The results of genome-wide association studies are known to be affected by epistasis and gene-by-environment interactions. Using a statistical model.... Daphnia magna susceptibility to infection by Pasteuria ramosa, we identify genomic regions consistent with G × G interactions. We conclude by outlining possible avenues for designing more powerful and more repeatable association studies.Genome-wide association studies are widely used to identify “disease genes” conferring resistance/susceptibility to infectious diseases. Using a combination of mathematical models and simulations, we demonstrate that genetic interactions between hosts and parasites [genotype-by-genotype (G × G) interactions] can drastically affect the results of these association scans and hamper our ability to detect genetic variation in susceptibility. When hosts and parasites coevolve, these G × G interactions often make genome-wide association studies unrepeatable over time or across host populations. Reanalyzing previously published data on  INFECTIOUS diseases are pervasive. So pervasive, in fact, that without effective mechanisms of resistance, host populations can be quickly reduced in size or even driven to extinction. For instance, chestnut blight effectively wiped out the American chestnut, which had little if any resistance to this novel pathogen, after its introduction to North America in the early 1900s . AlthougThe existence of substantial variation in resistance to infectious disease within host populations has generated hope that it may be possible to identify the genes conferring resistance. Identifying such resistance genes would pave the way for genetic engineering of resistant crops and livestock, focus drug development efforts on likely targets, and open the door to gene therapeutic approaches within human populations. As the genomic revolution has progressed, it has become increasingly common to search for these “resistance genes” using genome-wide association studies (GWAS)  describe epistatic interactions among loci. Finally, M, which is assumed to have mean 0, a constant variance, and be uncorrelated with an individual’s phenotype. Substituting Equation 4 into Equation 1 yields a model of host susceptibility as a function of host and pathogen genotypes.We consider a scenario, common in practice, where host resistance is measured as a continuous quantitative trait. This would be the case, for instance, if host resistance is assessed by measuring viral load, duration of infection, or damage to host tissues. Our model assumes that host resistance depends on the value of a quantitative trait in the host, ce model  Our goal now is to use this genetic model to predict the sensitivity of GWAS to the genetic composition of the pathogen population. We will explore both traditional, single-species GWAS approaches and a novel approach that takes genetic information from both host and pathogen into account (co-GWAS). Our investigation will rely on a pair of complementary approaches. First, we will develop and analyze analytical approximations that quantify the sensitivity of GWAS and co-GWAS approaches to changes in pathogen genotype frequencies. These analytical approximations will rely on simplified genotype–phenotype maps and will not explicitly integrate evolution and coevolution. Second, we will develop and analyze simulations that allow us to explore the consequences of rapid pathogen evolution and coevolution between the species on the performance of both GWAS and co-GWAS approaches.α in Equations 2 and 3). Under this assumption, Equation 1 can be approximated by its second order Taylor series expansion. This allows the genetic model of susceptibility to be simplified to the following approximate expression:To simplify the genetic model of resistance developed in the previous section sufficiently for mathematical analysis, we begin by considering the case where β coefficients can be found using least squares regression. The biological interpretation of this linear model is straightforward. The intercept coefficient, Mathematica notebook in File S1):i = {1,2}, where We envision a standard GWAS where susceptibility to infection has been measured for some number of host individuals, each of which has also been genotyped at a large number of marker loci. To focus our model on the effects of species interactions, we will assume this data accurately provides us with the genotype of individuals at the two host resistance loci. Using this data, the goal of the genetic association study is to partition host susceptibility between these genes relative to their effects. This can be done by fitting susceptibility with a linear combination of the genetic indicator variables:β’s) inferred by a host-only GWAS can be quite sensitive to the genetic composition of the pathogen population (As a result of the dependence of the coefficients in (7) on the pathogen allele frequencies and linkage disequilibrium, the allelic effects (pulation . ChangesHost–Parasite Coevolution section below).A second result that can be drawn from Equation 7 is that when the resistance function is approximately linear, ce model . A thirdIn addition to identifying the allelic effects on host resistance, an important metric of GWAS performance is the proportion of phenotypic variation explained by the identified causative loci. Given the dependence of the estimated allelic effects on pathogen allele frequencies, we calculated the total phenotypic variation explained by the host loci across the range of pathogen allele frequencies . When thThe results derived in the previous section demonstrate that traditional single-species GWAS may be sensitive to the genetic composition of the pathogen population at loci involved in host–pathogen specificity. In this section, we attempt to overcome this problem by developing an alternative GWAS design in which both host and pathogen genetics are incorporated. In contrast to the traditional method where only host genotypes are recorded, this design requires that both host and pathogen genotypes are known. As with Equation 6, we now attempt to fit host resistance as a linear function of the allelic indicator variables, but we include pathogen indicators as well as interaction terms between host and pathogen loci:Mathematica notebook in File S1):Despite the complexity of Equation 8, and hence the logistical and computational challenges of applying it, the expressions for each of these coefficients in terms of the host and pathogen phenotypic effects are simple (see Comparing the equations in (9) with the coefficients in (7) reveals an important conclusion: the effect sizes no longer depend on the pathogen allele frequencies nor the linkage disequilibrium . This reIn addition to stabilizing the estimated allelic effects across pathogen allele frequencies, the total phenotypic variation explained by the co-GWAS greatly exceeds that of the host-only GWAS. For the two-locus case explored here, the co-GWAS approach can explain 100% of the variation regardless of pathogen allele frequency . The conS, which is a function of the host and parasite genotype. Infected hosts experience a fitness cost r (see Mathematica notebook in File S1). We then use Equations 7 and 9 to calculate the inferred allelic effect sizes by using a host-only GWAS or co-GWAS for each generation over the course of coevolution for both the phenotypic-difference and phenotypic-matching models  and are available for download from the Dryad Digital Repository (DOI: https://doi.org/10.5061/dryad.tb25q).The analysis, numerical simulations, and scripts to generate the original figures were coded in Wolfram D. magna to two P. ramosa strains, C1 and C19, as a preliminary test of our analytical predictions. In particular, we compare the results of genome scans for C1 and C19 susceptibility analyzed separately to a single genome scan for susceptibility using all the data but ignoring pathogen strain type. Our analytical model predicts that, despite having half the sample size, the separate genome scans for C1 and C19 resistance should reveal loci that determine host–parasite specificity, whereas the full data scan will have lower power to do so. Note that strain type captures almost all of the relevant genetic information in this case, given that the parasite is clonal.Taken together, our analytical model and simulations illustrate that incorporating pathogen genetic information into the search for disease genes can greatly increase the explanatory power and repeatability of genome scans. Testing these theoretical predictions with biological data is a critical step in evaluating the power of the co-GWAS approach relative to a traditional single-species GWAS. Analysis of biological data will include several complications that we ignored above, including finite sample sizes, arbitrary forms of coevolutionary interactions, and complex genomic architectures. Unfortunately, we know of no studies that include full host and parasite genomic data as well as the outcome of infection experiments. Further, the computational tools to perform a co-GWAS in the form of Equation 8 do not yet exist. We can, however, use recently published data by D. magna clones from three distinct geographic regions—1 site in Germany, 1 in Switzerland, and 11 sites in Finland—and provided the sequence at 6403 SNPs. Host susceptibility  infection by each P. ramosa strain, C1 and C19, was determined by assessing whether fluorescently labeled spores attached to the host’s esophagus  determines overall host susceptibility regardless of pathogen strain and is thought to reside on linkage group 4 . The expressions for the β coefficients under this interaction model are unruly and difficult to interpret. Using a numerical approach, we observe that once again G × G interactions can explain a significant proportion of the variation in susceptibility , particularly in highly variable pathogen populations. Unlike the phenotypic-difference and phenotypic-matching models, however, the co-GWAS approach (Equation 8) no longer explains all of the variation in susceptibility and the coefficients vary with pathogen allele frequency. This is a result of higher order interactions not included in our model. Hence, although the co-GWAS approach performs significantly better than a single-species approach, it will not always capture the full genetic basis of infection because of the second order approximation used in Equation 8.The mathematical analysis presented above focuses on host–pathogen interactions of a specific form, given by Equation 1. Although we have relied on an approximation that assumes weak phenotype differences, Regardless of the form of the interaction, our analytical models and simulations illustrate that incorporating pathogen genetics into the search for disease genes can greatly increase the explanatory power and repeatability of genome scans. Unfortunately, several logistical and computational challenges preclude applying a full two-species GWAS. Most notably, such a design requires additional genetic data that is not currently available. More specifically, this design requires genotyping all hosts and the pathogens to which they are exposed, not just the host–parasite combinations observed in infected individuals. Future exploration is warranted to determine whether uninfected individuals can simply be treated as unknown with respect to pathogen exposure, and what the consequences of doing so would be for the statistical power of our approach.The complexity of the two-species design (Equation 8) relative to that of a single-species design (Equation 6) also introduces computational challenges. In addition to requiring larger sample sizes, estimating the effects of the large number of potential G × G interactions in a full host–genome by parasite–genome study is computationally unrealistic. In addition to the large number of pairwise interactions between hosts and pathogens, depending on the form of the interaction, higher order genetic interactions may be necessary to fully explain the variation in susceptibility. These higher order interactions can be particularly important as the number of loci underlying susceptibility, Daphnia–Pasteuria data set we analyzed provides a valuable test case for a two-species co-GWAS. In this study, we know exactly to which pathogen type individuals have been exposed, which is generally not known in natural populations. This information may have increased the power of the study to detect loci underlying C1 and C19 susceptibility. Despite this increased power, we chose to use the arguably lenient significance threshold of a log-likelihood score >2 plus clustering of four or more SNPs, as in the original article. Requiring more stringent threshold corrections for multiple sampling, such as a Bonferroni correction, does not yield any significant SNPs. Given the correspondence between the GWAS results and those of functional studies . Interestingly, this “mixed” GWAS not only identifies the same regions on linkage group 4 and 5 but also identifies regions on linkage groups 1, 9, and 10, as were found in the single pathogen-type GWAS. The fact that this mixed analysis picks up some of the potentially parasite-specific loci is likely due to randomly sampling an excess of C1- or C19-tested clones. Consistent with this interpretation, exactly which parasite-specific regions are identified varies with the random sample chosen. Nevertheless, as with the complete data set, a comparison between C1, C19, and mixed susceptibility provides additional information about which genes are involved in general health vs. parasite-specific susceptibility.The The results presented here highlight several important avenues for future research. First and foremost, designing genome-wide association methods that allow for G × G interactions is critically important, as is the collection of genotypic data from hosts and pathogens. This could be approached, for example, by adapting GWAS designs and analyses used to detect gene-by-environment interactions . Recogniwww.genetics.org/lookup/suppl/doi:10.1534/genetics.117.300481/-/DC1.Supplemental material is available online at Click here for additional data file.Click here for additional data file.Click here for additional data file.


