To investigate using quantitative imaging to assess the malignant potential of intraductal papillary mucinous neoplasms (IPMNs) in the pancreas.Pancreatic cysts are identified in over 2% of the population and a subset of these, including intraductal papillary mucinous neoplasms (IPMNs), represent pre-malignant lesions. Unfortunately, clinicians cannot accurately predict which of these lesions are likely to progress to pancreatic ductal adenocarcinoma (PDAC).We investigated 360 imaging features within the domains of intensity, texture and shape using pancreatic protocol CT images in 53 patients diagnosed with IPMN (34 “high-grade” [HG] and 19 “low-grade” [LG]) who subsequently underwent surgical resection. We evaluated the performance of these features as well as the Fukuoka criteria for pancreatic cyst resection.In our cohort, the Fukuoka criteria had a false positive rate of 36%. We identified 14 imaging biomarkers within Gray-Level Co-Occurrence Matrix (GLCM) that predicted histopathological grade within cyst contours. The most predictive marker differentiated LG and HG lesions with an area under the curve (AUC) of .82 at a sensitivity of 85% and specificity of 68%. Using a cross-validated design, the best logistic regression yielded an AUC of 0.96 (σ = .05) at a sensitivity of 97% and specificity of 88%. Based on the principal component analysis, HG IPMNs demonstrated a pattern of separation from LG IPMNs.HG IPMNs appear to have distinct imaging properties. Further validation of these findings may address a major clinical need in this population by identifying those most likely to benefit from surgical resection. Pancreatic cysts are identified in greater than 2% of the general patient population who present with symptoms unrelated to the pancreas , 2. The Retrospective histopathological studies on resected pancreatic cysts have shown that approximately half are intraductal papillary mucinous neoplasms (IPMNs) –5. IPMNsQuantitative imaging, sometimes referred to as “Radiomics,” has the potential to provide a higher level of care to a large number of patients. This imaging analysis utilizes algorithms to derive image texture , shape aWe identified 53 cases of IPMN (34 “high-grade” and 19 “low-grade”) for quantitative analysis of the pancreatic cysts and pancreas parenchyma to differentiate high grade from low grade lesions. The median age of study participants was 73 years, . Of the 53 cases, their demographics by gender and race are as follows: 57% male and 43% female; 90% Caucasian, 5% Hispanic and 5% African American.Quantitative imaging analysis of pancreatic cysts yielded 14 top performing markers in terms of area under the curve (AUC) as well as in terms of true positive rate (TPR) at fixed 5% false positive rate (FPR). All markers were within the cyst contour region of interest and within the domain of texture analysis, specifically in Gray-Level Co-Occurrence Matrix (GLCM) using correlation statistics. Receiver operating characteristic curves were constructed for all variables to differentiate between high and low grade IPMNs . The associated maximum of the Youden index was 0.8438 and its corresponding pair of sensitivity and specificity was 97% and 88% respectively.  in patients with worrisome features by Fukuoka criteria. As a case in point regarding the difficulty of classifying IPMNs based on radiologic appearance, we have included four of our samples with imaging and respective histology to show that cysts that appear grossly simple may harbor high grade dysplasia while suspicious, multi-lobulated cysts may yield only low grade dysplasia Figure .Promising new molecular markers have recently been cited in the literature in a retrospective, multi-institutional paper by Springer and colleagues where they were able to achieve 90-100% sensitivity and 92-98% specificity in classifying pancreatic cysts . CombiniLimitations of this study include its retrospective and single institution design as well as small sample size. In addition, as with most imaging biomarker studies, a large number of tested variables may increase the risk of false discovery. We addressed this potential problem by implementing a false discovery analysis, a cross-validated panel, and principal component analysis. Our data also suggest that a higher rate of prediction is possible by constructing a model from multiple variables. We decided to include this panel data without making strong conclusions as this approach may be a consequence of “over-fitting” and could lead to an inaccurate account of validity. Nonetheless, the accuracy we were able to achieve with our cross-validated model warrants further investigation. At present our findings provide a proof of principle and our next step is to evaluate our cross-validated model using data from other institutions, with a particular emphasis on branch duct IPMNs, which present the most challenging cases to manage due to highly variable malignant potential. As we refine our analysis and identify the key features that are associated with malignancy, we will prospectively evaluate these features in patients with IPMN.Notably, an independent study by Permuth et al , conductIn conclusion, our findings demonstrate that imaging-based, physical properties of IPMNs associate with their malignant potential and suggest the possibility of a non-invasive method to appropriately select patients for surgical or non-surgical management. In clinical practice, we envision a quantitative tool that provides a measure of malignancy risk for a given IPMN lesion and guides borderline cases to either resection or observation. This decision-making may be combined with clinical criteria and molecular markers to provide a higher degree of certainty. Future implementation of these findings into robust statistical models may provide a more scientifically meaningful and clinically relevant method to characterize IPMNs and fulfill a critical need for this patient population given the associated morbidity, potential mortality and associated health care costs of undergoing surgical intervention.We retrospectively examined patients with IPMN seen at our institution between March 2003 and October 2011 who received a pancreas protocol CT scan, subsequent surgical resection, and pathological diagnosis confirming IPMN. Cases were selected in reverse chronological order based on IPMN specimens that physically exist at our institution. Each specimen was then clinically correlated with patient record number, a procedure note outlining the surgical resection of the IPMN and a pre-operative pancreas protocol CT scan. We did not select patients for inclusion based on the radiographic appearance. All consecutive resected specimens with appropriate, matched imaging were used. We recorded the histological grade of the IPMN from surgical pathology using standard histological criteria . IPMNs wWe used a process of segmentation, where we contoured several sets that consisted of either: the cyst alone, the cyst and pancreas together, the pancreas excluding the cyst or pancreatic parenchyma. In situations where multiple cysts existed, we contoured the most radiologically suspicious cyst with review by an expert radiologist (ET). For main-branch duct IPMNs where a clear cystic lesion was not clearly identifiable; we contoured the area of the main pancreatic duct with greatest suspicion of harboring the IPMN. In situations where there were associated side branch cysts that made connections to the main branch, the side branch cysts were included in the contour. In a few cases where there was frank invasion of neoplasm into neighboring tissue, our contouring goal was to sample as much of the cystic component as possible; however, solid mural components as well as potential non-cystic components originating from the IPMN were also contoured. For patients with stents or other metallic artifacts, our contours excluded the margin around the artifact to prevent distortion of true signal. We used Velocity software  to register portal to arterial phases and to aid in contouring of pancreatic cysts, pancreas and parenchyma. Contours were reviewed independently by a radiation oncologist and radiologist for accuracy and consistency.A total of 360 physical features were extracted from the arterial phases using IBEX (Imaging Biomarker Explorer) , which rUltimately, the markers we identify are within the domain of Gray-Level Co-Occurrence Matrix (GLCM), a form of texture analysis, which provides statistical measures of the spatial relationship of pixels within an image. In simple terms, GLCM can be understood as a mathematical method to analyze texture. Within GLCM, identified markers in this study were within the correlation statistic which provides a joint probability occurrence of specific pixel pairs. Put simply, we propose that the correlation aspect of GLCM as it relates to our analysis likely provides information regarding physical properties of tissue, specifically the heterogeneity within the neoplasm as well as the tumor-stromal interface.In this pilot study, we implemented a trifold approach to our data analysis.Our primary goal was to identify the top performing markers in the prediction of pathological grade of IPMNs among all 360 imaging variables using receiver operating characteristic (ROC) analysis as well as false-discovery rate (FDR) analysis to control for Type I errors . From thAs a secondary goal for this pilot study, we constructed a panel based on the AIC (Akaike Information Criterion) to evaluate whether the use of a model combining a panel of markers could be used to increase diagnostic accuracy. We scrutinized all possible combinations of the top performing markers focusing on markers in terms of AUC as well as in terms of true positive rate (TPR) at fixed 5% false positive rate (FPR), in order to pick the best AIC performing logistic regression model.For the construction of a panel, we considered as an initial set the top 10 markers in terms of AUC and the top 5 in terms of sensitivity at a FPR = 0.05 (or ROC(0.05)). We obtain a panel after scrutinizing all possible models that can be built by this initial set using AIC as the selection criterion. This panel consists of 10 markers and was cross validated by the following two schemes of leave 1/3-out cross-validation:(1) Splitting the data into a training set (2/3 of the data) and a test set (1/3) of the data. Fit the model using the training set and apply it to the test obtaining also its AUC. Repeat 1000 times and average out the obtained AUCs. Under this scheme the model is refitted for each training set even though the markers that participate remain the same.(2) Cross-validate the whole procedure, namely use (2/3) of the data of all 360 markers, select the top 10 in terms of AUC and top 5 in terms of ROC(0.05) in the training set, scrutinize all possible models, build the model (still in the training set), fix its coefficients, and apply it on the test set (10 repetitions).Regarding the inference for the individual markers as well as for the panel, we used the bootstrap (1000 draws). We note that the variance of the estimated coefficients of the panel was taken into account during the bootstrap by refitting the model for every bootstrap iteration.Cross-validation provides a method of internally validating our data by minimizing the potential of over-fitting  by creating a model based on variations of training and testing sets.Finally, for completeness and to confirm that signal exists within the cysts contours, we implemented a principal component analysis (PCA), which was applied to all markers in all contours . Using the first two components we are allowed to visualize any potential regarding the discriminatory ability of our markers. PCA is a dimension reduction technique that employs an orthogonal transformation and reduces a set of variables to linearly uncorrelated principal components. The derived principal components account for the most part of the variability of the data. In our study, we collected a large number of variables within a single population, and PCA is a method of affirming that patterns exist among our data.We also obtained the maximum of the Youden index estimate  along wiMATLAB 2014b  and JMP11  were used for statistical analysis.


