QBS in the whole sample was significantly heritable  while the variance attributed to the shared household effect was not significant (p = 0.073). When subjects with psychiatric illness were removed, the QBS heritability was similar . These findings suggest that quantitative bipolarity as measured by QBS can separate BP from other psychiatric illnesses yet is significantly heritable with and without BP included in the pedigrees suggesting that the quantitative bipolarity describes a continuous heritable trait that is not driven by a discrete psychiatric diagnosis. Bipolarity trait assessment may be used to supplement the diagnosis of BP in future genetic studies and could be especially useful for capturing subclinical genetic contributions to a BP phenotype.Research has yet to provide a comprehensive understanding of the genetic basis of bipolar disorder (BP). In genetic studies, defining the phenotype by diagnosis may miss risk-allele carriers without BP. The authors aimed to test whether quantitatively detected subclinical symptoms of bipolarity identifies a heritable trait that infers risk for BP. The Quantitative Bipolarity Scale (QBS) was administered to 310 Old Order Amish or Mennonite individuals from multigenerational pedigrees; 110 individuals had psychiatric diagnoses , 3 psychotic disorders, 26 other psychiatric disorders). Familial aggregation of QBS was calculated using the variance components method to derive heritability and shared household effects. The QBS score was significantly higher in BP subjects (31.5 ± 3.6) compared to MDD (16.7 ± 2.0), other psychiatric diagnoses (7.0 ± 1.9), and no psychiatric diagnosis (6.0 ± 0.65) (all  Case-control genome-wide association studies (GWAS) are beginning to illuminate the genetic risk for this complex polygenic disorder and have identified a number of loci6 though findings have been difficult to replicate. Most genetic studies of BP define the study group by diagnosis. While this definition is important for clinical care of BP patients, whether this is the correct phenotype to use to search for genes conferring risks for BP is not clear. Genetically susceptible individuals without BP expression may be missed or even erroneously grouped into the control groups. Our hypothesis is that bipolarity is not only expressed in BP but may also be a heritable subclinical trait that is present even in non-bipolar individuals, yet much more severe in BP and as such separates BP from other psychiatric diagnoses.Bipolar disorder (BP) affects about 1% of the population, causing significant disability worldwide. BP is considered a highly heritable condition with an estimated 40–80% heritability7. BSDS was shown to have a sensitivity of 0.75 and a specificity of 0.93 for BP in an outpatient clinic population7, a range that was generally supported by other studies11. These data support the clinical validity of BSDS to quantify clinical and subclinical bipolarity symptoms. The original BSDS does not include all DSM-5 BP symptoms. We adopted much of the BSDS content and format but modified items to cover symptoms in the DSM-5 bipolar I criteria, and also implemented a new severity rating for each item, henceforth called the Quantitative Bipolarity Scale (QBS). To our knowledge the heritability of BSDS or other quantitative bipolarity tools, such as the Mood Disorders Questionnaire (MDQ)12, has never been evaluated. Although QBS is designed to be consistent with DSM-5, it is completed by patients, allowing independent evaluations of its validity using clinician assessed DSM-5 diagnoses.The Bipolar Spectrum Diagnostic Scale (BSDS) is a self-report scale designed to screen for bipolar spectrum disorders13. The Old Order Amish and Old Order Mennonite (OOA/M) population is a founder population whose large families and genealogical record keeping make the population a powerful resource for genetic and heritability analyses even in modest sample sizes. The prevalence of BP in the OOA/M appears similar to that in the general population14 though some pedigrees carry a heavier burden of mood disorders15. In addition, they share similar rural upbringing and school education. The high environmental homogeneity reduces between-subject variations of uncontrolled developmental and environmental factors theoretically yielding more precise estimates of genetic contributions. The low incidence of substance use disorders decreases another confounding factor commonly present in BP patients in the general population. Therefore, this study aims to develop a potentially novel phenotyping alternative to supplement traditional diagnosis-based genetic research in BP by taking advantage of the large family structures in the OOA/M, to test the hypothesis that QBS provides a heritable quantitative trait that separates BP from other psychiatric diagnoses.The ideal sample to test whether a quantitative bipolarity measurement is a heritable endophenotype would be a population with BP and with a family structure which enables estimation of genetic vs non-genetic effects. Pioneered by Egeland, there has been a long history of studying the genetics of BP through large pedigrees in the Amish/Mennonite population16 and the OOM communities17 and digitalized in the NIH Anabaptist Genealogy Database (AGDB)18. The genealogical data were converted to the pedigree format by the SOLAR-Eclipse software (http://www.nitrc.org/projects/se_linux). Exclusion criteria included major medical and neurological conditions and substance abuse in the past year. Recruitment was based on the Research Domain Criteria (RDoC) strategy19 and included all Axis-I psychiatric illnesses, starting with identifying families with at least two cases of any psychiatric illnesses and then followed by recruiting members from the same household regardless of diagnosis. Families without psychiatric illnesses were also recruited. Note that this traditional definition of case and control families is a relative term in a population isolate where families are interrelated. For this study individuals without psychiatric illness were labeled as controls irrespective of their family status. Figure The study included 310 members of OOA/M families  from 119 nuclear families in Pennsylvania and Maryland. The sample included 173 sibling (sibship size ranged from 2 to 9), 25 spouse pair, and 185 parent-child pair relationships. Sixty nuclear families had one individual participating , 24 families had two participants, 35 families had three or more participants. As this is a founder population and marriages are kept within the community, most families are connected using genealogical records maintained by the OOAn = 20, including 19 bipolar I and 1 bipolar II), major depressive disorder (MDD) (n = 61), schizophrenia spectrum disorders (n = 3), and other psychiatric disorders  and 200 individuals with no lifetime psychiatric disorders. The Schedule for Clinical Interview for DSM-5 (SCID-5) was used to determine diagnoses by trained clinicians. Each SCID interview was reviewed in consensus meetings. For SCID inter-rater reliability, a research team from another study interviewed 17 of the current OOA/M participants15. Diagnoses for 15 of 17 subjects were in agreement while 2 had minor differences (kappa = 0.87), supporting the reliability of the SCID in this population. All study participants gave written informed consent as approved by the University of Maryland IRB.The data included 110 individuals with a lifetime diagnosis of psychiatric disorders: bipolar disorders The original BSDS contains 19 items and a summary itemwww.mdbrain.org/QBS_instructions_and_scale.pdf).The QBS analysis was based on the summed values from all individual items as well as a item asking how much the scale as a whole describes the individual. Additional analysis was performed on predefined subscale scores. Items 3 through 8 refer to depressive symptoms. Items 11 through 24 refer to manic symptoms. Items 1, 2, 9, and 10 refer to mood fluctuation symptoms. Hereafter these respective sums are referred to as depression subscore, mania subscore, and mood fluctuation subscore. The Quantitative Bipolarity Scale (QBS) is available online  curve was performed to evaluate sensitivity and specificity of QBS. Cutoff was based on the Youden-Index to determine the point for which sensitivity plus specificity is maximalhttp://www.nitrc.org/projects/se_linux). Heritability (h2) is defined as the proportion of the total phenotypic variance that is explained by additive genetic factors in related individuals. The variance parameters are estimated by comparing the observed phenotypic covariance matrix with the covariance matrix predicted by kinship. Inverse Gaussian transformation was applied to ensure normality of the measurements. Household effects were simultaneously estimated for shared environmental effects. Siblings were grouped in the same household. Significance of the heritability is tested by comparing the likelihood of the model in which additive genetic factors is constrained to zero with that of a model in which additive genetic factors is estimated. Twice the difference between the loge likelihoods of these models yields a test statistic21. Age and sex were used as covariates when calculating the heritability of QBS scores. The familiality of QBS score was further evaluated by comparing family members (first or second degree relatives) of individuals with high QBS score  to the remaining individuals on QBS scores.Heritability estimates were obtained using the variance components method as implemented in the SOLAR-Eclipse software package  were explained by shared genetic factors. The genetic correlation (ρG) of the two traits is modeled as a linear function of kinship coefficients that express relatedness among all pairs of individuals in the pedigree; the phenotypic variance–covariance matrix and its additive genetic and random environmental components are then obtained. The significance of the components are then estimated directly by the likelihood ratio testTo further explore whether there are latent structures of QBS not captured by the predefined depression, mania, and mood fluctuation subscales, factor analysis was performed using the whole sample. Principal