The idea of forensic DNA intelligence is to extract from genomic data any information that can help guide the investigation. The clues to the externally visible phenotype are of particular practical importance. The high heritability of the physical phenotype suggests that genetic data can be easily predicted, but this has only become possible with less polygenic traits. The forensic community has developed DNA-based predictive tools by employing a limited number of the most important markers analysed with targeted massive parallel sequencing. The complexity of the genetics of many other appearance phenotypes requires big data coupled with sophisticated machine learning methods to develop accurate genomic predictors. A significant challenge in developing universal genomic predictive methods will be the collection of sufficiently large data sets. These should be created using whole-genome sequencing technology to enable the identification of rare DNA variants implicated in phenotype determination. It is worth noting that the correctness of the forensic sketch generated from the DNA data depends on the inclusion of an age factor. This, however, can be predicted by analysing epigenetic data. An important limitation preventing whole-genome approaches from being commonly used in forensics is the slow progress in the development and implementation of high-throughput, low DNA input sequencing technologies. The example of palaeoanthropology suggests that such methods may possibly be developed in forensics. The information included in genomic data can be used to generate investigative leads that, when properly used, can speed up the process of human identification in forensic investigations. Such forensic DNA intelligence can use a variety of methods, including relatedness testing, the inference of ancestry, the prediction of physical phenotype, and age estimation ,2,3,4. AA large meta-analysis of twin studies has confirmed that all human traits are heritable and showed that most of the traits can be explained by an additive genetic variation . The extGWAS data have been very effective at explaining the heritability of physical appearance traits. The heritability of human pigmentation traits has been assessed to be above 80% and, thus, provides a good starting point for DNA-based prediction, because it means that 80% of the variation in pigmentation in a population is due to genetic variation between individuals and that the influence of the environment is relatively small ,10,11.OCA2 gene, was implicated in eye colour via linkage and subsequent fine-mapping analyses [OCA2 genotypes and eye colour became stronger with additional reports [HERC2 in the determination of eye colour and suggested that this genomic region was responsible for the regulation of OCA2 gene expression [MC1R gene, which was also discovered long before the genome-wide association scans and confirmed in various population samples across the globe [IRF4 gene, which is now an important predictor of pigmentation phenotypes [Many candidate genes for human pigmentation were identified before the GWAS era through animal models and the linkage to diseases with mendelian inheritance modes, such as oculocutaneous albinism. Genome-wide association scans confirmed the importance of these genes and identified many of the novel gene variants influencing the variability of normal human pigmentation. The collected data confirmed a very promising perspective for the genetic prediction of pigmentation traits. The less complex nature of some pigmentation phenotypes, such as blue and brown eye colours and red hair colour, and the availability of DNA variants with relatively large effect sizes, similar to the genetic effects observed for mendelian traits, were particularly encouraging. The region on chromosome 15, including the analyses ,13. The  reports ,15,16. Tpression . This sppression ,19. The pression . Most ofhe globe ,22. The he globe ,24. Thesenotypes ,25. The enotypes ,27. Indeenotypes . The stuenotypes .SLC24A5, discovered thanks to a Zebrafish study, plays an important role in skin colour differences at the continental level, explaining more than 30% of skin colour differences between African and European populations [Human skin colour is highly variable at continental and intercontinental levels, complicating research on the genetic architecture and heritability of this trait . The rs1ulations ,32,33,34ulations ,36.SLC45A1, DSTYK, FOSL2, LHX2, EDNRB, SHC4, KRT31, and BCAS1. The study was highly successful at explaining up to 34.6% (red hair) of the heritability of human hair colour, despite an imperfect phenotyping regime involving self-reported hair colour in adulthood [MC1R gene may be more important for explaining red hair colour than the polymorphism within the MC1R exon, and it identified an additional eight loci that contribute to the genetics of red hair colour. This research also revealed 213 variants important to the determination of blond hair colour, accounting for 73% of SNP heritability. In addition, a set of 56 DNA variants was found to be important for brown hair colour and was assessed to account for 47% of SNP heritability of this hair category [A meta-analysis that involved almost 300,000 genomes from individuals of European ancestry included in two different cohorts  discovered 124 loci relevant to human hair colour, mostly novel associations, including genes with strong effect, such as dulthood . Anothercategory .AR/EDA2R) and 20p11 (PAX1/FOXA2). Of these two loci, only 20p11 is known to act in Asians, indicating the significance of population heterogeneity [HDAC9, WNT10, TARDBP, EBF1, SUCNR1, AUTS2, FGF5, IRF4, C1orf127, RUNX1, and TWIST2). Studies published in 2017–2018 led to significant advances in research on the genetics of hair loss. Four large GWA scans have been conducted on individuals of European descent. The first three of those studies, which investigated 20,000–50,000 genomes each, detected altogether more than 300 GWA signals, including 253 novel MPB associations [Along with hair colour, other features describing the properties of human hair can be useful to define the physical appearance of an individual. Research shows that genetics plays a key role in the determination of hair features. However, the level of heritability may differ for various hair traits. Very high heritability (85–95%) was estimated for hair shape . Heritabogeneity ,55. A lociations ,52,53. Tciations . These lciations ,57.TCHH gene is known to act only in Europeans, while EDAR is the main contributor to straight hair in East Asians [TCHH gene was discovered in the first GWA study published in 2009, which was conducted on three cohorts with a total of more than 4800 individuals of European descent [TCHH was the only gene in this study that reached genome-wide significance, but suggestive associations were also disclosed for several additional loci, including the FRAS1 and WNT10 genes. The role of the EDAR gene in hair straightness and thickness in Asians was discovered through candidate gene analyses [PRSS53 [ERRFI1/SLC45A1, PEX14, PADI3, TGFA, LGR4, HOXC13, KRTAP, and PTK6) [RPTN gene has been implicated in straight hair formation in Europeans and East Asians, but throughout interactions with different previously known head hair shape genes.For hair morphology (shape), four GWA studies have been published so far, two of which were carried out on Europeans, with one study on Latin Americans and one on East Asians ,58,59,60t Asians . Howeveranalyses ,62 and c [PRSS53 . The latnd PTK6) . The RPTEDAR, FOXL2, LIMS1, TMEM174, SOX2, and FOXD1 [PAX3 and 5q13.2 [EDAR, LNX1, PREP, and FOXP2 [IRF4, and KIF1A [Only a few studies have addressed the genetic basis of other hair traits. In recent years, the first genes responsible for the thickness of the eyebrows, e.g., nd FOXD1 ,64,65, md 5q13.2 ,51, bearnd FOXP2 , and haind KIF1A ,66, haveHuman height is heritable in approximately 80%, but only the single genes associated with this trait were known before the era of GWA studies. The study of human height genetics is a good example of the effectiveness of explaining the heritability of complex traits through the GWAS approach. An important advantage of stature studies is undoubtedly the ease of measuring this phenotype and thus the homogeneity of the phenotypic data. The genome-wide association scans for human stature identified gene variants with a small effect size, clearly confirming the high polygenicity of this trait. The first three GWA studies of human height, which collectively included more than 50,000 samples, detected only 54 loci with a statistically significant association with stature ,68,69. MGREM1 gene was associated with nose width, and another near the CCDC26 gene was associated with bizygomatic distance. The first GWAS scan that was aimed at investigating normal facial variation identified only a single intronic DNA variant in the PAX3 gene, which showed association with nasion position. This first study was conducted on a relatively small group of 2185 adolescents. The study used a 3D laser scanning method to collect phenotypic data. The 22 identified landmarks were then used to generate 54 3D and 2D distances featuring different facial characteristics. Additionally, following a previous method, a principal component analysis enabled the identification of 14 independent groups of correlated coordinates [PAX3 was replicated in an independent cohort of 1622 participants [PAX3, PRDM16, and TP63 have previously been linked to craniofacial development, while C5orf50 and COL17A1 were new findings [PAX3, which soon gained further confirmation in an independent study of about 6000 Latin Americans investigated in the large CANDELA project [PAX3, the most replicated gene for natural variation in facial appearance, cause Waardenburg syndrome, which involves some facial dysmorphism, including a broad nasal bridge. The phenotyping regime in Adhikari’s study involved a simple approach based on standard 2D photographs, and the study also implicated DCHS2, RUNX2, GLI3, and PAX1 in nose morphology and EDAR in chin protrusion [MAFB, PAX9, MIPOL1, ALX3, HDAC8, and PAX1 in normal facial variation, including the measures of eye, nose, and facial breadth. The study also provided additional evidence for the association between PRDM16 and C5orf50 and facial features [PCDH15, MBTPS1, and TMEM163, genes that have previously been associated with various pathological phenotypes involving craniofacial dysmorphias [SCHIP1 and PDE8A, identified that were associated with measures of human facial size [The human face represents a set of correlated complex phenotypes that are highly variable at inter- and intra-population levels and define what is apparently the most differentiating human trait . The higicipants . A largefindings . The str project . It is wotrusion . Anotherfeatures . Crouch morphias . The stumorphias . This apmorphias  and for morphias . The latial size . GWA stuial size .MC1R gene was soon proposed as an indicator of red hair colour [OCA2 variation was proposed for the inference of eye colour [Several factors determine the accuracy of DNA-based predictive methods, including high heritability of a trait, the identification of appropriate predictors, and the selection of the best mathematical approach to model development. The forensic community very early recognised the investigative potential of extracting phenotypes from DNA data. The practical importance of a simple amelogenin genetic sex test , and alsr colour , while te colour . The avaHERC2 rs12913832, OCA2 rs1800407, SLC24A4 rs12896399, SLC45A2 rs16891982, TYR rs1393350, and IRF4 rs12203592. The original IrisPlex method implements a multinomial logistic regression algorithm and a simple single base extension method based on SNaPshot minisequencing, which allows the PCR amplification and genotyping of several SNPs in a multiplex reaction. Importantly, the products of primer extension are analysed using capillary electrophoresis platforms, which are commonly used in human identification testing laboratories. Other tools based on other mathematical solutions were soon developed but, essentially, each of these algorithms relied on exploiting information in the HERC2-OCA2 gene complex. In general, these works were limited to the development of predictive algorithms using various sets of samples and mathematical approaches, but did not present specific tools for the collection of genetic data [In