Obesity is characterized as the excessive accumulation of body fat and has a complex genetic foundation in humans including monogenic high-risk mutations and polygenic contributions. Domestic pigs represent a valuable model on an obesity-promoting high-caloric diet while constantly evaluated for body characteristics. As such, we investigated the genetics of obesity-related traits, comprising subcutaneous fat thickness, lean mass percentage, and growth rate, in a pig population. We conducted genome-wide association analyses using an integrative approach of single-marker regression models and multi-marker Bayesian analyses. Thus, we identified 30 genomic regions distributed over 14 different chromosomes contributing to the variation in obesity-related traits. In these regions, we validated the association of four candidate genes that are functionally connected to the regulation of appetite, processes of adipogenesis, and extracellular matrix formation. Our findings revealed fundamental genetic factors which deserves closer attention regarding their roles in the etiology of obesity. Obesity and obesity related diseases, such as diabetes, high blood pressure and heart diseases, reach epidemic proportions worldwide with gravely consequences especially in industrialized and developing countries2). In agreement, obesity is a highly polygenic trait and heritability estimates of obesity related phenotypes  in humans revealed a proportion of 30–70% assigned to genetics4. Nevertheless, beside rare forms of obesity caused by monogenic high-risk mutations, the complex disease pattern is based on polygenic causes and suggested to be influenced by environmental factors, lifestyle, and dietary habits. Specifically, main driving factors are the permanent availability and consumption of energy dense foods combined with low levels of physical activity which potentially cause chronic imbalance between energy intake and expenditure5. Fundamental metabolic processes and accompanied genetic principles involved in the etiology of obesity related diseases are analogous in all mammalian species. Accordingly, commercial pigs represent an intact biological system on an obesity promoting high caloric diet throughout their lifetime coupled with low physical activity in the conventional production processes. Under these preconditions, pigs are highly susceptible for a disturbed fat metabolism and an obesity related phenotype but at the same time adapted to a diabetogenic environment6. However, due to the breeding structure and the continuous phenotypic evaluation of animals in terms of fat, muscle, and growth traits, basic molecular variations as well as minimal alterations in fat metabolism can be related to genetic causes. Therefore, the elucidation of the genetic architecture of traits related muscle and fat mass in these commercial pig lines provide interesting insights into major and minor factors contributing to the development of obesity related phenotypes.Several hypotheses are widely discussed giving different explanations for the genetic manifestation of obesity in humans , leptin (LEP), and fat mass and obesity-associated gene (FTO) are known key regulators of feed intake/appetite and energy homeostasis9. As many of these factors were derived and validated by genome-wide analyses across different species, the present study aimed to identify genetic markers and genomic regions associated with fat, leanness, and growth traits in pigs. Exploiting the genetic resource of mammalian livestock species can provide positional and functional candidate genes beyond the general knowledge about the main sources for the genetic predisposition of obesity.Genetic studies in different mammalian species, e.g. humans, mice, and pigs, revealed a consistently large number of genes affecting fat deposition and the occurrence and dimension of obesityp < 2.1e-06)  explained the highest proportion of the genetic variance of LMP with 1.39% , ADAM10 (ALGA0005610) and novel protein coding features (MARC0103791). Another region linked to the variation in LMP was identified between 98.6 and 104.0 Mb on chromosome 8 by single-marker analyses .The genome-wide analyses revealed 10 genomic windows with prominent contribution to the genetic variance in LMP , each contributing above 0.5% to the genetic variance.For D110, genetic factors explained 42.2% of the total variance. Eleven 1-Mb genomic windows on 5 different chromosomes were identified  showed significant (p < 0.05) higher SFT. Specifically, compared to carriers of the major allele these individuals showed 13% and 9% higher levels of subcutaneous fat for SPARC and BBS7, respectively. Moreover, the observed effects of these two tagging SNPs on LMP were in the opposite direction with a similar order of magnitude. Association analyses of the genotyped MC4R polymorphism revealed significant associations with LMP but not with SFT. Moreover, the tagging SNP in MC4R showed a tendency for affecting growth rates, with carriers of the minor G-allele growing slower but having higher LMP.Association analyses of SNPs located in 10, were applied to the dataset. Results were integrated with single-marker analyses which further provide supportive evidence for the association of complex traits and allelic variants12. In total, the genome-wide scan for DNA segments influencing fat, muscle, and growth traits revealed 30 unique 1-Mb genomic windows located on 14 different chromosomes. Proposed candidate genes are involved in the mediation of processes related to extracellular matrix formation, fatty acid transport, carbohydrate metabolism, homeostasis, bone metabolism, and melanocortin system. As such, the analyses provide a major resource of prominent genomic regions and derived genetic factors with specialized functions in the context of fat metabolism and, moreover, with an array of complex contributions to the etiology of obesity-related diseases.The current study elucidated the genetics of fat and muscle deposition in a pig population with a long-term breeding history of selection for leanness, growth and feed efficiency. Thus, it aims at the cross-species identification of genetic factors and genomic regions contributing to the genetic predisposition for an excess of body fat mass. Bayesian multi-marker approaches, which were shown to be beneficial to identify trait-associated genomic regions7). In fact, the MC4R locus represents one of the major obesity-associated loci in humans14 and is linked to fatness, growth, and efficiency traits in pigs15. However, the genotyping of the MC4R rs81219178 polymorphism in the current dataset and subsequent association analyses did not fully represent the highly significant associations obtained from the genome-wide analyses with LMP and D110. Other studies argue for at least a second genetic factor in this genomic region that influences fat deposition and/or muscle development as supported by conditional analysis including MC4R genotype information in pigs16. Moreover, for the human homologous gene, which is located on human chromosome 18, more than 130 mutations are described in the context of monogenic and polygenic obesity17. Depending on the localisation of the MC4R mutation, both response level and direction of effects on BMI vary, thus, arguing for a complex genomic structure of the MC4R locus with protective18 or causative19 implications in the context of obesity. Interestingly, in the current study, no significant associations between the MC4R rs81219178 polymorphism and STF were found. In agreement, MC4R genotypes observed in a long-term study in humans were shown to predominantly affect the level of weight traits, due to the regulation of appetite and food consumption, with more or less strong side and secondary effects on fat deposition20. However, although the MC4R locus has been intensively investigated in human and pigs for decades, the obtained results and the ambiguous situation in humans still argue for the careful dissection of the effects in this wide quantitative trait region. This is supported by several genome-wide analyses in humans consistently assigning variations in the MC4R locus to obesity-related traits22 but having difficulties to clearly narrow down the molecular causes even in large meta-analyses.The results partially overlap with genetic factors previously reported in human and mouse studies dealing with the genetics of obesity . Bardet-Biedl Syndrome is a developmental disorder which is independently caused by different genes and is characterised by diverse features including obesity24. Specifically, BBS7 plays a critical role in the assembly of BBS proteins to the BBSome complex. Beside obesity, molecular alterations of BBS7 are shown to be associated with secondary clinical features like developmental delay and hypertension25. Moreover, associations of BBS7 mutations and obesity were consistently observed in different population groups, as exemplified by analyses of a Russian26 and a Korean cohort27. Knock-out studies of genes that are involved in the pathophysiology of BBS, for instance BBS7, were shown to promote the obese phenotype due to altered feed intake and weight gain29. Furthermore, recent findings argue for an impaired trafficking of the leptin receptor due to alterations in the BBSome30. With regards to the significant associations between the BBS7 locus and fat thickness and leanness in pigs, this locus provides an interesting candidate not only for monogenic causes of obesity but also for its contribution to the polygenic implications on obesity rate.The region on chromosome 8 that showed the highest contribution to the genetic variance of SFT harbours the SLC27A6 locus, also known as FTP6, coded for a fatty acid transporter which predominantly acts on the transport of palmitate and linoleate in the plasma membrane of heart cells31. Nevertheless, SLC27A6 is also expressed in skeletal muscle32 and adipose tissue33. Evidence for an association of this locus with weight gain was previously obtained by genome-wide analyses in pigs34. Genetic variations in the porcine SLC26A6 gene are related to divergent lipid metabolism processes35. Although little is known about the role of SLC26A6 in the context of obesity, members of the fatty acid transport proteins are widely discussed as regulators of energy homeostasis, exogenous fatty acid uptake, and thermogenesis36.The examined SPARC (ENSSSCG00000017082) with fat deposition. SPARC, also known as osteonectin, is highly conserved