A number of significant SNP variants were identified for both traits. Many of the SNP variants associated either with susceptibility to – or recoverability from mastitis were located in or very near to genes that have been reported for their role in the immune system. Genes involved in lymphocyte developments  and genes involved in macrophage recruitment and regulation of inflammations (PDGFD and PTX3) were suggested as possible causal genes for susceptibility to – and recoverability from mastitis, respectively. However, this is the first GWAS study for recoverability from mastitis and our results need to be validated. The findings in the current study are, therefore, a starting point for further investigations in identifying causal genetic variants or chromosomal regions for both susceptibility to – and recoverability from mastitis.Because mastitis is very frequent and unavoidable, adding recovery information into the analysis for genetic evaluation of mastitis is of great interest from economical and animal welfare point of view. Here we have performed genome-wide association studies (GWAS) to identify associated single nucleotide polymorphisms (SNPs) and investigate the genetic background not only for susceptibility to – but also for recoverability from mastitis. Somatic cell count records from 993 Danish Holstein cows genotyped for a total of 39378 autosomal SNP markers were used for the association analysis. Single SNP regression analysis was performed using the statistical software package DMU. Substitution effect of each SNP was tested with a  Genome- ≈ 0.03) .Bos taurus autosomes (BTA) 6, 13, 14, and 20 in Nordic Holstein cattle  to determine association with a trait of interest assuming that a marker is in linkage disequilibrium (LD) with, or close to, a causative mutation . In cattn cattle ; BTA 6, n cattle ; 24 chron cattle ; and on n cattle . A revien cattle  indicateThese studies have been useful for identifying genetic variants and genes associated with CM and indicator traits. However, they lack consistency in reporting SNPs or genes implicated in mastitis, and to our knowledge, none of these studies have reported any genetic variant or chromosomal regions associated with recoverability from mastitis. We, therefore, performed GWAS using single SNP regression analysis for two main objectives: (1) to identify new or confirm previously identified regions of the genome for their association with mastitis susceptibility, and (2) identify variants and regions of the genome associated with recoverability from mastitis in Danish Holstein cows.H) and diseased (D). A cow may return to the same state more than once, meaning repeated disease cases are acknowledged by the model  and i had moved from H to D state), and consequently low values of In the above transition probability matrix, the first row consists of the probabilities of being in either of both states at time D state) . Note thThus, for each cow and lactation the sequence of H’s and D’s, indicating whether or not a cow had mastitis on subsequent test weeks, was converted into a new sequence of weekly transitions indicators: 0 if a cow remains in the same state and 1 if the cow changes state. This resulted into two series of transition records: one for healthy to diseased  and the other for diseased to healthy . A more detailed description of the data is given by f(DIM)] was modeled by a combination of Legendre polynomials and a Wilmink term (exp-0.05×DIM) (f(time)] were modeled with Legendre polynomials. The model for the phenotypic analyses included parity (1–3) and herd (1–7) as fixed effects, regression coefficients of a second order polynomial on DIM plus a Wilmink term and regression coefficients of a third order polynomial on time as covariates, and random effects of herd-test-week, cow-parity interaction, and cow effects. The observed transitions were linked to an underlying continues scale called liability  were adjusted for different fixed and random effects before using them in the association analysis. .05×DIM) , to refliability . This rey = 1 if λ > 0 and 0 otherwise. The mean on the liability scale models the average probability for y = 1. Accordingly, the observed transitions and liabilities were modeled with a threshold model as follows:where τ is a fixed threshold and usually set to an arbitrary value (here 0), such that y); b was a vector of all fixed effects (herd and parity) and covariates ; h, p, and c were vectors of random effects of herd-test-week, cow-parity interaction, and cow effects, respectively; X, Z1, Z2, and Z3 were respective incidence matrices, e was a vector of the residual effects. The vectors of random effects  were assumed to be normally distributed, i.e., I were the identity matrices. The residual variance 2, where PSD is the posterior standard deviation of the cow plus cow∗parity effects. Association analyses were carried out on four datasets  to investigate parity specific associations. The number of records, means and SD of the adjusted phenotype used in the association analyses are in Table 1.The phenotypic analyses were performed using the statistical software package MCMCglmm  in R R . Estimatanalysis , residuaFurther bivariate and trivariate analyses were performed to study correlations between traits as well as between parities but within traits. The bivariate model was similar to equation (2) and except the residual, all between traits random effects were assumed correlated. The trivariate model used to calculate correlations between parities was also similar to the model in equation (2) but did not include parity and cow-parity interaction effects. In this trivariate model only the cow effects across parities were correlated. Herd-test-week effects were included and assumed uncorrelated.The raw data contained 1957 cows genotyped with the Illumina BovineSNP50_v2  for a total of 46931 autosomal SNP markers. However, we use phenotypes records from our previous study  and the Multidimensional scaling (MDS) of SNP genotypes was performed for detection of population stratification for the 996 animals that passed genotyping quality control checks. The aim was to assess potential genetic clustering of animals by herd. The cows came from seven herds: one research herd and six commercial herds. In order to visualize possible population stratifications, MDS plots of an identity-by-state (IBS) matrix was generated using PLINK .Figures 1A and 1C, the first five MDS components explained 14.3, 6.2, 5.8, 5.4, and 3.9%, respectively, of the total genomic variance, and therefore, we reasoned that fitting these first five MDS components as clustering factor into our model could improve the GWAS results because it corrects for confounding effect of the population structure . After removing the three genetic outlier animals, the remaining animals formed no distinct cluster .As shown in tructure . The MDSAfter QC and removal of four cows (one for having less than 90% genotype data and three other cows for being genetic outliers), 993 cows and 39378 SNP markers remained and were used for final association analyses.Single SNP association analysis was performed with a linear mixed model in DMU . Each SNy was a vector of posterior mean of summed cow and cow∗parity effects obtained from equation (2); 1 was a vector of ones; μ was the general mean; X was a matrix containing the top five MDS components fitted as covariates, and b was a vector of associated effects; S was vector of SNP genotypes coded as 0, 1, or 2 for genotype copies of one of the alleles at each locus; α was the allele substitution effect; a was a vector of random additive polygenic effects, which was assumed to follow normal distribution, A is the pedigree-based additive genetic relationship matrix; Z was an incidence matrix relating elements of the vector of additive polygenic values a to individual phenotypes; e was a vector of random residuals and the residuals were assumed to follow a normal distribution, W was a diagonal matrix with diagonal element wi is used to account for the different residual variances due to parities. The pedigree for the animals (993) in the genotype data was extended to a maximum depth of 10.9 generations and an average of 4.4 known generations. The extended pedigree contained a total of 12779 animals and were offspring of 2659 sires and 9536 dams.where H0: b = 0 was tested with a t-test. A genome-wide Bonferroni corrected significance threshold can be obtained by dividing the nominal significance threshold (0.05) by the number of independent tests  in the analyses. The P-value after Bonferroni multiple testing corrections was 1.26974E-06, equivalent to -log10  = 5.90. However, in the current study no P-value exceeded the Bonferroni threshold and hence a liberal threshold of P-value < 10-4 was used to declare significant SNP-trait associations.The association analysis was conducted using the statistical software package DMU . The nul2 gene annotation system , which translates to less variation in susceptibility to- than in recoverability from mastitis within year across time. On the other hand, animal related variation was higher for susceptibility to mastitis (HD) than for recoverability from mastitis (DH) (Table 2). The between traits posterior mean of cow effect correction [rc = -0.90 (0.07)] was much higher than the cow-parity interaction correction [rcp = -0.29 (0.11)].Herd-test-week variation was lower for the HD trait than for the DH trait  across parities. As expected, for the HD trait, the smallest between parities cow effect correlation was observed between the non-consecutive parities 1 and 3 [rp13 = 0.08 (0.12)], whereas for the DH trait, the smallest between parities cow effect correlation was observed between the consecutive parities 2 and 3 [rp23 = 0.10 (0.20)] (Table 2).The variance components presented in P-value < 10-4 was considered to determine genome-wide significant SNP-trait association. For the full data , significant SNP-trait associations were detected on BTA3 and 7 for mastitis susceptibility and on BTA7 and 15 for recoverability from mastitis (Table 3). Parity specific significant SNP-trait association were also detected on the same chromosomes but no overlapping association signals were