the population mean shape given the locus genotypes vice versa): l is the additive effect of the locus l on the distance  from the distribution of maximum negative logistances . Analyseistances .1  because they look simultaneously at a large number of genotypes. In contrast, and similarly to the additive genome scan, pairwise interactions were corrected for the effect on the multivariate genotype means and then ran on residual Mahalanobis distances. The interactions were considered significant when p < 0.001. Because our data do not show any significant effect of relatedness among individuals and the epistasis test is relatively robust against it  in Mouse Genome Informatics . Once redundancies between lists and human-specific genes were removed, we were left with 1044 and 2829 genes for “Craniofacial” and “Growth/Size/Body” categories, respectively. Then, we assessed the over-representation of candidate genes within the two lists with Fisher’s exact tests , where A is the adjacency matrix  corresponds roughly to the crossproduct of the frequency-weighted additive effects of all causal loci , environmental variance, and the buffering effects of genetic and environmental canalization.The additive genetic covariance matrix , which were estimated simultaneously together with their correlations, and then a second block from PC11 to PC67 , which was modeled with diagonal variance components  and allows to fit independently these 51 additional PCs. This strategy represents a compromise between an ideal but intractable solution estimating every dimension simultaneously and an efficient but formally incorrect approach assuming a complete match between genetic and phenotypic PCs, an assumption that is, in most cases, unrealistic. Another option would have been factor analysis . This apP and the stochastic component of shape variation with the covariance matrix of shape FA. P and FA covariance matrices were estimated from the superimposed Procrustes coordinates of the symmetric and asymmetric components of skull shape, respectively . Similarly, the covariance matrix of shape FA results from the interplay between stochastic developmental noise affecting the two sides independently, and the buffering effect of developmental stability.We investigated the patterns of shape variation at two more levels: the global phenotypic variation present in the sample as the phenotypic covariance matrix ectively . P resulh2 = GP-1 . The validity of such an approach depends on the similarity between G and P, often assumed but seldom assessed , at least one population cannot be inferred from a linear combination of the eigenvectors of Mi defining the subspace. We can estimate how similar the corresponding eigenvector bk of H is to each population’s subspace with the angle To assess similarities among patterns of covariation, we first computed the Krzanowski subspace of the set of the four matrices  package .Si is the sample covariance, symmetric, and positive semi-definite . The strength of integration can thus be estimated by the unequal distribution of variance across dimensions of the shape space  to be explored further in pairwise analyses. The genetic architecture of skull shape canalization thus appears purely epistatic.No association was found between genetic markers and shape variance when the additive genetic scan was used . In contp < 10-9), three overlapped with genes known to contribute to craniofacial variation, five with genes having an effect on growth, and seven with other protein-coding genes  as well as growth genes (p = 3.07 × 10-4) among these significant interactions. For example, one marker detected in the three most significant pairwise interactions was found overlapping with Rapgef2, a gene involved in abnormal size and neural features .In the second step of the epistasis test, where specific pairwise interactions were associated with shape symmetric variance, 385 pairwise interactions involving 111 genomic markers  showed significant associations. These significant genomic markers overlapped with 893 candidate genes, 413 of which were protein-coding genes. Fisher’s exact test showed a significant over-representation of craniofacial genes (features . From thTnf (20 interactions) (Ddr1 (17 interactions)   and Ddr1actions) . The Tnfosophila . Two oth Cyp4f14 . The latao1/Tmx4 , as wellx4  or Smco2 (Notch3/Brd4 (on the negative side)   showed h  , we only obtained 11 common protein-coding candidate genes between canalization and developmental stability: Arhgef4 , Chd5  , and Gpx2)  showing the very good agreement between the variation described in the subspace and each reduced matrix  was obtained for all dimensionalities. These results suggest very similar patterns of variation among the first matrices eigenvectors, but then a marked difference of FA when the dimensionality becomes larger.The main patterns of variation appeared very similar among all matrices: the Kraznowski subspace analysis returned almost perfect d matrix . The D vPGE matrices. P and FA matrices showed similar eccentricity (P = 1.80 and FA = 1.78), while the G matrix showed a more spherical shape (1.17) and E was intermediate (1.34). The greater sphericity of G is apparent beyond the two first PCs with a slower decreasing of the proportion of variance explained by each PC for this matrix than for P, E, and FA  and more concentrated than E (2.18) and FA was even more concentrated (3.76).Analyses based on overall similarities between matrices suggested that FA is quite different . Both th, and FA . When thP, G, and E eigenvectors were more similar than random vectors  are smaller than those observed between the PC1s, and the vectors are significantly more related to each other than two random vectors. The matches are nonetheless not perfect and it thus appears more adequate to consider several PCs altogether to get a less biased comparison of the patterns of shape variation.The comparison of the angles between the first eigenvectors yielded striking differences:  vectors  but thisH) corresponds mainly to an expansion of the posterior part of the skull . The congruent pattern of shape changes shared across these axes . This is simply due to a global rotation of the axes: considered together, they contain roughly similar information, but they individually depict different aspects of shape variation . Although such rotation nevertheless indicates a phenotypic difference, it can completely mask the overall similarity. On the other hand, distances or correlation and subsequent multivariate ordination of matrices based on such metrics .Comparisons allowing both the assessment of the overall similarity and a hierarchical evaluation of this similarity such as the Krzanowski subspace should systematically be performed in conjunction with individual PCs or distance comparisons. In the present study, the subspace analysis indicated very strong similarities among all matrices for the main dimensions, although Could the discrepancy between mean, canalization and development stability genetic networks be due to the fact that only first-order interactions were considered? This might indeed limit the resolution of the inferred networks: using higher order interactions might potentially allow detecting many additional genes and genetic interactions with connections reflecting smaller effects. As explained in the recent omnigenic theory , pushed The genetic architecture of skull shape has been extensively studied. Although epistatic effects have been occasionally reported when searched ; factorThe genetic architecture of the skull shape canalization appears entirely epistatic and composed by a large proportion of craniofacial genes, as was also found for developmental stability . In contCanalization and developmental stability likely involve various processes acting at various scales and times during development. Some of these processes might be specific to the type of variation considered and would thus be different between developmental stability and canalization; other processes, like bone remodeling in reaction to biomechanical demand, might jointly alter variation both among and within individuals, reshaping covariation patterns characteristic of adults structures – these would contribute to both developmental stability and canalization. Such a mix of shared and specific