Affective disorders are highly heritable, but few genetic risk variants have been consistently replicated in molecular genetic association studies. The common method of defining psychiatric phenotypes in molecular genetic research is either a summation of symptom scores or binary threshold score representing the risk of diagnosis. Psychometric latent variable methods can improve the precision of psychiatric phenotypes, especially when the data structure is not straightforward. Using data from the British 1946 birth cohort, we compared summary scores with psychometric modeling based on the General Health Questionnaire (GHQ-28) scale for affective symptoms in an association analysis of 27 candidate genes (249 single-nucleotide polymorphisms (SNPs)). The psychometric method utilized a bi-factor model that partitioned the phenotype variances into five orthogonal latent variable factors, in accordance with the multidimensional data structure of the GHQ-28 involving somatic, social, anxiety and depression domains. Results showed that, compared with the summation approach, the affective symptoms defined by the bi-factor psychometric model had a higher number of associated SNPs of larger effect sizes. These results suggest that psychometrically defined mental health phenotypes can reflect the dimensions of complex phenotypes better than summation scores, and therefore offer a useful approach in genetic association investigations. Some work has previously been done to develop statistical tools for the detection of genetic associations in both candidate gene and GWAS.15 For example, van der Sluis et al.14 have developed the TATES method that combines P-values based on correlations between components of multivariate phenotype. The TATES method has demonstrated higher statistical power compared with methods based on composite scores or multivariate analysis of variance.Classification of affective disorder is usually based on self-reported symptoms intended to capture psychopathological syndromes through questionnaire responses, clinical ratings and evaluations or by field-trained interviewers. Psychiatric phenotypes defined this way may be especially susceptible to variance introduced by testing methods or other potentially bias-inducing elements that are not primarily related to the disorder itself. Moreover, typical effect sizes of common genetic variants are so small that many existing molecular genetic studies lack sufficient statistical power to detect such small effects. In order to better understand the genetic signals of complex psychiatric phenotypes, there is an argument for adopting approaches that can improve the definition of these phenotyps.a priori hierarchical and multidimensional structure, with specific clusters of psychiatric signs or symptoms combined into a summation score  or the Center for Epidemiological Studies Depression Scale).18 The GHQ-28 is a widely used self-report questionnaire to measure psychosocial dysfunction and psychological distress, with the following four subscales: Somatic Symptoms, Anxiety/Insomnia, Social Dysfunction, and Severe Depression.20 Traditionally, a single measure of global affective symptoms is derived from item responses and a threshold/cutpoint used to define ‘caseness'. This approach based on summation of items adopts a unidimensional data structure assumption, instead of fully taking into account the dimensions represented by the four subscales. When greater complexity is present, a unidimensional assumption leads to an inaccurate measure of affective symptoms and can alter the strength of association with covariates such as genetic polymorphisms.21 Psychometric methods that use latent variable methods to model multidimensional data in intelligence and personality have been developed and are widely used elsewhere.24 More recently, these methods have been applied to the understanding of psychiatric phenotypes.28 However, they are yet to be widely applied to phenotypes for molecular genetic associations.30 Ignoring the complex dimension structure intrinsic to the psychiatric instrument can result in less clear phenotype definition, and may dilute the strength of true genetic association. As a result, it becomes increasingly more difficult to observe consistent association signals for common genetic variants that have small effect sizes.Many scaled instruments have 28 In the bi-factor model, both the global factor representing total affective symptoms, and the specific factors corresponding to the symptoms of GHQ-28 subdimensions were psychometrically defined using latent variable technique. The bi-factor model is advantageous because it facilitates more flexible phenotype definition in the presence of an a priori multidimensional data structure. Hence, we hypothesize that, compared with the sum score approach, the global psychometric latent factor of affective symptoms in the bi-factor model would show stronger genetic associations in terms of effect sizes. Similarly, for specific dimensions of affective symptoms, we hypothesize that the dimension-specific phenotypes defined with the bi-factor models would show stronger genetic associations' effect sizes as compared with the sum score measures for each of these dimensions.In the present study using data from a population-representative birth cohort sample, we tested associations between candidate genes previously implicated in affective disorders and alternative outcomes based on two statistical approaches to define affective symptoms measured with the GHQ-28—the traditional sum score approach and the psychometric approach incorporating dimensional aspect of a complex latent phenotype. Rather than seek to identify novel genetic associations, we aim to compare the strength of associations for known candidate genes and phenotypes using standard summation score approach and potentially more accurate psychometric method. The psychometrical approach utilized a latent variable bi-factor model specification.31 Both the GHQ-28 data and the blood sample were collected in 1999, when the cohort members were 53 years old. The cohort was shown to be representative of a UK population of the same age.32 The data collection received Multi-Centre Research Ethics Committee approval, and participants gave informed consent.The study sample was based on the Medical Research Council National Survey of Health and Development, also known as the British 1946 birth cohort, which originally consisted of 5362 survey members  born in 1 week in March 1946 in England, Scotland and Wales.n=3035) answered 28 questions, coded on a four-point Likert scale rating from ‘not at all' to ‘much more than usual' on whether they had recently experienced symptoms from four affective disorder domains, i.e. Anxiety/Insomnia , Somatic Symptoms , Social Dysfunction  and Depression . On the basis of the GHQ-28 responses, phenotype measures of affective symptoms were developed using the following: (1) a traditional sum score approach and (2) a psychometric latent variable approach.Affective symptoms were assessed with GHQ-28 when the survey members were aged 53 years. Participants  were extracted and purified from whole blood using the PuregeneDNA Isolation Kit  according to the manufacturer's protocol.33DNA samples  or Sequenom by Medical Research Council Epidemiology Unit, Cambridge, UK (http://www.sequenom.com/).34In total, 249 single-nucleotide polymorphisms (SNPs) covering 27 candidate genes were included in the present study. The candidate genes were selected on the basis of existing evidence for their associations with affective disorders and/or neurocognitive and psychological functions known to be impaired in affective disorders . Some SNThe integrity of genotyping was checked with the call rates, concordance of duplicates and minor allele frequency. The call rates for the genotyped SNPs were >90%, with >95% concordance between duplicate samples. Only SNPs with minor allele frequency>0.05 were included in the analysis.a priori basis, and as our sample was not large enough to allow a high level of power for detection of small effects, we compared effect sizes obtained using the two different phenotype definition approaches by measuring the percentage of phenotype variances explained by a SNP (R-squared).As the candidate genes were chosen on an We adopted an additive genetic model for the association analyses. For each genotype, 0, 1 or 2 was assigned for an individual with a homozygote with the major allele, heterozygote and homozygote for the minor allele, respectively.37 (See Appendix for further details.) For the psychometric analysis based on categorical variables, we used the WLSMV estimator with theta parameterization. Scores for each questionnaire item were modeled as ordered polytomous outcomes, through a probit regression link, to the corresponding latent phenotype variables. This is a graded response, two-parameter normal ogive model in item response theory terms.38The final analysis sample consisted of 1337 individuals with complete data. All analyses were performed in the statistical software package Mplus 7.11.X2-statistic and other commonly used model fit indices. As the X2-measure is highly sensitive to the sample size, this measure needs to be interpreted with caution.40 Other sample size-independent fit indices include the Root Mean Square Error of Approximation (RMSEA), the Tucker–Lewis Index (TLI) and the Comparative Fit Index (CFI).43 The TLI and CFI vary along a 0-to-1 continuum, and values greater than 0.90 and 0.95 typically reflect an acceptable and excellent fit to the data. RMSEA values of less than 0.05 and 0.08 reflect a close fit and a minimally acceptable fit to the data, respectively.In order to evaluate the goodness of fit of the psychometric phenotype model, we presented the post hoc power analysis for SNPs with the largest effect sizes using statistical simulations. Sample size was fixed at 1337, the same as in the analytic sample. Population parameters were also