observed. Significant association signals in parity 1 and in the other parities (parity 2 or 3) detected on the same chromosome did not overlap, for either of the traits. For susceptibility to mastitis, the highest number of significant associations were observed on BTA7 and BTA13. For recoverability from mastitis, the highest number of significant associations were observed on BTA12 and BTA13. In total, 29 significant SNPs (14 for susceptibility to mastitis and 15 for recoverability) were detected.The nominal significance level of Figure 2) and Manhattan plot , only 6 SNPs (2 for susceptibility and 4 for recoverability) reached the defined genome-wide significance threshold when all parities were analyzed together. The QQ plots and Manhattan plots for the parity specific associations are provided in the Supplementary Material  further demonstrated that the GWAS results were not substantially influenced by the observed population stratification. Influence of population stratification was adequately controlled by including the MDS components in the model and this was quantified by a calculated genomic inflation factor ≤ 1.05 .As shown in the quantile–quantile (QQ) plot  indicates that highly susceptible cows are in general less likely to recover fast. Relatively, the trait correlation between cow-parity interaction effects was much smaller than the cow effect correlation, indicating even highly susceptible cows in parity one could recover relatively faster in the other next parity.The lower posterior mean of herd-test-week variance for HD than for DH could indicate that management to prevent mastitis (and other factors influencing susceptibility) is fairly stable across time, whereas management (and other factors) related to recoverability is more variable over time. This could suggest that there are possibilities to enhance recoverability by improvements in herd management . The posTable 2. The cow-parity interaction variance is larger than the cow variance, i.e., the larger part of cow variance is parity specific. Results from the trivariate analysis where parities are considered as different traits, also point to relatively low agreement between parities (Table 2). So, the data does not support the general expectation that parities may be similar, and this can explain the absence of common association signals across parities in the association results. A possible confounding effects of multiple mastitis pathogens may have contributed to the lack of consistent association signals across parities and traits. The cow effect correlations across parities but within traits were positive and ranged from low to moderate. For the HD trait, the weakest posterior mean of cow effect correlation across parities was observed between parities 1 and 3, indicating that susceptibility in parity 1 is distinct from susceptibility in parity 3. For the DH trait, the weakest posterior mean of correlation across parities was observed between parities 2 and 3, suggesting recoverability in parities 2 and 3 should not be considered as the same trait. In general, we would expect parities to be similar, but this data shows larger variation in cow-parity interaction than the cow effect variation as we presented it in TNF-α was associated with a lower number of mastitis cases in lower parities and a higher number of mastitis cases in higher parities. Similarly, the causal variants or genes could be different for susceptibility to – and recoverability from mastitis as association signals present on the same chromosome do not overlap. Our results support the findings that QTLs even for highly correlated traits  present on the same chromosome do not overlap  detected on the same chromosome do not overlap, suggesting that genetic regulation of mastitis could also be parity dependent. Effect of genes has been shown to vary depending on parity or age of cows . Wojdak- overlap . Most ofMARCH3 (E3 ubiquitin-protein ligase MARCH3). The gene MARCH3 encodes an E3 ubiquitin-protein ligase enzyme that may be involved in regulation of the endosomal transport pathway and it has been implicated in diverse biological functions, such as immune regulation, protein quality control, and membrane trafficking (STAB2 (stabilin-2 precursor). Studies in pigs . SLX4-deficient mice have been shown to develop epithelial cancers and to have a contracted hematopoietic stem cell pool  for their association with mastitis traits defined from SCCs. The SNP rs43732911 on BTA7, an intron variant and in significant association with susceptibility to mastitis in the full dataset, is located within a potential candidate protein coding gene fficking . The SNP in pigs  and in m in pigs  showed tell pool .ATG16L2 (autophagy related 16 like 2). The gene ATG16L2 has been reported to play a role in autophagy (PDGFD (platelet derived growth factor D). The protein encoded by the gene PDGFD plays an important role in wound healing and macrophage recruitment (PTX3 (pentraxin 3). The gene PTX3 may involve in the innate immune response against intra-amniotic infection and inflammation  and IFI30 . Both genes have been reported for their role in antigen processing. In a gene expression assays . The enzyme also facilitates MHC class I-restricted recognition of exogenous antigens containing disulfide bonds by CD8+ T-cells or cross-presentation (GeneCards).We have also identified some potential candidate genes from the nearby gene cluster, in addition to the genes identified by within gene SNP variants. The SNP rs41255569 on BTA7, an upstream gene variant and in significant association with susceptibility to mastitis in parity 3, is mapped very near to a functionally known candidate genes n assays , the geninvasion . The enzFigure 4 we present a 1Mb gene cluster surrounding the significantly associated variant in the region. This illustrates that complex traits could be regulated by several clusters of genes surrounding association signals and confirms the fact that mastitis is a complex trait and is determined by many genes with small effect limiting the ability of GWAS to identify causal genes . The gene EPS15L1 has been reported as an essential gene for T lymphocyte development in Zebrafish , MAST3 (highly expressed in antigen-presenting cells and in lymphocytes) and STAB2  as candidate for the causal genes for susceptibility to mastitis. For recoverability from mastitis we suggest EPS15L1 , PDGFD , and PTX3 (involved in regulating inflammation) as candidates for the causal genes. Regardless of the gene annotations related to immune responses, we were not fully convinced to suggest these genes as strong candidates as we were not able to confirm previously identified SNP variants or regions of the genome. However, this is the first GWAS study for recoverability from mastitis and our results need to be validated. Moreover, most of the functional annotations were derived from gene expression studies in mice and humans and gene functions may be different in cattle. There have also been discrepancies between gene-expression platforms and analyses methods reported to be obstacles in mapping loci that might underlie functional trait variation  determined at a significance level of BW performed the genome-wide association analysis and wrote the draft manuscript. PL and LJ contributed to the research hypothesis and discussion of the results. D-JK contributed to the research hypothesis, analysis, discussion of the results, and writing the manuscript. WF contributed to the research hypothesis, analysis of phenotype data, and discussion of the results. All authors approved the final version of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


To date, however, little consideration has been given to combining gene-based tests of association for the analysis of pedigree data. We propose a flexible framework for combining any number of family-based rare-variant tests of association into a single summary statistic and for assessing the significance of that statistic. We show that this approach maintains type I error and improves the robustness, to different genetic architectures, of the statistical power of family- and gene-based rare-variant tests through application to simulated phenotype data from Genetic Analysis Workshop 19.Current rare-variant, gene-based tests of association often suffer from a lack of statistical power to detect genotype–phenotype associations as a result of a lack of prior knowledge of genetic disease models combined with limited observations of extremely rare causal variants in population-based samples. The use of pedigree data, in which rare variants are often more highly concentrated than in population-based data, has been proposed as 1 possible method for enhancing power. Methods for combining multiple gene-based tests of association into a single summary  Over the past decade, the rapid decrease in costs for DNA sequence data have made it possible to consider the association of rare single nucleotide variants (SNVs) and complex disease phenotypes. However, the power of any single variant test of genetic association on such rare variants is limited. As such, many gene-based tests of association  have recently been proposed, with the intent to improve statistical power over single marker association tests. Recent insights have classified most of these gene-based tests of association into 2 broad classes: burden  tests and variance component  tests . Burden Despite improvements over single-marker approaches, gene-based tests may still have limited utility in detecting causal rare variants because of an overall lack of power. This lack of power is exacerbated by the fact that optimizing the limited power of gene-based tests of association (by selecting the appropriate burden or variance component test) requires some a priori knowledge of the underlying genetic architecture. In particular, burden tests are most powerful when the variants within a SNV set are mostly causal and have the same causal direction, whereas variance component tests perform optimally in circumstances where the causal direction varies  , even fop values from the gene-based tests of association into one summary p value for the gene, but are frequently limited by not being applicable to