particular, the discovery of eye colour markers with large phenotypic effects has made it easy to develop pretty accurate genetic predictors of this trait. The best-known tool commonly used in the forensic field today is the IrisPlex predictive system, which includes both a genetic test for data acquisition and a mathematical algorithm for predicting the three categories of eye colour . The algtic data ,103,104.tic data ,124,125.tic data  and finatic data , which wtic data . The skitic data . Notablytic data ,108,110.tic data . A more tic data ,112. It tic data . Furthertic data ,123. ThiEBF1, TARDBP, and HDAC9), trained and validated on >600 samples from six European populations [Numerous association studies conducted for MPB raised questions about the predictive ability of the discovered genetic variants. In 2015, a compact regression model was developed based on analysis of five SNPs from five genomic regions , and was trained on data generated for 528 samples from Polish individuals [EDAR gene, which has a significant effect on the determination of straight hair in non-European populations, primarily East Asian. In addition to the binomial model, a multinomial logistic regression model was developed to allow for a higher resolution of hair shape prediction, considering three categories—straight, wavy and curly—based on an analysis of 33 SNP positions. There are few or no prediction studies of the remaining hair features. In 2016, Adhikari et al. predicted different hair traits using the GWAS data generated for Latin Americans and reported the highest accuracy of prediction for beard thickness and the lowest for hair greying, with ~18% and ~7% of the phenotypic variation explained by the associated SNPs, respectively [KIF1A rs59733750, SEMA4D rs45483393) and literature resources . The model achieved a high accuracy of prediction with a cross-validated AUC = 0.87  but most of the prediction information was driven by age itself, while SNPs were found to explain merely ~7% of the variation in hair greying. As mentioned earlier, age is a very important factor in predicting hair loss. Sex and age were also shown to slightly improve the accuracy of prediction of hair shape [The first preliminary model for head hair shape was developed as a follow-up to the first GWA study conducted on hair characteristics , and incividuals . The modividuals . The binectively . Interesectively . A binarir shape .This implies that there is a need to determine the sex and age of an individual from the analysed biological sample. Information on a person’s sex is usually available in criminal investigations due to the inclusion of marker for the amelogenin gene located on the X and Y chromosome in standard STR DNA profiling, as previously mentioned, whereas age can be estimated via epigenetic analysis .Attempts at forensic human height prediction have not been particularly numerous and have been limited to the development of predictive algorithms that are not equipped with data collection tools. The reasons are related to the limitations of DNA analysis technology and stem from the need to analyse too many DNA variants. While the 5% heritability explained by the 54 DNA variants identified by the initial GWAS scans for human height was unlikely to predict the full range of human height, Aulchenko et al. tested whether it would allow the reliable prediction of extreme height. However, this turned out to be possible with only limited accuracy. Tall stature prediction was possible at AUC of 0.65, thus only moderately improving the accuracy resulting from a random hit (AUC = 0.5) . Using tDrawing a forensic sketch based on the instructions of a witness in a criminal case is a tool that has been used for years to identify the perpetrator of a crime. People recognise each other through the high variability of facial features. Therefore, having a good understanding of the genetics of human facial variation and being able to predict this complex phenotype is a very exciting prospect for forensic DNA intelligence. The small amount of explained heritability for craniofacial traits does not bring good prospects for the prediction of human facial phenotypes. Nevertheless, attempts have been made to develop models that would allow the prediction of facial appearance. The proposed methods are based on the indirect prediction of facial phenotypes, with ancestry and sex prediction DNA data playing a key role in this regard. The method by Claes et al. implements a bootstrapped response-based imputation modelling that makes use of information on genomic ancestry and sex first to create a sketch called a base-face. At the second stage, the information in 24 SNPs associated with facial variation is used to improve the prediction outcome . A similThe prediction of human externally visible characteristics using DNA markers can be treated as a supervised learning problem in which the considered appearance trait corresponds to a response (target) variable, whereas genetic markers correspond to explanatory variables . The supervised learning models are fitted using training data, which consist of observations for which the value of the target variable is known. Depending on the type of the target variable, three tasks can be distinguished: regression , binary classification , and multi-class classification .The specificity of the problem and the greatest challenge lies in the large number of potential features (genetic markers), which may significantly exceed the number of observations in the training data. Due to this, the use of traditional models and estimation methods (such as the maximum likelihood method in logistic regression) is not feasible. The simplest solution is to use some initial filtering method to reduce the total number of markers. However, simple filters only assess the marginal dependence between the variable and the trait; they may exclude variables that are potentially useful for the model, for example, variables that contribute by interacting with already selected ones. Therefore, there is a need to apply the estimation methods as well as feature selection approaches specially tailored to high-dimensional settings. This is one of the greatest challenges in designing learning models for appearance trait prediction.Finally, it is important to note that traditional genome-wide association studies focus on detecting the genetic variants associated with the trait with high statistical confidence, which, in particular, includes controlling the probability of at least one rejection via multiple-testing procedures. When the prediction is the main task, the paradigm shift is needed, because focusing on the accuracy of the model becomes the main objective . This apDespite its simplicity, the linear model and its generalisations are powerful tools for appearance trait prediction. The theory  and empilinear regression model, which assumes that for an In the case of the quantitative trait, it is natural to consider the ion 9 in ) in whicRegarding regularisation in the above models, the lasso penalty n height  or eye cn height . The lasAlthough the lasso method has many excellent properties and high predictive power, in recent years, several modifications have been proposed in statistical and machine learning literature. For example, it has been noticed that the lasso method produces biased estimators for truly significant variables with large coefficients, and this bias does not necessarily disappear for a large sample size. To overcome this drawback, non-convex penalties, such as SCAD (smoothly clipped absolute deviation)  or MCP , ncvreg , knockoff (knockoff filter), and earth (MARS method).The black-box model is a class of predictive models that are able to recover complex dependencies between explanatory variables and the dependent variable, including interaction terms, and which can potentially achieve higher accuracy then linear models. The main limitations are the high computational complexity, the difficulty in interpreting the model, and the necessity of parameter tuning. In this group, ensemble methods and neural networks play the leading role. The former are usually based on decision trees  and overon 10 in ). In GB,variants . The ensThe ensemble methods described above (RF and XGB) are often used to assess the importance of the features. The simplest approach is based on a permutation scheme and is very similar to the randomisation feature selection described above. The first method  involves fitting two ensemble models : the first is based on the original training data and the second is based on training data in which the values of the lgorithm , based olgorithm , in whicThe second important group of black-box models is artificial neural networks (ANN) . The latlgorithm  is now tlgorithm . A numbelgorithm ), are ofII):Feature selection is an essential element when building predictive models, as it prevents overfitting and allows discovering the dependency structure between variables and, in particular, recovering the features that affect the target variable. In the models described above, feature selection is usually embedded in learning algorithms. For example, in linear models as well as neural networks, selection is performed via regularisation, whereas in tree-based methods, the relevant features are selected when building the tree. However, including too many potential features may significantly increase the computational cost of fitting the model. Thus, very often in practice, there is a need to apply some fast preliminary filtering method. In the machine learning community, methods based on information theory have gained the most popularity in recent years . They arpistasis ,158, andpistasis . It has on model .The existing filters based on MI are forward sequential procedures that, in each step, add a candidate feature raction) ,161. It nsidered . In pracnsidered .The limitations of DNA sequencing technologies used in the forensic field are increasingly problematic because they are hindering the implementation of new methods that can improve law enforcement and justice, and which are therefore important for the safety of society. The lack of a suitable method for generating large amounts of SNP data from degraded DNA, validated for use in forensics, was considered to be a barrier to the forensic implementation of investigative genetic genealogy, an approach that was very successful at solving a number of criminal cases ,164. SucOne category of protocols involves library construction based on double-stranded DNA. A first protocol was described by Meyer and Kircher in 2010, and this was laborious and had limitations that resulted in the losses of ancient DNA sequences due to incompatible adapter combinations and three purification steps prior to amplification . Double-The second approach for library preparation from samples containing low amounts of degraded DNA is particularly interesting, as it implies a process of library construction based on single-stranded DNA, allowing the use of DNA that was preserved in a single-stranded state and which is considered to be more efficient compared to double-stranded approaches. The original protocol for single-stranded library preparation, although it recovered more endogenous DNA, was very expensive and laborious . HoweverResearch on the genetic architecture of natural variation in the human physical phenotype is growing in scale and involves different human populations. The genetic prediction of physical appearance traits occupies an important place in forensic research, although the available tools are limited to the least complex traits, mainly pigmentation. Notably, there are examples of using predictive methods that have been developed by the forensic community in ancient DNA research, and which have been carried out in the field of molecular anthropology ,178,179