processes might have contributed to the diversity of results obtained in the literature comparing those two components of phenotypic robustness.https://datadryad.org//resource/doi:10.5061/dryad.k543p)  .Here we re-used the data collected for a previously published study , so no nNN and CV-G designed the study. LP collected the data. NN performed the analyses. VD contributed to the discussion of the results. All authors contributed to writing the manuscript and approved its final version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Genome-wide association studies have helped us identify a wealth of genetic variants associated with complex human phenotypes. Because most variants explain a small portion of the total phenotypic variation, however, marker-based studies remain limited in their ability to predict such phenotypes. Here, we show how modern statistical genetic techniques borrowed from animal breeding can be employed to increase the accuracy of genomic prediction of complex phenotypes and the power of genetic mapping studies.Specifically, using the triglyceride data of the GAW20 data set, we apply genomic-best linear unbiased prediction (G-BLUP) methods to obtain empirical genetic values (EGVs) for each triglyceride phenotype and each individual. We then study 2 different factors that influence the prediction accuracy of G-BLUP for the analysis of human data: (a) the choice of kinship matrix, and (b) the overall level of relatedness. The resulting genetic values represent the total genetic component for the phenotype of interest and can be used to represent a trait without its environmental component.Finally, using empirical data, we demonstrate how this method can be used to increase the power of genetic mapping studies. In sum, our results show that dense genome-wide data can be used in a wider scope than previously anticipated. Genomic prediction (GP) refers to the use of genomic information for predicting an individual’s phenotype . SeveralThe generation of reliable EGV estimates constitutes one of their most important properties for the potential use of GP. Findings from the field of animal breeding strongly suggest that accuracy of GP can occasionally be low, and that the accuracy in relatedness estimates significantly affects the reliability of EGVs . While pHere, using the distributed GAW20 data , we studAll of the analyses were done using the entirety of the distributed GAW20 dataset. The initial GAW20 phenotype data file  presented 4 triglyceride (TG) measurements (trr1 to trr4), representing TG levels at 4 different time points, 2 pre- and 2 post-fenofibrate intervention. To reduce the effects of measurement error, pre- and posttreatment TG replicates were averaged. The resulting file was, together with the pedigree file, converted to SOLAR  format [The physical coordinates for GAW20 genotypes were converted to release 19 of the human genome (hg19) from UCSC. PREST-Plus  was thenPedigree kinship estimates were obtained from the original pedigree data file using SOLAR. Two different empirical kinship matrices were then calculated from the curated genotype data using LDAK version 4.9 and IBDLD version 3.33. Both software packages attempt to account for the linkage disequilibrium (LD) present in dense genotype data. However, they differ in how they account for LD. IBDLD uses a hidden Markov model to estimate identity-by-descent probabilities conditional on multilocus genotype information. LDAK, on the other hand, assesses local patterns of LD prior to kinship estimation, and then uses that information to give each single-nucleotide polymorphism (SNP) a specific weight during kinship calculations that accounts for the extent to which the genetic signal is replicated by its neighboring SNPs. The empirical kinship estimates from LDAK and IBDLD were both weighted and scaled, ensuring the diagonal elements were equal to 1.Genomic Selection function. G-BLUPs are calculated by solving the mixed model equation:y is a vector of phenotypic observations; b is a vector of fixed effects with design matrix X; u is a vector of random polygenic effects (EGV) with design matrix Z; and e is a vector of residual effects. There are 2 important features of G-BLUP worth noting. First, the variance structure of u is proportional to the relationship matrix and, therefore, we should expect u to be directly affected by kinship estimates. Second, G-BLUP does not directly make assumptions regarding the number of loci underlying the traits of interest. However, G-BLUP does assume that the underlying loci have similar effect sizes, which is not an accurate assumption when the number of underlying loci is small. Given the polygenic nature of TG phenotypes, however, we do not expect this assumption to have been violated in our case.The pre- and posttreatment TG levels, and relevant covariates  were exported to TASSEL  togetherσ2g is the additive genetic variance of the base population [A single common criterion was used to assess each matrix’s performance in producing accurate EGV estimates. In particular, we estimated the accuracy of individuals’ EGV based on the prediction error variance (PEV). In the absence of statistical bias, PEV is equal to the mean squared error (MSE). The accuracy was estimated as mentclass2pt{minimResearch in animal breeding suggests that the number of relatives in a pedigree can influence the accuracy of estimated EGVs . To testThe decomposition of a trait into its genetic and environmental components opens up the possibility of increasing the genetic signal in linkage studies through the removal of structured environmental effects. Therefore, we here regressed the linkage signals obtained by using EGVs as traits on the results obtained from traditional genetic mapping . See Blackburn et al.  for detaAccuracy estimates Fig.  suggest When regressing accuracy estimates on the number of SDRs Fig. , we founp < 0.001) and posttreatment- TG levels (p < 0.001), indicating that the use of EGVs considerably enhanced the genetic signal in both cases.When regressing logarithm of odds (LOD) scores obtained from EGV-based linkage scans on the traditional linkage scan LODs (pre-TG Fig. Reliable prediction of complex human phenotypes or diseases will be essential to attain the objective of truly personalized medicine. Marker-informed prediction based on SNPs has already become commonplace , 17–19 aGenome-wide approaches, on the other hand, are still rare, but have already helped us attain much higher predictive power. Yang et al. , for exaIn this study, we aimed to show how statistical techniques borrowed from animal breeding could be employed to predict complex phenotypes with relatively high accuracy see Fig. . FurtherIn general, prediction accuracy increases with the number of relatives an individual possesses in the data set see Fig. . This isIn any case, our results also suggest that using EGVs in linkage studies might be a fruitful way to increase the power to detect genomic regions underlying complex traits. By removing the structured environmental variance from the phenotypic variance, we see a pronounced increased in LOD scores of the linkage model. It should be noted, however, that a proper test of this hypothesis is to start with a system in which the genomic variants are known a priori.Because the GAW20 data set is composed of mostly unrelated individuals, one could anticipate that GP based on more closely related individuals would generate highly accurate EGV estimates. In other words, family-based studies might represent a particularly useful starting point when attempting to use EGVs in the analyses of complex traits or in personalized medicine.Our analysis of the GAW20 data set shows that dense genome-wide SNP data can be used to accurately estimate EGVs for use in personalized medicine or to increase the power of linkage scans. EGVs estimated based on empirical kinship matrices are slightly more reliable than pedigree-based matrices, largely as a consequence of their ability to capture distant relationships among individuals. Similarly, the prediction accuracy increases with the number of relatives an individual possesses in the data set. In sum, family-based studies, with empirically derived kinships, might be the ideal study design for the application of GP of complex traits in human health research.


