Much of the world’s population growth will occur in regions where food insecurity is prevalent, with large increases in food demand projected in regions of Africa and South Asia. While improving food security in these regions will require a multi-faceted approach, improved performance of crop varieties in these regions will play a critical role. Current rates of genetic gain in breeding programs serving Africa and South Asia fall below rates achieved in other regions of the world. Given resource constraints, increased genetic gain in these regions cannot be achieved by simply expanding the size of breeding programs. New approaches to breeding are required. The Genomic Open-source Breeding informatics initiative (GOBii) and Excellence in Breeding Platform (EiB) are working with public sector breeding programs to build capacity, develop breeding strategies, and build breeding informatics capabilities to enable routine use of new technologies that can improve the efficiency of breeding programs and increase genetic gains. Simulations evaluating breeding strategies indicate cost-effective implementations of genomic selection (GS) are feasible using relatively small training sets, and proof-of-concept implementations have been validated in the International Maize and Wheat Improvement Center (CIMMYT) maize breeding program. Progress on GOBii, EiB, and implementation of GS in CIMMYT and International Crops Research Institute for the Semi-Arid Tropics (ICRISAT) breeding programs are discussed, as well as strategies for routine implementation of GS in breeding programs serving Africa and South Asia. Crop improvement through plant breeding is a process of continuous genetic improvement through selection and recombination of superior lines. The response to selection, or rate of genetic gain, is dependent on multiple factors, expressed in the “breeder’s equation,”R is the response to selection per year, i is the selection intensity, r is the accuracy of selection, σa is the additive genetic standard deviation for the trait of interest, and L is the generation interval  , where the performance of new lines is predicted based on genome-wide information . Multipl (GBLUP)  and sing (GBLUP)  enabled r, Equation 1) estimates of line performance but significantly increases generation interval  due to the multiple years of testing. While simulation studies demonstrate that a rapid-cycle recurrent GS approach may ultimately provide the largest increases in genetic gains  selection of parents for crossing, (ii) selfing or use of doubled haploid technology (DH) to achieve the desired level of homozygosity, and (iii) multi-stage field trials of selection candidates (inbred lines or testcross hybrids) to identify best lines or hybrids for release and commercialization as varieties. We generalize this structure as a variety development pipeline 2 platform. EiB is being developed as a “complete platform” or set of interconnected tools and strategies designed to increase the efficiency of breeding programs through the adoption of modern technologies and optimal use of breeding resources.Identifying cost-effective approaches to routinely genotype lines entering the VDP is a critical first step. However, implementation also requires operational capabilities to sample, genotype and generate genomic predictions on a tight turn-around schedule. To do this effectively at scale, advanced breeding informatics systems that include biometrical and quantitative genetics, as well as bioinformatics, are needed. Breeding informatics systems require significant and sustained investment in foundational technologies and computational infrastructure. Fortunately, recent funding initiatives have begun to provide the resources needed to build the foundational capabilities required to modernize and improve the efficiency of public sector breeding programs. The Genomic Open-source Breeding informatics initiative (GOBii)To examine potential approaches for GS implementation, proof-of-concept studies were conducted by the International Crops Research Institute for the Semi-Arid Tropics (ICRISAT) and International Maize and Wheat Improvement Center (CIMMYT) in chickpea and maize, respectively. To examine optimal strategies for routine implementation of GS of lines entering the VDP, three approaches were compared: (i) the development of a dedicated training set (DTS) in parallel to the VDP to serve as a starting point for GS implementation, (ii) splitting full-sib families for training and prediction (FSTS), and (iii) the use of incomplete block designs across environments, or sparse testing (ST), to obtain good prediction accuracies while reducing plot numbers to offset the cost of genotyping. Here we present the results from these studies and discuss strategies for phased implementation of GS in public sector breeding programs. We also highlight breeding informatics capabilities being developed to enable large-scale implementation of genomic breeding strategies.n = 153) and Desi (n = 162), evaluated under rainfed and irrigated regimes in a randomized complete block design with three replicates. All lines were previously genotyped with 2,598 DArT markers (see The two datasets from ICRISAT and CIMMYT are described in detail in The maize dataset consists of 849 double haploid (DH) lines from 13 bi-parental families out of the CIMMYT Africa maize breeding program. For demonstration, the three families with the largest family sizes were used for this study . Each DH line was testcrossed to a single tester, and the testcrosses were evaluated in an alpha-lattice incomplete block design with two replications planted in the rainy season with supplemental irrigation as needed in both Kiboko and Kakamega, Kenya, as well as under managed drought conditions during the dry season in Kiboko. The DH lines were genotyped with 9,155 dominant repeat Amplification Sequencing (rAmpSeq) markers at Cornell Life Science Core Laboratory Center, Ithaca, NY, United States . MarkersAs the chickpea data consisted of fixed lines generated from many parents, FSTS predictions were not appropriate in this case, and only DTS and ST predictions were compared. In contrast, as the maize dataset consisted of DH generated from three bi-parental crosses, and as such, FSTS and ST were the most appropriate comparisons. In each comparison, the same number of individuals  were assigned to the training and test sets.K = UDU’, where U is a matrix of eigenvectors and D is a diagonal matrix of eigenvalues. The first two eigenvectors multiplied by their respective eigenvalues were plotted against each other to form a principal component (PC) plot. The proportion of variance explained by each PC is defined as Dii/tr(D), where tr is the trace.Population structure was evaluated using singular value decomposition of the additive genomic relationship matrix, An unstructured univariate genotype by environment interaction model was used to estimate genetic correlations across environments. This can be written asy is the vector of a phenotype in each environment, X is the design matrix for the vector β of fixed environmental effects, Z is an incidence matrix linking observations in y to individuals, u is the vector of genetic values and e is the vector of residuals. The random effects were both considered centered multivariate normal such that E[u] = E[e] = 0 andwhere G is the genetic covariance of environments (which must be estimated), and K is the additive genomic relationship of individuals, calculated from genetic markers , were used as true estimated breeding values (EBVs) to compare to the Best Linear Unbiased Predictors (BLUPs), or genomic estimated breeding values (GEBVs). These values were computed using the above model, but allowing For the Desi and Kabuli comparisons, genomic prediction accuracy was assessed using k-fold cross-validation with 10-fold, where records for a random 10th of the individuals were removed (or masked) from the dataset for each fold. Each fold was predicted before the accuracy was calculated as the Pearson correlation between the all predicted BLUPs and the observed BLUEs. The average accuracy across 10 replicates was used as the estimate of genomic prediction accuracy. This was accomplished among both seed-types, within each seed-type, and across seed-types.For DTS prediction, the population was randomly split into two sets. Phenotypic records from individuals in one set were removed in both environments before fitting the prediction model with records from the remaining set to predict GEBVs for the missing individuals in both environments. Prediction accuracy of unobserved genotype-environment combinations was then determined using the Pearson correlation of the predicted BLUPs to the observed BLUEs either separately by environment, or by combining predictions across environments. This process was repeated 10 times and averaged to produce an estimate of prediction accuracy.Similar to DTS prediction, FSTS prediction was accomplished by removing phenotypic records from a random half of the lines within each bi-parental family in all three environments. The remaining individuals were used to fit the prediction model and predict the GEBVs of unobserved individuals in all three environments. Results from ten replicates were averaged to estimate prediction accuracy.Genomic prediction accuracy of ST was determined by again randomly splitting the individuals into two equal sized sets. For ST in chickpea, phenotypic records of one half were removed in the rainfed environment while the records of the other half were removed in the irrigated environment. For ST in the maize dataset, half of the individuals within each family were removed from Kiboko, then further split in half and removed from either Kakamega or Kiboko Drought, along with an additional quarter from the remaining set see . PredictPopulation structures for the chickpea and maize datasets can be found in To determine whether these two chickpea groups should be combined for training and prediction, cross-validation was conducted among and within each group. Prediction across seed-types was also accomplished to determine if the allele frequency and linkage disequilibrium (LD) pattern is sufficiently shared between seed-types. Results from the cross-validation results are found in To determine whether the high prediction accuracies seen using Desi and Kabuli in both training and validation sets were due to the prediction of group differences between Desi and Kabuli, or due to predictions of phenotype variation within seed-type, we then compared (1) single seed-type training sets to predict phenotypes for the same seed-type, with (2) both seed-types to predict phenotypes for a single seed-type. Higher prediction accuracies were generally observed when the training population was consisted of a single seed-type .Genetic correlations between environments vary across traits and range from moderate to high . ResultsRoutine implementation of genomic information represents significant changes in the way plant breeding programs operate and how breeding decisions are made. To facilitate routine implementation, we recommend a phased implementation strategy . In PhasThe data used in this study were collected on crops with different breeding approaches and different initial strategies for building training sets and applying GS in early-stage trials. These contrasting crops make for an interesting dataset for testing widely applicable strategies for the initial adoption of GS approaches. The chickpea training set was developed to represent the full diversity of ICRISAT chickpea breeding programs, both Kabuli and Desi, for the purpose of predicting the performance of new lines prior to preliminary yield trials. The combined chickpea training set may be good at distinguishing phenotypic differences between the two known groups, but less accurate at discriminating within groups. The inability to predict across seed-types demonstrates that population specific allele frequency and LD patterns appear