family-based data. One exception is the seqMeta package in R [In response, new tests of association have been proposed that simultaneously leverage the strengths of both burden and variance component tests –5. Theseage in R .The use of family/pedigree data has become popular as another means of increasing statistical power to detect causal rare variants. Rare SNVs are more concentrated in affected families, meaning that causal SNVs may aggregate in pedigree data more than in the general population or in a traditional case-control study. In this article, we propose a general method of combining different rare-variant tests for use on family-based data. We then quantify to what extent combining family-based rare-variant tests of association increases power and maintains type I error rate for simulated phenotypes using data from Genetic Analysis Workshop 19 (GAW19).p values across a variety of family-based gene-level rare-variant tests of association into a single summary statistic. Given a set of k distinct family- and gene-based rare-variant tests of association, the method involves the following 5 steps: (a) Generate a vector of test statistics ith family-based rare-variant test of association . (b) Permute the phenotype of interest m times. Find jth permutation . (See next paragraph for consideration of pedigree structure.) (c) Convert each test statistic vector, p values, where m permutations, and S is a univariate statistic calculated by combining jth permutation. In this article we use p value combination options are possible. (e) Compute the significance level of m.Our approach, which extends the approach by Derkach et al  to the cPermuting phenotype values when pedigree structure exists in the samples fails to maintain appropriate type I error rates. To address this limitation, we modify the approach listed in the previous paragraph by first fitting a linear mixed effects model to the desired covariates, with familial correlation as a random effect, and the phenotype of interest as the response, as has been done in related settings . We estiAs a proof-of-concept, we applied the above method to data from GAW19. The data consists of real genotype data, on which blood pressure phenotypes were simulated, for 849 Mexican American individuals across 20 separate pedigrees. We considered the continuous response variable mean arterial pressure (MAP), calculated as (2/3)*(diastolic blood pressure) + (1/3)*(systolic blood pressure). We assessed the relationship between MAP and the 30 genes on chromosome 3 that possess at least 1 casual variant using 1000 permutations per test. Power was then calculated as the proportion of times among the 200 individual phenotype simulations where each gene was found to be significant, using a 0.05 threshold. Type I error was assessed in a similar method on the same 30 genes, but with the trait Q1  as the phenotype of interest.ith variant, ith genotype vector. In particular, 2 different variant weighting systems were considered. The weights suggested by Wu et al. [ith variant, are calculated using a Beta distribution and substantially downweight common variants,  Table .Table 1Tp value procedure, the combined test had power in the same range as more optimal tests. We further note that, depending on the characteristics of the gene being examined, tests featuring either the PDCD6IP, which contains a number of highly causal rare variants, and ZBTB38, which contains several causal common variants .Pairwise correlation between Few current methods allow for a flexible combination of family-based rare-variant tests of association. In response, we propose an adaptable framework for combining any number and type of gene-based rare-variant tests on pedigree data that condition on genotype data. Unlike current existing combination methods, our proposed method does not limit the researcher to combining just 1 family-based burden test with 1 family-based variance-component test, but instead allows for the combination of an arsenal of varied and specialized tests. Our proposed method also offers a distinct advantage over simply running all possible individual tests and choosing the most significant result; our combination approach appropriately controls for multiple testing and for the correlation between tests. When applied to the GAW19 data, the combination method appeared to sufficiently maintain type I error and demonstrated generally robust statistical power, even when applied to a wide variety of genetic architectures, or when low-powered tests were included.p value).The permutation component of our combination approach also poses computational challenges. The process of calculating p values when asymptotic distributions have not been derived is time intensive. The tests evaluated here were applied to only 30 genes in a single simulated data set. We note that if combining tests with known asymptotic distributions, it is straightforward to utilize asymptotically obtained p values from individual tests when creating However, our current approach does have several limitations. In the above proof-of-concept application, the combined test incorporates 8 highly correlated individual tests of association; the additional noise from this correlation diminishes the power of the combined test to detect any meaningful genotype–phenotype associations. Further research is needed to explore what combination of tests minimizes such correlation while also maximizing statistical power. Further work is also necessary to evaluate the benefits and drawbacks of different choices of combined test statistics (eg, alternatives to using the minimum entclass1pt{minimaOur analysis acts as a proof-of-concept for combined tests on family-based rare-variant data. In particular, our analysis suggests that the type I error rate is controlled by the proposed method, while power may be more robust to differing genetic architectures than individual, gene-based rare-variant tests. Further work is needed to explore the trade-off between combining many tests versus combining a smaller set of diverse tests in other data sets/genes to ensure transferability of these findings. Additionally, we need to explore alternatives to permutation strategies to compute statistical significance resulting from the inherent computational limitations of this approach.


N=10 064) from the China, Oxford, and VCU Experimental Research on Genetic Epidemiology study, aimed at identifying genetic risk factors for recurrent major depression among a rigorously ascertained cohort. Heritability of neuroticism as measured by the Eysenck Personality Questionnaire (EPQ) was estimated to be low but statistically significant at 10% . In addition to EPQ, neuroticism based on a three-factor model, data for the Big Five (BF) personality dimensions  measured by the Big Five Inventory were available for controls (n=5596). Heritability estimates of the BF were not statistically significant despite high power (>0.85) to detect heritabilities of 0.10. Polygenic risk scores constructed by best linear unbiased prediction weights applied to split-half samples failed to significantly predict any of the personality traits, but polygenic risk for neuroticism, calculated with LDpred and based on predictive variants previously identified from European populations (N=171 911), significantly predicted major depressive disorder case–control status (P=0.0004) after false discovery rate correction. The scores also significantly predicted EPQ neuroticism (P=6.3 × 10−6). Factor analytic results of the measures indicated that any differences in heritabilities across samples may be due to genetic variation or variation in haplotype structure between samples, rather than measurement non-invariance. Findings demonstrate that neuroticism can be significantly predicted across ancestry, and highlight the importance of studying polygenic contributions to personality in non-European populations.Biometrical genetic studies suggest that the personality dimensions, including neuroticism, are moderately heritable (~0.4 to 0.6). Quantitative analyses that aggregate the effects of many common variants have recently further informed genetic research on European samples. However, there has been limited research to date on non-European populations. This study examined the personality dimensions in a large sample of Han Chinese descent ( Neuroticism also predicts response to treatment for both mental and physical health problems12 and generates significant economic burden.13 Hence, research developing a biological understanding of the personality dimensions has strong relevance for public health and for the prevention of mental health disorders.14Personality dimensions predict numerous social and behavioral outcomes . However, despite the apparent role of biological factors in the etiology of the personality dimensions, genome-wide association studies (GWAS) have only observed a small number of SNPs significantly associated with any of the BF.24The BF personality dimension phenotypes appear to be highly polygenic, and may be most appropriate to explore with quantitative analyses that can aggregate the effects of many common single-nucleotide polymorphisms . Several twin and family  studies estimated significant heritabilities for the BF personality dimensions on the order of ~0.4 to 0.6 refs , 17, 18 24 neuroticism scores across >170 000 primarily European samples from the UK Biobank were harmonized and common variation from these GWAS explained up to 15% of the genetic variance in neuroticism.24 These results suggest that adequately powered samples can detect modest yet noteworthy aggregate molecular genetic effects on neuroticism in Europeans.In the largest genome-wide meta-analysis to date,N=10 064). This sample is substantial enough for analyses of common genetic variation (power=0.93 for heritability of 0.10), and evidences comparable rates of neuroticism. CONVERGE is the first study to identify and successfully replicate genome-wide significant loci hits for major depression,25 and this sample has other strengths as well: carefully ascertained Han Chinese ancestry allows for examination of whether genetic variation differs in samples of non-European descent compared with previously ascertained