axis factor analyses were performed to identify the latent constructs in the data using oblique (promax) rotations. The optimal solution was based on a combination of common factor solution eigenvalues > 1.0, factor structure using loadings > 0.35, and percent of variance explained.3,305 = 30.3, p < 0.001] (Table p < 0.001). The difference was not significant when comparing BP and psychotic disorders  although this may be due to the limited number of individuals with psychotic disorder in the sample (Table Age and sex ratio did not differ significantly across diagnostic groups Table . The QBSn = 15) to patients who have lifetime BP but are currently in full remission , and found that their QBS scores were not significantly different . This suggests that QBS captures the longitudinal trait aspect of bipolarity symptoms. There was no correlation between age and QBS score .The QBS score of bipolar patients was approximately double that of MDD, and individuals with MDD showed a two to three fold higher mean QBS score compared to individuals with other psychiatric illnesses. Controls showed the lowest mean QBS score Fig. . These cThe validity of QBS in terms of specificity and sensitivity was formally investigated with a ROC curve analysis. For BP vs controls, sensitivity and specificity was 0.90 and 0.88; for BP vs. all other psychiatric diagnoses, sensitivity and specificity was 0.90 and 0.81; for BP vs MDD sensitivity and specificity was 0.90 and 0.61 Fig. .Fig. 3Rep = 0.006 and p = 6 × 10−9 respectively), but the depression subscore was not (p = 0.34). This supports the validity of QBS for identifying the converging symptoms (depression) and diverging symptoms (mania and mood fluctuation) characterizing these two major mood disorders.In terms of the subscales, mood fluctuation, depression, and mania subscores were all significantly different across the five diagnostic groups Table . The keyFactor analyses identified two factors that together accounted for 60% of the variance in the data. All depression items and mood fluctuation items loaded onto factor 1 and all manic items loaded onto factor 2.2 = 0.71 ± 0.45, p = 0.03). The QBS score also was significantly heritable   and the proportion of phenotypic variance attributable to household effects was not significant  . Lastly, we removed all psychiatric illnesses and found that the heritability of the QBS score remained similarly significant  while the phenotypic variance from shared environment in controls was zero  than individuals without such relatives  .An alternative analysis of familiality showed that individuals with a first or second degree relative with a high QBS score had a significantly higher QBS score .In further support of genetic validity, genetic correlation analyses demonstrated significant shared genetic variance between BP and QBS score . In addition, all QBS subscores remained significantly heritable when the analysis was performed on controls only . These subscore findings suggest that the significant heritability of QBS is not driven by any one facet of the scale.Exploring the heritability of the QBS subscores, we found that the mood fluctuation, depression, and mania subscores all had significant heritability , MAD1L1 (encoding mitotic arrest deficient-like 1), and TRANK1 (encoding tetratricopeptide repeat and ankyrin repeat containing 1) genes6. It would be interesting to determine whether using the QBS phenotype can replicate some of these loci associated with BP or whether QBS identifies additional candidate loci. In addition to GWAS studies, more recently whole-genome or -exome sequencing has been used in the search for BP related genes. As rare alleles can be enriched in founder populations, with better characterized phenotypes, the OOA/M may provide an important cohort for discovering risk variants for BP15.BP is a highly heritable condition. GWAS of BP conducted outside the Amish have identified ~30 significant loci though many have not been replicated29. It is known that BP is prone to over- or under-diagnosis30, which has led to the development of tools to screen for BP such as the MDQ and BSDS. A quantitative trait measuring bipolarity may be more informative than diagnosis for genetic studies31. Furthermore, as the onset of BP can occur later in age, future patients can be erroneously included as controls or with diagnoses of MDD that may convert to BP later, an issue particularly problematic in pedigree-based genetic search where candidate gene identification has been largely dependent upon comparing case vs non-case status within pedigrees. A tool that can identify subclinical bipolar symptoms and their genetic influence may address this important concern. The bivariate genetic correlation analysis here showed a significantly shared genetic correlation between QBS and BP, supporting that this quantitative bipolarity is at least partially tagging the genetic risk of BP.Use of diagnosis as the primary phenotype in the search for genes conferring risk for BP has often gone unquestioned though some have investigated temperamental traits as endophenotypes for bipolar disorder33 and may indicate undiagnosed bipolar features in the general population or patients with MDD34 and may even predict the conversion of unipolar depression to BP35. This view is consistent with our findings in which patients with MDD have a three times higher total bipolarity score, as well as higher mania, mood fluctuation, and depression subscores, compared to non-psychiatric controls.This measure of bipolarity scored much higher in BP and clearly separated BP from other psychiatric illnesses. However, the detection of subthreshold bipolarity may also be of value. Subthreshold bipolarity has been extensively studied36. The original version of the BSDS has been applied to several populations leading to a range of values for sensitivity (0.70–0.90) and specificity (0.51–0.93) of bipolar vs non-bipolar patients depending on the cutoff score used11. Using the QBS, the sensitivity and specificity were comparable in OOA/M, which supports the generalizability of our findings.One of the limitations of this study is that the QBS findings were not evaluated in the general population. However, although OOA/M are culturally and environmentally separated from other Caucasians of European descent in North America, there is evidence that the clinical presentation of mood disorders is similar37, has since been replicated in multiple studies in the general population39. Similarly, genetic studies of non-psychiatric traits have identified associations and biological mechanisms that are replicable in the general population41.Furthermore, research indicates that genetic findings in the OOA/M may be highly applicable to the larger population. For example, the contactin-associated protein-2 (CNTNAP2) gene, first associated with autism in the OOA/MOne of the novel findings of this study in the OOA/M cohort is that the heritability of QBS was substantial even in subsamples without psychiatric diagnosis, suggesting that the range of subtle bipolarity symptoms assessed by QBS is not specific to mental illness but rather that QBS may be indexing a heritable trait characterized along a continuous dimension from subclinical to overtly clinical symptoms. However, controls in families of a founder population with many cases of BP, may have inflated risk as they are genetically more closely related than discreet families in cohorts from the general population. Therefore, this particular conclusion must be re-tested by applying QBS to family or twin samples from the general population. However, even if this caveat of inflated risk is true, it may actually further support QBS as a marker of genetic risk for BP. In addition, a OOA/M cohort with more densely sampled nuclear families than ours may be helpful in a replication study.To the best of our knowledge, there has not been a tool designed to quantitatively assess mania vs depression vs mood fluctuation and then used to assess their relative heritability. The three quantitative subscales for mood fluctuation, depression, and mania within QBS were each found to be significantly heritable. Our definitions of the subscores were based on the clinical description of each item. Factor analysis confirmed the clear distinction between manic and depression items. It is unclear why the depression and the mood fluctuation items clustered together. We speculate that it may reflect fluctuations between normal and depressed moods occurring more commonly than fluctuations between normal and manic moods. Factor analysis of QBS in a larger sample of BP may allow a clearer distinction.42. An additional limitation is that only about 6% of the current sample have BP but their QBS scores are by definition higher and predominate in the distribution of the overall sample, which is a limitation of the current sample. Future studies recruiting BP based samples are needed to further validate the questionnaire.Another notable limitation of this study is the lack of QBS administration over time, thus we did not address reliability. Our study and other studies using the BSDS address validity with measures of sensitivity and specificity, however, we do not know of any studies that address reliability. Furthermore we did not evaluate the predictive power of QBS for risk of conversion to BP, which we plan to address in subsequent studies. Some individuals in our study classified as controls were below the age of maximum risk for BP and may go on to develop BP. However this may also be a strength of the QBS approach because a main purpose of this study is to investigate quantitative bipolarity with and without BP. Another limitation of this study is the low number of individuals with a schizophrenia spectrum disorder , given the data supporting a genetic overlap between schizophrenia and BPHeritability is only the first step of genetic validity; whether QBS would assist in the search for genes conferring risk of developing BP remains to be seen. This study suggests that quantitative bipolarity as measured by the concise self-administered QBS task may be a useful phenotype in supplementing the diagnosis phenotype in BP genetic studies.