fixed to be equal to the sample data of the corresponding SNP and phenotype. For each power analysis, 1000 replication data sets were generated and analyzed, with results averaged across the 1000 analysis.We conducted a GHQ item responses were coded using the Likert scale (0-1-2-3). The global mental health sum scores were calculated, ranging from 1 to 65, with a mean score of 17.37. For dimension-specific phenotypes, the average score of somatic symptoms was 4.21, ranging from 0 to 20. For social dysfunction, the average score was 7.14, ranging from 0 to 20. Anxiety scores had a mean of 4.85, with a range of 0–21. The depression sum score ranged from 0 to 19, with an average score of 1.18.P<0.05.A bi-factor model  was fittAssociation analysis was performed for all SNPs for both the sum score version of the phenotype and the psychometric phenotype . ResultsDLG4 gene on global affective symptoms and GHQ-28 subscales are shown in Distributions of the effect size frequencies were estimated as densities  and plotPost hoc power analysis was performed for five SNPs with the largest effect sizes for global affective disorder, anxiety, depression, social dysfunction and somatic-symptom factors .10The sum score of depression items had the highest skewness (3.78 compared with between 1 and 1.5 for skewness from other domains) with a rather low mean of 1.18 out of a possible highest score of 21. Reduced power is present in data that are highly skewed; nevertheless, latent variable approach can improve the power of such data.DLG4 gene on anxiety symptoms emerged only under the bi-factor model method. Compelling evidence suggests that affective disorders are associated with dysfunction of brain glutamatergic transmission.44 The DLG4 gene, which encodes post-synaptic density protein 95 (PSD95), has a critical role in regulating N-methyl-d-aspartate receptor receptor activity and its signal transduction; PSD95 is a member of the synapse-associated protein family of scaffolding molecules that control the organization, composition and function of synapses.46 In particular, DLG4 knockout mice showed increased repetitive behaviors, abnormal communication and social behaviors, impaired motor coordination and increased stress reactivity and anxiety-related responses.47 Taken together, these findings indicate that the aberrant expression and function of PSD95 may contribute to the compromised N-methyl-d-aspartate receptor-mediated signaling in affective symptoms, and in particular anxiety and stress responses.In the present study, some genetic variants were shown to be associated with specific subscales of affective symptoms measured by GHQ-28. This pattern of results was most clearly shown under the bi-factor model. For example, the effect of the 48 Although the heritability of global affective symptoms was 44%, the percentage varied from 20% for social dysfunction to 44% for depression, indicating heterogeneity of the genetic underpinnings of the subscales. A more recent twin study examined the role of genetic factors in neuroticism, anxiety/depression and somatic distress.49 The study estimated that anxiety/depression shared 11% genetic variance with somatic stress. These findings demonstrate that even closely related subscales of affective symptoms can have unshared, unique genetic variances. Failure to incorporate the dimensionality aspect of complex traits could lead to reduced power to detect genetic associations.21 This has also been investigated in simulation studies of twin designs.13The heritability of domains and global measures of the GHQ-28 was previously studied in a representative twin sample from the United Kingdom.DLG4 gene and specific factor on anxiety indicates that the DLG4 potentially contributes to mental health symptoms mainly through anxiety symptoms that are not shared by other dimensions of the GHQ. If the ultimate purpose was to reduce global mental health symptoms (in contrast to specific factors for each symptom dimension), then targeting the DLG4 gene would not be an efficient route. However, for better understanding of severe anxiety disorder, it could be fruitful to explore DLG4 biochemical pathways.In terms of association analysis on the basis of the first-order model, whereas the first-order model reflected the dimensionality of the GHQ structure on the subscale level, it failed to take into account the global factor that represents global mental health. The bi-factor model, on the other hand, was able to overcome the dimensionality limitations of the first-order model and allowed for both a global factor as well as specific factors, which are essentially variances after taking into account of the global factor. The factors in the bi-factor model are all orthogonal to each other (covariances among factors are specified to be zero), reflecting an implied assumption that the global factor explains the correlations among the subdimensions. That is, the general mental health phenotype is only based on information that is no longer ‘contaminated' by variances from the specific dimensions; similarly, traits that originally overlap with each subdimension are modeled separately as distinct, nonoverlapping factors. The unique association between the The advantage of the bi-factor model specification is that it fully takes into account a global domain as well as the specific domains in the presence of data complexity/multidimensionality. For multidimensional phenotypes, a definition that emphasizes unidimensionality  produces a less accurate phenotype definition, thus underestimating the strength of association between a SNP and a phenotype of interest. This can consequently lead to reduced statistical power in detecting associations because the genetic link is masked by variances due to specific dimensions embedded in the phenotype measure. The bi-factor specification resolves this issue by separating the variances into phenotypes that are specific to both the global construct and the specific dimensions. Moreover, as the associations between a SNP and a phenotype of interest are simultaneously estimated in a latent variable framework, measurement errors are taken into account so as to enable more accurate genetic association estimation.It is worth noting that, even under the bi-factor psychometric method, genetic association analyses using the global affective symptom factor had weaker effect sizes than the subdimensions of affective symptoms. The disparity in the densities of SNPs with relatively low effect sizes between the sum score and bi-factor approach is less pronounced for the global factor compared with the differences between the two approaches in specific factors. This indicates genetic heterogeneity of the global factor from specific factors. It seems that the global, common mental health factor is less explained by the candidate gene SNPs included in the present studies. Instead of focusing on a global phenotype that highlights shared common variances among all dimension-specific symptoms, it might be more informative to look at dimension-specific phenotypes that are conceptually more homogeneous. The genetic architecture of dimension-specific phenotypes could be simpler than that of the broader global construct.a priori, and correlations between global and specific factors were specified to be orthogonal. This was because the GHQ-28 is an instrument with predefined symptom dimensions. In situations when the data structure is not known or not well established, it is possible to relax this specification and use a more exploratory framework, allowing dimensions to be correlated through bi-factor rotation methods.52Although in the present investigation the bi-factor model fitted the data very well, the factor structure is specified 55 A phenotype might consist of symptom items assessed by nurses, family members and self-completed questionnaires. Although the effect of an external assessor or self-administration should be irrelevant to the association between the target phenotype and genetic predictors, variances introduced by these unique external sources potentially contaminate the phenotype measure and prevent accurate genetic signals being observed.Psychometric approaches can be applicable in a variety of contexts in psychiatric genetics and beyond, where the data structure of phenotypes of interests is complex. For example, data collected from studies that utilize different forms of assessment methods might be prone to method effects.Another issue is that, although this type of analysis is fast when only several dozens of SNPs are included, it would require substantial computational power to test for genome-wide associations with hundreds of thousands of SNPs.et al.14 investigated how to approach gene finding research if the exact location of a SNP effect in multivariate data was unknown. In addition, when subgroups of individuals  are present in the data, it is possible that the measurement model of the phenotypes might differ across these groups, as will the association between a genetic variant and a phenotype. Ignoring hidden genetic–phenotype structures can lead to biased findings. Some previous studies have investigated the impact that phenotype measurement bias can have on the power to detect genetic variants.56 These topics are beyond the scope of the analytic framework in the present investigation, but need to be explored in future studies.It is likely that the relationship between genetic and phenotypic variables is much more complex. For example, there could be a direct effect from a SNP variable to the individual items even after accounting for the association between the SNP and the latent phenotypic variable. In particular, van de Sluis In conclusion, the psychometric approach for phenotype definition represents a useful step for genetic investigations of psychiatric disorders. This approach affords a more flexible phenotype definition, and thus provides greater statistical strength to detect genetic associations.