Mixed models are used to correct for confounding due to population stratification and hidden relatedness in genome-wide association studies. This class of models includes linear mixed models and generalized linear mixed models. Existing mixed model approaches to correct for population substructure have been previously investigated with both continuous and case-control response variables. However, they have not been investigated in the context of extreme phenotype sampling (EPS), where genetic covariates are only collected on samples having extreme response variable values. In this work, we compare the performance of existing binary trait mixed model approaches  on EPS data. Since linear mixed models are commonly used even with binary traits, we also evaluate the performance of a popular linear mixed model implementation (GEMMA).We used simulation studies to estimate the type I error rate and power of all approaches assuming a population with substructure. Our simulation results show that for a common candidate variant, both LEAP and GMMAT control the type I error rate while CARAT’s rate remains inflated. We applied all methods to a real dataset from a Québec, Canada, case-control study that is known to have population substructure. We observe similar type I error control with the analysis on the Québec dataset. For rare variants, the false positive rate remains inflated even after correction with mixed model approaches. For methods that control the type I error rate, the estimated power is comparable.The methods compared in this study differ in their type I error control. Therefore, when data are from an EPS study, care should be taken to ensure that the models underlying the methodology are suitable to the sampling strategy and to the minor allele frequency of the candidate SNPs.The online version contains supplementary material available at (10.1186/s12864-022-08297-y). In genetic studies involving human populations, researchers are interested in determining how genetic variation contributes to disease. Genome-Wide Association Studies (GWAS), which involve genotyping a large number of individuals at hundreds of thousands of genetic markers, have been useful for discovering relationships between common variants and complex diseases. Recently, sequencing has been used to discover rare variants associated with human traits . AlthougAn example of a cost saving design is extreme phenotype sampling (EPS), a design where genetic data are collected only on individuals in the tails of the phenotype distribution. The use of this study design was proposed by Lander and Botstein  for linkAs with all population-based genetic association designs, extreme phenotype sampling is prone to confounding by population structure or stratification. Differences in allele frequencies among members of a strata or subgroup in the population may lead to confounding if there are also differences in the phenotype distribution between the subgroups. Confounding is known to inflate the type I error rate, which can lead to spurious associations. Methods have been developed that can correct for the effects of population stratification using genomic data. The earliest approaches include Genomic Control  and STRUMixed model methods have recently become popular due to their robustness in tackling other sources of confounding in the study, in particular cryptic relatedness . Since mIn human genetic studies, the phenotype of interest is often a binary trait, such as presence or absence of disease. To correct for population stratification, binary traits are sometimes analysed using LMMs –21 even Mixed model approaches that do not treat disease status as a continuous random variable have recently been developed. One such approach is based on the liability threshold model, which assumes that there is an unobserved normally distributed latent variable known as the ‘liability’ and that individuals having liability values above a threshold are classified as cases. Liability threshold-based methods have been implemented in the software LEAP  and LTMLWe have previously shown that the false positive rates due to population stratification are substantially inflated with EPS designs relative to random sampling . TherefoIn this work, we aim to accomplish two goals. First, we present an overview of the mixed model-based approaches for correcting for population stratification with a binary response variable; we focus on the recently proposed algorithms LEAP, LTMLM, GMMAT and CARAT. Second, we compare the performance of these approaches and an LMM approach (GEMMA ) when thp1=0.25 and p2=0.85 and the phenotypic means of μ1=0.07 and μ2=−0.07 for subpopulations 1 and 2, respectively. LEAP and GMMAT show well controlled type I error rates, indicating adequate correction of the population structure in the data. For both approaches, the estimated type I error rate for all the sample sizes ranges between 0.041−0.052. All but one of these estimates are slightly lower than the nominal level of 0.05; however, these small deviations from the true value can be explained by Monte Carlo sampling error. The type I error rate for the PCA approach is also close to the nominal value, though possibly slightly elevated; similar results for the PCA based correction were observed in our previous work  distribution. To mimic population differentiation, the ‘1’ allele frequency within each of the two populations, p1 and p2, was then sampled from a Beta distribution with shape and scale parameters Fst level [Genetic data was simulated using the Balding-Nichols method , 44 as pfication . We labep1=0.25 in the first subpopulation and p2=0.85 in the second subpopulation. Although this allele frequency difference is probably not realistic in practice, it was chosen to reflect a ‘worst case’ scenario of a candidate SNP showing extreme population differentiation. We also included a smaller simulation where we varied the ‘1’ allele frequency difference between the populations; in particular, we set p2 to range from 0.5−0.9 while fixing p1 at 0.5.We simulated a candidate SNP separately. We first assumed that the ‘1’ allele frequency for the candidate SNP was μ1=0.07 and μ2=−0.07 for subpopulation 1 and 2, respectively, and a common variance of σ2=1. We note here that the genotypes and phenotypes have been simulated independently, which implies that the genotype at the candidate SNP is not causally associated with the phenotype. The EPS sample was then selected as the individuals in the upper and lower 10th percentile of the phenotype distribution. For the EPS sample, the binary response variable is membership in the upper or lower group; in practice, these are sometimes labelled as cases and controls, though it should be noted that there is no true control group in this design.In order to obtain the EPS sample, we simulated phenotypes from a normal distribution with mean values p=5000 SNPs simulated under the Balding-Nichols method was used to compute the GRM; the candidate SNP for the association test was not included in the GRM calculation. LEAP and GEMMA compute the GRM as part of the algorithm. For GMMAT, the GRM must be computed externally and then passed to the program; we used the standardized GRM computed by GEMMA.For methods requiring a GRM, the genetic data on the m=3000 datasets under the scenario where the candidate variant ‘1’ allele frequency was p1=0.25 and p2=0.85 in subpopulation 1 and 2, respectively. The computational time for CARAT is significantly longer than the other methods, particularly for the large sample sizes. Therefore, we were only able to complete CARAT analysis of m=1999 simulated datasets for the simulation with full cohort size of N=10,000. Due to limited computational time, we only performed m=1000 simulations for each setting under the scenario where p1=0.5 and p2 varied. For these simulations, we chose to focus on the trend in the rate as p2 varied for each method separately.We simulated α=0.05. Simulations were run in a cluster computing environment (CAC-FRONTENAC) and all analysis of the results was done in R [GMMAT is available as an R package . LEAP, Gone in R .ms [Ne=100,000, a per-site mutation rate of μ=10−8 and a per-adjacent site recombination rate of ρ=10−8. To incorporate population structure, we again assumed two subpopulations of equal size (i.e. 5000 haplotypes from each subpopulation) and a migration parameter M=10, which is representative of the population differentiation parameter Fst=0.01 in the case of a common variant [N=5,000 individuals, the 10,000 haplotypes were randomly paired within subpopulation. The continuous phenotype values and genetic data at 5000 non-candidate SNPs (for GRM estimation) were generated as previously described for the common variant simulation study.We also investigated the performance with a candidate region having rare variants. To simulate data for the candidate region, we simulated haplotype data in a 30kb region using the coalescent-based simulation program ms . We simuα=0.05.To test for association with rare variants while accounting for population structure, only the generalized linear model approach had software available. We used SMMAT (variant set mixed model association test) , which iFor methods that adequately controlled the type I error rate , we conducted additional simulations in order to compare their performance with respect to power. We did not include a rare variant power simulation since none of the methods we tested adequately controlled the type I error rate.μi+βGij where μi, the subpopulation means, are the same as for the type I error simulations, Gij is the genotype of individual j in subpopulation i, and β is the effect size of the causal allele (β=0.15 and β=0.25). We simulated a full cohort size of N=10,000 which gives us an EPS subsample of n=2000 when we select the top and bottom 10%. The number of simulations for power estimation was set to m=3000 and power was estimated by the proportion of tests rejected at level α=0.05.For the power simulations, the genetic data for estimating ancestry was simulated using the same procedure as described for the type I error simulations. In particular, we continue to assume that there is hidden population subdivision. To simulate the candidate SNP, we assumed no differences in allele frequency between the two populations and an allele frequency of 0.2 for the causal allele. The genotypes were sampled assuming Hardy-Weinberg equilibrium. The phenotype was again simulated assuming a normal distribution, with variance 1 and mean p-value of 10−6. We also checked that all the SNPs used were autosomal (i.e on chromosomes 1-22) and that all reported male individuals had an F value (based on the X chromosome inbreeding estimate) above 0.8. After quality control, genotype data was available on 574,885 SNPs and for 1295 cases and 1248 controls.We evaluated the mixed model methods for common variants on data collected from a population-based case-control study, conducted in Montréal, Canada. The study has been described elsewhere (e.g. ). BrieflP-value=0.48); we therefore used this as our continuous phenotype and pooled the cases and controls. We selected those in the top and bottom 15% of BMI in our extreme sampling design. After data cleaning, we observed that 2520 of the men with complete genotype data also had BMI data. With these numbers, the sample size of the final EPS sample was about 756.Data was collected on several continuous variables within this study. We found that body mass index (BMI) was not associated with prostate cancer status in this study . The red line indicates the standard threshold for a genome-wide significant association .Additional file 2 Title: Supplementary Figure 2. Description: Manhattan plot for results obtained from GMMAT for the GWAS with the BMI phenotype. The y-axis shows -log10 of the p-values from test for association between BMI extremes and genotype and the x-axis shows genomic position of the SNP. The blue line indicates the standard threshold for a suggestive association . The red line indicates the standard threshold for a genome-wide significant association .Additional file 3 Title: Supplementary Figure 3. Description: Manhattan plot for results obtained from GEMMA for the GWAS with the BMI phenotype. The y-axis shows -log10 of the p-values from test for association between BMI extremes and genotype and the x-axis shows genomic position of the SNP. The blue line indicates the standard threshold for a suggestive association . The red line indicates the standard threshold for a genome-wide significant association .Additional file 4 Title: Supplementary Figure 4. Description: Manhattan plot for results obtained from PLINK (uncorrected logistic regression) for the GWAS with the BMI phenotype. The y-axis shows -log10 of the p-values from test for association between BMI extremes and genotype and the x-axis shows genomic position of the SNP. The blue line indicates the standard threshold for a suggestive association . The red line indicates the standard threshold for a genome-wide significant association .