GWAS) of 17 wood traits in Norway spruce using 178 101 single nucleotide polymorphisms (SNPs) generated from exome genotyping of 517 mother trees. The wood traits were defined using functional modelling of wood properties across annual growth rings. We applied a  Least Absolute Shrinkage and Selection Operator (LASSO‐based) association mapping method using a functional multilocus mapping approach that utilizes latent traits, with a stability selection probability method as the hypothesis testing approach to determine a significant quantitative trait locus. The analysis provided 52 significant SNPs from 39 candidate genes, including genes previously implicated in wood formation and tree growth in spruce and other species. Our study represents a multilocus GWAS for complex wood traits in Norway spruce. The results advance our understanding of the genetics influencing wood traits and identifies candidate genes for future functional studies.Norway spruce is a boreal forest tree species of significant ecological and economic importance. Hence there is a strong imperative to dissect the genetics underlying important wood quality traits in the species. We performed a functional genome‐wide association study ( Wood provides both structural support and a transport route for water and solutes in trees. Our work provides a framework to dissect the genetic nature of wood formation and adds to our understanding of tree growth and development. With the current research focus on wood cell wall biosynthesis in general, and lignocellulose feedstock for biorefineries, we believe that this contribution will be of wide interest for the plant science community. Picea abies (L.) Karst.) is a dominant boreal species of significant economic and ecological importance  and red pine the relationship has been inconsistent  for wood density variation in loblolly pine using linkage analyses based on segregating family pedigrees  we detected 52 significant QTLs in 17 individual traits whose phenotypic variance explained (PVE) ranged from 0.01 to 4.93% (Table d/a| > 1.25), with the remaining being codominant (27) (|d/a| < 0.50) and 10 exhibiting partial to full dominance (0.50 < |d/a| < 1.25) . Applying the probability of stability selection (SSP) to the intercept, slope and two nodes β and β3 wet al., et al.  was determined through the pooling of all r2 values and plotting them against the physical distances between the same SNP pair . The top two explained a total of 5.3% of the variation. Population structure inference of clusters detected by PCA was performed by ADMIXTURE Figure  and the within each trait and across traits in the analysis. WD, Ring width (RW), Transitional ring witdh (TRW) and Latewood number of cells (LNC) had one (MA_33109_11804), two (MA_10434805_21408 and MA_20322_28351), one (MA_817099_1105) and one (MA_10428744_29330) QTLs shared by two or more latent traits, respectively. Common QTLs within RW were observed for slope, β2 and β3 latent traits, with moderate frequencies ranging from 0.521 to 0.615 and influenced their respective traits to modest degrees (PVE in ranges of 0.18–2.66%).Several associations were shared across the different latent traits, SNP MA_10434805_21408 was shared between latewood wood density (LWD), RW and Modulus of elasticity (MOE); this is not surprising because of the close correlation between MOE and wood density. Intron variant MA_10434805_21408 explained between 0.18 and 2.66% of the PVE observed in the respective traits. This SNP associated also had high frequencies of 0.602 and 0.615 in MOE and RW explaining PVE of 1.00 and 2.66%, respectively (Table For QTLs common Numbers of cells (NC), ENC, TNC and LNC traits were associated with a total of three putative genes and three protein domains. Of the three putative genes, two are associated with serine/kinase activity and one is involved in cysteine and methionine synthesis Table . All theWood percentage traits, early wood proportion (EP), LP, TP and the ratio of EP/LP had significant associations with 10 SNPs. Four of the six significant SNP variants for EP/LP are modifiers with the other two SNPs, being a synonymous (MA_96191_7122) and missense (MA_1045136_4310) variant. The synonymous SNP MA_96191_7122 was consistent with the codominant mode of inheritance , explaining a PVE ranging between 0.05 and 1.82% Table .d/a| > 1.25), codominant (|d/a| < 0.50) 12 and four SNPs were partial to fully dominant (0.50 < |d/a| < 1.25).Our study identified several interesting genes linked to the significant QTLs from the intercept latent trait, which represents the mean from our spline model. This resulted in 17 significant associations with a PVE ranging from 0.50 to 4.64% associated with the intercept latent trait. The modes of action determined by the non‐additive effects of these significant SNP associations to the intercept latent trait were one for overdominance  had two significant associations detected for the intercept. Both associations were detected by SNP MA_10435406_13733 and were both partial to fully dominant in their form of inheritance Table . The SNPThe analysis of slope (rate of change) of wood formation traits over cambial ages, to our knowledge, has never been dissected in any Norway spruce QTL or AM analyses.across related traits density, growth number of cells and MOE. The codominant SNP MA_10435406_13733 , were detected 33 Table  that is across LWD, RW and MOE with partial to fully dominance (0.50 < |d/a| < 1.25) for all three associations , with the 11 markers being dominant (0.50 < |d/a| < 1.25)   had three significant associations with the βts Table . The ups24 Table  and the s Figure .2 and β3, explaining PVE ranging from 0.01 to 4.51%  could indicate a possible relationship between the genes influencing RW and wood density. Contig MA_10434624 is homologous to a pectin esterase and was associated with the downstream variant MA_10434624_20686 with an over‐dominant mode of inheritance for LRW (β3 latent trait) at year 10  Voss identified a significant nonsynonymous SNP coding for cysteine associated with earlywood and total wood cell wall thickness associated with pectin methylesterase  had eight significant associations identified for the latent traits βts Table . Three p6 Figure  due to t10 Table . This siPicea sitchensis was associated with the EP/LP, β2 latent trait. The gene is highly expressed in vegetative shoots (June) and during the late afternoon in needles  we identified three putative candidate genes, DNA‐3‐methyladenine glycosylase II enzyme, phytochrome kinase substrate 1 and glycosyltransferase. Synonymous SNP (MA_96191_59480) within the gene MA_96191g0010, which