Picea abies (L.) Karst.), to the native white pine weevil (Pissodes strobi Peck). We developed single‐ and multi‐trait genomic selection (GS) models and selection indices considering the relationships between weevil resistance, intrinsic wood quality, and growth traits. Weevil resistance, acoustic velocity as a proxy for mechanical wood stiffness, and average wood density showed moderate‐to‐high heritability and low genotype‐by‐environment interactions. Weevil resistance was genetically positively correlated with tree height, height‐to‐diameter at breast height (DBH) ratio, and acoustic velocity. The accuracy of the different GS models tested  was high and did not differ among each other. Multi‐trait models performed similarly as single‐trait models when all trees were phenotyped. However, when weevil attack data were not available for all trees, weevil resistance was more accurately predicted by integrating genetically correlated growth traits into multi‐trait GS models. A GS index that corresponded to the breeders’ priorities achieved near maximum gains for weevil resistance, acoustic velocity, and height growth, but a small decrease for DBH. The results of this study indicate that it is possible to breed for high‐quality, weevil‐resistant Norway spruce reforestation stock with high accuracy achieved from single‐trait or multi‐trait GS.Plantation‐grown trees have to cope with an increasing pressure of pest and disease in the context of climate change, and breeding approaches using genomics may offer efficient and flexible tools to face this pressure. In the present study, we targeted genetic improvement of resistance of an introduced conifer species in Canada, Norway spruce ( Host–pest relationships may constantly coevolve over time when organisms share the same environment  is a conifer native to Scandinavia, Eastern Europe, and the mountainous regions of central Europe. Being the most important commercial softwood for lumber and pulp and paper production in Europe , a wood‐boring insect found in the temperate and southern boreal forests across the North American continent. Larvae feed on leader shoots, causing dieback; eventually lateral branches take over, causing kink and crooked stems of attacked trees. Besides its principal host eastern white pine (Pinus strobus L.), the weevil attacks different native spruces across the North American continent. In Norway spruce, moderate weevil damage leads to significant monetary loss due to stem defects and the resulting losses in lumber volume and quality    between trees to obtain predictions of genetic values  between trees to obtain predictions (GBLUPs) of their genetic value   better understand the genetic relationships between weevil attack and other growth and wood traits, in particular intrinsic wood quality traits; (b) evaluate the performance of different single‐trait genomic selection models, especially for weevil resistance; (c) test the performance of multi‐trait genomic selection models for predicting a target trait  when coupled with genetically correlated indicator traits ; and (d) develop multi‐trait genomic selection indices for the production of high‐quality and weevil‐resistant seedling stock in Norway spruce.22.1The data analyzed in this study are a subset of a larger breeding population derived from a partial diallel mating design  and diameter at breast height (DBH15) were measured at age 15. The height‐to‐diameter ratio (Height15/DBH15) was calculated as a proxy of stem taper. Three wood quality traits related to mechanical properties were assessed: average wood density and cellulose microfibril angle were determined from wood increment cores using X‐ray densitometry and diffractometry at age 15 (Density15 and MFA15) as described in Lenz et al. (16) with the ST300 Hitman tool. Acoustic velocity is a proxy for wood stiffness or modulus of elasticity measured at standing trees  if there was an attack prior to age 15, but no attack at age 10 or prior to age 10. The resulting CWA variable is ordinal . Table For each of two surveys at ages 10 and 15, the presence/absence of weevil damage in the current year (coded 0/1) and in previous years (coded 0/1) were recorded. Current year damage was detected by inspecting the terminal shoot for emergence holes or death of the leader shoot. Weevil damage in previous years was visible by forks, curves, bayonets, or multiple stems. We calculated the cumulative number of attacks (CWA) as: 2.2FIS| < 0.50. SNPs were well distributed across MAF classes with 86% of the SNPs with MAF ≥0.05  with the software LinkImpute , which was assembled from a catalog of high‐confidence gene SNPs obtained from exome capture and sequencing  and its inverse were first computed based on the registered pedigree information using the function “asreml.Ainverse” of the R package ASReml‐R v.3.0 , after checking model residuals . A pedigree‐based relationship matrix  is the random effect of block within site, with bs∼N; a is the random additive genetic effect, with a∼N0,σa2A; sa is the random interaction of site with additive genetic effects, with sa∼N; and e is the residual term, assuming homogeneity across sites with e∼N. For the ABLUP method, the matrix A is the pedigree‐based relationship matrix, which was replaced by the realized genomic relationship matrix G for the GBLUP method ). The X and Z matrices are incidence matrices of their corresponding effects, and xI is an identity matrix of its proper dimension. The symbol ⊗ refers to the Kronecker product. To test the hypothesis of greater than zero variance for each effect , we performed a likelihood‐ratio test with one degree of freedom between the full model in Equation  or the genomic‐based (GBLUP) individual‐tree mixed models  in ASReml‐R v.3.0:y=μ+Xs+Z1Equation , and, acEquation . We compEquation . Howevery=μ+Xs+Z1y=μ+Xs+Z1Narrow‐sense individual heritability was estimated as:r^B), was estimated as:The size of genotype‐by‐environment interaction (GxE), or type‐B correlation . The following model was fitted:iy and jy are the stacked vectors of phenotypic observations for trait i and trait j, respectively; t is the vector of fixed effects of traits ; b(t) is the random effect of block nested within trait, with bt∼N0,Ib⊗VB; a(t) is the random additive effect within trait, with at∼N0,A⊗VA; and e is the residual error, with e∼N0,Ie⊗VR. The matrix A (ABLUP) was replaced by the realized genomic relationship matrix (G) for the GBLUP method. The matrices BV, AV, and RV are 2 x 2 variance–covariance matrices defined by the correlation of effects between traits  and unique variances for each trait . To facilitate convergence, we provided starting values for the variance components in BV, AV, and RV matrices that were taken from the results of the single‐trait models . The genetic correlation between traits was directly provided by the estimated parameter r^a and the phenotypic correlation was calculated as:i,j)p is the phenotypic covariance between traits, and σ^pi2, σ^bi2, σ^ai2, and σ^ei2 are the estimated phenotypic, block, additive, and residual variance of trait i (same for trait j), respectively. The significance of the genetic correlation  was tested by performing a likelihood‐ratio test with one degree of freedom between the full model in Equation . The significance of the phenotypic correlation  was tested by performing a likelihood‐ratio test with three degrees of freedom between the full model in Equation . The single‐site heritability of trait i was given by:To estimate single‐site phenotypic and genetic correlations between traits, bivariate models were run for all pairs of traits in ASReml. Bivariate models were run for each site separately because of convergence problems of the multi‐site model due to large GxE for some traits  pedigree relationships. GBLUP relies on the “infinitesimal” model of quantitative genetics, assuming that the genetic control of complex traits is equally distributed across many (infinite) loci with small effects .We evaluated four single‐trait GS methods: GBLUP, Bayesian ridge regression (BRR), BayesCπ, and, in addition for CWA, threshold GBLUP (TGBLUP) developed for ordinal traits. GBLUP was implemented as described in Equation  in ASRemy is the phenotype; μ is the overall mean; s is the fixed site effect ; b(s) is the random effect of block within site, with bs∼N; ma is the random additive effect of markers; and e is the residual term, with e∼N. Note that compared with ABLUP and GBLUP models, the genotype‐by‐environment interaction (GxE) component was not fitted in BRR and BayesCπ models, thus assuming that marker effects were stable across environments. To account for different distributions of marker effects, the prior for ma changes depending on the method ). In BRR, all markers have a nonzero effect, and so this method is appropriate for traits controlled by a large number of genes with small effects. In contrast, the method BayesCπ takes into account that only a proportion π of markers have an effect, while a proportion (1 − π) of marker effects are shrunk toward zero  were obtained by summing over the effects of all markers, with GEBVi=∑j=1mZ'ija^j, where a^j is the estimated effect of the jth marker, and Zij′ is an indicator of the genotype of individual i at the thj marker.BRR and BayesCπ are from a different class of models, in which marker effects are estimated and used to predict breeding values based on tree genotypes. We fitted the following Bayesian model in the BGLR package:2.7y*) by taking the residuals (e) of a model that included a fixed site effect (s) and a random block within site effect (b(s)): y=μ+Xs+Zbs+e. After adjusting phenotypes, the portion of GxE due to rank changes in different sites remains, but the “level‐of‐expression GxE”  is controlled for ; a(t) is the random additive effect within trait, with at∼N0,G⊗VA; and e is the residual error, with e∼N0,Ie⊗VR. The matrices AV, and RV are p × p variance–covariance matrices, defined by correlations between all pairs of traits and unique variances for each trait. GxE (as rank‐changes interaction) was not fitted to simplify the model and facilitate convergence. We provided starting values for the variance components in AV and RV matrices that were taken from the results of the single‐trait models. We obtained GEBVs of individual trees for each trait separately from the BLUPs of the random additive effect (a) within trait.Genomic selection models that incorporated the information of multiple correlated traits into a single analysis were evaluated using GBLUP multivariate models in ASReml. To facilitate the convergence of multivariate models, the modeling was done in two steps. First, phenotypes were adjusted for block and site effects (15), and microfibril angle at age 15 (MFA15), for which measurements are difficult or costly to obtain for a large number of candidate trees. For each target trait, four multi‐trait GS model was tested: (a) a two‐trait model including one of the target traits and Height15 as an indicator trait; (b) a two‐trait model with one of the target traits and Height15/DBH15 ratio; (c) a two‐trait model with one of the target traits and Velocity16; and (d) a three‐trait model with one of the target traits, Height15/DBH15 ratio, and Velocity16. To simulate a situation where the target trait was only measured for a smaller subset of the trees, the percentage of missing data in the training set (see below) was varied from 0% to 90% by randomly adding missing values to the phenotypes of the target trait. We repeated this process 100 times for each trait and level of random missing values in cross‐validation (see below).We evaluated the performance of multi‐trait GS models for predicting a target trait, when coupled with genetically correlated indicator traits. We chose three target traits, the cumulative number of weevil attacks (CWA), average wood density at age 15 , TGBLUP, BRR, and BayesCπ models’ predictions was tested using a tenfold cross‐validation (CV) scheme combining data across sites as in Beaulieu, Doerksen, Clément, et al.  and Lenzy*) for block and site effects. The predictive accuracy (PACC) of models was estimated from PA as PACC=PA/h^ind2 of the models was evaluated as the Pearson correlation coefficient between the predicted breeding values of the validation trees and the adjusted phenotypes  and prediction accuracy (PACC) of multi‐trait GBLUP models were compared with the equivalent single‐trait GBLUP models using adjusted phenotypes as a response variable  or GBLUP (GEBVs) analysis Equation . These e0≤wi≤1 for all traits, andw1+w2+w3+w4=1.15 is an economically important trait, but was excluded from the SI scenarios because its heritability was not significantly different from zero (see results). The four weight coefficients were varied between 0 and 1 by intervals of 0.05, resulting in 1,771 different selection indices. For each SI, trees were ranked according to decreasing values of the index (I) and the top 5% trees were selected to calculate the expected genetic gain. For each trait, the relative genetic gain (%) was calculated as the ratio of the expected gain to the maximum possible gain from single‐trait selection. We present, a first SI scenario (SI‐1) that corresponded to the priorities for the Norway spruce breeding program in Québec, which put more emphasis on weevil resistance (w2 = 0.6), followed by growth, represented here by height growth (w1 = 0.3), and acoustic velocity (w3 = 0.1). We chose to present two further SIs that maximized the total relative gain of the following traits: Height15, CWA, and Velocity16 (SI‐2) and in the other case Height15, CWA, Velocity16, and Density15 (SI‐3).We assigned a negative sign to CWA since a decrease in the value of this trait (fewer weevil attacks) represents an improvement. Breeding values for each trait were scaled to a variance of one  prior to SI calculations. DBH33.1h^ind2 = 0.25–0.47; GBLUP: h^ind2 = 0.20–0.29) for the cumulative number of weevil attacks (CWA), wood quality traits , height growth (Height15), and height‐to‐DBH ratio (Height15/DBH15), whereas microfibril angle (MFA15) was under low additive genetic control (the additive variance was not significantly different from zero). For DBH15, the estimated heritability was null and the type‐B correlation was zero (r^B = 0) due to large genotype × environment interaction (GxE). Using the GBLUP method, weevil resistance (CWA), acoustic velocity (Velocity16), and average wood density (Density15) had the highest heritabilities (h^ind2 = 0.26–0.29). The lowest GxE estimates were also found for these traits (r^B = 0.76–0.86 using GBLUP), indicating little rank changes of families between sites. GxE was moderate for Height15 and Height15/DBH15 (r^B = 0.52–0.56 using GBLUP) and was higher for MFA15 and DBH15 (r^B < 0.43 using GBLUP).In the across‐site analyses, heritability ranged from 0 to 0.47 using the ABLUP method and from 0 to 0.29 using GBLUP  between the cumulative number of weevil attacks (CWA) and acoustic velocity (Velocity16), and a strong negative genetic correlation between CWA and Height15/DBH15 . There was a negative genetic correlation between CWA and Height15 on both sites , although this genetic correlation was only significant on the most severely affected site GPI by weevil attacks. Conversely, there was a positive, but not significant, genetic correlation between CWA and DBH15 . Overall, these results indicated that weevil‐resistant genotypes were taller, with larger height‐to‐DBH ratio and higher wood stiffness as measured by acoustic velocity.Phenotypic and genetic correlations between traits using the GBLUP method are presented for each site separately, in Table 16) was strongly positively correlated with Height15 and Height15/DBH15 for both sites (r^a > 0.58). Average wood density (Density15) was also weakly positively correlated with Height15/DBH15 (r^a = 0.36) for site GPI, but this genetic correlation was not significant for site STM. MFA was strongly positively correlated with DBH15 (r^a = 0.71) and negatively correlated with Height15/DBH15 (r^a = −0.65) for site STM, but these correlations were not significant for site GPI.Significant genetic correlations between wood quality and growth traits were found for both sites. Acoustic velocity  had the smallest PA, while PA for all other traits was above 0.35. After standardizing PA with the square root of heritability, the estimated predictive accuracy (PACC) was obtained and it was high for all traits (PACC > 0.69). PACC was very high (0.97) for Height15/DBH15 and above 0.80 for CWA, Velocity16, Height15, and MFA15. However for MFA15, the standard error of the estimated PACC was high, which is likely due to a low heritability estimate with large standard error. PACC for DBH15 was not estimated because of the null heritability observed for this trait.The four tested single‐trait genomic selection (GS) methods  and the conventional pedigree‐based method (ABLUP) resulted in similar predictive abilities and predictive accuracies. The PA, which was defined as the correlation between the predicted breeding values for the validation trees and the phenotypic values, ranged from 0.10 for DBHr = 0.997), and the heritability estimates were within the same range (GBLUP: h^ind2 = 0.27 (0.07); TGBLUP: h^ind2 = 0.30 (0.08)). Thus, the assumption of the normality of residuals in GBLUP , the three methods that considered ordinal data, namely TGBLUP, BRR, and BayesCπ, had PA and PACC similar to those of GBLUP, which assumed normality of residuals Figure . In addiP Figure  did not 3.415, or MFA15), when combined with genetically correlated indicator traits . The cumulative number of weevil attacks (CWA), average wood density at age 15 (Density15), and microfibril angle at age 15 (MFA15) were chosen as target traits because they are rather cumbersome and expensive to assess on a large number of trees, whereas indicator traits are easier to track for the majority of trees in a breeding population. The multi‐trait models were compared with the single‐trait GBLUP models. As expected, when the percentage of missing phenotypic data increased from 0% to 90% for the target traits CWA, Dens15, and MFA15, the predictive accuracy (PACC) of the single‐trait models  as an indicator trait  outperformed the single‐trait model only when CWA had 80% or more missing data. When CWA was coupled with the moderately correlated trait Velocity16 , PACC was similar to that of the single‐trait model. The three‐trait model including traits CWA, the Height15/DBH15 ratio, and Velocity16 (purple line) outperformed the single‐trait model for 40% or more missing data, but its PACC was slightly lower than that of the two‐trait model considering CWA and the Height15/DBH15 ratio (blue line).The PACC of multi‐trait models was not improved over single‐trait models when all of the phenotypic data for the target trait were included for model training (0% missing data) Figure . However15 and MFA15 , are given in Table w2 = 0.6), followed by Height15 (w1 = 0.3), and by Velocity16 (w3 = 0.1). This scenario achieved the largest gains in terms of reduction of cumulative weevil attacks (CWAs) (83% of maximum possible gains from single‐trait selection in Table 15 (86% of maximum) and Velocity16 (74% of maximum). The SI‐2 scenario that simultaneously maximized the genetic gain in Height15, Velocity16, and CWA reached as much as 89%, 87%, and 76% of the maximum possible gains for each trait, respectively. In this scenario, MFA15 was also improved by 5.6% (62% of maximum). For SI‐1 and SI‐2, the gain in Density15 was marginal. For SI‐3, Density15 was added as an important trait to maximize along with Height15, Velocity16, and CWA. This SI scenario slightly improved gains in Density15 by 2.6% (51% of maximum), but at the cost of smaller improvements for Height15, Velocity16, CWA, and MFA15. In all three SIs, stem taper, as estimated by the Height15/DBH15 ratio, was improved  by ~10%.The expected gains from the multi‐trait selection indices (SIs) are shown in Table 44.1Hylobius abietis), mostly at the seedling stage after clearfelling operations . These results are not surprising given that height growth is mechanistically stunted in consequence of attack, while the tree continues to grow in DBH and volume. Indeed, with increasing age, a higher height/DBH ratio and thus a lower stem taper was observed in more weevil‐resistant trees Holst,  also repThe genetic control of variation in wood quality traits, such as average wood density and acoustic velocity as a proxy for wood stiffness, was in the same range as that for weevil resistance. Compared to recent wood quality studies of Norway spruce from Scandinavia , genomic selection models using GBLUP, BRR, or BayesCπ had comparable PA and accuracy, confirming early proof‐of‐concept studies in other conifers and spruces   for weevil resistance was in the same range as that for other traits , as compared with approaches that assumed normality of residuals (ABLUPs and GBLUPs). Second, the BayesCπ algorithm, which assumed that only a portion of genes had an effect, did not lead to improvement of PA or accuracy compared with methods assuming that all genes had small effects . In BayesCπ, the estimated proportion of markers having an effect . Other empirical plant or tree breeding studies found little benefits of using multi‐trait GS to predict nonphenotyped selection candidates when 100% of the individuals in the training set were phenotyped  as an indicator trait, the multi‐trait models predicted resistance to weevil attacks more accurately when there was a large amount of missing data for this trait. However, the advantage of multi‐trait over single‐trait GS disappeared as the genetic correlation between weevil resistance and the indicator trait decreased to r^a¯ = −0.55 when using Velocity16 as the indicator trait. Similarly, there was no advantage of using multi‐trait models to predict the target traits Density15 or MFA15, which was likely due to weak or inconsistent genetic correlations across sites between the target and indicator traits. Previous studies similarly found that multi‐trait models performed best when the genetic correlation between traits was high  in our multi‐trait models. We adjusted the phenotypes for site and block effects, but this standardization does not control for GxE due to rank changes between sites. Nevertheless, this seemed to be a reasonable model simplification in the present case, given that we found the same predictive accuracy for single‐trait GBLUP models that accounted for GxE Equation  compared15/DBH15 ratio and Velocity16 did not outperform our best two‐trait model using only Height15/DBH15 ratio as an indicator trait to predict resistance to weevil attacks. This is because Velocity16 was much less correlated with weevil resistance (r^a¯ = −0.55) than was the Height15/DBH15 ratio (r^a¯ = −0.89), and thus, Velocity16 added little information to the predictions. Because of the rapidly increasing complexity of models as the number of correlated traits increases, computer resource limitations and convergence problems due to collinearity can arise  appears more challenging. The low genetic control and the important genotype‐by‐environment interaction observed for DBH  framework. We found that in such a realistic context, it was possible to improve significantly for weevil resistance using GS, given that weevil resistance was moderately to highly heritable and that it was positively genetically correlated with the height/DBH ratio and wood stiffness (acoustic velocity). By taking advantage of these existing genetic relationships, we showed that multi‐trait genomic selection models could improve the accuracy of the prediction for a scarcely phenotyped target trait (weevil resistance) by using the information from a readily available indicator trait. Finally, by combining multiple correlated traits into a selection index, we obtained the best compromise for all traits of interest that corresponded to the priorities of the breeders. Thus, this integrated approach showed how genomic selection can be used to breed simultaneously for taller, stiffer, and more weevil‐resistant Norway spruces. We conclude that single/multi‐trait GS models and index selection are efficient selection tools that can be integrated into operational breeding programs to accelerate the realization of genetic gains for most traits of interest.Another advantage of integrating genomic selection to a breeding program is that, once the breeding population has been genotyped, models can easily be recalibrated for additional traits, such as pest resistance or resilience to episodic climate extremes such as droughts. Such traits may be costly and difficult to measure in forest trees . More details can be found in Azaiez et al. (SNP data and the description of the genotyping array were deposited in the European Variation Archive (EVA, z et al. . The phe Click here for additional data file. Click here for additional data file. Click here for additional data file.