European samples, and the CONVERGE sample is female. Rates of major depressive disorder (MDD) in women are approximately twice that of men  and an all-female sample is optimal because it controls for clinical heterogeneity owing to sex. Finally, CONVERGE used a clinically rich protocol with personality and interview-based diagnostic data on over 12 000 participants.However, to date, there has been no research aggregating common variation in large non-European samples. The current study examined heritabilities of the personality dimensions in the large China, Oxford and VCU Experimental Research on Genetic Epidemiology (CONVERGE) study of individuals of Han Chinese descent  scores,30 with the intention of testing whether variants predicting neuroticism also predict MDD status, as has been found using European samples. Finally, we calculate polygenic risk scores for neuroticism based on the European sample (N>170 000) meta-analysis summary statistics, to predict both MDD status and neuroticism in CONVERGE. Scoring weights based on a subsample were recently found to significantly predict MDD status and neuroticism in independent cohorts of European ancestry.23This study reflects a personality trait-based approach to the study of psychiatric disorders, consistent with the National Institute of Mental Health's Research Domain Criteria initiative to look beyond dichotomous, diagnostic status to identify core processes implicated in the development and maintenance of psychological disorders.25 We controlled for potential clinical heterogeneity by recruiting only female participants, and to control for ethnic stratification, only participants whose grandparents  were of Han Chinese descent were recruited to participate. Cases and controls (age M (s.d.)=44.4 (8.9) and 47.7 (5.6), respectively) were excluded for diagnosis of bipolar disorder, any psychosis and any significant mental deficit such as a diagnosis of intellectual disability. Cases had to have had at least two major depressive episodes with the first episode occurring before age 50, and could not have abused drugs or alcohol before their first episode of depression. Controls were clinically screened to rule out prior depressive episodes and had to be at least 40 years of age, past the age of most typical MDD onset.Cases and controls were recruited from 51 mental health centers and psychiatric departments of general medical hospitals, in 40 cities across 21 provinces. Please refer to previously published research for full details of sample ascertainment.The study protocol was approved centrally by the Ethical Review Board of Oxford University, and by the ethics committees of all of the participating hospitals in China. All the participants provided written informed consent.24 Briefly, the DNA was extracted from saliva using Oragene and sequenced reads were obtained from Illumina Hiseq machines aligned to Genome Reference Consortium Human Build 37 patch release 5 (GRCh37.p5) with Stampy 35 and imputation was performed at sites with no reference panel using BEAGLE (version 3.3.2).36 A second round of imputation was performed at biallelic polymorphic SNPs using 1000 G Phase 1 ASN haplotypes as a reference panel. To determine the final number of SNPs, we applied a conservative inclusion threshold for SNPs: (1) a P-value for violation HWE>10-6, (2) information score >0.9 and (3) minor allele frequency in CONVERGE >0.5%.GATK's UnifiedGenotyper (version 2.7-2-g6bda569) VariantRecalibrator (version 2.7-2-g6bda569) were used on post subsequent base quality recalibration files for variant discovery and genotyping at all polymorphic SNPs in 1000 G Phase 1 ASN panelAll the participants were interviewed using a computerized assessment system and the participant interview sessions lasted approximately 2 h. The interviewers were largely trained psychiatrists with a small number representing postgraduate medical students or psychiatric nurses, and all were clinically trained by the CONVERGE team for at least 1 week. The interviews were recorded and included an assessment of psychopathology, demographic characteristics and psychosocial functioning. Trained editors listened to a portion of the interviews to provide ratings of interview quality. We excluded participants who had incomplete assessment information or were lacking high-quality genetic data, to arrive at a final 4728 controls and 5612 case samples for analysis.38 which had been developed in English and translated into Mandarin. The measure consists of 44 items rated on a 1–5 Likert Scale (strongly disagree to strongly agree) and subscales are scored for each of the five BF traits.During the interview, control the participants completed the Big Five Inventory (BFI)39 a 40-item true–false self-report scale. This assessment was also developed in English and translated to Mandarin. Because the Mandarin version of the BFI had not previously been examined for internal reliability, all translated scales were examined carefully and any items with poor subscale factor loadings were removed from the subscale calculations. For example, one of the BFI Openness items generated a negative factor loading, thus analyses of this subscale were run with and without the poorly performing item. Before removing any items, both BFI and Eysenck Personality Questionnaire (EPQ) assessments generally showed adequate to high reliability, and Cronbach's alphas for each of the the scales in this sample are presented in The control and case samples completed the Neuroticism subscale of the Eysenck Personality Questionnaire,41 To correct for dependence between markers, and thereby avoid the potential disruption of the eigenvalue structure, SNPs were pruned at r2>0.7 before construction of principal component scores, as recommended by Patterson et al.41 The principal component analysis was conducted to obtain principle components of population stratification. A total of 144 929 autosomal SNPs with Pr(G)⩾0.9 and <1% missing rate were used to generate 10 intracontinental principal component scores. To circumvent over-fitting,42 only the first two principal components, which distinguished north–south regional differences, were used in the subsequent analyses.To reduce the effects of population stratification, ancestry principal components were constructed using EIGENSOFT 3.0 ref.  and SMAR45). GCTA-based methods can examine information from all genotyped DNA in one analysis, providing a genome-based approach to quantify the heritability of complex psychiatric phenotypes.46The GWAS methods can be complimented by GREML (genomic relatedness restricted maximum likelihood) methods, namely genome-wide complex trait analysis (GCTA). Genomic relatedness analyses integrate and test the effect of variation across all genotyped variants. The use of genomic relatedness data have been informative across many disorders where univariate genome-wide association tests have been unsuccessful in accounting for a significant proportion of genetic variance  indicated greater than 85% power to detect heritability estimates of 0.20.For each analysis presented, the GCTA package29 using the first random half of the sample and then testing the aggregate score in the remaining half. We then reversed the analysis to predict scores in the first half of the sample from the second half. Linear regressions were conducted for each dimension including a full model (BLUP score and the two primary principle components as predictors of the trait) and a restricted model, removing the BLUP score. The difference in Nagelkerke R2 between models (rsq) was computed for each trait and the P-value associated with the SNP score variable within the full model was examined. The Nagelkerke rsq generated from these models is a difference in pseudo-R2, thus P-values are not derived from full and restricted model comparisons. Instead, the P-value associated with the dropped component (score) is reported here.Genetic risk scores were constructed using estimated SNP effects by the BLUP method24 Polygenic risk profile scores calculated from the UK Biobank summary statistics were used to test predictive power in CONVERGE. LDpred48 was the preferred method for these analyses, owing to the ability of LDpred to account for linkage disequilibrium (LD) structure using our EUR test sample, and because P-value thresholds do not need to be specified using LDpred, nor do variant lists need to be pruned for LD. The method does require prior proportions of causal variants in the genome to be assumed for score calculations, and a range  was tested to avoid making errors based on incorrect theory. Regressions were run using R to compare full  and restricted models where RPS was removed.Analyses also generated polygenic risk profile scores based on weights from previous meta-analyses that had successfully predicted MDD and neuroticism in independent cohorts.RPSs were used to predict both MDD status (binary case–control) and neuroticism (quantitative EPQ score) in CONVERGE. For these analyses, neuroticism was examined as both a sum score and a factor score, but results did not appreciably differ across the two types of scores.Characteristics of the trait distributions for the BFI are listed in σ2G/σ2P) of the BFI scales in the controls are presented in P=0.06) and none of the other subscales exhibited statistically significant heritability estimates. This was true even when adjusting starting values (the —reml-priors function in GCTA) and increasing the number of iterations (the —reml-maxit function in GCTA).Heritability estimates (P<0.0001). This estimate, at 10% with 3% standard error, was statistically significant. For this sample, power was approximately 0.93 to detect a heritability of 0.10. The controls and cases were also examined separately, with heritability analyses resulting in nonsignificant estimates.Polygenic prediction of the BF using BLUP scores was then conducted. 