across species and the human homologous gene mapped on chromosome 5 at 151.6 Mb. In white adipose tissues, SPARC is known to be mainly located in the extracellular matrix (ECM). As such, SPARC is involved in the regulation of metabolic processes during adipogenesis and participates in the stabilisation of ECM structures through regulating the expression and modification of collagen38. Beside the knowledge gap regarding genetic-based alterations of SPARC, the gene is well characterised in the background of obesity. Analyses in mouse models revealed a role of different SPARC isoforms in the systemic mobilization and migration of adipose stem cells and established interactions between SPARC expression and obesity related phenotypes40. Furthermore, based on correlations between osteonectin plasma concentrations, SPARC expression and BMI, these connections were also validated in humans42. On the functional level, osteonectin inhibits the differentiation of mesenchymal stem cells and preadipocytes into adipocytes and induce the production of osteoblasts by stimulating osteoblastogenesis37. Accordingly, SPARC-null mice showed no difference in weight traits but excessive accumulation of white adipose tissue due to increased size and number of adipose cells43. Phenotypic observations further revealed impaired infarct healing and collagen formation after myocardial infarction in SPARC-null mice indicating multi-dimensional functions of SPARC in adipose tissue organisation and cardio-vascular system44. Moreover, osteonectin represents a substantial proportion of non-collagenous proteins in mineralized tissues and is involved in bone calcification, collagen I deposition, fibrillogenesis, and bone turnover45. Interestingly, total lean mass was previously mentioned as predictor for bone mineral density46 and, thus, the current associations between SPARC and LMP provide evidence for a genetic link between SPARC mutations and altered bone metabolism. Consequently, in the context of recently discussed connections between osteoporosis and obesity48, SPARC could act as an important mediator in the balance of adipogenesis and osteogenesis49.The combined analyses further provided first evidence for an association of genetic variants of 50.The presented list of candidate genes and their functional contribution to the etiology of obesity highlighted the usability of genome-wide analysis as a valuable tool to elucidate the genetic architecture of complex traits across species. Nevertheless, the power of the approach is influenced by many factors such as population structure, confounding variables, and the informative value of analysed traits. With respect to these factors, animal models provide several advantages51. Accordingly, the performed association analyses assigned alterations of leanness to the MC4R locus while less pronounced effects were observed for the association with fat thickness. Another advantage of translational studies in model organisms is the environmental stability within the population including homogeneous feeding regimes and the absence of confounding factors such as smoking and alcohol consumption. Moreover, the polygenic inheritance of obesity is suggested to be less complex in pigs due to the on-going selection based on body characteristics and subsequent fixation of loci51. Consequently, there is less genetic heterogeneity which provides the possibility to uncover particular pathways and genetic factors contributing to the predisposition of obesity. The conducted genome-wide analyses of obesity-related traits in pigs revealed prominent genetic factors like MC4R and BBS7 with known contributions to monogenic and polygenic causes in the etiology of obesity. The analyses provided a list of so far unknown and/or not examined candidate genes spotlighting pathways related to bone metabolism, extracellular matrix formation, and fatty acid transport. Genome-wide studies in livestock species provide supporting evidence for candidates discussed in humans and mouse and shed light into putative genetic factors with contribution to the polygenic cause of obesity. In particular, SPARC, as an interesting candidate gene involved in the bone-adipose axis, deserves further investigation with particular focus on human gene polymorphisms affecting obesity rate.Although the analysed pigs were not classified as having an obese phenotype, LMP and SFT varied considerably within the population enabling to identify genetic drivers for differences of body fat mass. Moreover, the analysed traits are more suitable compared to indirect measures like BMI to distinguish between genetic factors of fat or lean mass developmentad libitum access to feed and water. Diets had a net energy of 9.90 MJ/kg with 16.5% protein, 3.2% oil, 3.6% fibre and 4.8% ash as previously described52. Prior to the end of the feeding trial (at approximately 110 kg), subcutaneous fat thickness (SFT) and percent lean, as indicator for lean mass percentage (LMP), were measured between the 3rd and 4th last rib, 5 cm and 7 cm from the midline using a Piglog 105 ultrasonic device . Individual growth rates were expressed as the number of days necessary to gain a final body weight of 110 kg (D110) and recoded for each animal. Descriptive statistics of the traits are presented in Table Vena jugularis in EDTA containing tubes.All procedures described in this experiment were conducted under experimental licence from the Irish Department of Health in accordance with the Cruelty to Animals Act 1876 and the 1994 European Communities Regulations . Data of boars of the Maxgro sire line recorded between 2006 and 2012 were provided by Hermitage Genetics . Animals have been grouped in standard commercial fully slatted pens which were mechanically ventilated to provide an ambient temperature of 18 °C with 53. Marker genotype information was subsequently merged with the latest version of the Sus scrofa build 10.2 available at http://www.animalgenome.org/repository/pig/ (2014-07-07). The map file contains markers mapping to all 18 porcine autosomes, both sex chromosomes and a contig of unmapped markers combined as chromosome ‘UWGS’, resulting in 51661 annotated SNPs used for downstream analyses.Blood samples were used to extract DNA employing the QIAamp DNA Blood Mini Kit . In total, 951 individuals were genotyped using porcine SNP60 Beadchips . Eleven samples were excluded from analyses due to sample call rates <0.97. In total, 861 individuals with complete phenotypic data were used. After filtering, 52920 SNPs were used for downstream processing. Applied criteria to retain SNP markers in analyses were: call frequencies ≥0.95, and minor allele frequency (MAF) ≥0.03. Imputation of missing genotypes was performed using fastPHASE (v1.2) to close gaps in the genotype matrix54. Based on 23496 independent test , thresholds were set to p = 4.3e-05 (1/23496) for suggestive significance and p = 2.1e-06 (0.05/23496) for genome-wide significance. Results were depicted as Manhattan plot using the qqman R package55.Mixed linear models implemented in JMP genomics 6  were employed for multiple single-SNP-trait association analyses. The models included random effects of dam subline and sire subline to account for relatedness between individuals. To account for age related differences, linear models of SFT and LMP included growth rate as covariate. Regarding the setting of significance thresholds, the simpleM R script was used to estimate the number of independent tests56. Parameters were set to a chain length of 51000 iterations including 1000 cycles as burn-in phase, and an output was created at 50 iteration intervals. The π-value, representing the number of SNPs considered as having no effects, was set to 0.995 as previously described for pigs57. Thus, approximately 260 SNPs were reported in a single iteration of the Markov chain Monte Carlo (MCMC) chain. Variance components were estimated using initial Bayes C analyses implemented in GenSel. Taking the estimated residual and genetic variance into account, the multi-marker approach was applied to the dataset employing a Bayes B algorithm. Values of growth rates were included as covariate in the analyses of SFT and LMP. Subsequently, the individual marker results were combined to estimate the contribution of non-overlapping 1-Mb windows to the genetic variance of the three analysed traits. In total, 2577 1-Mb windows were considered (excluding linkage group ‘UWGS’) resulting in a theoretical proportion to the genetic variance of a single window of about 0.04% (100%/2577). Windows that explain more than 0.5% of the genetic variance of a trait were considered in downstream investigations.All multi-marker analyses were performed using a Bayesian approach implemented in the web-based GenSel software (version 4.73 R)http://www.ensembl.org/Sus_scrofa). Moreover, corresponding genomic regions were screened for functional candidate genes using the information of the GeneCards database (http://www.genecards.org/). For promising positional and functional candidate genes, polymorphisms (tagging SNPs) were identified by sanger-sequencing of phenotypic-divergent individuals or by using the SNP database . Subsequently, DNA-based tools for genotyping polymorphisms of Solute Carrier Family 27 Member 6 , Secreted Protein Acidic And Cysteine Rich , and Bardet-Biedl Syndrome 7  were developed based on restriction fragment length polymorphism (RFLP) assays. Moreover, the MC4R polymorphism rs81219178, previously suggested to be causal for effects on fat deposition traits in pigs15, was analysed. In brief, RFLP assays were carried out in a standard PCR mix with SupraTherm Taq Polymerase  according to manufactor’s specifications. Used primer pairs and restriction enzymes  are shown in Supplementary Table Based on the integration of both genome-wide approaches, positional candidate genes were obtained using the porcine genome resource (Ensemble pig genome release 84, The data that support the findings of this study are available from the corresponding author upon reasonable request.Supplementary Information