to be driving the observed prediction accuracy. While using Kabuli lines to predict the performance of Desi, and vice versa, may be viewed as an extreme case, the large decreases in prediction accuracy when compared to the use of Desi to predict Desi and Kabuli to predict Kabuli highlight the importance of building appropriate training sets.In the chickpea case, the estimates of prediction accuracy using both seed-types were overly optimistic and could have disappointed and discouraged funders of these early GS efforts. Indeed, many reported genomic prediction accuracies are likely upward biased when it comes to selection, as the (unobserved) accuracy of new lines formed from relatively few crosses will not be inflated by the same degree of population structure within the diverse training population. The inability to predict across demonstrates that the two seed-types comprise effectively separate breeding programs and should be treated as such for training population designs in order to provide realistic expectations to funders. It may be prudent to refrain from reporting accuracies in diverse populations, instead focusing on the average of within group/family to guide expectations.It has been shown that, when assuming the infinitesimal model, the expected prediction accuracy is a function of population structure, trait heritability, training set size, and the accuracy with which genomic relationships calculated using genetic markers estimate the true genomic relationships at the QTL regions controlling the trait of interest . The latWhen comparing approaches to initiate a dual purpose VDP, the ST approach consistently outperforms both FSTS and DTS in terms of prediction accuracy. Given the differences in crops and population structure of the training sets in this study, the fact that ST delivered higher prediction accuracies in both cases indicates that it could be a robust strategy across crops and breeding programs. It should be noted that the ST method does necessitate the generation of seed from all lines, where the FSTS does not, however, the amount of seed required is less, presenting the potential for time savings during seed multiplication for inbred crops. In lower throughput programs where seed multiplication occurs in the field, this could allow material to enter the VDP an entire year earlier. However, for hybrid crops, the cost implication of seed multiplication for ST is greater since hybrid crops require testcrossing all candidates. The tradeoffs between cost and accuracy need to be carefully considered when considering implementation strategies.Traits that benefited most from the sparse testing approach were of moderate to high heritability. Traits with low heritability, such as seed yield, also tended to have low genetic correlations across environments. Often, moderate to high heritability traits are under selection in small plot trials during seed multiplication, meaning sparse testing may not be as advantageous for these cases as indicated here. More importantly, the observation of all lines in the field, as is done in ST, allows for a breeder to identify and cull lines with other undesirable, but highly heritable traits, before they enter into extensive field trials. Sparse testing also presents opportunities for cross program collaboration, including across countries or continents. If both programs share a marker platform, implementation of germplasm sharing could be expedited by predicting performance in the other program, and exchange of promising materials for the other environment(s). However, this may be limited to programs that already share related materials which can be reliably predicted.While prediction accuracy is a major factor in determining the best approaches to implement GS, the cost and complexity of implementation must also be considered. For simplicity and ease of comparison, the same number of plots were used in training predictive models for each approach presented here. This does not mean that each approach would have roughly the same cost or the same efficiency in VDP design. The FSTS approach has the advantage of reducing the number of lines for which seed must be produced for yield trial testing as with this approach phenotypic data is not collected on all genotyped lines. The DTS approach enables prediction of new lines prior to the collection of any information on the line itself or on full-sibs, but requires significant initial investment to develop the training set. Thus, it is difficult to envision an implementation that is cost neutral in terms of the total breeding budget. The ST approach combines genomic prediction and advancement decisions into a single analysis. The fact that implementation can be viewed as a change in experimental design is appealing, but it does increase the complexity of models that need to be run to advance lines through the VDP. In an ST approach, genomic relationship matrices need to be calculated for variety trials and used in mixed models for variety advancements. This adds complexity to the traditional advancement process that could quickly overwhelm even a moderate sized breeding program without breeding informatics tools to support the process.It is important to note that incomplete block designs typically have some explicit genetic overlap, with some lines shared across each pair of environments, as is the case here in maize. With this overlap, the genetic correlation of environments can be estimated even when lines are considered independent. When genotypes are available, however, the genetic correlation of environments can be estimated without the need to replicate any lines across environments. These correlations are instead estimated through replication of alleles across environments, as is the case here in chickpea. The ST approach does require estimation of genetic correlations across environments, and this should be taken into account when designing multi-environment trials. Generally, greater levels of genetic overlap will increase the precision of these correlation estimates.While these results provide some general guidance on promising approaches for initial implementation of routine GS, the optimal implementation strategy will depend on the specifics of each breeding pipeline. The heritability of traits of interest, cost of phenotyping, amount of historical data available for training prediction models, field testing resources, structure of breeding populations, and access to cost-effective genotyping platforms are all factors that will influence decisions about optimal approaches for implementation of GS. Even within a program there may be a need for hybrid approaches given the expected prediction accuracy for a given population using historical data. It is recommended that any breeding program test the potential efficiency of new approaches using simulation prior to implementation. Fortunately, there are freely available simulation packages , and EiBImplementation of any of the approaches examined in this study will require full integration of genomic information into routine breeding decisions, requiring a shift in how data is viewed and handled in a breeding program. The need to build a large training set through a dual purpose VDP means that variety testing trials can no longer be viewed as independent experiments for the identification and advancement of superior varieties. The data collected should be treated as a resource for increasing understanding of breeding germplasm and improving the accuracy of breeding decisions . The capTo address this critical need, GOBii, EiB, and several other projects are working with public sector breeding programs to build and deploy the foundational capabilities needed to digitize breeding data, support breeding processes and implement GS routinely. Given the size and capacity of many public sector breeding programs, open-source breeding software needs to be both scalable and customizable to meet the needs of diverse crop breeding programs. To accomplish this communities of practice associated with projects like the Breeding Application Program Interface BrAPI;  and EiB 3. Projects like GOBii are focused on building databases designed to achieve optimal performance with specific data types  to a price per line that is comparable to the cost of running a single yield trial plot. Using the HTPG platform, EiB is implementing low-cost genotyping services for public sector breeding programs. Access to these low-cost genotyping services, combined with open-source databases and analytic pipelines greatly reduces barriers to cost-effective implementation of GS strategies and should pave the way for routine use of GS in public sector breeding programs in the near future.To enable cost-effective genotyping, EiB, in collaboration with the High Throughput Genotyping (HTPG)Notably, adoption of new technology demands a skilled workforce. Rapid creation, quality control and turnover of genotypic and phenotypic data will be necessary to make and implement breeding decisions. This will result in many moving parts, and all these steps require a high degree of skill. Many programs will need to adopt a team-oriented approach where expertise is split across many individuals, with enough overlap for effective communication. Future members of plant breeding teams will need skills and expertise outside of what has traditionally been associated with plant breeding. Expertise in database management, machine learning, biometrics, software development, engineering, and operations research will be needed to augment the biology, genetics, and agronomy skills of the team. We acknowledge that building this expertise for every program would be impractical, therefore movement towards regional networks with shared services and expertise will be necessary.There are several barriers to routine implementation of GS at a breeding program scale. These barriers are currently being addressed and we foresee movement towards routine adoption in several public breeding programs. We suggest that breeding programs approach the implementation of GS in a phased approach with the initial step being the routine genotyping of all materials that are evaluated for yield. These materials will be genetically and environmentally close to the materials to be predicted in later stages. We stress that genotyping should be a regular process instead of a series of isolated efforts as is often practiced today. Modification of a traditional variety development pipeline will include implementation of experimental designs that optimize resources allocated to phenotyping and genotyping. Changes in experimental designs and VDP structure should focus on reductions in replications, sparse testing, and faster germplasm turnover. Marker data must be seamlessly integrated with pedigree information, phenotypes, and experimental design to facilitate data processing and analysis for making breeding decisions at a fast turnover rate. Adoption of standardized databases and analysis platforms is necessary to streamline decision making processes. Many of these platforms exist or are currently being constructed, but adoption will be key to successful implementation of GS into the 21st century public breeding program.y.beyene@cgiar.org (maize); Rajeev Varshney, r.k.varshney@cgiar.org (chickpea).The datasets analyzed in this article are not publicly available. Requests to access the datasets should be directed to Yoseph Beyene, NS analyzed the chickpea data, contributed to the design of the study, and drafting of the manuscript. SA analyzed the maize data, contributed to the design of the study, and the drafting of the manuscript. KR, MO, and EJ contributed to the design of the study and drafting of the manuscript. YB, RV, MG, PG, XZ, CB, and MR contributed to the design of the study and collection of phenotypic and genotypic data. CH, AR, KD, JC, and PP-R contributed to the curation, QC, and analysis of the data. SG and SM contributed to the drafting 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.