24 and calculated by LDpred, was significantly predictive of MDD status and neuroticism in CONVERGE at a prior threshold of 0.3 after false discovery rate correction . Risk profile scores did not significantly predict BFI neuroticism (only available in the Han controls).Finally, we examined RPSs based on the sample weights from discovery samples that had significantly predicted MDD and neuroticism in independent cohorts of European ancestry. Polygenic risk of neuroticism, based on the discovery setThe current study examined common genomic variation in relation to the major dimensions of personality. Results suggest that when examining a sufficiently large sample, EPQ neuroticism can be successfully predicted from common variants with a genome-based heritability of approximately 10% in individuals of Han Chinese ancestry. When examining controls separately using the BFI, common genetic variants appeared to have a negligible effect on personality dimensions.24), and is consistent with genetic correlations found in twin research.51Although scientific consensus converged on three- and five-factor dimensional models of personality decades ago, research has only now begun to successfully link these dimensions to molecular genetic variants. Both GPC and UK Biobank analyses suggest shared genetic architecture of MDD and EPQ neuroticism. This is supported by successful polygenic risk prediction of neuroticism and MDD status from discovery sample statistics . This is not inconsistent with the previous univariate GWAS findings or with lower estimates reported from studies using adoption designs.63 Non-additive genetic variance could also be less relevant for neuroticism compared with, for example, extraversion, accounting for the lower SNP-based h2 estimates for extraversion and the other BF traits. This could contribute to the differences between the 10–15% and the ~40–50% heritability observed in biometrical studies of neuroticism.It remains unclear why heritability estimates from genome-based studies, including this one, are largely inconsistent with the increased heritability estimates derived from twin studies. One explanation for decreased genome-based estimates could be that these dimensions are associated with rare variants, insertions, deletions or other types of genomic variation. It is also possible that twin studies overestimate narrow sense heritability and underestimate the variance due to nonadditive genetic and common environmental factors. Twin research by Loehlin Although all of these explanations are possible and by no means mutually exclusive, we believe it is likely the case that the effect sizes of the individual variants on personality are likely to be very small. Although our sample was relatively large, much larger samples have been necessary to detect the very small additive genetic effects of individual molecular variants on personality.h2 was detected for neuroticism in the entire sample, yet polygene signals in split-halves were not significantly predictive of neuroticism. This may be a statistical artifact of the case–control design used in CONVERGE, and the differential distributions of neuroticism that were observed across the MDD and control samples. Another possibility is that reduced power from splitting the sample, skewness or zero-inflation in the neuroticism distribution led to these results.It is somewhat incongruous that significant 56 The rate of MDD in women is approximately twice that of men27 and longitudinal studies indicate that this sex difference first emerges mid-puberty,69 which could implicate dynamic epigenetic, endocrine and/or environmental changes.In addition, some generalizability concerns are relevant to this study. First, though examination of only females controlled for sex-related clinical heterogeneity, it is possible that this led to different point estimates than would be found with males and females. This would be consistent with previous research examining sex differences in genome-based estimation of heritability of neuroticism and extraversion in males and females.Second, the trait scales used were originally developed in English, and although reliability of the scales in this sample was generally adequate, not all of the translated items performed well. However, although measurement non-invariance across European and Chinese samples should be further explored, factor analytic results of the measures suggest that differences in heritabilities across samples may be owing to genetic variation or variation in haplotype structure between samples.h2 results across measures. It is also important to point out that the five factors have been critiqued as only partially empirically derived.73 Some have speculated that the five personality dimensions may be statistical artifacts of the factor analytic methods used. Common pathway modeling has indicated overlap of these constructs, with a general violation of collinearity constraints .Finally, general measurement issues relating to the BF may benefit from further scrutiny. Two items on the BFI corresponding to remaining calm in stressful situations  were the least correlated with EPQ items. This difference between the scales could account for discrepant Results from this well-powered study suggest that with a sufficiently large sample, neuroticism based on the EPQ can be successfully predicted from common genetic variants. Overall, common genetic variation appeared to have a very limited effect on the major dimensions of personality in our sample of controls. Conceivably, a clinically enriched sample with MDD (and thus with greater mean levels of neuroticism) could enrich the burden of relevant alleles in the sample, increasing power to detect risk variants. Nevertheless, the use of both a healthy control group and a depressive disorder case group in this study separately produced low and nonsignificant heritability estimates. Future genome-based personality studies would likely benefit from the use of larger samples, and given the paucity of significant, replicated GWAS association findings, future research on the genetics of personality may also benefit from further use of gene-based and gene pathway methods to examine enrichment relating to personality dimensions.


Manihot esculenta Crantz) is an important security crop that faces severe yield loses due to cassava brown streak disease (CBSD). Motivated by the slow progress of conventional breeding, genetic improvement of cassava is undergoing rapid change due to the implementation of quantitative trait loci mapping, Genome-wide association mapping (GWAS), and genomic selection (GS). In this study, two breeding panels were genotyped for SNP markers using genotyping by sequencing and phenotyped for foliar and CBSD root symptoms at five locations in Uganda. Our GWAS study found two regions associated to CBSD, one on chromosome 4 which co-localizes with a Manihot glaziovii introgression segment and one on chromosome 11, which contains a cluster of nucleotide-binding site-leucine-rich repeat (NBS-LRR) genes. We evaluated the potential of GS to improve CBSD resistance by assessing the accuracy of seven prediction models. Predictive accuracy values varied between CBSD foliar severity traits at 3 months after planting (MAP) (0.27–0.32), 6 MAP (0.40–0.42) and root severity (0.31–0.42). For all traits, Random Forest and reproducing kernel Hilbert spaces regression showed the highest predictive accuracies. Our results provide an insight into the genetics of CBSD resistance to guide CBSD marker-assisted breeding and highlight the potential of GS to improve cassava breeding.Cassava ( Manihot esculenta Crantz) is a primary source of income and dietary calories for millions of people, and the high starch content of its storage roots is exploited in industry1. Although cassava is a resilient crop, its production in East Africa is often constrained by viral diseases including cassava brown streak virus disease (CBSD) which causes significant yield loses4. This disease is caused by two virus species of the genus Ipomovirus, family Potyviridae: cassava brown streak virus (CBSV) and Ugandan cassava brown streak virus (UCBSV)7. Both cassava brown streak viruses have successfully colonized a broad altitudinal range in South, East, and Central Africa and the steady spread of the disease is a threat to cassava production in West Africa6. In the field, CBSVs are transmitted by the whitefly (Bemisia tabaci) in a semi-persistent manner and through the exchange of infected cassava cuttings among farmers9. In susceptible clones, the viruses cause a myriad of symptoms including yellow chlorotic patterns along minor veins of leaves, necrotic streaks on the stems and brown or grey corky root necrosis15.Cassava  and Albert (susceptible), has facilitated the identification of a set of candidate genes that collectively may confer resistance to CBSD in Namikonga25. QTL mapping in biparental crosses successfully identified a set of QTL and candidate genes associated with resistance to CBSD induced root necrosis and CBSD foliar symptoms20. Notably, the characterization of QTL regions associated with CBSD resistance in the Tanzanian local cultivar Namikonga and the Tanzanian landrace Kiroba, suggest that some of those regions were introgressed from Manihot glaziovii20.So far, few studies have used genomic-enabled approaches to increase the understanding of the mechanism of resistance and identify candidate genes and/or molecular markers associated with CBSD resistance or tolerance28. Evaluation in the greenhouse and the field of transgenic lines, has shown efficient, durable and stable siRNA-derived resistance to CBSD across agro-ecological regions28.Crop biotechnology approaches such as RNAi technology have also been proposed to accelerate the integration of CBSD resistance into farmer-preferred cultivarswww.nextgencassava.org). Preliminary studies suggest that Genome-wide association studies (GWAS) and genomic selection (GS) are effective approaches for cassava breeding. Genome-wide association mapping studies in cassava have led to the identification of QTL regions associated with cassava mosaic disease resistance (CMD)29, beta-carotene content30 and dry matter content31. Genomic selection is particularly promising as an alternative method to marker-assisted selection (MAS) and conventional phenotypic selection because it can accelerate genetic gains due to the selection of parental genotypes with superior breeding values at the seedling stage based on genotypes alone34. Indeed, the evaluation of the performance