is homologous to Glucosyltransferase in across traits   homologues were identified D) Table . ApproxiD) Table , in whicD) Table . SimilarGIBBERELLIC ACID INSENSITIVE (GAI), REPRESSOR of GAI (RGA) and SCARECROW (SCR) (GRAS)  and the optimal model was selected based on a compromise of model fit and complexity.EBVs were calculated for growth and wood quality traits for 14 consecutive annual growth rings. The variance and covariance components were estimated using , et al. . In brieThe EBVs were plotted against cambial age  to produce time trajectories for each trait Figures  and S1. Ki  are defined as knots, and (t − Ki)+ = (t − Ki) if t > Ki , and otherwise is equal to zero. The number of knots has to be properly defined to provide an accurate description of the data under investigation, while avoiding overadaptation to data ) and β3 (at Knot 2 (K2)) are estimated by standard least squares , the intercept βio Team, , and the2 and β3 represent inflection points in the cambial age trajectories where the development of the EBVs enters new phases. These two points (β2 and β3) are therefore supposed to have biological significance, warranting a closer analysis of the genes imparting these shifts in the EBVs dynamics. The four latent traits show lower correlations compared with the direct measurements on the original scales and they also have constant variances, thereby reducing the need to account for residual dependencies in the model  Assay Kit . Extracted DNA was submitted to RAPiD Genomics (USA) where DNA library preparation and capture sequencing were performed. Sequence capture was performed using the 40 018 diploid probes designed and evaluated for P. abies  protocol with DNA quantification performed using the QubitP. abies reference genome v.1.0 using bwa‐mem  were used for sorting and marking of PCR duplicates. Variant calling was performed using gatk haplotypecaller v.3.6 , MappingQuality (MQ) and BaseQRankSum, with tranches 100, 99.9, 99.0 and 90.0 were then applied for the determination of the good versus bad variant annotation profiles. After obtaining the variant annotation profiles, the recalibration was then applied to filter the raw variants. Using vcftools v.0.1.13  method was performed to avoid the use of hard filtering for exome/sequence capture data. For the VQSR analysis two datasets were created, a training subset and an input file. The training dataset was derived from the Norway spruce genetic mapping population showing expected segregation patterns (Bernhardsson r2), globally with special attention given to all the contigs with significant associations in vcftools v.0.1.13 software using the ‘geno‐r2’ routine  to additive (a). The ranges were: partial or complete dominance (−0.50 < |d/a| < 1.25) and additive (−0.50 ≤ |d/a| ≤ 0.50), with |d/a| > 1.25 being equal to over‐ or underdominance  was determined using the Tracy‐Widom (TWi) distribution and a significance threshold of P = 0.01. For population clustering, admixture v.1.3.0   and selecting only a single representative SNP from the group of highly dependent SNPs. The LASSO model as described by Li et al.  for the latent trait β0, β1, β2 or β3, 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 penalty term is able to shrink the additive effects of some of the markers exactly to zero, and select a subset of the most important markers into the model. The tuning parameter λ determines the degree of shrinkage, and the number of markers having non‐zero effects. Cross‐validation is used to decide an optimal value for λ.The LASSO model:stability selection  was used for all traits. This frequency was inferred conditional on the expected number of false selected markers being less than one  of all the QTLs  hosted by congenie (http://congenie.org/). The complete P. abies 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, a gene enrichment analysis of putative genes and their associated orthologs was performed against the This work has dissected the genetic basis of wood properties in Norway spruce with use of functional AM. In total, we identified 52 significant QTLs for wood properties and mining of candidate genes located in the vicinity of significant QTLs identified genes that could be directly or indirectly responsible for variations in the observed traits. Functional mapping analyses allowed us to utilize all the longitudinal data for a trait simultaneously and may better account for the temporal trends and correlation structures across years for the complex traits associated with wood formation. It can therefore be applied to the detection of QTLs stable over time (i.e. the QTLs associated with intercept traits) with greater statistical evidence. The slope latent trait over cambial ages or the rate of juvenile‐to‐mature wood transition has allowed for the dissection the dynamics of the transition process itself and can be applied to other important plant breeding traits. The significance of our results is provided by the identification of QTLs associated to both high wood density and fast growth, therefore larger biomass. These QTLs can now be a basis for future functional genomics in Norway spruce.Populus sp.However, the direct use of QTLs for marker‐assisted breeding has not been successful, mainly due to the difficulty in transferring the associations across populations and species of forest trees. With the small percentage variances detected and no direct information about the developmental change of QTL expression, breeders will be unable to make use of these QTLs in direct early selection. Non‐additive interactions especially epistasis, play an important role in accounting for the total genetic variance of a trait. Therefore this study will be a good basis for initiating the detection and estimation of possible epistatic influence on these complex traits. Future work should focus on replicated sampling from a larger number of representative genotypes across different environments, which take into consideration genotype × environment interactions. Additional support for marker‐assisted tree breeding may also be provided by the functional genetics studies, systems mapping and consideration of biological mechanisms : list of variables and examples of data.Table S3. Curve shape data (A): list of variables for each property and example of data.Click here for additional data file.Methods S1. PVE evaluation of a QTL.Methods S2. Trait data set used for GWAS identification of novel candidate loci affecting number of tracheids formed, radial growth, density, stiffness and mass at breast height of young Norway spruce.Click here for additional data file. Click here for additional data file.