Venous thromboembolism (VTE) is a common disease where known genetic risk factors explain only a small portion of the genetic variance. Then, the analysis of intermediate phenotypes, such as thrombin generation assay, can be used to identify novel genetic risk factors that contribute to VTE.To investigate the genetic basis of distinct quantitative phenotypes of thrombin generation and its relationship to the risk of VTE.Lag time, thrombin peak and endogenous thrombin potential (ETP) were measured in the families of the Genetic Analysis of Idiopathic Thrombophilia 2 (GAIT-2) Project. This sample consisted of 935 individuals in 35 extended families selected through a proband with idiopathic thrombophilia. We performed also genome wide association studies (GWAS) with thrombin generation phenotypes.F2 gene. However, other suggestive signals were observed.The results showed that 67% of the variation in the risk of VTE is attributable to genetic factors. The heritabilities of lag time, thrombin peak and ETP were 49%, 54% and 52%, respectively. More importantly, we demonstrated also the existence of positive genetic correlations between thrombin peak or ETP and the risk of VTE. Moreover, the major genetic determinant of thrombin generation was the F2 was identified as a major determinant of thrombin generation. We reported suggestive signals that might increase our knowledge to explain the variability of this important phenotype. Validation and functional studies are required to confirm GWAS results.The thrombin generation phenotypes are strongly genetically determined. The thrombin peak and ETP are significantly genetically correlated with the risk of VTE. In addition,  Venous thromboembolism (VTE) is a common disease involving genetic and environmental risk factors and their interactions . VTE, whet al. [One of these intermediate phenotypes could be a measure of the thrombin generation assay, which measures the capacity to generate thrombin, a key enzyme in the coagulation cascade . This phet al. . There aet al. ,9.Several studies have reported an association between these quantitative thrombin generation phenotypes and the risk of cardiovascular diseases using different test conditions. Specifically, an association has been described between thrombin generation and the risk of first VTE –13. HoweIn addition to the Genetic Analysis of Idiopathic Thrombophilia 1 (GAIT-1) Project ,20, we iThe Spanish families in our study were recruited in the GAIT-2 Project at the Hospital de la Santa Creu i Sant Pau of Barcelona, Spain.The recruitment and the criteria used for inclusion were the same as that in GAIT-1 and have been described in detail previously . BrieflyOur subjects were questioned also about previous episodes of venous and arterial thrombosis, the age at which these events occurred, and the presence of potentially correlated disorders such as diabetes, lipid disease, asthma, allergic rhinitis, atopic dermatitis, autoimmune disease and cancer. The residence of each subject was determined to assess the contribution of shared environmental influences (such as diet) common to members of the same household. The study was performed according to the Declaration of Helsinki. All procedures were reviewed and approved by the Institutional Review Board of the Hospital de la Santa Creu i Sant Pau, Barcelona, Spain. Adult subjects gave written informed consent for themselves and for their minor children.This sample consisted of 935 individuals in 35 extended families. All of the pedigrees contained at least 3 generations, and 14 families had more than 3 generations. The individuals ranged in age from 2.6 to 101 years old (SD = 21.4), with a median age of 39.5 and approximately an equal number of males (465) and females (470). The depth and complexity of the pedigrees are illustrated in The sample had 120 subjects with thromboembolism, including venous and arterial thrombosis. Specifically, they were 86 subjects with venous thrombosis, 47 with arterial thrombosis and 13 with both venous and arterial thrombosis. Of the 935 subjects, we obtained the thrombin generation phenotypes of 919 subjects.g for 20 minutes at room temperature (22±2°C) and stored at −80°C before performing the thrombin generation assay. DNA was extracted from whole blood samples using a standard salting out procedure [Blood was collected by venipuncture following a 12-hour fast. Samples were collected in 1/10 volume containing 0.129 mol/L sodium citrate. None of the participants was using oral anticoagulants or heparins at the time of blood collection. Platelet-poor plasma (PPP) was obtained by centrifugation at 2000 perature ±2°C and et al. [2 FluCa-Kit from Thrombinoscope BV  were used. Thrombin generation curves were calculated using the Thrombinoscope™ software  and the parameters analyzed were lag time (min), thrombin peak (nM), and ETP (nM*min). The medians of crude lag time, crude thrombin peak and crude ETP were 2.67 min (first quartile 2.33 min and third quartile 3.08 min), 315.80 nM (first quartile 224.80 nM and third quartile 391.50 nM) and 1,594 nM*min . The intra-assay and inter-assay coefficients of variation for thrombin generation parameters were below 6% and below 8%, respectively.Thrombin generation was evaluated in PPP according to the method described by Hemker et al.  by means-06) and MAF (>1%). We merged the data and obtained 395,556 SNPs in all of the samples. Then, we estimated haplotypes using SHAPEIT v2 [We genotyped the samples with a combination of HumanOmniExpressExome-8v1.2  and HumanCoreExome-12v1.1 . We applied inclusion filters in the datasets based on call rate (>98%), HWE  was performed using the variance component method [c2); and (3) a residual random environmental variance that is specific to each individual. The random environmental variance also includes non-additive genetic effects such as interactions between alleles within loci (dominance effects), interactions between alleles at different loci (epistatic effects), and effects caused by gene-environment interactions. Therefore, this model generally underestimates the role of genetics in the determination of the trait.The statistical methods used in our study have been described elsewhere ,26. The t method . The totThe covariances among individuals within a family that are due to additive genetic effects were estimated as a function of their expected genetic kinship relationships. Covariances among individuals that are due to shared environments were modeled by using the information whether individuals live in the same household. The power of this variance component approach of partition genetic and environmental effects stems from the high information content in the case of the extended pedigrees where families cut across multiple household . Because2 captured the non-linear relationship between the trait and age. The regression coefficients for the discrete covariates  represent the effect of the covariate versus its absence. P-values of less than 0.05 were considered statistically significant.Trait-specific covariates in the variance component models were evaluated from the following list of candidate covariates: age, sex, physical activity, oral contraceptives and smoking. The regression coefficient for the continuous covariate age represents the effect associated with a 1-year deviation from the mean age. The covariate ageThe disease status of VTE is recorded as a binary trait encoding affected or unaffected status. In the statistical model, it is transformed to a continuous trait referred to as liability or susceptibility (risk) to the disease by means of probit link function. An interpretation of the effect of a continuous covariate like age in our model with probit link function follows a rule: the negative sign of the coefficient, for example for age covariate, means positive effect in the model (increase liability to the disease).g), and the environmental (ρe) correlations between traits. The phenotypic correlation (ρp) can be derived from these two constituent correlations and the heritabilities of the traits as follows:h21 and h22 are the heritabilities for trait one and trait two.The correlations between a given pair of traits were analyzed by multivariate variance component models, which are an extension of the univariate model . Similar−8, and suggestive significance was defined by p-values <1x10−5. The association analysis was adjusted for F2 G20210A mutation as known genetic determinant of thrombin generation, if needed.For the association analysis with the imputed genotypes it was applied two excluding filters based on imputation score (info <0.3) and MAF (<1%). The final number of SNPs was 9,303,497. Analyses were performed using the measured genotype method by testing for genotype-specific differences in the means of traits while allowing for the nonindependence among family members. Genome wide significance was defined by p-values <5x10All statistical analyses were performed employing the computer package Sequential Oligogenic Linkage Analysis Routines  . SOLAR eThe effects of significant covariates of the risk of VTE and thrombin generation phenotypes are shown in h2) was performed after the correction by its significant covariates. It is notable that genetic factors accounted for 67% of the variation in the risk of VTE. Furthermore, the heritabilities of lag time, thrombin peak and ETP were estimated as 49%, 54% and 52%, respectively. The estimates of additive genetic effects and the household effect on the variability of the traits are shown in The analysis of the contribution of genetics to the variability of each trait  in association with the ETP trait. The significant SNPs rs61828128  and rs61828133  on Chromosome 1 are located in the HHAT gene. Interestingly, the SNP rs1799963  in F2 gene is included in the association peak at chromosome 11 region. After adjustment for G20210A (rs1799963) mutation in the F2 gene, the signals did not remain significant. However, our results suggested associations involved in both thrombin peak and ETP traits in or near IRF6 , OCLN , CDKAL1 , CAMK2B , and NUDCD3  genes. We defined as “near” a distance between 1 and 1,500 bp upstream.No inflation of the test statistic was observed . No variants showed genome wide significance for association with the risk of VTE (data not shown). Manhattan plots of the results from genome wide associations on the thrombin generation traits are shown in To investigate the risk of diseases, the study of complex intermediate phenotypes provide a useful means to identify genetic risk factors. The intermediate phenotypes are closer to the action of genes than the presence or absence of a complex disease. In addition, the susceptibility to a complex disease is primarily a process that represents an unobservable continuous liability. This means that this variable can not be measured directly in an individual, and consequently the use of intermediate phenotypes is statistically more powerful . Our stu2, sex and oral contraceptives and the ETP was influenced by age, age2 and oral contraceptives. These results are consistent with previous reports under different experimental conditions. Specifically, it has been reported that thrombin generation increases with age and is higher in women than in men [Using a variance component method and maximum likelihood estimations age was found to be the covariate with statistically significant effect on the risk of VTE. This is not surprising since the covariate age has been associated previously with VTE . Except n in men . In addin in men ,35.Our study was based on the recruitment of families, which provided direct data of familial transmission and allowed an estimation of genetic factors affecting the variation in the risk of VTE within a Spanish population . It is nOne of our most important results is that they add to the previous evidence of the relationship between the risk of VTE and thrombin generation. For the first time to our knowledge, we determined genetic correlations between the susceptibility to VTE and thrombin peak or ETP. Not surprisingly, we did not find phenotypic, genetic nor environmental correlations between the risk of VTE and lag time. Our results agree with previous publications which reported no association between the lag time and the risk of first or recurrent VTE . In contF5, F2, FGA, F10, F12 and TFPI [ORM1 locus [Taking together, our results support the hypothesis that the thrombin generation test is a very useful test to investigate the risk of VTE. Specifically, thrombin generation phenotypes have been associated significantly with genetic variants in haemostatic genes such as and TFPI ,36. It hM1 locus . It is iM1 locus . ConsequIn agreement with a previous study  we obserThe thrombin generation assay is sensitive to preanalytic conditions and there is no standardized protocol ,40. In dF2 has been shown as the main contributor to thrombin generation.In summary, the high heritabilities that we found indicate that genetic factors play a significant role in the risk of VTE and in the thrombin generation test in this population. In addition, the significant genetic correlations suggest that there are pleiotropic genetic effects between the risk of VTE and thrombin peak or ETP. Finally, our study indicates that the use of the thrombin generation assay should help to detect genes responsible for thrombophilia. In fact, from results of the GWAS,