A Mendelian transmission produces phenotypic and genetic relatedness between family members, giving family-based analytical methods an important role in genetic epidemiological studies—from heritability estimations to genetic association analyses. With the advance in genotyping technologies, whole-genome sequence data can be utilized for genetic epidemiological studies, and family-based samples may become more useful for detecting de novo mutations. However, genetic analyses employing family-based samples usually suffer from the complexity of the computational/statistical algorithms, and certain types of family designs, such as incorporating data from extended families, have rarely been used.We present a Workbench for Integrated Superfast Association studies for Related Data (WISARD) programmed in C/C++. WISARD enables the fast and a comprehensive analysis of SNP-chip and next-generation sequencing data on extended families, with applications from designing genetic studies to summarizing analysis results. In addition, WISARD can automatically be run in a fully multithreaded manner, and the integration of R software for visualization makes it more accessible to non-experts.Comparison with existing toolsets showed that WISARD is computationally suitable for integrated analysis of related subjects, and demonstrated that WISARD outperforms existing toolsets. WISARD has also been successfully utilized to analyze the large-scale massive sequencing dataset of chronic obstructive pulmonary disease data (COPD), and we identified multiple genes associated with COPD, which demonstrates its practical value.The online version of this article (10.1186/s12920-018-0345-y) contains supplementary material, which is available to authorized users. Family-based samples have different properties from population-based samples because of Mendelian transmission, and this well-known feature has allowed family-based designs to play a key role in genetic epidemiology from the very beginning of genetic analysis. For instance, phenotypic correlations between family members enable the estimation of heritability via a linear mixed effects model , and linFurthermore, in the presence of population substructure, statistical methods for association analysis with population-based samples are often similar to those for family-based samples. The presence of population substructure generates correlations between population-based samples, and the magnitude of the correlation can be substantial for phenotypes with a large polygenic effect. For instance, around 30% of the phenotypic variance of height is captured by the genetic relationship matrix (GRM)  and the However, in spite of such improvement, there is no integrated toolset for an analysis of large-scale family-based samples, and statistical analysis has often suffered from computational intensity. In this paper, we introduce a Workbench for Integrated Superfast Analyses for Related Data (WISARD), which is thoroughly optimized for analysis in a multi-core system and has comprehensive features for various analyses. Furthermore we propose two novel methods for rare variant association analysis with related samples, cFARVAT and famVT. WISARD features three major functionalities: data management, quality control (QC), and association analysis. WISARD provides various tasks for large-scale genetic data management such as retrieval, conversion, split and merging of datasets with various formats. For extended families, the family-based imputation performed by WISARD is useful for handling missing genotypes. Second, genotype quality for each variant or each subject can be evaluated using several statistics, and samples and variants can be filtered based on quality scores. Third, WISARD provides useful functions for association analyses ranging from heritability estimations to joint association analysis with multiple genotypes and phenotypes. With the integration of R, WISARD enables longitudinal data analysis, and the graphical summarization of analysis results. A list of tasks supported by WISARD with family-based samples is shown in Fig. To demonstrate the performance and practicality of WISARD, we compared its performance with existing toolsets by using genetic analysis workshop (GAW) 18 dataset, and statistical powers of two new methods for rare variant association analysis were evaluated with GAW17 dataset. Moreover, we also analyzed the Boston Early Onset Chronic Obstructive Pulmonary Disease (EOCOPD) dataset. We found that WISARD outperforms existing toolsets. These results illustrate the practical value of WISARD, and how it can strengthen the analytical power of analysis of large-scale genetic datasets for newcomers.WISARD has many functions related to data management and can simply conduct retrieval, conversion, splitting and merging of a large-scale genetic data with various formats. At the same time, we provide a simple method to impute missing genotypes for typed variants based on the familial relationship and calculate the expected genotypes for untyped subjects. For instance, if the phenotypes of any untyped subjects in each family are available, their genotypes can be imputed from their relatives’ genotypes and then imputed genotypes can be utilized for genetic association analysis. More than 60 functions for data managements are available, providing enormous convenience for analysis. Furthermore 15 different file formats including variant call format (VCF) can be easily converted to other file formats.Many functions for quality control and summarization measures for both chip-based and sequencing-based data are implemented. The quality control process can be accomplished by many QC measures such as Hardy-Weinberg equilibrium, minor allele frequency (MAF), minor allele count (MAC), genotype missing rate, or Mendelian error rate. In addition, many elementary statistics for genetic datasets are also provided, including Ts/Tv ratio, inbreeding coefficient and Fixation index.WISARD can perform various association analyses in multi-threaded manner. Statistical analyses implemented in WISARD are listed in Table http://eigen.tuxfamily.org), and LAPACK (http://www.netlib.org/lapack/). We developed our own C/C++ library for matrix operations, and its computational efficiency improves the computational time of WISARD. Implemented C/C++ library has four different property, compared to existing software; (1) row-wise matrix access, (2) efficient use of symmetric matrix, (3) application of Single-Instruction-Multiple-Data (SIMD), and (4) sweep-operator. Detailed explanation is provided in Additional file Distinctive feature of WISARD is an implementation of functions for various statistical analyses such as linear mixed effects model, quasi-likelihood approaches. However in spite of their statistical efficiency, parameter estimation for linear mixed models and quasi-likelihood approaches require many matrix related operation and it is computationally very intensive. For instance, genome-wide analyses often take more than a few months if sample size is a few thousands or more. Therefore multiple software have been proposed to improve the computational complexity, and they utilized existing C/C++ libraries for matrix calculation such as EIGEN  matrix. For gene-level tests, we consider only rare variants whose MAFs are less than 0.05. For GRM, IBS calculation and variant-level tests, we considered variants whose MAFs are larger than 0.05. Multithreaded analyses with 2, 4 and 8 threads were also performed , and rare variants of which MAFs were less than 0.05 were used to make a gene set file for gene-level tests. To adjust the population substructure, variance-covariance matrix was parameterized with GRM, and variants whose MAFs are larger than 0.05, were used to get GRM matrix.GAW 17 dataset were used to evaluate validity of proposed gene-level tests (famVT and cFARVAT-o). GAW17 is an artificial dataset that consists of a single set of odd-numbered chromosomes generated from 697 subjects from 1000 Genomes Projects, and 200 replicates of simulated phenotypes. We considered continuous phenotype Q1. We considered variants whose P values are less than several significance levels with 1000 permuted phenotypes. The statistical powers were estimated by using six predefined causal genes of 200 simulated phenotypes in GAW17. Their estimated statistical powers were compared with existing toolsets for family-based analyses: MONSTER [For evaluation of proposed methods, we estimated the empirical type 1 errors and statistical powers. The empirical type-1 errors were estimated by calculating proportions of non-causal genes whose  MONSTER , famSKAT MONSTER , and fam MONSTER , as wellP > 10− 8), and average sequencing depth (> 12). Relatedness of subjects was evaluated by comparing the kinship coefficient matrix (KCM) and GRM. Heterozygous/homozygous genotype ratio, Mendelian errors, the proportion of variants in dbSNP, and proportion of nonsynonymous variants were used to identify outliers. After subjects with missing phenotypes or covariates were filtered out and 254 subjects from 49 families were analyzed.We applied the proposed rare variant association statistics to whole-exome sequencing data from the Boston Early-onset COPD (EOCOPD) Study . Whole eFor gene-level rare variant association analyses, we assumed that variants with MAFs less than 0.05 were rare. We then annotated the rare variants to genes with UCSC Genome Browser (genome version GRCh37). We considered genes with at least two rare variants, and 4 or more MAC, and thus 8126 genes that consist of 88,373 rare variants were analyzed. We considered five COPD-related phenotypes: forced expiratory volume in 1 s. prebronchodilator (FEVPRE); forced vital capacity postbronchodilator (FVCPST); forced expiratory flow 25–75% prebronchodilator (DPRF2575); FEVPRE divided by FVCPRE (RATIO); DPRF2575 divided by FVCPRE (F2575RAT). Sex, age, height, and pack-years of cigarette smoking were utilized as covariates. For variance-covariance matrix, we applied both KCM and GRM according to the status of population substructure. Population substructure was not detected and KCM was utilized. The significance level α was set to 0.05, and Bonferroni correction was applied for multiple testing problem.Table Computational efficiency of WISARD was compared with GAW18 dataset. Figure Figure Last we compared results from GAW18 dataset by WISARD and compared toolsets, and check whether their results are same. Additional file We estimated type-1 errors and statistical powers of the proposed methods with GAW17 dataset and they were compared with other methods. Table P values for all phenotypes, which leads to a large number of false positives. Four methods implemented in FREGAT  consistently showed inflated pattern except for RATIO as well.Analyses results for 5 phenotypes of EOCOPD dataset are summarized in Fig. P values from