Saccharum spp.) has a complex genome with variable ploidy and frequent aneuploidy, which hampers the understanding of phenotype and genotype relations. Despite this complexity, genome-wide association studies (GWAS) may be used to identify favorable alleles for target traits in core collections and then assist breeders in better managing crosses and selecting superior genotypes in breeding populations. Therefore, in the present study, we used a diversity panel of sugarcane, called the Brazilian Panel of Sugarcane Genotypes (BPSG), with the following objectives: (i) estimate, through a mixed model, the adjusted means and genetic parameters of the five yield traits evaluated over two harvest years; (ii) detect population structure, linkage disequilibrium (LD) and genetic diversity using simple sequence repeat (SSR) markers; (iii) perform GWAS analysis to identify marker-trait associations (MTAs); and iv) annotate the sequences giving rise to SSR markers that had fragments associated with target traits to search for putative candidate genes. The phenotypic data analysis showed that the broad-sense heritability values were above 0.48 and 0.49 for the first and second harvests, respectively. The set of 100 SSR markers produced 1,483 fragments, of which 99.5% were polymorphic. These SSR fragments were useful to estimate the most likely number of subpopulations, found to be four, and the LD in BPSG, which was stronger in the first 15 cM and present to a large extension (65 cM). Genetic diversity analysis showed that, in general, the clustering of accessions within the subpopulations was in accordance with the pedigree information. GWAS performed through a multilocus mixed model revealed 23 MTAs, six, three, seven, four and three for soluble solid content, stalk height, stalk number, stalk weight and cane yield traits, respectively. These MTAs may be validated in other populations to support sugarcane breeding programs with introgression of favorable alleles and marker-assisted selection.Sugarcane ( Saccharum spp.) is an important industrial crop and a vital component for food and energy security, providing sucrose, bioethanol and bioelectricity [Sugarcane  to identify those with the potential to become new cultivars –13. EvenClearly, there is a need to complement the classical breeding of sugarcane with other tools, such as molecular approaches, which have been applied for other crops –22. QuanOn the other hand, GWAS has been widely used to identify marker-trait associations (MTAs) in genetically diverse populations of plants ,21,28–32For the latter, several algorithms and software have been developed to improve statistical power, increase computational efficiency, and reduce spurious associations in the GWAS approach . Among GIn the current assignment, our objectives were to (i) estimate, through a mixed model, the adjusted means and genetic parameters of the five yield traits evaluated over two harvest years in a diversity panel composed of ancestral and modern sugarcane accessions; (ii) detect population structure, LD and genetic diversity using SSR markers; (iii) perform GWAS analysis to identify MTAs; and iv) annotate the sequences giving rise SSR markers that had fragments associated with target traits to search for putative candidate genes.Saccharum species complex. The BPSG accessions represent an important genetic background in Brazilians breeding programs.In this study, 134 accessions  of the B–1). Phenotypic yield trait data were collected according to Balsalobre et al. [The 134 accessions of BPSG were planted in a field experiment performed in 2013 at the Agricultural Science Center of the Federal University of São Carlos (UFSCar) in Araras City, São Paulo State, Brazil. Araras is located at 22°21’25”S, 47°23’3”W at an altitude of 611 m; the experimental area soil is Typic Eutroferric Red Latosol. The experimental design consisted of a randomized complete block, which was fully replicated four times. The plots consisted of two rows 3 m long and spaced 1.5 m apart. Each plot was composed of 12 presprouted seedlings at the planting of the experiment in 2013. The experimental plants were harvested when they were approximately 18 months of age during the plant cane and first ratoon. The BPSG was evaluated for five yield components: soluble solid content , stalk height , stalk number (SN), stalk weight , and cane yield ; bkm is the fixed effect of the kth replication  at the mth harvest; gimk is the random effect of the ith accession  at the mth harvest evaluated in the kth replication; rumk and cvmk are the random effects of the uth row and vth column, both evaluated at the mth harvest and kth replication; and eimkuv is the random residual error. In addition, for the SN, SW, and TCH traits, the number of clumps per plot was included in the mixed model as a fixed covariate. Aiming to model the accession effects, the genetic variance–covariance (VCOV) matrix G = GM ⊗ IIg, i.e., g~N was considered, where M is the number of harvests, and ⊗ represents the Kronecker product of both the genetic GM and identity IIg matrices with the respective dimensions of 2 x 2 and 1 x 134. For the GM matrix, four structures ) were examined and compared via Akaike [P < 0.05). After the GM matrix structure selection, the adjusted means for accessions and genetic parameters for each evaluated trait were obtained. The phenotypic A multiharvest mixed model produced the joint adjusted means. The analysis was conducted for each trait using GenStat 19th edition  based ona Akaike   and Baya Akaike ) informaa Akaike . For the effects .Approximately 3.0 g of tissue from the leaf primordia of each accession was collected, and the genomic DNA was extracted according to methods described by Aljanabi et al. . The SSRDue to the polyploid and complex nature of sugarcane, the amplified SSR fragments, which cannot depict ploidy levels and allele dosages, were evaluated as dominant markers ,62, i.e.Population structure was analyzed by a discriminant analysis of principal components (DAPC)  using SSK) was generated.The kinship coefficient was calculated between pairs of accessions using the kinship2 package  in R, coLogP) logarithmic probabilities of the associations between only linked fragments were plotted with the respective genetic distances [Marker data were used to assess the level of LD in the BPSG as described by Raboin et al. . Brieflyistances  in centip-value less than 1% following an FDR controlling procedure [p-values obtained for each MTA, against their expected distribution under the null hypothesis of no genetic association. For significant MTAs detected by FarmCPU, the phenotypic variance explained for each SSR fragment was estimated one at a time using a linear model with the lm function in R software.GWAS analysis was conducted using both the Genomic Association and Prediction Integrated Tool  and Farrocedure  and Bonfe-values ≤ 1 × 10−3 through BLASTX and ii) the Phytozome website [Viridiplantae protein databases.Functional annotation of the loci associated with traits was performed using the available sequences that gave rise to the SSR marker. These sequences were annotated using i) the nonredundant NCBI database with  website , which wGM matrix were based on AIC and BIC criteria. AR1(het) had the lowest AIC and BIC values, which indicated that it was the best model for all evaluated traits  (-1) was 7.6 times greater than the accession POJ2878 (38.90 t ha-1). The SN trait also showed high variation, i.e., the accession IN84-58  was 7.03 times greater than the accession POJ2878 . On the other hand, the BRIX trait had a relatively low variation, i.e., the accession TUC71-7 (22.55°Brix) was 1.48 times greater than the accession IN84-58 (15.14°Brix).The VCOV models selected for the and TCH) . This reG) and phenotypic (CVP) variations were for the BRIX trait, while the higher values for CVG and CVP were for SN, SW and TCH.Estimates for P < 0.05) were observed between the evaluated traits in the BPSG. According to the degree of correlation between traits, correlations were grouped into low (≤0.35), moderate (0.36–0.70) and strong (≥0.71) categories [Pairwise genotypic correlations among the five evaluated traits, considering both harvests (plant cane and first ratoon), are shown in tegories . Thus, fS. officinarum) at ESTB45 and SMC319; Ganda Cheni (S. barberi) at ESTB45, ESTB118, ESTA51, and ESTC17; and especially IN84-58 (S. spontaneum) at CIR23, ESTA26, ESTA61, CIR55, ESTB69, ESTA33, ESTB94, ESTA63, CIR18, ESTB63, CIR36, ESTB45, ESTA16, ESTC55, ESTA48, SMC222 and CIR25.The use of 100 SSR markers generated 1483 fragments, 1476 of which were polymorphic (99.5%), in the 134 accessions of the BPSG. Considering all polymorphic fragments, 484 (32.8%) were produced by SSR dinucleotides, 689 (46.7%) were produced by SSR trinucleotides, and 303 (20.5%) were produced by SSR tetranucleotides. The number of fragments ranged from four (ESTC52 and ESTC55) to 36 (ESTA31), with an average of 14.83 fragments per SSR. Species-specific fragments were observed for the ancestral accessions Badila  and Ganda Cheni (S. barberi) were placed in subpopulation 2, the ancestral accessions Badila (S. officinarum) and IN84-58 (S. spontaneum) were positioned in subpopulation 1, and the ancestral accession White Transparent (S. officinarum) was positioned in subpopulation 4.The phylogram using the SM genetic distance among accessions also suggested the presence of four subpopulations. A total of 99.25% of the group assignments made by the DAPC analysis were also made by the phylogram . Only acLogP) logarithm probabilities were plotted against the distances, in centiMorgan (cM), between linked marker fragments in the same cosegregation group of