R2 of 0.91. This accuracy exceeds narrow-sense heritability, approaches limits imposed by measurement repeatability and is higher than achieved with a single assay in the laboratory. Our results prove that very accurate prediction of complex traits is possible, and suggest that additional data from families rather than reference cohorts may be more useful for this purpose.In spite of decades of linkage and association studies and its potential impact on human health, reliable prediction of an individual's risk for heritable disease remains difficult. Large numbers of mapped loci do not explain substantial fractions of heritable variation, leaving an open question of whether accurate complex trait predictions can be achieved in practice. Here, we use a genome sequenced population of ∼7,000 yeast strains of high but varying relatedness, and predict growth traits from family information, effects of segregating genetic variants and growth in other environments with an average coefficient of determination  Heritability of complex traits can be finely dissected using yeast crosses. Here, Kaspar Märtens and colleagues show that quantitative traits in yeast can be predicted from genotype and phenotype data with an average coefficient of determination of 0.91, providing the most accurate predictions of complex traits to date. Disease incidence can be predicted based on the health record246An important question of whether this is due to paucity of data, or perhaps more fundamental limitations, can be attacked by predicting phenotypes in model organisms1012131516192020Here, we use a recent resource of over 7,000 diploid hybrid yeast strains of high relatednessSaccharomyces cerevisiae strains with phased whole-genome sequences from the collection of diploid phased outbred linesf=0.5), and 7,225 ones for which no complete chromosome is shared, but a substantial part of linkage blocks and allele combinations are  and repeatabilities , and the quality of prediction depends on the strength of pairwise correlations of the phenotypes. The genomic best linear unbiased predictor (BLUP), an additive model based on realized genetic relatedness alone, captures the pedigree structure in the population, and achieves prediction accuracies very close to the narrow-sense heritability estimates . These predictions are near-identical to a simple midparent approach , with predictions very similar to BLUP , mainly due to dominance effects of strongest QTLs for allantoin and galactose (R2=0.86).Next, we mapped quantitative trait loci (QTLs) in each environment, and asked how well they predict growth in that environment. A small number of single nucleotide polymorphisms (SNPs) with the largest effects explain a sizeable portion of additive variance, but for all traits the prediction accuracy remains lower than BLUP's . For each of the measured traits, our predictions of the mean phenotype  have lower error than a single growth experiment , and ‘distant relatives', where test set individuals are not as closely related to anyone in the training set . Accuracy increases markedly even with a small number of close relatives included in the training data, whereas adding more distant relatives to close ones does not improve predictions .So far, our predictions for each test individual were obtained from models that were trained with data from its close relatives that share half of the complete chromosomes. We observed that errors were larger when close relatives were not available , the accuracies were similar for both training scenarios  ‘QTLs'. tructure , which etructure . NotablyCombining genomic and phenotypic information (LMM+P) to predict from distant relatives gives accuracies similar to combining QTLs and phenotypic information. For traits where genomic prediction on distant relatives does not work well , this model performs similarly to using other phenotypes only or even slightly worse  for close relatives, with no consistent difference across the traits , which is not caused by a single dominant allele . Any remaining difference is potentially due to both weak interaction and dominance effects not included in the LMM during model selection. Standardization, distribution of replicates across multiple pre-culture and experimental batches, and normalization of phenotypes to very densely spaced internal controls are expected to minimize the influence of shared environmental variation across plates23Genomic prediction methods have recently been extended to include more fine-grained decomposition of trait variances, both for phenotypes  from a cross between YPS128 and DVPBG6044 . Let y be the vector containing the phenotype of interest for all N individuals, and let P1, …, P8 be the remaining phenotypes. We modelled y as β used for prediction.Best linear unbiased predictor. Let xj be the genotype vector for SNP j=1, …, M, and let X be the genotype matrix X=. In the genomic BLUP model, c being the average diagonal value of XXT. Prediction for the test individual can be obtained by conditioning on the observed data in a standard way for multivariate normal distributions. When calculating the standard deviation of the predictive distribution .Midparent. Let yij be the phenotype for individual who has parents i and j. Let Pi1 and Pj2 be the parental phenotype values. We model yij as the mid-parent value ɛij is uncorrelated noise. We first fit the parental values from the yij observed in training data, and used them to predict phenotypes of test individuals.LMM with dominance and interaction effects. The LMM model combines additive, dominance and interaction effects with genetic relatedness, xi and xj, where xi has previously been selected into the model. Although we miss interactions where neither locus has a significant additive effect, it has been shown that such occurrences are rareLMM including phenotypes (‘LMM+P'). The LMM+P model combines additive, dominance and interaction effects with genetic relatedness and other traits, P) part. The latter includes the linear combination of all other traits P1, …, P8. First, we regress y on P, and then we construct the genetic component as described for the LMM model.Multi-trait LMM. MT-LMMs model multiple phenotypes jointly. The correlation between two traits is modelled in two parts, via a genetic and non-genetic component as followsY= be the matrix for phenotypes y!,…,y9, and let F denote the fixed effects for each of these phenotypes, F=. We used the same fixed effects fi that we constructed in the LMM model. Let C be the genetic covariance matrix between phenotypes and Σ the non-genetic one. Then Mixed random forest. We applied the MRF approachN2 as a test set to obtain predictions and the remaining three as a training set to fit the models. First, we did not take into account the relatedness structure and divided individuals into subsets randomly , as well as sampled N2 from the 2N2 remaining individuals who do share one parent with someone in the test set (‘close relatives').All models were fitted with the Python package LIMIXrij=yi+ɛij, where ri1, ri2, ri3, ri4 are the four replicate measurements for individual i, yi is the average rij value for this individual and ɛij∼N. Repeatability was estimated as 1−σ2/Var(r).Narrow-sense heritability was estimated from the genomic BLUP model as et al.23 Analysis code is available at https://github.com/kasparmartens/y10k-prediction.The data used in this study are available in the Supporting Information of Hallin Code availability: All the code used in the current study is available at https://github.com/kasparmartens/y10k-prediction.How to cite this article: Märtens, K. et al. Predicting quantitative traits from genome and phenome with near perfect accuracy. Nat. Commun. 7:11512 doi: 10.1038/ncomms11512 (2016).Supplementary Figures 1-10, Supplementary Tables 1-3 and Supplementary References


GMPS and xanthosine using single variant analysis and associations between HAL and histidine, PAH and phenylalanine, and UPB1 and ureidopropionate using gene-based tests , highlighting novel coding variants that may underlie inborn errors of metabolism. Further, we show how an examination of variants across the spectrum of allele frequency highlights independent association signals at select loci and generates a more integrated view of metabolite heritability. These studies build on prior metabolomics genome wide association studies to provide a more complete picture of the genetic architecture of the plasma metabolome.The study of rare variants may enhance our understanding of the genetic determinants of the metabolome. Here, we analyze the association between 217 plasma metabolites and exome variants on the Illumina HumanExome Beadchip in 2,076 participants in the Framingham Heart Study, with replication in 1,528 participants of the Atherosclerosis Risk in Communities Study. We identify an association between  Several GWAS have identified many common variants associated with blood metabolites. Here, the authors use an exome array to identify low frequency, potentially functional variants that impact human metabolism. Several independent genome wide association studies (GWAS) have identified dozens of common variants associated with plasma or serum metabolite levels123456The study of low frequency and potentially functional variants, not captured on standard GWAS arrays, has the potential to refine and expand our understanding of the genetic determinants of circulating metabolite levels. Thus, we analyzed the relationship of plasma metabolites in FHS with coding variants captured on the Illumina HumanExome Beadchip, which includes functional exonic variants identified from exome and whole-genome sequencing of over 12,000 individualsP<6.2 × 10−7 in linear mixed effects models) in FHS. Six of these eight metabolites were also measured in ARIC, and in the replication analysis, the association between a missense variant in GMPS (rs61750370) and xanthosine levels remained significant   and plasma cystathionine levels identified in FHS (P=5.4 × 10−14) and has clear biologic and clinical underpinnings: the variant encodes p.Thr67Ile in cystathionine gamma-lyase, a cytoplasmic enzyme that converts cystathionine into cysteine, and even more convincing, prior reports have identified homozygosity for this same missense mutation as a cause of cystathioninuria (MIM#219500)9As detailed in the Methods section, we restricted our analysis to the subset of variants that were (1) polymorphic, (2) nonsynonymous, stop-altering or located in a splice site and (3) had a minor allele frequency (MAF) ≤5% . Eight snalysis) . This vad in FHS . HoweverP<3.8 × 10−6 in linear mixed effects models). Using this approach, we identified six additional gene-metabolite associations in FHS. Four of these six metabolites were also measured in ARIC, and in the replication analysis, three of the associations remained significant —mutations in HAL are known to cause the autosomal recessive metabolic disorder histidinemia (MIM # 609457)PAH and phenylalanine —mutations in PAH cause the autosomal recessive inborn error of metabolism phenylketonuria (MIM#612349); and UPB1 and ureidopropionate —mutations in UPB1 are the cause of the autosomal recessive disorder Beta-ureidopropionase deficiency (MIM # 60667).Because the number of study participants harbouring any single low-frequency or rare variant is limited, we performed gene-based burden tests to identify additional locus-metabolite associations. These gene-based tests can improve the power to detect associations compared to single variant tests, provided that multiple variants within the gene are causalnificant , with alHAL . At PRODH, rs5747933 encodes p.Thr167Asn and is independent of the intronic GWAS variant rs2078743 . By contrast, the association between rs3135506, which encodes p.Ser19Trp in APOA5, and diacylglycerol (DAG) 36:2 was no longer significant after adjusting for the intronic GWAS variant rs964184 . Similarly, the common