RNF17, and can explain 2.3% of the phenotypic variation. This locus is also significantly associated with residual feed intake (RFI), and can explain 3% of this phenotypic variation. Among 12 SNPs associated with the feed conversion ratio (FCR), the most significant SNP , which was located in the region between the 3rd and 4th exon of the SORCS1 gene on Chr6, explained 3% of the phenotypic variance. Using gene-set analysis, a total of two significant genes were detected be associated with RFI on Chr1. This study is the first GWAS for growth and feeding efficiency related traits in ducks. Our results provide a list of candidate genes for marker assisted selection for growth and feeding efficiency, and also help to better understand the genetic mechanisms of feed efficiency and growth in ducks.Growth rate and feeding efficiency are the most important economic traits for meat animals. Pekin duck is one of the major global breeds of meat-type duck. This study aims to identify QTL for duck growth and feeding efficiency traits in order to assist artificial selection. In this study, the growth and feeding related phenotypes of 639 Pekin ducks were recorded, and each individual genotype was evaluated using a genotyping-by-sequencing (GBS) protocol. The genetic parameters for growth and feeding efficiency related traits were estimated. Genome-wide association analysis (GWAS) was then performed for these traits. In total, 15 non-overlapping QTLs for the measured traits and 12 significant SNPs for feed efficiency traits were discovered using a mixed linear model. The most significant loci of feed intake (FI) is located in a 182Mb region on Chr1, which is downstream of gene  One of the biggest global challenges is how to ensure that an increasingly large population has enough food to meet nutritional needs in the near future. It is expected that the population of the entire planet will reach around 10 billion in 2050 . Food avThe growth and feeding efficiency of animals are affected by a variety of genetic factors. Previous studies discovered a considerable number of QTL, some of which have been used in animal breeding. In the AnimalQTLdb , 13,385 Domestic ducks are important poultry and have a huge global consumer, especially in Asia. More than 35 million Pekin ducks are reared in Asia per annum. However, few QTL-related studies have been reported in ducks due to the non-availability of genotyping arrays. We firstly applied genotyping-by-sequencing (GBS) in ducks , and useBased on our recent genotyping results in the same flock , this stad libitum access to feed and water. Feed was commercial duck feed as detailed in our previous studies  were provided by Golden Star Duck Co., Ltd. and were randomly divided into five batches. The interval between each batch was 5 days. At 42 days, the valid phenotype records of the five batches equated to 120, 114, 116, 135 and 154 ducks, respectively. All ducks were raised to 6 weeks of age and had  studies . Measure studies , and inc studies . Feed coQ < 20 were removed, and barcode sequences were clipped. The data were deposited in the NCBI sequence read archive (SRP068685 and SRP172425). The clean reads were aligned to the duck reference genome using Burrows-Wheeler Aligner (BWA) with the default parameters. In this study, the BGI1.0 duck genome was reassembled based on the radiation hybrid (RH) map using ALLMAP (http://bigd.big.ac.cn/) (The same flock has been genotyped and described in our recent publication . All ducg ALLMAP  and was .ac.cn/) .R2 > 0.9 as the lowest standard. We identified a subset tagging SNPs which matched the following conditions: minor allele frequency MAF > 0.1%, sample call rate ≥ 95%, SNP call rate ≥ 95%, and Hardy–Weinberg equilibrium test P > 10E-6 using PLINK (v 1.90)  .via the PLINK command (--indep-pairwise 25 5 0.2) for PCA analysis. The top 10 eigenvectors were used in related analysis only when the genomic inflation factor is greater than 1.05, to account for the effect of population stratification. The effects of covariates, such as sex and batch, on quantitative phenotypes were assessed with analysis of variance (ANOVA) using R software (https://www.r-project.org/), and covariates explaining more than 1% of the variance at P < 0.05 were included in a mixed linear regression model as the fixed effects. The results of ANOVA are shown in Normality test was performed using the Shapiro–Wilk test to check the distribution of the studied traits. If the traits are skewed from the normal test, the phenotypic data were normalized by the rank-transformation method for application of mixed linear model analysis . The pheThe mixed linear model association analyses were performed using GEMMA . Associay is an n × 1 vector of the normalized phenotype for n individuals; W is an n × i matrix of fixed effects , including a column vector of 1. The α is a c × 1 vector of corresponding coefficients including the intercept, x is an n × 1 vector of marker genotypes at the locus tested and β is the corresponding effect size of the marker. All effects are reported for the minor allele in each marker. The u is an n × 1 vector of random polygenic effects and G is the genomic relationship matrix (GRM) calculated using genotypes and I is an n × n identity matrix and where The SNP-based heritability was estimated using the GREML-LDMS method . The mixy is an n × 1 vector of phenotypic values for n individuals, b is a vector of fixed effects with its incidence matrix X, Gg is a vector of aggregate effects of all SNPs, and the variance of Gg, Rg being the SNP-derived genomic relationship matrix  and e is a random residual term with I represents an identity matrix. The SNP effects were calculated as (2pqβ^2/σ^2), in which p and q are the allele frequencies, β is the estimated SNP effect, and σ^2 is the phenotypic variance.where P-value) higher than the −log10  of the top SNP minus 3 units. If there was another significant SNP within 1kb of the core QTL region, this SNP was also merged into the core QTL region. The extended QTL region did not consider the presence or absence of non-significant sites within the QTL region.Bonferroni correction was performed to establish proper thresholds for genome-wide suggestive and significant associations. The independent locus number was calculated to reduce the confounding effect of LD structure by the simpleM method . Thus, tF-test to compute the gene P-value. The association level for each gene is the weighted sum of the associated statistics for SNP sites in the region. The linkage of the genome and the regulatory patterns of the genes were taken into account when conducting association analysis. The influence range of a single gene was extended to 0.1 MB upstream and downstream, which can increase the sensitivity of associated gene detection.The MAGMA Top model was used in gene-set analysis . Gene-seThe functional annotation of candidate genes was completed using the online tool KOBAS . Due to In this study, 639 Pekin ducks  aged 42 days were used for GWAS. The descriptive statistics of all traits are shown in After quality control, 62,067 SNPs were kept for further analysis. The distribution of SNPs is illustrated in Rg = −0.03).The heritability and genetic correlation Table 2 P < 3.48347E-05) for five different traits. Information on the most significant QTL is shown in The results of the association analysis are shown in P value = 1.90E-05) of the highest genetic additive effect can explain 3.3% of the phenotypic variation of BW21, which is located upstream of SLC39A10 on Chr7. The only suggestive significant locus for ADG was identified at 41.26 MB on Chr1, but no candidate gene was observed.For body weight traits, a total of 4 suggestive significant QTLs were identified Table 3.RNF17, explaining 2.3% and 3% phenotypic variation in FI and RFI, respectively .In total, 12 QTL were significantly associated with feeding traits Table 3.SLC39A10 and DNAJC19, which correspond to the average daily gain QTL in chicken. The results of full GO and KEGG annotations are listed in In total, 14 potential candidate genes were annotated within all detected QTL, with the results shown in We used a genomic relationship matrix constructed from genomic SNP information to estimate the genetic parameters of growth and feeding efficiency related traits in Pekin ducks from the age of 4 to 6 weeks. As animal breeding is a complex, multistep process and pedigree errors are common in commercial breeding practices, the accuracy of genetic parameter estimates is reduced, thus affecting genetic gain from traditional pedigree records. The relationship matrix derived from high density genotypes can, therefore, efficiently correct errors . We estiThe BW21 has both moderate and/or high phenotypic and genetic correlations with FI and FCR Table 2.SLC10A2; Solute carrier family 39 member 10, SLC39A10) for growth traits were found to be involved in metabolism. Solute carrier proteins are a family of transmembrane transporters that play an important role in the exchange of physiological molecules. SLC39A10 is a key transporter for the maintenance of hematopoietic homeostasis and is abundantly expressed during blood cell development and zinc metabolism  are associated with growth and gut length. This study found that SNP between the fourth and fifth exon of the IL1RAPL1 gene could explain 2.5% of FCR phenotypic variation. IL1RAPL1 is an important receptor for interleukins and mutations within this gene in human have been shown to cause mental retardation .All experiments were performed according to regulations and guidelines established by the Animal Care and Use Committee of China Agricultural University (permit number: DK996). All protocols and procedures were approved by the Beijing Administration Committee of Laboratory Animals under the leadership of the Beijing Association for Science and Technology (permit number: SYXK 2007–0023). All efforts were made to minimize animal suffering during the study.Z-CH conceived and designed the experimental plan. S-RC, Y-zY, F-XY, and J-PH participated in collecting tissues, sampling and maintaining and recording the phenotypic data. FZ analyzed the data and interpreted the results. FZ and Z-CH drafted this manuscript. All authors read and approved the final manuscript.The work was supported by the National Waterfowl-Industry Technology Research System (CARS-42-09), the National Scientific Supporting Projects of China (2015BAD03B06), Primary Research & Development Plan of Jiangsu Province (BE2017349), and the Program for Changjiang Scholar and Innovation Research Team in University (IRT1191).Authors F-XY and J-PH were employed by company Golden Star Duck Inc. The remaining 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.