the SP80-180 × SP80-4966 integrated genetic map (Fisher's (−etic map . This stS. spontaneum (IN84-58). The three SSR fragments associated with TCH were also associated with SW, and two of these fragments (CIR51_11 and SMC319_09) were in the group of marker fragments associated with SH. Although not in the same fragment as TCH, SW and SN, the genomic SSR marker SMC319 was also present among the SH MTAs and was therefore associated with four yield-related traits. Likewise, the genomic SSR marker CIR51 was associated with four yield-related traits, namely, BRIX, SN, SW and TCH.The QQ plots obtained with FarmCPU and GAPIT software for phenotypic traits are presented individually in Viridiplantae protein database using Phytozome  and Zea mays (for the SH trait). A functional description of the sequences showed possible candidate genes for all traits except for TCH. Despite this result, the CIR51 marker, which was found near (approximately 5.3 kb) the cytochrome P450 transcript region in S. bicolor, had fragments significantly associated with TCH in addition to BRIX, SN and SW. Overall, the homologies found for significant SSR markers associated with BRIX  suggest a role in the accumulation and trafficking of lipids and sucrose, while the homologies for significant SSR markers associated with SH (ESTC19), SN  and SW (ESTB130) were related to plant growth and development.The available sequences of the SSR markers significantly associated with the BRIX, SH, SN, SW and TCH traits were blasted against the nonredundant NCBI database using BLASTX and against the hytozome . SequencS. spontaneum), Badila (S. officinarum) and Ganda Cheni (S. barberi). The identification of new alleles controlling sugar and yield metabolism in alternative Saccharum species and the introduction of these alleles into core germplasms would be one way to overcome obstacles in sugarcane breeding, increasing the productivity of commercial cultivars [The complexity of the sugarcane genome and the quantitative nature of sugar- and yield-related traits are challenging for geneticists and breeders searching for higher genetic gains for this crop. Moreover, assessing genetic variables free of environmental effects and estimating their real genotypic value are extremely important for breeding purposes. Here, the genetic information obtained with SSR markers was able to efficiently distinguish ancestral and improved accessions of the BSPG due the high polymorphism and presence of unique alleles in some accessions, such as IN84-58 ) at the genetic level, thereby improving the predictive accuracy directly related to heritability and genetic gain ,53,80,81The phenotypic range for each trait reflected the high genetic variability of the BSPG, and the broad-sense heritability values showed that much of the observed phenotypic variation can be attributed to differences at the genotypic level . TherefoIn addition to genotypic correlations, genetic variability is essential to breeders for the generation of improved cultivars. In the present study, population structure and genetic diversity were assessed in the BSPG through DAPC analysis and by a genetic dissimilarity matrix calculated with the SM distance and visualized as an NJ phylogram, both based on SSR markers. DAPC analysis divided the BSPG into four subpopulations , and thiS. spontaneum, S. officinarum and modern hybrids, respectively. The existence of a large LD extent and, consequently, the presence of large gene clusters indicate that a high density of markers is not required to detect MTAs by the GWAS approach in sugarcane. Thus, single-dose markers could be useful for this purpose as an initial step. On the other hand, the LD caused by population structure and familial relatedness can promote false positive detection in GWAS analysis [LD is affected by genetic and nongenetic factors, such as recombination, genetic drift, population stratification, genetic relatedness, mutation, selection and linkage ,94. Theranalysis –97, and analysis ,96.The QQ plots obtained with GAPIT software showed that the association tests were inflated and resulted in false positives when compared with the QQ plots generated by FarmCPU software, mainly for the BRIX and SN traits . The comS. spontaneum accession IN84-58, was positively and negatively associated with the SN and BRIX traits, respectively. ESTA61_15 may be a unique allele that causes important phenotypic variation. Previous studies detected MTAs for the SW [The GWAS analysis with FarmCPU software revealed 23 MTAs associated with five traits when the Bonferroni-corrected threshold was set to 1% . All butr the SW ,39, SN [r the SW ,39,40,42r the SW ,39,40,42r the SW ,40–42 trr the SW ,41,42.cortical cell-delineating protein, which is a member of the alpha-amylase inhibitors, lipid transfer and seed storage (AAI-LTSS) protein family according to SMART annotation in Phytozome [S. spontaneum and other BPSG accessions [vacuolar fusion protein MON1, which is a member of the MON1/SAND protein family. In Arabidopsis, the MON1 and CCZ1 proteins form a complex that is critical for vacuolar trafficking, vacuole biogenesis, and plant growth. The mon1 mutants show pleiotropic growth defects, fragmented vacuoles, and altered vacuolar trafficking [To understand the plant metabolism functions of the SSR marker regions associated with traits and search for candidate genes, we annotated the available sequences from which the associated markers originated. Thus, the sequence that produced the ESTA61 marker showed similarity with hytozome . This recessions ,104. Thefficking . Therefoexonuclease DPD1, chloroplastic/mitochondrial could indicate that the nucleotides, i.e., purines and pyrimidines, released during the leaf senescence process provide nitrogen, sugar and phosphate to maintain or increase the plant tillering ability [auxin response factor 5 (ARF5), which acts as a transcriptional activator of auxin-responsive promoter elements. This homology suggests that a modification in the ARF5 protein could affect plant growth and development and consequently affect the weight and stalk production of sugarcane [Arabidopsis, the overexpression of DVL1 was associated with plants with a shortened stature, smaller and rounder rosette leaves, clustered inflorescences, shortened pedicles, and siliques with pronged tips resembling horns [For the ESTB111 marker, which was associated with SN, similarity with  ability . Likewisugarcane –109. Forng horns . Thus, tThe GWAS analysis with FarmCPU software, which used population structure information derived from DAPC analysis as a covariate, was able to detect MTAs with efficient control of spurious associations in sugarcane. In addition, the verification of possible candidate genes for MTAs showed the importance of providing insights into gene networks that are related to the expression of target traits. This approach has great potential for assisting breeding programs in increasing the genetic gain rate of target traits. However, the development of statistical approaches to enable mapping association with markers in multiple doses is important to enhance the probability of finding higher numbers of significant associations and, consequently, increase the use of molecular markers in breeding programs of outcrossing heterozygous species, such as sugarcane.S1 Table(PDF)Click here for additional data file.S2 TableGM matrix were selected according to the lowest value of the BIC criterion for BRIX as °Brix, stalk height (SH) in m, stalk number (SN) by direct counting, stalk weight (SW) in kg and cane yield (TCH) in t ha-1 for BPSG over two harvest years (plant cane and first ratoon). Bold numbers represent the smallest AIC and BIC values.The Akaike (AIC) and Bayesian (BIC) information criteria were used to compare the structures of the variance–covariance matrix. The models for the (PDF)Click here for additional data file.S3 TableA threshold of 0.005 was used to declare the major contributions. LD: linear discriminant.(PDF)Click here for additional data file.S1 FigThe x-axis represents the different number of subpopulations that could be presented in the Brazilian Panel of Sugarcane Genotypes (BPSG). The y-axis represents the BIC value associated with each number of subpopulations.(PDF)Click here for additional data file.S2 FigThe x-axis represents the number of PCs retained in each DAPC. The y-axis represents the proportion of successful outcome prediction. Each dot represents the individual replicate of the analysis.(PDF)Click here for additional data file.S3 FigThe α-score on the y-axis depicts the difference between the proportion of successful reassignment of the analysis (observed discrimination) and the values obtained using random groups (random discrimination). The x-axis represents the number of retained PCs for each random group. The spline interpolation approximates the optimal number of PCs to be retained.(PDF)Click here for additional data file.S4 FigThe red regions inside the columns indicate the set of accessions grouped in the corresponding subpopulation according to the membership probabilities.(PDF)Click here for additional data file.S5 FigA threshold of 0.005 was used to declare major contributions.(PDF)Click here for additional data file.S6 FigThe subpopulations obtained through discriminant analysis of principal components (DAPC) are also shown.(PDF)Click here for additional data file.S7 FigThe thresholds corresponding to the Bonferroni and false discovery rate (FDR) corrections are indicated on a logarithmic scale, showing significant and nonsignificant associations above and below their values, respectively. Genetic distances were obtained through the Kosambi mapping function.(PDF)Click here for additional data file.