variant signal at this locus was abrogated after adjusting for the coding variant . Given the plausible functional effect at rs3135506, predicted to be damaging by dbNSFPWe merged the exome chip and common variant data sets in FHS and performed conditional analyses in order to test whether the exome array is able to identify independent signals at loci highlighted by our prior metabolomics GWAS. We restricted our analysis to exome variants not captured in our prior GWAS and identified three coding variants that replicated prior locus-metabolite associations at a genome-wide significant threshold in linear mixed effects models. Results of conditional analyses, whereby exome array variants were adjusted for previously identified noncoding common variants and vice versa, are shown in Because we have now integrated metabolite data with both common variant and exome variant arrays in the same 2,076 individuals, we next estimated the proportion of interindividual metabolite variation captured across a broad range of allele frequency . Looking45To date, common variant association studies have shown that many loci have relatively large effect sizes on circulating metabolite levels, as compared to GWAS for common diseases and most risk factor phenotypes. As a result significant findings have emerged from samples as small as 284 subjectsCTH, HAL, PAH and UPB1, and plasma cystathionine, histidine, phenylalanine, and ureidopropionate levels, respectively. Mutations in each of these genes are established causes of autosomal recessive disorders characterized by the defective catabolism and subsequent plasma accumulation of these metabolites. With a MAF of 1% the association between a mutation in CTH and plasma cystathionine levels could be detected in single variant analysis. By contrast, the association between the less common mutations in the other genes and their respective metabolites were detected with gene-based testing. Given widespread newborn screening for phenylketonuria, dozens of mutations in the PAH locus have now been identified, and indeed the three variants most strongly associated with phenylalanine in our data have been extensively reported in the literature. By contrast, the missense mutation at rs118092776, which encodes p.Arg53His has not been published UPB1, the mutation associated with ureidopropionate levels has only been described in one patient in the literatureHAL are known causes of histidinemia, the mutations we highlight in association with plasma histidine levels in FHS are novel. Prior reports have identified seven other mutations in HAL as either a cause of histidinemia or associated with plasma histidine levels, including four in Japanese individualsOur data demonstrate significant associations between variants in ASS1 and SLC25A13, respectively. The most commonly reported mutation in type I citrullinemia, rs121908641, demonstrated a trend for association with higher citrulline levels , despite being present as a single copy in only two individuals in FHS. In SLC25A13, we identified a loss-of-function splice mutation at rs150021522 nominally associated with citrulline . This mutation, present in three individuals in FHS, has not been reported in the literature, perhaps because type II citrullinemia is primarily described in East Asian populationsCompared to studies of complex diseases, the study of quantitative phenotypes—in some cases immediately downstream of gene function—can dramatically lower the sample size required to detect statistically significant associations. Nevertheless, the sample sizes examined in FHS and ARIC constrain our power to detect weak associations, a limitation that is mitigated but not eliminated by gene-based testing. It is likely that numerous biologically important associations did not reach statistical significance because of limited effect size and/or limited allele frequency. For example, using established disease loci as a positive control, we highlight the examples of types I and II citrullinemia , which are caused by mutations in vels β=1., P=0.09,In our analysis of the estimated proportion of interindividual metabolite variation captured by SNPs across both common variant and exome arrays, we found that low frequency and rare variants made moderate contributions to overall heritability. Although a larger sample size would be required to capture the full contribution of rare variants to metabolite heritabilityIn summary, we have extended our prior study of the common genetic determinants of plasma metabolites to now include an analysis of lower frequency coding variants sequenced using an exome array. Notably, metabolites were measured using different LC–MS platforms in discovery and replication, to our knowledge the first time this has been done in a study of the genetic determinants of the metabolome, providing increased confidence in our significant findings. By integrating GWAS and exome array data, we also highlight independent association signals at select loci and provide a more granular view of metabolite heritability. Finally, all of our single variant and gene-based analyses have been made publicly available as a resource to the scientific community. Future efforts will seek to examine larger data sets, both in regards to the number of individuals genotyped and the number of metabolites assayed.The FHS Offspring cohort is a prospective, observational, community-based cohortThe ARIC study is a prospective cohort originally designed to assess risk factors for cardiovascular disease in the general populationBlood samples were collected after an overnight fast, immediately centrifuged and stored at −80 °C until assayed.2 column (Phenomenex); mobile phase A: 20 mM ammonium acetate, 20 mM ammonium hydroxide; mobile phase B: 10 mM ammonium hydroxide in 25:75 methanol/acetonitrile v/v. The column was eluted isocratically with 10% mobile phase A for 1 min followed by a linear gradient to 100% mobile phase A over 9 min. MS data were acquired using a 5500 QTRAP triple quadrupole mass spectrometer (AB SCIEX) using ESI and MRM in the negative ion mode. The ion spray voltage was −4.5 kV and the source temperature was 500 °C. For lipids, including lysophosphatidylcholines, lysophosphatidylethanolamines, phosphatidylcholines, sphingomyelins, cholesterol esters, DAGs and triacylglyerols, 10 μl of plasma were extracted in 190 μl of isopropanolFor FHS samples, metabolites were measured at the Broad Institute. For amino acids, amino acid derivatives, urea cycle intermediates, nucleotides and other positively charged polar metabolites, 10 μl of plasma were extracted in nine volumes of 74.9:24.9:0.2 v/v/v acetonitrile/methanol/formic acidFor ARIC samples, metabolites were measured using fasting serum, sampled at the baseline ARIC examination. Following receipt at Metabolon Inc. , untargeted gas chromatography-mass spectrometry and liquid chromatography-mass spectrometry-based metabolomic quantification protocols were used to detect and quantify metabolites27http://genome.sph.umich.edu/wiki/Exome_Chip_Design.Genotyping was performed using the Illumina HumanExome BeadChip, which captures putative functional exonic variants selected from over 12,000 individual exome and whole-genome sequences. In order to be included, nonsynonymous variants had to be observed two or more times in at least two datasets, and stop-altering and splice variants had to be observed two or more times in at least two datasets. In addition to nonsynonymous, stop-altering and splice variants, additional array content included tags for previously described GWAS hits, ancestry informative markers, random synonymous SNPs, mitochondrial SNPs and human leukocyte antigen tags. In sum, >240,000 variants were included on the exome array. Of these, 109,911 variants were polymorphic in the FHS sample, and a further subset of 81,021 was nonsynonymous, nonsense, or located in a splice site and had a MAF≤5% . DetailsP<0.0001, and the final genomic control parameter lambda was between 0.92 and 1.11 with a median of 1.02 for all analyses.Due to right-skewed distributions of metabolite levels and differences in scaling, genetic analyses were conducted using normalized residuals of metabolite levels, adjusted for age and sex. The association of genetic variants and metabolite concentrations was tested using linear mixed effects models to accommodate pedigree structure under an additive genetic model. Population stratification was accounted for by adjusting for PC1 if P<6.2 × 10−7). For replication, results were considered significant if (1) they reached a significance threshold adjusted for the number of associations examined (P<8 × 10−3) and (2) following meta-analysis across the discovery and replication cohorts, the P value of association reached genome-wide significance (P<5 × 10−8).Analyses were performed using the R GWAF packageP<3.8 × 10−6). For replication, results were considered significant if (1) they reached a significance threshold adjusted for the number of associations examined (P<1.2 × 10−2) and (2) following meta-analysis across the discovery and replication cohorts, the P value of association reached genome-wide significance (P<5 × 10−8).The effects of single variant association within a gene were aggregated by summing up the score statistics using the collapsing method in Li and Lealet al.Single variant meta-analysis was carried out using the inverse variance weighted method: the meta-analysis beta was the sum of betas from the two cohorts weighted by the inverse of respective variances. To combine the gene-based results from the two cohorts, meta-analysis of each single variant was done first, then the single variant meta-analysis statistics were combined using the method by Pan SNPs with significant metabolite associations identified in our prior GWAS were included as covariates in the linear mixed effect model of single variant analyses to examine whether the association is attenuated by adjusting for known associationsData on common variant associations from our prior GWASData on singled variant and gene-based results for all metabolites measured in FHS have been deposited in the database of Genotypes and Phenotypes (dbGaP) under accession code phs000007.v28.p10. All other data is available within the manuscript or from the authors upon request.How to cite this article: Rhee, E. P. et al. An exome array study of the plasma metabolome. Nat. Commun. 7:12360 doi: 10.1038/ncomms12360 (2016).Supplementary Tables 1-3


Clinical end-point traits are often characterized in terms of quantitative precursors. For psychiatric disorders, traits such as symptom counts often serve as endophenotypes of interest for understanding the genetic basis of the clinical end-point trait. Since such traits are discrete in nature, it may not be optimal to use standard approaches such as Analysis of Variance (ANOVA) to detect association.For population level data, we propose a Poisson regression approach that computes the likelihood of the count phenotype conditional on an additive allele count at a SNP. The test statistic is asymptotically distributed as chi-squares with one degree of freedom under no association between the SNP and the phenotype. For family-based data involving trios with at least one heterozygous parent at a SNP, we use a similar Poisson regression model conditional on two indicator variables: the marker allele transmitted by the heterozygous parent and the marker allele transmitted by the other parent. A one degree of freedom test based only on the coefficient of the first indicator is protected against population stratification as it tests for association in the presence of linkage. Two degrees of freedom test based on both the indicators is also a valid test for association, but is susceptible to population stratification.Based on extensive simulations under different genetic models, we find that for population level data, while the asymptotic tests for ANOVA yield an inflated rate of false positives, especially when there is heteroskedasticity in the distribution of the trait across the QTL genotypes, our proposed method maintains the correct size. Moreover, our method yields uniformly more power compared to ANOVA for the different genetic parameters in our simulations. For the trio design, we find that the two degrees of freedom test is more powerful than the one degree of freedom test. We applied our method to analyze externalizing symptoms, an endophenotype correlated with alcoholism using data generated in the Collaborative Study On the Genetics of Alcoholism (COGA) project. We found significant evidence of association in the class 1 alcohol dehydrogenase subunit ADH1C in the 4q22.3 region.