In contrast, linear models failed to detect any genome-wide significant SNPs. The number of top significant genes overlapping between the two samples in the GCC-based GWAS was higher than when applying linear models. The GCC model identified significant genetic variants missed by conventional linear models, with more replicated genes and biological pathways related to cognitive function. Moreover, the GCC-based GWAS was robust in handling correlated samples like twin pairs. GCC is a useful statistical method for GWAS that complements traditional linear models for capturing genetic effects beyond the additive assumption.Despite a strong genetic background in cognitive function only a limited number of single nucleotide polymorphisms (SNPs) have been found in genome-wide association studies (GWASs). We hypothesize that this is partially due to mis-specified modeling concerning phenotype distribution as well as the relationship between SNP dosage and the level of the phenotype. To overcome these issues, we introduced an assumption-free method based on generalized correlation coefficient (GCC) in a GWAS of cognitive function in Danish and Chinese twins to compare its performance with traditional linear models. The GCC-based GWAS identified two significant SNPs in Danish samples (rs71419535, p  In addition, different limitations in current GWASs might be the reason for the situation due to the distribution of cognitive measurements as well as the complex relationship between SNP genotypes and cognitive performance. One of the assumptions in the popular linear models used for GWAS is normality of the phenotype of interest. Unfortunately, the distribution of cognitive measurements is typically skewed [Cognitive function is an important phenotype involving multiple mental abilities including learning, thinking, reasoning, remembering, problem-solving, decision-making, and attention. Thus, cognitive function affects health and quality of life in the elderly. Recent twin and family studies have shown that the heritability of general cognitive function is more than 50% in adolescence through adulthood to older age , suggesty skewed . Also, ty skewed , 5.x and y and the product of their marginal rank [2) relative to the regression function, but also to detect non-linear relationships between variables. Recently, Murrell et al., 2016 [2, but powerful when the parametric form of association is unknown. A point of particular importance is that through applying this method both generality and equitability can be attained by estimating a generalized R2 based on density approximation. We believe that this method has advantages that merit it to be considered in GWAS as the associations between the SNP dosage and the phenotype are expected to be complex. More importantly, this method does not rely on strict assumptions such as normality of the phenotype distribution as well as linearity of the genetic effect . Additionally, when related samples such as twins or family data are included in association studies, more model assumptions are imposed. Hence, the assumption-free or generalized measurements of association may be advantageous in genetic association studies.As a recent development, the generalized measure of dependency for analysis of omics data has been proposed –6. The cnal rank . In 2012nal rank  and his l., 2016  proposedl., 2016 . MurrellIn this study, we aimed to compare the performance of popular linear regression models and the generalized correlation coefficient (GCC) method to identify genetic variants, genes and pathways in Danish and Chinese GWASs of cognitive function.The study population comprised Danish and Chinese GWAS data. A description of the two samples is illustrated in = 1.47e-08) on chromosome 2 near the gene THSD7B, and rs905838 (p = 1.69e-08) on chromosome 5 near the gene CTD-2533K21.4; and two genome-wide significant SNPs in Chinese samples: rs2292999 (p = 9.27e-10) on chromosome 3 near the gene ABCC5, and rs17019635 (p = 2.50e-09) on chromosome 4 near the gene GRID2. Circos plots for the genome-wide significant SNPs identified using GCC are shown in The summary results for the top 30 SNPs from the GWASs performed in Danish and Chinese samples using each of the three models are shown in 2 < 0.6) with the independent significant SNPs, respectively. Most of the tagged SNPs identified in FUMA from the Danish GWAS results are positioned in intergenic and ncRNA intronic regions   and PTPRD , which were both replicated in the Chinese data  and MS4A2 , while the two top genes identified in the Chinese sample were TEX26  and WDPCP .The two top genes identified from the discovery data GCC GWAS results were ese data , 8. The Finally, the results of the overrepresentation analysis performed in GSEA based on genes with p < 0.05 in the gene-based analysis are shown in The literature on GWAS data analysis has been dominated by multiple assumptions , which, if not fulfilled, could be speculated to be responsible for low statistical power, missing heritability and the lack of replication of GWAS findings. Our study, through model comparison and validation of GWAS results from two independent samples of different ethnicity, illustrates the strength of the GCC method over popular linear models, with more replicated GCC-based results and cognition-implicated SNPs and functional pathways.THSD7B, which is associated with melanoma, metabolism, and Cutaneous Malignant 1 diseases. Variation in this gene has been shown to associate with multiple system atrophy, which is an adult-onset neurodegenerative disorder [CTD-2533K21.4, which is a novel transcript highly expressed in brain hippocampus. Among the two genome-wide significant SNPs found in the GCC-based GWAS in the Chinese sample, rs2292999 on chromosome 3 is located near ABCC5, which is expressed in both brain and muscle tissue, and rs17019635 on chromosome 3 near GRID2, which is expressed in the brain. The ABCC5 gene has previously been found to encode a general glutamate conjugate and analog transporter that can limit the brain levels of endogenous metabolites, drugs, and toxins [ABCC5 is additionally related to the ‘Blood-Brain Barrier’ and ‘Immune Cell Transmigration’ pathways. Glutamate is the principal excitatory neurotransmitter in the brain. It is crucial for learning and memory in everyday brain functions but causes excitotoxic damage in traumatic brain injury and stroke [GRID2 gene is highly expressed in brain and associated with depression. This gene plays a role in synapse organization between parallel fibers and Purkinje cells (a class of GABAergic neurons in the cerebellum in the brain). The implications of GABAergic neurotransmission in Alzheimer’s disease (AD) have been discussed [CSMD1 and PTPRD, are very interesting; a variant in CSMD1 was recently associated with cognitive function [PTPRD gene was discussed as a druggable target, and a marker for and an important constituent of brain circuits of likely importance for major brain-based phenotypes [Among the two genome-wide significant SNPs identified in the GCC-based GWAS in the Danish sample, rs71419535 is near disorder . The othd toxins . ABCC5 id stroke , 12. Theiscussed –15, and function , and in Although some significant pathways were identified from the GWAS results generated using all models, more biologically meaningful pathways were found when using the GCC method. Importantly, more genes and pathways identified from the CGG GWAS results were replicated in the Chinese sample than was the case for the linear models. Some replicated pathways from the GCC model GWAS results were pathways in calcium signaling, Type II diabetes mellitus, long-term depression, melanogenesis, axon guidance, focal adhesion, MAPK-signaling, Hedgehog signaling, GnRH signaling, and some related to cardiomyopathy . The repCalcium signaling and potassium channels have been reported in aging-related diseases, including cognitive function. Calcium signaling is a crucial messenger between synapse and nucleus as calcium transient in nucleus is needed for neuroadaptations to switch on the necessary genes . AdditioThe Wnt signaling pathway is also important and many recent studies have discussed and reported this pathway in relation to aging and AD, where the loss of canonical Wnt signaling has been found to be involved in the pathogenesis of AD, and play a role in synaptic plasticity and maintenance in the adult brain , 26.In the literature, there is evidence regarding the role of MAPK-signaling, long-term depression, Hedgehog signaling, GnRH signaling, melanogenesis, T cell receptor pathways, cancer, vascular smooth muscle cells and hypertrophic cardiomyopathy (HCM) on cognitive function, Alzheimer’s, Parkinson's and Huntington’s diseases, and age-related neurodegenerative disorders , 27–34.Moreover, worth mentioning are the pathways ‘neuroactive ligand receptor interaction’ and ‘Alzheimer's disease’ that were identified among the 28 KEGG pathways identified from the 476 genes overlapping between the gene-based analysis of Danish and Chinese GCC-based GWAS results . The neuIn A limitation of the GCC method, and different from the conventional linear models that report regression coefficients with a direction of effect (+ or -), is that the association parameter from GCC, A, has no direction, which is understandable because direction of effect does not make sense in case of a non-linear relationship. We argue that, as a hypothesis-free or agnostic approach, the primary goal is to identify reliably significant SNP markers. The pattern of association for specific markers can be examined empirically by plotting the data to reveal the various relationships not limited to minus or plus. For SNPs not captured by the linear model, the linear model results can still provide reference information about the direction of the GCC correlation. However, as an assumption-free approach, GCC is inherently robust in handling correlated or structured samples in association testing. The QQ plots in As a generalized measurement of association, GCC is capable of capturing different patterns of genotype-phenotype correlation, not limited to additive genetic effects. As an assumption-free approach, GCC is robust in dealing with correlated samples, e.g. twins, and skewed phenotype distributions, e.g. cognition, as compared to the linear models. By exemplifying and illustrating the advantages and the necessity of GCC in GWAS, this study could serve as a precedent for application and further development of assumption-free methods to complement current GWAS on cognitive traits and on other complex traits as well.https://pubmed.ncbi.nlm.nih.gov/31544734/). General cognitive functioning was assessed by a cognitive composite score computed from five brief cognitive tests evaluating verbal fluency, attention and working memory (digits forward and digits backward), and memory  [The Danish sample comprises 1172 participants aged 50-80 years, including 440 same-sex dizygotic (DZ)  twins, 460  opposite-sex DZ twins and 272  single monozygotic (MZ) twins recruited by the Danish Twin Registry as a part of the study of Middle-Aged Danish Twins (MADT)  . Scores  recall)  (Table 1Participants in the Chinese sample were 278 dizygotic (DZ) twins aged 40-70 years, including 41 male, 39 female, and 59 opposite sex twin pairs, who completed the Montreal Cognitive Assessment (MoCA) questionnaire to measure their cognitive function and had blood samples taken for DNA extraction and genotyping . All volhttp://snpseq.medsci.uu.se/genotyping/snp-services/). Pre-imputation quality control (QC) removed SNPs on genotype call rate < 98%, Hardy-Weinberg equilibrium (HWE) p < 10-6, and MAF = 0, and individuals on sample call rate < 99%, relatedness and gender mismatch. Pre-phasing and imputation to the 1000 Genomes phase 3 reference panel was performed using IMPUTE2 [Genome-wide SNP genotyping for all the twins in Danish data was conducted using Illumina PsychArray . Genotyping was conducted by the SNP&SEQ Technology Platform, Science for Life Laboratory, Uppsala, Sweden  from the Kinship2 package , the mix package , and the package  in R. In2, can be expressed as: 1-x and y is the product of the marginal density estimation as P(x) P(y). The alternative model is considered as a mixture distribution of both dependent and independent components. It calculates a generalized R2 based on the probability density ratio of null and alternative models [The GCC is a generalized measure of association between variables. The association strength, which is called A, ranges from 0 (in case variables are independent) to 1 (in the case of perfect association between variables). A is the square of the correlation coefficient and can be considered as the proportion of variance in one variable explained by another variable or a number of variables. As the explained proportion of variance is one minus the unexplained proportion of variance, then the proportion of explained variance, R density . In a die models .CVLRS):To calculate a p-value for assessing the significance of the test, i.e. to check the departure of observations from independence, it calculates a cross-validation likelihood for both the null and the alternative model. Equation (2) shows the calculation of the cross-validation likelihood statistics (Lcv(Null) and Lcv(Alt) are cross-validation likelihoods for the null and alternative models [where e models .https://gtexportal.org/home/), for the significant SNPs from the GCC-based GWAS to detect tissue-specific gene expression associated with the identified genetic.Genomic risk loci were defined from the SNP-based GWAS results of the GCC, kinship and LME model analyses using functional mapping and annotation of genetic associations (FUMA) . As parthttp://software.broadinstitute.org/gsea/index.jsp) to test the enrichment of genes against the KEGG pathways from MSigDB gene-sets. Results with FDR q-value < 0.05 are reported.To further investigate the biological function of the genes identified in the gene-based analysis from MAGMA, we used the top genes with p < 0.05 identified using the results of analyses with each of the three models in both Danish and Chinese samples as input for GSEA using KEGG pathways (N = 186). A hypergeometric test is used in GSEA (Supplementary FiguresSupplementary Tables