The software was written in R, to simplify installation and implementation. Because crossword is a domain-specific language, it allows complex and unique simulations to be performed, but the language is supported by a graphical interface that guides users through functions and options. We first show crossword’s utility in QTL-seq design, where its output accurately reflects empirical data. By introducing the concept of levels to reflect family relatedness, crossword can simulate a broad range of breeding programs and crops. Using levels, we further illustrate crossword’s capabilities by examining the effect of family size and number of selfing generations on phenotyping accuracy and genomic selection. Additionally, we explore the ramifications of large phenotypic difference between parents in a QTL mapping cross, a scenario that is common in crop genetics but often difficult to simulate.Quantitative genetic simulations can save time and resources by optimizing the logistics of an experiment. Current tools are difficult to use by those unfamiliar with programming, and these tools rarely address the actual genetic structure of the population under study. Here, we introduce  Current open-source packages have expanded the realism and utility of simulated scenarios by incorporating elements such as variation in recombination frequencies, novel transgenic approaches, and genomic selection5. Still, this realism comes at a significant cost, both, in terms of computational speed and difficulty of use. We have developed a simulation platform that uses real genotypic data and emerging genomics data in order to make realistic simulations much easier to implement. The platform, called “crossword”, is essentially a domain-specific language that, when executed, is interpreted into and executed by the R statistical programming environment. This layer of abstraction allows users to focus less on the mechanics of implementing the simulation and more on their actual experimental goals and/or breeding ideas. In addition, this precise syntax allows crossword to take full advantage of the familial structure unique to controlled crosses.The simulation of controlled crosses has been useful in comparing breeding strategies  Most users will initially interact with crossword via a semi-graphical editor run under gWidgets/tcltk, a lightweight R windowing environment  All R functions underlying crossword are available and well-documented for those who have experience in R and need to directly modify or expand a behavior.ent Fig. . This incrossword simplifies these problems, and we give examples of how the resultant realism can be critical to major aspects of experimental design and interpretation.In what follows, we describe some of hurdles to realistic breeding and genetic mapping simulations. We report on the methods by which crossword run script is interpreted into a set of R functions; thus, crossword can be installed on any system capable of running R. Crossword allows the user to submit a text file defining a small number of input files and describing the crossing scheme in a simple syntax . When a phenotyping or genotyping step is performed on a population, genetic coordinates are converted to genomic coordinates, the appropriate SNPs for a given founder haplotype are assigned, and phenotypes calculated. Given the computational demand on this step, crossword uses Rcpp/C+ + to optimize the speed of operations ~60-fold.Any variant in the supplied VCF file can be selected as a simulated quantitative trait nucleotide (QTN), which are causal variants with some genetic effect on the phenotype. For example, if the genetic architecture consists of 30 QTN, then these QTN are sampled from across population-wide polymorphisms, and, thus, a single cross may or may not be segregating for all 30 QTN. If a user wants all specified QTNs to be segregating in a biparental cross, they can filter VCF files to the appropriate parents. . Given that these represent only two populations within each species, the fit generally approximates the maps with the exception that the centromere displays slightly less recombination than predicated by gene density alone Fig. . As seen10. In order to examine crossword’s capacity to reflect actual experiment results,we simulated one such previously published QTL-seq experiment and included all parameters relevant to that population and its development11 .QTL-seq is an emerging genetic mapping strategy that combines bulk segregant analysis with next-generation sequencing2 = 0.5 and 0.8. The study used peanut genotypes SPT06-06 as a resistant parent and Florida07 as a susceptible parent with 5,513 markers segregating. A population of 166 individuals was advanced to F6. The highest and lowest 5% was bulked for QTL-seq analysis. A simulation was run using the same parameters, which showed similar trends for the three peaks  is quite minor.The empirical data revealed two strong peaks at chromosomes Aradu.A05 and Araip.B05, and one weak peak at chromosome Araip.B03 correlated with late leaf spot resistance of peanut. We simulated both Haks Fig. . Howeverata Fig. . Heritabcrossword is to supply a flexible but powerful syntax for the exploration of realistic breeding strategies. Previous simulation frameworks often underestimate the importance of how particular genetic pools get advanced and evaluated and, therefore, underestimating the impact of factors such as residual heterozygosity and family size on genetic variance and phenotyping accuracy. For example, bulk advancement versus single-seed descent can have a substantial impact on the logistics of variety management. Some population level processes can be modeled using scripting libraries such as simuPop, SliM, etc.13, but scripting requires a general proficiency in programming and, more importantly, is not designed for dealing directly with controlled crosses. We have addressed this operational gap by incorporating the concept of “levels” into many crossword functions. Where applicable, users can specify one of four levels: “individual”, “family”, “cross”, or “population”. All populations have “individual” and “population” levels, but other levels depend on how the population is created and managed , nearly all phenotypic replication of a “genotype” is actually based on a population of closely related individuals, usually referred to as a “recombinant inbred family”. Crossword gives users the power to fine-tune this family-creation function in order to precisely explore the implications of sample size on their final result.The “level” concept carries over into phenotyping and selection. Generally, the costliest aspect in genetic mapping and breeding is phenotyping. Unless clones or double haploids are used . As with family homogenization and family size, optimal genomic selection logistics are additionally complicated by the need to determine the training population size that will result in the highest prediction accuracy given the available resources.Genomic selection (GS) is another area in which phenotypic accuracy is crucial. GS was developed within the cattle breeding community as a way of predicting complex traits solely from marker-based