Wolbachia bacteria are perhaps the most common endosymbionts, manipulating host reproduction to propagate. Many Wolbachia cause cytoplasmic incompatibility (CI), which results in reduced egg hatch when uninfected females mate with infected males. Wolbachia that cause intense CI spread to high and relatively stable frequencies, while strains that cause weak or no CI tend to persist at intermediate, often variable, frequencies. Wolbachia could also contribute to host reproductive isolation (RI), although current support for such contributions is limited to a few systems. To test for Wolbachia frequency variation and effects on host RI, we sampled several local Prosapia ignipectus (Fitch) (Hemiptera: Cercopidae) spittlebug populations in the northeastern United States over two years, including closely juxtaposed Maine populations with different monomorphic color forms, “black” and “lined.” We discovered a group‐B Wolbachia (wPig) infecting P. ignipectus that diverged from group‐A Wolbachia—like model wMel and wRi strains in Drosophila—6 to 46 MYA. Populations of the sister species Prosapia bicincta (Say) from Hawaii and Florida are uninfected, suggesting that P. ignipectus acquired wPig after their initial divergence. wPig frequencies were generally high and variable among sites and between years. While phenotyping wPig effects on host reproduction is not currently feasible, the wPig genome contains three divergent sets of CI loci, consistent with high wPig frequencies. Finally, Maine monomorphic black and monomorphic lined populations of P. ignipectus share both wPig and mtDNA haplotypes, implying no apparent effect of wPig on the maintenance of this morphological contact zone. We hypothesize P. ignipectus acquired wPig horizontally as observed for many Drosophila species, and that significant CI and variable transmission produce high but variable wPig frequencies.Animals serve as hosts for complex communities of microorganisms, including endosymbionts that live inside their cells.  Wolbachia (wPig) infects the spittlebug Prosapia ignipectus. wPig was likely recently acquired and has little apparent effect on the maintenance of a P. ignipectus morphological contact zone in Maine, USA. The wPig genome contains three sets of loci associated with cytoplasmic incompatibility (CI), and we hypothesize that significant CI and variable transmission produce high but variable wPig frequencies in nature.We report that a group‐B  The 2020 specimens (N = 9 sites) were collected on August 9 , August 17 , and August 20   (N = 25), Philaenus spumarius (L.) (N = 5), and Philaenarcys killa (Hamilton) (N = 24), all of the family Aphrophoridae. Like, P. ignipectus, P. killa a is monophage on Little bluestem. L. quadrangularis is a polyphage but often abundant on Little bluestem. P. spumarius is an extreme polyphage, with a preference for forbs  but is occasionally collected from Little bluestem in the company of P. ignipectus. By screening them for Wolbachia, we tested for the possibility of horizontal Wolbachia transfer through plant interactions  and Florida (N = 40) to screen for infections. P. bicincta is native to the southeastern United States  Table . Collects) Table . Specime2.2Wolbachia data to type the Wolbachia infecting P. ignipectus and to search for loci associated with CI. We extracted 800ng of high molecular weight DNA  from one black New Vineyard female (see below), and then input and sequenced it  for 48 hr (Oxford Nanopore Technologies). We mapped raw nanopore reads (5.8Gb of data) to all known Wolbachia sequences (NCBI taxid 953) with BLASTn and extracted reads where at least 60% of their length mapped (qcovs ≥ 60). We then corrected and assembled reads using canu 2.1.1 , but we performed Bayesian analyses using the GTR + Γ + I model for sequence evolution using whole‐genome data to confirm this  in both directions , from each of the four populations  sampled in both years  and New Portland (monomorphic lined) P. ignipectus, we amplified and Sanger sequenced the cytochrome C oxidase I (CoI) mitochondrial locus from one male and one female from these populations, with the exception of one  that did not produce a usable sequence. We also produced CoI sequences for one black and one lined female from the polymorphic Strong population. Covariance of Wolbachia and mtDNA haplotypes with P. ignipectus color forms would support a potential role for Wolbachia in maintaining the morphological contact zone.To specifically assess whether Wolbachia  associated with WO prophage in Wolbachia genomes  transgenically expressed in male D. melanogaster induce CI, while one gene (cifA) expressed in females rescues it. To identify cif loci, we used BLASTn to search for cif homologs in our whole‐genome raw reads, querying the Type 1 cif pair in wMel, the Type 2 pair in wRi, the Type 3 pair in wNo, the Type 4 pair in wPip, and the Type 5 pair in wStri     to determine differences in frequencies among sites, between years, between sexes, and between color forms.To test for 33.1Wolbachia infection frequency (p) in P. ignipectus is high . Based on five Sanger sequenced loci, the multiple sequence query of the MLST database supports that a group‐B strain, most closely related to Wolbachia in Chloropidae (Diptera) , infects our P. ignipectus samples—we call this strain wPig. Preliminary phylogenetic analyses using only our five Sanger sequenced genes also placed wPig in group B. Our draft wPig assembly size  falls in the range of complete Wolbachia genomes , despite its fragmentation (50 contigs). In total, we extracted 65 single‐copy homologs of equal length  for our phylogenetic analysis, which also places wPig in group B  from wMel, wRi, wPip, and wMau. This indicates that significant residual error in the wPig assembly reduces the number of homologs meeting our equal length criteria for inclusion. Finer placement of wPig among group‐B strains will require the generation of short‐read data to further correct our draft wPig assembly. Thus, we do not attempt to place wPig precisely among group‐B strains.Across all samples, P. bicincta samples from Hawaii and Florida were Wolbachia infected. Even if some P. bicincta are Wolbachia infected, as previously reported for one individual used as a PCR control in another study  must be very low across the P. bicincta range, given our species estimate and credible interval , keeping in mind the possibility that the Hawaiian population may have experienced a recent bottleneck during introduction and may not be representative of the species in the native range. Very low frequency Wolbachia infections in global P. bicincta populations, in combination with generally high wPig frequencies in P. ignipectus, indicate that P. ignipectus likely acquired wPig after its initial divergence from P. bicincta, although it is also possible that P. bicincta lost its resident Wolbachia following cladogenic acquisition. Because testing predictions about modes of Wolbachia acquisition requires formal analysis of Wolbachia, host nuclear, and host mtDNA phylograms and chronograms, we are unable to distinguish between introgressive and horizontal wPig transfer , all P. spumarius were infected , and only one P. killa individual was infected . Because Wolbachia that infect P. spumarius and wPig in P. ignipectus are both at high frequency, we also typed the Wolbachia infecting P. spumarius to determine whether a wPig‐like variant infects this host species. The multiple sequence query in the MLST database supports that a different group‐B strain, most closely related to the thrip species Aptinothrips rufus  infects P. spumarius. Generating more sequence data will be required to resolve the phylogenetic relationships of these and other group‐B strains, including Wolbachia in P. spumarius , between color forms for males only , or among females , across all samples. wPig frequency also did not differ between New Vineyard (monomorphic black) and New Portland (monomorphic lined) populations .We found no evidence for CoI mtDNA haplotype between the New Vineyard and New Portland P. ignipectus populations, where all samples were identical across the 680 bp that we recovered. The black and lined females from the polymorphic Strong population also did not differ from each other, or from other populations, across this region. Thus, wPig and mtDNA haplotypes were not differentiated between populations or color forms.We found no evidence for differentiation in P. ignipectus samples included in the Barcode of Life Database (BOLD) , but less than 90% similar to any others , but less than 95% identical to other Type 4 cifs. The Wolbachia infecting D. spinosa does not have Type 4 cifs, distinguishing it from wPig. None of the wPig cifs are truncated relative to copies with 99% identity. Additional sequencing is required to make more detailed cif comparisons.We identified Type 1, 3, and 4 3.4wPig varied in frequency in several ways. First, frequency varied spatially among all samples  (Table P < 0.0001), and 2020 . This variation occurred over a geographic radius of only 20 km in 2019 and 70 km in 2020  and 2020  . For the four sites we sampled in both years, frequencies were only significantly different between 2019  and 2020  in New Vineyard . Third, across all samples wPig frequency was higher in females  than males  . However, this was driven mostly by a paucity of infected males in New Vineyard , with no differences in wPig frequency between males and females in other populations. wPig frequency in males was relatively low in 2019 , but fixed in 2020 . We interpret these results as pervasive spatial, and rare temporal and sex‐specific, variation in wPig frequency.4wPig is a group‐B Wolbachia acquired after the initial divergence of P. ignipectus from P. bicincta. Analysis of Wolbachia and mtDNA haplotypes indicates that wPig has no apparent effect on the P. ignipectus morphological contact zone in Maine. Across all samples, wPig occurs at very high frequencies, consistent with our discovery of three divergent sets of CI loci in the wPig genome. Finally, we document pervasive spatial, and rare temporal, wPig frequency variation. We discuss this in more detail below.Our results suggest that 4.1wPig frequencies in P. ignipectus, we found no evidence of Wolbachia in our sample of 100 P. bicincta. A prior report of one infected P. bicincta sample indicates that Wolbachia could infect this species . Mathematical models predict that intense CI drives Wolbachia to high frequencies, balanced by imperfect maternal transmission , harbor Wolbachia with 16S rRNA sequence that is identical to Wolbachia in two psyllid species, two whiteflies, an aphid, a planthopper, two leafhoppers, two grasshoppers, a mosquito, and a weevil. Likewise, Lis et al.  (Aphrophoridae). Based on five MLST loci, their N. campestris strain is closely related to Wolbachia found in a leafhopper (Hemiptera) and cluster with Wolbachia from a planthopper, a scale insect and a psyllid , as well as two chrysomelid beetles, two butterflies, a parasitic wasp, and a mosquito. Koga et al. (Wolbachia in the spittlebug Cosmoscarta heros (F.) (Cercopidae), in addition to A. quadrinotata and P. maghresignus.Overall, the limited data are consistent with relatively recent noncladogenic transmission, a process that seems to be common among i et al.  report ts et al.  report ti et al.  also repa et al.  report tPoophilus costalis  (Aphrophoridae)  and lined (New Portland) populations that are separated by only 10 km in Maine, with no obvious barriers to dispersal or reproduction ; Investigation (lead); Validation (supporting); Visualization (lead); Writing‐original draft (supporting); Writing‐review & editing (supporting). Vinton Thompson: Conceptualization ; Data curation (supporting); Formal analysis (supporting); Investigation ; Methodology ; Project administration (supporting); Resources ; Visualization (supporting); Writing‐original draft (supporting); Writing‐review & editing . William R. Conner: Data curation (supporting); Formal analysis ; Investigation ; Writing‐original draft (supporting); Writing‐review & editing (supporting). Brandon Cooper: Conceptualization ; Data curation (supporting); Formal analysis ; Funding acquisition (lead); Investigation ; Methodology ; Project administration (lead); Resources ; Supervision (lead); Validation ; Visualization (supporting); Writing‐original draft (lead); Writing‐review & editing .Table S1Click here for additional data file.Table S2Click here for additional data file.


A comprehensive analysis of 17 RSA traits showed high variability among the different accessions, unveiling correlations between traits and conditions of the natural habitat of the plants. A sub-selection of these accessions was grown in water-limiting conditions in a rhizotron set-up, which revealed that especially the spatial distribution showed a high consistency between in vitro and ex vitro conditions, while in particular, a large root area in the lower zone favored drought tolerance. The collected RSA phenotype data were used to perform genome-wide association studies (GWAS), which stands out from the previous studies by its exhaustive measurements of RSA traits on more mature Arabidopsis accessions used for GWAS. As a result, we found not only several genes involved in the lateral root (LR) development or auxin signaling pathways to be associated with RSA traits but also new candidate genes that are potentially involved in the adaptation to the natural habitats.Root system architecture (RSA) has a direct influence on the efficiency of nutrient uptake and plant growth, but the genetics of RSA are often studied only at the seedling stage. To get an insight into the genetic blueprint of a more mature RSA, we exploited natural variation and performed a detailed  Arabidopsis thaliana has been used as a model organism to study all aspects of plant biology and the root has turned out to be a very elegant model organ for cell and developmental biology studies. Typically, roots are studied on young seedlings grown for a short period on vertical agar plates, commonly for 5 or 7 days . The results show a broad range of variability for all parameters tested and represent a unique description of the existing natural variation in root system architecture in the A. thaliana accessions has been selected from the Hap-Map population based on habitat, geographical information, or previously described interesting root or/and shoot phenotype. About 120 accessions were obtained from an in-house collection derived from the Nottingham Arabidopsis Stock Centre (NASC), the rest was retrieved directly from NASC . After germination, the seedlings of similar size were transferred to large Nunc plates (24 cm × 24 cm) and placed back to the same conditions of growth. Placement of accessions on each plate and placement of plates in the growth chamber were randomized based on incomplete block design. According to this design, each accession was present in each repeat but with a variable number, different for each repeat and each accession. Each plate contained three plants from three different accessions. Col-0 was incorporated in the block design so that it was present in each repeat in a random number. At 14 days after germination, the plates were scanned on a high-resolution flatbed scanner, equipped with backlight illumination . First, the rosette was excised carefully to keep only the root on the plate for clear imaging. Shoot biomass was measured immediately after excision. The fresh weight of the root was measured instantly after image acquisition.Phenotyping occurred in 10 experimental repeats, each lasting 21 days. Seeds were stratified for 4 days and sown on small petri dish plates  on 1/2 MS medium with 0.6% Gelrite as a gelling agent according to the protocol . Plates via the Root Reader 2D plugin  and additionally, each sheet was photographed including the above-ground parts. Next, the shoots were excised and the length of the inflorescence stems was measured. The rosettes were imaged and the weight of the above-ground parts was determined. The rosette area and the dry and fresh weight of the stem and rosette were measured via an ImageJ in-house created script. The length measurements of roots were scored by hand: Root Width, Depth, PR Length, Convex Area, and Solidity. Additionally, an in-house written ImageJ script was used to determine how root growth is spatially distributed in the sheets. Two models were created. The first model consisted of 4 equal zones out of the whole length of the rhizosheet. The second model used 3 equal zones out of the whole length of the root system. The first model provides insight into the root development in function of the depth of the rhizosheet, while the second is more informative about the overall root development in function of root depth  for all RSA traits was calculated as the ratio of the genetic variation over the total phenotypic variation, with the total phenotypic variation being the sum of the genetic and environmental variation. Genetic variation was estimated as the variance explained by the genotype in a random effect model. The environmental variation was estimated as the residual variation from the random effect model. The random-effect model was constructed using the R package, “sommer” (4). To perform a genome-wide association study (GWAS), a publicly available collection of 250k single nucleotide polymorphism (SNP) array data was used (5). The QTCAT approach was chosen to perform the association analysis based on its in literature highlighted feature to outperform other methods via implementation of hierarchical testing to control the population structure (via the JBrowse platform (7) and listed . About 2,659 root pictures were collected and 15 RSA traits were measured  on shaping the convex area as compared to the root depth of which the positive correlation is rather modest.Logically, root depth and primary root length are strongly correlated which aligns with a correlation between the convex area and primary root length. Convex area and length distribution were moderately negatively correlated with solidity. Solidity at the same time was strongly negatively correlated with parameters that relate to the width of the root system such as root width and width to depth ratio. In other words, the wider, the more diffuse root systems seem to develop. A similar strong negative correlation was observed between convex area and solidity indicating that reaching a larger area of soil can be accomplished by economizing on root growth in the central part of the root system.Traits related to LR growth also clearly vary among accessions. Some accessions like ICE-70 and Sap-0, have long laterals that branch out mostly in the shootward region of the primary root and thus are growing in the topsoil levels. In contrast, some accessions, such as Knox-10 or ICE-106 are characterized by shorter but more condensed LRs distributed evenly along the primary root. The total number of LRs was much more variable than the order of LRs. Most of the accessions initiate LRs up to the second order. Only a few reached just the first level of branching. An increase in the number of LRs has a direct impact on how compacted a root system becomes. This relation emerges clearly from the correlation analysis . The totAdditionally, a PCA was used to explain the variation in the shape of the root system between accessions. Since the shape is different from the convex area and is insensible to size, orientation, not rotation, the most appropriate way of scoring the shape of the root system was to give vectoral artificial points to each root system and analyze the PCA. The analysis divided the shape into 3 PCs collectively explaining 58% of the total variation of that trait observed in this dataset. The PC1 was an indicator of the root system width in the upper half of the total root depth, explaining 36% of the whole root shape variation. The PC2 was influenced by the width of the bottom section of the root system, explaining 13% of the variation. The PC1 and PC2 components can be used to discriminate the different genotypes. The PC3 was responsible for about 9% of total shape variation and was indicative of a very peculiar trait representing the bending of the root system .For each accession, the sampling location data is known (longitude and latitude). Based on this data, climate-related parameters , for each geographical location were assembled. This data collection aimed to analyze if habitat location and/or climate may have any influence on root traits. The influence of habitat conditions on root traits was analyzed as correlations and have also yielded striking dependencies . The yeaTo test whether contrasting phenotypes were stable in the genetically encoded traits or rather the expression of growth under artificial conditions in petri-dishes, we decided to grow a selection of contrasting accessions in soil conditions in a rhizotron set-up. For practical reasons , an experimental setup of 4 accessions was considered feasible. First, we classified the 20 most contrasting accessions based on the width to depth ratio. Following, we narrowed down the selection to 3 accessions, Dr-0, Had-1b, and Wc-1, based on their contrast in primary root length and convex area . Those 3In conditions of sufficient water availability, Col-0 and Dr-0 performed similarly to each other for all traits measured . For allNext, we compared the results obtained in petri-dish conditions with