F) was estimated at KCTD8, GNPDA2) point to phenotypic characteristics related to environmental adaptation and robustness of Maremmana cattle. These results are of important because they will help design and implement breeding and conservation strategies for Maremmana cattle, and provide guidelines for other local cattle breeds.In this study, we estimated genetic diversity in semi-feral Maremmana cattle using both pedigree- and genomic-based approaches, and detected regions of homozygosity (ROH) and heterozygosity (ROHet) in the genome, which are still poorly characterized in the Maremmana breed. A sensitivity analysis on the parameters used to detect ROH and ROHet provided information which can be useful to guide studies on the detection of genomic runs in general, and in semi-feral cattle populations in particular. The average observed and expected heterozygosity were estimated at F) was estimated at F and genomic-based approaches (F and Semi-feral local livestock populations, like Maremmana cattle, are the object of renewed interest for the conservation of biological diversity and the preservation and exploitation of unique and potentially relevant genetic material. The aim of this study was to estimate genetic diversity parameters in semi-feral Maremmana cattle using both pedigree- and genomic-based approaches ( Semi-feral local livestock populations are an object of renewed interest for the conservation of biological diversity and the preservation and exploitation of unique and potentially relevant genetic material ,2. BasicDespite such advancements, the merit of genomic-based relative to pedigree-based methods has not been fully explored under all scenarios, particularly those related to local and/or marginal livestock populations. In addition, the sensitivity of ROH detection to the parameters used in the analysis  is still unclear: so far only a handful of works addressed this issue , limiteThe aim of this study was to estimate genetic diversity parameters in semi-feral Maremmana cattle using both pedigree- and genomic-based approaches, and to detect regions of homozygosity and heterozygosity in the genome, which are still poorly characterized in the Maremmana breed. A sensitivity analysis on the parameters used to detect ROH and heterozygosity-rich regions provided information which can be useful to guide studies on the detection of genomic runs in general, and in semi-feral cattle populations in particular.N = 25; Tormancina: N = 124) were obtained from previously published studies [PLINK v.1.9 [SNP genotype data ) for 149 heads of Maremmana cattle  from two farms in central Italy  a sliding window of 50 SNPs across the genome; (ii) the proportion of homozygous overlapping windows was 0.05 ; (iii) the minimum number of consecutive SNPs included in an ROH was 30; (iv) the minimum length of an ROH was set to 2 ROH was identified in every individual using From ROH, individual inbreeding coefficients  were detectRUNS  by applytectRUNS . The folR package pedigree was used for pedigree calculations [Pedigree data for the animals from the two farms were available: the pedigree file contained 1439 records and included 119 sires and 512 dams, with average depth of 4.2 generations. The numerator relationship matrix  SNP genotypes, and (ii) ROH. When genotypes were used directly, the method described by Van Raden  was apple” v.4.1 . The imphttps://www.ncbi.nlm.nih.gov/genome/gdv/browser/genome/?id=GCF_002263795.1). The Bos taurus genome assembly ARS-UCD1.2 was used as reference.Information on the annotated genes within the highly homozygous (ROH islands) and heterozygous (ROHet islands) genomic regions detected in Maremmana cattle were obtained from the Genome Data Viewer tool provided by NCBI  pedigree . On . On F ans . An impRuns of homozygosity have been characterized in many cattle populations, not yet in the Maremmana breed. In the present study, 3332 ROH longer than 2 a et al.  reportedl breeds ,46,47.g is the number of generations since the common ancestor [Clustering ROH into different size classes makes it possible to interpret patterns of homozygosity. Our study revealed that in the Maremmana breed most ROH are short or middle-size. The expected length of autozygous segments that are IBD follows an exponential distribution with mean equal to ancestor : 16 MB Rć et al.  using a Heterozygosity-rich regions are far less characterized than ROH in livestock. Williams et al.  were theć et al.  reporteds et al.  found 52It is known that the parameters used to detect ROH influence results and vary widely across studies . There in et al.  looked a relaxed . These rNothing has been reported so far on the effect of tweaking parameters for the detection of heterozygosity-rich regions (ROHet): in KCTD8 gene is associated with climate adaptation [YIPF7, GUF1, GNPDA2, GABRG1 and GABRA2 have been associated with carcass traits (rib-eye area) in a composite beef cattle breed [GNPDA2 has been found to have a potential role in the regulation of body weight and of fat and energy metabolism in chickens [ROH islands in cattle have been used to identify genomic regions under selection, which may be involved in defining breed-specific traits or the adaptation to the environment or production systems ,53,54. Taptation  and has aptation . This gele breed . GNPDA2 chickens .GABRB1 gene on BTA6 in Maremmana cattle, Hou et al. [TARSL2, TM2D3, PCSK6 and SNRPA1) related to reproduction traits in livestock [PCSK6 (involved in iron homeostasis) may be associated with the evolutionary response to anemia in Sheko cattle [Unlike ROH islands, heterozygosity-rich regions have not been analysed much in the genome of livestock populations. Using a high-density SNP array Williams et al.  found thu et al.  reportedivestock . PCSK6 (o cattle . In thosThis is the first exhaustive genome-wide study of the genetic diversity in semi-feral Maremmana cattle using both pedigree- and genomic-based data. Estimators of inbreeding based on different data types and statistical approaches were compared. The correlation between pedigree-based and genome-based inbreeding was low, plausibly due to the limited depth of the available pedigree data for these animals. The genetic diversity parameters and the patterns of ROH found in Maremmana cattle were in agreement with results from previous studies in local cattle breeds. Genes found to be within the identified ROH and ROHet islands point to phenotypic characteristics related to environmental adaptation and robustness of Maremmana cattle. Taken together, these results indicate that genome-based measures of inbreeding and heterozygosity through ROH and ROHet are able to detect differences between chromosomal regions, providing a more detailed picture of the genetic diversity. The results are of significant importance because they will help design and implement breeding and conservation strategies for Maremmana cattle.


One of the most basic human traits is language. Linguistic ability, and disability, have been shown to have a strong genetic component in family and twin studies, but molecular genetic studies of language phenotypes are scarce, relative to studies of other cognitive traits and neurodevelopmental phenotypes. Moreover, most genetic studies examining such phenotypes do not incorporate parent-of-origin effects, which could account for some of the heritability of the investigated trait. We performed a genome-wide association study of receptive language, examining both child genetic effects and parent-of-origin effects.−8). We further confirmed that this association was not driven by broader neurodevelopmental diagnoses in the child or a family history of psychiatric diagnoses by incorporating covariates for the above and repeating the analysis.Using a family-based cohort with 400 children with receptive language scores, we found a genome-wide significant paternal parent-of-origin effect with a SNP, rs11787922, on chromosome 9q21.31, whereby the T allele reduced the mean receptive language score by ~ 23, constituting a reduction of more than 1.5 times the population SD (P = 1.04 × 10Our study reports a genome-wide significant association for receptive language skills; to our knowledge, this is the first documented genome-wide significant association for this phenotype. Furthermore, our study illustrates the importance of considering parent-of-origin effects in association studies, particularly in the case of cognitive or neurodevelopmental traits, in which parental genetic data are not always incorporated. Language is one of the most fundamental human traits, and, as such, has been studied from many different angles, in disciplines such as philosophy, linguistics, psychology, anthropology, neuroscience and, more recently, genetics.In the previous century, many studies showed that linguistic traits were heritable, i.e. that the genetic variation across individuals can explain some of the phenotypic  variation in the studied population. The investigated traits in those studies were either manifestations of atypical language development or of specific language skills in the general population , althougFOXP2 [FOXP2 was monogenic (caused by a disruption of a single gene), the precise mechanism through which this gene caused the speech and language disorder is still unknown, and many subsequent studies examined the gene’s molecular function, its molecular evolution and its involvement in vocal communication in other species [FOXP2 were not the cause of a common form of language impairment, namely specific language impairment (SLI) [With advances in molecular genetic techniques and the establishment of samples with available language-related phenotypes, studies were performed whose goal was to identify genes (or genetic variants) associated with linguistic ability. It was a series of studies of one extended pedigree which ultimately reported the first gene implicated in a speech and language disorder, FOXP2 . Despite species . The disnt (SLI) . SLI is nt (SLI) . This innt (SLI) . A discrThe first genome-wide screen for susceptibility loci for SLI was a non-parametric linkage analysis performed by the SLI Consortium , and it With the advent of dense marker maps and single-nucleotide polymorphism (SNP) arrays towards the end of the 2000s, and in relation to theoretical considerations in complex disease studies, disease-gene mapping strategies in the field of human genetics shifted from linkage studies towards genome-wide association studies (GWAS) . In 2013HLA-B allele and receptive language in SLI [A parent-of-origin effect denotes a change in the way an allele may influence a trait dependent on which parent it was inherited from. There are good reasons to examine parent-of-origin effects in general, and in studies of language-related traits and disorders in particular: firstly, they may account for some of the so-called “missing heritability” (the difference between disease heritability estimates and the variance explained by significant GWAS associations across published studies) . A recene in SLI . Secondle in SLI . Importae in SLI , 26, as e in SLI , both ofe in SLI , althouge in SLI –35. A ree in SLI .At this point it would be useful to discuss the expressive language-receptive language divide, as several of the aforementioned studies examined or found significant results for only one type of language trait. Expressive language refers to language production, and receptive language refers to language comprehension. An illustration of what can be measured by scores of expressive or receptive language ability can be found in the Clinical Evaluation of Language Fundamentals , which wThe above studies provided insight into the genetic basis of language, but genome-wide studies of language-related traits and language disorders remain scarce, relative to studies of other cognitive phenotypes and neurodevelopmental disorders. Studies in which parent-of-origin effects are modeled are even scarcer, as parental genotype data are not always available. In this study, we attempted to fill in some of these gaps by examining receptive language (which has not been the focus in follow-up studies to the first genome-wide screen of SLI) in a relatively large family cohort, and by examining parent-of-origin effects as well as child genetic effects in a GWAS design.In total, 400 children had Test for Reception of Grammar 2 (TROG-2) scores in our dataset. The distribution of the TROG-2 scores is shown in Fig. −6), and rs9397338 in the maternal parent-of-origin test (P = 8 × 10−6). Manhattan and QQ plots for the discovery analyses are shown in Additional file −5) from all three tests can be found in Additional file In the discovery stage, we found a genome-wide significant association in the paternal parent-of-origin test; the top hits in the other two tests showed only suggestive association: rs12297354 in the general test (P = 7 × 10−8), which is more than 1.5 SD away from the population mean. The frequency of the T allele in our entire sample was 7.54%. It was 5.88%, 10.64% and 6.98% in the ASD, ADHD and index