Despite centuries of research, much about the barbarian migrations that took place between the fourth and sixth centuries in Europe remains hotly debated. To better understand this key era that marks the dawn of modern European societies, we obtained ancient genomic DNA from 63 samples from two cemeteries  that have been previously associated with the Longobards, a barbarian people that ruled large parts of Italy for over 200 years after invading from Pannonia in 568 CE. Our dense cemetery-based sampling revealed that each cemetery was primarily organized around one large pedigree, suggesting that biological relationships played an important role in these early medieval societies. Moreover, we identified genetic structure in each cemetery involving at least two groups with different ancestry that were very distinct in terms of their funerary customs. Finally, our data are consistent with the proposed long-distance migration from Pannonia to Northern Italy. The Longobards invaded and conquered much of Italy after the fall of the Western Roman Empire. Here, the authors sequence and analyze ancient genomic DNA from 63 samples from two cemeteries associated with the Longobards and identify kinship networks and two distinct genetic and cultural groups in each. Because barbarian populations of the Migration Period left no written record, the only direct evidence of their societies comes from their archeological remains, chiefly grave goods, that have been used to make inferences about group identities, social structures, and migration patterns5. Unfortunately, grave goods represent a limited and highly curated portion of material culture, and with little other archeological data available, fundamental questions about barbarian social organization and migration remain unanswered6 and vigorously debated8. Were specific barbarian peoples described in texts culturally and ethnically homogeneous populations, or were they ad-hoc and opportunistic confederations of diverse, loosely connected groups? What role did biological relatedness, being that of close kinship relations or long-term shared ancestry, play in the organization of these barbarian communities and how are such relationships related to patterns of material culture? Did this period involve long-distance migrations as described by late-antique authors?Western Europe underwent a major socio-cultural and economic transformation from Late Antiquity to the Early Middle Ages . This period is often characterized by two major events: the collapse of the Western Roman Empire, and its invasion by various western and eastern barbarian/non-Romanized peoples such as the Goths, Franks, Anglo-Saxons, and Vandals, as well as by nomadic Huns; as such it has come to be known as the Migration Period, in German the Völkerwanderung, and in French Les invasions barbares. However, written accounts of these events are laconic, stereotypical, and largely written decades or even centuries later11, see Supplementary Note 12. Etymologically the word fara is rooted in “to travel”, but its precise meaning is ambiguous. While some have interpreted it to represent cognatic, kin-based clans, others suggest that it may simply refer to military units of mixed background13.One group with a relatively voluminous historical description are the Longobards  analyses suggested that Szólád was occupied for only ~20/30 years by a mobile group of Longobard-era settlers14. The female to male ratio  is 0.65. Graves in this cemetery are organized such that a core group (N = 18), mostly of male individuals, is surrounded by a half-ring of females (N = 11) . See Supplementary Note We performed a deep genomic characterization of individuals buried in two cemeteries of the sixth to seventh centuries CE that have material culture associated with the Longobards. Both are considered key sites with regard to the proposed migration from Pannonia to Italy. The first cemetery, Szólád, is located in present-day Hungary  characteristic of ancient DNA  for 10 male samples from Szólád to undergo whole-genome sequencing (WGS), with a mean genome-wide coverage across samples of 11.3×. The remaining 53 samples underwent an in-solution capture targeting 1.2 M single nucleotide polymorphisms (SNPs) (henceforth 1240 K capture)20. The average coverage at these SNPs (excluding the whole genomes) was ~1.5x and the mean number of genotyped SNPs per sample was ~522 K  of samples from Szólád and Collegno against modern reference sets infers that our ancient samples possess genetic ancestry that overlaps overwhelmingly with modern Europeans Fig. –25. Howe30 against a worldwide panel of modern reference samples  that estimates uncertainty in genetic ancestry assignment finds that individuals with high CEU + GBR ancestry are assigned to countries from all over modern central, northern, and northwest Europe , Neolithic farmers (EEF), and Bronze Age Steppe herders (SA). Both PCA and supervised and unsupervised ADMIXTURE analyses .Individuals in this kindred were buried with a rich diversity of grave goods, and all but one was buried in elaborate ledge graves. Only two members of this kindred are female, SZ8 and SZ19, who are estimated to be 3–5 and 17–25 yo. respectively at death. The rest are males, aged from ~1 to ~65 yo. These graves occupy a prominent position in the northwest of the cemetery, with all but SZ19 found amongst the core group , we inferred  Supplementary Note In Collegno, we identified three kindreds, with one particularly extensive one. Nine of the ten individuals from the largest kindred  or Southern (S) groups based on their proportion of CEU + GBR + FIN ancestry versus TSI + IBS ancestry, and used these to conduct a series of Fisher’s exact tests for their association with material culture  for Collegno to complement the existing data from Szólád14 and analyzed them within the context of our genomic data in order to better understand patterns of immigration to these two sites (Supplementary Note 14 described this as Range I)  Fig. . This miIn contrast, in Collegno it was notable that the five individuals with major southern ancestry primarily assigned to Italy using PAA exhibited local strontium isotope signatures. When examining the two major kindred, we observe the striking general pattern that earlier generations had strontium isotope values that diverged from the local range more than later generations Fig. . This apThe most striking feature of our data is the inference of two main clusters of genetic ancestry that are shared amongst our two sixth- to seventh-century cemeteries separated by almost 1000 km. In both Szólád and Collegno this genetic structure mirrors the variation that emerges from their mortuary practices, i.e., how living members of the community represented the individuals that they buried. This perhaps suggests that in these two cemeteries there may indeed have been a biological basis to the notion that long-term shared common descent can shape social identity and that this is reflected in the material culture. However, whether the association between genetic ancestry and material culture reflects specific peoples mentioned in historical texts  or stemmed from a deeper/long-term descent (of mixed barbarian ancestries) is as yet unclear. Future research may reveal how widespread such a link between ancestry and material culture was in the Migration Period.Our genomic characterization of Szólád and Collegno provides novel insight into the structures and hierarchies of societies from the Migration Period in two very different contexts. On the one hand, a previous study of Szólád had identified this cemetery as belonging to a highly mobile community that was settled in the region for approximately one generation. We were able to show in particular that the burials in this cemetery were organized around a three-generation kindred with ten members (Kindred SZ1), all but one of which were of predominantly central/northern ancestry. Members of this kindred stand out in relation to others for a number of reasons: (i) access to diet higher in animal protein; (ii) graves occupying a prominent position in the cemetery; (iii) the presence of the oldest individual in the cemetery (SZ24); and (iv) the individual (SZ13) with the deepest grave and the only one buried with a horse, Thuringian type pottery, and a scale with weights and coins.Surrounding this kindred within the area demarcated by the half-ring of women there are ten males. Of the nine for which we have genetic data, all have predominantly central/northern European genetic ancestry but some have both additional TSI and IBS ancestry (for example SZ5), suggesting a somewhat diverse genetic makeup and thus possibly different geographic origins from each other and compared to the focal Kindred SZ1. All adults and teenagers have weapons, and three out of four adults share the same non-local Sr signature as adults in Kindred SZ1. The half-ring of women itself is made up of a mixture of individuals (ten adults and one child), with five that have predominantly central/northern ancestry and three that have majority TSI + IBS ancestry. They also have a wide range of strontium isotope ratios. The female SZ19 in this half-ring is part of Kindred SZ1. She lacks the ledge graves of the other members, has a distinct material culture, and is of southern ancestry. To what extent her lack of IBD with SZ24 and her different ancestry contributed to her more peripheral burial compared to both the female SZ8 (who is buried within the core) and the male SZ15 (who is similarly unrelated to SZ24 but again found within the core) is an open question.37).Since the adults were almost all non-local, it is tempting to suggest that we may be observing the historically described fara during migration. Regardless, this group appears to be a unit organized around one high-status, kin-based group of predominantly males, but also incorporating other males that may have some common central/northern European descent. The relative lack of adult female representatives from Kindred SZ1, the diverse genetic and isotope signatures of the sampled women around the males and their rich graves goods suggest that they may have joined the unit during the process of migration  is composed of individuals of mainly southern European genetic ancestry that are conspicuously lacking grave goods and occupy the southeastern part of the cemetery; they were buried in randomly oriented graves with straight walls. While the lack of grave goods does not necessarily imply that these individuals were of lower status, it does point to them belonging to a different social group. The strontium isotope data suggest that they may have migrated together with the warrior-based group from outside Szólád, but barriers to gene flow were largely maintained.The remaining part of this community for which we have genomic data  of predominant central/northern ancestry who does not belong to the major kindred unit (though our sampling of this cemetery is not as complete as Szólád). On the other hand, individuals of southern ancestry of the type that would typically be found in the region today appear to be local to the Collegno area based on isotope data, show much more scattered burials and are poorer with regard to grave goods and animal protein consumption. As such it is tempting to infer a scenario of these large immigrant barbarian families exerting a dominating influence on the original resident population.36.Our two cemeteries overlap chronologically with the historically documented migration of Longobards from Pannonia to Italy at the end of the sixth century. We observe that central/northern European ancestry is dominant in both Szólád and Collegno, and that modern genetic data do not show a preponderance of such ancestry in either Hungary or northern Italy. While we do not yet know the general genomic background in these geographic regions before the establishment of Szólád and Collegno, other Migration Period genomes show a fairly strong correlation with modern geography. Going further back in time, samples from Szólád and Collegno with high central/northern ancestry are genetically closer to Bronze Age populations north of Hungary than of Hungary itself. Hungary has demonstrated a diverse and shifting genetic profile from the Bronze Age through to today. Observing some mix of resident individuals with both primarily central/northern and southern ancestry in Szólád