Sea lice have significant negative economic and welfare impacts on marine Atlantic salmon farming. Since host resistance to sea lice has a substantial genetic component, selective breeding can contribute to control of lice. Genomic selection uses genome-wide marker information to predict breeding values, and can achieve markedly higher accuracy than pedigree-based methods. Our aim was to assess the genetic architecture of host resistance to sea lice, and test the utility of genomic prediction of breeding values. Individual lice counts were measured in challenge experiments using two large Atlantic salmon post-smolt populations from a commercial breeding programme, which had genotypes for ~33 K single nucleotide polymorphisms (SNPs). The specific objectives were to: (i) estimate the heritability of host resistance; (ii) assess its genetic architecture by performing a genome-wide association study (GWAS); (iii) assess the accuracy of predicted breeding values using varying SNP densities (0.5 to 33 K) and compare it to that of pedigree-based prediction; and (iv) evaluate the accuracy of prediction in closely and distantly related animals.Heritability of host resistance was significant (0.22 to 0.33) in both populations using either pedigree or genomic relationship matrices. The GWAS suggested that lice resistance is a polygenic trait, and no genome-wide significant quantitative trait loci were identified. Based on cross-validation analysis, genomic predictions were more accurate than pedigree-based predictions for both populations. Although prediction accuracies were highest when closely-related animals were used in the training and validation sets, the benefit of having genomic-versus pedigree-based predictions within a population increased as the relationships between training and validation sets decreased. Prediction accuracy reached an asymptote with a SNP density of ~5 K within populations, although higher SNP density was advantageous for cross-population prediction.Host resistance to sea lice in farmed Atlantic salmon has a significant genetic component. Phenotypes relating to host resistance can be predicted with moderate to high accuracy within populations, with a major advantage of genomic over pedigree-based methods, even at relatively sparse SNP densities. Prediction accuracies across populations were low, but improved with higher marker densities. Genomic selection can contribute to lice control in salmon farming.The online version of this article (doi:10.1186/s12711-016-0226-9) contains supplementary material, which is available to authorized users. Genomic selection (GS) involves the prediction of individual breeding values for complex traits by combining statistical methods with genome-wide single nucleotide polymorphism (SNP) data. Relationships between SNPs and traits of interest are first determined within a reference (or training) population, and then they are used to identify selection candidates with high genetic merit in the absence of phenotype records , 2. The Lepeophtheirus salmonis in Europe and Caligus rogercresseyi in Chile), are the primary threat to sustainable production, and have a negative economic, animal welfare, and environmental impact. Symptoms of L. salmonis infection include skin lesions, osmotic imbalance, and increased susceptibility to other infections as a result of host immune suppression and skin damage [In Atlantic salmon farming, ectoparasitic copepods, commonly known as sea lice , which n damage , 11. GivThe quantitative genetic models that underpin GS can be broadly split into two categories based on the assumptions that underlie the genetic architecture of the trait. The first category assumes an even distribution of the genetic variance across the genome and includes genomic best linear unbiased prediction (GBLUP) methods. The second category allows for heterogeneity in the contribution of the markers to the genetic variance, which is typically modelled using Bayesian methods (e.g. ). While Family-based selective breeding programmes for Atlantic salmon have traditionally focused on economically important traits that can be easily measured on the selection candidates (e.g. growth) and on traits that can be measured on close relatives , such as disease resistance and processing traits. Studies of GS in aquaculture using both simulated and ‘experimental’ data have suggested that genomic prediction can result in more accurate breeding values than traditional pedigree-based approaches e.g. , 19–21).–21.19–21The objectives of this study were to (i) estimate the heritability of host resistance to sea lice using both genomic and pedigree-based methods, (ii) analyse the genetic architecture of host resistance by performing a GWAS, (iii) assess the accuracy of genomic prediction using various SNP densities up to 33 K SNPs and compare it to that of pedigree-based prediction, and (iv) test genomic prediction accuracies in closely and more distantly related reference and validation populations.The animals used in the study originated from a commercial Atlantic salmon breeding programme . Due to the 4-year generation interval, the breeding program consists of four sub-populations (referred to as year groups), two of which were studied. Full details for population I  were previously described in Tsai et al. . BrieflyAll animals were reared in accordance with relevant national and EU legislation concerning health and welfare. The challenge experiment was performed by the Marine Environmental Research Laboratory  under approval of the ethics review committee of the University of Stirling  and according to Home Office license requirements. Landcatch are accredited participants in the RSPCA Freedom Foods standard, the Scottish Salmon Producers Organization Code of Good Practice, and the EU Code-EFABAR Code of Good Practice for Farm Animal Breeding and Reproduction Organizations.DNA was extracted from fin tissue samples using the DNeasy 96 tissue DNA extraction kit . Population I was genotyped with an Affymetrix Axiom SNP array that included ~132 K SNPs  and popuThe raw data for lice counts showed a positively skewed distribution  with the following mixed model:e is a vector of residuals. If the SNPs applying sex as the fixed effect did not surpass the genome-wide significance threshold (Bonferroni correction (0.05/N), where N represents the number of QC-filtered SNPs across the entire genome), it was omitted from subsequent analyses. The genomic relationship matrix was constructed by the Genabel R package [https://www.r-project.org/). Narrow sense heritability was estimated as the ratio of additive genetic variance to total phenotypic variance. Phenotypic correlations between traits were estimated using ASReml 3.0 [The heritability of host resistance to sea lice count (and of weight and length traits) was estimated using both genomic and pedigree-based analyses for the two populations. Only fish with complete phenotype and genotype records were included, resulting in 531 and 588 fish in populations I and II, respectively. Heritabilities were estimated by ASReml 3.0  using ge package  using th package  and thenReml 3.0  and geneReml 3.0  as well.a) of the SNP was calculated as half the difference between the predicted phenotypic means of the two homozygotes, i.e. d) was calculated as The two-step ‘GRAMMAR’ approach was used to perform the GWAS using the GenABEL R Package . The GWAThe utility of genomic prediction for resistance to lice was assessed by cross-validation analyses under various scenarios (see below) in which (i) varying SNP densities , and 33 K (full dataset)) and (ii) varying degrees of relationships between training and validation sets were applied.Within each population , cross-validation analysis was performed by selecting five random non-overlapping training and validation sets as described previously . At eachWithin each population, training and validation sets were established such that both sets contained representatives of each family. The same cross-validation analyses were performed as for Scenario (i).Within each population, training and validation sets were established such that full siblings were not included in either set . The resulting training and validation sets were more distantly related than for Scenarios (i) and (ii), although they did contain some half-sibs. The same cross-validation analyses were performed as for Scenarios (i) and (ii).To assess prediction accuracy across populations per year group, population I was used as the training set and population II as the validation set, and vice versa. The same genomic prediction and cross-validation analyses were performed as for Scenarios (i) to (iii), but pedigree-based prediction was not possible since genetic links between the two populations were absent from the available pedigree.The five-fold cross-validation analyses for each scenario described above were performed using the methods described in Tsai et al. . BrieflyEstimated heritability for lice count was moderate (~0.3) and relatively consistent when using a pedigree relationship matrix Table . EstimatThe results of the GWAS suggest that lice resistance is a polygenic trait, with no SNPs surpassing the Bonferroni-corrected significance thresholds Fig. . Indeed,The putative polygenic architecture of lice resistance in these populations means that genomic prediction may be a practical and effective method of predicting breeding values for lice resistance, which was tested using cross-validation analyses under different scenarios in which varying SNP densities and varying levels of relatedness between training and validation sets were applied see “.Fig. 2AcThe results of genomic prediction under the “random selection” , and “sibling”  scenarios were very similar for both populations Fig. a–d. TherWhen the training and validation sets were less related, predictions of both pedigree- and genomic-based methods were less accurate, as expected. In the “non-sibling” scenario , accuracies of prediction obtained with both GBLUP and PBLUP were substantially lower than those in the previous two scenarios. However, the benefit of genomic prediction was greatest under this scenario, with prediction accuracies increasing fivefold (population I) and 2.5-fold (population II) relative to pedigree-based prediction accuracies. Perhaps surprisingly, there was little benefit from increasing SNP density above ~5 K SNPs under this scenario as well Fig. , which sGenomic selection is an increasingly important component of modern aquaculture breeding schemes, with simulated and applied studies highlighting its benefits over pedigree-based selection , 28. HowThe heritability of resistance to lice was estimated at ~0.3 and 0.2 in populations I and II, respectively, which is similar to the findings of Gharbi et al.  (~0.3) aThe GWAS indicated that host resistance to lice likely has a polygenic architecture, with no major QTL segregating in these populations Fig. . TherefoThe degree of the genetic relationships between training and validation sets is critical for the efficacy of genomic prediction. In our study, genomic prediction was found to be highly effective and showed a significant advantage in terms of accuracy over pedigree-based methods within populations  , 33. HowGenomic prediction is an effective method for predicting breeding values for host resistance to sea lice in Atlantic salmon populations from a commercial breeding programme. The GWAS results suggested that lice resistance is a polygenic trait. Cross-validation tests of genomic prediction highlighted the substantial improvements in prediction accuracy compared to that of pedigree-based prediction. The accuracy of GBLUP was highest when training and validation sets were closely related but the relative advantage over pedigree-based prediction within a population was largest when relationships were more distant. Relatively low SNP densities (from 1 to 5 K SNPs) were sufficient for accuracy to reach the asymptote under most of the scenarios tested. Prediction accuracy is generally much lower across distantly-related populations, although a trend was evident that increased marker density was advantageous in such situations. Therefore, larger population sample sizes and high-density SNP genotypes are probably necessary to improve across-population prediction. Given the economic importance of resistance to sea lice, and the efficacy of genomic prediction, it is likely that selective breeding for this trait using genomic data will become an important component of sea lice control.


Robustness in lettuce, defined as the ability to produce stable yields across a wide range of environments, may be associated with below-ground traits such as water and nitrate capture. In lettuce, research on the role of root traits in resource acquisition has been rather limited. Exploring genetic variation for such traits and shoot performance in lettuce across environments can contribute to breeding for robustness. A population of 142 lettuce cultivars was evaluated during two seasons (spring and summer) in two different locations under organic cropping conditions, and water and nitrate capture below-ground and accumulation in the shoots were assessed at two sampling dates. Resource capture in each soil layer was measured using a volumetric method based on fresh and dry weight difference in the soil for soil moisture, and using an ion-specific electrode for nitrate. We used these results to carry out an association mapping study based on 1170 single nucleotide polymorphism markers. We demonstrated that our indirect, high-throughput phenotyping methodology was reliable and capable of quantifying genetic variation in resource capture. QTLs for below-ground traits were not detected at early sampling. Significant marker-trait associations were detected across trials for below-ground and shoot traits, in number and position varying with trial, highlighting the importance of the growing environment on the expression of the traits measured. The difficulty of identifying general patterns in the expression of the QTLs for below-ground traits across different environments calls for a more in-depth analysis of the physiological mechanisms at root level allowing sustained shoot growth. Agronomic research has contributed to the design of lettuce cropping systems that maximize yields and optimize quality by supplying abundant water and nutrients, avoiding stress conditions  × wild (L. serriola L.) lettuce population subjected to drought, salinity and nutrient deficiency. Jenni et al.  × wild (L. serriola L.) lettuce F2:3 families. Thirteen QTLs, each accounting for 28–83% of the phenotypic variation in root traits, were identified, and they showed that the loci for taproot length co-localized with the ability to extract water from deeper soil layers.One way to improve resource capture and use efficiency and consequently the robustness of new lettuce cultivars may thus be to select for genotypes with a longer, more developed root system able to forage water and nutrients in the lower layers of the soil and compensate for the unavailability of resources in the top layers during a period of drought. With this idea, Johnson et al.  tested wHowever, assessing the genetic diversity of root systems with the objective to breed for improved root system architecture, is very intensive and labor-consuming, especially under field conditions where roots have to be sampled, washed, manually cleaned to remove organic litter and scanned. Instead, it might be easier to take soil samples to measure resource capture, and by a modeling approach, predict root characteristics—based on the assumption that root characteristics and resource capture are strongly correlated within relevant ranges, as shown by King et al.  in barleKerbiriou et al.  showed tThis study by Kerbiriou et al.  also revWhile the mechanisms involved in resource capture and use were analyzed in Kerbiriou et al. , the curTwo-hundred-fifty lettuce accessions, commercially available in the period between 1960 and 2008 were grown under field conditions in 2008 and were evaluated for a broad range of crop growth parameters. Out of these 250 accessions from various seed companies, 148 butterhead types suitable for field cultivation under either spring or summer conditions, or both, were selected for this study. Criteria for selection included diversity in head characteristics , commercial origin (seed company), and country and date of release. Criteria for selection did not include traits related to root characteristics, as they were not known for the entire set of cultivars, but we surmised that cultivars released before 1970 had larger root systems and lower harvest indices than more recent cultivars. In the selected population, 27 cultivars were released before 1970, 24 cultivars were released between 1970 and 1990, and 95 cultivars were released after 1990; the time of release of two cultivars was unknown. Eight cultivars were known to be grown by amateur gardeners, and two cultivars came from breeding programmes targeting specifically organic farming systems.Seeds used originated from randomly selected plants from the screening trial in 2008. Prior to transplanting, seeds were sown in 4 × 4 × 4 cm organic peat blocks  after breaking seed dormancy by exposure to 4°C for 24 h. Transplants were raised in a greenhouse with a day temperature of 20°C and a night temperature of 15°C.Transplanting was done when the transplants had 5–7 leaves and few roots started to emerge out of the peat block. In the field, plant arrangement was 0.3 × 0.3 m.Four field trials were performed: two in Wageningen , in spring and summer 2010, and two in Voorst , in spring and summer 2011. Each trial included two repetitions. The experimental set up was a randomized complete block design, each block consisting of 150 plots. Two plots per block were left empty for measurements in bare soil. Not bare plots were planted to 25 plants (5 × 5 plants) of the same cultivar  and previous crop management. All sites had been cropped uniformly in the previous 5 years on a larger surface than the area covered by the trials, in order to avoid influence of previous crops or field management on soil characteristics. In both locations the soil was sandy, poor with a low content in organic matter (8–10%), and low water retention capability. All trial fields were certified organic and managed according to organic standards during the experiments.For even distribution of nutrients, fertilization was provided by applying 100 kg/ha nitrogen, from seaweed pellets  on the day before transplanting, instead of using compost or manure. Weeding was done manually every week. Irrigation was not applied.http://www.met.wau.nl/ and for the Voorst trials, data were collected from the on-farm weather station). Soil temperatures were measured in 4 horizons  using a data logger. Cumulative degree-days , as well as cumulative rainfall at each sampling date for each trial are shown in Table For each trial, weather data  were recorded daily (Voorst) or hourly (Wageningen) at the nearest weather station  and at final harvest . For three plants per plot, soil samples taken in each soil layer were pooled to account for plant-to-plant variation.Volumetric soil moisture content (% v:v) was recorded after drying at 40°C for 48 h.3], assessed in ppm) in each 0.1 m soil layer was measured using an Ion Selective Electrode  using the method described previously by Sibley et al.  were assessed based on three plants per plot at final harvest, which took place 5–9 weeks after transplanting depending on the trial  estimates were then calculated based on the variance components as follows: h2 = The genotypic and residual variance components were estimated using the Residual Maximum Likelihood Estimations (REML) analysis of Genstat 15th Edition  with the following