estimates of kinship to a phenotyped training set2 between the TGV of an individual that has full-sibling relatedness to the training set and its phenotypic prediction based on the trained model, “kinship.BLUP” function of rrBLUP was used  apiece.We simulated four scenarios that highlight the consequences of asymmetrical effect polarity. In one set, 3 QTN and 15 QTN had randomly polarized effects (“QTN_random”) that are roughly evenly distributed between parents. Alternatively, in the second set (“highLow”), all effects are positive in one parent and negative in the other. For these simulations, we used a HAs expected, in both cases of 3 QTN, the polarity of the effects was generally irrelevant due to the fact that QTN are rarely in strong linkage with one another Fig. . Here w. In the crossword, which advances the ability to simulate genetic and breeding experiments in the following ways:crossword uses actual structures derived from genetic variation data and supplied as VCF or Hapmap files .Parents with highly divergent phenotypes are often used to genetically map traits. ait Fig. . In addiCrossword is designed to cater to numerous programming proficiency levels and offers a graphical interface as an entry point into the platform  to bias randomization toward the regions having more genes.It is assumed that causal variants are generally located in or near genes. Instead of randomly picking positions across the genome, QTN_random, QTN_supplied, or high_low_parent. In QTN_random mode, each QTN (Quantitative Trait Nucleotide) position is drawn at random across the genome and underlying allele effects are assigned based on the supplied distribution. In QTN_supplied mode, positions and the effects associated with alleles are defined by the user. In high_low_parent mode, positions are drawn at random, as in QTN_random above, but allelic effects are biased to be positive in the user-specified “high” parent and negative in the “low” parent. True genetic values (TGVs) are then summed for a particular genotype.  Epistatic interactions are currently unsupported.The genetic architecture of traits is simulated in one of three modes: 2) is the relationship between genetic variance (VG) and residual variance (VR). VG can change substantially depending on the parents in a cross and downstream selection in a population, among other things. Alternatively, VR will be essentially constant assuming individuals are evaluated in a consistent set of environments. The common approach in many simulation frameworks of calculating VR from the supplied H2 for every phenotyping step is therefore conceptually dubious, since VG and VR are not expected to show substantial covariance. Indeed, in simulation of long-term selection experiments, such a conventional approach will dramatically overestimate the ability to accurately select superior genotypes in late generations. To that end, in crossword, the user supplies either “absolute” or “average” H2. “Absolute” H2 is the heritability in a theoretical cross that segregates for all possible QTN. “Average” H2 considers the average number of QTN segregating in all possible crosses between founders.  In these theoretical crosses, we assume a very large population size, free recombination among loci, and complete homozygosity. Dominant effects are irrelevant in this calculation. VR is then calculated using a set of 10,000 individuals randomly selected from all possible combinations of individuals binomially distributed for positive and negative alleles. The subsample approach is used to simplify calculations when effects are not equal and/or do not follow a formal distribution, such as in used-supplied mode. The actual H2, including dominance effects, for a population is reported whenever that population is phenotyped, such that the user can modify the supplied H2 if inappropriate.In its simplest form, heritability  supplied by user per chromosome .Founder haplotypes are imported in either HapMap or VCF formats. The names of founders in these files can be used directly in ata Fig. . GFF filcrossword uses the Rcpp external R library and custom C++ programs to optimize the speed of operations ~60-fold as a part of “get_parental_genotypes” function. The script recognizes and reads the VCF or hapmap files, differentiates homozygous/heterozygous/indel loci, reformats the output and then applies it to a prediction function to estimate the genomic coordinates from genetic coordinates.Halotypes are converted to genotypes, using the transform function above, when a genotyping or phenotyping set is required. Given the computational demand on this step, All polymorphisms in the input founder file (VCF or hapmap) are used to select QTN. Therefore, when making a cross, the actual number of segregating QTN will be lower if the founder file contains more than the two individuals in the cross. This approach allows users to apply genome-wide association data from the diversity panels that they may be using as founders and to accurately simulate the reduction in relative genetic variance that occurs in derived populations. A helper function is supplied to assist in reducing VCF files depending on the level of polymorphism desired.17. To focus on effect estimates, the true genetic positions for all markers were used directly as opposed to being estimated from recombinants. Using the ‘stepwiseqtl’ function, the maximum number of QTL to discover (“max.qtl”) was iteratively increased until the minimum LOD score for every discovered QTL was <3. Effects were assessed based on the final resultant model. The R script to perform mapping is available in the auxillary_scripts folder released with crossword, but depends on r/qtl, which is not included with crossword.In simulations Fig. , QTL wer11. The dataset contained 5,513 SNPs between the two peanut varieties SPT06-06 and Flordia07. QTL-seq analysis revealed three QTLs. Crossword offers the capability of simulating reads from a genomic sequence generated from a set of individuals in a simulation. Reads can then be simulated from these sequences if a user wants to explore a full genotyping-by-sequencing pipeline. Alternatively, since this read simulation approach can be computationally demanding, crossword offers an auxiliary program to add sampling and read coverage variation to a set of underlying genotypes. In actual experiments involving combining DNA from multiple genotypes as a single sample, such as QTL-seq, individual genotypes are obviously not known. Conversely, in simulations users can exploit this knowledge to get a realistic sampling of each bulked sample. In our simulation, we used an average coverage from the original experiment (30x across the bulk) and a standard deviation of 7. For each locus, a random variant was sampled from a normal distribution based on these values, and this random number of alleles was sampled with replacement from the set of all bulked genotypes.Data and parameters for Fig. https://peanutbase.org] and Phytozome [https://phytozome.jgi.doe.gov], respectively. Further location details are described in crossword manual.The genomic data of peanut and soybean was collected from PeanutBase [supplemental materials