the observations made in the soil . While HLimited water conditions had a visible influence on all accessions. Had-1b still scored the highest values for all parameters in water stress conditions while it showed also a significant reduction in convex area , soliditConcerning the shoot, for all accessions, a drop in values in shoot parameters was observed in water limiting conditions. The size and weight of the rosette were decreased  and the in vitro conditions, on gel-filled medium plates. The measurements and analysis of traits are described in the section “Materials and Methods.” Broad-sense heritability (H2) was calculated and the results varied from 21 to 56% (The suppressor of MAX 2 1 (SMAX1) was one of the genes found in the 20 kb window around the SNP associated with shoot to root biomass ratio. The SMAX1 is known to act downstream of MAX2 and is involved in response to strigolactones and karrikins  was also detected for the root-to-shoot trait. It belongs to the known auxin efflux carrier protein family  that besides serving a role in plant immunity is also required for the SCF(TIR1)-mediated degradation of Aux/IAA proteins in the canonical auxin signaling pathway, linking it to RSA development (Root Meristem Growth Factor 6 (RGF6) or GOLVEN1, can also be regarded as a proof of concept of the value of our GWAS as it is required for the maintenance of the root stem cell niche and transit amplifying cell proliferation in the root meristem which would be in agreement with its association with PR length , is involved in the regulation of abscisic acid (ABA) and drought responses of the root and was found in our study to be linked to vertical root growth as well (RGF1 Insensitive 2 (RGI2) that also plays role in the maintenance of root stem cell niche by influencing PLETHORA (PLT) transcription factors and is a receptor of RGFs. Association for shoot biomass also emerged as two interesting genes. One candidate gene is Aberrant Lateral Root Formation 5 (ALF5)  was found for width to depth ratio and categorized as a gene of interest for further investigation. Among associations for RSA total length, Dioxygenase for Auxin Oxidation 1 (DAO1) was detected. This is the major indoleacetic acid (IAA) oxidase in plants, manipulating the levels of auxin degradation  . The ALFpressors . Becausepressors . Transcrpressors . Similarradation . For thiradation . It is din vitro experiments is performed on very young seedlings compared to our study. At the end of the day, the phenotyping method, however, will always be a limiting factor as there is no single, universal method that allows for investigating all possible root traits at once  yielded no significant SNPs, confirming that low heritability escapes the power of GWAS to detect causative regions of the genome.In our study, we have detected a high amount of genes of interest, similar to many other root GWASs . This mise genes . We havese genes . Based ose genes . Additiose genes . This isArabidopsis enhancing our insight into genotype-phenotype relationships.Indeed, the GWAS produces a high amount of data that can be further exploited. For instance, in crops, marker-trait associations are used for breeding strategies . Our resThe datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/AD-M and TB conceptualized the manuscript. AD-M performed the phenotyping and GWAS experiments. AD-M, BP, HM, and TB wrote the manuscript. AD-M and DO performed the rhizotron experiments. AD-M and MN made the figures. VS, GL, and PC shared their GWAS expertise and plant material. All authors contributed to the article.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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Dicentrarchus labrax L.) to viral nervous necrosis (VNN) is well-known. Interest towards selective breeding as a tool to enhance genetic resistance in this species has increased sharply due to the major threat represented by VNN for farmed sea bass and limitations concerning specific therapeutical measures. A sea bass experimental population (N = 650) was challenged with nervous necrosis virus (NNV) to investigate genetic variation in VNN mortality. In addition, relationships of this trait with serum cortisol concentration after stress exposure, antibody titer against NNV antigens, and body weight at a fixed age were studied to identify potential indicator traits of VNN resistance.Susceptibility of European sea bass  to 0.23 . Heritability estimates for cortisol concentration, antibody titer, and body weight were 0.19 , 0.36  and 0.57 , respectively. Phenotypic relationships between traits were trivial and not statistically significant, except for the estimated correlation between antibody titer and body weight (0.24). Genetic correlations of mortality with body weight or antibody titer (− 0.39) exhibited a 0.89 probability of being negative. A negligible genetic correlation between mortality and cortisol concentration was detected. Antibody titer was estimated to be positively correlated with body weight (0.49).Antibody titer against NNV offers the opportunity to use indirect selection to enhance resistance, while the use of cortisol concentration as an indicator trait in breeding programs for VNN resistance is questionable. The estimate of heritability for VNN mortality indicates the feasibility of selective breeding to enhance resistance to NNV and raises attention to the development of genomic prediction tools to simplify testing procedures for selection candidates. Betanodavirus. Major damages caused by this virus have been observed in the central nervous system of the fish  .Genomic DNA (gDNA) was extracted from approximately 20 mg of tissue (muscle or fin) of each fish using the commercial kits Invisorb® Spin Tissue Mini Kit and Invisorb® DNA Tissue HTS 96 Kit . Integrity of gDNA was assessed by visualization on a 1% TAE agarose gel stained with SYBR® Safe DNA Gel Stain (Invitrogen—ThermoFisher Scientific). Genomic DNA concentration and purity were quantified with a NanoDrop ND-1000 spectrophotometer  in terms of absorbance at 260/230 nm and at 260/280 nm. This procedure ensured that similar concentrations of high-quality gDNA were obtained for each fish, as required for 2b-RAD library preparation. A 2b-RAD library was constructed for each individual following the protocol from  with minhttp://bioinformatics.babraham.ac.uk) was used to visualize and confirm the quality of the raw demultiplexed reads. A custom-made script was used to filter the reads for the presence of the restriction endonuclease recognition site and to trim the adaptors used in the process of libraries construction, which resulted in 34-bp fragments ready to be processed by the STACKS software 2.0 [http://seabass.mpipz.mpg.de/DOWNLOADS/dicLab1_scaffold.fasta) [refmap_map.pl in STACKS. First, the program runs gstacks, to build loci according to the alignment positions provided for each read before calling SNPs in each sample. The STACKS module populations was used to filter the outputs, excluding the loci that were shared by less than 75% of the analysed individuals. SNPs that had a minor allele frequency lower than 1% and a missing genotype in more than 15% of the individuals, and those that deviated from the expected Hardy–Weinberg equilibrium frequencies (P < 0.001) were discarded. Missing genotypes for the remaining SNPs were then imputed using the FImpute software [Demultiplexing and an initial check of raw data quality were performed by the sequencing services Fasteris SA and UC Davis. The software FastQC (ware 2.0 . The trid.fasta)  using a software .10-likelihood ratio (LLR) required for accepting a proposed relationship to 0.5, and the threshold LLR used to identify candidate relatives to − 2.Parentage assignment was performed with the likelihood-based program CERVUS 3.0 , 34. ForDescriptive statistics for the investigated traits were obtained using the MEANS and UNIVARIATE procedures in SAS 9.4 (SAS Institute Inc.). The frequency distribution of serum cortisol concentration, measured as ng per mL of serum, was skewed, and it was normalized by a square root transformation of the original data. The Kaplan–Meier product-limit survival curve of the experimental fish challenged with NNV was estimated using the LIFETEST procedure in SAS 9.4. Preliminary analyses to identify non-genetic effects on traits were performed with logistic regression and linear models, using the LOGISTIC and GLM procedures of SAS 9.4.Variance components and genetic parameters were estimated using Bayesian procedures for the statistical models described in the next section. Marginal posterior distributions for (co)variance components and related parameters were estimated using Monte–Carlo Markov chain (MCMC) methods, performing numerical integration via the Gibbs sampler, as implemented in the software TM . The sizVariance components for mortality coded as 0  and 1 (dead) were estimated using the following univariate sire-dam threshold model:ortality , 39 by uVariance components for body weight, cortisol concentrations and antibody titer were estimated using the following univariate animal model:2 distributions were assumed as prior distributions for the sire, dam, additive genetic and residual variance. The hyperparameters of scaled inverted χ2 distributions  , which rkage BOA .Bivariate Bayesian analyses were used to estimate (co)variance components and to investigate the relationships (genetic and phenotypic) between the traits, using the same models as used in the univariate analyses. When mortality was one of the analysed traits, a bivariate sire-dam model was used, otherwise a bivariate animal model was fitted to the data. Prior densities for the model effects were those described for univariate analyses. Inverse Wishart prior distributions, which are very vague priors, were used as priors for all (co)variances.For the bivariate sire-dam models, genetic (σd2+σe2) .For bivariate animal models, correlations were estimated as:σa2+σe2) .Similar to the univariate analyses, the median of the estimated posterior densities of (co)variances, Typical clinical signs related to NNV infection were detected in each experimental tank starting day 3 after injection. As illustrated by the estimated Kaplan–Meier curve Fig. , mortaliIn total, 340 million demultiplexed and filtered-quality reads were obtained for the 714 samples (including 12 TR). The number of reads per individual was on average 3,500,000. STACKS identified 351,203 loci (or tags) at 12,018,544 sites . After filtering, the dataset consisted of 18,097 SNPs. Some SNPs were discarded based on a minor allele frequency lower than 1%, genotype frequencies deviating from Hardy–Weinberg equilibrium, and a missing genotype rate higher than 15%. After imputation, the