famSKAT, famFLM, FREGAT and MLR, tend to be liberal and it is why they have many significant results. WISARD was a unique toolset that preserves the nominal significance level and identified one or more significant genes for all phenotypes. P values from MONSTER are generally stable, but it was not able to discover any significant gene except DPOF2575. Thus we focused on genes identified by WISARD due to its unacceptably high Q-Q trend of famSKAT, famFLM, famSKAT and FFBSKAT, and SKAT. Additional file Table WISARD enables automatic visualization of the results of statistical analysis, using commands in the R program. Furthermore, the web-based WISARD can annotate each marker and provide information about the disease susceptibility loci reported in the GWAS catalogue, the Human Gene Mutation Database (HGMD) and Online Mendelian inheritance in Man (OMIM). Figure Over the last decade, thousands of GWAS have been conducted to identify disease susceptibility loci, and many causal variants for phenotypes have been identified. However, missing heritability remains a challenge, and genetic analysis with next-generation sequencing technology has been expected to provide some clues. Even though various genetic analyses have been conducted to address these unsolved questions, most of them have not yet been answered, and development of an analysis toolset that enables thorough and comprehensive analysis is in demand.In this paper, we present a comprehensive workbench, WISARD, for the analysis of large-scale genetic data with family-based samples. WISARD provides various functions for quality control, data management and extensive statistical analyses for family-based samples, and it is also useful for population-based samples in the presence of a population substructure. The quality of each variant and subject can be evaluated with familial relationship, and statistical analyses can be conducted by allowing for phenotypic and genetic correlation between subjects. WISARD takes account of correlations between subjects, and our analysis with simulated data showed that WISARD outperforms similar existing toolsets with respect to computational time, which implies that the genome-wide analysis is achievable in a relatively short time. Furthermore, we proposed two novel methods for rare variant association analyses with related samples, and found that it achieves reasonable statistical power and preserves the nominal significance levels. Moreover, application of the proposed methods to EOCOPD dataset successfully identified significant genes, and thus these results illustrate its practical value.Recent improvements in genotyping technology enable the identification of rare variants for common diseases, and large families have been expected to play a key role for rare variant association analysis. However, in spite of the various advantages of family-based designs, their statistical analysis has often been complicated because of relatedness between family members. WISARD provides comprehensive functions for various genetic analyses with large families, and it enables researchers’ efficient large-scale genetic analysis.Additional file 1:Table S1. Accuracy of WISARD’s implementation. GAW18 dataset was analyzed with WISARD and existing toolset. Then P-values from WISARD and existing toolsets were compared, and averages of their differences were obtained. Regression and Fisher’s exact test from WISARD were compared with results from R. Table S2. List of significant genes from statistics implemented in WISARD. famVT and cFARVAT-o are newly proposed methods. . Figure S1. Multithreading efficiency of WISARD analyses with varying number of threads. Acceleration folds of the nine analyses with (A) 2 threads, (B) 4 threads, and (C) 8 threads were obtained. X and Y axes respectively represent chromosomes of GAW18 dataset and acceleration folds compared to the single-thread execution time. Solid lines represent observed acceleration folds of nine different analyses, and red dashed line represents upper limit of speedup with given number of threads. Regression and Fisher tests by WISARD were compared with results by R. (DOCX 138 kb)Supplementary Text.


Pisum sativum cultivars based on 36 manually derived root traits. Through combining random forest and support vector machine models, machine learning algorithms were successfully used for unbiased identification of most distinguishing root traits and subsequent pairwise cultivar differentiation. Up to 86% of pea cultivar pairs could be distinguished based on top five important root traits (Timp5) – Timp5 differed widely between cultivar pairs. Selecting top important root traits (Timp) provided a significant improved classification compared to using all available traits or randomly selected trait sets. The most frequent Timp of mature pea cultivars was total surface area of lateral roots originating from tap root segments at 0–5 cm depth. The high classification rate implies that culturing did not lead to a major loss of variability in root system architecture in the studied pea cultivars. Our results illustrate the potential of machine learning approaches for unbiased (root) trait selection and cultivar classification based on rather small, complex phenotypic data sets derived from pot experiments. Powerful statistical approaches are essential to make use of the increasing amount of (root) phenotyping information, integrating the complex trait sets describing crop cultivars.Phenotyping local crop cultivars is becoming more and more important, as they are an important genetic source for breeding – especially in regard to inherent root system architectures. Machine learning algorithms are promising tools to assist in the analysis of complex data sets; novel approaches are need to apply them on root phenotyping data of mature plants. A greenhouse experiment was conducted in large, sand-filled columns to differentiate 16 European  A policy report of the European Union noted recently that protein crops, e.g., bean, lentil, lupine, pea, and soya, are currently grown on 1.8% of arable land in the EU only, compared with 4.7% in 1961, and about 8% in Australia and Canada, 14.5% in North America, and 25.5% in South America . This isPlant phenotyping intends measuring complex traits related to growth, yield, and adaptation to stress at different macroscopically scales of plant organization . ExampleEspecially the modification of root system architecture (RSA) could contribute to improvements of desirable agronomic traits such as yield, drought tolerance, and resistance to nutrient deficiencies; thus, RSA was described key to a second green revolution improving resource use efficiency of crops . For exain situ or under more realistic growth conditions, which mostly rely on manual, destructive methods .Comparing the efficiency of ML techniques conducted by RF and SVMs with a univariate permutation test, only 46.6% (Bonferroni-corrected) and 47.5% (fdr-corrected) of HACCs (based on Timp5) have significantly different root traits. Significantly different root traits in univariate permutation tests can be found in (supplementary) figures . In contrast, cultivars ps3.Estonia vs. ps9.Norway are classified with high accuracy (90.9%), however, without any significant root traits identified by univariate permutation test . There are (visibly) no significant different root traits between cultivars ps1.Estonia vs. ps14.Portugal and they are not accurately classified either – accuracy 69.2% . The most significantly different root traits  can be different from the most important root traits (ranked by RF) in each pairwise classification, e.g., Timp5 in the pair ps3.Estonia vs. ps9.Norway are tapdw7.5, tapRDW, rootdw, latn5, and latRDW while the rank order based on p values from permutation test changed to latn5, tapdw7.5, tapRDW, latRDW, and rootdw. No tied importance values of Timp5, indicating higher ranking root traits are more important in RF, have been found.Three examples are given to visualize the mean difference of root traits in pairwise comparisons. There are significant different root traits between ps1.Estonia vs. ps8.Latvia and the cultivars are also classified with high accuracy  genomics/proteomics . To the So far, most of the techniques developed for RSA phenotyping involve the of use seedlings . Althougp values from univariate permutation test, i.e., the most significantly different root traits differ with the most important root traits identified by pairwise classification. This might be due to that SVMs classification concerns more about parameters (root traits) involved in the SVMs models while traditional multi/univariate analyses focus more on differences of specific root traits between two groups  were not able to increase the overall accuracy, which confirmed the necessity of root traits selection through RF in cultivar differentiation. This finding is in accordance with previous ML approaches in other scientific fields . The impeedlings . Miguel Completely intact root systems can hardly be collected by destructive harvesting methods, especially of mature plants with deeper root systems grown in the field; similar, lateral root traits are likely more affected by destructive sampling, e.g., by root tip shedding, than tap root traits. RSA information retrieved from top soil layers is thus likely more accurate . InteresRoot system depth and average radius were previously identified as frequently top-ranked root traits in linear SVMs classification to distinguish different rice genotypes . In our Correlation among traits is generally considered as an indication of their redundancy for classification. However, they may still provide complementary information and an otherwise inconclusive variable can provide a significant performance in combination with others . The corThe accurate classification of 86% (103 of 120) genotype pairs of pea indicated that most of the studied cultivars could be well differentiated by using a few most distinguishing root traits, as selected through RF. This implies that past culturing did not lead to a major loss of RSA variability of the studied European pea cultivars. Breeders are envisioned to work more effectively in future breeding programs by knowing distinguishing (pea) root traits in advance . In specJZ, GB, and BR conceived and planned the experiment; JZ and BR performed the experiment; JZ analyzed the data; all authors jointly wrote the manuscript.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.