family children subgroups, respectively. The T allele was not genome-wide significantly associated in the general test  or in the maternal parent-of-origin test , but it showed suggestive association in the former.The top hit in the paternal parent-of-origin test was with an intergenic SNP on chromosome 9:82,788,836, rs11787922 . In our second post hoc test, we repeated the association test having added covariates for ASD, ADHD and for whether the family was an index family or a control family, as we had observed lower scores (on average) in children from all three groups compared to all children in the study. While the covariates explained some of the reduction in the TROG-2 score, the genetic effect was still large . When using a covariate for sex, the effect of T allele on TROG-2 score was − 22.9, (P = 1.13 × 10−8).To assess the evidence for a paternal association at the above locus further, we performed five post hoc tests. The first test assessed whether there was a significant difference between the maternal and paternal alleles; it is possible that, when looking only at paternally inherited alleles, the association is significant, and that, when looking at maternally inherited alleles, it is not (or vice versa). However, that alone does not mean that the difference itself between the associations with paternal and maternal alleles is significant. QTDT has a special test for that, and our first post hoc test confirmed that the difference was indeed significant in this case (P = 5 × 10−7), with the T allele being associated with a reduced score, but the effect was somewhat diminished at − 1.3 SD of the transformed score. Variance component models are sensitive to departure from normality; we used them in our model to account for relatedness in 11 families with a sibship of 2. If we remove one sibling from each of these families  and do not include variance components in the model, the results are similar to the original, with an effect of the T allele = − 22.9 (P = 2 × 10−8).In the fourth post hoc test, where scores were transformed to approach normality and scaled, the association signal between rs11787922 and receptive language remained significant . This method relies on unrelated individuals and was designed with larger samples in mind, and we also lost power by excluding individuals. Nonetheless, even with this method, we observed some signal at this locus. Table 2  of > 5%, compared to values of close to 0% for ASD and ADHD (see footnote in the Methods section for more information about this). A different way of estimating additive genetic overlaps between disorders is estimating genetic correlations between them. This can be achieved in several ways e.g. by using family data or through examining genetic and phenotypic variation across unrelated individuals, the latter being more similar to the PRS analyses. Earlier studies, which examined genetic correlations across ASD and ADHD or specific traits associated with the two disorders, reported moderate to high significant genetic correlations [Our GWAS identified a genome-wide significant association for receptive language with a paternal parent-of-origin effect of a SNP on chromosome 9, rs11787922, supported by both a family-based association method and an independent method of assessing parent-of-origin effects which is based on the variance of the trait in unrelated individuals. This association was not driven by the presence of two other neurodevelopmental disorders in which language may be impaired, namely ASD and ADHD, nor was it driven by the parents’ having a diagnosis of schizophrenia or bipolar disorder, despite the fact that the receptive language scores across individuals with ASD, ADHD or whose parents had the above psychiatric diagnoses were lower on average compared to the entire cohort. This suggests that the association of this SNP with receptive language is not part of a larger behavioral phenotype encompassing ASD, ADHD, familial risk of schizophrenia or bipolar disorder. In this context, it would be useful to note that a study of polygenic risk which used the same cohort used in this study  found thelations , 42, butelations  or non-selations .To our knowledge, this is the first genome-wide significant association reported for receptive language; a previous GWAS of receptive language (which did not model parent-of-origin effects) found only suggestive associations .TLE4 , which was a suggestive expression quantitative trait locus (eQTL) for IMP5  (P = 7.18 × 10−6) [SPPL2C has been highlighted in a G × E study which examined the influence of common variants on language through low-frequency hearing ability [Through PhenoScanner , we foun × 10−6) . Deletio × 10−6) , 50, alt × 10−6) . Recentl ability .PTCH1 gene [PTCH1, that were still associated with language problems, including associations with receptive language [RORB, TRPM6, NMRK1 and OSTF1. Of note, RORB was reported to be involved in verbal intelligence [Our top SNP, rs11787922, lies in a region encompassed by several pathogenic copy-number variants (CNVs) (found with the ClinGen track on UCSC Genome Browser) associated with various forms of developmental delay, including a CNV identified in an individual with delayed speech and language development (ClinVar accession number: VCV000148901.1). This CNV, however, was quite large  and contains many genes, and so it could, at most, provide suggestive evidence for the region around the SNP’s being associated with a language phenotype. A survey of the literature found that the chromosomal band encompassing the SNP on chromosome 9 (i.e. 9q21.31), as well as adjacent bands, were implicated in several studies of phenotypes such as developmental delay, language delay and intellectual disability. At times these were part of a broad syndrome, namely Gorlin syndrome, which typically involves 9q22 and the CH1 gene , but thelanguage . A studylanguage . Among tlligence .PTCH1) associated with language delay or intellectual disability are of paternal origin, suggesting that the paternal allele is expressed in that region [not expressed. Further insight can be gained by considering uniparental disomy (UPD) cases, i.e. scenarios in which both chromosomes of the same kind  in the child come from one parent. If a region is paternally or maternally imprinted, then a UPD involving it may result in an abnormal phenotype, either due to having too much gene expression from two non-imprinted chromosomes, or insufficient gene expression, if both chromosomes are imprinted. One study [While there seems to be some evidence for the involvement of the locus on chromosome 9 in language-related phenotypes, one further point to consider is that our top association displayed a paternal parent-origin effect. Information regarding imprinted regions in the human genome is not readily available in online databases . A “Catalogue of Imprinted Genes and Parent-of-origin Effects” exists , but it t region , 60. Thine study  reportedne study , which i−6 in the paternal parent-of-origin test) was shown to be an eQTL for PHIP [−8), a gene which has been implicated in intellectual disability and developmental delay [−6) in a study of autism [Some of the suggestive associations  are scarce. Specifically, even though samples meeting those criteria do exist, they are often ascertained for having language disorders or related neurodevelopmental conditions, meaning that the receptive language scores are already likely to be quite below the population mean, which could potentially mask an effect observed in non-ascertained samples, and/or they may also comprise fewer families, as is the case for some of the previously mentioned studies. Our association thus requires replication in a suitable, independent sample. Lastly, the effect size of the top SNP in our study is quite large . In the general test of association (without parent-of-origin effects), the effect of the T allele was a reduction of 9.46 in the TROG-2 score, and it was not genome-wide significant. While we could not find relevant examples of studies employing parent-of-origin tests, the result in the general test is of the same order of magnitude  as observed in associations between with SLI , intelliwith SLI , and hanwith SLI . The facwith SLI , 71.We report a genome-wide significant association with receptive language in a sample not selected for language impairment. The association displays a parent-of-origin effect, whereby the minor allele reduces the language test score by more than 1.5 times the population SD, when inherited from the father. Our results contribute to the scientific literature and shed light on a relatively understudied research topic–the genetic underpinnings of linguistic ability. Moreover, our study illustrates the importance of considering parent-of-origin effects in genetic studies, which are often not modeled, in part due to constraints in data availability. In the same vein, we hope that our study will encourage further investigations into this and similar phenotypes.NB: in other VIA 7 publications “index” may refer to the biological parents themselves who were selected for having a psychiatric diagnosis as well as to matched control parents . However, here, “index” refers to the families of parents who have a psychiatric diagnosis, and “control” refers to families of parents who do not have a psychiatric diagnosis. The families in the study were recruited from across Denmark. Just under half of the families lived in densely populated areas, with no significant differences in urbanization between index families (neither schizophrenia nor bipolar disorder) and control families. The parents in the schizophrenia group were younger and had lower levels of education compared to the other two groups. Both biological parents in the index families had lower levels of functioning and were less often in employment or enrolled in a study program [The cohort used in this study comes from the Danish High Risk and Resilience Study–the VIA 7 study . The VIA program . In the A subset of the families in VIA 7 provided DNA samples, which were subsequently genotyped on the Illumina PsychChip v1.1. This subset was used in this study. Following genetic quality control (QC), individuals from 429 families remained, consisting of child-parents trios, child-parent duos, or only children, and, in a minority of families, a sibling as well . Due to the nature of the analyses, the precise number of children used in the association test with a given marker may differ across markers, as explained later in this section, but we note the precise number of informative children in the test for the top association observed and for the suggestive associations in Additional file The test used in this study was the Test for Reception of Grammar (TROG-2) . This teP value threshold of 1 × 10−6 was employed for QC-passing SNPs, as well as a minor allele frequency threshold of 1%. In total, 299,604 autosomal SNPs passed all QC steps. Positions in the text and tables are in hg19.The QC steps for the genetic data are described in detail in a recent study of PRS for language impairment . QC was 2 statistic (in most cases). We included variance components in all models (-wega), incorporating an environmental component, a polygenic component and an additive major locus component. This allowed for the use of families with multiple children. In the two parent-of-origin analyses, the same model was used, except that only paternally inherited alleles and maternally inherited alleles were used in the paternal parent-of-origin analysis and maternal parent-of-origin analysis, respectively. The phenotype used in the discovery analyses was the standardized TROG-2 score. LocusZoom [The software package QTDT v2.6.1  was usedocusZoom  was used2 of 0.1, risk allele frequency of 0.5, significance level of 0.001 and including parental genotypes, a sample of 480 children (families with a sibship of 1) resulted in a power estimate of 97.4% [2 of 0.1, and a risk allele frequency of 0.3 [The power of a QTDT analysis depends on factors such as: the marker allele frequencies, the effect size, the linkage disequilibrium between the marker and the quantitative trait locus, the number of genotypes in the analysis (determined by the number of children/siblings), whether parental genotypes are included or not, and the significance level. Power analyses for QTDT are carried out through simulation and were performed in several studies focusing on the method itself or on how it compares with other association methods. In the original QTDT paper, assuming a maximum D’, hof 97.4% . Anothery of 0.3 . Howevery of 0.3 , and .The second test repeated the original analysis in which the top SNP was highlighted, incorporating covariates for a diagnosis of ASD, a diagnosis of ADHD, and whether the family was an index family or a control family. These were included as “dummy variables” having a value of 0  or 1 . We also repeated the test with a covariate for sex (third test). This was done separately due to the sex bias observed in ASD and ADHD (to avoid correlation between the covariates).The fourth test repeated the original analysis but used Z-scores of transformed TROG-2 standardized scores . The method for the transformation, the Yeo-Johnson transformation , and theAdditionally, we employed a newer approach for the detection of parent-of-origin effects for quantitative traits in unrelated individuals (fifth test). To this end we used the “POE method” as implemented in QUICKTEST v0.99b . In thisAdditional file 1: Figure S1. Manhattan and QQ plots for the discovery analyses.Additional file 2: Table S1. Suggestive associations (P ≤ 0.00001) from the discovery analyses.