final number of available SNP genotypes per animal was 16,075. Two individuals of the challenged group were discarded from the dataset because of poor genotyping results.Parentage assignment to a unique parental pair was achieved for 100% of the experimental fish. A total of 41 parents were assigned to 22 dummy parental pairs (14 grandsires and 8 granddams), 3 to 3 single dummy parents (1 grandsire and 2 granddams), while 8 parents remained unassigned. In total, 136 full-sib families were identified among the fish that were challenged with NNV, with the number of offspring per family ranging from 1 to 17 (on average 4.8 full-sibs per family). Two males and one female of the parent group used to generate the experimental fish were not assigned to any individual in the challenged sample. The number of offspring per sire ranged from 4 to 45 (on average 21 offspring per sire), while the number of offspring per dam ranged from 10 to 68 (on average 34 offspring per dam).0.5/mL0.5), and of antibody titer are in Table Descriptive statistics for body weight (g) at 548 d post-hatching, post-stress cortisol concentration in blood serum (ng/mL), the square root of cortisol concentration  were estimated. The heritability of mortality on the liability scale, estimated using a univariate sire-dam model, was low in comparison to estimates obtained in other studies , 15. DifIn our study, age (548 d post-hatching) and size of the experimental fish at the time of infection were rather unusual compared with those of previous investigations on sea bass , 15 or oThe use of small fish in challenge tests is preferred when the infection is carried out by placing the animals in a water environment that contains the pathogen. Such experimental trials based on infection by immersion closely mimic the conditions of natural infections, where pathogens must cross the external protective barriers of the host , penetrate the host cell barriers in adequate numbers, attack target cells, and replicate. Conversely, when animals are infected by injection of the pathogen, the trial is less representative in terms of natural disease outbreaks, but the advantages are the application of a systematic infection, with an equal level of exposure to the virus for all individuals, and greater effectiveness when large-sized individuals are tested . An equavs. injection) is not straightforward because, with injection, only part of the mechanisms of resistance is considered, i.e. the components of specific immunity  and those of the non-specific immune system related to physical, chemical and cellular defences . Variation in resistance due to differences in the characteristics of the external barriers  is ignored with injection, which may lead to a decrease in the observed variability in resistance and affect heritability. With infection by immersion, physical and mechanical barriers can play a significant role in preventing penetration of the virus and contribute additional variation to the observed resistance.We can hypothesize that different methods to infect the animals lead to different results, particularly when the focus of the investigation is on genetic variation of disease resistance. The comparison between genetic studies in which experimental infections were carried out with different modalities , may haHeritability estimates for post-stress cortisol concentration were moderate and showed a posterior probability of being higher than 0.1 greater than 90%. These heritability estimates are in the range of those reported in the literature for sea bass , 21. PosThe heritability of antibody titer against NNV had a point estimate equal to 0.36 and a probability of being greater than 0.1 close to 1. This estimate was higher than estimates reported for salmonids , 23, whiOther possible indicator traits for disease resistance can also be exploited in indirect selection approaches. In general, nodavirus infection appears to be cooperatively prevented by activation of innate immunity , cellular T cell type I interferon immunity, and humoral immunity (immunoglobulins antibodies) , 58. In Other future perspectives for selective breeding for disease resistance could be the exploitation of genomic selection as an alternative to traditional pedigree-based methods . Genomicvs injection) on the observed variation in resistance to VNN and its additive genetic component.Identification of a genetic basis for mortality, body weight, cortisol concentration, and antibody titer suggests that selective breeding can be an effective approach to enhance these traits in European sea bass and should be integrated in the pool of VNN prevention tools for farmed sea bass. Antibody titer against NNV offers the opportunity of indirect selection approaches to enhance resistance to VNN through the exploitation of its negative genetic correlation with mortality. Traditional pedigree-based breeding programs require individual phenotyping for mortality and antibody titer, which may be difficult or impossible for routine implementation. Such difficulties can be overcome by developing genomic tools for the prediction of the genetic merit of selection candidates for the traits of interest. In this view, the effectiveness and accuracy of genomic prediction of the genetic merit of selection candidates for VNN mortality and antibody titer deserve specific investigations. Studies are also required to elucidate the effects of the various infection methods that are currently used in challenge tests (immersion


The distinction between genetic influences on the covariance (or bivariate heritability) and genetic correlations in bivariate twin models is often not well-understood or only one is reported while the results show distinctive information about the relation between traits. We applied bivariate twin models in a large sample of adolescent twins, to disentangle the association between well-being (WB) and four complex traits , aggressive behaviour (AGG), and educational achievement (EA)). Optimism and AD showed respectively a strong positive and negative phenotypic correlation with WB, the negative correlation of WB and AGG is lower and the correlation with EA is nearly zero. All four traits showed a large genetic contribution to the covariance with well-being. The genetic correlations of well-being with optimism and AD are strong and smaller for AGG and EA. We used the results of the models to explain what information is retrieved based on the bivariate heritability versus the genetic correlations and the  implications.The online version contains supplementary material available at 10.1007/s10519-021-10046-y. The main goal of behavior genetics research is to understand the causes of individual differences in human traits and behavior. A genetically informative design, such as the classical twin design, can be applied to decompose the observed variance of a trait into genetic and environmental sources of variation using data from reared together monozygotic (MZ) and dizygotic (DZ) twins. The difference in genetic relatedness of MZ twins  and DZ twins  allows the decomposition of the observed variance in genetic and environmental sources  correlation of 1 indicates a perfect overlap of the genetic  factors, indicating that the factors that influence both traits are identical. In contrast, a correlation of 0 indicates no overlap and thus independent genetic  factors for both traits.For example, applying a bivariate model to well-being and depressive symptoms, Baselmans et al.  found thIn behavior genetics studies, often the distinction between the results of the covariance decomposition and the genetic/environmental correlation is either not well-understood or only one of the two is reported while the results show distinctive information about the covariance and overlap between traits. For example, while the influence of genetic factors on the phenotypic correlation can be rather substantial, the genetic correlation can be low, indicating that although genes play an important role in the observed overlap between traits, most of the genetic influences are trait specific. Or the opposite can be observed, that is while there is only a small influence of genetic factors on the covariance, the genetic correlation is substantial. This indicates that the sets of genes that influence each trait are shared to a large extent, but that genes are actually not that important in understanding the association between two traits.Therefore, in this paper we will explain the difference between the results of the covariance decomposition and genetic and environmental correlations in greater depth. Given our longstanding interest, we will apply the bivariate model to well-being and four other traits known to have a different phenotypic association with well-being in a large sample of adolescent twins. The four traits included are optimism, symptoms of anxiety or depression, aggressive behavior, and educational achievement. We chose these traits based on previous work (summarized below), since we expect different associations of the traits with well-being . These differences (e.g. high/low bivariate heritability and high/low genetic correlation) have different  implications and will help clarify the difference between both sets of results of the bivariate twin model.Well-being is characterized by high levels of positive affect, low levels of negative affect, and a positive subjective evaluation of life satisfaction  , established by the Department of Biological Psychology, Vrije Universiteit Amsterdam , 42.3% of the participants were male. The sample included 5242 MZ twins and 4442 DZ twins, from complete or incomplete same sex twin pairs. For the bivariate models, subsets of the sample were used with data on well-being and optimism ), well-being and anxious/depressed symptoms , well-being and aggressive behavior  and well-being and educational achievement ..Zygosity for same-sex twin pairs was determined by DNA analysis (27%) or by previously collected parental report on physical similarity and the frequency of confusion of the twins by parents, other family members and strangers  . As Mavioğlu et al.  and temporal reliability (4 month test–retest: 0.68)   and temporal reliability (1 week test–retest: 0.88) of the anxious-depressed scale are both high  and temporal reliability (1 week test–retest: 0.83) of the aggressive behavior scale are both high . The Cito-test consists of four parts, including items on language, mathematics, study skills, and world orientation. Combining the scores on the four tests results in a standardized Cito-score, ranging from 500 to 550. The Cito-test results are used in the primary school’s advice on the most appropriate level of secondary education and reflects educational