of genomic prediction models using phenotypic and genotyping by sequencing (GBS) datasets from three African cassava breeding programs highlight the potential of GS as a breeding tool for some traits36.Currently, genetic improvement of cassava is undergoing rapid change through the implementation of genomic-enabled tools by breeding programs in Africa  to identify sources of resistance to CBSD in the GWAS panel, (2) to assess the current predictive accuracy for CBSD (3) to identify genomic prediction models that account for CBSD genetic architecture including the evaluation of a synergistic implementation of GWAS and GS and (4) to identify significant polymorphisms to guide CBSD marker-assisted breeding to improve cassava breeding in the face of increasing disease threats to agricultural production.Foliar and root disease scoring was performed according to a standard CBSD scoring scale that ranges from 1 to 5   estimates across panels and locations spanned a wide range of values from 0.11 for 3 MAP at Namulonge (hotspot CBSD location) (Panel 1) to 0.75 for scorings 9 MAP at Kamuli  (Panel 2) (Table 2) estimates ranged between 0.11 for 3 MAP at Namulonge and 0.73 for CBSDRS at Ngetta (low CBSD pressure). For GWAS Panel 2, H2 estimates ranged from 0.24 for scorings 3 MAP to 0.75 at Kamuli for 9 MAP.For both panels and across locations the heritability estimates for CBSD symptom severity scorings at 3 MAP, 6 MAP, 9 MAP, and CBDRS were low to intermediate. Broad-sense heritability  that displayed an H2 value of 0.11 and for the multi-location dataset (3 MAP), which showed the most significant difference between H2 and SNP heritability estimates. For Panel 2, H2 estimates were consistently higher than SNP heritability estimates except for Namulonge, Serere, Kamuli and the multi-location dataset for CBSDRS.Further, we combined genotypic and phenotypic data and estimated SNP heritability values using variance components that were obtained as a result of fitting a one-step model for each panel, each location, and multi-location datasets. For Panel 1, HThe genetic structure of the GWAS panels was estimated by using principal components analysis (PCA). Overall, the first three principal components (PCs) accounted for 60% of the genetic variation observed in the data Fig. , the firManhattan plots of the genotype-phenotype associations at 3 MAP, 6 MAP, and CBDRS based on the combination of ~1000 clone multi-location de-regressed BLUPs are presented in Fig. 10 which exceeded the Bonferroni threshold >5.9 were considered to be statistically significant and were further annotated into coding regions (genes) of the cassava genome  = 9.38 within the QTL on chromosome 11 explained 6% of the observed phenotypic variance (Supplementary Table 2 > 0.6) with the highest −log10 SNP hit.SNP markers with a −log2 > 0.2 with the highest −log10 marker, were annotated and classified as candidate genes. The genes identified were Manes.11G130500, Manes.11G130000, Manes.11G130200 and Manes.11G131100. Manes.11G130500 is known to encode glycine-rich protein. Manes.11G130000 encodes a Leucine-rich repeat (LRR) containing protein. Manes.11G130200 encodes the trigger factor chaperone and peptidyl-prolyl trans. Finally, Manes.11G131100 encodes a protein kinase 38. We further confirmed the presence and segregation of the introgressed genome segment in both panels using a set of diagnostic markers from M. glaziovii  > 6.5), which explained 8, 6 and 10% of the phenotypic variance respectively.SNPs surpassing the Bonferroni threshold could not be identified for CBSDRS across panels. However, analysis of the multi-location data for Panel 1 identified significant regions of CBSDRS association on chromosomes 5, 11 and 18  in that they are sums of additive effects of markers, while other model predictions represent genomic estimated total genetic values (GETGV) because they include non-additive effects. Prediction accuracy for CBSD related traits had mean values across methods of 0.29 (3 MAP), 0.40 (6 MAP) and 0.34 (CBSDRS) Fig. .Figure 5These accuracies varied from 0.27 (BayesB and GBLUP) to 0.32 (RF) for 3 MAP, from 0.40 (most methods) to 0.41 (RF) and 0.42 (RKHS) for 6 MAP and from 0.31  to 0.42 (RF and RKHS) for CBSDRS. It is clear from the results that higher predictive accuracies were consistently achieved when using RF and RKHS for the prediction of both foliar and root CBSD resistance traits. For foliar symptoms, the increase in predictive accuracy using RF and RKHS was modest in comparison to GBLUP, whereas for CBSDRS the predictive accuracy increased from 0.31 using GBLUP to 0.42 using RF and RKHS models.Based on the genome-wide association results, we identified for foliar 3 MAP and 6 MAP and CBSDRS the strongest marker associations on chromosomes 4 and 11. To test if GWAS results can help to improve genomic prediction accuracy markers from chromosomes 4, 11 and markers on other chromosomes were used independently to construct covariance matrices that were fitted together in a multikernel GBLUP model  reflect the differences in CBSD pressure at these locations. For example, Kasese and Namulonge are both hotspots for CBSD and showed a higher correlation in comparison to Kasese and Ngetta, which differ on their CBSD prevalence. The high variability observed within and across GWAS panels reflects differences in population composition, trait genetic architecture, field design and environmental effects including differences in virus strains or species which may contribute to poor correlations across locations.41. Nonetheless, the correlation among foliar CBSD severities, in both panels and across locations, were consistently higher than the correlation between foliar and root severities. Similar to other studies21, our results demonstrate variability of symptom expression across environments and suggest that mechanisms of CBSD resistance operate somewhat independently in leaves and storage roots17, or are under different genetic control.In addition to scoring variability within and across locations, both panels showed variation in foliar CBSD symptom expression at 3, 6 and 9 MAP and root necrosis CBSDRS. These results are consistent with the observation that symptom expression changed with the age of the plant, with the loss of lower symptomatic leaves or symptoms being obscured in older plants19. However, when Panel 1 was analyzed independently, significant regions associated with CBSDRS were identified on chromosomes 5, 11 and 18. Similarly, Nzuki et al.19 in an analysis of a biparental population identified a region on chromosome 5 associated with root necrosis and a region on chromosome 11 associated with both root necrosis and foliar symptoms. Moreover, Masumba et al.20 found chromosomes 11 and 18 associated with CBSD root necrosis. Both QTL mapping studies were developed from crosses between Tanzanian clones and support the idea that much of the CBSD resistance present in the Ugandan breeding population has its origin in Tanzania.Given the availability of multi-location phenotypic data and GBS genotyping datasets, we assessed the potential of GWAS to identify QTL associated with CBSD resistance in leaves and storage roots. For all traits, several factors played a role in the identification of significantly associated SNPs including differences in panel size, age of the plant at scoring, CBSD prevalence and environmental condition at the different locations. Based on preliminary GWAS results in individual panels, we decided to increase the study power and resolution of our GWAS by combining multi-location scores from both panels. GWAS in the combined population detected SNPs significantly associated  with foliar CBSD symptoms at 3 and 6 MAP on chromosomes 4 and 11. In contrast, no significant SNPs associated with CBSDRS could be detected, possibly due genotype-by-environment (GxE) interaction with CBSD and polygenic control or mechanisms of resistance under different genetic control contributing to CBSDRSet al.42 previously reported Manes.11G130000 when studying the distribution of NBS-LRR gene family in the cassava genome. In addition,Manes.11G130000 was among the differentially expressed genes during early transcriptome response to brown streak virus infection in the susceptible line 60444 from the ETH cassava germplasm collection40. The QTL on chromosome 11 is particularly unstable across locations, which may be related to NBS-LRR genes conferring resistance to a particular viral strain7.Genomic annotation of the foliar GWAS results showed that a cluster of genes underlies the significant QTL associated with foliar disease resistance on chromosome 11; candidate genes that were identified for further study are Manes.11G131100, Manes.11G130500, Manes.11G130200 and Manes.11G130000. Lozano M. glaziovii and cassava varieties of Brazilian origin were successfully used for crosses to obtain varieties which showed high levels of field resistance to CBSD43. Similar to Bredeson et al.38, in our study, we identified widespread evidence for interspecific hybridization in the Ugandan cassava breeding panels. One of these introgression regions is located on chromosome 4, which in our study was associated with foliar severity. However, the degree of linkage disequilibrium in that region of chromosome 4 was a major constraint for the identification of a gene or genes responsible for CBSD resistance38. Interestingly, this region was not detected in the QTL mapping population involving the inter-specific hybrid, Namikonga21, but was detected in a cross with Kiroba20, another inter-specific hybrid presumably originating from the Amani breeding program.Throughout the 1940s and 1950s at the Amani Research Station in Tanzania, 34. We applied genomic prediction models that have different underlying assumptions: the GBLUP model assumes an infinitesimal genetic architecture, Bayesian methods such as BayesA and BayesB relax the assumption of common variance across marker effects46 and RKHS and RF can model epistasis. For all traits, using the combined dataset, we found moderate predictive accuracies in the 0.27–0.42 range; in general, predictive accuracies were in accordance with broad-sense heritability estimates, which ranged for Panel 1 between 