J. regia 700 K SNP array, with phenological data from 2018, 2019 and legacy data. These accessions come from the INRAE walnut germplasm collection which is the result of important prospecting work performed in many countries around the world. In parallel, an F1 progeny of 78 individuals segregating for phenology-related traits, was genotyped with the same array and phenotyped for the same traits, to construct linkage maps and perform Quantitative Trait Loci (QTLs) detection.Unravelling the genetic architecture of agronomic traits in walnut such as budbreak date and bearing habit, is crucial for climate change adaptation and yield improvement. A Genome-Wide Association Study (GWAS) using multi-locus models was conducted in a panel of 170 walnut accessions genotyped using the Axiom™ Using GWAS, we found strong associations of SNPs located at the beginning of chromosome 1 with both budbreak and female flowering dates. These findings were supported by QTLs detected in the same genomic region. Highly significant associated SNPs were also detected using GWAS for heterodichogamy and lateral bearing habit, both on chromosome 11. We developed a Kompetitive Allele Specific PCR (KASP) marker for budbreak date in walnut, and validated it using plant material from the Walnut Improvement Program of the University of California, Davis, demonstrating its effectiveness for marker-assisted selection in Persian walnut. We found several candidate genes involved in flowering events in walnut, including a gene related to heterodichogamy encoding a sugar catabolism enzyme and a cell division related gene linked to female flowering date.This study enhances knowledge of the genetic architecture of important agronomic traits related to male and female flowering processes and lateral bearing in walnut. The new marker available for budbreak date, one of the most important traits for good fruiting, will facilitate the selection and development of new walnut cultivars suitable for specific climates. Juglans regia L.) is one of the oldest food sources known [www.fao.org). At more than 22,000 ha, Persian walnut is the second leading tree crop in France, after apple. In the last 3 years, France has oscillated between 7th and 9th position for in-shell walnut production (circa 40 kt) [Persian walnut  . The abi.a 40 kt)a 40 kt) .Climate change, particularly global warming, is no longer to be proven within the scientific community , and res2 progeny in the United-States [Genetic control of phenology-related traits is fundamental for the development of new, resilient cultivars, able to adapt to changing climatic conditions. Many studies have focused on genetic dissection of phenological traits  in diverse fruit crops, such as peach, apricot and sweet cherry , 24. In d-States , but hasJ. regia 700 K SNP genotyping array [Release of the first walnut genome sequence  facilitang array . Applicang array  and wateng array , 34. A rng array .J. regia 700 K SNP array to genotype both a panel of 170 walnut accessions of diverse geographical provenience and an F1 progeny segregating for these traits. This study sought to identify candidate genes for both female and male flowering dates and to develop the first Kompetitive Allele Specific PCR (KASP) marker for phenology in walnut. This will be useful for walnut breeding programs in selecting of new resilient varieties to climate change.Here, we studied for the first time in walnut, the genetic control of budbreak date and female/male flowering dates, using the Axiom™ 1 mapping progeny of 78 individuals resulting from a bi-parental controlled cross between ‘Franquette’ (late flowering), and ‘UK 6–2’ (intermediate to early flowering). Both populations were maintained at the INRAE of Bordeaux field station and phenotyped during 2018 and 2019. For the GWAS panel, we also used previously collected (legacy) phenotypical data taken between 1989 and 2011.Two populations were used in this study: a GWAS panel of 170 diverse accessions of worldwide origin and an F1 progeny in 2019 exhibited a smaller range of 76 to 102 Julian days  than in 2018 .For the GWAS panel, the 2018–2019 data exhibited high variation in phenology-related traits, particularly for budbreak which ranged in 2019 from 57 Julian days for ‘Early Ehrhardt’ to 128 for ‘Fertignac’ (Feb 27th to May 9th) Figures  and S2. s Figure  and S4. 1 progeny . However, we found low values for male flowering duration (H2 = 0.22) and female flowering duration (H2 = 0.26) within GWAS panel (using the recent two phenotyping years), while no genetic effect was found for the F1 progeny. Therefore, we did not consider both male and female flowering durations in the GWAS and QTL mapping analyses.High broad-sense heritability values were observed for budbreak date, with Hta Table . OverallA total of 364,275 SNPs were retained after filtering for high resolution SNPs categories (Poly High Resolution and No Minor Homozygotes), for genotyping rate > 90%, and minor allele frequency > 5% Table . At K = PCA shows similar clustering of our germplasm collection as fastSTRUCTURE Figure . PC1, wh1 progeny  with the KING method. To identify first-degree relationships and differentiate “parent-offspring” from “full sibling” pairs, we used the estimates of k and the proportion of zero identical-by-state (IBS0) observed in the Fy Figure . In party Figure . Moreovep-value: 2.98E-14), and two additional associations are also found on Chr6  and Chr8 , according to the false discovery rate (FDR) threshold (≥ 0.05).For bearing habit, we found no influence of population structure , leading to an increased yield.The boxplots show the bearing habit phenotypes of 2019 for the different alleles of the three associated SNPs Fig. . For the1 progeny data. The allele T of this SNP is linked to a late budbreak date   on Chr1 was systematically found associated with all stages of female flowering , linked with a delayed budbreak date. Since we designed the KASP primers using the complementary strand, we expect to find the allele A (versus C) as involved in late leafing genotypes. The three different genotypes were clearly separated for 95 accessions, whereas one of them could not be determined . Then, we ran a pairwise comparison using Dunn’s test with Bonferroni p-value adjustment. Results of pairwise comparisons showed that A/A vs. C/A, and A/A vs. C/C have a significant effect on leafing date , contrary to C/A vs. C/C genotypes . The homozygous accessions A/A have a median leafing date of almost 110 Julian days, while genotypes C/A and C/C have a median leafing date of only 76 Julian days  were developed. These primers were used to genotype 96 unreleased breeding line accessions of the Walnut Improvement Program of the University of California, Davis. Among them, 48 are early leafing accessions and 48 are late leafing accessions. Leafing date occurs 3 days after budbreak date. Our GWAS results showed that the allele T of the SNP ‘AX-171179714’ (versus G) has an allelic estimated effect of 5.9  the loci identified in the GWAS likely are not segregating in the F1 progeny, and (ii) the QTLs found in the linkage mapping analysis have rare alleles in the association panel. For both GWAS results and QTLs mapping, the minor trait-loci associations were not stable across years, suggesting they are more affected by environmental conditions. For this reason, we decided to focus mostly on the GWAS results based on the two-year BLUPs. Nevertheless, results based on the legacy data allowed confirmation of the major SNP associated with budbreak date.While the major loci controlling the traits studied were identified with both GWAS and linkage mapping, this did not happen for the minor associations. For instance, for budbreak date, GWAS found a second trait-locus association on Chr2, while one minor QTL was detected on LG 6 of the ‘UK 6–2’ map. This finding can be explained in two ways:  [1 progeny, due to monomorphism in the parents, likely preventing accurate QTL detection. This is the case, for example, of the QTLs detected on LG 2 for both parental maps. By checking the physical position of the associated markers, we observed that the beginning of the Chr2 lacks regions from 0 to 5 Mbp for the ‘Franquette’ map, and from 0 to 7.7 Mbp for the ‘UK 6–2’ map.We observed a different scenario for all the stages of male flowering date: while the major QTL found on LG 1 co-localizes with those for female flowering dates and budbreak date, these results were not confirmed in the GWAS panel. Here, instead, we identified the most significant associations on chromosomes 11 and 4. Then, since heterodichogamy is the measurement of how the male and female flowering dates overlap, it is not surprising to find a marker-trait association on Chr11 with more consistency across years. Since the F effect) , we deciCombining multi-locus models for GWAS has become widespread , and theIn this work, classical QTL mapping mostly confirmed the major marker-trait associations identified. We found a major locus on Chr1 for budbreak date and female flowering dates, and a major locus on Chr11 for male flowering dates. Separating the flowering stages  allowed “repeat” phenotyping, generating robust data for identification of the major loci required for marker development purposes. Also, the high complexity of phenology-related traits indicates that many loci may be involved in their expression , making 2 progeny from ‘Chandler’, a lateral bearing cultivar heterozygous for this locus, the genetic basis of lateral fruitfulness was found to be located in the centromeric region of Chr11 [1 progeny from ‘Chandler’ by ‘Idaho’ cross [Lateral bearing is a crucial trait in walnut breeding since it contributes to increased yield . Fruitinof Chr11 . The samo’ cross . Additioo’ cross .1 progeny, this trait did not segregate and all hybrids are lateral bearing. ‘Franquette’ is terminal bearing and homozygous G/G for this locus, whereas ‘UK 6–2’ is lateral bearing and heterozygous G/C for this locus. Maybe because our progeny is too small or because of segregation distorsion, we did not observe