Increasing our understanding of the genetic architecture of complex traits, through analyses of genotype-phenotype associations and of the genes/polymorphisms accounting for trait variation, is crucial, to improve the integration of molecular markers into forest tree breeding. In this study, two full-sib families and one breeding population of maritime pine were used to identify quantitative trait loci (QTLs) for height growth and stem straightness, through linkage analysis (LA) and linkage disequilibrium (LD) mapping approaches.n = 197 and n = 477). These populations were assessed for height growth or stem straightness and genotyped for 248 and 217 markers, respectively. The population used for LD mapping consisted of 661 founders of the first and second generations of the breeding program. This population was phenotyped for the same traits and genotyped for 2,498 single-nucleotide polymorphism (SNP) markers corresponding to 1,652 gene loci. The gene-based reference genetic map of maritime pine was used to localize and compare the QTLs detected by the two approaches, for both traits. LA identified three QTLs for stem straightness and two QTLs for height growth. The LD study yielded seven significant associations (P ≤ 0.001): four for stem straightness and three for height growth. No colocalisation was found between QTLs identified by LA and SNPs detected by LD mapping for the same trait.The populations used for LA consisted of two unrelated three-generation full-sib families (This study provides the first comparison of LA and LD mapping approaches in maritime pine, highlighting the complementary nature of these two approaches for deciphering the genetic architecture of two mandatory traits of the breeding program. Populus hybrids [Picea mariana [The genetic variation of key traits used as selection criteria in forest tree breeding programs is estimated by quantitative genetics approaches, in progeny testing and/or common garden experiments . In quan hybrids  and for  mariana .Pinus pinaster) is an important forest tree species in the southwestern part of the Mediterranean basin. It is grown in intensive plantations over large areas of France, Spain and Portugal. The optimization of silvicultural practices and genetic improvements through breeding, selection and testing have had a significant impact on the productivity and quality of plantations established during the last 30 years. A breeding program was first set up for this species in the 1960s, to improve biomass production and stem straightness. The breeding program was initially based on the "Landes" provenance of the Aquitaine region in southern France [Maritime pine  have recently been developed for maritime pine , 24. GivThe objective of this study was to determine the genetic architecture of height growth and stem straightness, two major traits of the maritime pine breeding program, by LA and LD mapping approaches. LA was applied to two mapping progenies, one specifically designed to maximize the segregation of height growth, and the other designed to maximize the segregation of stem straightness. The LD approach was applied to a set of trees from breeding populations in which these two traits are used as selection criteria. The QTL results for the LA and LD mapping approaches were compared, to check for consistency and complementarity.Three-generation inbred mapping population–The first mapping population considered for QTL mapping was a three-generation inbred progeny (F2 population) obtained by the self-pollination of a “Landes x Corsica” hybrid. This accession, called H12, originated from the control cross between genotypes L146  and C10 . This cross was specially designed for dissection of the genetic architecture of stem straightness (STR). STR is the characteristic differing most markedly between the Landes and Corsican ecotypes of maritime pine: Corsican ecotype being straighter than Landes ecotype [ ecotype . An F2 p ecotype .et al. [2 tests to test if the allelic segregation of each locus fitted the expected 1:2:1 Mendelian segregation ratio.Two medium-throughput genotyping technologies were used to genotype the F2 population: the Goldengate VeraCode  and MassARRAY iPLEX  systems. The design of the SNP arrays is detailed in et al. . JoinMapet al.  was usedThree-generation outbred mapping population–The second QTL mapping population was a three-generation outbred progeny (G2 population), specifically designed for elucidation of the genetic architecture of height growth. The four grandparents were selected from the base population (G0 trees). These trees were subjected to progeny testing between 1970 and 1980 and were classified on the basis of breeding value as 'Vigor +' for vigorous trees or 'Vigor–' for less vigorous trees. Two G1 trees obtained from two different biparental 'Vigor +' x 'Vigor–' crosses were then crossed, and the resulting G2 seedlings were planted in autumn 1982, in Malente, France. The seedlings were planted in rows, with 1.1 m between trees within the same row and 4 m between rows. The G2 trees were felled in March 1997, when they were 15 years old, for retrospective height increment analysis. The structure of maritime pine, with a tier of branches at the top of each annual shoot, facilitates retrospective measurement of the length of successive annual shoots along the trunk. We were able to measure 12 annual height increments (between 1985 and 1997) precisely, on 197 trees. STR was not assessed, because this trait did not segregate in this family. Genotyping and linkage analysis were performed in previous studies of this population [pulation , 35. Briet al. [The population used for LD mapping was described in a previous study by Isik et al. . Brieflyet al.  have alret al. . We remoet al. ) for theqtl [stepwiseqtl with the imputation method and a maximum of five QTLs for forward selection. In total, 1000 permutations were performed for each trait with the function scantwo, to estimate the type I error rate at genome level. A threshold error rate of 5% was used to define significant QTLs. The 95% Bayesian credible interval for each QTL was calculated using the function bayesint with default parameter [s = ABμ − AAμ. For the F2 mapping population the additive (a) and dominance (d) effects of a QTL were calculated as follows: a = (BBμ − AAμ)/2 and d = ABμ − (BBμ − AAμ)/2.We used the multiple QTL mapping procedure implemented in the R package qtl , 38 for arameter . The effet al. [A) was calculated from pedigree information for the 661 individuals. The 184 G0 trees were considered to be unrelated. A complete pedigree (mother and father known) was available for 355 (74.4%) of the 477 G1 trees, whereas only the mother was known for the remaining trees. In addition to matrix A, we also calculated the realized coefficients of relationship (assembled in matrix G) between the 661 individuals from the available marker data . Matrix G was constructed as follows [For the LD approach, the putative population structure had already been analyzed by Plomion et al.  for the  follows :G=(M−P)M is a matrix of dimensions n  × p (number of markers) containing the three classes of marker alleles for each individual. One homozygote is coded– 1, the heterozygote is coded 0, and the other homozygote as coded 1. P is a matrix of dimensions n × p containing marker allele frequencies calculated as follows: 2(pi− 0.5), where pi is the observed allele frequency for marker i.Where GenABEL [polygenic function was used in combination with mmscore (FASTA) or grammar (GRAMMAR-gamma) in GenABEL for the analysis. Given the absence of population stratification [A and G matrices were compared, to determine whether family relationships led to p-value inflation. The genomic inflation factor (λ) was calculated [10 was used to visualize and identify marker-trait associations exceeding a threshold of 3 (p < 0.001), which were considered to be significant. Significance levels were adjusted for multiple testing by the false discovery rate method [Associations between SNPs and the two traits (HT and STR) were analyzed with the R package GenABEL . In addiGenABEL ) and theGenABEL ). The pofication , only refication . For botlculated . The tese method , to obtasynbreed [BLR [et al. [In addition to classical single marker-trait association studies, we estimated marker effects with two different genomic prediction models: ridge regression best linear unbiased prediction  and Bayesian least absolute shrinkage and selection operator (B-LASSO). The R packages synbreed  and BLR eed [BLR  were use [et al. . In totaLPmerge [et al. [A composite linkage map of maritime pine was established by merging 14 component maps obtained by genotyping seven mapping progenies, including the three-generation inbred (F2) and outbred (G2) mapping populations studied here. This reference map was produced with the R package LPmerge , by de M [et al. . For the [et al.  that wer [et al. . For the [et al. .p < 0.01) accounted for 3.6% of the mapped markers. These SNPs with segregation distortion were located in four different LGs and represent four different regions  and 76 (iPLEX) polymorphic SNPs were available for construction of the linkage map of the hybrid parent of the F2 population. The final map included 248 markers: 200 VeraCode and 48 iPLEX SNPs. Information for each marker and its location on the genetic maps is provided in  regions . The higF2 population–As expected from previous studies, stem straightness had a high coefficient of variation  and the realized kinship coefficients obtained with genomic information (matrix G) highlighted differences between the two estimates  values on Q-Q plots  than for a model including kinship coefficients from matrix G (λ = 0.5 for HT and λ = 0.51 for STR). Given the low level of bias observed with the FASTA method and matrix G, we can conclude that population structure was effectively controlled by the family relatedness captured in matrix G. We therefore used this model for the detection of marker-trait associations.Relatedness between individuals is known to bias -Q plots . As expea method , with a p<0.001, p-value profiles (in terms of–log10(p)) for all tested SNPs for both traits were plotted on Manhattan plots  yielded significant results p<0.001, . The p-van plots . HT was pinaster . After cpinaster . Estimatpinaster . Similarpinaster .Most traits of interest in forest tree breeding, including height growth and wood properties, are quantitative traits with complex genetic architectures and low to medium heritabilities . The ideP. pinaster genome as associated with the traits considered: three regions for stem straightness and two for annual height growth increment. Interestingly, the favorable allele of one of the three detected QTLs for stem straightness comes from the Landes grand-parent. Consistent with this observation made at the molecular level, it should be mentioned that stem straightness is genetically variable within the Landes provenance and heritable, resulting in positive genetic gains for the first breeding generations [et al. [P. radiata including about 500 genotypes. Conversely, in Pseudotsuga menziesii, two QTLs were detected for height growth, explaining 16.1 and 17.7% of the phenotypic variation in multiple small (n = 10) full-sib families [P. elliottii × P. caribaea hybrid has addressed the genetic architecture of stem straightness in forest trees [LA identified five regions of the erations . The pererations , 56–60. families , 62. Givfamilies , water-ufamilies , 65, woofamilies , and trafamilies , but no st trees . Howeverst trees .p-value inflation, for both traits. Similar results were reported for wood quality and growth traits in Eucalyptus globulus [Cryptomeria japonica [Q < 0.1) with stem straightness. In previous studies of growth traits in conifer, a small number of associations were highlighted through the use of candidate genes [et al. [et al. [P. taeda, Cumbie et al. [et al. [In addition to QTL mapping in dedicated full-sib families, we also performed genome-wide association with related genotypes from the first two generations of the maritime pine breeding population. Population stratification or relatedness can result in the detection of spurious marker-trait associations , 69. Howglobulus , 71 and japonica . Only sete genes , 16, 73 te genes , 74. In  [et al.  tested 1 [et al. , 75, 76, [et al. –79. It h [et al. , 73, 80.e et al.  found on [et al. , 81, 82, [et al. –85, and  [et al. , was sliet al. [P. radiata [As pointed out by Grattapaglia et al. , tree gret al. , 52, 87. radiata , only tw radiata  and in p radiata  reportedi.e. a larger number of genes but a smaller number of SNPs per gene [Two different strategies have been used for LD mapping in conifers: i) early studies focused on a selected set of candidate genes with the depth of SNP coverage clearly favored, resulting in the discovery of significant associations , 82, 88,per gene , 72, 86.Current technologies have made it possible to capture and sequence the coding fraction of any conifer genome . Such apS1 Data(XLSX)Click here for additional data file.S1 Fig(PDF)Click here for additional data file.S2 Fig(PDF)Click here for additional data file.S3 FigP. pinaster and the F2 map (panel A), G2 male map (panel B) and G2 female map (panel C).Comparison between the composite map of  The composite map is represented in blue and the parental maps of the F2 and G2 populations in green. The numbers at the top of each linkage group indicate the number of markers common to different maps for each linkage group (LG).(PDF)Click here for additional data file.S4 FigThe number of markers per linkage group is indicated beneath each linkage group.(PDF)Click here for additional data file.S5 FigHorizontal dotted lines represent the threshold above which SNPs are significantly distorted (p < 0.01).(PDF)Click here for additional data file.S6 FigThe genotype of the grandparents (Landes or Corsican) is indicated below the corresponding class.(PDF)Click here for additional data file.S7 Fig(PDF)Click here for additional data file.S8 FigPinus pinaster composite map.The absolute values of markers are plotted on the 12 linkage groups of the (PDF)Click here for additional data file.S1 Note(PDF)Click here for additional data file.S2 Note(PDF)Click here for additional data file.S1 TablePinus pinaster composite map when available. , the associated linkage group, and the position on the F2 map and on the (XLSX)Click here for additional data file.S2 TableFor the F2 mapping population: stem straightness (STR) and height growth (HT) and for the G2 mapping population: annual height increment (HI) from 1985 to 1997.(PDF)Click here for additional data file.S3 Tablep < 0.05) are listed.Only significant correlations ((PDF)Click here for additional data file.S4 Table(PDF)Click here for additional data file.S5 Table(XLSX)Click here for additional data file.S6 Table(PDF)Click here for additional data file.