achievement. In the DHBQ, adolescents were asked whether they took the Cito-test and if so, to report their Cito score.For educational achievement, we used the Dutch Cito-elementary test score. The Cito-test is a standardized test for educational achievement in the Netherlands. Children take the test in the final grade of elementary school (around 11 or 12 years old)  and dizygotic twins (share on average 50% of their genes) to decompose the variance and covariance in genetic and environmental components. In bivariate models, the within-twin cross-trait covariances/correlations show whether there are common etiological influences in the traits; importantly, the cross-twin cross-trait covariances/correlations give information about whether familial  common etiological influences contribute to the covariation.The genetic variance and covariance components are additive genetic variance (A) and non-additive genetic variance (D). Whereas A represents the additive variance explained by summing the effects of all alleles that influence the phenotype, D arises due to interactions between alleles at the same locus (dominance) or between alleles at different loci (epistasis) and cannot be explained by a linear model. The environmental variance and covariance consist of a common environmental variance component (C) (variance shared by family members) and a non-shared environmental component (E) . The effects of C and D cannot be estimated in the same model unless an extended family design is applied. Therefore, a choice for an ACE or ADE model was made based on the ratio of the cross-twin cross-trait correlations. If the MZ correlations are smaller than twice the DZ correlations, common environment (C) effects are expected and an ACE model is used. If the MZ correlations are larger than twice the DZ correlations, dominant genetic (D) effects are expected and an ADE model is appropriate  by constraining the variance estimates of both phenotypes and the covariance between the phenotypes of females and males to be equal. We did not test for qualitative sex differences, as modelling sex specific genes in multivariate models has inherent limitations  equalling the difference in df between the two models. If a p value from the χ2 -test is significant (threshold for the genetic models p < 0.01 due to multiple testing), the fit of the constrained model is significantly worse than the fit of the more complex model. For the best fitting model, 95% confidence intervals were estimated for the parameters.The fit of the different models and nested models (both for the saturated as well as the genetic models) was compared by means of the log-likelihood ratio test (LRT). The difference in minus two times the log-likelihood (−2LL) between two nested models has a χTable The twin correlations  was explained by genetic influences for respectively 66% and 40% in females and males, whereas the rest was explained by non-shared environmental influences.The phenotypic correlation between optimism and well-being  and lower in males , indicating that the genetic factors underlying both traits overlap to a significant extent and to a greater extent in females than in males. In addition, the non-shared environmental correlations were moderate, with 0.22 and 0.28 for females and males, respectively, indicating an overlap in environmental factors as well.The genetic correlations between well-being and optimism were relatively strong in females  was primarily influenced by genetic influences , whereas the rest was explained by non-shared influences.The phenotypic correlation between well-being and anxious-depressed symptoms  as well, indicating that there were largely overlapping genetic influences on the phenotypes. The non-shared environmental correlations were −0.28 and −0.19 for females and males respectively. The negative correlations indicated that the effects of the influences are in opposite directions.The genetic correlation between well-being and anxious-depressed symptoms was negative and strong in both sexes  was mainly explained by genetic influences (71% and 70% respectively), whereas the rest was explained by non-shared environmental influences.For well-being and aggressive behavior, the phenotypic correlation . Overlapping genetic factors influence both well-being and aggressive behavior, but the effects of the genetic factors on the traits were opposite. The non-shared environmental correlations were −0.13 in females and −0.10 in males, indicating some overlap in the environmental factors underlying the traits as well, and they also went in the opposite direction.The genetic correlation between well-being and aggressive behavior was moderately negative  was made up of genetic (69% and 97%) and non-shared environmental 31% and 3%) influences in females and males respectively. However, there are large confidence intervals, as can be seen in Fig. % and 3% The genetic and environmental correlations between well-being and educational achievement were close to zero (< 0.10) in both sexes, with zero in the confidence intervals, indicating no significant overlapping genetic and environmental influences on well-being and educational achievement.The four bivariate twin models fitted to data of adolescent females and males using well-being  and four other complex traits, namely optimism, anxious-depressed symptoms, aggressive behavior, and educational achievement resulted in variability in the phenotypic correlations, bivariate heritability, and genetic and environmental correlations. Bivariate models like the ones that we applied in this paper provide insight into the underlying source of overlap between two traits and provide an indication of how strong the genetic link is between two phenotypes. If interested in exploring the relationship between all these traits taken together, other models (e.g. multivariate models) that were not the scope of this paper have to be fitted.The bivariate heritability indicates the proportion of the correlation between the phenotypes that is accounted for by additive genetic influences. While the phenotypic correlation between WB and the four phenotypes varied from moderate (r about 0.4 for WB with optimism and anxious-depressed symptoms) to small , a significant part of all these varying phenotypic correlations was accounted for by genetic factors. For optimism, anxious-depressed symptoms and aggressive behavior the bivariate heritability with well-being ranged between 0.40 and 0.70, indicating that around half to more than half of the overlap between well-being and these phenotypes can be explained by genetic factors. For educational achievement, the estimates for the bivariate heritability are 69% and even 97% in females and males respectively, indicating that almost all covariance between educational achievement and well-being was explained by genetic factors. However, as the phenotypic correlation between well-being and educational achievement is really small and the confidence intervals around the bivariate heritability estimate were large, this estimate may not be relevant and must be interpreted with care.The genetic correlation reflects a measure of overlap in the genetic factors underlying the traits and is different than the bivariate heritability. For example, if the bivariate heritability is large and the genetic correlation is low, genes do play a substantial role in the observed overlap between traits, but most of the genetic influences are trait specific. In our examples, the bivariate heritability between well-being and anxious-depressed symptoms and well-being and aggressive behavior was similar, whereas the genetic correlation between well-being and anxious-depressed symptoms was higher than between well-being and aggressive behavior. This indicated that genes play a role in the overlap between WB and the traits to a similar extent, whereas the biological mechanisms underlying well-being and anxious-depressed symptoms are more similar than those underlying well-being and aggressive behavior. The genetic correlation between well-being and optimism was relatively strong as well, indicating that the biological mechanism underlying the positive traits of well-being and optimism is similar as well. The genetic factors underlying well-being and educational achievement overlapped only a little or not at all, as indicated by a genetic correlation lower than 0.1 suggesting that the biological mechanisms underlying well-being and educational achievement are almost completely separate.e ~ 0.30) overlapped compared to aggressive behavior (re ~ 0.10). For educational achievement, none of the environmental factors overlapped, indicated by an environmental correlation of zero.A similar pattern can be observed in the unique environmental influences on the covariance between the traits and the unique environmental correlations between well-being and the four traits. Although the phenotypic correlations between well-being and the other traits vary, roughly a similar proportion is accounted for by environmental factors. Additionally, as indicated by the environmental correlations, more environmental factors underlying well-being and both optimism and anxious-depressed symptoms , whereas the heritability is constrained to be equal. We compared the fit of both quantitative sex limitation models. Only for the bivariate model of well-being and educational achievement, the scalar sex limitation model resulted in a better fit than the non-scalar bivariate model . This indicates scaled sex differences in the unstandardized variance components, but no sex differences in the standardized variance components (i.e. the heritability). However, the difference between the standardized variance components of this scalar AE model and the reported non-scalar AE model were small.For every bivariate model, the best fitting model included quantitative sex differences, i.e. the (bivariate) heritability for males and females differed. Sex differences in the genetic architecture of traits might be quantitative  or qualitative. As mentioned before, the literature and our data indicated no qualitative sex differences, therefore we did not test these sex differences. An alternative model, to the one used in the paper, to test non-scalar quantitative sex differences is the scalar sex limitation model. In this model, the total variance in males and females is allowed to differ by a factor k  and LD score regression  over time  implications. It is highly recommended that future behavior genetics studies report both sets of results.Supplementary file1 (PNG 100 KB)Supplementary file2 (PNG 94 KB)Supplementary file3 (DOCX 41 KB)Supplementary file4 (R 10 KB)Supplementary file5 (R 13 KB)Below is the link to the electronic supplementary material.