any terminal bearing individuals.In the present study, we reconfirmed that walnut bearing habit is controlled by a major QTL in the centromeric region of Chr11. In line with previous studies, we observed that an allelic substitution at the most associated locus, ‘AX-171191765’, causes lateral bud fruiting. We confirm previous results regarding genetic control of lateral bearing by performing GWAS in genetically different plant materials, further supporting the power and high resolution of this method in walnut. In our Fchromosome transmission fidelity (ctf) protein 8 homolog gene as candidate gene for all female flowering dates. Previous studies in Arabidopsis thaliana suggest the involvement of the ctf genes family in the cell division processes [ctf7–1 and ctf7–2 genes resulted in developmental defects, including aberrations in flower morphology and male and female gametophytes. Interestingly, a TPX2 encoding gene required for spindle assembly during cell division process was also found likely to be involved in harvest date determination in walnut [ctf8 protein homolog locus is an excellent candidate gene for female flowering.Availability of the new chromosome-scale reference genome Chandler v2.0  allowed rocesses . In partn walnut . These fJ. regia, during staminate flower development, the tepals differentiate into anthers and filaments shortly before anthesis, enveloping the stamens [trichome birefringence-like 13 protein encoding gene within the LD block surrounding the SNP on Chr4 which is associated with male flowering dates. Also, the SNP on Chr11 associated with heterodichogamy and male flowering dates is in LD with a candidate gene encoding a probable trehalose-phosphate phosphatase D. This dephosphorylating enzyme and other members of the gene family are highly expressed in male flowers of Jatropha, a perennial woody plant, and in transgenic plants of A. thaliana with delayed flowering [probable trehalose-phosphate phosphatase D encoding gene for heterodichogamy.Trichomes consist of only one cell type, appearing as a long, slender appendage . In J. r stamens . Since tlowering , suggestlowering , 49. In A. thaliana, sweet cherry or other species, these mechanisms are regulated by flowering locus, early flowering, and MADS-box homologous genes, among others [agamous-like MADS-box protein AGL11 encoding gene (approx. 2.0 Mbp) and a MADS-box transcription factor 6-like encoding gene (8.8 Mbp), both at the beginning of the Chr1. The first one co-localizes with the major QTL found for budbreak date using the F1 progeny and the second is very near to the major marker-trait association found for female flowering dates (9.3 Mbp). Then, our results showed several marker-trait associations for both female and male flowering dates on Chr7 between 23.0 and 48.0 Mbp. Interestingly, we found the following encoding genes within this window: a flowering time control protein FCA-like (33 Mbp), a flowering-promoting factor 1-like protein 3 (34.4 Mbp), a dormancy-associated protein homolog 4 (42.9 Mbp), a protein early flowering 3-like (44.3 Mbp), a MADS-box protein SOC1-like (48.0 Mbp), and a protein flowering locus D-like (48.6 Mbp). Finally, we noticed that two genes are near to our marker-trait associations for the dichogamy (31.9 Mbp) and the male flowering dates (from 29.4 to 31.9 Mbp) on Chr11: a MADS-box transcription factor 27-like (29.9 Mbp) and a protein embryonic flower 1 (34.3 Mbp).Surprisingly, we did not find widely known genes to be involved in flowering process or dormancy in our candidate genes. In g others –52. For For the first time in walnut, a KASP marker related to phenology is released. By designing specific primers for the SNP AX-171179714, we found that the homozygous accessions A/A are significantly later leafing. Since this marker is dominant, it will greatly help breeders to accurately select individuals with delayed budbreak. However, due to the complex genetic basis of phenology-related traits in perennials , additio1 progeny is too small to pursue investigations but this study confirms that the INRAE walnut germplasm repository contains an array of plant material suitable for this type of work. New genome-wide analyses now are being initiated to further increase our knowledge concerning the genetic architecture of the main traits of interest in walnut.Due to the significant influence of environment on phenology-related traits in walnut, unravelling their genetic architecture is of upmost importance for the development of markers that could assist the selection of superior genotypes and, therefore, the release of new walnut cultivars adapted to different climatic conditions. Using GWAS with two different multi-locus models, we identified significant associations for budbreak date, and male and female flowering dates, confirmed by classical QTL mapping. In addition, we provide a list of candidate genes for these traits, that will be fundamental in future studies of functional genomics and understanding the metabolic pathways underlying phenology in walnut. We also developed and validated the first KASP marker for budbreak date in walnut, which will allow accurate selection of individuals with a delayed budbreak and, therefore, suitable for cultivation in France and other regions where late spring frosts are challenging. In parallel, the genetic bases for the expression of lateral bearing were confirmed. Since the future French walnut breeding program needs cultivars with high quality kernels, efforts are underway phenotype the entire collection regarding chemical content (e.g. fatty acids and tocopherols) and physical characteristics . Our FJ. regia grafted trees accessions originating worldwide and maintained at the Prunus and Juglans Genetic Resources Center located in the Fruit Experimental Unit of the INRAE, Toulenne, France (44°34′37.442″N – 0°16′51.48″W), near Bordeaux. This INRAE walnut germplasm collection is publicly available and is a result of important collecting work performed between 1988 and 2000 by Eric Germain  in 23 countries including the European, American, and Asian continents. Eric Germain initiated an international cooperation during his activity concerning the exchange of walnut plant materials  and obtained all permissions to bring them. Experimental research on this plant material complies with our INRAE institutional guidelines. The original source of each accession (institute/lab source or collecting source) is given . ‘UK 6–2’ has an early to intermediate budbreak date and lateral bearing. The progeny consists of 78 individuals, also located at the Fruit Experimental Unit of the INRAE in Toulenne, which were previously genotyped using the same SSR markers as those of the GWAS panel [The intraspecific FAS panel  to confi1 progeny, at a rate of two to three visits in orchards per week during the months of March, April and May: budbreak date, beginning, peak, end, and duration of female flowering, heterodichogamy, and beginning, peak, end, and duration of male flowering  for both the GWAS panel and the Fng Table . Heterodng Table  means th1 progeny, so only the GWAS panel was evaluated. Lateral bearing was found in recently released cultivars from France  and the USA , and in accessions coming from the Central Botanical Garden of Kiev, Ukraine  on three sites. These legacy data are available from the INRAE walnut germplasm collection database . Moreovene Table , suppose1 progeny, the means of genotypic effects were obtained for each accession by adjusting for known environmental factors using the BLUPs. When using two-year data, the means were predicted using a mixed linear model considering the year effect (a). When using phenological data from many years and three sites only available for the GWAS panel [For both the GWAS panel and the FAS panel , the meaPi(j)k refers to the observed phenotype of the kth accession in the ith year in the jth site; μ is the mean value of the trait; Yi is the fixed effect of the ith year, Sj is the fixed effect of the jth site, gk is the random effect of the k genotype; and ei(j)k is the residuals of the model. The BLUPs were performed using “R” package “lme4” [where e “lme4” .Based on the previous mixed linear models, broad-sense heritability of each trait was estimated using the variance components. When using two-year data, we accounted for the variance component of genotype × year interaction (a). When using phenological data from many years and three sites, we accounted for the variance components of genotype × year and genotype × site interactions (b):2G is the genotypic effect variance; σ2GxY is the genotype × year interaction variance; σ2GxS is the genotype × site interaction variance; σ2ε is the variance of residuals; ny is the number of years; and ns is the number of sites. We have always only one replication by genotype. For two-year data, ny = 2 (2018 and 2019). Nevertheless, the legacy data are very unbalanced for the years and sites available considering each genotype. We consider nyns = 5 because, in average, we have 5 observations by genotype.where σJ. regia accessions) and F1 progeny  were extracted from young leaves as described in Bernard et al. [J. regia 700 K SNP array containing 609,658 SNPs uniformly distributed over the 16 J. regia chromosomes [1 progeny were removed. After, Poly High Resolution (PHR) and No Minor Homozygotes (NMH) SNPs were filtered using stringent thresholds: SNP call rate (> 90%), minor allele frequency (MAF > 5%), and redundancy in the genome . Finally, 364,275 robust SNPs  were retained for the following genome-wide analysis.The genomic DNAs of both the GWAS panel . Single trait analysis was performed using a Multiple Interval Mapping (MIM) method [chr) was performed as follows:QTLs were determined using MultiQTL 2.6 software  method , in whicM is the total number of markers used for the QTL detection on each parental map, and m is the number of markers in the LG carrying the QTL [.where  the QTL .For the GWAS panel, the R packages “SNPRelate” and “gdsfmt”  were useGWAS was carried out using the R package “GAPIT” , accounthttps://www.ncbi.nlm.nih.gov/genome/annotation_euk/Juglans_regia/100/) and mapped onto the new chromosome-scale reference genome v2.0 [https://www.hardwoodgenomics.org/Genomeassembly/2539069).LD levels around the most associated loci were estimated using HaploView v4.2 software . Each phome v2.0   markers are based on the dual Fluorescence Resonance Energy Transfer (FRET) method, in which the sample DNA is amplified with allele specific primers conjugated to fluorometric dyes HEX and FAM at their 5′ end. Based on GWAS results regarding the budbreak date, the following KASP primers were developed  to target the most significantly associated SNP with this trait:Allele A: 5′-AGGACAGCAATAAACTCAATCACACA-3′.Allele C: 5′-GGACAGCAATAAACTCAATCACACC-3′.Allele A tail (FAM tail): 5′-GAAGGTGACCAAGTTCATGCT-3′.Allele C tail (HEX tail): 5′-GAAGGTCGGAGTCAACGGATT-3′.Common reverse: 5′-AGGTTCTGCCAAGCTACAGGGTATA-3′.https://www.biosearchtech.com/support/education/kasp-genotyping-reagents/how-does-kasp-work. The KASP reaction was prepared as follows: 1.95 μL of PCR mix PACE , 2 μL of genomic DNA (2 ng/μL), and 0.053 μL of the three primers , for a total volume reaction of 4.003 μL. Amplification was performed in a hydrocycler, starting with 15 min at 94 °C, a touchdown phase of 10 cycles at 94 °C for 20 s and at 61 °C for 60 s with a 0.6 °C decrease in temperature per cycle, followed by 35 cycles of 94 °C for 20 s and 55 °C for 60 s. Once the cycle was complete, Fluostar Omega reader  was used to read the fluorescence signal. The KASP method was tested using DNA from a set of 96 unreleased breeding line accessions from the Walnut Improvement Program of the University of California, Davis.These primers were developed by the BioGEVES laboratory  on the complementary strand. The KASP reaction and its components are explained at Additional file 1: Table S1. List of association panel accessions with their origin, breeding level, pedigree when known, and their membership to clusters based on fastSTRUCTURE results for K = 3.Additional file 2: Table S2. Number of principal components required for bearing habit trait using Bayesian Information Criterion.Additional file 3: Table S3. ‘Franquette’ and ‘UK 6–2’ parental maps for QTL detection.Additional file 4: Table S4. Genotyping results of SNP AX-171179714 by KASP.Additional file 5: Table S5. Walnut trait ontology.Additional file 6: Table S6. Phenotypic raw dataset of the GWAS panel.Additional file 7: Table S7. Phenotypic raw dataset of the F1 progeny.Additional file 8: Figure S1. Scatter plots showing the two-year data related to phenological traits in Julian days for the 170 accessions of the GWAS panel.Additional file 9: Figure S2. Density plots showing the two-year data related to phenological traits in Julian days for the 170 accessions of the GWAS panel.Additional file 10: Figure S3. Scatter plots showing the two-year data related to phenological traits in Julian days for the 78 accessions of the F1 progeny.Additional file 11: Figure S4. Density plots showing the two-year data related to phenological traits in Julian days for the 78 accessions of the F1 progeny.Additional file 12: Figure S5. Detection of the number of clusters using a) Prob(K), and b) deltaK method  in the GWAS panel.Additional file 13: Figure S6. Principal Component Analysis performed on the GWAS panel. The two first principal components show accessions colored according to fastSTRUCTURE results with EEAs for Eastern Europe and Asia, and WEAm for Western Europe and America.Additional file 14: Figure S7. Scatter plot showing the estimated kinship coefficients by the proportion of zero Identical-By-State (IBS0) in the F1 progeny.Additional file 15: Figure S8. Level plot showing the Identical-By-State (IBS) values for the 170 accessions of the GWAS panel.Additional file 16: Figure S9. Genetic maps and QTLs detected using two-year data. F(X) and U(X) are the linkage groups of ‘Franquette’ and ‘UK 6–2’ parental maps respectively. Legend of the QTLs: black for budbreak date, red for beginning female flowering date, deep green for full female flowering date, blue for end female flowering date, yellow for beginning male flowering date, pink for full female flowering date, and light green for end female flowering date. Solid bars indicate the 95% confidence interval of the QTL, and terminal bars indicate the 99.9% confidence interval of the QTL. The marker names were changed with the corresponding chromosome number and its physical position for a better visualization.


Because hyper-excitability has been shown to be a shared pathophysiological mechanism, we used the latest and largest genome-wide studies in amyotrophic lateral sclerosis  and epilepsy  to determine genetic overlap between these conditions. First, we showed no significant genetic correlation, also when binned on minor allele frequency. Second, we confirmed the absence of polygenic overlap using genomic risk score analysis. Finally, we did not identify pleiotropic variants in meta-analyses of the 2 diseases. Our findings indicate that amyotrophic lateral sclerosis and epilepsy do not share common genetic risk, showing that hyper-excitability in both disorders has distinct origins. Cramps and fasciculations are common in ALS patients and indicate axonal hyper-excitability in motor neurons . Proposed mechanisms for hyper-excitability in ALS include impaired inhibitory signaling through GABAergic interneurons, astrocytes failing to adequately regulate glutamate levels and extracellular K+ concentrations at the synaptic cleft, and altered ion channel expression in motor neurons . Ion channel dysfunction has been correlated to muscle weakness and motor neuron degeneration , while peripheral hyper-excitability and central hyper-excitability are furthermore strong predictors of shorter survival . Similarly, in epilepsy, an imbalance of excitatory and inhibitory mechanisms in the brain contributes to seizure pathogenesis. Interestingly, the anti-epileptic drug retigabine has shown acute beneficial effects on peripheral nerve excitability in ALS . Of further note, riluzole—a drug used in the treatment of ALS—has been shown to decrease hippocampal epileptiform activity and reduce seizure activity in epilepsy animal models .The beforementioned observations and the availability of recent and large genome-wide association study (GWAS) datasets , showing2.We used individual level data of subjects with European ancestry from recent large GWASs in ALS   and epil3.BMP8A in the ALS-epilepsy  analysis  in both studies independently  and a genome-wide significant p-value in meta-analysis . This variant, however, was not associated with ALS (p = 0.11) in a larger GWAS for which only summary statistics were available .We found no significant genetic correlations between ALS and epilepsy, including focal and generalized subtypes . Furtheranalysis  fulfille4.We have used the latest and largest individual-level genotype datasets for ALS and epilepsy to perform the most comprehensive cross-disorder genome-wide study between these diseases available to date. This has enabled careful control of sample overlap and relatedness between datasets. Also, many tools for cross-disorder analyses have increased power because they use individual-level genotype data instead of summary statistics. Moreover, the genetic correlation estimate between these disorders was not reported in a recent large-scale study on shared polygenic risk in ALS . Overall, we showed with the currently available sample sizes that ALS and epilepsy are not genetically correlated, and that GRS capturing combined effects of common variants in one disease do not explain phenotypic variance in the other disease and that no loci with an effect in both ALS and epilepsy were identified in meta-analyses.BMP8A and PTPRK initially showed evidence for pleiotropy, because of their nominal significant association in both ALS and epilepsy GWAS and stronger statistical association in the meta-analysis and sign-independent meta-analysis, respectively. Nevertheless, the associations of top single-nucleotide polymorphisms (SNPs) in these loci with ALS were weak and did not reach nominal significance in a more recent and larger ALS GWAS meta-analysis , which combined logistic regression association results from summary-level data of p-values around the genome-wide significance threshold . Detailed C9orf72 repeat expansion status was currently unavailable in our ALS dataset. Although several sodium and potassium channel genes have been associated with epilepsy, and involvement of these channels in the development of cramps in ALS has been proposed , although case reports of co-occurrence in α = 0.05 using GCTA-REML . Second, rare genetic variation plays a role in both diseases. Although we found non-significant genetic correlation estimates in the lower minor allele frequency (MAF) spectrum of our data, variants with MAF < 0.01 cannot be reliably included in these GWAS analyses. Future GWAS studies employing rare-variant imputation or large-scale sequencing efforts may potentially provide more insight into shared heritability in the rare variant spectrum. Third, phenotypic heterogeneity in ALS and epilepsy might result in a dilution of signal of possible shared genetic underpinnings. Although we have tested genetic correlations with epilepsy subtypes , a detailed characterization of both ALS and epilepsy patients could identify subgroups that share phenotypic and pathological characteristics that might share a genetic basis. However, at present such phenotypic information is unavailable at sufficiently large sample sizes for cross-disorder analyses.We have considered possible limitations of our study. First, we cannot rule out that a study with even larger datasets may possibly yield significant results. However, such a correlation would then be expected to be very small, as we have shown no sign of substantial shared genetic risk with the current large GWAS datasets we have analyzed. The current sample size provides 80% power to detect a genetic correlation of 0.091 at Supplemental