Litopenaeus vannamei, the deterioration of water quality from intensive culture systems and environmental pollution is a common but troublesome problem in the cultivation of this species. The toxicities that result from deteriorating water quality, such as that from ammonia stress, have lethal effects on juvenile shrimp and can increase their susceptibility to pathogens. The toxicity of ammonia plays an important role in the frequently high mortality during the early stage on shrimp farms. However, little information is available regarding the genetic parameters of the ammonia tolerance of juveniles in the early stage, but this information is necessary to understand the potential for the genetic improvement of this trait. Considering the euryhalinity of L. vannamei and the fact that low salinity can increase the toxicity of ammonia stress, we estimated the heritability of ammonia tolerance in juveniles in 30‰  and 5‰ (low) salinity in this study using the survival time (ST) at individual level and the survival status at the half-lethal time (SS50) at the family level. In the normal and low salinity conditions and for the merged data, the heritability estimates of the ST  and SS50  were all significantly greater than zero, which indicates that the ammonia-tolerance of shrimp can be greatly improved. So it might provide an alternative method to reduce mortality, help to enhance resistance to pathogens and reduce the occurrence of infectious diseases. The significant positive genetic correlation between ST and body length suggested that ammonia is more toxic to shrimp in the early stage. The medium-strength genetic correlations of the ST and SS50 between the two environments  indicate a strong genotype-by-environment (G×E) interaction for ammonia tolerance between the different salinity levels. Therefore, salinity-specific breeding programs for ammonia tolerance in shrimp should be purposefully implemented.Regarding the practical farming of  Litopenaeus vannamei is an important worldwide aquaculture species [L. vannamei is becoming the most popular cultured species in the world and generally uses semi-intensive and intensive culture modes [L. vannamei in farms [The Pacific white shrimp  species , 2. Due re modes . Howeverin farms –6.L. vannamei, and we found that the majority of the key genes involved in the response to ammonia stress are potentially involved in immune defense function [High concentrations of ammonia are the most common and greatest toxic factor in the deteriorating water quality, which does serious harm to crustaceans, mollusks and fish , 7–9. Mufunction .L. vannamei is cultured at different salinity levels due to its euryhalinity. However, many studies have revealed that low salinity can increase the toxicity of ammonia to the shrimp [L. vannamei.Because the aquaculture environment is becoming worse, and ammonia stress causes grievous harm to shrimp aquaculture, it is necessary to seek possibilities for culturing shrimp that efficiently tolerate ammonia stress, which might represent alternative methods for reducing mortality and infectious diseases. Selective breeding programs are an effective method to gain genetic improvements and have been utilized to improve performance in terms of growth, survival, reproduction, resistances to viruses and adverse environmental stress in shrimp –27. Knowe shrimp , 41–43, e shrimp . If the e shrimp . Based oL. vannamei occurs in seawater with normal salinity and freshwater with low salinity, in the present study, we estimated the heritability of ammonia tolerance in L. vannamei juveniles in the early stage  at two different salinity levels (30‰ and 5‰) and estimated the genetic correlations between body length and ammonia tolerance within the two environments to verify the relationship between ammonia tolerance and ontogenetic development. Additionally, in the present study, we also detected the G×E interaction between the two salinity levels. Our goal was to understand the potential for the genetic improvement of ammonia tolerance in L. vannamei in the early stage in normal and low salinity because such improvement might represent an alternative method for reducing the mortality of L. vannamei and will also be helpful for enhancing resistance to pathogen and reducing infectious diseases.Considering that the majority of the production of 0) via an incomplete diallel cross experiment. The G0 was constructed with 130 full-sib families using artificial insemination with 114 males and 108 females in 2011 and included 69 half-sib families. Individual estimated breeding values (EBV) for body weight and the family EBV for survival rate from the test were weighted into the selection index and used to rank the breeding candidates as described by Luan et al. [The program was conducted at a Hebei Xinhai Aquatic Biological Technology Co., Ltd. Facility located in the Huanghua , Hebei province, China. The founders were collected from seven improved commercial strains that were introduced from different companies in the United States and Singapore. The virus-free individuals from the seven strains were tagged using numbered rings that were placed on one ocular peduncle and used to produce the base population . The half-sib families were produced by two females mating with one male or two males mating with one female, and all of the families were produced by artificial insemination. The processes of family construction, hatching, and larvae rearing were described in detail in our previous study [3) that were separately fixed in two large ponds (60 m3) for separate rearing at the same density for each family. The seawater in one of the ponds was diluted with filtered fresh water from 30‰ to 5‰, which lasted for approximately three weeks. However, the seawater in the other pond was maintained at 30‰. Aside from the salinity, the other management conditions were maintained identically between the two ponds.The present experimental shrimp were from 91 full-sib families  of the fifth generation .To acquire the individual survival times for the estimations of the genetic parameters, we performed a pre-experiment to obtain the proper concentrations of ammonia in the 30‰  and 5‰ (low) salinity conditions. Based on the results of our pre-experiment, the concentrations of ammonia required to kill all of the shrimp after 72-h challenges were 32 mg/L and 18 mg/L in the normal and low salinity conditions, respectively. The ammonia solution was prepared using NH-1, the pH ranged from 8.00 to 8.06, and the temperature was approximately 27±0.5℃. The tanks were cleaned daily by suction to remove feces, and the water that was removed by suction was replaced with clean water with the same concentration of ammonia. The dead shrimp were collected and recorded every hour during the experiment. When the shrimp sank, stopped movement and lost responsiveness to external stimulation, they were defined as dead and removed from the tanks with a scoop net. The family ID and death time of each dead shrimp were recorded to quantify the individual survival times (STs) and survival statuses  at the half lethal time (SS50). Because the animals were too small that body weight measures would introduce large measurement errors due to the different degrees of excess water on the shrimp, we selected body length (BL) as an index of growth. Therefore, the individual BLs were also recorded when the shrimp died, which enabled us to investigate the relationship between ammonia tolerance and growth stage.The juveniles were too small (the average body weight of 0.50 g and average length of 3.73 cm) that they could not be tagged with visible implant elastomer (VIE) when the experiment began. Therefore, forty individuals were randomly selected from each net cage (each family) and transferred to separate 100 L tanks filled with 35 L 30‰ or 5‰ salinity water for ammonia stress tests under the same conditions of density and environmental management. After a period of three days for a temporary rearing, the ammonia acute stress experiment was initiated. During the experiment, the shrimp were given feedstuff twice per day, and the management environments were maintained identically between the tanks. The dissolved oxygen level was no less than 6 mg L0 to G5 was used in the following analysis to account for the genetic relationships among the individuals. The common environmental effect was omitted in this analysis due to the separate rearing of the families during the experiment. The ammonia tolerance was defined by the ST  and the SS50 (family level) in the present study. The variance components and heritabilities were calculated for ST and SS50 using the data from the normal and low salinity conditions and the merged data from the two conditions. Therefore, these variables are denoted as the STH/SS50H, STL/SS50L, and STM/SS50M for the normal salinity, low salinity, and merged data, respectively.An analysis of the descriptive statistics was conducted using the MEANS procedure . The comP < 0.01) and was fit as a covariate in the model. The fitted model was as follows:yijk is the observed ST of the kth individual, μ is the overall mean, Salti is the fixed effect of the ith salt level (two levels) and it was not contained when estimated for the separate environments, BLj is the covariate of the jth body length, b is the regression coefficient, ak is the random additive genetic effect of the kth individual, and eijk is the random residual effect associated with observation ijk. The phenotypic variance (h2) was calculated as the ratio between the genetic variance and the total phenotypic variance  of the jth shrimp, λijkl is the underlying liability of yijkl, which was assumed to be a cumulative standard normal distribution, μ is the overall mean, BLj is the covariate of the jth body length, b is the regression coefficient, and Sirek and Daml are the additive genetic effects of the kth sire and the lth dam, Sire