seems plausible. However, the observation of a majority of individuals with central/northern ancestry in modern or Bronze Age northern Italy is unexpected. Analyzing the paleogenomic data alongside the strontium isotope data further favors a migration hypothesis, as this indicates that the earliest individuals of central/northern ancestry in Collegno were probably migrants while those with southern ancestry were local residents. Our results are thus consistent with an origin of this group east of the Rhine and north of the Danube and we cannot reject the migration, its route, and settlement of the Longobards described in historical texts. This is also consistent with a parallel study that explicitly modeled this scenario using whole mitochondrial genomes39. It is surprising to find significant diversity within small, individual cemeteries. Even amongst the two family groups of primarily central/northern ancestry there is clear evidence of admixture with individuals with more southern ancestry. Whether these people identified as Longobard or any other particular barbarian people is therefore impossible to assess. If we are seeing evidence of movements of barbarians, there is no evidence that these were genetically homogenous groups of people.Modern European genetic variation is generally highly structured by geographyWe have observed an intriguing association between genetic evidence, isotope data and material culture that sheds new light on the social organization of sixth-century communities during both migration and settlement phases. A key aspect of our approach is the in-depth sampling of entire cemeteries. We propose that this is a conceptual and methodological advance: in the future one must use a similar whole cemetery-based genomic methodology to explore whether the results observed here are common to other sites from late antiquity and the early middle ages. The genetic complexity observed within these cemeteries presents a new set of questions concerning population structure within past societies. Moreover, if the genetic similarity we observe between Szólád and Collegno appears in other contemporary cemeteries, we will be able to better appreciate the extent and dynamics of these movements and of the invasion of barbarian peoples across the Roman Empire.40. Genomic libraries were prepared at the University of Florence and at the Max Planck Institute for the Science of Human History in Jena according to modified Illumina protocols and were screened for endogenous human DNA via mapping to the human reference genome after re-sequencing. DNA postmortem damage (PMD) patterns typical of ancient DNA were assessed with MapDamage41. The ten samples  with best DNA quality and concentration were submitted for WGS on an Illumina HiSeq 2500 1TB at the New York Genome Center. Another 53 samples underwent a capture and NextSeq sequencing for ~1.2 M SNPs for an average coverage of ~1x per sample. Reads were trimmed, merged (where applicable), mapped, and filtered for PCR duplications according to a protocol optimized for ancient DNA43. Genotype likelihood estimation and pseudo-haploid and diploid calling were performed using in-house Python scripts (www.github.com/kveeramah). For samples subject to partial UDG treatment, genotype likelihood estimation was implemented, ignoring the first and last three bases of reads. Genotype calling considered PMD for the remaining samples44. Finally, an additional pseudo-haploid call set was generated by sampling a random read at each position. See Bone specimens from Szólád and Collegno were prepared in clean-room facilities dedicated to ancient DNA in the Laboratory of Molecular Anthropology and Paleogenetics, University of Florence. DNA extraction was performed using a silica-based protocol45 European POPRES46 SNP set with ~300 K SNPs, an imputed Eurasian24 SNP set with ~700 K, and a 1000 Genomes21 and SGDP22 whole genome set matching all 1240 K SNPs. Our ancient reference set consisted of 435 ancient West Eurasians from 6500 to 400 BCE26 with calls made at the 1240 K capture SNPs. Depending on the context, we made pseudo-haploid calls by randomly drawing one allele from a diploid genotype.We assembled SNP data matching the 1240 K capture for three modern reference datasets and one ancient reference dataset for comparison to the early medieval samples generated in this study. The three modern reference sets consisted of an imputed32 to estimate biological relatedness and coancestry coefficients for every pair of samples. In addition to using the allele frequencies of the ancient samples themselves, we also adapted the software to utilize allele frequencies from other sources, in this case the CEU and TSI 1000 Genomes populations, and incorporate admixture.We used the software lcMLkin47. When analyzing Migration Period individuals against reference populations, individual pseudo-haploid PCAs were conducted for each ancient sample separately, and individual analyses were then combined using a Procrustes transformation in R using the vegan package48. LD pruning was performed using the indep-pairwise function in PLINK49.PCA of SNP data was conducted using smartpca30. Dependent on the analysis, target Migration Period samples were analyzed individually (to avoid the effects of relatedness) or together .Supervised and unsupervised model-based clustering was performed using ADMIXTURE28 using the POPRES and HellBus datasets.PAA was conducted as described in Veeramah et al.50 was performed using custom Python software that allowed multithreading.D-statistic analysis as described in Patterson et al.51 to analyze the POPRES imputed SNP dataset. We also further extended the software to allow the use of pseudo-haploid calls in our ancient samples, and to infer the location of one parent of an admixed individual given the known location of another parent.We applied the software SPA27 to examine the relative sharing of central/northern European and southern European-specific rare variants, and used the software rarecoal to assign our ancient whole genomes to a branch on a bifurcating demographic model based on the analysis of rare variants in modern European populations (TSI, IBS, and GBR from the 100 Genomes project21, Denmark52, and the Netherlands. Analysis was performed using both pseudo-haploid calls and diploid genotypes.We followed the approach of Schiffels et al.55. The lack of base calls due to the absence of reads at a position in a particular sample was resolved either as an ancestral or derived allele by a hierarchical inferential method according to the phylogenetic context based on a cladistics approach. The phylogenetically informative SNPs were used to build a parsimony-based phylogenetic tree using the Pars application of the Phylip v3.69 package. FigTree v1.4.2 software was used to display the generated tree.The phylogenetic position of each Y chromosome variant observed in the whole sample was established according to its occurrence in public database or in the published literature56. Collagen was extracted from bones for nitrogen and carbon isotope analysis, based on the method detailed by Privat et al.57. These analyses were carried out at the Godwin Laboratory, University of Cambridge. To determine local bioavailable strontium values, samples of water, vegetation, and soil were collected both at the site of the cemetery of Collegno and at locations considered to be about a day’s walk from the site and then pre-treated following the procedures outlined in Maurer et al.58. Faunal samples were taken from six species from a third-/fourth-century site in Piazza Castello in Turin to provide an ecological baseline for human diet.Strontium, oxygen, carbon, and nitrogen isotope analysis was carried out on 33 first-period individuals from Collegno, together with faunal and environmental reference samples. Tooth enamel powder primarily from second premolars or second molars was used for strontium isotope analysis at the Isotope Geochemistry Laboratory of the Department of Earth Sciences, University of Cambridge. Preparation of tooth enamel powder for oxygen isotope analysis followed the method described by Balasse et al.https://github.com/kveeramah/.Code generated to call variants in the ancient samples is available at Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5


Solanum tuberosum) is a staple food crop and is considered one of the main sources of carbohydrates worldwide. Late blight (Phytophthora infestans) and common scab (Streptomyces scabies) are two of the primary production constraints faced by potato farming. Previous studies have identified a few resistance genes for both late blight and common scab; however, these genes explain only a limited fraction of the heritability of these diseases. Genomic selection has been demonstrated to be an effective methodology for breeding value prediction in many major crops . However, the technology has received little attention in potato breeding. We present the first genomic selection study involving late blight and common scab in tetraploid potato. Our data involves 4,110  and phenotypic field evaluations for late blight  and common scab  collected in seven and nine years, respectively. We report moderately high genomic heritability estimates . The extent of genotype-by-year interaction was high for late blight and low for common scab. Our assessment of prediction accuracy demonstrates the applicability of genomic prediction for tetraploid potato breeding. For both traits, we found that more than 90% of the genetic variance could be captured with an additive model. For common scab, the highest prediction accuracy was achieved using an additive model. For late blight, small but statistically significant gains in prediction accuracy were achieved using a model that accounted for both additive and dominance effects. Using whole-genome regression models we identified SNPs located in previously reported hotspots regions for late blight, on genes associated with systemic disease resistance responses, and a new locus located in a WRKY transcription factor for common scab.Potato ( Solanum tuberosum L.) is considered the sixth most important agricultural commodity worldwide after sugar cane, maize, rice, wheat and milk. In 2014, the global production of potatoes exceeded 385 million tons, positioning China as the largest producer with more than 66 million tons, followed by Russia, India and the United States (The potato (d States . As a std States .Phytophthora infestans (Mont.) de Bary), represent the most devastating disease for potato worldwide. Late blight infects vegetative tissues, typically killing the entire plant, within 7 to 10 days. This pathogen accounts for annual losses of 16% of the global potato production , reduces the potato quality and marketability by causing superficial lesions on the tuber surface  nine years of field evaluations for late blight and common scab at two Michigan State University’s (MSU) research centers. We considered models that accounted for additive effects and various forms of dominance and evaluated two different statistical methods. Our results suggest that sizable fraction of the inter-individual differences in disease resistance (∼46% for late blight and 45% for common scab) can be captured by the SNP set used in the study. The extent of G×E was low for common scab and high for late blight. We found that additive models can capture more than 90% of the genetic variance. We report large-effect SNPs contributing to late blight resistance in chromosomes V and IX, that have been previously reported to harbor resistance genes to this pathogen. We also report the first SNP associated with common scab resistance, located on chromosome IX, and positioned in a transcription factor known for its role in systemic defense and resistance responses. Our results demonstrate that genomic selection can yield moderately accurate prediction of disease resistance for genotypes that have been not evaluated in field trials. Thus, GS could be used for rapid cycling selection for resistance to both late blight and common scab in tetraploid potato.Data were collected from early generation and advanced tetraploid potato genotypes derived from bi-parental crosses at the MSU potato breeding program. Additional advanced breeding genotypes from other United States breeding programs and reference varieties were also included. The available genotypes (n = 381) represent