0.37–0.61 and Panel 2 between 0.25–0.46. In general, we observed a superiority of the RF and RKHS models to predict both CBSD foliar symptoms and root necrosis, though the increase in predictive accuracy was more prominent for root necrosis. Similar to our results, previous studies that contrasted the performance of various prediction methods in cassava showed that RF and RKHS displayed higher predictive accuracy, particularly with phenotypes known to have a significant amount of non-additive genetic variation such as yield-related traits23. Based our findings, non-additive effects are likely to play a role shaping CBSD resistance in cassava in particular for root necrosis.Genomic selection (GS) can accelerate genetic gains through the use of phenotypic and genotypic data from a training population for early selection of seedlings to develop superior varietiesa priori knowledge of the loci associated with a trait is not needed for GS, we tested a multiple kernel approach by fitting three kernels with genomic relationship matrices constructed with SNP markers from chromosomes 4 (Gchr4), 11 (Gchr11) and SNPs from other chromosomes . While the use of a multiple kernel approach did not increase predictive accuracies, the highest contribution of each kernel to the total predictive accuracy modeled the genetic architecture of CBSD traits. Based on our results, we conclude that genomic selection is a promising breeding tool for selecting for CBSD resistance. Based on our results, we conclude that genomic selection is a promising breeding tool for selecting for CBSD resistance and that the use of prediction models that consider both additive and non-additive effects could be advantageous.Even though 39 and unstable across environments, which is an indication of quantitative resistance47. Although we were able to identify a candidate NBS-LRR gene on chromosome 11, the function of this gene in CBSD resistance requires further validation and more importantly, there is the possibility that this gene might not be a source of durable resistance to CBSVs. Further work will require screening large diversity panels, possibly including wild relatives, in multiple environments, and efforts to identify QTL specific to certain viral strains to further support the development of CBSD resistance.Cassava brown streak disease is devastating cassava production in regions already affected by CBSVs and poses a high risk to countries in Central and West Africa where CBSD is not currently present. The GWAS and GS results presented in this study support previous findings which indicate that resistance to CBSD is polygenicWe assembled two cassava GWAS panels composed of 429 and 872 clones, respectively. These clones represented germplasm diversity derived from the International Institute for Tropical Agriculture (IITA) and the International Center for Tropical Agriculture (CIAT)  on a 1–5 scaleThe first stage in data analysis involved accounting for trial design using a linear mixed model to obtained de-regressed BLUPs, and the second stage involved the use of de-regressed BLUPs in GWAS and Genomic prediction.For Panel 1 we fit the model:β included a fixed effect for the population mean and location. The incidence matrix Zclone and the vector c represent a random effect for clone Zrange(loc.) and random effects vector block(range) and effects vector ε were distributed In this model, y was the vector of raw phenotypes, β included fixed effects for the population mean, the location and finally an effect for checks. The incidence matrix Zclone and the vector c are the same as the model above, and the blocks were also modeled with incidence matrix b represents the random effect for block. The best linear predictors (BLUPs) of the clone effect (ĉ) were extracted as de-regressed BLUPS following the formula52:lmer function from the lme4 R package53 to fit the models described above.For Panel 2 we fit the model54. GBS libraries were constructed using the ApeKI restriction enzyme as previously described55. Marker genotypes were called using TASSEL GBS pipeline V456 after aligning the reads to the Cassava v6 reference genome58. Markers with more than 60% missing calls were removed.Total genomic DNA was extracted from young leaves according to standard procedures using the DNAeasy plant mini extraction kit59. After imputation, 63,016 SNPs had an AR2 (Estimated Allelic r-squared) higher than 0.3 and were kept for downstream analysis. From the remaining imputed markers, 41,530 had a minor allele frequency (MAF) higher than 0.01.The resulting marker dataset consisted of 173,647 bi-allelic SNP markers and was imputed using Beagle 4.1Correlation across CBSD traits and within each location was estimated using using the de-regressed BLUP values obtained after fitting the aforementioned linear mixed model. Broad-sense heritability values on an entry-mean basis were calculated using the variance components estimated using the mixed-models described above, which represent the first-step of the two-step genomic analysis.emmreml function from the EMMREML R package60. The random effect was modeled as having a co-variance proportional to the kinship matrix, which was calculated using the A.mat function from the rrBLUP R package61.Finally, SNP-based heritability was calculated for each GWAS panel by fitting a single-step mixed-effects model using the princomp in R62.A principal component analysis (PCA) was performed across panels to identify any population stratification between the two GWAS panels. We used the imputed dataset of 63,016 SNP markers to calculate the PCs with the function 63. We followed a leave one chromosome out approach in which the chromosome with the tested candidate SNP markers is excluded from the genomic relationship (GRM) calculation. Manhattan plots were generated using R package qqman with a Bonferroni threshold of 5.964.With the imputed dataset of 63,016 SNP markers and 986 individuals, GWAS was performed using a mixed linear model association analysis (MLMA) accounting for kinship and filtering by MAF > 0.05 as implemented in GCTA (v 1.26.0)A.mat in the R package rrBLUP61. GBLUP predictions were made with the function emmreml in the R package EMMREML60.In this prediction model, the GEBVs are obtained after fitting a linear mixed model where the genomic realized relationship matrix is based on SNP marker dosages. Accordingly, the genomic relationship matrix was constructed using the function chr4), 11 (Gchr11) and SNPs from the other chromosomes . Multi-kernel GBLUP predictions were made with the function emmremlMultiKernel in the R package EMMREML60.Because the most significant QTLs for foliar CBSD severity at 3 and 6 MAP were mapped on chromosomes 4 and 11 (this paper) we followed a multi-kernel approach by fitting three kernels with genomic relationship matrices constructed with SNP markers from chromosomes 4  parameter that determines the rate of decay of correlation among individuals. This function is nonlinear, and the kernels used for RKHS can capture non-additive as well as additive genetic variation65. To fit a multiple-kernel model with six covariance matrices, we used the emmremlMultiKernel function in the EMMREML package, with the following bandwidth parameters: 0.0000005, 0.00005, 0.0005, 0.005, 0.01, 0.05 (Multi-kernel RKHS) and allowed REML to find optimal weights for each kernel.We use a Gaussian kernel function:46, the Bayesian LASSO66, BayesA, and BayesB32. The Bayesian models we tested allow for alternative genetic architecture by way of differential shrinkage of marker effects. We performed Bayesian predictions with the R package BGLR67.We tested four Bayesian prediction models: BayesCpi70. Random Forest regression with marker data has been shown to capture epistatic effects and has been successfully used for prediction74. We implemented RF using the Random Forest package in R75 with the parameter, ntree set to 500 and the number of variables sampled at each split (mtry) equal to 300.Random Forest (RF) is a machine learning method used for regression and classificationet al.38. We used the M. glaziovii diagnostic markers identified in Supplementary Dataset 2 of Bredeson et al.38. These ancestry informative (AI) SNPs were identified as being fixed for different alleles in a sample of two pure M. esculenta (Albert and CM33064) and two pure M. glaziovii.To identify introgression segments in the two GWAS panels, we followed the approach described in Bredeson M. glaziovii homozygous (G/G), M. esculenta homozygous (E/E), or heterozygous (G/E). We assigned G/G, G/E or E/E ancestry to each window, for each individual only when the proportion of the most common genotype in that window was at least twice the proportion of the second most common genotype. If this was not the case, we assigned windows a “No Call” status.Out of 173,647 SNP in our imputed dataset, 12,502 matched published AI SNPs. For these AI SNPs, we divided each chromosome into non-overlapping windows of 20 SNP. Within each window, for each individual, we calculated the proportion of SNPs that were 63. Briefly, the LD score for a given marker is calculated as the sum of R2 adjusted between the index marker and all markers within a specified window:2 is the usual estimator of the squared Pearson’s correlation76. The resulting LD scores were then plotted against log10 from GWAS of every marker on chromosome 4.To confirm the presence of a haplotype on chromosome 4, we calculated LD scores for every SNP marker on chromosome 4 in a 1 Mb window using GCTAr2) between the top significant GWAS SNP hit on this chromosome and neighboring markers in a window of 2 Mb (1 Mb upstream and 1 Mb downstream)78.To highlight the importance of the associated markers on chromosome 11 we calculated pairwise squared Pearson’s correlation coefficient  > 5.9. The resulting significant SNP markers were then mapped onto genes using the SNP location and gene description from the M.esculenta_305_v6.1.gene.gff3 available in Phytozome 1158 for Manihot esculenta v6.1 using bedtools79.We used the https://www.cassavabase.org/.The phenotypic and genotypic data generated and analyzed during this study are available in the Cassavabase repository, Supplementary FiguresSupplementary Tables and Data