or Dam ~ and a sire–dam model were used in this study to estimate the heritability of the SS follows :λijkl=μd Lerner .P < 0.01). The fitted model was as follows:yijk is the observed BL of the kth individual, μ is the overall mean, Salti is the fixed effect of the ith salt level (two levels), and it was not contained when estimated for the separate environments, b is the regression coefficient, Agej is the covariate of the jth individual’s age when the experiment ended, ak is the random additive genetic effect of the kth individual, and eijk is the random residual effect associated with observation ijk. The phenotypic variance  interactions were estimated based on the genetic correlations of these traits between environments. The phenotypic (rp) and genetic (rg) correlations of the ST and BL values between the two salinity environments were estimated using the bivariate animal model described above. Because one animal can only be tested in one environment, there were no environmental  correlations between the homologous traits, and the environmental covariance was set to be zero in the bivariate analysis [g between the two salinity environments for the SS50 was calculated with the estimated breeding values. A G×E interaction was measured as the difference between the genetic correlation and 1. Thus, genetic correlations closer to 1 indicated smaller the G×E interactions. The genetic correlations between the environments were used to measure the degree of re-ranking due to G×E interactions. Additionally, the rp and rg values between the STs and BLs within environments were estimated using the bivariate animal model, which is the extension of g, the standard errors (SEs) and Z0.025, which is 1.96 . If 1 or 0 was contained within the CI, the difference between the genetic correlation and 1 or 0 was not significant.The homologous ST, SSanalysis . Because50), ST, and BL are given in th percentiles, median percentiles, 75th percentiles, minima, maxima, and outliers of the SS50 and average ST values of each family are displayed in 50 values under acute ammonia stress in the normal and low salinity conditions varied substantially between the families, but the variance was higher following exposure to acute ammonia stress in the normal salinity condition , which might have been due to cannibalism during the experimental process. The numbers of samples, simple means, minima, maxima, standard deviations and the coefficients of variation for the survival rate of each family at the half-lethal time . According to the classification reported by Cardellino and Rovira [H (0.784±0.070) was very high, the heritabilities of STL (0.575±0.068) and STM (0.517±0.058) were high, and the heritabilities of SS50H (0.402±0.061), SS50L (0.216±0.050) and SS50M (0.264±0.050) were medium. The heritability estimate of STH was significantly greater than those of STL and STM (P<0.05), but there was no significant difference between STL and STM (P>0.05). The heritability estimates of ST were all significantly greater than that of SS50 (P < 0.01). The heritability of SS50H was significantly greater than that of SS50L (P < 0.05), but there was no significant difference between SS50H and SS50M (P>0.05), and there was also no significant difference between SS50L and SS50M (P>0.05). The heritability estimates of BLH (0.346±0.052), BLL (0.386±0.054), and BLM (0.291±0.042) were all medium based on the above classification, and the values for BLH and BLL were higher than that of BLM but were not significantly different from each other (P > 0.05).Estimates of the variance components and heritability of ST, SSd Rovira   . These results indicate a strong re-ranking effect for ammonia tolerance and growth between the two environments.To understand the effects of the G×E interactions on the three traits, particularly the trait of ammonia tolerance, genetic correlations were estimated between the two environments, and the results are displayed in 7±0.098) . The genp = 0.416±0.017; rg = 0.779±0.037) than in the low salinity condition .The phenotypic and genetic correlations within the two environments for ST and BL are given in L. vannamei individuals with the an average body weight of 3.3 g were dead following exposure to 63 mg/L ammonia for 218 h  [L. vannamei with an average body length of 5.0 cm. Although high temperatures and low salinities can increase the toxicity of ammonia stress, the shorter survival time at lower concentrations of ammonia in our study also suggests that younger shrimp are more sensitive to ammonia toxicity. Additionally, we also observed that the lower salinity increased the sensitivity of shrimp to the toxic effects of ammonia. The lethal concentration of ammonia at 72 h in the normal salinity condition (30‰) was 32 mg/L, which is significantly higher than the 18 mg/L value observed in the low salinity condition (5‰). The average survival time of all the individuals under acute ammonia stress in the normal salinity condition was 36.61 h, which is substantially greater than the 24.80 h observed in the low salinity condition  for juveniles in the early stage in normal salinity 30‰) and low salinity (5‰) conditions and the G×E interaction between the two environments to explore the possibility of reducing the mortality of shrimp by selection. Many studies have reported that ammonia stress is more toxic to shrimp in early stages ‰ and low–55, and y = 30‰) . Sun et y = 30‰)  reportedondition , and theondition . The cumondition . The abo50 were 0.154±0.045 and 0.148±0.040, respectively, and these values were not significantly different from each other. In the present study, we estimated the heritability of ammonia tolerance of shrimp with an average body weight of 0.5 g under ammonia stress in normal and low salinity conditions. The heritability estimates of ST  and SS50  under ammonia stress at the two salinity levels were all significantly greater than that reported in the previous study, which indicates that improving the ammonia tolerance of shrimp via selection performed at the early stage could be quite advantageous. Although we did our best to maintain the same environment between the tanks in the present study, the absent of common environmental effects because of the juveniles without communal rearing during the experiment may have contributed to the overestimation of the heritability estimates of ammonia tolerance, because the exclusion of common environmental effects can inflate heritability estimates [Va, i.e., the heritable phenotypic variation). Because shrimp in the early stage are more sensitive to the ammonia stress, the CGV is more easily released. Additional information about CGV can be found in the review by Paaby and Rockman that was published in Nature Reviews Genetics [A high heritability of a trait suggests that rapid and highly accurate genetic improvement can be obtained by selection. Although many studies have been performed to detect ammonia toxicity for aquatic organisms –18, verystimates . In our stimates , 60. TheGenetics , 63. Wit50, which is inconsistent with our previous results that indicated that the heritability estimates of ST and SS50 are basically the same [50 compared with ST might have resulted from the smaller sample size at the family level (91) for SS50 compared to the individual level (above 3500) for ST. Additionally, the heritability estimate of ammonia tolerance at normal salinity was significantly greater than that at low salinity in the present study, which might have resulted from the incomplete release of the additive genetic variance among the individuals and families because the time to death was shorter and more intensive in the low salinity condition; after 20 h of ammonia stress, the cumulative mortality in the low salinity condition was 50%, whereas this value was only 24% in the normal salinity condition . Fortunately, the ammonia-tolerant shrimp can be improved largely due to high heritability, which might provide an alternative method to reduce mortality, aid the enhancement of resistance to pathogens, and reduce infectious diseases.In the present study, the heritability estimates of ST were all significantly higher than those for SSthe same . The sigondition . The herL. vannamei have primarily focused on harvest body weight [L. vanname [Growth is a very important trait in selective breeding programs because it is highly correlated with economic return. The estimates of the heritability of growth in y weight , 64, and vanname , 24. How vanname , 38. TheG×E interactions refer to genotypes that respond differently in different environments and thus result in different performances –67. GeneL. vannamei juveniles, which suggests that rapid genetic gains in terms of ammonia tolerance could be obtained by increasing the selection intensity in our selective breeding program. However, the heritability of ammonia tolerance in the normal salinity condition was significantly higher than that in the low salinity condition. Additionally, a strong G×E interaction for ammonia tolerance was detected between the normal salinity and low salinity conditions, which suggests that salinity-specific breeding programs for ammonia tolerance in shrimp should be purposefully implemented and that the normal salinity environment is a better choice based on the faster rate of genetic improvement due to higher heritability. Additionally, the significantly and strong positive correction between ammonia tolerance and body size suggests that ammonia-tolerant shrimp should be selected in the early stage.Overall in this study, we first reported the heritability of ammonia tolerance in juveniles in the early stage and the G×E interaction between the normal and low salinity to explore the potential for improving ammonia tolerance in shrimp by selection. The results revealed that the heritability of ammonia tolerance was medium to high in S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.