different market classes for fresh market, chip-processing, and russet-type fresh market and processing varieties. These genotypes were evaluated in field trials that included annual selections from MSU’s potato breeding program, where each year poorly performing genotypes were replaced with new genotypes, while maintaining control genotypes during consecutive years.Late blight field resistance trials  were conducted in inoculated foliar field trials during seven years (2010-2015 and 2017) at the MSU’s Clarksville Research Center . Potato seed tubers were hand planted early- to mid-June as four-plant hills in 1.5 m plots in a randomized complete block design with one to three replicates. Late blight-susceptible rows and plots were planted around the perimeter and between blocks to promote an even late blight distribution in the field. After approximately 60 days, all plots were inoculated with a zoospore suspension of late blight at 3x106 spores/mL at the end of July or beginning of August of each year. Over the 7-year period, different isolates were used to infect the trial depending on the prominent isolate in the region. The P. infestans strain  detected in each year in the trial can be found in Table S1 in File S4. Following inoculation, plots were rated visually for the percentage of foliar area affected by late blight. Ratings were taken at 3 to 7-day intervals, based on the rate of disease progression during 35-50 days post inoculation (DPI). Finally, the percent defoliation data were used to calculate the relative area under the disease progress curve - RAUDPC  were conducted under field conditions during nine years (2009-2017) in a disease nursery at the MSU’s Montcalm Research Center . The field was inoculated with common scab from aggressive Michigan isolates, and has been cultivated for high disease pressure for the past nine years. The trials were planted in a randomized complete block design consisting of one to four replications of five-hill plots. After harvesting, mature tubers in plots were assessed for their overall plot disease rating scale of 0-5. The rating was based on a combined score for common scab coverage and lesion severity in which a rating of 0 indicates zero infection and 3.0 or greater scores represent highly susceptible genotypes with >50% infection and severe pitted lesions , following manufacturer’s instructions. DNA was quantified using the Quant-iT PicoGreen dsDNA Assay kit . Genotype scoring was performed using the GenomeStudio software . The tetraploid SNP calling was performed as per e.g., B) where 0 denotes fully homozygous allele (AAAA), 1-3 represent heterozygous genotypes  and 4 the other homozygous genotype (BBBB). The genotype file was filtered by retaining SNPs with minor allele frequency (MAF) >0.05 and missing rate <0.15. The remaining missing SNP-based genotypes were imputed with the SNP means. The final number of SNPs that passed the quality filtering were 4,110.2 of 0.974. Moreover, we did not identify any clear outlier SNP that may have indicated a significant deviation of the observed frequency of heterozygous relative to the one predicted from the estimated allele frequency of the locus.We compared the observed and expected rates of heterozygous loci, the later derived under the assumption of Hardy-Weinberg (HW) equilibrium. Averaged across loci, the observed rates of heterozygosity (0.663) was only slightly higher than the one predicted from estimated allele frequencies (0.647). The regression of the observed and expected frequency of heterozygous loci had an estimated slope of 0.98 (SE = 0.0025) and a RGenomic relationships were computed from centered and scaled SNP-based genotypes according to thm loci. Subtracting the eigen R-function to GRM.thk replicate of the thi genotype collected in year j, thi genotype , iid) with year-specific variances, that is We use whole-genome regression models  and was treated as a random effect. Genetic and genetic-by-year interactions were also modeled as random effects. We considered four specifications for modeling the main effect of genotypes:Genotype effect. In this specification we assumed that the main effects of the genotypes where iid draws from normal distributions Additive model (A)thm SNP in the thi genotype and Additive + Dominance (A+D). In this case, the main effects of genotypes have an additive component plus one that accounted for dominance; therefore in this model thm SNP of the thi genotype was in heterozygous (homozygous) state and General model (G)n copies of the reference allele and Marker effects  were treated as random. We considered two prior distributions of effects: (i) treating SNP effects as draws from normal distributions with null mean and model-specific variances , this approach was implemented using the Bayesian Ridge Regression (BRR) specification in the Bayesian Generalized Linear Regression (BGLR) R-package  of each of the terms entering in the model of expression [1]. The sequence of models considered is summarized in We used the whole-genome regression models described above for three purposes: (1) estimation of variance components, (2) identification of variants with high contribution to additive variance and (3) assessment of prediction accuracy in cross-validation.The amount of variance accounted for by each of the terms included in the model was estimated using the methods described in Response to selection is directly proportional to additive variance . Thus, ii.e., prediction based on genotype data only) and is equivalent to the method labeled as Cross-Validation one (CV1) in We implemented two cross-validation schemes. First, we used a fivefold cross-validation, assigning genotypes to folds. When using this approach all the phenotypic records of a genotype are assigned to either training or testing populations. Thus, this approach yields an estimate of the prediction accuracy that can be achieved predicting the performance of genotypes that have not been evaluated in field trials . Thus, when analyzing the thj fold, data from the thj year was assigned to testing and data from all the other years was used for training. This CV approach yields an estimate of the prediction accuracy that can be achieved when attempting to predict future year performance based on past data. Note that in this case, unlike CV1, when predicting data for the thi genotype on the thj year all the data from the thi genotype collected in other years was part of the training dataset.In a second prediction scheme (CV2), we assigned years to folds  analysis showed that potato genotypes clustered in two groups, one involving 391 genotypes, and a small one including 22 genotypes . The eiglate blight, the amount of genetic variance captured by the A model was roughly 94% of the variance captured by the G model (computed as 0.330/0.352).The variance components analyses for late blight resistance  and low for common scab .i.e., those with the larger single-SNP variance) for each trait. For both pathogens, there were a few regions with large single-SNP-variance. Specifically, for late blight, there were multiple SNPs distributed across the potato chromosomes (For the A model (fitted using BayesB), we computed single-locus variances and used these estimates as proxies for the SNP relevance  and 4b. The results from the first cross-validation analysis (CV1) yielded an estimated prediction correlation of about 0.31 for late blight resistance using the G model. For this trait, there was a relatively small, albeit significant, increase in prediction correlation for the G model relative to the A model. Likewise, there was a slight superiority of BayesB over BRR . In the e.g., M2 and M3) are not presented because in CV1 they render zero within-year correlation. This happens because in CV1 predictions are entirely depending on borrowing of information between genotypes, a feature that is not possible in models that do not use genotype or pedigree information.Note that in i.e., where years were assigned to folds, CV2) yielded higher estimates of prediction accuracy than those obtained in CV1  can be explained using 4,110 codominant SNPs from the Infinium 8303 Potato Array used in this study. These moderately high genomic heritability estimates for complex disease phenotypes indicates that, in principle, genomic prediction could be used successfully to select for resistance to late blight and common scab.vs. tetraploid, hybrids vs. genotypes) and of the environmental conditions  makes the comparisons across studies difficult  and cultivated potatoes (di-haploid S. tuberosum × S. chacoense), For common scab, previous heritability estimates are also highly variable, depending on the genetic material and the environmental conditions. For instance, using diploid potatoes derived from a cross between wild relatives  reflecting changes on the late blight aggressiveness and late blight genotypes present in different years, resulting in a large extent of genotype-by-year interactions for this pathogen. On the other hand, our common scab data were generated in a nursery that has been used to evaluate common scab resistance in potato breeding genotypes for several years. Consequently, there was less variability in the mean scores across years and therefore we observed substantially less extent of G×E. A similar result was reported under comparable conditions by The amount of variance in disease resistance that could be attributed to genotype-by-year interactions was high for late blight and very small for common scab. These differences are likely to be due to the different nature and characteristics of infection on the fields used to evaluate late blight and common scab. Specifically, for late blight, the mean scores varied substantially between years . However, when dominance is included in the model (A+D), the estimated ‘additive variance’ no longer represents the variance explained by allele substitution effects; therefore, the additive component in the A+D model is smaller than the additive component estimated with the A model.The comparison of the genomic variance estimates obtained with the A model and those obtained with the G model suggest that, for both pathogens, a sizable fraction of the total genetic variance  can be captured by an additive model  and priming the synthesis of metabolites associated with the production of defense-related compounds (S. demissum and S. berthaultii (The presence of linkage disequilibrium (LD) between loci makes the partition of the total genetic variance into  locus-specific components not possible . OverallFor common scab resistance, our results suggest an additive resistance effect with a clear major-effect SNP located on chromosome IX. This SNP is associated with a WRKY transcription factor known for their role in the modulation of the resistance responses in systemic and acquired plant resistance, activating or repressing the transcription of genes involved in the synthesis of defense related-proteins such as R proteins . In addiFor instance, we found one SNP located in chromosome III (Table S4 in File S4) associated with the primary metabolism-related protein fructokinase, whose concentration increases under pathogen attack as a mechanism to reduce the costs attributed to the defense response in soil-borne pathogens . These analyses rendered moderately low CV-correlations .We considered two different prediction problems and implemented different CV schemes to represent each prediction problem. Our first CV focused on the prediction of future scores from genotypes that were not evaluated in field trials . This result agrees with previous studies , give a baseline estimates of the prediction accuracy that can be achieved with phenotypic prediction. In CV2, we obtained higher prediction correlations  of the total genetic variance. However, for late blight, we found small  gains in prediction accuracy when accounting for dominance. Our analyses confirm strong associations with disease resistance to SNPs in previously reported resistance hotspot regions for late blight and reported a novel locus that has a sizable contribution to common scab resistance. We demonstrated that prediction of disease resistance, using genomic prediction applied to autotetraploid potato, is feasible and can be implemented for SNP-based selection in potato breeding. Further research is needed to explore ways  in which genomic prediction accuracy can be further improved.