mixed model: response = general mean + genotype + block + error. Heritability  were mined from various transcriptome sequencing projects done on the leaves of two lettuce lines (proprietary markers by Enza Zaden). SNPs were identified in lettuce Expressed Sequence Tags (EST) and only the 1348 SNPs with high probability scores were conferred into KASP™ assays . Six cultivars from the 148 cultivars tested in the field were discarded in the association mapping studies because of large amounts of missing values (more than 10%) for these cultivars, therefore only 142 cultivars in total were kept in the analyses. Markers of poor quality or rare alleles (less than 10% occurrence) were also removed from the analysis and at the end 1170 markers were used. SNP markers were run with the DNA of the lettuce population on a Fluidigm chip on a Biomark HD system . The percentage of heterozygous scores over all SNPs and all accessions was 0.87%, with one accession having 6.6% heterozygosity over all SNPs and the remaining accessions all having less than 1.5% heterozygosity. The SNPs were mapped to nine linkage groups  were investigated on the set of 1170 SNP markers correcting for relatedness using the significant eigenvectors as covariates in the QLDDECAY procedure in Genstat 15th Edition . For each chromosome, pairwise LD between markers was calculated using the square of the corrected correlation coefficient, p-values were –log10 transformed. To account for multiple testing, a number of effective tests (# tests) was calculated as the ratio of the total genome size to the average LD over the nine chromosomes, and used to calculate the threshold of significance to claim a significant QTL as: threshold = −log10(0.05/# tests). Because a threshold of 3.5 was more stringent than a 5% false discovery rate, this value was used to identify significant marker-trait associations throughout the analyses.All the mean shoot and soil measurements obtained for each cultivar in each environment were used as phenotypic data to be related to the genotypic data. Association mapping studies were carried out for each trait at each sampling date within each environment using the QASSOCIATION procedure in Genstat 15th Edition . Population structure was corrected for using the eigenvectors as random covariates in a linear mixed model with QTL as fixed effect at the marker position. The Wald-test was used to test significance; Wald The threshold for the minor allele frequency (MAF) was set to 7%  for testing marker-trait associations.Figure Moisture or nitrate measurements in the soil at intermediate or final sampling did not show significant variation caused by cultivar differences for Trial 1, 2010, with relatively mild temperatures and dry weather. Significant genetic variation was found in moisture content in each soil layer and over the whole soil profile at final sampling in Trial 1, 2011, with relatively low temperatures and dry weather; nitrate left in the soil did not show much genetic variation in Trial 1, 2011. Highest levels of nitrate left in each soil layer and over the whole soil profile at final harvest were recorded for Trial 2, 2010, under optimal growing conditions, with in most cases significant genetic variation. Also in Trial 2, 2011, an experiment under conducive growing conditions, several soil variables showed significant genetic variation Figure ; Table 3Under optimal growing conditions  the highest dry matter production and highest Nitrogen Use Efficiency (NUE) at final harvest were achieved Figure . SignifiPer trial, the heritability estimates were low for the soil moisture content measurements at each layer and over the whole soil profile, except for the measurements made at final harvest in Trial 1, 2011 (moderately dry conditions), where estimates ranged from 14 to 38% , and Trial 2, 2011 (wet conditions), with values ranging from 7 to 17% in the layers 0.1–0.4 m of the soil  were generally the traits for which the heritability was the largest, with values up to 55% Table . The higr2 beyond which LD was assumed to be due to genetic linkage, was estimated to be 0.2 over the whole genome. For each chromosome, intra-chromosomal LD was calculated as the intersection of the LD trend line with the basal r2 .Many significant QTLs were found in the association mapping study, especially for the traits measured at final harvest. Most of the QTLs found were located on chromosomes 4, 5, 7, and 9, while only very few significant associations were found on chromosomes 1, 2, 3, 6, and 8  and chromosome 9 (52.0 cM), for layer 0.2–0.3 m on chromosome 9 (53.7 and 57.7 cM) and for layer 0.3–0.4 m on chromosome 7 (97.2 cM).2O and NO3  in the deeper soil layers than the rest of the group , from a single seed company (3), or from diverse seed companies (3). The ANOVA based on this grouping showed that this cluster left significantly more Hp Figure . The culSeveral significant QTLs were detected for the shoot traits, mainly for the shoot fresh and dry weights. For Trial 2, 2011, only few significant QTLs were found for shoot traits Table . Signifi3] in a 0.10 m soil layer at final harvest in Trial 2, 2010 , and in Trial 2, 2011 (0.1–0.2 m). On chromosome 8, the region around 100 cM was significantly associated with NO3 content over the whole soil profile in Trial 2, 2010 (intermediate sampling), and Trial 1, 2011 .The QTLs detected for the below-ground traits showed reasonable consistency across trials: for instance on chromosome 7, the region around 43.6 cM was significantly associated with  in layers 0.1–0.2 and 0.3–0.4 in Trial 2, 2010 . On chromosome 7, the region around 43.6 cM was very significantly associated with shoot fresh weight in Trial 2, 2010, and NO3 left over the soil profile in the same trial. On chromosome 9, the region between 50 and 60 cM was significantly associated with the dry weight at final harvest (marker at 52 cM) and diverse below-ground traits in Trial 2, 2011, at final harvest : the same marker was associated with several traits.The same QTLs were often detected for multiple traits across trials. For instance, the region around 50 cM on chromosome 5 was associated with fresh and dry weights in Trial 1, 2010. On this same chromosome, the region around 90 cM was significantly associated with fresh weight and NO15N labeling . As shown by Hodge (3] and [H2O] in the lower layers of the soil profile) in this very specific environment  that barely any genetic variance and consequently no QTLs were detected for these traits. Although heritability values were higher for the shoot traits, the significant QTLs detected for these traits were relatively less consistent and less numerous than the significant marker-trait associations detected for the below-ground traits. One reason for this discrepancy might be that, as shown in Kerbiriou et al. , the ranby Hodge , Gallardby Hodge  and Kerbby Hodge  localize3] concentration in the different layers of the soil profile. One can imagine that in relatively warm and optimal conditions with regular rainfall, which replenished the soil profile at regular intervals  was significantly negatively correlated with the [NOnd Table .3] of the lower layers of the soil profile in Trial 2, 2011; in Uwimana et al. (Overall, several regions showed to be significantly associated with below-ground traits, e.g., the region around 80 cM on chromosome 4, the region around 90 cM on chromosome 7, and the region between 50 and 60 cM on chromosome 9. The exact same regions were not identified before, although there seems to be some overlap with some regions previously identified by Uwimana et al. . For insa et al.  a neighba et al.  would imGiven the observations above, the genotypic data used in this study may be further transformed to gain a more accurate understanding of the G × E, and exploited more in-depth to get a better insight into the mechanisms explaining the results. As this study was based on the assumption that root characteristics are strongly correlated with resource capture, it would be interesting to assess the root system architecture of the cultivars used in this association panel. It would also be interesting to investigate further how the regions identified in this study relate to each other, and how they interact with the environment, by for instance designing experiments where different stresses are applied (such as in Kerbiriou et al., Although these traits can be more easily measured in greenhouse experiments, such greenhouse experiments may not always reflect the reality of the field conditions. This was illustrated by a study by Hartman et al.  who founMost of the recent literature investigating the potential of marker use in lettuce breeding has been focusing on cultivated × wild lettuce crosses (Johnson et al., However, the high frequency of the alleles shown in Tables Furthermore, the development of more affordable and faster molecular techniques will soon allow systematic genotyping as a molecular-assisted breeding tool and might replace current techniques using genotypic markers. Indeed, sequencing the whole genome allows a more precise localization of genomic regions of interest and thus the identification of potential candidate genes regulating the expression of the trait of interest. In contrast, marker technologies can only point out potential regions of interest but do not bring much information in regards to the expression of the trait. For breeding for complex traits though, the bottleneck remains in the phenotyping. As pointed out by Johnson et al.  below-grPK, MK, EL, and PS conceived and designed the study. MK carried out the pre-screening of the varieties. PK carried out the field experiments and managed the data collection and data analysis. EL and PS supervised experimentation. DF and IR carried out the genotyping. CM supervised data analysis. PK, CM, TS, EL, and PS interpreted the results and 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